From 13817c9fc7d168a34c7332d0eed84f41b382bd2f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Wed, 21 Apr 2021 01:28:54 +0200 Subject: [PATCH 01/33] Einsum decomposition --- _doc/sphinxdoc/source/api/testing.rst | 21 +- _unittests/ut_testing/test_einsum.py | 187 ++++++++++++ mlprodict/testing/einsum_impl.py | 411 ++++++++++++++++++++++++++ mlprodict/tools/data_types.py | 6 +- 4 files changed, 619 insertions(+), 6 deletions(-) create mode 100644 _unittests/ut_testing/test_einsum.py create mode 100644 mlprodict/testing/einsum_impl.py diff --git a/_doc/sphinxdoc/source/api/testing.rst b/_doc/sphinxdoc/source/api/testing.rst index 598e3d155..028b8197e 100644 --- a/_doc/sphinxdoc/source/api/testing.rst +++ b/_doc/sphinxdoc/source/api/testing.rst @@ -15,12 +15,27 @@ Implementation of ONNX operators Experimental implementations for algorithm. -.. autosignature:: mlprodict.testing.experimental.custom_einsum_float +Einsum +^^^^^^ + +.. autosignature:: mlprodict.testing.einsum_impl.analyse_einsum_equation + +.. autosignature:: mlprodict.testing.einsum_impl.apply_sequence + +.. autosignature:: mlprodict.testing.einsum_impl.decompose_einsum_equation + +.. autosignature:: mlprodict.testing.experimental_c.custom_einsum_float .. autosignature:: mlprodict.testing.experimental_c.custom_einsum_double +Pad +^^^ + .. autosignature:: mlprodict.testing.experimental.custom_pad -.. autosignature:: mlprodict.testing.experimental.custom_reducesum_rk_double +ReduceSum +^^^^^^^^^ + +.. autosignature:: mlprodict.testing.experimental_c.custom_reducesum_rk_double -.. autosignature:: mlprodict.testing.experimental.custom_reducesum_rk_float +.. autosignature:: mlprodict.testing.experimental_c.custom_reducesum_rk_float diff --git a/_unittests/ut_testing/test_einsum.py b/_unittests/ut_testing/test_einsum.py new file mode 100644 index 000000000..595ff5f3b --- /dev/null +++ b/_unittests/ut_testing/test_einsum.py @@ -0,0 +1,187 @@ +""" +@brief test log(time=6s) +""" +import unittest +import io +from contextlib import redirect_stdout +import numpy +from pyquickhelper.pycode import ExtTestCase +from mlprodict.testing.einsum_impl import ( + analyse_einsum_equation, decompose_einsum_equation, EinsumSubOp, + apply_sequence) + + +class TestEinsum(ExtTestCase): + + def test_analyse_einsum_equation(self): + self.assertRaise(lambda: analyse_einsum_equation("abc"), + NotImplementedError) + self.assertRaise(lambda: analyse_einsum_equation("abc0,ch->ah"), + ValueError) + self.assertRaise(lambda: analyse_einsum_equation("abc,ch->a0"), + ValueError) + res = analyse_einsum_equation("abc,ch->ah") + self.assertEqual(len(res), 3) + letters, mat, lengths = res + self.assertEqual(letters, "abch") + self.assertEqualArray(lengths, numpy.array([3, 2, 2])) + self.assertEqualArray( + mat, numpy.array([[0, 1, 2, -1], + [-1, -1, 0, 1], + [0, -1, -1, 1]])) + + def test_decompose_einsum_equation_exc(self): + self.assertRaise( + lambda: decompose_einsum_equation("abc,ch->ah", (2, 2, 2), (2, 2), + strategy="donotexist"), + ValueError) + self.assertRaise( + lambda: decompose_einsum_equation("abc,ch->ah"), ValueError) + self.assertRaise( + lambda: decompose_einsum_equation("abc,ch->ah", (2, 2, 2), (2, 2), + "donotexist"), + TypeError) + self.assertRaise( + lambda: decompose_einsum_equation("abc,ch->ah", (2, 2, 2)), + ValueError) + self.assertRaise( + lambda: decompose_einsum_equation("abc,ch->ah", (2, 2), (2, 2)), + ValueError) + self.assertRaise( + lambda: decompose_einsum_equation("aac,ch->ah", (2, 2), (2, 2)), + NotImplementedError) + + def test_decompose_einsum_equation(self): + m1 = numpy.arange(0, 8).astype(numpy.float32).reshape((2, 2, 2)) + m2 = numpy.arange(0, 4).astype(numpy.float32).reshape((2, 2)) + exp = numpy.einsum("bac,ch->ah", m1, m2) + + f = io.StringIO() + with redirect_stdout(f): + seq = decompose_einsum_equation( + "bac,ch->ah", (2, 2, 2), (2, 2), verbose=True) + res = apply_sequence(seq, m1, m2, verbose=True) + import pprint + pprint.pprint(seq) + + out = f.getvalue() + print(out) + self.assertEqual(exp, res) + + def test_einsum_sub_op(self): + self.assertRaise(lambda: EinsumSubOp("er", (2, 2)), ValueError) + self.assertRaise(lambda: EinsumSubOp("reshape"), RuntimeError) + self.assertRaise(lambda: EinsumSubOp("gemm", (2, 2)), RuntimeError) + self.assertRaise(lambda: EinsumSubOp("id", (2, 2)), TypeError) + + # Taken from https://github.com/numpy/numpy/blob/main/numpy/ + # core/tests/test_einsum.py. + + def _test_hadamard_like_products(self): + # Hadamard outer products + self.optimize_compare('a,ab,abc->abc') + self.optimize_compare('a,b,ab->ab') + + def _test_index_transformations(self): + # Simple index transformation cases + self.optimize_compare('ea,fb,gc,hd,abcd->efgh') + self.optimize_compare('ea,fb,abcd,gc,hd->efgh') + self.optimize_compare('abcd,ea,fb,gc,hd->efgh') + + def _test_complex(self): + # Long test cases + self.optimize_compare('acdf,jbje,gihb,hfac,gfac,gifabc,hfac') + self.optimize_compare('acdf,jbje,gihb,hfac,gfac,gifabc,hfac') + self.optimize_compare('cd,bdhe,aidb,hgca,gc,hgibcd,hgac') + self.optimize_compare('abhe,hidj,jgba,hiab,gab') + self.optimize_compare('bde,cdh,agdb,hica,ibd,hgicd,hiac') + self.optimize_compare('chd,bde,agbc,hiad,hgc,hgi,hiad') + self.optimize_compare('chd,bde,agbc,hiad,bdi,cgh,agdb') + self.optimize_compare('bdhe,acad,hiab,agac,hibd') + + def _test_collapse(self): + # Inner products + self.optimize_compare('ab,ab,c->') + self.optimize_compare('ab,ab,c->c') + self.optimize_compare('ab,ab,cd,cd->') + self.optimize_compare('ab,ab,cd,cd->ac') + self.optimize_compare('ab,ab,cd,cd->cd') + self.optimize_compare('ab,ab,cd,cd,ef,ef->') + + def _test_expand(self): + # Outer products + self.optimize_compare('ab,cd,ef->abcdef') + self.optimize_compare('ab,cd,ef->acdf') + self.optimize_compare('ab,cd,de->abcde') + self.optimize_compare('ab,cd,de->be') + self.optimize_compare('ab,bcd,cd->abcd') + self.optimize_compare('ab,bcd,cd->abd') + + def _test_edge_cases(self): + # Difficult edge cases for optimization + self.optimize_compare('eb,cb,fb->cef') + self.optimize_compare('dd,fb,be,cdb->cef') + self.optimize_compare('bca,cdb,dbf,afc->') + self.optimize_compare('dcc,fce,ea,dbf->ab') + self.optimize_compare('fdf,cdd,ccd,afe->ae') + self.optimize_compare('abcd,ad') + self.optimize_compare('ed,fcd,ff,bcf->be') + self.optimize_compare('baa,dcf,af,cde->be') + self.optimize_compare('bd,db,eac->ace') + self.optimize_compare('fff,fae,bef,def->abd') + self.optimize_compare('efc,dbc,acf,fd->abe') + self.optimize_compare('ba,ac,da->bcd') + + def _test_inner_product(self): + # Inner products + self.optimize_compare('ab,ab') + self.optimize_compare('ab,ba') + self.optimize_compare('abc,abc') + self.optimize_compare('abc,bac') + self.optimize_compare('abc,cba') + + def _test_random_cases(self): + # Randomly built test cases + self.optimize_compare('aab,fa,df,ecc->bde') + self.optimize_compare('ecb,fef,bad,ed->ac') + self.optimize_compare('bcf,bbb,fbf,fc->') + self.optimize_compare('bb,ff,be->e') + self.optimize_compare('bcb,bb,fc,fff->') + self.optimize_compare('fbb,dfd,fc,fc->') + self.optimize_compare('afd,ba,cc,dc->bf') + self.optimize_compare('adb,bc,fa,cfc->d') + self.optimize_compare('bbd,bda,fc,db->acf') + self.optimize_compare('dba,ead,cad->bce') + self.optimize_compare('aef,fbc,dca->bde') + + def _test_combined_views_mapping(self): + # gh-10792 + a = numpy.arange(9).reshape(1, 1, 3, 1, 3) + b = numpy.einsum('bbcdc->d', a) + assert_equal(b, [12]) + + def _test_broadcasting_dot_cases(self): + # Ensures broadcasting cases are not mistaken for GEMM + + a = numpy.random.rand(1, 5, 4) + b = numpy.random.rand(4, 6) + c = numpy.random.rand(5, 6) + d = numpy.random.rand(10) + + self.optimize_compare('ijk,kl,jl', operands=[a, b, c]) + self.optimize_compare('ijk,kl,jl,i->i', operands=[a, b, c, d]) + + e = numpy.random.rand(1, 1, 5, 4) + f = numpy.random.rand(7, 7) + self.optimize_compare('abjk,kl,jl', operands=[e, b, c]) + self.optimize_compare('abjk,kl,jl,ab->ab', operands=[e, b, c, f]) + + # Edge case found in gh-11308 + g = numpy.arange(64).reshape(2, 4, 8) + self.optimize_compare('obk,ijk->ioj', operands=[g, g]) + + +if __name__ == "__main__": + TestEinsum().test_decompose_einsum_equation() + # stop + unittest.main() diff --git a/mlprodict/testing/einsum_impl.py b/mlprodict/testing/einsum_impl.py new file mode 100644 index 000000000..42e84cace --- /dev/null +++ b/mlprodict/testing/einsum_impl.py @@ -0,0 +1,411 @@ +""" +@file +@brief Function to dig into Einsum computation. +""" +import numpy + + +def analyse_einsum_equation(equation): + """ + Analyses an einsum equation. + + :param equation: :epkg:`numpy:einsum` equation + :return: + """ + spl = equation.strip(' ,').split("->") + if len(spl) != 2 or len(spl[1]) == 0 or len(spl[0]) == 0: + raise NotImplementedError( + "The function only implements the case when there are " + "two sides in the equation: %r." % equation) + inputs = list(map(lambda s: s.strip(), spl[0].split(','))) + for inp in inputs: + if len(inp) != len(set(inp)): + raise NotImplementedError( + "One input uses more than once the same indice %r in " + "equation %r." % (inp, equation)) + output = spl[1] + all_letters = set(inputs[0]) + for inp in inputs[1:]: + all_letters |= set(inp) + letters = list(sorted(all_letters)) + for c in letters: + if not(('a' <= c <= 'z') or ('A' <= c <= 'Z')): + raise ValueError( + "Equation %r must only contain lower or upper letters " + "but %r is not." % (equation, c)) + rev = {c: i for i, c in enumerate(letters)} + for c in output: + if c not in letters: + raise ValueError( + "Output contains one unexpected letter %r in " + "equation %r." % (c, equation)) + mat = numpy.full((len(inputs) + 1, len(letters)), -1, dtype=numpy.int8) + for i, inp in enumerate(inputs): + for k, c in enumerate(inp): + mat[i, rev[c]] = k + for k, c in enumerate(output): + mat[len(inputs), rev[c]] = k + lengths = [len(inp) for inp in inputs] + lengths.append(len(output)) + return "".join(letters), mat, lengths + + +def decompose_einsum_equation(equation, *shapes, strategy="simple", verbose=False): + """ + Decomposes an equation used in :epkg:`numpy:einsum` knowing + the input shapes. It returns a sequence of operations + to do to compute the results. + + :param equation: a string + :param shapes: sequence of input shapes + :param strategy: there are different way to decompose the equation, + this parameters defines the way to do it (see below) + :param verbose: verbosity + :return: sequence of operations of typ @see cl EinsumSubOp + + About *strategy*: + * `'simple'`: align all dimensions in the alphabetical order + + Available operations: *expand_dims*, *transpose*, *matmul*, *reduce_sum*, + *id*, *squeeze*. + """ + if len(shapes) == 0: + raise ValueError("No input shapes.") + for sh in shapes: + if not isinstance(sh, tuple): + raise TypeError( + "All shapes must be tuples for %r is not." % sh) + if strategy == "simple": + return _decompose_einsum_equation_simple(equation, *shapes, verbose=verbose) + raise ValueError("Unknown strategy %r." % strategy) + + +def apply_sequence(seq, *inputs, verbose=False): + """ + Applies a sequence of operations on a list of inputs. + + :param seq: sequence of operations + :param inputs: inputs: + :return: output + """ + data = {i: inp for i, inp in enumerate(inputs)} + for op in seq: + op.apply(data) + return data[id(seq[-1])] + + +def _basic_verification(lengths, shapes, equation): + if len(lengths) - 1 != len(shapes): + raise ValueError( + "Equation %r has %d inputs but %d shapes are given." + "" % (equation, len(lengths), len(shapes))) + for i, (le, sh) in enumerate(zip(lengths, shapes)): + if le != len(sh): + raise ValueError( + "Inputs %d has %d dimensions but shapes %r has %d " + " in equation %r." % (i, le, sh, len(sh), equation)) + + +class EinsumSubOp: + """ + Defines a sub operation used in Einsum decomposition. + + :param name: name (reshape, transpose, reduce_sum, matmul, id) + :param inputs: inputs + :param kwargs: arguments + """ + _allowed = {'expand_dims', 'transpose', 'reduce_sum', 'matmul', 'id', + 'squeeze'} + + def __init__(self, name, *inputs, **kwargs): + self.name = name + self.inputs = inputs + self.kwargs = kwargs + if name not in EinsumSubOp._allowed: + raise ValueError( + "Unexpected name %r. It should be in %r." + "" % (name, EinsumSubOp._allowed)) + if len(inputs) not in (1, 2): + raise RuntimeError( + "Inputs must contains 1 or 2 inputs not %d." % len(inputs)) + if name == 'matmul' and len(inputs) != 2: + raise RuntimeError( + "Inputs must contains 2 inputs not %d for operator 'matmul'." + "" % len(inputs)) + for i, inp in enumerate(inputs): + if not isinstance(inp, (int, EinsumSubOp)): + raise TypeError( + "Input %d has type %r, int or EinsumSubOp is expected." + "" % (i, type(inp))) + + def __repr__(self): + inps = ", ".join(map(str, self.inputs)) + kw = ", ".join("%s=%r" % (k, w) for k, w in self.kwargs.items()) + m = "%s(%r, %s, %s)" % ( + self.__class__.__name__, self.name, inps, kw) + return m + + def _check_arg_(self, name, typ): + if name not in self.kwargs: + raise RuntimeError( + "Parameter %r not found for operator %r." % (name, self.name)) + if not isinstance(self.kwargs[name], typ): + raise TypeError( + "Unexpected type %r for parameter %r and parameter %r." + "" % (type(self.kwargs[name]), name, self.name)) + + def _check_row_(self, row, inp=False, verbose=False): + """ + Checks input or output is valid. + """ + if verbose: + if inp: + print() + print('<-' if inp else '->', self.name, row, self.kwargs) + if not inp or self.name != 'id': + if row.max() == -1: + raise RuntimeError( # pragma: no cover + "Shape is empty %r." % row) + + def compute_output_row(self, row, row2=None, verbose=False): + """ + Updates *row* based on the operator. + """ + self._check_row_(row, True, verbose=verbose) + + if self.name == "id": + row[:] = row2[:] + self._check_row_(row, verbose=verbose) + return + + if self.name == "transpose": + self._check_arg_('perm', tuple) + if len(self.kwargs['perm']) != len(row): + raise RuntimeError( + "Unexpected permutation %r (row=%r)." + "" % (self.kwargs['perm'], row)) + cpy = row.copy() + for i, p in enumerate(self.kwargs['perm']): + row[i] = cpy[p] + self._check_row_(row, verbose=verbose) + return + + if self.name == "expand_dims": + self._check_arg_('axis', tuple) + if row[self.kwargs['axis'][1]] != -1: + raise RuntimeError( + "Dimension should be -1 in row %r axis=%r." % ( + row, self.kwargs['axis'])) + self._check_row_(row, verbose=verbose) + return + + if self.name == "reduce_sum": + self._check_arg_('axes', tuple) + for a in self.kwargs['axes']: + row[a] = -1 + self._check_row_(row, verbose=verbose) + return + + if self.name == "matmul": + self._check_arg_('axes', tuple) + if row2 is None: + raise RuntimeError("matmul expects two inputs.") + if verbose: + print(" MATMUL %r @ %r" % (row, row2)) + row2[:] = numpy.maximum(row, row2) + for a in self.kwargs['axes']: + row2[a] = -1 + self._check_row_(row2, verbose=verbose) + return + + if self.name == "squeeze": + self._check_arg_('axes', tuple) + for a in self.kwargs['axes']: + row[a] = -1 + self._check_row_(row, verbose=verbose) + return + + raise NotImplementedError( + "compute_output_row not implemented for %r." % self.name) + + def _check_inputs_(self, n_expected): + if len(self.inputs) != n_expected: + raise RuntimeError( + "Number of inputs must be %d not %d for operator %r." + "" % (n_expected, len(self.inputs), self.name)) + + def _get_data(self, data, key): + if isinstance(key, int): + return data[key] + if isinstance(key, EinsumSubOp): + return data[id(key)] + raise TypeError( + "Unexpected input type %r." % type(key)) + + def apply(self, data): + """ + Applies one operator on the data. + + :param data: dictionary storing the results + """ + if self.name == 'id': + self._check_inputs_(1) + inp = self.inputs[0] + return self._get_data(data, inp) + + if self.name == 'expand_dims': + self._check_inputs_(1) + + raise NotImplementedError( + "apply not implemented for %r." % self.name) + + +def _apply_transpose_reshape(op, row): + """ + Put all dimensions in the same order. + """ + axes = [] + p = 0 + perm = [] + for i, r in enumerate(row): + if r == -1: + axes.append((p, i)) + perm.append(-1) + else: + p += 1 + perm.append(r) + for a in reversed(axes): + op = EinsumSubOp('expand_dims', op, axis=a) + yield op + dec = [0] + for i in range(1, len(perm)): + if perm[i - 1] == -1: + dec.append(dec[-1] + 1) + else: + dec.append(dec[-1]) + for i in range(0, len(perm)): # pragma: disable=C0200 + if perm[i] == -1: + perm[i] = i + else: + perm[i] = perm[i] + dec[i] + op = EinsumSubOp('transpose', op, perm=tuple(perm)) + yield op + + +def _apply_squeeze_transpose(op, row_last, row_output): + """ + Put output dimension in the expected order. + """ + + perm = [] + sq = [] + for i, d in enumerate(row_output): + if d == -1: + perm.append((i, i)) + sq.append(i) + else: + perm.append((d, i)) + perm = [p[1] for p in perm] + op = EinsumSubOp('transpose', op, perm=tuple(perm)) + yield op + if len(sq) > 0: + op = EinsumSubOp('squeeze', op, axes=tuple(sq)) + yield op + + +def _decompose_einsum_equation_simple(equation, *shapes, verbose=False): + """ + Applies strategy simple of function @see fct decompose_einsum_equation. + """ + letters, mat, lengths = analyse_einsum_equation(equation) + if len(letters) != mat.shape[1]: + raise RuntimeError( # pragma: no cover + "Unexpected number of letters %r, shape=%r." % ( + letters, mat.shape)) + _basic_verification(lengths, shapes, equation) + ops = [] + # last_row, current_row (row = shape) + rows = numpy.full((2, mat.shape[1]), -1) + last_op = None + if verbose: + print("EQUATION=%r" % equation) + + for i, sh in enumerate(shapes): + if verbose: + print() + print("######### ROW %d shape=%r row=%r" % (i, sh, rows[1, :])) + + # Input matrix aligned to the same dimensions. + op = EinsumSubOp('id', i) + op.compute_output_row(rows[1, :], mat[i, :], verbose=verbose) + ops.append(op) + for op in _apply_transpose_reshape(op, mat[i]): + op.compute_output_row(rows[1, :], verbose=verbose) + ops.append(op) + + # Reduction? (a dimension not used later) + red = [] + for d in range(0, mat.shape[1]): + if (mat[i + 1:, d].max() == -1 and rows[1, d] != -1 and + rows[0, d] == -1): + red.append(d) + if len(red) > 0: + if verbose: + print("-- REDUCE1 row=%d axes=%r" % (i, red)) + print(mat) + print('-') + print(rows) + op = EinsumSubOp('reduce_sum', ops[-1], axes=tuple(red)) + op.compute_output_row(rows[1, :], verbose=verbose) + + if last_op is not None: + # Matrix multiplication? + common_dims = [] + for d in range(0, mat.shape[1]): + if rows[:, d].min() >= 0: + common_dims.append(d) + if verbose: + print("-- MATMUL common_dims=%r" % common_dims) + print(rows) + if len(common_dims) > 0: + op = EinsumSubOp('matmul', last_op, op, + axes=tuple(common_dims)) + ops.append(op) + op.compute_output_row(rows[0, :], rows[1, :], verbose=verbose) + else: + raise NotImplementedError( + "Unable to interpret equation %r at position %i " + "(starting at 0) common_dims=%r rows-=%r rows+=%r." + "" % (equation, i, common_dims, rows[0, :], rows[1, :])) + + # End + rows[0, :] = rows[1, :] + last_op = op + + # Final output + if verbose: + print() + print("######### FIN row=%r" % rows[1, :]) + if mat[len(shapes), :].max() >= 0: + rows[1, :] = mat[len(shapes), :] + red = [] + for d in range(0, mat.shape[1]): + if rows[0, d] > 0 and rows[1, d] == -1: + red.append(d) + elif rows[0, d] == -1 and rows[1, d] >= 0: + raise RuntimeError( + "Issue in equation %r, last_result is %r, " + "output is %r." % (equation, rows[0, :], rows[1, :])) + if len(red) > 0: + if verbose: + print("-- REDUCE2 axes=%r" % red) + print(mat) + op = EinsumSubOp('reduce_sum', op, axes=red) + ops.append(op) + op.compute_output_row(rows[1, :], verbose=verbose) + + # Final transpose and reshape if needed + for op in _apply_squeeze_transpose(op, rows[1, :], mat[len(shapes), :]): + op.compute_output_row(rows[1, :], verbose=verbose) + ops.append(op) + return ops diff --git a/mlprodict/tools/data_types.py b/mlprodict/tools/data_types.py index 23c37589f..464d9615b 100644 --- a/mlprodict/tools/data_types.py +++ b/mlprodict/tools/data_types.py @@ -4,11 +4,11 @@ .. versionadded:: 0.6 """ -from onnx import onnx_pb as onnx_proto # pylint: disable=W0611 -from skl2onnx.common.data_types import ( # pylint: disable=W0611 +from onnx import onnx_pb as onnx_proto # pylint: disable=W0611,E0611 +from skl2onnx.common.data_types import ( # pylint: disable=W0611,E0611 TensorType, FloatTensorType, Int64TensorType, DoubleTensorType, StringTensorType, Int32TensorType, BooleanTensorType, UInt8TensorType) -from skl2onnx.common.data_types import ( # pylint: disable=W0611 +from skl2onnx.common.data_types import ( # pylint: disable=W0611,E0611 Int16TensorType, Int8TensorType, UInt16TensorType, UInt32TensorType, UInt64TensorType, Float16TensorType) From da470a1afda5359904fec8aba9a8b7d820bf1aea Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Wed, 21 Apr 2021 01:29:15 +0200 Subject: [PATCH 02/33] Update test_einsum.py --- _unittests/ut_testing/test_einsum.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_unittests/ut_testing/test_einsum.py b/_unittests/ut_testing/test_einsum.py index 595ff5f3b..3857137d8 100644 --- a/_unittests/ut_testing/test_einsum.py +++ b/_unittests/ut_testing/test_einsum.py @@ -182,6 +182,6 @@ def _test_broadcasting_dot_cases(self): if __name__ == "__main__": - TestEinsum().test_decompose_einsum_equation() + # TestEinsum().test_decompose_einsum_equation() # stop unittest.main() From 9f760b927c5abeb8ef3b44d193c5a0bb1440d2f5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Wed, 21 Apr 2021 19:11:48 +0200 Subject: [PATCH 03/33] fix a couple of issues --- _unittests/ut_testing/test_einsum.py | 79 ++++-- mlprodict/testing/einsum_impl.py | 362 +++++++++++++++++++++++---- 2 files changed, 375 insertions(+), 66 deletions(-) diff --git a/_unittests/ut_testing/test_einsum.py b/_unittests/ut_testing/test_einsum.py index 3857137d8..70554caf4 100644 --- a/_unittests/ut_testing/test_einsum.py +++ b/_unittests/ut_testing/test_einsum.py @@ -56,39 +56,73 @@ def test_decompose_einsum_equation(self): m2 = numpy.arange(0, 4).astype(numpy.float32).reshape((2, 2)) exp = numpy.einsum("bac,ch->ah", m1, m2) - f = io.StringIO() - with redirect_stdout(f): + def fct(): + print("########################## DECOMPOSE") seq = decompose_einsum_equation( "bac,ch->ah", (2, 2, 2), (2, 2), verbose=True) + print("########################## APPLY") + dot = seq.to_dot() + print(dot) + red = dot.split('red') + self.assertEqual(len(red), 4) res = apply_sequence(seq, m1, m2, verbose=True) - import pprint - pprint.pprint(seq) + print("########################## END") + return res + + f = io.StringIO() + try: + with redirect_stdout(f): + res = fct() + except Exception as e: + raise AssertionError("Issue. Logs =\n%s" % f.getvalue()) from e out = f.getvalue() - print(out) + self.assertIn("numpy_extended_dot", out) self.assertEqual(exp, res) def test_einsum_sub_op(self): - self.assertRaise(lambda: EinsumSubOp("er", (2, 2)), ValueError) - self.assertRaise(lambda: EinsumSubOp("reshape"), RuntimeError) - self.assertRaise(lambda: EinsumSubOp("gemm", (2, 2)), RuntimeError) - self.assertRaise(lambda: EinsumSubOp("id", (2, 2)), TypeError) + self.assertRaise(lambda: EinsumSubOp(2, "er", (2, 2)), ValueError) + self.assertRaise(lambda: EinsumSubOp(2, "expand_dims"), RuntimeError) + self.assertRaise(lambda: EinsumSubOp( + 2, "matmul", (2, 2)), RuntimeError) + self.assertRaise(lambda: EinsumSubOp(2, "id", (2, 2)), TypeError) # Taken from https://github.com/numpy/numpy/blob/main/numpy/ # core/tests/test_einsum.py. - def _test_hadamard_like_products(self): + def optimize_compare(self, equation, verbose=False): + eqs = equation.split("->")[0].split(",") + inputs = [] + for eq in eqs: + i = numpy.arange(2 ** len(eq)).reshape( + (2,) * len(eq)).astype(numpy.float32) + inputs.append(i + numpy.array([10], dtype=numpy.float32)) + + exp = numpy.einsum(equation, *inputs) + if verbose: + print("###### equation", equation) + path = numpy.einsum_path(equation, *inputs, optimize=False) + print(path[1]) + path = numpy.einsum_path(equation, *inputs) + print(path[1]) + + shapes = [m.shape for m in inputs] + seq = decompose_einsum_equation(equation, *shapes, verbose=verbose) + got = apply_sequence(seq, *inputs, verbose=verbose) + self.assertEqualArray(exp, got) + + def test_numpy_test_hadamard_like_products(self): # Hadamard outer products self.optimize_compare('a,ab,abc->abc') self.optimize_compare('a,b,ab->ab') - def _test_index_transformations(self): + def np_test_index_transformations(self): # Simple index transformation cases self.optimize_compare('ea,fb,gc,hd,abcd->efgh') self.optimize_compare('ea,fb,abcd,gc,hd->efgh') self.optimize_compare('abcd,ea,fb,gc,hd->efgh') - def _test_complex(self): + def np_test_complex(self): # Long test cases self.optimize_compare('acdf,jbje,gihb,hfac,gfac,gifabc,hfac') self.optimize_compare('acdf,jbje,gihb,hfac,gfac,gifabc,hfac') @@ -99,16 +133,16 @@ def _test_complex(self): self.optimize_compare('chd,bde,agbc,hiad,bdi,cgh,agdb') self.optimize_compare('bdhe,acad,hiab,agac,hibd') - def _test_collapse(self): + def np_test_np_test_collapse(self): # Inner products - self.optimize_compare('ab,ab,c->') - self.optimize_compare('ab,ab,c->c') - self.optimize_compare('ab,ab,cd,cd->') + self.optimize_compare('ab,ab,c->c', verbose=True) self.optimize_compare('ab,ab,cd,cd->ac') self.optimize_compare('ab,ab,cd,cd->cd') + self.optimize_compare('ab,ab,c->') + self.optimize_compare('ab,ab,cd,cd->') self.optimize_compare('ab,ab,cd,cd,ef,ef->') - def _test_expand(self): + def np_test_expand(self): # Outer products self.optimize_compare('ab,cd,ef->abcdef') self.optimize_compare('ab,cd,ef->acdf') @@ -117,7 +151,7 @@ def _test_expand(self): self.optimize_compare('ab,bcd,cd->abcd') self.optimize_compare('ab,bcd,cd->abd') - def _test_edge_cases(self): + def np_test_edge_cases(self): # Difficult edge cases for optimization self.optimize_compare('eb,cb,fb->cef') self.optimize_compare('dd,fb,be,cdb->cef') @@ -132,7 +166,7 @@ def _test_edge_cases(self): self.optimize_compare('efc,dbc,acf,fd->abe') self.optimize_compare('ba,ac,da->bcd') - def _test_inner_product(self): + def np_test_inner_product(self): # Inner products self.optimize_compare('ab,ab') self.optimize_compare('ab,ba') @@ -140,7 +174,7 @@ def _test_inner_product(self): self.optimize_compare('abc,bac') self.optimize_compare('abc,cba') - def _test_random_cases(self): + def np_test_random_cases(self): # Randomly built test cases self.optimize_compare('aab,fa,df,ecc->bde') self.optimize_compare('ecb,fef,bad,ed->ac') @@ -154,13 +188,13 @@ def _test_random_cases(self): self.optimize_compare('dba,ead,cad->bce') self.optimize_compare('aef,fbc,dca->bde') - def _test_combined_views_mapping(self): + def np_test_combined_views_mapping(self): # gh-10792 a = numpy.arange(9).reshape(1, 1, 3, 1, 3) b = numpy.einsum('bbcdc->d', a) assert_equal(b, [12]) - def _test_broadcasting_dot_cases(self): + def np_test_broadcasting_dot_cases(self): # Ensures broadcasting cases are not mistaken for GEMM a = numpy.random.rand(1, 5, 4) @@ -183,5 +217,4 @@ def _test_broadcasting_dot_cases(self): if __name__ == "__main__": # TestEinsum().test_decompose_einsum_equation() - # stop unittest.main() diff --git a/mlprodict/testing/einsum_impl.py b/mlprodict/testing/einsum_impl.py index 42e84cace..04434c458 100644 --- a/mlprodict/testing/einsum_impl.py +++ b/mlprodict/testing/einsum_impl.py @@ -5,6 +5,59 @@ import numpy +def numpy_extended_dot(m1, m2, axes, left, right, verbose=False): + """ + Extended version of a matrix multiplication + with two matrices *m1*, *m2* of the same dimension. + Loops over *left* axes for *m1* and *right* axes for *m2*, + summation is done over *axes*. + Other axes must be empty. + + :param m1: first matrix + :param m2: second matrix + :param axes: summation axes + :param left: left axes + :param right: right axes + :param verbose: display intermediate information + :return: output + """ + if len(m1.shape) != len(m2.shape): + raise RuntimeError( + "Matrices m1 and m2 must have the same dimension, " + "m1=%r, m2=%r." % (m1.shape, m2.shape)) + # This implementation should not use einsum. + # Temporary solution. + l1 = [chr(i + 97) for i in range(len(m1.shape))] + l2 = [chr(i + 97) for i in range(len(m2.shape))] + l3 = [chr(i + 97) for i in range(len(m2.shape))] + for a in left: + l1[a] = l1[a].upper() + l3[a] = l3[a].upper() + for a in right: + l2[a] = l2[a].upper() + l3[a] = l3[a].upper() + for a in axes: + l1[a] = l1[a].lower() + l2[a] = l2[a].lower() + if a not in right: + l3[a] = None + else: + l3[a] = l3[a].lower() + eq = "%s,%s->%s" % ("".join(l1), "".join(l2), + "".join(s for s in l3 if s)) + if verbose: + print(" [numpy_extended_dot] %s: %r @ %r" % (eq, m1.shape, m2.shape)) + output = numpy.einsum(eq, m1, m2) + new_shape = list(output.shape) + for a in axes: + if a not in right: + new_shape.insert(a, 1) + if verbose: + print(" [numpy_extended_dot] %r reshaped into %r " % ( + output.shape, new_shape)) + return output.reshape(tuple(new_shape)) + + def analyse_einsum_equation(equation): """ Analyses an einsum equation. @@ -61,7 +114,7 @@ def decompose_einsum_equation(equation, *shapes, strategy="simple", verbose=Fals :param strategy: there are different way to decompose the equation, this parameters defines the way to do it (see below) :param verbose: verbosity - :return: sequence of operations of typ @see cl EinsumSubOp + :return: instance @see cl GraphEinsumSubOp About *strategy*: * `'simple'`: align all dimensions in the alphabetical order @@ -88,10 +141,7 @@ def apply_sequence(seq, *inputs, verbose=False): :param inputs: inputs: :return: output """ - data = {i: inp for i, inp in enumerate(inputs)} - for op in seq: - op.apply(data) - return data[id(seq[-1])] + return seq.apply_sequence(*inputs, verbose=verbose) def _basic_verification(lengths, shapes, equation): @@ -117,7 +167,8 @@ class EinsumSubOp: _allowed = {'expand_dims', 'transpose', 'reduce_sum', 'matmul', 'id', 'squeeze'} - def __init__(self, name, *inputs, **kwargs): + def __init__(self, full_dim, name, *inputs, **kwargs): + self.full_dim = full_dim self.name = name self.inputs = inputs self.kwargs = kwargs @@ -208,13 +259,20 @@ def compute_output_row(self, row, row2=None, verbose=False): if self.name == "matmul": self._check_arg_('axes', tuple) + self._check_arg_('left', tuple) + self._check_arg_('right', tuple) if row2 is None: raise RuntimeError("matmul expects two inputs.") if verbose: - print(" MATMUL %r @ %r" % (row, row2)) + axes = self.kwargs['axes'] + left = self.kwargs['left'] + right = self.kwargs['right'] + print(" MATMUL %r @ %r axes=%r left=%r right=%r" % ( + row, row2, axes, left, right)) row2[:] = numpy.maximum(row, row2) for a in self.kwargs['axes']: - row2[a] = -1 + if a not in self.kwargs['right']: + row2[a] = -1 self._check_row_(row2, verbose=verbose) return @@ -228,36 +286,243 @@ def compute_output_row(self, row, row2=None, verbose=False): raise NotImplementedError( "compute_output_row not implemented for %r." % self.name) - def _check_inputs_(self, n_expected): + def _check_inputs_(self, n_expected, check_dim=False): if len(self.inputs) != n_expected: raise RuntimeError( "Number of inputs must be %d not %d for operator %r." "" % (n_expected, len(self.inputs), self.name)) + def _check_shape_(self, m): + if len(m.shape) != self.full_dim: + raise RuntimeError( + "Number of dimensions %r is different from expected value " + "%d." % (m.shape, self.full_dim)) + def _get_data(self, data, key): if isinstance(key, int): + if key not in data: + raise RuntimeError( + "Unable to find key %d in %r." % ( + key, list(sorted(data)))) return data[key] if isinstance(key, EinsumSubOp): + if id(key) not in data: + raise RuntimeError( + "Unable to find key %d in %r." % ( + id(key), list(sorted(data)))) return data[id(key)] raise TypeError( "Unexpected input type %r." % type(key)) - def apply(self, data): + def apply(self, data, verbose=False): """ Applies one operator on the data. :param data: dictionary storing the results """ + if verbose: + print() if self.name == 'id': self._check_inputs_(1) inp = self.inputs[0] - return self._get_data(data, inp) + output = self._get_data(data, inp) - if self.name == 'expand_dims': + elif self.name == 'expand_dims': self._check_inputs_(1) + inp = self.inputs[0] + m = self._get_data(data, inp) + if verbose: + print("- %s, shape=%r axis=%r" % ( + self.name, m.shape, self.kwargs['axis'])) + output = numpy.expand_dims(m, self.kwargs['axis'][0]) - raise NotImplementedError( - "apply not implemented for %r." % self.name) + elif self.name == 'transpose': + self._check_inputs_(1, True) + inp = self.inputs[0] + m = self._get_data(data, inp) + self._check_shape_(m) + if verbose: + print("- %s, shape=%r perm=%r" % ( + self.name, m.shape, self.kwargs['perm'])) + output = numpy.transpose(m, self.kwargs['perm']) + self._check_shape_(output) + + elif self.name == 'matmul': + self._check_inputs_(2) + inp1 = self.inputs[0] + inp2 = self.inputs[1] + m1 = self._get_data(data, inp1) + m2 = self._get_data(data, inp2) + self._check_shape_(m1) + self._check_shape_(m2) + axes = self.kwargs['axes'] + left = self.kwargs['left'] + right = self.kwargs['right'] + + if verbose: + print("- %s, shapes=%r @ %r axes=%r left=%r right=%r" % ( + self.name, m1.shape, m2.shape, axes, left, right)) + + output = numpy_extended_dot(m1, m2, axes, left, right, + verbose=verbose) + self._check_shape_(output) + + elif self.name == 'reduce_sum': + self._check_inputs_(1) + inp = self.inputs[0] + m = self._get_data(data, inp) + self._check_shape_(m) + axes = self.kwargs['axes'] + if verbose: + print("- %s, shape=%r axes=%r" % ( + self.name, m.shape, self.kwargs['axes'])) + output = numpy.sum(m, axis=axes, keepdims=True) + self._check_shape_(output) + + elif self.name == 'squeeze': + self._check_inputs_(1) + inp = self.inputs[0] + m = self._get_data(data, inp) + axes = self.kwargs['axes'] + if verbose: + print("- %s, shape=%r axes=%r" % ( + self.name, m.shape, self.kwargs['axes'])) + output = m + for a in axes[::-1]: + output = numpy.squeeze(output, axis=a) + return output + + else: + raise NotImplementedError( + "apply not implemented for %r." % self.name) + + data[id(self)] = output + if verbose: + print("+ %s, shape=%r -- %d" % (self.name, output.shape, id(self))) + return output + + +class GraphEinsumSubOp: + """ + Class gathering all nodes produced to explicit einsum + operators. + """ + + def __init__(self): + self._nodes = {} + self._mark = {} + self._ops = [] + self.last_op = None + self.last_added_op = None + + def append(self, op): + """ + Adds one input or result. + + :param op: integer (an input) or an instance of @see cl EinsumSubOp. + :return: op or None if op is an integer + """ + if isinstance(op, int): + if op in self._nodes: + raise RuntimeError("Key %d already added." % op) + self._nodes[op] = op + self.last_added_op = op + return None + if isinstance(op, EinsumSubOp): + if op in self._nodes: + raise RuntimeError( + "Key %d already added, op=%r." % (id(op), op)) + self._nodes[id(op)] = op + self._ops.append(op) + self.last_added_op = op + return op + raise TypeError("Unexpected type %r." % type(i)) + + def mark(self, i, op): + """ + Marks one input or result as an intermediate result + after a full einsum step. + + :param op: integer (an input) or an instance of @see cl EinsumSubOp. + """ + if not isinstance(i, int): + raise TypeError("i must an integer not %r." % type(i)) + if isinstance(op, EinsumSubOp): + if id(op) not in self._nodes: + raise RuntimeError( + "Key %d not found, op=%r." % (id(op), op)) + self._mark[i] = op + self.last_op = op + else: + raise TypeError("Unexpected type %r." % type(i)) + + def __iter__(self): + "Iterates on nodes." + for op in self._ops: + yield op + + def to_graph(self, layout='ascii'): + """ + Draws a graph. + + :param layout: ascii (relies on package :epkg:`graphscii` + :return: string + """ + if layout == 'ascii': + pass + + raise NotImplementedError("Unexpected layout %r." % layout) + + def to_dot(self): + """ + Produces a graph in :epkg:`dot`. + :return: string + """ + def d2s(d): + it = [] + for k, v in sorted(d.items()): + it.append("%s=%s" % (k, v)) + return " ".join(it) + + rows = ["digraph{"] + for k, v in self._nodes.items(): + if isinstance(v, int): + lab = str(v) + sk = v + else: + lab = "%s\\n%s" % (v.name, d2s(v.kwargs)) + sk = id(v) + if sk in self._mark: + s = '%d [label="%s" fillcolor=red];' % (k, lab) + else: + s = '%d [label="%s"];' % (k, lab) + rows.append(s) + if not hasattr(v, 'inputs'): + continue + for i in v.inputs: + vid = i if isinstance(i, int) else id(i) + s = "%d -> %d;" % (vid, k) + rows.append(s) + rows.append("}") + return "\n".join(rows) + + def apply_sequence(self, *inputs, verbose=False): + """ + Applies a sequence of operations on a list of inputs. + + :param inputs: inputs: + :return: output + """ + if verbose: + print('######### apply_sequence') + data = {i: inp for i, inp in enumerate(inputs)} + last = None + for op in self: + last = op.apply(data, verbose=verbose) + if last is None: + raise RuntimeError( + "Sequence of operations is empty.") + return last def _apply_transpose_reshape(op, row): @@ -275,7 +540,7 @@ def _apply_transpose_reshape(op, row): p += 1 perm.append(r) for a in reversed(axes): - op = EinsumSubOp('expand_dims', op, axis=a) + op = EinsumSubOp(len(row), 'expand_dims', op, axis=a) yield op dec = [0] for i in range(1, len(perm)): @@ -288,7 +553,7 @@ def _apply_transpose_reshape(op, row): perm[i] = i else: perm[i] = perm[i] + dec[i] - op = EinsumSubOp('transpose', op, perm=tuple(perm)) + op = EinsumSubOp(len(row), 'transpose', op, perm=tuple(perm)) yield op @@ -306,10 +571,10 @@ def _apply_squeeze_transpose(op, row_last, row_output): else: perm.append((d, i)) perm = [p[1] for p in perm] - op = EinsumSubOp('transpose', op, perm=tuple(perm)) + op = EinsumSubOp(len(row_last), 'transpose', op, perm=tuple(perm)) yield op if len(sq) > 0: - op = EinsumSubOp('squeeze', op, axes=tuple(sq)) + op = EinsumSubOp(len(row_last), 'squeeze', op, axes=tuple(sq)) yield op @@ -326,7 +591,8 @@ def _decompose_einsum_equation_simple(equation, *shapes, verbose=False): ops = [] # last_row, current_row (row = shape) rows = numpy.full((2, mat.shape[1]), -1) - last_op = None + graph = GraphEinsumSubOp() + fd = mat.shape[1] if verbose: print("EQUATION=%r" % equation) @@ -334,14 +600,16 @@ def _decompose_einsum_equation_simple(equation, *shapes, verbose=False): if verbose: print() print("######### ROW %d shape=%r row=%r" % (i, sh, rows[1, :])) + graph.append(i) # Input matrix aligned to the same dimensions. - op = EinsumSubOp('id', i) + op = EinsumSubOp(fd, 'id', i) op.compute_output_row(rows[1, :], mat[i, :], verbose=verbose) - ops.append(op) + marked = graph.append(op) + for op in _apply_transpose_reshape(op, mat[i]): op.compute_output_row(rows[1, :], verbose=verbose) - ops.append(op) + marked = graph.append(op) # Reduction? (a dimension not used later) red = [] @@ -351,41 +619,49 @@ def _decompose_einsum_equation_simple(equation, *shapes, verbose=False): red.append(d) if len(red) > 0: if verbose: - print("-- REDUCE1 row=%d axes=%r" % (i, red)) + print(" -- REDUCE1 row=%d axes=%r" % (i, red)) print(mat) - print('-') + print(' -') print(rows) - op = EinsumSubOp('reduce_sum', ops[-1], axes=tuple(red)) + op = EinsumSubOp(fd, 'reduce_sum', + graph.last_added_op, axes=tuple(red)) op.compute_output_row(rows[1, :], verbose=verbose) + marked = graph.append(op) - if last_op is not None: + if graph.last_op is not None: # Matrix multiplication? common_dims = [] + left = [] + right = [] for d in range(0, mat.shape[1]): if rows[:, d].min() >= 0: common_dims.append(d) + if mat[i + 1:, d].max() >= 0: + left.append(d) + right.append(d) + else: + if rows[0, d] >= 0: + left.append(d) + if rows[1, d] >= 0: + right.append(d) if verbose: - print("-- MATMUL common_dims=%r" % common_dims) + print(" -- MATMUL common_dims=%r" % common_dims) print(rows) - if len(common_dims) > 0: - op = EinsumSubOp('matmul', last_op, op, - axes=tuple(common_dims)) - ops.append(op) - op.compute_output_row(rows[0, :], rows[1, :], verbose=verbose) - else: - raise NotImplementedError( - "Unable to interpret equation %r at position %i " - "(starting at 0) common_dims=%r rows-=%r rows+=%r." - "" % (equation, i, common_dims, rows[0, :], rows[1, :])) + op = EinsumSubOp(fd, 'matmul', graph.last_op, op, + axes=tuple(common_dims), + left=tuple(left), right=tuple(right)) + op.compute_output_row(rows[0, :], rows[1, :], verbose=verbose) + marked = graph.append(op) # End + graph.mark(i, marked) rows[0, :] = rows[1, :] - last_op = op # Final output if verbose: print() print("######### FIN row=%r" % rows[1, :]) + if mat[len(shapes), :].max() >= 0: rows[1, :] = mat[len(shapes), :] red = [] @@ -400,12 +676,12 @@ def _decompose_einsum_equation_simple(equation, *shapes, verbose=False): if verbose: print("-- REDUCE2 axes=%r" % red) print(mat) - op = EinsumSubOp('reduce_sum', op, axes=red) - ops.append(op) + op = EinsumSubOp(fd, 'reduce_sum', op, axes=tuple(red)) + graph.append(op) op.compute_output_row(rows[1, :], verbose=verbose) - # Final transpose and reshape if needed + # Removes empty axes. for op in _apply_squeeze_transpose(op, rows[1, :], mat[len(shapes), :]): op.compute_output_row(rows[1, :], verbose=verbose) - ops.append(op) - return ops + graph.append(op) + return graph From c2e0f47305b428eee10d69de8e0a141b2e7c369b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Thu, 22 Apr 2021 10:04:13 +0200 Subject: [PATCH 04/33] improve dot graph --- mlprodict/onnxrt/onnx_inference_exports.py | 4 ++- mlprodict/testing/einsum_impl.py | 38 ++++++++++++++++++---- 2 files changed, 34 insertions(+), 8 deletions(-) diff --git a/mlprodict/onnxrt/onnx_inference_exports.py b/mlprodict/onnxrt/onnx_inference_exports.py index 68816457b..2e2417b2a 100644 --- a/mlprodict/onnxrt/onnx_inference_exports.py +++ b/mlprodict/onnxrt/onnx_inference_exports.py @@ -47,6 +47,7 @@ def to_dot(self, recursive=False, prefix='', # pylint: disable=R0914 'nodesep': '0.05', 'width': '0.5', 'height': '0.1', + 'size': '5', } One example: @@ -98,6 +99,7 @@ def dot_label(text): 'nodesep': '0.05', 'width': '0.5', 'height': '0.1', + 'size': '5', } options.update(params) @@ -114,7 +116,7 @@ def dot_label(text): inter_vars = {} exp = ["digraph{"] - for opt in {'orientation', 'pad', 'nodesep', 'ranksep'}: + for opt in {'orientation', 'pad', 'nodesep', 'ranksep', 'size'}: if opt in options: exp.append(" {}={};".format(opt, options[opt])) fontsize = 10 diff --git a/mlprodict/testing/einsum_impl.py b/mlprodict/testing/einsum_impl.py index 04434c458..3aea1ecd9 100644 --- a/mlprodict/testing/einsum_impl.py +++ b/mlprodict/testing/einsum_impl.py @@ -408,12 +408,15 @@ class GraphEinsumSubOp: operators. """ - def __init__(self): + def __init__(self, letters, mat, lengths): self._nodes = {} self._mark = {} self._ops = [] self.last_op = None self.last_added_op = None + self.metadata = dict( + letters=letters, mat=mat, lengths=lengths, + mat0=mat.copy()) def append(self, op): """ @@ -452,6 +455,7 @@ def mark(self, i, op): raise RuntimeError( "Key %d not found, op=%r." % (id(op), op)) self._mark[i] = op + self._mark[id(op)] = i self.last_op = op else: raise TypeError("Unexpected type %r." % type(i)) @@ -473,11 +477,24 @@ def to_graph(self, layout='ascii'): raise NotImplementedError("Unexpected layout %r." % layout) - def to_dot(self): + def to_dot(self, **kwargs): """ Produces a graph in :epkg:`dot`. + + :param kwargs: additional graph option :return: string """ + options = { + 'orientation': 'portrait', + 'ranksep': '0.25', + 'nodesep': '0.05', + 'width': '0.5', + 'height': '0.1', + 'size': '5', + 'node': '[shape=record]', + } + options.update(kwargs) + def d2s(d): it = [] for k, v in sorted(d.items()): @@ -485,15 +502,22 @@ def d2s(d): return " ".join(it) rows = ["digraph{"] + for k, v in options.items(): + if "[" in v: + rows.append("{} {};".format(k, v)) + else: + rows.append("{}={};".format(k, v)) for k, v in self._nodes.items(): if isinstance(v, int): - lab = str(v) + lab = "input %d\\\\n%s" % (v, str(self.metadata['mat0'][v])) sk = v else: - lab = "%s\\n%s" % (v.name, d2s(v.kwargs)) + lab = "%s\\\\n%s" % (v.name, d2s(v.kwargs)) sk = id(v) - if sk in self._mark: - s = '%d [label="%s" fillcolor=red];' % (k, lab) + if sk in self._mark and isinstance(self._mark[sk], int): + la = self._mark[sk] + s = ('%d [label="%s - I%d" style=filled ' + 'fillcolor=red];' % (k, lab, la)) else: s = '%d [label="%s"];' % (k, lab) rows.append(s) @@ -591,7 +615,7 @@ def _decompose_einsum_equation_simple(equation, *shapes, verbose=False): ops = [] # last_row, current_row (row = shape) rows = numpy.full((2, mat.shape[1]), -1) - graph = GraphEinsumSubOp() + graph = GraphEinsumSubOp(letters, mat, lengths) fd = mat.shape[1] if verbose: print("EQUATION=%r" % equation) From 6758a9aaaa026863ac902bd86ea9fca492c6a2f7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Thu, 22 Apr 2021 20:36:03 +0200 Subject: [PATCH 05/33] documentation --- _doc/notebooks/einsum_decomposition.ipynb | 674 ++++++++++++++++++++++ _unittests/ut_testing/test_einsum.py | 191 ++++-- mlprodict/onnxrt/ops_cpu/op_einsum.py | 5 +- mlprodict/testing/einsum_impl.py | 153 +++-- 4 files changed, 919 insertions(+), 104 deletions(-) create mode 100644 _doc/notebooks/einsum_decomposition.ipynb diff --git a/_doc/notebooks/einsum_decomposition.ipynb b/_doc/notebooks/einsum_decomposition.ipynb new file mode 100644 index 000000000..8da3ffa3e --- /dev/null +++ b/_doc/notebooks/einsum_decomposition.ipynb @@ -0,0 +1,674 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Einsum decomposition\n", + "\n", + "This notebook shows a way to decompose [einsum](https://numpy.org/doc/stable/reference/generated/numpy.einsum.html) into a subset of operations (expand_dims, squeeze, transpose, extended matrix multiplication)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
run previous cell, wait for 2 seconds
\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from jyquickhelper import add_notebook_menu\n", + "add_notebook_menu()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Operator explanation with equation bac,cd,def->ebc\n", + "\n", + "The operator einsum takes an equation and some inputs. Every letter involved in the equation is a loop. Let's see on one example." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[ 8866198., 9864696.],\n", + " [12090270., 13152928.]],\n", + "\n", + " [[ 8883886., 9884376.],\n", + " [12114390., 13179168.]]], dtype=float32)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy\n", + "\n", + "m1 = numpy.arange(0, 8).astype(numpy.float32).reshape((2, 2, 2)) + 10\n", + "m2 = numpy.arange(0, 4).astype(numpy.float32).reshape((2, 2)) + 100\n", + "m3 = numpy.arange(0, 8).astype(numpy.float32).reshape((2, 2, 2)) + 1000\n", + "\n", + "equation = \"bac,cd,def->ebc\"\n", + "truth = numpy.einsum(equation, m1, m2, m3)\n", + "truth" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This summation is equalent to:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[ 8866198., 9864696.],\n", + " [12090270., 13152928.]],\n", + "\n", + " [[ 8883886., 9884376.],\n", + " [12114390., 13179168.]]])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res = numpy.zeros((2, 2, 2))\n", + "for a in range(0, 2):\n", + " for b in range(0, 2):\n", + " for c in range(0, 2):\n", + " for d in range(0, 2):\n", + " for e in range(0, 2):\n", + " for f in range(0, 2):\n", + " res[e, b, c] += m1[b, a, c] * m2[c, d] * m3[d, e, f]\n", + "res" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Theoritically, this summation is in this case has a cost of $O(N^6)$. However this simple computation is usually much longer than using matrix multiplications along the path. $O(N^4)$ is the cost of the heaviest matrix multiplication in this case). But to do that, the equation needs to be decomposed into a sequence of matrix multiplications." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Decomposition of bac,cd,def->ebc" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy\n", + "from mlprodict.testing.einsum_impl import (\n", + " decompose_einsum_equation, apply_einsum_sequence)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "m1 = numpy.arange(0, 8).astype(numpy.float32).reshape((2, 2, 2)) + 10\n", + "m2 = numpy.arange(0, 4).astype(numpy.float32).reshape((2, 2)) + 100\n", + "m3 = numpy.arange(0, 8).astype(numpy.float32).reshape((2, 2, 2)) + 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "seq = decompose_einsum_equation(\"bac,cd,def->ebc\", m1.shape, m2.shape, m3.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from jyquickhelper import RenderJsDot\n", + "RenderJsDot(seq.to_dot(size=7))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then the result can be obtained as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[ 8866198., 9864696.],\n", + " [12090270., 13152928.]],\n", + "\n", + " [[ 8883886., 9884376.],\n", + " [12114390., 13179168.]]], dtype=float32)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "apply_einsum_sequence(seq, m1, m2, m3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## onnxruntime" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "import onnx\n", + "from onnx import helper, numpy_helper\n", + "from onnxruntime import InferenceSession\n", + "\n", + "\n", + "def make_model(equation):\n", + " model = helper.make_model(\n", + " opset_imports=[helper.make_operatorsetid('', 13)],\n", + " graph=helper.make_graph(\n", + " name='einsum_test',\n", + " inputs=[helper.make_tensor_value_info(\"X\", onnx.TensorProto.FLOAT, None),\n", + " helper.make_tensor_value_info(\"Y\", onnx.TensorProto.FLOAT, None),\n", + " helper.make_tensor_value_info(\"Z\", onnx.TensorProto.FLOAT, None)],\n", + " outputs=[helper.make_tensor_value_info(\"A\", onnx.TensorProto.FLOAT, None)],\n", + " nodes=[\n", + " helper.make_node(\"Einsum\", [\"X\", \"Y\", \"Z\"], [\"A\"], equation=equation)\n", + " ]\n", + " )\n", + " )\n", + " return model\n", + "\n", + "\n", + "model = make_model(\"bac,cd,def->ebc\")\n", + "sess = InferenceSession(model.SerializeToString())" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[ 8866198., 9864696.],\n", + " [12090270., 13152928.]],\n", + "\n", + " [[ 8883886., 9884376.],\n", + " [12114390., 13179168.]]], dtype=float32)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sess.run(None, {'X': m1, 'Y': m2, 'Z': m3})[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Benchmark\n", + "\n", + "It clearly shows the summation done with the basic algorithm is clearly the slowest." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 10/10 [00:03<00:00, 2.98it/s]\n" + ] + }, + { + "data": { + "text/html": [ + "
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averagedeviationmin_execmax_execrepeatnumbertotalnameN
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300.0772210.0091780.0667670.09466510100.772209numpy.einsum40
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" + ], + "text/plain": [ + " average deviation min_exec max_exec repeat number total \\\n", + "28 0.010033 0.000136 0.009900 0.010392 10 10 0.100332 \n", + "29 0.002508 0.000182 0.002332 0.002898 10 10 0.025076 \n", + "30 0.077221 0.009178 0.066767 0.094665 10 10 0.772209 \n", + "31 0.014716 0.000698 0.013814 0.015954 10 10 0.147156 \n", + "32 0.003906 0.000792 0.003125 0.005847 10 10 0.039060 \n", + "\n", + " name N \n", + "28 custom_einsum 35 \n", + "29 onnxruntime 35 \n", + "30 numpy.einsum 40 \n", + "31 custom_einsum 40 \n", + "32 onnxruntime 40 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from mlprodict.onnxrt.validate.validate_helper import measure_time\n", + "from tqdm import tqdm\n", + "from pandas import DataFrame\n", + "\n", + "\n", + "def raw_product(m1, m2, m3):\n", + " N = m1.shape[0]\n", + " res = numpy.zeros((N, N, N))\n", + " for a in range(0, N):\n", + " for b in range(0, N):\n", + " for c in range(0, N):\n", + " for d in range(0, N):\n", + " for e in range(0, N):\n", + " for f in range(0, N):\n", + " res[e, b, c] += m1[b, a, c] * m2[c, d] * m3[d, e, f]\n", + " return res\n", + "\n", + "\n", + "equation = \"bac,cd,def->ebc\"\n", + "sess = None\n", + "seq = None \n", + "\n", + "results = []\n", + "for N in tqdm([2, 3, 4, 10, 15, 20, 25, 30, 35, 40]):\n", + " m1 = numpy.random.randn(N, N, N)\n", + " m2 = numpy.random.randn(N, N)\n", + " m3 = numpy.random.randn(N, N, N)\n", + " \n", + " if seq is None:\n", + " seq = decompose_einsum_equation(\n", + " equation, m1.shape, m2.shape, m3.shape)\n", + " if sess is None:\n", + " model = make_model(equation)\n", + " sess = InferenceSession(model.SerializeToString())\n", + "\n", + " res = measure_time(lambda x: numpy.einsum(equation, *x, optimize=True),\n", + " [m1, m2, m3],\n", + " repeat=10, number=10)\n", + " res['name'] = \"numpy.einsum\"\n", + " res[\"N\"] = N\n", + " results.append(res)\n", + "\n", + " if N <= 4:\n", + " res = measure_time(lambda x: raw_product(*x),\n", + " [m1, m2, m3],\n", + " repeat=10, number=10)\n", + " res['name'] = \"raw_product\"\n", + " res[\"N\"] = N\n", + " results.append(res) \n", + "\n", + " res = measure_time(lambda x: apply_einsum_sequence(seq, *x),\n", + " [m1, m2, m3],\n", + " repeat=10, number=10)\n", + " res['name'] = \"custom_einsum\"\n", + " res[\"N\"] = N\n", + " results.append(res) \n", + "\n", + " res = measure_time(lambda x: sess.run(None, {'X': x[0], 'Y': x[1], 'Z': x[2]}),\n", + " [m1.astype(numpy.float32), m2.astype(numpy.float32),\n", + " m3.astype(numpy.float32)],\n", + " repeat=10, number=10)\n", + " res['name'] = \"onnxruntime\"\n", + " res[\"N\"] = N\n", + " results.append(res) \n", + " \n", + "\n", + "df = DataFrame(results)\n", + "df.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "piv = df.pivot(\"N\", \"name\", \"average\")\n", + "ax = piv.plot(logy=True, logx=True)\n", + "ax.set_title(\"Benchmark einsum function\");" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/_unittests/ut_testing/test_einsum.py b/_unittests/ut_testing/test_einsum.py index 70554caf4..6609fee78 100644 --- a/_unittests/ut_testing/test_einsum.py +++ b/_unittests/ut_testing/test_einsum.py @@ -4,11 +4,12 @@ import unittest import io from contextlib import redirect_stdout +import itertools import numpy from pyquickhelper.pycode import ExtTestCase from mlprodict.testing.einsum_impl import ( analyse_einsum_equation, decompose_einsum_equation, EinsumSubOp, - apply_sequence) + apply_einsum_sequence) class TestEinsum(ExtTestCase): @@ -65,7 +66,7 @@ def fct(): print(dot) red = dot.split('red') self.assertEqual(len(red), 4) - res = apply_sequence(seq, m1, m2, verbose=True) + res = apply_einsum_sequence(seq, m1, m2, verbose=True) print("########################## END") return res @@ -87,16 +88,90 @@ def test_einsum_sub_op(self): 2, "matmul", (2, 2)), RuntimeError) self.assertRaise(lambda: EinsumSubOp(2, "id", (2, 2)), TypeError) + def common_test_case_2(self, equation, verbose=False): + m1 = numpy.arange(2 * 2 * 2).reshape((2, 2, 2)) + 10 + m2 = numpy.arange(4).reshape((2, 2)) + 100 + exp = numpy.einsum(equation, m1, m2) + seq = decompose_einsum_equation( + equation, m1.shape, m2.shape, verbose=verbose) + res = apply_einsum_sequence(seq, m1, m2, verbose=verbose) + self.assertEqualArray(exp, res) + + def test_case_2_A(self): + self.common_test_case_2('abc,cd->abc') + + def test_many_2(self): + m1 = numpy.arange(2 * 2 * 2).reshape((2, 2, 2)) + 10 + m2 = numpy.arange(4).reshape((2, 2)) + 100 + + res = [] + for p1 in itertools.permutations(list("abc")): + for p2 in itertools.permutations(list("cd")): + for i in [1, 2]: + for j in [0, 1]: + sp1 = "".join(p1) + sp2 = "".join(p2) + if len(set([sp1[0], sp1[i], sp2[j]])) != 3: + continue + equation = "%s,%s->%s%s%s" % ( + sp1, sp2, sp1[0], sp1[i], sp2[j]) + try: + r = numpy.einsum(equation, m1, m2) + res.append((equation, r)) + except ValueError: + # Not viable equation. + continue + + for i, (eq, exp) in enumerate(res): + with self.subTest(equation=eq, index=i, total=len(res)): + seq = decompose_einsum_equation( + eq, m1.shape, m2.shape) + res = apply_einsum_sequence(seq, m1, m2) + self.assertEqualArray(exp, res) + + def test_many_3(self): + m1 = numpy.arange(2 * 2 * 2).reshape((2, 2, 2)) + 10 + m2 = numpy.arange(4).reshape((2, 2)) + 100 + m3 = numpy.arange(8).reshape((2, 2, 2)) + 1000 + + res = [] + for p1 in itertools.permutations(list("abc")): # pylint: disable=R1702 + for p2 in itertools.permutations(list("cd")): + for p3 in itertools.permutations(list("def")): + for i in [1, 2]: + for j in [0, 1]: + sp1 = "".join(p1) + sp2 = "".join(p2) + sp3 = "".join(p3) + equation = "%s,%s,%s->%s%s%s" % ( + sp1, sp2, sp3, sp1[0], sp1[i], sp3[j]) + try: + r = numpy.einsum(equation, m1, m2, m3) + res.append((equation, r)) + except ValueError: + # Not viable equation. + continue + + for i, (eq, exp) in enumerate(res): + with self.subTest(equation=eq, index=i, total=len(res)): + seq = decompose_einsum_equation( + eq, m1.shape, m2.shape, m3.shape) + res = apply_einsum_sequence(seq, m1, m2, m3) + self.assertEqualArray(exp, res) + # Taken from https://github.com/numpy/numpy/blob/main/numpy/ # core/tests/test_einsum.py. - def optimize_compare(self, equation, verbose=False): - eqs = equation.split("->")[0].split(",") - inputs = [] - for eq in eqs: - i = numpy.arange(2 ** len(eq)).reshape( - (2,) * len(eq)).astype(numpy.float32) - inputs.append(i + numpy.array([10], dtype=numpy.float32)) + def optimize_compare(self, equation, operands=None, verbose=False): + if operands is not None: + inputs = operands + else: + eqs = equation.split("->")[0].split(",") + inputs = [] + for eq in eqs: + i = numpy.arange(2 ** len(eq)).reshape( + (2,) * len(eq)).astype(numpy.float32) + inputs.append(i + numpy.array([10], dtype=numpy.float32)) exp = numpy.einsum(equation, *inputs) if verbose: @@ -108,7 +183,7 @@ def optimize_compare(self, equation, verbose=False): shapes = [m.shape for m in inputs] seq = decompose_einsum_equation(equation, *shapes, verbose=verbose) - got = apply_sequence(seq, *inputs, verbose=verbose) + got = apply_einsum_sequence(seq, *inputs, verbose=verbose) self.assertEqualArray(exp, got) def test_numpy_test_hadamard_like_products(self): @@ -116,33 +191,22 @@ def test_numpy_test_hadamard_like_products(self): self.optimize_compare('a,ab,abc->abc') self.optimize_compare('a,b,ab->ab') - def np_test_index_transformations(self): + def test_np_test_np_test_collapse(self): + # Inner products + self.optimize_compare('ab,ab,c->c') + self.optimize_compare('ab,ab,cd,cd->ac') + self.optimize_compare('ab,ab,cd,cd->cd') + # self.optimize_compare('ab,ab,c->') + # self.optimize_compare('ab,ab,cd,cd->') + # self.optimize_compare('ab,ab,cd,cd,ef,ef->') + + def test_np_test_index_transformations(self): # Simple index transformation cases self.optimize_compare('ea,fb,gc,hd,abcd->efgh') self.optimize_compare('ea,fb,abcd,gc,hd->efgh') self.optimize_compare('abcd,ea,fb,gc,hd->efgh') - def np_test_complex(self): - # Long test cases - self.optimize_compare('acdf,jbje,gihb,hfac,gfac,gifabc,hfac') - self.optimize_compare('acdf,jbje,gihb,hfac,gfac,gifabc,hfac') - self.optimize_compare('cd,bdhe,aidb,hgca,gc,hgibcd,hgac') - self.optimize_compare('abhe,hidj,jgba,hiab,gab') - self.optimize_compare('bde,cdh,agdb,hica,ibd,hgicd,hiac') - self.optimize_compare('chd,bde,agbc,hiad,hgc,hgi,hiad') - self.optimize_compare('chd,bde,agbc,hiad,bdi,cgh,agdb') - self.optimize_compare('bdhe,acad,hiab,agac,hibd') - - def np_test_np_test_collapse(self): - # Inner products - self.optimize_compare('ab,ab,c->c', verbose=True) - self.optimize_compare('ab,ab,cd,cd->ac') - self.optimize_compare('ab,ab,cd,cd->cd') - self.optimize_compare('ab,ab,c->') - self.optimize_compare('ab,ab,cd,cd->') - self.optimize_compare('ab,ab,cd,cd,ef,ef->') - - def np_test_expand(self): + def test_np_test_expand(self): # Outer products self.optimize_compare('ab,cd,ef->abcdef') self.optimize_compare('ab,cd,ef->acdf') @@ -151,29 +215,16 @@ def np_test_expand(self): self.optimize_compare('ab,bcd,cd->abcd') self.optimize_compare('ab,bcd,cd->abd') - def np_test_edge_cases(self): + def test_np_test_edge_cases(self): # Difficult edge cases for optimization - self.optimize_compare('eb,cb,fb->cef') - self.optimize_compare('dd,fb,be,cdb->cef') - self.optimize_compare('bca,cdb,dbf,afc->') - self.optimize_compare('dcc,fce,ea,dbf->ab') - self.optimize_compare('fdf,cdd,ccd,afe->ae') - self.optimize_compare('abcd,ad') - self.optimize_compare('ed,fcd,ff,bcf->be') - self.optimize_compare('baa,dcf,af,cde->be') + self.optimize_compare( + 'eac->ace', operands=[numpy.arange(24).reshape((2, 3, 4))]) + self.optimize_compare('eac->ace') self.optimize_compare('bd,db,eac->ace') - self.optimize_compare('fff,fae,bef,def->abd') + self.optimize_compare('eb,cb,fb->cef') self.optimize_compare('efc,dbc,acf,fd->abe') self.optimize_compare('ba,ac,da->bcd') - def np_test_inner_product(self): - # Inner products - self.optimize_compare('ab,ab') - self.optimize_compare('ab,ba') - self.optimize_compare('abc,abc') - self.optimize_compare('abc,bac') - self.optimize_compare('abc,cba') - def np_test_random_cases(self): # Randomly built test cases self.optimize_compare('aab,fa,df,ecc->bde') @@ -192,9 +243,9 @@ def np_test_combined_views_mapping(self): # gh-10792 a = numpy.arange(9).reshape(1, 1, 3, 1, 3) b = numpy.einsum('bbcdc->d', a) - assert_equal(b, [12]) + self.assertEqual(b, [12]) - def np_test_broadcasting_dot_cases(self): + def test_np_test_broadcasting_dot_cases1(self): # Ensures broadcasting cases are not mistaken for GEMM a = numpy.random.rand(1, 5, 4) @@ -202,19 +253,49 @@ def np_test_broadcasting_dot_cases(self): c = numpy.random.rand(5, 6) d = numpy.random.rand(10) - self.optimize_compare('ijk,kl,jl', operands=[a, b, c]) + # self.optimize_compare('ijk,kl,jl', operands=[a, b, c]) self.optimize_compare('ijk,kl,jl,i->i', operands=[a, b, c, d]) e = numpy.random.rand(1, 1, 5, 4) f = numpy.random.rand(7, 7) - self.optimize_compare('abjk,kl,jl', operands=[e, b, c]) + # self.optimize_compare('abjk,kl,jl', operands=[e, b, c]) self.optimize_compare('abjk,kl,jl,ab->ab', operands=[e, b, c, f]) + def test_np_test_broadcasting_dot_cases2(self): # Edge case found in gh-11308 g = numpy.arange(64).reshape(2, 4, 8) self.optimize_compare('obk,ijk->ioj', operands=[g, g]) + def np_test_complex(self): + # Long test cases + self.optimize_compare('acdf,jbje,gihb,hfac,gfac,gifabc,hfac') + self.optimize_compare('acdf,jbje,gihb,hfac,gfac,gifabc,hfac') + self.optimize_compare('cd,bdhe,aidb,hgca,gc,hgibcd,hgac') + self.optimize_compare('abhe,hidj,jgba,hiab,gab') + self.optimize_compare('bde,cdh,agdb,hica,ibd,hgicd,hiac') + self.optimize_compare('chd,bde,agbc,hiad,hgc,hgi,hiad') + self.optimize_compare('chd,bde,agbc,hiad,bdi,cgh,agdb') + self.optimize_compare('bdhe,acad,hiab,agac,hibd') + + def np_test_inner_product(self): + # Inner products + self.optimize_compare('ab,ab') + self.optimize_compare('ab,ba') + self.optimize_compare('abc,abc') + self.optimize_compare('abc,bac') + self.optimize_compare('abc,cba') + + def np_test_edge_cases_duplicate_indices(self): + # Difficult edge cases for optimization + self.optimize_compare('bca,cdb,dbf,afc->') + self.optimize_compare('dd,fb,be,cdb->cef') + self.optimize_compare('dcc,fce,ea,dbf->ab') + self.optimize_compare('fdf,cdd,ccd,afe->ae') + self.optimize_compare('abcd,ad') + self.optimize_compare('ed,fcd,ff,bcf->be') + self.optimize_compare('baa,dcf,af,cde->be') + self.optimize_compare('fff,fae,bef,def->abd') + if __name__ == "__main__": - # TestEinsum().test_decompose_einsum_equation() unittest.main() diff --git a/mlprodict/onnxrt/ops_cpu/op_einsum.py b/mlprodict/onnxrt/ops_cpu/op_einsum.py index 69d77ec6a..fe78d5ad4 100644 --- a/mlprodict/onnxrt/ops_cpu/op_einsum.py +++ b/mlprodict/onnxrt/ops_cpu/op_einsum.py @@ -26,7 +26,7 @@ def __init__(self, onnx_node, desc=None, **options): raise TypeError("equation is empty.") # pragma: no cover def _run(self, *args): # pylint: disable=W0221 - return (numpy.einsum(self.equation, *args), ) + return (numpy.einsum(self.equation, *args, optimize=True), ) def _infer_shapes(self, *args): # pylint: disable=W0221 try: @@ -38,4 +38,5 @@ def _infer_type(self, *args): return ShapeObject._infer_merged_type(*args) def to_python(self, inputs): - return "import numpy", "return numpy.einsum(equation, *inputs)" + return ("import numpy", + "return numpy.einsum(equation, *inputs, optimize=True)") diff --git a/mlprodict/testing/einsum_impl.py b/mlprodict/testing/einsum_impl.py index 3aea1ecd9..1df3695b3 100644 --- a/mlprodict/testing/einsum_impl.py +++ b/mlprodict/testing/einsum_impl.py @@ -20,6 +20,9 @@ def numpy_extended_dot(m1, m2, axes, left, right, verbose=False): :param right: right axes :param verbose: display intermediate information :return: output + + The current implementation still uses :epkg:`numpy:einsum` + but this should be replaced. """ if len(m1.shape) != len(m2.shape): raise RuntimeError( @@ -63,7 +66,17 @@ def analyse_einsum_equation(equation): Analyses an einsum equation. :param equation: :epkg:`numpy:einsum` equation - :return: + :return: three results, list of letters, + a matrix (see below), lengths of each components + + The returned a matrix is defined as follows: + + .. math:: + + m_{ij}=\\left\\{\\begin{array}{ll}-1 & + \\text{if letter j is involved in input i} \\\\ + p & \\text{p is position of letter j in equation i} + \\end{array}\\right. """ spl = equation.strip(' ,').split("->") if len(spl) != 2 or len(spl[1]) == 0 or len(spl[0]) == 0: @@ -120,7 +133,30 @@ def decompose_einsum_equation(equation, *shapes, strategy="simple", verbose=Fals * `'simple'`: align all dimensions in the alphabetical order Available operations: *expand_dims*, *transpose*, *matmul*, *reduce_sum*, - *id*, *squeeze*. + *id*, *squeeze*. It analyses an equation and produces a graph + where node are instance of class @see cl EinsumSubOp. + + .. runpython:: + :showcode: + + from mlprodict.testing.einsum_impl import decompose_einsum_equation + seq = decompose_einsum_equation( + "bac,cd,def->ebc", (2, 2, 2), (2, 2), (2, 2, 2)) + for op in seq: + print(op) + + It can be better displayed as the following. + + .. gdot:: + :script: DOT-SECTION + :process: + + from mlprodict.testing.einsum_impl import decompose_einsum_equation + seq = decompose_einsum_equation( + "bac,cd,def->ebc", (2, 2, 2), (2, 2), (2, 2, 2)) + print("DOT-SECTION", seq.to_dot()) + + See notebook :ref:`einsumdecompositionrst`. """ if len(shapes) == 0: raise ValueError("No input shapes.") @@ -133,13 +169,32 @@ def decompose_einsum_equation(equation, *shapes, strategy="simple", verbose=Fals raise ValueError("Unknown strategy %r." % strategy) -def apply_sequence(seq, *inputs, verbose=False): +def apply_einsum_sequence(seq, *inputs, verbose=False): """ Applies a sequence of operations on a list of inputs. + The sequence of operations is produced by function + @see fn decompose_einsum_equation. :param seq: sequence of operations :param inputs: inputs: :return: output + + .. runpython:: + :showcode: + + from mlprodict.testing.einsum_impl import ( + decompose_einsum_equation, apply_einsum_sequence) + + m1 = numpy.arange(2 * 2 * 2).reshape((2, 2, 2)) + 10 + m2 = numpy.arange(4).reshape((2, 2)) + 100 + m3 = numpy.arange(2 * 2).reshape((2, 2)) + 1000 + + seq = decompose_einsum_equation( + "bac,cd,def->ebc", (2, 2, 2), (2, 2), (2, 2, 2)) + res = apply_einsum_sequence(seq, m1, m2, verbose=verbose) + print(res) + + See notebook :ref:`einsumdecompositionrst`. """ return seq.apply_sequence(*inputs, verbose=verbose) @@ -188,6 +243,16 @@ def __init__(self, full_dim, name, *inputs, **kwargs): raise TypeError( "Input %d has type %r, int or EinsumSubOp is expected." "" % (i, type(inp))) + self._check_() + + def _check_(self): + if self.name == 'transpose': + self._check_arg_('perm', tuple) + perm = self.kwargs['perm'] + if len(perm) != len(set(perm)): + raise RuntimeError( + "perm has duplicated values %r (name=%r)." + "" % (perm, self.name)) def __repr__(self): inps = ", ".join(map(str, self.inputs)) @@ -213,10 +278,6 @@ def _check_row_(self, row, inp=False, verbose=False): if inp: print() print('<-' if inp else '->', self.name, row, self.kwargs) - if not inp or self.name != 'id': - if row.max() == -1: - raise RuntimeError( # pragma: no cover - "Shape is empty %r." % row) def compute_output_row(self, row, row2=None, verbose=False): """ @@ -439,7 +500,7 @@ def append(self, op): self._ops.append(op) self.last_added_op = op return op - raise TypeError("Unexpected type %r." % type(i)) + raise TypeError("Unexpected type %r." % type(op)) def mark(self, i, op): """ @@ -465,22 +526,10 @@ def __iter__(self): for op in self._ops: yield op - def to_graph(self, layout='ascii'): - """ - Draws a graph. - - :param layout: ascii (relies on package :epkg:`graphscii` - :return: string - """ - if layout == 'ascii': - pass - - raise NotImplementedError("Unexpected layout %r." % layout) - def to_dot(self, **kwargs): """ Produces a graph in :epkg:`dot`. - + :param kwargs: additional graph option :return: string """ @@ -494,7 +543,7 @@ def to_dot(self, **kwargs): 'node': '[shape=record]', } options.update(kwargs) - + def d2s(d): it = [] for k, v in sorted(d.items()): @@ -503,21 +552,28 @@ def d2s(d): rows = ["digraph{"] for k, v in options.items(): - if "[" in v: + if isinstance(v, str) and "[" in v: rows.append("{} {};".format(k, v)) else: rows.append("{}={};".format(k, v)) for k, v in self._nodes.items(): if isinstance(v, int): - lab = "input %d\\\\n%s" % (v, str(self.metadata['mat0'][v])) + let = [(r, self.metadata['letters'][i]) + for i, r in enumerate(self.metadata['mat0'][v]) + if r != -1] + let.sort() + letters = "".join(_[1] for _ in let) + lab = "input %d\\\\n%s\\\\n%s" % ( + v, letters, str(self.metadata['mat0'][v])) sk = v else: lab = "%s\\\\n%s" % (v.name, d2s(v.kwargs)) sk = id(v) if sk in self._mark and isinstance(self._mark[sk], int): la = self._mark[sk] - s = ('%d [label="%s - I%d" style=filled ' - 'fillcolor=red];' % (k, lab, la)) + lab = lab.replace("\\\\n", " - I%d\\\\n" % la) + s = ('%d [label="%s" style=filled ' + 'fillcolor=red];' % (k, lab)) else: s = '%d [label="%s"];' % (k, lab) rows.append(s) @@ -559,43 +615,45 @@ def _apply_transpose_reshape(op, row): for i, r in enumerate(row): if r == -1: axes.append((p, i)) - perm.append(-1) else: p += 1 - perm.append(r) + perm.append((r, i)) for a in reversed(axes): op = EinsumSubOp(len(row), 'expand_dims', op, axis=a) yield op - dec = [0] - for i in range(1, len(perm)): - if perm[i - 1] == -1: - dec.append(dec[-1] + 1) - else: - dec.append(dec[-1]) - for i in range(0, len(perm)): # pragma: disable=C0200 - if perm[i] == -1: - perm[i] = i - else: - perm[i] = perm[i] + dec[i] - op = EinsumSubOp(len(row), 'transpose', op, perm=tuple(perm)) + perm.sort() + p = 0 + new_perm = numpy.arange(len(row)) + for i, r in enumerate(row): + if r == -1: + continue + new_perm[perm[p][1]] = i + p += 1 + op = EinsumSubOp(len(row), 'transpose', op, perm=tuple(new_perm)) yield op def _apply_squeeze_transpose(op, row_last, row_output): """ - Put output dimension in the expected order. + Puts output dimension in the expected order. """ - perm = [] sq = [] for i, d in enumerate(row_output): if d == -1: - perm.append((i, i)) sq.append(i) else: perm.append((d, i)) + perm.sort() + new_perm = numpy.arange(len(row_last)) + p = 0 + for i, d in enumerate(row_output): + if d == -1: + continue + new_perm[i] = perm[p][1] + p += 1 perm = [p[1] for p in perm] - op = EinsumSubOp(len(row_last), 'transpose', op, perm=tuple(perm)) + op = EinsumSubOp(len(row_last), 'transpose', op, perm=tuple(new_perm)) yield op if len(sq) > 0: op = EinsumSubOp(len(row_last), 'squeeze', op, axes=tuple(sq)) @@ -612,13 +670,14 @@ def _decompose_einsum_equation_simple(equation, *shapes, verbose=False): "Unexpected number of letters %r, shape=%r." % ( letters, mat.shape)) _basic_verification(lengths, shapes, equation) - ops = [] + # last_row, current_row (row = shape) rows = numpy.full((2, mat.shape[1]), -1) graph = GraphEinsumSubOp(letters, mat, lengths) fd = mat.shape[1] if verbose: print("EQUATION=%r" % equation) + print("LETTERS=%r" % letters, "LENGTHS=%r" % lengths) for i, sh in enumerate(shapes): if verbose: @@ -694,8 +753,8 @@ def _decompose_einsum_equation_simple(equation, *shapes, verbose=False): red.append(d) elif rows[0, d] == -1 and rows[1, d] >= 0: raise RuntimeError( - "Issue in equation %r, last_result is %r, " - "output is %r." % (equation, rows[0, :], rows[1, :])) + "Issue in equation %r, variable %d, last_result is %r, " + "output is %r." % (equation, d, rows[0, :], rows[1, :])) if len(red) > 0: if verbose: print("-- REDUCE2 axes=%r" % red) From 14eba4663e9f55d89228bfbea1eff280454bedf7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Fri, 23 Apr 2021 00:51:43 +0200 Subject: [PATCH 06/33] remove optimize=True in some cases --- _unittests/ut_onnxrt/test_onnxrt_python_runtime_.py | 2 +- mlprodict/onnxrt/ops_cpu/op_einsum.py | 7 +++++-- 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/_unittests/ut_onnxrt/test_onnxrt_python_runtime_.py b/_unittests/ut_onnxrt/test_onnxrt_python_runtime_.py index 77af7ddd0..8098df97c 100644 --- a/_unittests/ut_onnxrt/test_onnxrt_python_runtime_.py +++ b/_unittests/ut_onnxrt/test_onnxrt_python_runtime_.py @@ -1632,7 +1632,7 @@ def test_onnxt_runtime_dropout(self): def test_onnxt_runtime_einsum(self): X = numpy.random.randn(5, 2, 3).astype(numpy.float32) Y = numpy.random.randn(5, 3, 4).astype(numpy.float32) - equation = 'bij, bjk -> bik' + equation = 'bij,bjk->bik' onx = OnnxEinsum( 'X', 'Y', equation=equation, output_names=['Z'], op_version=get_opset_number_from_onnx()) diff --git a/mlprodict/onnxrt/ops_cpu/op_einsum.py b/mlprodict/onnxrt/ops_cpu/op_einsum.py index fe78d5ad4..2416d0bb0 100644 --- a/mlprodict/onnxrt/ops_cpu/op_einsum.py +++ b/mlprodict/onnxrt/ops_cpu/op_einsum.py @@ -26,7 +26,10 @@ def __init__(self, onnx_node, desc=None, **options): raise TypeError("equation is empty.") # pragma: no cover def _run(self, *args): # pylint: disable=W0221 - return (numpy.einsum(self.equation, *args, optimize=True), ) + try: + return (numpy.einsum(self.equation, *args, optimize=True), ) + except TypeError as e: + return (numpy.einsum(self.equation, *args), ) def _infer_shapes(self, *args): # pylint: disable=W0221 try: @@ -39,4 +42,4 @@ def _infer_type(self, *args): def to_python(self, inputs): return ("import numpy", - "return numpy.einsum(equation, *inputs, optimize=True)") + "return numpy.einsum(equation, *inputs)") From 7943226d5466f87d5422d4f479d15b8b159bd22c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Fri, 23 Apr 2021 02:16:31 +0200 Subject: [PATCH 07/33] Add diagonal --- _unittests/ut_testing/test_einsum.py | 83 ++++++++++++++---- mlprodict/onnxrt/ops_cpu/op_einsum.py | 2 +- mlprodict/testing/einsum_impl.py | 119 +++++++++++++++++++++++--- 3 files changed, 176 insertions(+), 28 deletions(-) diff --git a/_unittests/ut_testing/test_einsum.py b/_unittests/ut_testing/test_einsum.py index 6609fee78..9e7038b00 100644 --- a/_unittests/ut_testing/test_einsum.py +++ b/_unittests/ut_testing/test_einsum.py @@ -9,11 +9,28 @@ from pyquickhelper.pycode import ExtTestCase from mlprodict.testing.einsum_impl import ( analyse_einsum_equation, decompose_einsum_equation, EinsumSubOp, - apply_einsum_sequence) + apply_einsum_sequence, numpy_diagonal) class TestEinsum(ExtTestCase): + def test_numpy_diagonal(self): + mat = numpy.arange(8).reshape((2, 2, 2)) + diag = numpy_diagonal(mat, 1, [1, 2]) + self.assertEqualArray(diag, numpy.array([[0, 3], [4, 7]])) + diag = numpy_diagonal(mat, 2, [1, 2]) + self.assertEqualArray(diag, numpy.array([[0, 3], [4, 7]])) + + diag = numpy_diagonal(mat, 0, [0, 1]) + self.assertEqualArray(diag, numpy.array([[0, 1], [6, 7]])) + diag = numpy_diagonal(mat, 1, [0, 1]) + self.assertEqualArray(diag, numpy.array([[0, 1], [6, 7]])) + + diag = numpy_diagonal(mat, 0, [0, 2]) + self.assertEqualArray(diag, numpy.array([[0, 2], [5, 7]])) + diag = numpy_diagonal(mat, 2, [0, 2]) + self.assertEqualArray(diag, numpy.array([[0, 2], [5, 7]]).T) + def test_analyse_einsum_equation(self): self.assertRaise(lambda: analyse_einsum_equation("abc"), NotImplementedError) @@ -22,14 +39,29 @@ def test_analyse_einsum_equation(self): self.assertRaise(lambda: analyse_einsum_equation("abc,ch->a0"), ValueError) res = analyse_einsum_equation("abc,ch->ah") - self.assertEqual(len(res), 3) - letters, mat, lengths = res + self.assertEqual(len(res), 4) + letters, mat, lengths, duplicates = res self.assertEqual(letters, "abch") self.assertEqualArray(lengths, numpy.array([3, 2, 2])) self.assertEqualArray( mat, numpy.array([[0, 1, 2, -1], [-1, -1, 0, 1], [0, -1, -1, 1]])) + self.assertEqual(duplicates, [None, None, None]) + + def test_analyse_einsum_equation_duplicates(self): + res = analyse_einsum_equation("aac,ca->aa") + self.assertEqual(len(res), 4) + letters, mat, lengths, duplicates = res + self.assertEqual(letters, "ac") + self.assertEqualArray(lengths, numpy.array([3, 2, 2])) + self.assertEqual(duplicates, [{'a': [0, 1], 'c': [2]}, + None, + {'a': [0, 1]}]) + self.assertEqualArray( + mat, numpy.array([[1, 2], + [1, 0], + [1, -1]])) def test_decompose_einsum_equation_exc(self): self.assertRaise( @@ -48,9 +80,6 @@ def test_decompose_einsum_equation_exc(self): self.assertRaise( lambda: decompose_einsum_equation("abc,ch->ah", (2, 2), (2, 2)), ValueError) - self.assertRaise( - lambda: decompose_einsum_equation("aac,ch->ah", (2, 2), (2, 2)), - NotImplementedError) def test_decompose_einsum_equation(self): m1 = numpy.arange(0, 8).astype(numpy.float32).reshape((2, 2, 2)) @@ -88,6 +117,26 @@ def test_einsum_sub_op(self): 2, "matmul", (2, 2)), RuntimeError) self.assertRaise(lambda: EinsumSubOp(2, "id", (2, 2)), TypeError) + def test_case_1_iii_ii_i(self): + verbose = False + equation = 'ii->i' + m1 = numpy.arange(2 * 2).reshape((2, 2)) + 10 + exp = numpy.einsum(equation, m1) + seq = decompose_einsum_equation( + equation, m1.shape, verbose=verbose) + res = apply_einsum_sequence(seq, m1, verbose=verbose) + self.assertEqualArray(exp, res) + + def test_case_1_iii_ii_i_j(self): + verbose = False + equation = 'iij->ij' + m1 = numpy.arange(2 * 2 * 2).reshape((2, 2, 2)) + 10 + exp = numpy.einsum(equation, m1) + seq = decompose_einsum_equation( + equation, m1.shape, verbose=verbose) + res = apply_einsum_sequence(seq, m1, verbose=verbose) + self.assertEqualArray(exp, res) + def common_test_case_2(self, equation, verbose=False): m1 = numpy.arange(2 * 2 * 2).reshape((2, 2, 2)) + 10 m2 = numpy.arange(4).reshape((2, 2)) + 100 @@ -225,21 +274,16 @@ def test_np_test_edge_cases(self): self.optimize_compare('efc,dbc,acf,fd->abe') self.optimize_compare('ba,ac,da->bcd') - def np_test_random_cases(self): + def test_np_test_random_cases(self): # Randomly built test cases self.optimize_compare('aab,fa,df,ecc->bde') - self.optimize_compare('ecb,fef,bad,ed->ac') - self.optimize_compare('bcf,bbb,fbf,fc->') self.optimize_compare('bb,ff,be->e') - self.optimize_compare('bcb,bb,fc,fff->') - self.optimize_compare('fbb,dfd,fc,fc->') self.optimize_compare('afd,ba,cc,dc->bf') - self.optimize_compare('adb,bc,fa,cfc->d') self.optimize_compare('bbd,bda,fc,db->acf') self.optimize_compare('dba,ead,cad->bce') self.optimize_compare('aef,fbc,dca->bde') - def np_test_combined_views_mapping(self): + def test_np_test_combined_views_mapping(self): # gh-10792 a = numpy.arange(9).reshape(1, 1, 3, 1, 3) b = numpy.einsum('bbcdc->d', a) @@ -285,17 +329,22 @@ def np_test_inner_product(self): self.optimize_compare('abc,bac') self.optimize_compare('abc,cba') - def np_test_edge_cases_duplicate_indices(self): + def test_np_test_random_cases_difficult(self): + self.optimize_compare('adb,bc,fa,cfc->d') + self.optimize_compare('ecb,fef,bad,ed->ac') + self.optimize_compare('fdf,cdd,ccd,afe->ae') + + def test_np_test_edge_cases_duplicate_indices(self): # Difficult edge cases for optimization - self.optimize_compare('bca,cdb,dbf,afc->') + # self.optimize_compare('bca,cdb,dbf,afc->') self.optimize_compare('dd,fb,be,cdb->cef') self.optimize_compare('dcc,fce,ea,dbf->ab') - self.optimize_compare('fdf,cdd,ccd,afe->ae') - self.optimize_compare('abcd,ad') + # self.optimize_compare('abcd,ad') self.optimize_compare('ed,fcd,ff,bcf->be') self.optimize_compare('baa,dcf,af,cde->be') self.optimize_compare('fff,fae,bef,def->abd') if __name__ == "__main__": + # TestEinsum().test_case_1_iii_ii_i_j() unittest.main() diff --git a/mlprodict/onnxrt/ops_cpu/op_einsum.py b/mlprodict/onnxrt/ops_cpu/op_einsum.py index 2416d0bb0..0b5d363f6 100644 --- a/mlprodict/onnxrt/ops_cpu/op_einsum.py +++ b/mlprodict/onnxrt/ops_cpu/op_einsum.py @@ -28,7 +28,7 @@ def __init__(self, onnx_node, desc=None, **options): def _run(self, *args): # pylint: disable=W0221 try: return (numpy.einsum(self.equation, *args, optimize=True), ) - except TypeError as e: + except TypeError: return (numpy.einsum(self.equation, *args), ) def _infer_shapes(self, *args): # pylint: disable=W0221 diff --git a/mlprodict/testing/einsum_impl.py b/mlprodict/testing/einsum_impl.py index 1df3695b3..0b30d5080 100644 --- a/mlprodict/testing/einsum_impl.py +++ b/mlprodict/testing/einsum_impl.py @@ -61,13 +61,44 @@ def numpy_extended_dot(m1, m2, axes, left, right, verbose=False): return output.reshape(tuple(new_shape)) +def numpy_diagonal(m, axis, axes): + """ + Extracts diagonal coefficients from an array. + + :param m: input array + :param axis: kept axis among the diagonal ones + :param axes: diagonal axes (axis must be one of them) + :return: output + """ + if axis not in axes: + raise RuntimeError( + "axis %r must be in axes %r." % (axis, axes)) + shape = [] + out_axis = None + for i, s in enumerate(m.shape): + if i not in axes or i == axis: + if i == axis: + out_axis = len(shape) + shape.append(s) + output = numpy.empty(tuple(shape), dtype=m.dtype) + index_in = [slice(s) for s in m.shape] + index_out = [slice(s) for s in shape] + for i in range(0, shape[axis]): + for a in axes: + index_in[a] = i + index_out[out_axis] = i + output[tuple(index_out)] = m[tuple(index_in)] + return output + + def analyse_einsum_equation(equation): """ Analyses an einsum equation. :param equation: :epkg:`numpy:einsum` equation :return: three results, list of letters, - a matrix (see below), lengths of each components + a matrix (see below), lengths of each components, + duplicates The returned a matrix is defined as follows: @@ -84,13 +115,10 @@ def analyse_einsum_equation(equation): "The function only implements the case when there are " "two sides in the equation: %r." % equation) inputs = list(map(lambda s: s.strip(), spl[0].split(','))) - for inp in inputs: - if len(inp) != len(set(inp)): - raise NotImplementedError( - "One input uses more than once the same indice %r in " - "equation %r." % (inp, equation)) output = spl[1] all_letters = set(inputs[0]) + + # Set of letters for inp in inputs[1:]: all_letters |= set(inp) letters = list(sorted(all_letters)) @@ -99,6 +127,7 @@ def analyse_einsum_equation(equation): raise ValueError( "Equation %r must only contain lower or upper letters " "but %r is not." % (equation, c)) + rev = {c: i for i, c in enumerate(letters)} for c in output: if c not in letters: @@ -113,7 +142,23 @@ def analyse_einsum_equation(equation): mat[len(inputs), rev[c]] = k lengths = [len(inp) for inp in inputs] lengths.append(len(output)) - return "".join(letters), mat, lengths + + # Look for duplicates + duplicates = [] + for inp in inputs + [output]: + if len(inp) == len(set(inp)): + duplicates.append(None) + continue + # There is some duplicates. + counts = {} + for i, c in enumerate(inp): + if c in counts: + counts[c].append(i) + else: + counts[c] = [i] + duplicates.append(counts) + + return "".join(letters), mat, lengths, duplicates def decompose_einsum_equation(equation, *shapes, strategy="simple", verbose=False): @@ -133,7 +178,7 @@ def decompose_einsum_equation(equation, *shapes, strategy="simple", verbose=Fals * `'simple'`: align all dimensions in the alphabetical order Available operations: *expand_dims*, *transpose*, *matmul*, *reduce_sum*, - *id*, *squeeze*. It analyses an equation and produces a graph + *id*, *squeeze*, *diagonal*. It analyses an equation and produces a graph where node are instance of class @see cl EinsumSubOp. .. runpython:: @@ -220,7 +265,7 @@ class EinsumSubOp: :param kwargs: arguments """ _allowed = {'expand_dims', 'transpose', 'reduce_sum', 'matmul', 'id', - 'squeeze'} + 'squeeze', 'diagonal'} def __init__(self, full_dim, name, *inputs, **kwargs): self.full_dim = full_dim @@ -344,6 +389,30 @@ def compute_output_row(self, row, row2=None, verbose=False): self._check_row_(row, verbose=verbose) return + if self.name == "diagonal": + self._check_arg_('diag', list) + to_remove = [] + for choice, choices in self.kwargs['diag']: + for ch in choices: + if ch != choice: + to_remove.append(ch) + for i in range(len(row)): # pylint: disable=C0200 + if row[i] in choices: + if row[i] != choice: + row[i] = choice + to_remove.sort() + for r in to_remove: + for i in range(len(row)): # pylint: disable=C0200 + if row[i] == r: + raise RuntimeError( + "Unexpected result r=%r row=%r to_remove=%r " + "diag=%r." % ( + r, row, to_remove, self.kwargs['diag'])) + if row[i] > r: + row[i] -= 1 + self._check_row_(row, verbose=verbose) + return + raise NotImplementedError( "compute_output_row not implemented for %r." % self.name) @@ -383,11 +452,28 @@ def apply(self, data, verbose=False): """ if verbose: print() + print("apply %r." % self.name) + if self.name == 'id': self._check_inputs_(1) inp = self.inputs[0] output = self._get_data(data, inp) + elif self.name == 'diagonal': + self._check_inputs_(1) + inp = self.inputs[0] + m = self._get_data(data, inp) + if verbose: + print("- %s, shape=%r diag=%r" % ( + self.name, m.shape, self.kwargs['diag'])) + diag = self.kwargs['diag'] + if len(diag) != 1: + raise NotImplementedError( + "Not implemented with more than one duplicated indice " + "%r." % diag) + diag0 = diag[0] + output = numpy_diagonal(m, axis=diag0[0], axes=diag0[1]) + elif self.name == 'expand_dims': self._check_inputs_(1) inp = self.inputs[0] @@ -664,7 +750,7 @@ def _decompose_einsum_equation_simple(equation, *shapes, verbose=False): """ Applies strategy simple of function @see fct decompose_einsum_equation. """ - letters, mat, lengths = analyse_einsum_equation(equation) + letters, mat, lengths, duplicates = analyse_einsum_equation(equation) if len(letters) != mat.shape[1]: raise RuntimeError( # pragma: no cover "Unexpected number of letters %r, shape=%r." % ( @@ -678,6 +764,7 @@ def _decompose_einsum_equation_simple(equation, *shapes, verbose=False): if verbose: print("EQUATION=%r" % equation) print("LETTERS=%r" % letters, "LENGTHS=%r" % lengths) + print("DUPLICATES=%r" % duplicates) for i, sh in enumerate(shapes): if verbose: @@ -690,6 +777,18 @@ def _decompose_einsum_equation_simple(equation, *shapes, verbose=False): op.compute_output_row(rows[1, :], mat[i, :], verbose=verbose) marked = graph.append(op) + duplicate = duplicates[i] + if duplicate is not None: + # Diagonal + diag = [] + for _, v in duplicate.items(): + if len(v) == 1: + continue + diag.append((v[0], tuple(v))) + op = EinsumSubOp(fd, 'diagonal', op, diag=diag) + op.compute_output_row(rows[1, :], mat[i, :], verbose=verbose) + marked = graph.append(op) + for op in _apply_transpose_reshape(op, mat[i]): op.compute_output_row(rows[1, :], verbose=verbose) marked = graph.append(op) From 8d9ded8bb68236865e40f7fe6f95064c9e277741 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Fri, 23 Apr 2021 12:06:08 +0200 Subject: [PATCH 08/33] refactoring, fix duplicates indices --- _unittests/ut_testing/test_einsum.py | 58 ++- mlprodict/testing/einsum_impl.py | 566 +++++------------------ mlprodict/testing/einsum_impl_classes.py | 453 ++++++++++++++++++ mlprodict/testing/einsum_impl_ext.py | 193 ++++++++ 4 files changed, 823 insertions(+), 447 deletions(-) create mode 100644 mlprodict/testing/einsum_impl_classes.py create mode 100644 mlprodict/testing/einsum_impl_ext.py diff --git a/_unittests/ut_testing/test_einsum.py b/_unittests/ut_testing/test_einsum.py index 9e7038b00..53aad6fae 100644 --- a/_unittests/ut_testing/test_einsum.py +++ b/_unittests/ut_testing/test_einsum.py @@ -7,9 +7,11 @@ import itertools import numpy from pyquickhelper.pycode import ExtTestCase +from mlprodict.testing.einsum_impl_ext import ( + numpy_diagonal, numpy_extended_dot) from mlprodict.testing.einsum_impl import ( analyse_einsum_equation, decompose_einsum_equation, EinsumSubOp, - apply_einsum_sequence, numpy_diagonal) + apply_einsum_sequence) class TestEinsum(ExtTestCase): @@ -31,6 +33,47 @@ def test_numpy_diagonal(self): diag = numpy_diagonal(mat, 2, [0, 2]) self.assertEqualArray(diag, numpy.array([[0, 2], [5, 7]]).T) + def test_numpy_extended_dot_2(self): + m1 = numpy.arange(4).reshape((2, 2)) + m2 = m1 + 10 + + self.assertRaise(lambda: numpy_extended_dot(m1, m2.T, [0], [1], [2]), + ValueError) + dm1 = m1.reshape((2, 2, 1)) + dm2 = m2.reshape((1, 2, 2)) + dot = numpy_extended_dot(dm1, dm2, axes=[1], left=[0], right=[2]) + exp = m1 @ m2 + self.assertEqual(exp, numpy.squeeze(dot)) + + dm1 = m1.reshape((2, 1, 2)) + dm2 = m2.reshape((1, 2, 2)) + dot = numpy_extended_dot(dm1, dm2, axes=[2], left=[0], right=[1]) + exp = m1 @ m2.T + self.assertEqual(exp, numpy.squeeze(dot)) + + dm1 = m1.reshape((2, 2, 1)) + dm2 = m2.reshape((1, 2, 2)) + dot = numpy_extended_dot(dm1, dm2, axes=[2], left=[0], right=[1, 2]) + exp = numpy.array([[[10, 11], [12, 13]], [[50, 55], [60, 65]]]) + self.assertEqual(exp, numpy.squeeze(dot)) + + def test_numpy_extended_dot_3(self): + m1 = numpy.arange(8).reshape((2, 2, 2)) + m2 = m1 + 10 + + dot = numpy_extended_dot(m1, m2, [1], [0], [2]) + exp = numpy.array([[[164, 176]], [[580, 624]]]) + self.assertEqual(exp, dot) + + dot = numpy_extended_dot(m1, m2, [1], [2], [0]) + exp = numpy.array([[[284, 376]], [[380, 504]]]) + self.assertEqual(exp, dot) + + dot = numpy_extended_dot(m1, m2, [1], [2], [0, 1]) + exp = numpy.array([[[84, 126], [200, 250]], + [[116, 174], [264, 330]]]) + self.assertEqual(exp, dot) + def test_analyse_einsum_equation(self): self.assertRaise(lambda: analyse_einsum_equation("abc"), NotImplementedError) @@ -134,6 +177,8 @@ def test_case_1_iii_ii_i_j(self): exp = numpy.einsum(equation, m1) seq = decompose_einsum_equation( equation, m1.shape, verbose=verbose) + dot = seq.to_dot() + self.assertIn("i=0,1", dot) res = apply_einsum_sequence(seq, m1, verbose=verbose) self.assertEqualArray(exp, res) @@ -330,9 +375,18 @@ def np_test_inner_product(self): self.optimize_compare('abc,cba') def test_np_test_random_cases_difficult(self): + self.optimize_compare('cac,c,h->h') + self.optimize_compare('cfc,c,h->h') + self.optimize_compare('cfc,c,d->d') + self.optimize_compare('c,cfc,d->d') + self.optimize_compare('d,c,cfc->d') + self.optimize_compare('d,bc,cfc->d') + self.optimize_compare('db,bc,cfc->d') + self.optimize_compare('adb,bc,cfc->d') self.optimize_compare('adb,bc,fa,cfc->d') self.optimize_compare('ecb,fef,bad,ed->ac') self.optimize_compare('fdf,cdd,ccd,afe->ae') + self.optimize_compare('adb,cfc->d') def test_np_test_edge_cases_duplicate_indices(self): # Difficult edge cases for optimization @@ -346,5 +400,5 @@ def test_np_test_edge_cases_duplicate_indices(self): if __name__ == "__main__": - # TestEinsum().test_case_1_iii_ii_i_j() + # TestEinsum().test_np_test_random_cases_difficult() unittest.main() diff --git a/mlprodict/testing/einsum_impl.py b/mlprodict/testing/einsum_impl.py index 0b30d5080..063e34e5f 100644 --- a/mlprodict/testing/einsum_impl.py +++ b/mlprodict/testing/einsum_impl.py @@ -3,12 +3,13 @@ @brief Function to dig into Einsum computation. """ import numpy +from .einsum_impl_classes import EinsumSubOp, GraphEinsumSubOp def numpy_extended_dot(m1, m2, axes, left, right, verbose=False): """ - Extended version of a matrix multiplication - with two matrices *m1*, *m2* of the same dimension. + Extended version of a matrix multiplication (:epkg:`numpy:dot`) + with two matrices *m1*, *m2* of the same dimensions. Loops over *left* axes for *m1* and *right* axes for *m2*, summation is done over *axes*. Other axes must be empty. @@ -21,6 +22,77 @@ def numpy_extended_dot(m1, m2, axes, left, right, verbose=False): :param verbose: display intermediate information :return: output + The dot product is equivalent to: + + .. runpython:: + :showcode: + + import numpy + from mlprodict.testing.einsum_impl import numpy_diagonal + + m1 = numpy.arange(4).reshape((2, 2)) + m2 = m1 + 10 + print("dot product") + print(m1 @ m2) + + dm1 = m1.reshape((2, 2, 1)) + dm2 = m2.reshape((1, 2, 2)) + dot = numpy_extended_dot(dm1, dm2, axes=[1], left=[0], right=[2], + verbose=True) + print("extended dot product") + print(dot) + + Empty axes should be squeezed to get identical results. + Dot product when the second matrix is transposed. + + .. runpython:: + :showcode: + + import numpy + from mlprodict.testing.einsum_impl import numpy_diagonal + + m1 = numpy.arange(4).reshape((2, 2)) + m2 = m1 + 10 + print("dot product") + print(m1 @ m2.T) + + dm1 = m1.reshape((2, 1, 2)) + dm2 = m2.reshape((1, 2, 2)) + dot = numpy_extended_dot(dm1, dm2, axes=[2], left=[0], right=[1], + verbose=True) + print("extended dot product") + print(dot) + + An example when right axes include the summation axis. + + .. runpython:: + :showcode: + + import numpy + from mlprodict.testing.einsum_impl import numpy_diagonal + + m1 = numpy.arange(4).reshape((2, 2)) + m2 = m1 + 10 + dm1 = m1.reshape((2, 2, 1)) + dm2 = m2.reshape((1, 2, 2)) + dot = numpy_extended_dot(dm1, dm2, axes=[2], left=[0], right=[1, 2], + verbose=True) + print(dot) + + Example in higher dimension: + + .. runpython:: + :showcode: + + import numpy + from mlprodict.testing.einsum_impl import numpy_diagonal + + m1 = numpy.arange(8).reshape((2, 2, 2)) + m2 = m1 + 10 + + dot = numpy_extended_dot(m1, m2, [1], [0], [2], verbose=True)) + print(dot) + The current implementation still uses :epkg:`numpy:einsum` but this should be replaced. """ @@ -28,6 +100,17 @@ def numpy_extended_dot(m1, m2, axes, left, right, verbose=False): raise RuntimeError( "Matrices m1 and m2 must have the same dimension, " "m1=%r, m2=%r." % (m1.shape, m2.shape)) + + def _check_(axs, n): + for a in axs: + if a < 0 or a >= n: + raise ValueError( + "One axis %d (in %r) is negative or above the maximum " + "dimension %d." % (a, axs, n)) + _check_(axes, len(m1.shape)) + _check_(left, len(m1.shape)) + _check_(right, len(m1.shape)) + # This implementation should not use einsum. # Temporary solution. l1 = [chr(i + 97) for i in range(len(m1.shape))] @@ -69,26 +152,46 @@ def numpy_diagonal(m, axis, axes): :param axis: kept axis among the diagonal ones :param axes: diagonal axes (axis must be one of them) :return: output + + .. runpython:: + :showcode: + + import numpy + from mlprodict.testing.einsum_impl import numpy_diagonal + + mat = numpy.arange(8).reshape((2, 2, 2)) + print(mat) + diag = numpy_diagonal(mat, 1, [1, 2]) + print(diag) """ if axis not in axes: raise RuntimeError( "axis %r must be in axes %r." % (axis, axes)) shape = [] - out_axis = None + new_shape = [] for i, s in enumerate(m.shape): - if i not in axes or i == axis: + if i in axes: if i == axis: - out_axis = len(shape) + shape.append(s) + new_shape.append(s) + else: + shape.append(1) + else: shape.append(s) + new_shape.append(s) + + # Extracts coefficients. output = numpy.empty(tuple(shape), dtype=m.dtype) index_in = [slice(s) for s in m.shape] - index_out = [slice(s) for s in shape] + index_out = [slice(s) for s in m.shape] for i in range(0, shape[axis]): for a in axes: index_in[a] = i - index_out[out_axis] = i + index_out[a] = i if a == axis else 0 output[tuple(index_out)] = m[tuple(index_in)] - return output + + # Removes axis. + return output.reshape(tuple(new_shape)) def analyse_einsum_equation(equation): @@ -256,444 +359,13 @@ def _basic_verification(lengths, shapes, equation): " in equation %r." % (i, le, sh, len(sh), equation)) -class EinsumSubOp: - """ - Defines a sub operation used in Einsum decomposition. - - :param name: name (reshape, transpose, reduce_sum, matmul, id) - :param inputs: inputs - :param kwargs: arguments - """ - _allowed = {'expand_dims', 'transpose', 'reduce_sum', 'matmul', 'id', - 'squeeze', 'diagonal'} - - def __init__(self, full_dim, name, *inputs, **kwargs): - self.full_dim = full_dim - self.name = name - self.inputs = inputs - self.kwargs = kwargs - if name not in EinsumSubOp._allowed: - raise ValueError( - "Unexpected name %r. It should be in %r." - "" % (name, EinsumSubOp._allowed)) - if len(inputs) not in (1, 2): - raise RuntimeError( - "Inputs must contains 1 or 2 inputs not %d." % len(inputs)) - if name == 'matmul' and len(inputs) != 2: - raise RuntimeError( - "Inputs must contains 2 inputs not %d for operator 'matmul'." - "" % len(inputs)) - for i, inp in enumerate(inputs): - if not isinstance(inp, (int, EinsumSubOp)): - raise TypeError( - "Input %d has type %r, int or EinsumSubOp is expected." - "" % (i, type(inp))) - self._check_() - - def _check_(self): - if self.name == 'transpose': - self._check_arg_('perm', tuple) - perm = self.kwargs['perm'] - if len(perm) != len(set(perm)): - raise RuntimeError( - "perm has duplicated values %r (name=%r)." - "" % (perm, self.name)) - - def __repr__(self): - inps = ", ".join(map(str, self.inputs)) - kw = ", ".join("%s=%r" % (k, w) for k, w in self.kwargs.items()) - m = "%s(%r, %s, %s)" % ( - self.__class__.__name__, self.name, inps, kw) - return m - - def _check_arg_(self, name, typ): - if name not in self.kwargs: - raise RuntimeError( - "Parameter %r not found for operator %r." % (name, self.name)) - if not isinstance(self.kwargs[name], typ): - raise TypeError( - "Unexpected type %r for parameter %r and parameter %r." - "" % (type(self.kwargs[name]), name, self.name)) - - def _check_row_(self, row, inp=False, verbose=False): - """ - Checks input or output is valid. - """ - if verbose: - if inp: - print() - print('<-' if inp else '->', self.name, row, self.kwargs) - - def compute_output_row(self, row, row2=None, verbose=False): - """ - Updates *row* based on the operator. - """ - self._check_row_(row, True, verbose=verbose) - - if self.name == "id": - row[:] = row2[:] - self._check_row_(row, verbose=verbose) - return - - if self.name == "transpose": - self._check_arg_('perm', tuple) - if len(self.kwargs['perm']) != len(row): - raise RuntimeError( - "Unexpected permutation %r (row=%r)." - "" % (self.kwargs['perm'], row)) - cpy = row.copy() - for i, p in enumerate(self.kwargs['perm']): - row[i] = cpy[p] - self._check_row_(row, verbose=verbose) - return - - if self.name == "expand_dims": - self._check_arg_('axis', tuple) - if row[self.kwargs['axis'][1]] != -1: - raise RuntimeError( - "Dimension should be -1 in row %r axis=%r." % ( - row, self.kwargs['axis'])) - self._check_row_(row, verbose=verbose) - return - - if self.name == "reduce_sum": - self._check_arg_('axes', tuple) - for a in self.kwargs['axes']: - row[a] = -1 - self._check_row_(row, verbose=verbose) - return - - if self.name == "matmul": - self._check_arg_('axes', tuple) - self._check_arg_('left', tuple) - self._check_arg_('right', tuple) - if row2 is None: - raise RuntimeError("matmul expects two inputs.") - if verbose: - axes = self.kwargs['axes'] - left = self.kwargs['left'] - right = self.kwargs['right'] - print(" MATMUL %r @ %r axes=%r left=%r right=%r" % ( - row, row2, axes, left, right)) - row2[:] = numpy.maximum(row, row2) - for a in self.kwargs['axes']: - if a not in self.kwargs['right']: - row2[a] = -1 - self._check_row_(row2, verbose=verbose) - return - - if self.name == "squeeze": - self._check_arg_('axes', tuple) - for a in self.kwargs['axes']: - row[a] = -1 - self._check_row_(row, verbose=verbose) - return - - if self.name == "diagonal": - self._check_arg_('diag', list) - to_remove = [] - for choice, choices in self.kwargs['diag']: - for ch in choices: - if ch != choice: - to_remove.append(ch) - for i in range(len(row)): # pylint: disable=C0200 - if row[i] in choices: - if row[i] != choice: - row[i] = choice - to_remove.sort() - for r in to_remove: - for i in range(len(row)): # pylint: disable=C0200 - if row[i] == r: - raise RuntimeError( - "Unexpected result r=%r row=%r to_remove=%r " - "diag=%r." % ( - r, row, to_remove, self.kwargs['diag'])) - if row[i] > r: - row[i] -= 1 - self._check_row_(row, verbose=verbose) - return - - raise NotImplementedError( - "compute_output_row not implemented for %r." % self.name) - - def _check_inputs_(self, n_expected, check_dim=False): - if len(self.inputs) != n_expected: - raise RuntimeError( - "Number of inputs must be %d not %d for operator %r." - "" % (n_expected, len(self.inputs), self.name)) - - def _check_shape_(self, m): - if len(m.shape) != self.full_dim: - raise RuntimeError( - "Number of dimensions %r is different from expected value " - "%d." % (m.shape, self.full_dim)) - - def _get_data(self, data, key): - if isinstance(key, int): - if key not in data: - raise RuntimeError( - "Unable to find key %d in %r." % ( - key, list(sorted(data)))) - return data[key] - if isinstance(key, EinsumSubOp): - if id(key) not in data: - raise RuntimeError( - "Unable to find key %d in %r." % ( - id(key), list(sorted(data)))) - return data[id(key)] - raise TypeError( - "Unexpected input type %r." % type(key)) - - def apply(self, data, verbose=False): - """ - Applies one operator on the data. - - :param data: dictionary storing the results - """ - if verbose: - print() - print("apply %r." % self.name) - - if self.name == 'id': - self._check_inputs_(1) - inp = self.inputs[0] - output = self._get_data(data, inp) - - elif self.name == 'diagonal': - self._check_inputs_(1) - inp = self.inputs[0] - m = self._get_data(data, inp) - if verbose: - print("- %s, shape=%r diag=%r" % ( - self.name, m.shape, self.kwargs['diag'])) - diag = self.kwargs['diag'] - if len(diag) != 1: - raise NotImplementedError( - "Not implemented with more than one duplicated indice " - "%r." % diag) - diag0 = diag[0] - output = numpy_diagonal(m, axis=diag0[0], axes=diag0[1]) - - elif self.name == 'expand_dims': - self._check_inputs_(1) - inp = self.inputs[0] - m = self._get_data(data, inp) - if verbose: - print("- %s, shape=%r axis=%r" % ( - self.name, m.shape, self.kwargs['axis'])) - output = numpy.expand_dims(m, self.kwargs['axis'][0]) - - elif self.name == 'transpose': - self._check_inputs_(1, True) - inp = self.inputs[0] - m = self._get_data(data, inp) - self._check_shape_(m) - if verbose: - print("- %s, shape=%r perm=%r" % ( - self.name, m.shape, self.kwargs['perm'])) - output = numpy.transpose(m, self.kwargs['perm']) - self._check_shape_(output) - - elif self.name == 'matmul': - self._check_inputs_(2) - inp1 = self.inputs[0] - inp2 = self.inputs[1] - m1 = self._get_data(data, inp1) - m2 = self._get_data(data, inp2) - self._check_shape_(m1) - self._check_shape_(m2) - axes = self.kwargs['axes'] - left = self.kwargs['left'] - right = self.kwargs['right'] - - if verbose: - print("- %s, shapes=%r @ %r axes=%r left=%r right=%r" % ( - self.name, m1.shape, m2.shape, axes, left, right)) - - output = numpy_extended_dot(m1, m2, axes, left, right, - verbose=verbose) - self._check_shape_(output) - - elif self.name == 'reduce_sum': - self._check_inputs_(1) - inp = self.inputs[0] - m = self._get_data(data, inp) - self._check_shape_(m) - axes = self.kwargs['axes'] - if verbose: - print("- %s, shape=%r axes=%r" % ( - self.name, m.shape, self.kwargs['axes'])) - output = numpy.sum(m, axis=axes, keepdims=True) - self._check_shape_(output) - - elif self.name == 'squeeze': - self._check_inputs_(1) - inp = self.inputs[0] - m = self._get_data(data, inp) - axes = self.kwargs['axes'] - if verbose: - print("- %s, shape=%r axes=%r" % ( - self.name, m.shape, self.kwargs['axes'])) - output = m - for a in axes[::-1]: - output = numpy.squeeze(output, axis=a) - return output - - else: - raise NotImplementedError( - "apply not implemented for %r." % self.name) - - data[id(self)] = output - if verbose: - print("+ %s, shape=%r -- %d" % (self.name, output.shape, id(self))) - return output - - -class GraphEinsumSubOp: - """ - Class gathering all nodes produced to explicit einsum - operators. - """ - - def __init__(self, letters, mat, lengths): - self._nodes = {} - self._mark = {} - self._ops = [] - self.last_op = None - self.last_added_op = None - self.metadata = dict( - letters=letters, mat=mat, lengths=lengths, - mat0=mat.copy()) - - def append(self, op): - """ - Adds one input or result. - - :param op: integer (an input) or an instance of @see cl EinsumSubOp. - :return: op or None if op is an integer - """ - if isinstance(op, int): - if op in self._nodes: - raise RuntimeError("Key %d already added." % op) - self._nodes[op] = op - self.last_added_op = op - return None - if isinstance(op, EinsumSubOp): - if op in self._nodes: - raise RuntimeError( - "Key %d already added, op=%r." % (id(op), op)) - self._nodes[id(op)] = op - self._ops.append(op) - self.last_added_op = op - return op - raise TypeError("Unexpected type %r." % type(op)) - - def mark(self, i, op): - """ - Marks one input or result as an intermediate result - after a full einsum step. - - :param op: integer (an input) or an instance of @see cl EinsumSubOp. - """ - if not isinstance(i, int): - raise TypeError("i must an integer not %r." % type(i)) - if isinstance(op, EinsumSubOp): - if id(op) not in self._nodes: - raise RuntimeError( - "Key %d not found, op=%r." % (id(op), op)) - self._mark[i] = op - self._mark[id(op)] = i - self.last_op = op - else: - raise TypeError("Unexpected type %r." % type(i)) - - def __iter__(self): - "Iterates on nodes." - for op in self._ops: - yield op - - def to_dot(self, **kwargs): - """ - Produces a graph in :epkg:`dot`. - - :param kwargs: additional graph option - :return: string - """ - options = { - 'orientation': 'portrait', - 'ranksep': '0.25', - 'nodesep': '0.05', - 'width': '0.5', - 'height': '0.1', - 'size': '5', - 'node': '[shape=record]', - } - options.update(kwargs) - - def d2s(d): - it = [] - for k, v in sorted(d.items()): - it.append("%s=%s" % (k, v)) - return " ".join(it) - - rows = ["digraph{"] - for k, v in options.items(): - if isinstance(v, str) and "[" in v: - rows.append("{} {};".format(k, v)) - else: - rows.append("{}={};".format(k, v)) - for k, v in self._nodes.items(): - if isinstance(v, int): - let = [(r, self.metadata['letters'][i]) - for i, r in enumerate(self.metadata['mat0'][v]) - if r != -1] - let.sort() - letters = "".join(_[1] for _ in let) - lab = "input %d\\\\n%s\\\\n%s" % ( - v, letters, str(self.metadata['mat0'][v])) - sk = v - else: - lab = "%s\\\\n%s" % (v.name, d2s(v.kwargs)) - sk = id(v) - if sk in self._mark and isinstance(self._mark[sk], int): - la = self._mark[sk] - lab = lab.replace("\\\\n", " - I%d\\\\n" % la) - s = ('%d [label="%s" style=filled ' - 'fillcolor=red];' % (k, lab)) - else: - s = '%d [label="%s"];' % (k, lab) - rows.append(s) - if not hasattr(v, 'inputs'): - continue - for i in v.inputs: - vid = i if isinstance(i, int) else id(i) - s = "%d -> %d;" % (vid, k) - rows.append(s) - rows.append("}") - return "\n".join(rows) - - def apply_sequence(self, *inputs, verbose=False): - """ - Applies a sequence of operations on a list of inputs. - - :param inputs: inputs: - :return: output - """ - if verbose: - print('######### apply_sequence') - data = {i: inp for i, inp in enumerate(inputs)} - last = None - for op in self: - last = op.apply(data, verbose=verbose) - if last is None: - raise RuntimeError( - "Sequence of operations is empty.") - return last - - def _apply_transpose_reshape(op, row): """ Put all dimensions in the same order. + + :param op: integer (for one input) or an operator + :param row: letter involved in this input (as a vector of binaries) + :return: last created operator """ axes = [] p = 0 @@ -759,7 +431,7 @@ def _decompose_einsum_equation_simple(equation, *shapes, verbose=False): # last_row, current_row (row = shape) rows = numpy.full((2, mat.shape[1]), -1) - graph = GraphEinsumSubOp(letters, mat, lengths) + graph = GraphEinsumSubOp(letters, mat, lengths, duplicates) fd = mat.shape[1] if verbose: print("EQUATION=%r" % equation) @@ -787,9 +459,13 @@ def _decompose_einsum_equation_simple(equation, *shapes, verbose=False): diag.append((v[0], tuple(v))) op = EinsumSubOp(fd, 'diagonal', op, diag=diag) op.compute_output_row(rows[1, :], mat[i, :], verbose=verbose) + tr_row = rows[1, :] marked = graph.append(op) + else: + diag = None + tr_row = mat[i] - for op in _apply_transpose_reshape(op, mat[i]): + for op in _apply_transpose_reshape(op, tr_row): op.compute_output_row(rows[1, :], verbose=verbose) marked = graph.append(op) diff --git a/mlprodict/testing/einsum_impl_classes.py b/mlprodict/testing/einsum_impl_classes.py new file mode 100644 index 000000000..d14c60f69 --- /dev/null +++ b/mlprodict/testing/einsum_impl_classes.py @@ -0,0 +1,453 @@ +""" +@file +@brief Function to dig into Einsum computation. +""" +import numpy +from .einsum_impl_ext import numpy_extended_dot, numpy_diagonal + + +class EinsumSubOp: + """ + Defines a sub operation used in Einsum decomposition. + + :param name: name (reshape, transpose, reduce_sum, matmul, id) + :param inputs: inputs + :param kwargs: arguments + """ + _allowed = {'expand_dims', 'transpose', 'reduce_sum', 'matmul', 'id', + 'squeeze', 'diagonal'} + + def __init__(self, full_dim, name, *inputs, **kwargs): + self.full_dim = full_dim + self.name = name + self.inputs = inputs + self.kwargs = kwargs + if name not in EinsumSubOp._allowed: + raise ValueError( + "Unexpected name %r. It should be in %r." + "" % (name, EinsumSubOp._allowed)) + if len(inputs) not in (1, 2): + raise RuntimeError( + "Inputs must contains 1 or 2 inputs not %d." % len(inputs)) + if name == 'matmul' and len(inputs) != 2: + raise RuntimeError( + "Inputs must contains 2 inputs not %d for operator 'matmul'." + "" % len(inputs)) + for i, inp in enumerate(inputs): + if not isinstance(inp, (int, EinsumSubOp)): + raise TypeError( + "Input %d has type %r, int or EinsumSubOp is expected." + "" % (i, type(inp))) + self._check_() + + def _check_(self): + if self.name == 'transpose': + self._check_arg_('perm', tuple) + perm = self.kwargs['perm'] + if len(perm) != len(set(perm)): + raise RuntimeError( + "perm has duplicated values %r (name=%r)." + "" % (perm, self.name)) + + def __repr__(self): + inps = ", ".join(map(str, self.inputs)) + kw = ", ".join("%s=%r" % (k, w) for k, w in self.kwargs.items()) + m = "%s(%r, %s, %s)" % ( + self.__class__.__name__, self.name, inps, kw) + return m + + def _check_arg_(self, name, typ): + if name not in self.kwargs: + raise RuntimeError( + "Parameter %r not found for operator %r." % (name, self.name)) + if not isinstance(self.kwargs[name], typ): + raise TypeError( + "Unexpected type %r for parameter %r and parameter %r." + "" % (type(self.kwargs[name]), name, self.name)) + + def _check_row_(self, row, inp=False, verbose=False): + """ + Checks input or output is valid. + """ + if verbose: + if inp: + print() + print('<-' if inp else '->', self.name, row, self.kwargs) + + def compute_output_row(self, row, row2=None, verbose=False): + """ + Updates *row* based on the operator. + """ + self._check_row_(row, True, verbose=verbose) + + if self.name == "id": + row[:] = row2[:] + self._check_row_(row, verbose=verbose) + return + + if self.name == "transpose": + self._check_arg_('perm', tuple) + if len(self.kwargs['perm']) != len(row): + raise RuntimeError( + "Unexpected permutation %r (row=%r)." + "" % (self.kwargs['perm'], row)) + cpy = row.copy() + for i, p in enumerate(self.kwargs['perm']): + row[i] = cpy[p] + self._check_row_(row, verbose=verbose) + return + + if self.name == "expand_dims": + self._check_arg_('axis', tuple) + if row[self.kwargs['axis'][1]] != -1: + raise RuntimeError( + "Dimension should be -1 in row %r axis=%r." % ( + row, self.kwargs['axis'])) + self._check_row_(row, verbose=verbose) + return + + if self.name == "reduce_sum": + self._check_arg_('axes', tuple) + for a in self.kwargs['axes']: + row[a] = -1 + self._check_row_(row, verbose=verbose) + return + + if self.name == "matmul": + self._check_arg_('axes', tuple) + self._check_arg_('left', tuple) + self._check_arg_('right', tuple) + if row2 is None: + raise RuntimeError("matmul expects two inputs.") + if verbose: + axes = self.kwargs['axes'] + left = self.kwargs['left'] + right = self.kwargs['right'] + print(" MATMUL %r @ %r axes=%r left=%r right=%r" % ( + row, row2, axes, left, right)) + row2[:] = numpy.maximum(row, row2) + for a in self.kwargs['axes']: + if a not in self.kwargs['right']: + row2[a] = -1 + self._check_row_(row2, verbose=verbose) + return + + if self.name == "squeeze": + self._check_arg_('axes', tuple) + for a in self.kwargs['axes']: + row[a] = -1 + self._check_row_(row, verbose=verbose) + return + + if self.name == "diagonal": + self._check_arg_('diag', list) + to_remove = [] + for choice, choices in self.kwargs['diag']: + for ch in choices: + if ch != choice: + to_remove.append(ch) + for i in range(len(row)): # pylint: disable=C0200 + if row[i] in choices: + if row[i] != choice: + row[i] = choice + to_remove.sort() + for r in to_remove: + for i in range(len(row)): # pylint: disable=C0200 + if row[i] == r: + raise RuntimeError( + "Unexpected result r=%r row=%r to_remove=%r " + "diag=%r." % ( + r, row, to_remove, self.kwargs['diag'])) + if row[i] > r: + row[i] -= 1 + self._check_row_(row, verbose=verbose) + return + + raise NotImplementedError( + "compute_output_row not implemented for %r." % self.name) + + def _check_inputs_(self, n_expected, check_dim=False): + if len(self.inputs) != n_expected: + raise RuntimeError( + "Number of inputs must be %d not %d for operator %r." + "" % (n_expected, len(self.inputs), self.name)) + + def _check_shape_(self, m): + if len(m.shape) != self.full_dim: + raise RuntimeError( + "Number of dimensions %r is different from expected value " + "%d." % (m.shape, self.full_dim)) + + def _get_data(self, data, key): + if isinstance(key, int): + if key not in data: + raise RuntimeError( + "Unable to find key %d in %r." % ( + key, list(sorted(data)))) + return data[key] + if isinstance(key, EinsumSubOp): + if id(key) not in data: + raise RuntimeError( + "Unable to find key %d in %r." % ( + id(key), list(sorted(data)))) + return data[id(key)] + raise TypeError( + "Unexpected input type %r." % type(key)) + + def apply(self, data, verbose=False): + """ + Applies one operator on the data. + + :param data: dictionary storing the results + """ + if verbose: + print() + print("apply %r." % self.name) + + if self.name == 'id': + self._check_inputs_(1) + inp = self.inputs[0] + output = self._get_data(data, inp) + + elif self.name == 'diagonal': + self._check_inputs_(1) + inp = self.inputs[0] + m = self._get_data(data, inp) + if verbose: + print("- %s, shape=%r diag=%r" % ( + self.name, m.shape, self.kwargs['diag'])) + diag = self.kwargs['diag'] + if len(diag) != 1: + raise NotImplementedError( + "Not implemented with more than one duplicated indice " + "%r." % diag) + diag0 = diag[0] + output = numpy_diagonal(m, axis=diag0[0], axes=diag0[1]) + + elif self.name == 'expand_dims': + self._check_inputs_(1) + inp = self.inputs[0] + m = self._get_data(data, inp) + if verbose: + print("- %s, shape=%r axis=%r" % ( + self.name, m.shape, self.kwargs['axis'])) + output = numpy.expand_dims(m, self.kwargs['axis'][0]) + + elif self.name == 'transpose': + self._check_inputs_(1, True) + inp = self.inputs[0] + m = self._get_data(data, inp) + self._check_shape_(m) + if verbose: + print("- %s, shape=%r perm=%r" % ( + self.name, m.shape, self.kwargs['perm'])) + output = numpy.transpose(m, self.kwargs['perm']) + self._check_shape_(output) + + elif self.name == 'matmul': + self._check_inputs_(2) + inp1 = self.inputs[0] + inp2 = self.inputs[1] + m1 = self._get_data(data, inp1) + m2 = self._get_data(data, inp2) + self._check_shape_(m1) + self._check_shape_(m2) + axes = self.kwargs['axes'] + left = self.kwargs['left'] + right = self.kwargs['right'] + + if verbose: + print("- %s, shapes=%r @ %r axes=%r left=%r right=%r" % ( + self.name, m1.shape, m2.shape, axes, left, right)) + + output = numpy_extended_dot(m1, m2, axes, left, right, + verbose=verbose) + self._check_shape_(output) + + elif self.name == 'reduce_sum': + self._check_inputs_(1) + inp = self.inputs[0] + m = self._get_data(data, inp) + self._check_shape_(m) + axes = self.kwargs['axes'] + if verbose: + print("- %s, shape=%r axes=%r" % ( + self.name, m.shape, self.kwargs['axes'])) + output = numpy.sum(m, axis=axes, keepdims=True) + self._check_shape_(output) + + elif self.name == 'squeeze': + self._check_inputs_(1) + inp = self.inputs[0] + m = self._get_data(data, inp) + axes = self.kwargs['axes'] + if verbose: + print("- %s, shape=%r axes=%r" % ( + self.name, m.shape, self.kwargs['axes'])) + output = m + for a in axes[::-1]: + output = numpy.squeeze(output, axis=a) + return output + + else: + raise NotImplementedError( + "apply not implemented for %r." % self.name) + + data[id(self)] = output + if verbose: + print("+ %s, shape=%r -- %d" % (self.name, output.shape, id(self))) + return output + + +class GraphEinsumSubOp: + """ + Class gathering all nodes produced to explicit einsum + operators. + """ + + def __init__(self, letters, mat, lengths, duplicates): + self._nodes = {} + self._mark = {} + self._ops = [] + self.last_op = None + self.last_added_op = None + self.metadata = dict( + letters=letters, mat=mat, lengths=lengths, + mat0=mat.copy(), duplicates=duplicates) + + def append(self, op): + """ + Adds one input or result. + + :param op: integer (an input) or an instance of @see cl EinsumSubOp. + :return: op or None if op is an integer + """ + if isinstance(op, int): + if op in self._nodes: + raise RuntimeError("Key %d already added." % op) + self._nodes[op] = op + self.last_added_op = op + return None + if isinstance(op, EinsumSubOp): + if op in self._nodes: + raise RuntimeError( + "Key %d already added, op=%r." % (id(op), op)) + self._nodes[id(op)] = op + self._ops.append(op) + self.last_added_op = op + return op + raise TypeError("Unexpected type %r." % type(op)) + + def mark(self, i, op): + """ + Marks one input or result as an intermediate result + after a full einsum step. + + :param op: integer (an input) or an instance of @see cl EinsumSubOp. + """ + if not isinstance(i, int): + raise TypeError("i must an integer not %r." % type(i)) + if isinstance(op, EinsumSubOp): + if id(op) not in self._nodes: + raise RuntimeError( + "Key %d not found, op=%r." % (id(op), op)) + self._mark[i] = op + self._mark[id(op)] = i + self.last_op = op + else: + raise TypeError("Unexpected type %r." % type(i)) + + def __iter__(self): + "Iterates on nodes." + for op in self._ops: + yield op + + def to_dot(self, **kwargs): + """ + Produces a graph in :epkg:`dot`. + + :param kwargs: additional graph option + :return: string + """ + options = { + 'orientation': 'portrait', + 'ranksep': '0.25', + 'nodesep': '0.05', + 'width': '0.5', + 'height': '0.1', + 'size': '5', + 'node': '[shape=record]', + } + options.update(kwargs) + + def d2s(d): + it = [] + for k, v in sorted(d.items()): + it.append("%s=%s" % (k, v)) + return " ".join(it) + + def d2sd(d): + it = [] + for k, v in sorted(d.items()): + if len(v) > 1: + it.append("%s=%s" % (k, ",".join(map(str, v)))) + return " ".join(it) + + rows = ["digraph{"] + for k, v in options.items(): + if isinstance(v, str) and "[" in v: + rows.append("{} {};".format(k, v)) + else: + rows.append("{}={};".format(k, v)) + for k, v in self._nodes.items(): + if isinstance(v, int): + let = [(r, self.metadata['letters'][i]) + for i, r in enumerate(self.metadata['mat0'][v]) + if r != -1] + dup = self.metadata['duplicates'][v] + if dup is None: + dup = "" + else: + dup = " - %s" % d2sd(dup) + let.sort() + letters = "".join(_[1] for _ in let) + lab = "input %d\\\\n%s\\\\n%s%s" % ( + v, letters, str(self.metadata['mat0'][v]), dup) + sk = v + else: + lab = "%s\\\\n%s" % (v.name, d2s(v.kwargs)) + sk = id(v) + if sk in self._mark and isinstance(self._mark[sk], int): + la = self._mark[sk] + lab = lab.replace("\\\\n", " - I%d\\\\n" % la) + s = ('%d [label="%s" style=filled ' + 'fillcolor=red];' % (k, lab)) + else: + s = '%d [label="%s"];' % (k, lab) + rows.append(s) + if not hasattr(v, 'inputs'): + continue + for i in v.inputs: + vid = i if isinstance(i, int) else id(i) + s = "%d -> %d;" % (vid, k) + rows.append(s) + rows.append("}") + return "\n".join(rows) + + def apply_sequence(self, *inputs, verbose=False): + """ + Applies a sequence of operations on a list of inputs. + + :param inputs: inputs: + :return: output + """ + if verbose: + print('######### apply_sequence') + data = {i: inp for i, inp in enumerate(inputs)} + last = None + for op in self: + last = op.apply(data, verbose=verbose) + if last is None: + raise RuntimeError( + "Sequence of operations is empty.") + return last diff --git a/mlprodict/testing/einsum_impl_ext.py b/mlprodict/testing/einsum_impl_ext.py new file mode 100644 index 000000000..e0077a1c3 --- /dev/null +++ b/mlprodict/testing/einsum_impl_ext.py @@ -0,0 +1,193 @@ +""" +@file +@brief Function to dig into Einsum computation. +""" +import numpy + + +def numpy_extended_dot(m1, m2, axes, left, right, verbose=False): + """ + Extended version of a matrix multiplication (:epkg:`numpy:dot`) + with two matrices *m1*, *m2* of the same dimensions. + Loops over *left* axes for *m1* and *right* axes for *m2*, + summation is done over *axes*. + Other axes must be empty. + + :param m1: first matrix + :param m2: second matrix + :param axes: summation axes + :param left: left axes + :param right: right axes + :param verbose: display intermediate information + :return: output + + The dot product is equivalent to: + + .. runpython:: + :showcode: + + import numpy + from mlprodict.testing.einsum_impl_ext import numpy_extended_dot + + m1 = numpy.arange(4).reshape((2, 2)) + m2 = m1 + 10 + print("dot product") + print(m1 @ m2) + + dm1 = m1.reshape((2, 2, 1)) + dm2 = m2.reshape((1, 2, 2)) + dot = numpy_extended_dot(dm1, dm2, axes=[1], left=[0], right=[2], + verbose=True) + print("extended dot product") + print(dot) + + Empty axes should be squeezed to get identical results. + Dot product when the second matrix is transposed. + + .. runpython:: + :showcode: + + import numpy + from mlprodict.testing.einsum_impl_ext import numpy_extended_dot + + m1 = numpy.arange(4).reshape((2, 2)) + m2 = m1 + 10 + print("dot product") + print(m1 @ m2.T) + + dm1 = m1.reshape((2, 1, 2)) + dm2 = m2.reshape((1, 2, 2)) + dot = numpy_extended_dot(dm1, dm2, axes=[2], left=[0], right=[1], + verbose=True) + print("extended dot product") + print(dot) + + An example when right axes include the summation axis. + + .. runpython:: + :showcode: + + import numpy + from mlprodict.testing.einsum_impl_ext import numpy_extended_dot + + m1 = numpy.arange(4).reshape((2, 2)) + m2 = m1 + 10 + dm1 = m1.reshape((2, 2, 1)) + dm2 = m2.reshape((1, 2, 2)) + dot = numpy_extended_dot(dm1, dm2, axes=[2], left=[0], right=[1, 2], + verbose=True) + print(dot) + + Example in higher dimension: + + .. runpython:: + :showcode: + + import numpy + from mlprodict.testing.einsum_impl_ext import numpy_extended_dot + + m1 = numpy.arange(8).reshape((2, 2, 2)) + m2 = m1 + 10 + + dot = numpy_extended_dot(m1, m2, [1], [0], [2], verbose=True)) + print(dot) + + The current implementation still uses :epkg:`numpy:einsum` + but this should be replaced. + """ + if len(m1.shape) != len(m2.shape): + raise RuntimeError( + "Matrices m1 and m2 must have the same dimension, " + "m1=%r, m2=%r." % (m1.shape, m2.shape)) + + def _check_(axs, n): + for a in axs: + if a < 0 or a >= n: + raise ValueError( + "One axis %d (in %r) is negative or above the maximum " + "dimension %d." % (a, axs, n)) + _check_(axes, len(m1.shape)) + _check_(left, len(m1.shape)) + _check_(right, len(m1.shape)) + + # This implementation should not use einsum. + # Temporary solution. + l1 = [chr(i + 97) for i in range(len(m1.shape))] + l2 = [chr(i + 97) for i in range(len(m2.shape))] + l3 = [chr(i + 97) for i in range(len(m2.shape))] + for a in left: + l1[a] = l1[a].upper() + l3[a] = l3[a].upper() + for a in right: + l2[a] = l2[a].upper() + l3[a] = l3[a].upper() + for a in axes: + l1[a] = l1[a].lower() + l2[a] = l2[a].lower() + if a not in right: + l3[a] = None + else: + l3[a] = l3[a].lower() + eq = "%s,%s->%s" % ("".join(l1), "".join(l2), + "".join(s for s in l3 if s)) + if verbose: + print(" [numpy_extended_dot] %s: %r @ %r" % (eq, m1.shape, m2.shape)) + output = numpy.einsum(eq, m1, m2) + new_shape = list(output.shape) + for a in axes: + if a not in right: + new_shape.insert(a, 1) + if verbose: + print(" [numpy_extended_dot] %r reshaped into %r " % ( + output.shape, new_shape)) + return output.reshape(tuple(new_shape)) + + +def numpy_diagonal(m, axis, axes): + """ + Extracts diagonal coefficients from an array. + + :param m: input array + :param axis: kept axis among the diagonal ones + :param axes: diagonal axes (axis must be one of them) + :return: output + + .. runpython:: + :showcode: + + import numpy + from mlprodict.testing.einsum_impl_ext import numpy_diagonal + + mat = numpy.arange(8).reshape((2, 2, 2)) + print(mat) + diag = numpy_diagonal(mat, 1, [1, 2]) + print(diag) + """ + if axis not in axes: + raise RuntimeError( + "axis %r must be in axes %r." % (axis, axes)) + shape = [] + new_shape = [] + for i, s in enumerate(m.shape): + if i in axes: + if i == axis: + shape.append(s) + new_shape.append(s) + else: + shape.append(1) + else: + shape.append(s) + new_shape.append(s) + + # Extracts coefficients. + output = numpy.empty(tuple(shape), dtype=m.dtype) + index_in = [slice(s) for s in m.shape] + index_out = [slice(s) for s in m.shape] + for i in range(0, shape[axis]): + for a in axes: + index_in[a] = i + index_out[a] = i if a == axis else 0 + output[tuple(index_out)] = m[tuple(index_in)] + + # Removes axis. + return output.reshape(tuple(new_shape)) From 9290046a1049e863c8a81f5bc8580a257410adcd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Fri, 23 Apr 2021 18:39:01 +0200 Subject: [PATCH 09/33] refactoring --- _doc/notebooks/einsum_decomposition.ipynb | 127 ++++---- _unittests/ut_testing/test_einsum.py | 41 ++- mlprodict/testing/einsum_impl.py | 195 +---------- mlprodict/testing/einsum_impl_classes.py | 380 ++++++++++++---------- mlprodict/testing/einsum_impl_ext.py | 244 ++++++++++++-- 5 files changed, 510 insertions(+), 477 deletions(-) diff --git a/_doc/notebooks/einsum_decomposition.ipynb b/_doc/notebooks/einsum_decomposition.ipynb index 8da3ffa3e..8f49e574d 100644 --- a/_doc/notebooks/einsum_decomposition.ipynb +++ b/_doc/notebooks/einsum_decomposition.ipynb @@ -285,16 +285,16 @@ { "data": { "text/html": [ - "
\n", + "
\n", "" ], "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -419,7 +419,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 10/10 [00:03<00:00, 2.98it/s]\n" + "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:10<00:00, 1.28it/s]\n" ] }, { @@ -456,64 +456,64 @@ " \n", " \n", " \n", - " 28\n", - " 0.010033\n", - " 0.000136\n", - " 0.009900\n", - " 0.010392\n", + " 37\n", + " 0.028620\n", + " 0.000279\n", + " 0.028299\n", + " 0.029155\n", " 10\n", " 10\n", - " 0.100332\n", + " 0.286203\n", " custom_einsum\n", - " 35\n", + " 50\n", " \n", " \n", - " 29\n", - " 0.002508\n", - " 0.000182\n", - " 0.002332\n", - " 0.002898\n", + " 38\n", + " 0.007170\n", + " 0.000217\n", + " 0.006561\n", + " 0.007377\n", " 10\n", " 10\n", - " 0.025076\n", + " 0.071695\n", " onnxruntime\n", - " 35\n", + " 50\n", " \n", " \n", - " 30\n", - " 0.077221\n", - " 0.009178\n", - " 0.066767\n", - " 0.094665\n", + " 39\n", + " 0.273039\n", + " 0.004524\n", + " 0.265275\n", + " 0.283283\n", " 10\n", " 10\n", - " 0.772209\n", + " 2.730388\n", " numpy.einsum\n", - " 40\n", + " 55\n", " \n", " \n", - " 31\n", - " 0.014716\n", - " 0.000698\n", - " 0.013814\n", - " 0.015954\n", + " 40\n", + " 0.051407\n", + " 0.000719\n", + " 0.050533\n", + " 0.052883\n", " 10\n", " 10\n", - " 0.147156\n", + " 0.514065\n", " custom_einsum\n", - " 40\n", + " 55\n", " \n", " \n", - " 32\n", - " 0.003906\n", - " 0.000792\n", - " 0.003125\n", - " 0.005847\n", + " 41\n", + " 0.009407\n", + " 0.001364\n", + " 0.008197\n", + " 0.012437\n", " 10\n", " 10\n", - " 0.039060\n", + " 0.094069\n", " onnxruntime\n", - " 40\n", + " 55\n", " \n", " \n", "\n", @@ -521,18 +521,18 @@ ], "text/plain": [ " average deviation min_exec max_exec repeat number total \\\n", - "28 0.010033 0.000136 0.009900 0.010392 10 10 0.100332 \n", - "29 0.002508 0.000182 0.002332 0.002898 10 10 0.025076 \n", - "30 0.077221 0.009178 0.066767 0.094665 10 10 0.772209 \n", - "31 0.014716 0.000698 0.013814 0.015954 10 10 0.147156 \n", - "32 0.003906 0.000792 0.003125 0.005847 10 10 0.039060 \n", + "37 0.028620 0.000279 0.028299 0.029155 10 10 0.286203 \n", + "38 0.007170 0.000217 0.006561 0.007377 10 10 0.071695 \n", + "39 0.273039 0.004524 0.265275 0.283283 10 10 2.730388 \n", + "40 0.051407 0.000719 0.050533 0.052883 10 10 0.514065 \n", + "41 0.009407 0.001364 0.008197 0.012437 10 10 0.094069 \n", "\n", " name N \n", - "28 custom_einsum 35 \n", - "29 onnxruntime 35 \n", - "30 numpy.einsum 40 \n", - "31 custom_einsum 40 \n", - "32 onnxruntime 40 " + "37 custom_einsum 50 \n", + "38 onnxruntime 50 \n", + "39 numpy.einsum 55 \n", + "40 custom_einsum 55 \n", + "41 onnxruntime 55 " ] }, "execution_count": 12, @@ -564,7 +564,7 @@ "seq = None \n", "\n", "results = []\n", - "for N in tqdm([2, 3, 4, 10, 15, 20, 25, 30, 35, 40]):\n", + "for N in tqdm([2, 3, 4, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55]):\n", " m1 = numpy.random.randn(N, N, N)\n", " m2 = numpy.random.randn(N, N)\n", " m3 = numpy.random.randn(N, N, N)\n", @@ -618,9 +618,9 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "
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" ] }, "metadata": { @@ -630,9 +630,19 @@ } ], "source": [ + "import matplotlib.pyplot as plt\n", + "\n", "piv = df.pivot(\"N\", \"name\", \"average\")\n", - "ax = piv.plot(logy=True, logx=True)\n", - "ax.set_title(\"Benchmark einsum function\");" + "piv2 = piv.copy()\n", + "np = piv[\"numpy.einsum\"]\n", + "for c in piv2.columns:\n", + " piv2[c] /= np\n", + " \n", + "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n", + "piv.plot(logy=True, logx=True, ax=ax[0])\n", + "ax[0].set_title(\"Benchmark einsum function\")\n", + "piv2.plot(logy=True, logx=True, ax=ax[1])\n", + "ax[1].set_title(\"Benchmark einsum function\\n(ratio, baseline=numpy)\");" ] }, { @@ -641,13 +651,6 @@ "metadata": {}, "outputs": [], "source": [] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/_unittests/ut_testing/test_einsum.py b/_unittests/ut_testing/test_einsum.py index 53aad6fae..412efa3e9 100644 --- a/_unittests/ut_testing/test_einsum.py +++ b/_unittests/ut_testing/test_einsum.py @@ -8,7 +8,7 @@ import numpy from pyquickhelper.pycode import ExtTestCase from mlprodict.testing.einsum_impl_ext import ( - numpy_diagonal, numpy_extended_dot) + numpy_diagonal, numpy_extended_dot, numpy_extended_dot_python) from mlprodict.testing.einsum_impl import ( analyse_einsum_equation, decompose_einsum_equation, EinsumSubOp, apply_einsum_sequence) @@ -33,8 +33,8 @@ def test_numpy_diagonal(self): diag = numpy_diagonal(mat, 2, [0, 2]) self.assertEqualArray(diag, numpy.array([[0, 2], [5, 7]]).T) - def test_numpy_extended_dot_2(self): - m1 = numpy.arange(4).reshape((2, 2)) + def test_numpy_extended_dot_2_a(self): + m1 = numpy.arange(4).reshape((2, 2)).astype(numpy.float32) m2 = m1 + 10 self.assertRaise(lambda: numpy_extended_dot(m1, m2.T, [0], [1], [2]), @@ -43,19 +43,31 @@ def test_numpy_extended_dot_2(self): dm2 = m2.reshape((1, 2, 2)) dot = numpy_extended_dot(dm1, dm2, axes=[1], left=[0], right=[2]) exp = m1 @ m2 - self.assertEqual(exp, numpy.squeeze(dot)) + self.assertEqualArray(exp, numpy.squeeze(dot)) + dot2 = numpy_extended_dot_python( + dm1, dm2, axes=[1], left=[0], right=[2]) + self.assertEqualArray(exp, numpy.squeeze(dot2)) dm1 = m1.reshape((2, 1, 2)) dm2 = m2.reshape((1, 2, 2)) dot = numpy_extended_dot(dm1, dm2, axes=[2], left=[0], right=[1]) exp = m1 @ m2.T - self.assertEqual(exp, numpy.squeeze(dot)) + self.assertEqualArray(exp, numpy.squeeze(dot)) + dot2 = numpy_extended_dot_python( + dm1, dm2, axes=[2], left=[0], right=[1]) + self.assertEqualArray(exp, numpy.squeeze(dot2)) + def test_numpy_extended_dot_2_b(self): + m1 = numpy.arange(4).reshape((2, 2)).astype(numpy.float32) + m2 = m1 + 10 dm1 = m1.reshape((2, 2, 1)) dm2 = m2.reshape((1, 2, 2)) - dot = numpy_extended_dot(dm1, dm2, axes=[2], left=[0], right=[1, 2]) - exp = numpy.array([[[10, 11], [12, 13]], [[50, 55], [60, 65]]]) - self.assertEqual(exp, numpy.squeeze(dot)) + exp = numpy_extended_dot(dm1, dm2, axes=[2], left=[0], right=[1, 2]) + dot = numpy_extended_dot_python( + dm1, dm2, axes=[2], left=[0], right=[1, 2]) + self.assertNotEmpty(dot) + self.assertNotEmpty(exp) + # self.assertEqualArray(exp, numpy.squeeze(dot)) def test_numpy_extended_dot_3(self): m1 = numpy.arange(8).reshape((2, 2, 2)) @@ -63,16 +75,16 @@ def test_numpy_extended_dot_3(self): dot = numpy_extended_dot(m1, m2, [1], [0], [2]) exp = numpy.array([[[164, 176]], [[580, 624]]]) - self.assertEqual(exp, dot) + self.assertEqualArray(exp, dot) dot = numpy_extended_dot(m1, m2, [1], [2], [0]) exp = numpy.array([[[284, 376]], [[380, 504]]]) - self.assertEqual(exp, dot) + self.assertEqualArray(exp, dot) dot = numpy_extended_dot(m1, m2, [1], [2], [0, 1]) exp = numpy.array([[[84, 126], [200, 250]], [[116, 174], [264, 330]]]) - self.assertEqual(exp, dot) + self.assertEqualArray(exp, dot) def test_analyse_einsum_equation(self): self.assertRaise(lambda: analyse_einsum_equation("abc"), @@ -151,7 +163,7 @@ def fct(): out = f.getvalue() self.assertIn("numpy_extended_dot", out) - self.assertEqual(exp, res) + self.assertEqualArray(exp, res) def test_einsum_sub_op(self): self.assertRaise(lambda: EinsumSubOp(2, "er", (2, 2)), ValueError) @@ -332,7 +344,7 @@ def test_np_test_combined_views_mapping(self): # gh-10792 a = numpy.arange(9).reshape(1, 1, 3, 1, 3) b = numpy.einsum('bbcdc->d', a) - self.assertEqual(b, [12]) + self.assertEqualArray(b, [12]) def test_np_test_broadcasting_dot_cases1(self): # Ensures broadcasting cases are not mistaken for GEMM @@ -400,5 +412,6 @@ def test_np_test_edge_cases_duplicate_indices(self): if __name__ == "__main__": - # TestEinsum().test_np_test_random_cases_difficult() + TestEinsum().test_numpy_extended_dot_2_b() + TestEinsum().test_numpy_extended_dot_3() unittest.main() diff --git a/mlprodict/testing/einsum_impl.py b/mlprodict/testing/einsum_impl.py index 063e34e5f..f27d6911c 100644 --- a/mlprodict/testing/einsum_impl.py +++ b/mlprodict/testing/einsum_impl.py @@ -6,194 +6,6 @@ from .einsum_impl_classes import EinsumSubOp, GraphEinsumSubOp -def numpy_extended_dot(m1, m2, axes, left, right, verbose=False): - """ - Extended version of a matrix multiplication (:epkg:`numpy:dot`) - with two matrices *m1*, *m2* of the same dimensions. - Loops over *left* axes for *m1* and *right* axes for *m2*, - summation is done over *axes*. - Other axes must be empty. - - :param m1: first matrix - :param m2: second matrix - :param axes: summation axes - :param left: left axes - :param right: right axes - :param verbose: display intermediate information - :return: output - - The dot product is equivalent to: - - .. runpython:: - :showcode: - - import numpy - from mlprodict.testing.einsum_impl import numpy_diagonal - - m1 = numpy.arange(4).reshape((2, 2)) - m2 = m1 + 10 - print("dot product") - print(m1 @ m2) - - dm1 = m1.reshape((2, 2, 1)) - dm2 = m2.reshape((1, 2, 2)) - dot = numpy_extended_dot(dm1, dm2, axes=[1], left=[0], right=[2], - verbose=True) - print("extended dot product") - print(dot) - - Empty axes should be squeezed to get identical results. - Dot product when the second matrix is transposed. - - .. runpython:: - :showcode: - - import numpy - from mlprodict.testing.einsum_impl import numpy_diagonal - - m1 = numpy.arange(4).reshape((2, 2)) - m2 = m1 + 10 - print("dot product") - print(m1 @ m2.T) - - dm1 = m1.reshape((2, 1, 2)) - dm2 = m2.reshape((1, 2, 2)) - dot = numpy_extended_dot(dm1, dm2, axes=[2], left=[0], right=[1], - verbose=True) - print("extended dot product") - print(dot) - - An example when right axes include the summation axis. - - .. runpython:: - :showcode: - - import numpy - from mlprodict.testing.einsum_impl import numpy_diagonal - - m1 = numpy.arange(4).reshape((2, 2)) - m2 = m1 + 10 - dm1 = m1.reshape((2, 2, 1)) - dm2 = m2.reshape((1, 2, 2)) - dot = numpy_extended_dot(dm1, dm2, axes=[2], left=[0], right=[1, 2], - verbose=True) - print(dot) - - Example in higher dimension: - - .. runpython:: - :showcode: - - import numpy - from mlprodict.testing.einsum_impl import numpy_diagonal - - m1 = numpy.arange(8).reshape((2, 2, 2)) - m2 = m1 + 10 - - dot = numpy_extended_dot(m1, m2, [1], [0], [2], verbose=True)) - print(dot) - - The current implementation still uses :epkg:`numpy:einsum` - but this should be replaced. - """ - if len(m1.shape) != len(m2.shape): - raise RuntimeError( - "Matrices m1 and m2 must have the same dimension, " - "m1=%r, m2=%r." % (m1.shape, m2.shape)) - - def _check_(axs, n): - for a in axs: - if a < 0 or a >= n: - raise ValueError( - "One axis %d (in %r) is negative or above the maximum " - "dimension %d." % (a, axs, n)) - _check_(axes, len(m1.shape)) - _check_(left, len(m1.shape)) - _check_(right, len(m1.shape)) - - # This implementation should not use einsum. - # Temporary solution. - l1 = [chr(i + 97) for i in range(len(m1.shape))] - l2 = [chr(i + 97) for i in range(len(m2.shape))] - l3 = [chr(i + 97) for i in range(len(m2.shape))] - for a in left: - l1[a] = l1[a].upper() - l3[a] = l3[a].upper() - for a in right: - l2[a] = l2[a].upper() - l3[a] = l3[a].upper() - for a in axes: - l1[a] = l1[a].lower() - l2[a] = l2[a].lower() - if a not in right: - l3[a] = None - else: - l3[a] = l3[a].lower() - eq = "%s,%s->%s" % ("".join(l1), "".join(l2), - "".join(s for s in l3 if s)) - if verbose: - print(" [numpy_extended_dot] %s: %r @ %r" % (eq, m1.shape, m2.shape)) - output = numpy.einsum(eq, m1, m2) - new_shape = list(output.shape) - for a in axes: - if a not in right: - new_shape.insert(a, 1) - if verbose: - print(" [numpy_extended_dot] %r reshaped into %r " % ( - output.shape, new_shape)) - return output.reshape(tuple(new_shape)) - - -def numpy_diagonal(m, axis, axes): - """ - Extracts diagonal coefficients from an array. - - :param m: input array - :param axis: kept axis among the diagonal ones - :param axes: diagonal axes (axis must be one of them) - :return: output - - .. runpython:: - :showcode: - - import numpy - from mlprodict.testing.einsum_impl import numpy_diagonal - - mat = numpy.arange(8).reshape((2, 2, 2)) - print(mat) - diag = numpy_diagonal(mat, 1, [1, 2]) - print(diag) - """ - if axis not in axes: - raise RuntimeError( - "axis %r must be in axes %r." % (axis, axes)) - shape = [] - new_shape = [] - for i, s in enumerate(m.shape): - if i in axes: - if i == axis: - shape.append(s) - new_shape.append(s) - else: - shape.append(1) - else: - shape.append(s) - new_shape.append(s) - - # Extracts coefficients. - output = numpy.empty(tuple(shape), dtype=m.dtype) - index_in = [slice(s) for s in m.shape] - index_out = [slice(s) for s in m.shape] - for i in range(0, shape[axis]): - for a in axes: - index_in[a] = i - index_out[a] = i if a == axis else 0 - output[tuple(index_out)] = m[tuple(index_in)] - - # Removes axis. - return output.reshape(tuple(new_shape)) - - def analyse_einsum_equation(equation): """ Analyses an einsum equation. @@ -275,7 +87,7 @@ def decompose_einsum_equation(equation, *shapes, strategy="simple", verbose=Fals :param strategy: there are different way to decompose the equation, this parameters defines the way to do it (see below) :param verbose: verbosity - :return: instance @see cl GraphEinsumSubOp + :return: instance of @see cl GraphEinsumSubOp About *strategy*: * `'simple'`: align all dimensions in the alphabetical order @@ -420,7 +232,7 @@ def _apply_squeeze_transpose(op, row_last, row_output): def _decompose_einsum_equation_simple(equation, *shapes, verbose=False): """ - Applies strategy simple of function @see fct decompose_einsum_equation. + Applies strategy simple of function @see fn decompose_einsum_equation. """ letters, mat, lengths, duplicates = analyse_einsum_equation(equation) if len(letters) != mat.shape[1]: @@ -507,7 +319,8 @@ def _decompose_einsum_equation_simple(equation, *shapes, verbose=False): print(rows) op = EinsumSubOp(fd, 'matmul', graph.last_op, op, axes=tuple(common_dims), - left=tuple(left), right=tuple(right)) + left=tuple(left), right=tuple(right), + ndim=rows.shape[1]) op.compute_output_row(rows[0, :], rows[1, :], verbose=verbose) marked = graph.append(op) diff --git a/mlprodict/testing/einsum_impl_classes.py b/mlprodict/testing/einsum_impl_classes.py index d14c60f69..9caf0de57 100644 --- a/mlprodict/testing/einsum_impl_classes.py +++ b/mlprodict/testing/einsum_impl_classes.py @@ -3,7 +3,9 @@ @brief Function to dig into Einsum computation. """ import numpy -from .einsum_impl_ext import numpy_extended_dot, numpy_diagonal +from .einsum_impl_ext import ( + numpy_extended_dot, numpy_diagonal, + _numpy_extended_dot_equation) class EinsumSubOp: @@ -56,6 +58,20 @@ def __repr__(self): self.__class__.__name__, self.name, inps, kw) return m + def dot_label(self): + """ + Displays some informations useful to understand the operator. + """ + if self.name == "matmul": + ndim = self.kwargs['ndim'] + axes = self.kwargs['axes'] + left = self.kwargs['left'] + right = self.kwargs['right'] + eq = _numpy_extended_dot_equation(ndim, ndim, axes, left, right) + eq = eq.replace(">", "\\\\>") + return "~" + eq + return None + def _check_arg_(self, name, typ): if name not in self.kwargs: raise RuntimeError( @@ -74,97 +90,97 @@ def _check_row_(self, row, inp=False, verbose=False): print() print('<-' if inp else '->', self.name, row, self.kwargs) + def _compute_output_row_id(self, row, row2=None, verbose=False): + row[:] = row2[:] + self._check_row_(row, verbose=verbose) + + def _compute_output_row_transpose(self, row, row2=None, verbose=False): + self._check_arg_('perm', tuple) + if len(self.kwargs['perm']) != len(row): + raise RuntimeError( + "Unexpected permutation %r (row=%r)." + "" % (self.kwargs['perm'], row)) + cpy = row.copy() + for i, p in enumerate(self.kwargs['perm']): + row[i] = cpy[p] + self._check_row_(row, verbose=verbose) + + def _compute_output_row_expand_dims(self, row, row2=None, verbose=False): + self._check_arg_('axis', tuple) + if row[self.kwargs['axis'][1]] != -1: + raise RuntimeError( + "Dimension should be -1 in row %r axis=%r." % ( + row, self.kwargs['axis'])) + self._check_row_(row, verbose=verbose) + + def _compute_output_row_reduce_sum(self, row, row2=None, verbose=False): + self._check_arg_('axes', tuple) + for a in self.kwargs['axes']: + row[a] = -1 + self._check_row_(row, verbose=verbose) + + def _compute_output_row_matmul(self, row, row2=None, verbose=False): + self._check_arg_('axes', tuple) + self._check_arg_('left', tuple) + self._check_arg_('right', tuple) + self._check_arg_('ndim', int) + if row2 is None: + raise RuntimeError("matmul expects two inputs.") + if verbose: + ndim = self.kwargs['ndim'] + axes = self.kwargs['axes'] + left = self.kwargs['left'] + right = self.kwargs['right'] + print(" MATMUL %r @ %r axes=%r left=%r right=%r - eq=%s" % ( + row, row2, axes, left, right, + _numpy_extended_dot_equation(ndim, ndim, axes, left, right))) + row2[:] = numpy.maximum(row, row2) + for a in self.kwargs['axes']: + if a not in self.kwargs['right']: + row2[a] = -1 + self._check_row_(row2, verbose=verbose) + + def _compute_output_row_squeeze(self, row, row2=None, verbose=False): + self._check_arg_('axes', tuple) + for a in self.kwargs['axes']: + row[a] = -1 + self._check_row_(row, verbose=verbose) + + def _compute_output_row_diagonal(self, row, row2=None, verbose=False): + self._check_arg_('diag', list) + to_remove = [] + for choice, choices in self.kwargs['diag']: + for ch in choices: + if ch != choice: + to_remove.append(ch) + for i in range(len(row)): # pylint: disable=C0200 + if row[i] in choices: + if row[i] != choice: + row[i] = choice + to_remove.sort() + for r in to_remove: + for i in range(len(row)): # pylint: disable=C0200 + if row[i] == r: + raise RuntimeError( + "Unexpected result r=%r row=%r to_remove=%r " + "diag=%r." % ( + r, row, to_remove, self.kwargs['diag'])) + if row[i] > r: + row[i] -= 1 + self._check_row_(row, verbose=verbose) + def compute_output_row(self, row, row2=None, verbose=False): """ Updates *row* based on the operator. """ self._check_row_(row, True, verbose=verbose) - if self.name == "id": - row[:] = row2[:] - self._check_row_(row, verbose=verbose) - return - - if self.name == "transpose": - self._check_arg_('perm', tuple) - if len(self.kwargs['perm']) != len(row): - raise RuntimeError( - "Unexpected permutation %r (row=%r)." - "" % (self.kwargs['perm'], row)) - cpy = row.copy() - for i, p in enumerate(self.kwargs['perm']): - row[i] = cpy[p] - self._check_row_(row, verbose=verbose) - return - - if self.name == "expand_dims": - self._check_arg_('axis', tuple) - if row[self.kwargs['axis'][1]] != -1: - raise RuntimeError( - "Dimension should be -1 in row %r axis=%r." % ( - row, self.kwargs['axis'])) - self._check_row_(row, verbose=verbose) - return - - if self.name == "reduce_sum": - self._check_arg_('axes', tuple) - for a in self.kwargs['axes']: - row[a] = -1 - self._check_row_(row, verbose=verbose) - return - - if self.name == "matmul": - self._check_arg_('axes', tuple) - self._check_arg_('left', tuple) - self._check_arg_('right', tuple) - if row2 is None: - raise RuntimeError("matmul expects two inputs.") - if verbose: - axes = self.kwargs['axes'] - left = self.kwargs['left'] - right = self.kwargs['right'] - print(" MATMUL %r @ %r axes=%r left=%r right=%r" % ( - row, row2, axes, left, right)) - row2[:] = numpy.maximum(row, row2) - for a in self.kwargs['axes']: - if a not in self.kwargs['right']: - row2[a] = -1 - self._check_row_(row2, verbose=verbose) - return - - if self.name == "squeeze": - self._check_arg_('axes', tuple) - for a in self.kwargs['axes']: - row[a] = -1 - self._check_row_(row, verbose=verbose) - return - - if self.name == "diagonal": - self._check_arg_('diag', list) - to_remove = [] - for choice, choices in self.kwargs['diag']: - for ch in choices: - if ch != choice: - to_remove.append(ch) - for i in range(len(row)): # pylint: disable=C0200 - if row[i] in choices: - if row[i] != choice: - row[i] = choice - to_remove.sort() - for r in to_remove: - for i in range(len(row)): # pylint: disable=C0200 - if row[i] == r: - raise RuntimeError( - "Unexpected result r=%r row=%r to_remove=%r " - "diag=%r." % ( - r, row, to_remove, self.kwargs['diag'])) - if row[i] > r: - row[i] -= 1 - self._check_row_(row, verbose=verbose) - return - - raise NotImplementedError( - "compute_output_row not implemented for %r." % self.name) + method_name = "_compute_output_row_%s" % self.name + meth = getattr(self, method_name, None) + if meth is None: + raise NotImplementedError( + "compute_output_row not implemented for %r." % self.name) + meth(row, row2=row2, verbose=verbose) def _check_inputs_(self, n_expected, check_dim=False): if len(self.inputs) != n_expected: @@ -194,6 +210,97 @@ def _get_data(self, data, key): raise TypeError( "Unexpected input type %r." % type(key)) + def _apply_id(self, data, verbose=False): + self._check_inputs_(1) + inp = self.inputs[0] + output = self._get_data(data, inp) + return output + + def _apply_diagonal(self, data, verbose=False): + self._check_inputs_(1) + inp = self.inputs[0] + m = self._get_data(data, inp) + if verbose: + print("- %s, shape=%r diag=%r" % ( + self.name, m.shape, self.kwargs['diag'])) + diag = self.kwargs['diag'] + if len(diag) != 1: + raise NotImplementedError( + "Not implemented with more than one duplicated indice " + "%r." % diag) + diag0 = diag[0] + output = numpy_diagonal(m, axis=diag0[0], axes=diag0[1]) + return output + + def _apply_expand_dims(self, data, verbose=False): + self._check_inputs_(1) + inp = self.inputs[0] + m = self._get_data(data, inp) + if verbose: + print("- %s, shape=%r axis=%r" % ( + self.name, m.shape, self.kwargs['axis'])) + output = numpy.expand_dims(m, self.kwargs['axis'][0]) + return output + + def _apply_transpose(self, data, verbose=False): + self._check_inputs_(1, True) + inp = self.inputs[0] + m = self._get_data(data, inp) + self._check_shape_(m) + if verbose: + print("- %s, shape=%r perm=%r" % ( + self.name, m.shape, self.kwargs['perm'])) + output = numpy.transpose(m, self.kwargs['perm']) + self._check_shape_(output) + return output + + def _apply_matmul(self, data, verbose=False): + self._check_inputs_(2) + inp1 = self.inputs[0] + inp2 = self.inputs[1] + m1 = self._get_data(data, inp1) + m2 = self._get_data(data, inp2) + self._check_shape_(m1) + self._check_shape_(m2) + axes = self.kwargs['axes'] + left = self.kwargs['left'] + right = self.kwargs['right'] + + if verbose: + print("- %s, shapes=%r @ %r axes=%r left=%r right=%r" % ( + self.name, m1.shape, m2.shape, axes, left, right)) + + output = numpy_extended_dot(m1, m2, axes, left, right, + verbose=verbose) + self._check_shape_(output) + return output + + def _apply_reduce_sum(self, data, verbose=False): + self._check_inputs_(1) + inp = self.inputs[0] + m = self._get_data(data, inp) + self._check_shape_(m) + axes = self.kwargs['axes'] + if verbose: + print("- %s, shape=%r axes=%r" % ( + self.name, m.shape, self.kwargs['axes'])) + output = numpy.sum(m, axis=axes, keepdims=True) + self._check_shape_(output) + return output + + def _apply_squeeze(self, data, verbose=False): + self._check_inputs_(1) + inp = self.inputs[0] + m = self._get_data(data, inp) + axes = self.kwargs['axes'] + if verbose: + print("- %s, shape=%r axes=%r" % ( + self.name, m.shape, self.kwargs['axes'])) + output = m + for a in axes[::-1]: + output = numpy.squeeze(output, axis=a) + return output + def apply(self, data, verbose=False): """ Applies one operator on the data. @@ -204,94 +311,12 @@ def apply(self, data, verbose=False): print() print("apply %r." % self.name) - if self.name == 'id': - self._check_inputs_(1) - inp = self.inputs[0] - output = self._get_data(data, inp) - - elif self.name == 'diagonal': - self._check_inputs_(1) - inp = self.inputs[0] - m = self._get_data(data, inp) - if verbose: - print("- %s, shape=%r diag=%r" % ( - self.name, m.shape, self.kwargs['diag'])) - diag = self.kwargs['diag'] - if len(diag) != 1: - raise NotImplementedError( - "Not implemented with more than one duplicated indice " - "%r." % diag) - diag0 = diag[0] - output = numpy_diagonal(m, axis=diag0[0], axes=diag0[1]) - - elif self.name == 'expand_dims': - self._check_inputs_(1) - inp = self.inputs[0] - m = self._get_data(data, inp) - if verbose: - print("- %s, shape=%r axis=%r" % ( - self.name, m.shape, self.kwargs['axis'])) - output = numpy.expand_dims(m, self.kwargs['axis'][0]) - - elif self.name == 'transpose': - self._check_inputs_(1, True) - inp = self.inputs[0] - m = self._get_data(data, inp) - self._check_shape_(m) - if verbose: - print("- %s, shape=%r perm=%r" % ( - self.name, m.shape, self.kwargs['perm'])) - output = numpy.transpose(m, self.kwargs['perm']) - self._check_shape_(output) - - elif self.name == 'matmul': - self._check_inputs_(2) - inp1 = self.inputs[0] - inp2 = self.inputs[1] - m1 = self._get_data(data, inp1) - m2 = self._get_data(data, inp2) - self._check_shape_(m1) - self._check_shape_(m2) - axes = self.kwargs['axes'] - left = self.kwargs['left'] - right = self.kwargs['right'] - - if verbose: - print("- %s, shapes=%r @ %r axes=%r left=%r right=%r" % ( - self.name, m1.shape, m2.shape, axes, left, right)) - - output = numpy_extended_dot(m1, m2, axes, left, right, - verbose=verbose) - self._check_shape_(output) - - elif self.name == 'reduce_sum': - self._check_inputs_(1) - inp = self.inputs[0] - m = self._get_data(data, inp) - self._check_shape_(m) - axes = self.kwargs['axes'] - if verbose: - print("- %s, shape=%r axes=%r" % ( - self.name, m.shape, self.kwargs['axes'])) - output = numpy.sum(m, axis=axes, keepdims=True) - self._check_shape_(output) - - elif self.name == 'squeeze': - self._check_inputs_(1) - inp = self.inputs[0] - m = self._get_data(data, inp) - axes = self.kwargs['axes'] - if verbose: - print("- %s, shape=%r axes=%r" % ( - self.name, m.shape, self.kwargs['axes'])) - output = m - for a in axes[::-1]: - output = numpy.squeeze(output, axis=a) - return output - - else: + method_name = "_apply_%s" % self.name + meth = getattr(self, method_name, None) + if meth is None: raise NotImplementedError( "apply not implemented for %r." % self.name) + output = meth(data, verbose) data[id(self)] = output if verbose: @@ -414,16 +439,21 @@ def d2sd(d): lab = "input %d\\\\n%s\\\\n%s%s" % ( v, letters, str(self.metadata['mat0'][v]), dup) sk = v + extended_lab = "" else: lab = "%s\\\\n%s" % (v.name, d2s(v.kwargs)) sk = id(v) + extended_lab = v.dot_label() + if extended_lab: + extended_lab = "\\\\n" + extended_lab + if sk in self._mark and isinstance(self._mark[sk], int): la = self._mark[sk] lab = lab.replace("\\\\n", " - I%d\\\\n" % la) - s = ('%d [label="%s" style=filled ' - 'fillcolor=red];' % (k, lab)) + s = ('%d [label="%s%s" style=filled ' + 'fillcolor=red];' % (k, lab, extended_lab)) else: - s = '%d [label="%s"];' % (k, lab) + s = '%d [label="%s%s"];' % (k, lab, extended_lab) rows.append(s) if not hasattr(v, 'inputs'): continue diff --git a/mlprodict/testing/einsum_impl_ext.py b/mlprodict/testing/einsum_impl_ext.py index e0077a1c3..c0b3ebce8 100644 --- a/mlprodict/testing/einsum_impl_ext.py +++ b/mlprodict/testing/einsum_impl_ext.py @@ -5,6 +5,61 @@ import numpy +def _numpy_extended_dot_equation(m1_dim, m2_dim, axes, left, right): + """ + Returns the equation equivalent to an extended version + of a matrix multiplication (see @see fn numpy_extended_dot). + + :param m1: number of dimensions of the first matrix + :param m2: number of dimensions of the second matrix + :param axes: summation axes + :param axes: summation axes + :param left: left axes + :param right: right axes + :return: equation + """ + if m1_dim != m2_dim: + raise RuntimeError( + "Matrices m1 and m2 must have the same number of dimensions, " + "m1=%r, m2=%r." % (m1_dim, m2_dim)) + total = set(axes) | set(left) | set(right) + if len(total) > m1_dim: + raise ValueError( + "Whole set of involved axes should be inferior to the number " + "of dimensions: %r = {%r} | {%r} | {%r} has more than %d elements" + "." % (total, axes, left, right, m1_dim)) + + def _check_(axs, n): + for a in axs: + if a < 0 or a >= n: + raise ValueError( + "One axis %d (in %r) is negative or above the maximum " + "dimension %d." % (a, axs, n)) + _check_(axes, m1_dim) + _check_(left, m1_dim) + _check_(right, m1_dim) + + l1 = [chr(i + 97) for i in range(m1_dim)] + l2 = [chr(i + 97) for i in range(m1_dim)] + l3 = [chr(i + 97) for i in range(m1_dim)] + for a in left: + l1[a] = l1[a].upper() + l3[a] = l3[a].upper() + for a in right: + l2[a] = l2[a].upper() + l3[a] = l3[a].upper() + for a in axes: + l1[a] = l1[a].lower() + l2[a] = l2[a].lower() + if a not in right: + l3[a] = None + else: + l3[a] = l3[a].lower() + eq = "%s,%s->%s" % ("".join(l1), "".join(l2), + "".join(s for s in l3 if s)) + return eq + + def numpy_extended_dot(m1, m2, axes, left, right, verbose=False): """ Extended version of a matrix multiplication (:epkg:`numpy:dot`) @@ -12,6 +67,8 @@ def numpy_extended_dot(m1, m2, axes, left, right, verbose=False): Loops over *left* axes for *m1* and *right* axes for *m2*, summation is done over *axes*. Other axes must be empty. + This multiplication combines matrix multiplication (dot) + and broadcasted multiplication term by term. :param m1: first matrix :param m2: second matrix @@ -95,41 +152,12 @@ def numpy_extended_dot(m1, m2, axes, left, right, verbose=False): The current implementation still uses :epkg:`numpy:einsum` but this should be replaced. """ - if len(m1.shape) != len(m2.shape): - raise RuntimeError( - "Matrices m1 and m2 must have the same dimension, " - "m1=%r, m2=%r." % (m1.shape, m2.shape)) - - def _check_(axs, n): - for a in axs: - if a < 0 or a >= n: - raise ValueError( - "One axis %d (in %r) is negative or above the maximum " - "dimension %d." % (a, axs, n)) - _check_(axes, len(m1.shape)) - _check_(left, len(m1.shape)) - _check_(right, len(m1.shape)) - - # This implementation should not use einsum. - # Temporary solution. - l1 = [chr(i + 97) for i in range(len(m1.shape))] - l2 = [chr(i + 97) for i in range(len(m2.shape))] - l3 = [chr(i + 97) for i in range(len(m2.shape))] - for a in left: - l1[a] = l1[a].upper() - l3[a] = l3[a].upper() - for a in right: - l2[a] = l2[a].upper() - l3[a] = l3[a].upper() - for a in axes: - l1[a] = l1[a].lower() - l2[a] = l2[a].lower() - if a not in right: - l3[a] = None - else: - l3[a] = l3[a].lower() - eq = "%s,%s->%s" % ("".join(l1), "".join(l2), - "".join(s for s in l3 if s)) + if m1.dtype != m2.dtype: + raise TypeError( + "Both matrices should share the same dtype %r != %r." + "" % (m1.dtype, m2.dtype)) + eq = _numpy_extended_dot_equation( + len(m1.shape), len(m2.shape), axes, left, right) if verbose: print(" [numpy_extended_dot] %s: %r @ %r" % (eq, m1.shape, m2.shape)) output = numpy.einsum(eq, m1, m2) @@ -143,6 +171,152 @@ def _check_(axs, n): return output.reshape(tuple(new_shape)) +def numpy_extended_dot_python(m1, m2, axes, left, right, verbose=False): + """ + Implementation of @see fn numpy_extended_dot in pure python. + This implementation is not efficient but shows how to + implement this operation without :epkg:`numpy:einsum`. + """ + if m1.dtype != m2.dtype: + raise TypeError( + "Both matrices should share the same dtype %r != %r." + "" % (m1.dtype, m2.dtype)) + m1_dim = len(m1.shape) + m2_dim = len(m2.shape) + if m1_dim != m2_dim: + raise RuntimeError( + "Matrices m1 and m2 must have the same number of dimensions, " + "m1=%r, m2=%r." % (m1_dim, m2_dim)) + total = set(axes) | set(left) | set(right) + if len(total) > m1_dim: + raise ValueError( + "Whole set of involved axes should be inferior to the number " + "of dimensions: %r = {%r} | {%r} | {%r} has more than %d elements" + "." % (total, axes, left, right, m1_dim)) + + new_shape = numpy.full(m1_dim, 1, dtype=numpy.int64) + for i in left: + new_shape[i] = m1.shape[i] + for i in right: + if i in left and m1.shape[i] != m2.shape[i]: + raise RuntimeError( + "Matrices should the same dimension for dimension %d, " + "shapes=%r @ %r." % (i, m1.shape, m2.shape)) + new_shape[i] = m2.shape[i] + + t_left = 1 + d_left = [] + for n in left: + t_left *= m1.shape[n] + d_left.append(n) + + t_right = 1 + d_right = [] + d_common = [] + for n in right: + if n not in left: + t_right *= m2.shape[n] + d_right.append(n) + else: + d_common.append(n) + + t_axes = 1 + d_axes = [] + d_common_axes_right = [] + for n in axes: + if n not in left and n not in right: + t_axes *= m2.shape[n] + d_axes.append(n) + elif n in right and n not in left: + d_common_axes_right.append(n) + else: + raise NotImplementedError() + + if len(d_common_axes_right) == 0: + res = numpy.full(tuple(new_shape), numpy.nan, dtype=m1.dtype) + else: + res = numpy.zeros(tuple(new_shape), dtype=m1.dtype) + + i_left = [0 for i in m1.shape] + i_right = [0 for i in m1.shape] + i_out = [0 for i in m1.shape] + + for i in range(t_left): + + for j in range(t_right): # pylint: disable=W0612 + + if len(d_common_axes_right) == 0: + for d in d_common: + i_left[d] = i_right[d] + add = 0 + for s in range(t_axes): # pylint: disable=W0612 + + add += m1[tuple(i_left)] * m2[tuple(i_right)] + + p = len(d_axes) - 1 + i_left[d_axes[p]] += 1 + i_right[d_axes[p]] += 1 + while i_left[d_axes[p]] >= m1.shape[d_axes[p]]: + i_left[d_axes[p]] = 0 + i_right[d_axes[p]] = 0 + p -= 1 + if p < 0: + break + i_left[d_axes[p]] += 1 + i_right[d_axes[p]] += 1 + + res[tuple(i_out)] = add + elif len(d_axes) == 0: + for s in range(t_axes): + + for d in d_common_axes_right: + i_out[d] = i_right[d] + + res[tuple(i_out)] += m1[tuple(i_left)] * m2[tuple(i_right)] + + p = len(d_common_axes_right) - 1 + i_right[d_common_axes_right[p]] += 1 + while (i_left[d_common_axes_right[p]] >= + m1.shape[d_common_axes_right[p]]): + i_right[d_common_axes_right[p]] = 0 + p -= 1 + if p < 0: + break + i_right[d_common_axes_right[p]] += 1 + for d in d_common_axes_right: + i_out[d] = i_right[d] + for d in d_common: + i_left[d] = i_right[d] + else: + raise NotImplementedError() + + p = len(d_right) - 1 + i_right[d_right[p]] += 1 + i_out[d_right[p]] += 1 + while i_right[d_right[p]] >= m2.shape[d_right[p]]: + i_right[d_right[p]] = 0 + i_out[d_right[p]] = 0 + p -= 1 + if p < 0: + break + i_right[d_right[p]] += 1 + i_out[d_right[p]] += 1 + + p = len(d_left) - 1 + i_left[d_left[p]] += 1 + i_out[d_left[p]] += 1 + while i_left[left[p]] >= m1.shape[d_left[p]]: + i_left[d_left[p]] = 0 + i_out[d_left[p]] = 0 + p -= 1 + if p < 0: + break + i_left[d_left[p]] += 1 + i_out[d_left[p]] += 1 + + return res + + def numpy_diagonal(m, axis, axes): """ Extracts diagonal coefficients from an array. From 9a8d38538cf10cd6bec456f925a30cca81a4aaef Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Sat, 24 Apr 2021 12:56:43 +0200 Subject: [PATCH 10/33] fix python implemented, one broadcast case is still missing --- _unittests/ut_testing/test_einsum.py | 68 +- .../ut_testing/test_einsum_generic_dot.py | 1446 +++++++++++++++++ mlprodict/testing/einsum_impl.py | 3 +- mlprodict/testing/einsum_impl_classes.py | 12 + mlprodict/testing/einsum_impl_ext.py | 327 ++-- 5 files changed, 1673 insertions(+), 183 deletions(-) create mode 100644 _unittests/ut_testing/test_einsum_generic_dot.py diff --git a/_unittests/ut_testing/test_einsum.py b/_unittests/ut_testing/test_einsum.py index 412efa3e9..6ffd198c2 100644 --- a/_unittests/ut_testing/test_einsum.py +++ b/_unittests/ut_testing/test_einsum.py @@ -34,8 +34,8 @@ def test_numpy_diagonal(self): self.assertEqualArray(diag, numpy.array([[0, 2], [5, 7]]).T) def test_numpy_extended_dot_2_a(self): - m1 = numpy.arange(4).reshape((2, 2)).astype(numpy.float32) - m2 = m1 + 10 + m1 = numpy.arange(4).reshape((2, 2)).astype(numpy.float32) + 10 + m2 = m1 + 90 self.assertRaise(lambda: numpy_extended_dot(m1, m2.T, [0], [1], [2]), ValueError) @@ -58,33 +58,44 @@ def test_numpy_extended_dot_2_a(self): self.assertEqualArray(exp, numpy.squeeze(dot2)) def test_numpy_extended_dot_2_b(self): - m1 = numpy.arange(4).reshape((2, 2)).astype(numpy.float32) - m2 = m1 + 10 + m1 = numpy.arange(4).reshape((2, 2)).astype(numpy.float32) + 10 + m2 = m1 + 90 dm1 = m1.reshape((2, 2, 1)) dm2 = m2.reshape((1, 2, 2)) - exp = numpy_extended_dot(dm1, dm2, axes=[2], left=[0], right=[1, 2]) - dot = numpy_extended_dot_python( + dot = numpy_extended_dot(dm1, dm2, axes=[2], left=[0], right=[1, 2]) + dot2 = numpy_extended_dot_python( dm1, dm2, axes=[2], left=[0], right=[1, 2]) - self.assertNotEmpty(dot) - self.assertNotEmpty(exp) - # self.assertEqualArray(exp, numpy.squeeze(dot)) + self.assertEqualArray(dot, numpy.squeeze(dot2)) + + def test_numpy_extended_dot_2_b2(self): + m1 = numpy.arange(4).reshape((2, 2)).astype(numpy.float32) + 10 + m2 = m1 + 90 + dm1 = m1.reshape((2, 2, 1)) + dm2 = m2.reshape((1, 2, 2)) + dot = numpy_extended_dot(dm1, dm2, axes=[2], left=[0, 1], right=[2]) + dot2 = numpy_extended_dot_python( + dm1, dm2, axes=[2], left=[0, 1], right=[2]) + self.assertEqualArray(dot, numpy.squeeze(dot2)) def test_numpy_extended_dot_3(self): - m1 = numpy.arange(8).reshape((2, 2, 2)) - m2 = m1 + 10 + m1 = numpy.arange(8).reshape((2, 2, 2)) + 10 + m2 = m1 + 90 dot = numpy_extended_dot(m1, m2, [1], [0], [2]) - exp = numpy.array([[[164, 176]], [[580, 624]]]) - self.assertEqualArray(exp, dot) + dot2 = numpy_extended_dot_python(m1, m2, [1], [0], [2]) + self.assertEqualArray(dot, dot2) dot = numpy_extended_dot(m1, m2, [1], [2], [0]) - exp = numpy.array([[[284, 376]], [[380, 504]]]) - self.assertEqualArray(exp, dot) + dot2 = numpy_extended_dot_python(m1, m2, [1], [2], [0]) + self.assertEqualArray(dot, dot2) + + def test_numpy_extended_dot_3b(self): + m1 = numpy.arange(8).reshape((2, 2, 2)) + 10 + m2 = m1 + 90 dot = numpy_extended_dot(m1, m2, [1], [2], [0, 1]) - exp = numpy.array([[[84, 126], [200, 250]], - [[116, 174], [264, 330]]]) - self.assertEqualArray(exp, dot) + dot2 = numpy_extended_dot_python(m1, m2, [1], [2], [0, 1]) + self.assertEqualArray(dot, dot2) def test_analyse_einsum_equation(self): self.assertRaise(lambda: analyse_einsum_equation("abc"), @@ -198,6 +209,7 @@ def common_test_case_2(self, equation, verbose=False): m1 = numpy.arange(2 * 2 * 2).reshape((2, 2, 2)) + 10 m2 = numpy.arange(4).reshape((2, 2)) + 100 exp = numpy.einsum(equation, m1, m2) + seq = decompose_einsum_equation( equation, m1.shape, m2.shape, verbose=verbose) res = apply_einsum_sequence(seq, m1, m2, verbose=verbose) @@ -274,10 +286,10 @@ def optimize_compare(self, equation, operands=None, verbose=False): else: eqs = equation.split("->")[0].split(",") inputs = [] - for eq in eqs: + for d, eq in enumerate(eqs): i = numpy.arange(2 ** len(eq)).reshape( (2,) * len(eq)).astype(numpy.float32) - inputs.append(i + numpy.array([10], dtype=numpy.float32)) + inputs.append(i + numpy.array([3 ** d], dtype=numpy.float32)) exp = numpy.einsum(equation, *inputs) if verbose: @@ -288,9 +300,10 @@ def optimize_compare(self, equation, operands=None, verbose=False): print(path[1]) shapes = [m.shape for m in inputs] + seq = decompose_einsum_equation(equation, *shapes, verbose=verbose) got = apply_einsum_sequence(seq, *inputs, verbose=verbose) - self.assertEqualArray(exp, got) + self.assertEqualArray(exp, got, decimal=6) def test_numpy_test_hadamard_like_products(self): # Hadamard outer products @@ -321,16 +334,21 @@ def test_np_test_expand(self): self.optimize_compare('ab,bcd,cd->abcd') self.optimize_compare('ab,bcd,cd->abd') - def test_np_test_edge_cases(self): + def test_np_test_edge_cases1(self): # Difficult edge cases for optimization self.optimize_compare( 'eac->ace', operands=[numpy.arange(24).reshape((2, 3, 4))]) self.optimize_compare('eac->ace') self.optimize_compare('bd,db,eac->ace') - self.optimize_compare('eb,cb,fb->cef') self.optimize_compare('efc,dbc,acf,fd->abe') self.optimize_compare('ba,ac,da->bcd') + def test_np_test_edge_cases2(self): + # Difficult edge cases for optimization + self.optimize_compare( + 'eac->ace', operands=[numpy.arange(24).reshape((2, 3, 4))]) + self.optimize_compare('eb,cb,fb->cef') + def test_np_test_random_cases(self): # Randomly built test cases self.optimize_compare('aab,fa,df,ecc->bde') @@ -354,12 +372,10 @@ def test_np_test_broadcasting_dot_cases1(self): c = numpy.random.rand(5, 6) d = numpy.random.rand(10) - # self.optimize_compare('ijk,kl,jl', operands=[a, b, c]) self.optimize_compare('ijk,kl,jl,i->i', operands=[a, b, c, d]) e = numpy.random.rand(1, 1, 5, 4) f = numpy.random.rand(7, 7) - # self.optimize_compare('abjk,kl,jl', operands=[e, b, c]) self.optimize_compare('abjk,kl,jl,ab->ab', operands=[e, b, c, f]) def test_np_test_broadcasting_dot_cases2(self): @@ -412,6 +428,4 @@ def test_np_test_edge_cases_duplicate_indices(self): if __name__ == "__main__": - TestEinsum().test_numpy_extended_dot_2_b() - TestEinsum().test_numpy_extended_dot_3() unittest.main() diff --git a/_unittests/ut_testing/test_einsum_generic_dot.py b/_unittests/ut_testing/test_einsum_generic_dot.py new file mode 100644 index 000000000..866d4185d --- /dev/null +++ b/_unittests/ut_testing/test_einsum_generic_dot.py @@ -0,0 +1,1446 @@ +""" +@brief test log(time=6s) +""" +import unittest +import io +from contextlib import redirect_stdout +import numpy +from pyquickhelper.pycode import ExtTestCase +from mlprodict.testing.einsum_impl_ext import ( + numpy_extended_dot, numpy_extended_dot_python) + + +confs = [ + dict(shape1=(1, 5, 4, 1), shape2=(1, 1, 4, 6), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(1, 5, 1, 6), shape2=(1, 5, 1, 6), + axes=(1, 3), left=(0,), right=()), + dict(shape1=(1, 1, 1, 1), shape2=(10, 1, 1, 1), + axes=(), left=(0,), right=(0,)), + dict(shape1=(1, 1, 5, 4, 1), shape2=(1, 1, 1, 4, 6), + axes=(3,), left=(0, 1, 2), right=(4,)), + dict(shape1=(1, 1, 5, 1, 6), shape2=(1, 1, 5, 1, 6), + axes=(2, 4), left=(0, 1), right=()), + dict(shape1=(1, 1, 1, 1, 1), shape2=(7, 7, 1, 1, 1), + axes=(), left=(0, 1), right=(0, 1)), + dict(shape1=(2, 2, 2, 1), shape2=(1, 1, 2, 1), + axes=(), left=(0, 1, 2), right=(2,)), + dict(shape1=(2, 2, 2, 1), shape2=(1, 1, 2, 1), + axes=(), left=(0, 1, 2), right=(2,)), + dict(shape1=(2, 2, 2, 1), shape2=(1, 1, 2, 2), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 1, 2, 1), shape2=(1, 1, 2, 2), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 2, 2, 1), shape2=(1, 1, 2, 2), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 2, 1), shape2=(1, 1, 2, 1), + axes=(), left=(0, 1, 2), right=(2,)), + dict(shape1=(2, 1, 2, 1), shape2=(1, 1, 2, 2), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 1), shape2=(1, 1, 2, 2), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 2, 2, 1), shape2=(1, 1, 2, 1), + axes=(), left=(0, 1, 2), right=(2,)), + dict(shape1=(2, 2, 2, 1), shape2=(1, 1, 2, 2), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 1, 2, 1), shape2=(1, 1, 2, 2), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 2, 2, 1), shape2=(1, 1, 2, 2), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 2, 1), shape2=(1, 1, 2, 1), + axes=(), left=(0, 1, 2), right=(2,)), + dict(shape1=(2, 2, 2, 1), shape2=(1, 1, 2, 1), + axes=(), left=(0, 1, 2), right=(2,)), + dict(shape1=(2, 2, 2, 1), shape2=(1, 1, 2, 2), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(1, 2, 2, 1), shape2=(1, 1, 2, 2), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(2, 2, 2, 1), shape2=(1, 1, 2, 2), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 2, 1), shape2=(1, 1, 2, 1), + axes=(), left=(0, 1, 2), right=(2,)), + dict(shape1=(1, 2, 2, 1), shape2=(1, 1, 2, 2), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 1), shape2=(1, 1, 2, 2), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(2, 2, 2, 1), shape2=(1, 1, 2, 1), + axes=(), left=(0, 1, 2), right=(2,)), + dict(shape1=(2, 2, 2, 1), shape2=(1, 1, 2, 2), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(1, 2, 2, 1), shape2=(1, 1, 2, 2), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(2, 2, 2, 1), shape2=(1, 1, 2, 2), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 2, 1), shape2=(1, 1, 2, 1), + axes=(), left=(0, 1, 2), right=(2,)), + dict(shape1=(2, 1, 2, 1), shape2=(1, 1, 2, 2), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 1), shape2=(1, 1, 2, 2), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 1), shape2=(1, 1, 2, 2), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 1), shape2=(1, 1, 2, 2), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 1), shape2=(1, 1, 2, 2), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 1), shape2=(1, 1, 2, 2), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 1), shape2=(1, 1, 2, 2), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 1), shape2=(1, 1, 2, 2), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 1, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 1), right=(5,)), + dict(shape1=(2, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(2, 2, 1, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 1), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(1, 2, 3), right=(3,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 1), + axes=(), left=(0, 2, 3), right=(3,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(1, 2), right=(5,)), + dict(shape1=(1, 2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(1, 2), right=(2, 3)), + dict(shape1=(1, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(1, 2), right=(4,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 1, 2), + axes=(3,), left=(0, 2), right=(5,)), + dict(shape1=(2, 1, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 2), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 2, 2, 1), + axes=(3,), left=(0, 2), right=(4,)), + dict(shape1=(1, 5, 4, 1), shape2=(1, 1, 4, 6), + axes=(2,), left=(0, 1), right=(3,)), + dict(shape1=(1, 5, 1, 6), shape2=(1, 5, 1, 6), + axes=(1, 3), left=(0,), right=()), + dict(shape1=(1, 1, 1, 1), shape2=(10, 1, 1, 1), + axes=(), left=(0,), right=(0,)), + dict(shape1=(1, 1, 5, 4, 1), shape2=(1, 1, 1, 4, 6), + axes=(3,), left=(0, 1, 2), right=(4,)), + dict(shape1=(1, 1, 5, 1, 6), shape2=(1, 1, 5, 1, 6), + axes=(2, 4), left=(0, 1), right=()), + dict(shape1=(1, 1, 1, 1, 1), shape2=(7, 7, 1, 1, 1), + axes=(), left=(0, 1), right=(0, 1)), + dict(shape1=(1, 1, 1, 8, 2), shape2=(1, 2, 4, 8, 1), + axes=(3,), left=(4,), right=(1, 2)), + dict(shape1=(1, 2, 1, 2, 1), shape2=( + 1, 2, 1, 2, 1), axes=(1, 3), left=(), right=()), + dict(shape1=(1, 1, 1, 1, 1), shape2=(2, 1, 2, 1, 2), + axes=(), left=(), right=(0, 2, 4)), + dict(shape1=(1, 1, 2, 1, 2, 2), shape2=(1, 2, 2, 2, 1, 1), + axes=(), left=(2, 4, 5), right=(1, 2, 3)), + dict(shape1=(1, 2, 2, 2, 2, 2), shape2=(2, 1, 2, 1, 1, 2), + axes=(2,), left=(1, 3, 4, 5), right=(0, 5)), + dict(shape1=(2, 2, 1, 2, 2, 2), shape2=(1, 1, 1, 2, 1, 2), + axes=(3, 5), left=(0, 1, 4), right=()), + dict(shape1=(2, 2, 1, 1), shape2=(2, 1, 2, 1), + axes=(), left=(0, 1), right=(0, 2)), + dict(shape1=(2, 2, 2, 1), shape2=(2, 1, 1, 2), + axes=(0,), left=(1, 2), right=(3,)), + dict(shape1=(2, 1, 2, 1), shape2=(2, 2, 1, 1), + axes=(), left=(0, 2), right=(0, 1)), + dict(shape1=(2, 2, 2, 1), shape2=(2, 1, 1, 2), + axes=(0,), left=(1, 2), right=(3,)), + dict(shape1=(1, 1, 2, 1, 1), shape2=(2, 1, 1, 1, 2), + axes=(), left=(2,), right=(0, 4)), + dict(shape1=(2, 1, 2, 1, 2), shape2=(2, 1, 1, 2, 1), + axes=(), left=(0, 2, 4), right=(0, 3)), + dict(shape1=(2, 1, 2, 2, 2), shape2=(2, 2, 2, 1, 1), + axes=(0, 2), left=(3, 4), right=(1,)), + dict(shape1=(1, 1, 2, 2, 1, 1), shape2=(1, 1, 2, 1, 2, 2), + axes=(2,), left=(3,), right=(4, 5)), + dict(shape1=(1, 1, 1, 2, 2, 2), shape2=(2, 1, 1, 1, 2, 1), + axes=(4,), left=(3, 5), right=(0,)), + dict(shape1=(2, 1, 1, 2, 1, 2), shape2=(1, 2, 1, 2, 1, 2), + axes=(3, 5), left=(0,), right=(1,)), + dict(shape1=(2, 2, 1, 1, 1, 1), shape2=(1, 1, 2, 2, 1, 1), + axes=(), left=(0, 1), right=(2, 3)), + dict(shape1=(2, 2, 2, 2, 1, 1), shape2=(1, 1, 1, 1, 2, 2), + axes=(), left=(0, 1, 2, 3), right=(4, 5)), + dict(shape1=(2, 1, 1, 1, 1, 1), shape2=( + 1, 1, 2, 2, 1, 1), axes=(), left=(0,), right=(2, 3)), + dict(shape1=(2, 1, 2, 2, 1, 1), shape2=(1, 1, 1, 1, 1, 2), + axes=(), left=(0, 2, 3), right=(5,)), + dict(shape1=(2, 2, 1, 1, 1), shape2=(1, 1, 2, 2, 1), + axes=(), left=(0, 1), right=(2, 3)), + dict(shape1=(2, 2, 2, 2, 1), shape2=(1, 1, 1, 2, 2), + axes=(), left=(0, 1, 2, 3), right=(3, 4)), + dict(shape1=(1, 2, 1, 1, 1), shape2=( + 1, 1, 1, 2, 1), axes=(), left=(1,), right=(3,)), + dict(shape1=(1, 2, 1, 2, 1), shape2=(1, 1, 1, 2, 2), + axes=(3,), left=(1,), right=(4,)), + dict(shape1=(2, 2, 1, 1), shape2=(1, 2, 2, 2), + axes=(), left=(0, 1), right=(1, 2, 3)), + dict(shape1=(2, 2, 2, 2), shape2=(1, 1, 2, 2), + axes=(), left=(0, 1, 2, 3), right=(2, 3)), + dict(shape1=(2, 2, 1, 1), shape2=(1, 2, 2, 2), + axes=(), left=(0, 1), right=(1, 2, 3)), + dict(shape1=(2, 2, 2, 2), shape2=(1, 1, 2, 2), + axes=(2,), left=(0, 1, 3), right=(3,)), + dict(shape1=(2, 1, 1, 1, 2, 1, 1, 1), shape2=( + 1, 2, 1, 1, 1, 2, 1, 1), axes=(), left=(0, 4), right=(1, 5)), + dict(shape1=(2, 2, 1, 1, 2, 2, 1, 1), shape2=(1, 1, 2, 1, + 1, 1, 2, 1), axes=(), left=(0, 1, 4, 5), right=(2, 6)), + dict(shape1=(2, 2, 2, 1, 2, 2, 2, 1), shape2=(1, 1, 1, 2, 1, 1, 1, 2), + axes=(), left=(0, 1, 2, 4, 5, 6), right=(3, 7)), + dict(shape1=(2, 2, 2, 2, 2, 2, 2, 2), shape2=(2, 2, 2, 2, 1, 1, 1, 1), + axes=(0, 1, 2, 3), left=(4, 5, 6, 7), right=()), + dict(shape1=(2, 2, 1), shape2=(2, 2, 1), axes=(0, 1), left=(), right=()), + dict(shape1=(1, 1, 1), shape2=(1, 1, 2), axes=(), left=(), right=(2,)), + dict(shape1=(2, 2, 1, 1), shape2=(2, 2, 1, 1), + axes=(1,), left=(0,), right=(0,)), + dict(shape1=(2, 1, 1, 1), shape2=(1, 1, 2, 2), + axes=(), left=(0,), right=(2, 3)), + dict(shape1=(2, 1, 2, 2), shape2=(1, 1, 2, 2), + axes=(3,), left=(0, 2), right=(2,)), + dict(shape1=(2, 2, 1, 1), shape2=(2, 2, 1, 1), + axes=(0, 1), left=(), right=()), + dict(shape1=(1, 1, 1, 1), shape2=(1, 1, 2, 2), + axes=(), left=(), right=(2, 3)), + dict(shape1=(1, 1, 2, 2), shape2=(1, 1, 2, 2), + axes=(), left=(2, 3), right=(2, 3)), + dict(shape1=(2, 2, 1, 1, 1, 1), shape2=( + 2, 1, 1, 1, 1, 2), axes=(0,), left=(1,), right=(5,)), + dict(shape1=(1, 2, 1, 1, 1, 2), shape2=( + 1, 1, 1, 2, 1, 2), axes=(5,), left=(1,), right=(3,)), + dict(shape1=(1, 2, 1, 2, 1, 1), shape2=( + 1, 1, 1, 1, 2, 1), axes=(), left=(1, 3), right=(4,)), + dict(shape1=(2, 1, 1), shape2=(1, 1, 1), axes=(), left=(0,), right=()), + dict(shape1=(2, 1, 1), shape2=(2, 2, 1), axes=(0,), left=(), right=(1,)), + dict(shape1=(2, 1, 1, 2, 2), shape2=(2, 2, 1, 1, 1), + axes=(0,), left=(3, 4), right=(1,)), + dict(shape1=(1, 2, 1, 2, 2), shape2=(1, 1, 2, 1, 1), + axes=(), left=(1, 3, 4), right=(2,)), + dict(shape1=(1, 2, 2, 2, 2), shape2=(1, 1, 2, 2, 1), + axes=(2, 3), left=(1, 4), right=()), + dict(shape1=(1, 2, 1), shape2=(1, 2, 1), axes=(1,), left=(), right=()), + dict(shape1=(1, 1, 1), shape2=(1, 1, 2), axes=(), left=(), right=(2,)), + dict(shape1=(2, 1, 1), shape2=(2, 1, 1), axes=(0,), left=(), right=()), + dict(shape1=(1, 1, 1), shape2=(1, 1, 2), axes=(), left=(), right=(2,)), + dict(shape1=(2, 1, 1), shape2=(2, 1, 1), axes=(0,), left=(), right=()), + dict(shape1=(1, 1, 1), shape2=(1, 2, 1), axes=(), left=(), right=(1,)), + dict(shape1=(2, 1, 1), shape2=(2, 1, 1), axes=(0,), left=(), right=()), + dict(shape1=(1, 1, 1), shape2=(1, 2, 1), axes=(), left=(), right=(1,)), + dict(shape1=(1, 2, 1), shape2=(2, 1, 1), axes=(), left=(1,), right=(0,)), + dict(shape1=(2, 2, 1), shape2=(2, 1, 1), axes=(0,), left=(1,), right=()), + dict(shape1=(1, 1, 2, 1), shape2=(1, 2, 1, 1), + axes=(), left=(2,), right=(1,)), + dict(shape1=(1, 2, 2, 1), shape2=(1, 2, 1, 1), + axes=(1,), left=(2,), right=()), + dict(shape1=(2, 1, 2, 1), shape2=(2, 2, 1, 1), + axes=(0,), left=(2,), right=(1,)), + dict(shape1=(1, 2, 2, 1), shape2=(1, 2, 1, 1), + axes=(1,), left=(2,), right=()), + dict(shape1=(1, 2, 1, 2, 1), shape2=(1, 2, 2, 1, 1), + axes=(1,), left=(3,), right=(2,)), + dict(shape1=(1, 1, 2, 2, 1), shape2=( + 1, 1, 2, 1, 1), axes=(2,), left=(3,), right=()), + dict(shape1=(2, 2, 1, 2, 1), shape2=(1, 2, 2, 1, 1), + axes=(1,), left=(0, 3), right=(2,)), + dict(shape1=(2, 1, 2, 2, 1), shape2=(2, 1, 1, 1, 2), + axes=(0,), left=(2, 3), right=(4,)), + dict(shape1=(1, 1, 2, 2, 2), shape2=(1, 1, 2, 1, 2), + axes=(2, 4), left=(3,), right=()), + dict(shape1=(2, 2, 1), shape2=(1, 2, 2), axes=[1], left=[0], right=[2]), + dict(shape1=(2, 1, 2), shape2=(1, 2, 2), axes=[2], left=[0], right=[1]), + dict(shape1=(2, 2, 1), shape2=(1, 2, 2), axes=[2], left=[0], right=[1, 2]), + dict(shape1=(2, 2, 1), shape2=(1, 2, 2), axes=[2], left=[0, 1], right=[2]), + dict(shape1=(2, 2, 2), shape2=(2, 2, 2), axes=[1], left=[0], right=[2]), + dict(shape1=(2, 2, 2), shape2=(2, 2, 2), axes=[1], left=[2], right=[0]), + dict(shape1=(2, 2, 2), shape2=(2, 2, 2), axes=[1], left=[2], right=[0, 1]), + dict(shape1=(2, 1, 1), shape2=(2, 2, 1), axes=(), left=(0,), right=(0, 1)), + dict(shape1=(2, 2, 1), shape2=(2, 2, 2), + axes=(), left=(0, 1), right=(0, 1, 2)), + dict(shape1=(2, 1), shape2=(1, 2), axes=(), left=(0,), right=(1,)), + dict(shape1=(2, 2), shape2=(2, 2), axes=(), left=(0, 1), right=(0, 1)), +] + + +class TestEinsumGenericdot(ExtTestCase): + + def test_generic_dot(self): + + for i, conf in enumerate(confs): + with self.subTest(i=i, conf=conf): + r = self.common_test(conf["shape1"], conf["shape2"], + conf["axes"], conf["left"], conf["right"]) + if not r: + print(i, conf) + + def common_test(self, sh1, sh2, axes, left, right): + + m1 = numpy.empty(sh1).ravel() + m1 = numpy.arange(len(m1)).reshape(sh1).astype(numpy.float64) + 10 + m2 = numpy.empty(sh2).ravel() + m2 = numpy.arange(len(m2)).reshape(sh2).astype(numpy.float64) + 10000 + + try: + exp = numpy_extended_dot(m1, m2, axes, left, right) + except ValueError: + return False + try: + dot = numpy_extended_dot_python(m1, m2, axes, left, right) + except (IndexError, NotImplementedError, ValueError): + dot = numpy_extended_dot_python( + m1, m2, axes, left, right, verbose=True) + + try: + self.assertEqualArray(exp, dot) + redo = False + except AssertionError: + redo = True + if not redo: + return True + + f = io.StringIO() + with redirect_stdout(f): + exp = numpy_extended_dot(m1, m2, axes, left, right) + dot = numpy_extended_dot_python( + m1, m2, axes, left, right, verbose=True) + try: + self.assertEqualArray(exp, dot) + except AssertionError: + raise AssertionError( + "shape1=%r shape2=%r\naxes=%r left=%r right=%r\n" + "m1=%r\nm2=%r\nexp=\n%r\ndot=\n%r" + "\n-----\n%s" % (m1.shape, m2.shape, axes, left, right, + m1.ravel(), m2.ravel(), + exp.ravel(), dot.ravel(), f.getvalue())) + return True + + +if __name__ == "__main__": + # TestEinsumGenericdot().test_generic_dot() + unittest.main() diff --git a/mlprodict/testing/einsum_impl.py b/mlprodict/testing/einsum_impl.py index f27d6911c..07811ed82 100644 --- a/mlprodict/testing/einsum_impl.py +++ b/mlprodict/testing/einsum_impl.py @@ -305,10 +305,11 @@ def _decompose_einsum_equation_simple(equation, *shapes, verbose=False): right = [] for d in range(0, mat.shape[1]): if rows[:, d].min() >= 0: - common_dims.append(d) if mat[i + 1:, d].max() >= 0: left.append(d) right.append(d) + else: + common_dims.append(d) else: if rows[0, d] >= 0: left.append(d) diff --git a/mlprodict/testing/einsum_impl_classes.py b/mlprodict/testing/einsum_impl_classes.py index 9caf0de57..0fa2d33e8 100644 --- a/mlprodict/testing/einsum_impl_classes.py +++ b/mlprodict/testing/einsum_impl_classes.py @@ -50,6 +50,18 @@ def _check_(self): raise RuntimeError( "perm has duplicated values %r (name=%r)." "" % (perm, self.name)) + elif self.name == 'matmul': + self._check_arg_('axes', tuple) + self._check_arg_('left', tuple) + self._check_arg_('right', tuple) + axes = self.kwargs['axes'] + left = self.kwargs['left'] + right = self.kwargs['right'] + for a in axes: + if a in left and a in right: + raise RuntimeError( + "One axis belongs to every set (axes, left, right). " + "axes=%r, left=%r, right=%r." % (axes, left, right)) def __repr__(self): inps = ", ".join(map(str, self.inputs)) diff --git a/mlprodict/testing/einsum_impl_ext.py b/mlprodict/testing/einsum_impl_ext.py index c0b3ebce8..a1e1f84bf 100644 --- a/mlprodict/testing/einsum_impl_ext.py +++ b/mlprodict/testing/einsum_impl_ext.py @@ -5,6 +5,56 @@ import numpy +def numpy_diagonal(m, axis, axes): + """ + Extracts diagonal coefficients from an array. + + :param m: input array + :param axis: kept axis among the diagonal ones + :param axes: diagonal axes (axis must be one of them) + :return: output + + .. runpython:: + :showcode: + + import numpy + from mlprodict.testing.einsum_impl_ext import numpy_diagonal + + mat = numpy.arange(8).reshape((2, 2, 2)) + print(mat) + diag = numpy_diagonal(mat, 1, [1, 2]) + print(diag) + """ + if axis not in axes: + raise RuntimeError( + "axis %r must be in axes %r." % (axis, axes)) + shape = [] + new_shape = [] + for i, s in enumerate(m.shape): + if i in axes: + if i == axis: + shape.append(s) + new_shape.append(s) + else: + shape.append(1) + else: + shape.append(s) + new_shape.append(s) + + # Extracts coefficients. + output = numpy.empty(tuple(shape), dtype=m.dtype) + index_in = [slice(s) for s in m.shape] + index_out = [slice(s) for s in m.shape] + for i in range(0, shape[axis]): + for a in axes: + index_in[a] = i + index_out[a] = i if a == axis else 0 + output[tuple(index_out)] = m[tuple(index_in)] + + # Removes axis. + return output.reshape(tuple(new_shape)) + + def _numpy_extended_dot_equation(m1_dim, m2_dim, axes, left, right): """ Returns the equation equivalent to an extended version @@ -39,6 +89,12 @@ def _check_(axs, n): _check_(left, m1_dim) _check_(right, m1_dim) + for a in axes: + if a in left and a in right: + raise RuntimeError( + "One axis belongs to every set (axes, left, right). " + "axes=%r, left=%r, right=%r." % (axes, left, right)) + l1 = [chr(i + 97) for i in range(m1_dim)] l2 = [chr(i + 97) for i in range(m1_dim)] l3 = [chr(i + 97) for i in range(m1_dim)] @@ -198,170 +254,131 @@ def numpy_extended_dot_python(m1, m2, axes, left, right, verbose=False): for i in left: new_shape[i] = m1.shape[i] for i in right: - if i in left and m1.shape[i] != m2.shape[i]: + if (i in left and m1.shape[i] != m2.shape[i] and + m1.shape[i] != 1 and m2.shape[i] != 1): raise RuntimeError( "Matrices should the same dimension for dimension %d, " "shapes=%r @ %r." % (i, m1.shape, m2.shape)) new_shape[i] = m2.shape[i] - t_left = 1 - d_left = [] - for n in left: - t_left *= m1.shape[n] - d_left.append(n) - - t_right = 1 - d_right = [] - d_common = [] - for n in right: - if n not in left: - t_right *= m2.shape[n] - d_right.append(n) - else: - d_common.append(n) - - t_axes = 1 - d_axes = [] - d_common_axes_right = [] - for n in axes: - if n not in left and n not in right: - t_axes *= m2.shape[n] - d_axes.append(n) - elif n in right and n not in left: - d_common_axes_right.append(n) + # output shapes + res = numpy.full(tuple(new_shape), 0, dtype=m1.dtype) + + # indices + l1 = [chr(i + 97) for i in range(m1_dim)] + l2 = [chr(i + 97) for i in range(m1_dim)] + l3 = [chr(i + 97) for i in range(m1_dim)] + for a in left: + l1[a] = l1[a].upper() + l3[a] = l3[a].upper() + for a in right: + l2[a] = l2[a].upper() + l3[a] = l3[a].upper() + for a in axes: + l1[a] = l1[a].lower() + l2[a] = l2[a].lower() + if a not in right: + l3[a] = "-" else: - raise NotImplementedError() - - if len(d_common_axes_right) == 0: - res = numpy.full(tuple(new_shape), numpy.nan, dtype=m1.dtype) - else: - res = numpy.zeros(tuple(new_shape), dtype=m1.dtype) - - i_left = [0 for i in m1.shape] - i_right = [0 for i in m1.shape] - i_out = [0 for i in m1.shape] - - for i in range(t_left): - - for j in range(t_right): # pylint: disable=W0612 - - if len(d_common_axes_right) == 0: - for d in d_common: - i_left[d] = i_right[d] - add = 0 - for s in range(t_axes): # pylint: disable=W0612 - - add += m1[tuple(i_left)] * m2[tuple(i_right)] - - p = len(d_axes) - 1 - i_left[d_axes[p]] += 1 - i_right[d_axes[p]] += 1 - while i_left[d_axes[p]] >= m1.shape[d_axes[p]]: - i_left[d_axes[p]] = 0 - i_right[d_axes[p]] = 0 - p -= 1 - if p < 0: - break - i_left[d_axes[p]] += 1 - i_right[d_axes[p]] += 1 - - res[tuple(i_out)] = add - elif len(d_axes) == 0: - for s in range(t_axes): - - for d in d_common_axes_right: - i_out[d] = i_right[d] - - res[tuple(i_out)] += m1[tuple(i_left)] * m2[tuple(i_right)] - - p = len(d_common_axes_right) - 1 - i_right[d_common_axes_right[p]] += 1 - while (i_left[d_common_axes_right[p]] >= - m1.shape[d_common_axes_right[p]]): - i_right[d_common_axes_right[p]] = 0 - p -= 1 - if p < 0: - break - i_right[d_common_axes_right[p]] += 1 - for d in d_common_axes_right: - i_out[d] = i_right[d] - for d in d_common: - i_left[d] = i_right[d] + l3[a] = l3[a].lower() + + def intermediate(l1, l2, l3): + names = list(sorted(set(l1 + l2))) + kind = numpy.zeros(len(names), dtype=numpy.int64) + cols = {} + + for i, n in enumerate(names): + if n in l1: + kind[i] += 1 + cols[n] = l1.index(n) + if n in l2: + kind[i] += 2 + cols[n] = l2.index(n) + if n in l3: + kind[i] += 4 + + pos = numpy.zeros(len(names), dtype=numpy.int64) + for j in range(0, pos.shape[0]): + pos[j] = cols[names[j]] + common = [(kind[i] & 3) == 3 for i in range(len(kind))] + broadcast = [common[i] and m1.shape[pos[i]] != m2.shape[pos[i]] + for i in range(len(common))] + + return names, kind, cols, common, broadcast, pos + + names, kind, cols, common, broadcast, pos = intermediate(l1, l2, l3) + + if any(broadcast): + for i in range(len(broadcast)): # pylint: disable=C0200 + if broadcast[i] and not (kind[i] & 3) == 3: + raise RuntimeError( + "Broadcast should only happen on common axes, " + "axes=%r left=%r right=%r shape1=%r shape2=%r." + "" % (axes, left, right, m1.shape, m2.shape)) + # We split letters. + p = cols[names[i]] + dim = (m1.shape[p], m2.shape[p]) + let = [l1[p], l2[p], l3[p]] + inp = 1 if dim[0] == 1 else 0 + if (kind[i] & 4) > 0: + if let[inp].lower() == let[inp]: + let[inp] = let[inp].upper() + else: + let[inp] = let[inp].lower() + l3[p] = let[inp] + if inp == 1: + l2[p] = let[inp] + else: + l1[p] = let[inp] else: raise NotImplementedError() - p = len(d_right) - 1 - i_right[d_right[p]] += 1 - i_out[d_right[p]] += 1 - while i_right[d_right[p]] >= m2.shape[d_right[p]]: - i_right[d_right[p]] = 0 - i_out[d_right[p]] = 0 - p -= 1 - if p < 0: - break - i_right[d_right[p]] += 1 - i_out[d_right[p]] += 1 - - p = len(d_left) - 1 - i_left[d_left[p]] += 1 - i_out[d_left[p]] += 1 - while i_left[left[p]] >= m1.shape[d_left[p]]: - i_left[d_left[p]] = 0 - i_out[d_left[p]] = 0 - p -= 1 - if p < 0: - break - i_left[d_left[p]] += 1 - i_out[d_left[p]] += 1 - - return res - + names, kind, cols, common, broadcast, pos = intermediate(l1, l2, l3) -def numpy_diagonal(m, axis, axes): - """ - Extracts diagonal coefficients from an array. + indices = [0 for n in names] + pl1 = [names.index(c) for c in l1] + pl2 = [names.index(c) for c in l2] + limits = [m1.shape[pos[n]] if (kind[n] & 1) == 1 else m2.shape[pos[n]] + for n in range(len(names))] + plo = [-1 if c not in names else names.index(c) for c in l3] - :param m: input array - :param axis: kept axis among the diagonal ones - :param axes: diagonal axes (axis must be one of them) - :return: output - - .. runpython:: - :showcode: - - import numpy - from mlprodict.testing.einsum_impl_ext import numpy_diagonal - - mat = numpy.arange(8).reshape((2, 2, 2)) - print(mat) - diag = numpy_diagonal(mat, 1, [1, 2]) - print(diag) - """ - if axis not in axes: - raise RuntimeError( - "axis %r must be in axes %r." % (axis, axes)) - shape = [] - new_shape = [] - for i, s in enumerate(m.shape): - if i in axes: - if i == axis: - shape.append(s) - new_shape.append(s) - else: - shape.append(1) - else: - shape.append(s) - new_shape.append(s) - - # Extracts coefficients. - output = numpy.empty(tuple(shape), dtype=m.dtype) - index_in = [slice(s) for s in m.shape] - index_out = [slice(s) for s in m.shape] - for i in range(0, shape[axis]): - for a in axes: - index_in[a] = i - index_out[a] = i if a == axis else 0 - output[tuple(index_out)] = m[tuple(index_in)] + if verbose: + def dispb(c): + return "".join("o" if b else "." for b in c) + + print("GENERICDOT: %s,%s->%s or %s" % ( + "".join(l1), "".join(l2), "".join(l3), + _numpy_extended_dot_equation( + len(m1.shape), len(m1.shape), axes, left, right))) + print("GENERICDOT: shape1=%r shape2=%r shape=%r" % ( + m1.shape, m2.shape, res.shape)) + print("GENERICDOT: pl1=%r pl2=%r plo=%r" % (pl1, pl2, plo)) + print("GENERICDOT: names=%s kind=%r common=%s broadcast=%s" % ( + "".join(names), kind.tolist(), + dispb(common), dispb(broadcast))) + print("GENERICDOT: pos=%r" % pos.tolist()) + print("GENERICDOT: cols=%r" % cols) + print("GENERICDOT: limits=%r" % limits) + + while indices[0] < limits[0]: + + t1 = tuple(indices[n] for n in pl1) + t2 = tuple(indices[n] for n in pl2) + to = tuple(0 if n == -1 else indices[n] for n in plo) + c = m1[tuple(t1)] * m2[tuple(t2)] + + if verbose: + print(" %r x %r -> %r v=%r I=%r" % (t1, t2, to, c, indices)) + + res[tuple(to)] += c + + last = len(indices) - 1 + indices[last] += 1 + for i in range(last, 0, -1): + if indices[i] < limits[i]: + break + indices[i] = 0 + if i > 0: + indices[i - 1] += 1 - # Removes axis. - return output.reshape(tuple(new_shape)) + return res From f298ac66efd2003c7796b3c95866aa83c36bb75c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Sun, 25 Apr 2021 01:16:06 +0200 Subject: [PATCH 11/33] fix python version of matmul --- _doc/notebooks/einsum_decomposition.ipynb | 74 +++++++++---------- _unittests/ut_onnxrt/test_onnx_profiling.py | 4 +- _unittests/ut_testing/test_einsum.py | 9 +++ .../ut_testing/test_einsum_generic_dot.py | 4 +- mlprodict/testing/einsum_impl.py | 10 ++- mlprodict/testing/einsum_impl_classes.py | 46 ++++++++---- mlprodict/testing/einsum_impl_ext.py | 57 +++++++++++--- 7 files changed, 135 insertions(+), 69 deletions(-) diff --git a/_doc/notebooks/einsum_decomposition.ipynb b/_doc/notebooks/einsum_decomposition.ipynb index 8f49e574d..d2409df2f 100644 --- a/_doc/notebooks/einsum_decomposition.ipynb +++ b/_doc/notebooks/einsum_decomposition.ipynb @@ -285,16 +285,16 @@ { "data": { "text/html": [ - "
\n", + "
\n", "" ], "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -407,7 +407,7 @@ "source": [ "### Benchmark\n", "\n", - "It clearly shows the summation done with the basic algorithm is clearly the slowest." + "It clearly shows the summation done with the basic algorithm is the slowest." ] }, { @@ -419,7 +419,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:10<00:00, 1.28it/s]\n" + "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:11<00:00, 1.13it/s]\n" ] }, { @@ -457,61 +457,61 @@ " \n", " \n", " 37\n", - " 0.028620\n", - " 0.000279\n", - " 0.028299\n", - " 0.029155\n", + " 0.027626\n", + " 0.000687\n", + " 0.027092\n", + " 0.029385\n", " 10\n", " 10\n", - " 0.286203\n", + " 0.276258\n", " custom_einsum\n", " 50\n", " \n", " \n", " 38\n", - " 0.007170\n", - " 0.000217\n", - " 0.006561\n", - " 0.007377\n", + " 0.006592\n", + " 0.000820\n", + " 0.005656\n", + " 0.008152\n", " 10\n", " 10\n", - " 0.071695\n", + " 0.065923\n", " onnxruntime\n", " 50\n", " \n", " \n", " 39\n", - " 0.273039\n", - " 0.004524\n", - " 0.265275\n", - " 0.283283\n", + " 0.271303\n", + " 0.009298\n", + " 0.258212\n", + " 0.287778\n", " 10\n", " 10\n", - " 2.730388\n", + " 2.713027\n", " numpy.einsum\n", " 55\n", " \n", " \n", " 40\n", - " 0.051407\n", - " 0.000719\n", - " 0.050533\n", - " 0.052883\n", + " 0.052446\n", + " 0.001392\n", + " 0.050480\n", + " 0.055732\n", " 10\n", " 10\n", - " 0.514065\n", + " 0.524461\n", " custom_einsum\n", " 55\n", " \n", " \n", " 41\n", - " 0.009407\n", - " 0.001364\n", - " 0.008197\n", - " 0.012437\n", + " 0.009457\n", + " 0.000586\n", + " 0.008699\n", + " 0.010656\n", " 10\n", " 10\n", - " 0.094069\n", + " 0.094571\n", " onnxruntime\n", " 55\n", " \n", @@ -521,11 +521,11 @@ ], "text/plain": [ " average deviation min_exec max_exec repeat number total \\\n", - "37 0.028620 0.000279 0.028299 0.029155 10 10 0.286203 \n", - "38 0.007170 0.000217 0.006561 0.007377 10 10 0.071695 \n", - "39 0.273039 0.004524 0.265275 0.283283 10 10 2.730388 \n", - "40 0.051407 0.000719 0.050533 0.052883 10 10 0.514065 \n", - "41 0.009407 0.001364 0.008197 0.012437 10 10 0.094069 \n", + "37 0.027626 0.000687 0.027092 0.029385 10 10 0.276258 \n", + "38 0.006592 0.000820 0.005656 0.008152 10 10 0.065923 \n", + "39 0.271303 0.009298 0.258212 0.287778 10 10 2.713027 \n", + "40 0.052446 0.001392 0.050480 0.055732 10 10 0.524461 \n", + "41 0.009457 0.000586 0.008699 0.010656 10 10 0.094571 \n", "\n", " name N \n", "37 custom_einsum 50 \n", @@ -618,7 +618,7 @@ "outputs": [ { "data": { - "image/png": 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V0a6xOXCw3N+7qppg1CYDoLXOwqhZt38fT/d5EEKcm1SMCgZ7ZcpEpZS3UuoTWxOKDGAlEGi7m3QmDTCSqoN2yw5SeRlZkcPl9nUDGiilXJRSb9iaPWRgJIol5wSjhvVK4KBS6i+lVH/b8pbA4/Y1uxjlV2nTrzOorCxqCfxgd8xdQDHQqIrHrco5quJsrwfl39+S9+FHoKNSKgKjpjpda73ObtuSz03aWcQmqkiSZSeltS7WWn+P8c9/IZCE8Y/XSWsdaPsJ0EZnwDNJAvKA1jUXsdGmGuOb9RSgkS3p/wUjoS2hK9g1FeO2/xdKqQGnOcVhjNt8gXY/XlrrNWcbq9b6fa11L4yak3bAk7ZV2RhfSkrU2KgV5c9luxg2rHzzMg4DLVTFHYIqeo/txWNcaErO64Nxe/RoFc8thDg3WzHKG3vl/18fx2gS0Fdr7Y/RZAxOlqOn+/9OwqhdbWm3rAVn979tPwJTC9vxkoCxwEjgMoxmHuH2cWmt12utR2I00ViIcUcKjLLqtXLltrfW+nRN6KriMHBFueN6aq2PQulwepX9PFvFc9TE9aD8+xsPpf2UvsVo0ncLRtNHe5FAnNY6oxpiEOVIsuyklGEkRnvbXVprK/AZRhvgUNs2TZVSZ2xLatt3BvC2UqqJrYag/1m0Gasqd8ADo3lAkVLqCox2WWektV4B3Ax8r5TqU8lmHwPPKFunEmV0ZLnhbINUSvVWSvW1dZzIxvgiUdJ+ezMwyla70waj7XdN2YtRU3yVLZbnMd6/qlgHHAPeUEr5KKU87b5oJADNVAUdDW3mABOUUt1tn4HJwFqtddw5vxIhRFX8gtHG9nT8MCpG0pTRYe2lcusTMNojn8J2x+9b4DWllJ8yOkA/BpTvNBd+mvOPU0p1tPWbeQWYbzuuH5CPcRfKG6PcwHZcd6XUzUqpAG20sc3gZJn6GXCPrcxVtvLqKqVU+Rr2s/Wx7XW2tMXQ0HbNLHkvfE/zM7nSo5a1GbhSKRVsu8P7yHnGDPCC7frSCaMtuf34yV9hNKMbwanJ8kUYTRFFDZBk2fksVkplYRQ2rwG3aa132NY9hdFR7V/bbbDlGDUQVfEEsA1Yj9F84U2q+fOhtc7E6MzwLUZt8ViMTmlV3f83jI4li8t1CixZ/wNG3HNtr387Rqe5s+WPUYCnYtwGSwb+z7buHYw2egnAl5TtwFKtbE1O7gM+x6j5yQaqNEGL7eI1HKOd4iHbfjfaVv8B7ACOK6WSKth3OfACxl2AYxh3HG46n9cihKiSrzCSL6/TbPMu4IVRm/svRnM7e+8B1ytjpIz3OdWDGGVJLEY/kNkYlSVga77F6Wuav8Zoq3sco/leSQe1r+z23UnZzudg1IbG2crmezAqP9BaRwN3YjR9S8W4ho0/zfmr6j2M68sypVSmLZ6+1XBce18DWzCanCyjbGJ7rv7CeA9+B6ZorZeVrNBa/43xJWOj1vpguf3GcB7jc4vTU1qf6Y6sEEIIIWqDUmoykKi1fteEcz+P0UZakq5aZqvNPwC4na6viVLqD2C21vpzu2XDgVu01qMr20+cH0mWhRBCCCFMVJVkWSnVG2M8/ua2O7WilkgzDCGEEEIIB6aU+hKjaeUjkijXPqlZFkIIIYQQohJSsyyEEEIIIUQlJFkWQgghhBCiEhVNWOAwGjRooMPDw80OQwghztqGDRuStNZVnUSmTpAyWwjhrE5XZjt0shweHk50dLTZYQghxFlTSpUfB7XOkzJbCOGsTldmSzMMIYQQ50UpNVwp9Wl6errZoQghRLWTZFkIIcR50Vov1lrfFRAQYHYoQghR7SRZFkIIIYQQohIO3WZZmKewsJAjR46Ql5dndijiHHl6etKsWTPc3NzMDkUIIeoEuTY6v3O5NkqyLCp05MgR/Pz8CA8PRylldjjiLGmtSU5O5siRI0RERJgdjqjjlFLDgeFt2rQxOxQhapRcG53buV4bHbIZhnQWMV9eXh4hISFSGDgppRQhISFS+yFqhbRZFvWFXBud27leGx0yWZaC1zFIYeDc5O9XDXJTYcdC0NrsSISTy1i2jKLUVLPDENVAylbndi5/P4dMloUQwiGsnALfjYekGLMjEU6s4NAhjj76GMkff2J2KEKIcyDJshBCVCR5P6z9BHqMg4btzI7GoUnTudNL+uhjlKsrwbffbnYoQohzIMmyqBPi4uKIjIzkzjvvpFOnTlx++eXk5uby2Wef0bt3b7p168Z1111HTk4OAOPHj+fee++lX79+tGrVihUrVnD77bcTGRnJ+PHjS4+7bNky+vfvT8+ePbnhhhvIysoy6RWKWrf8JXBxh0ueNzsShydN5ypXEBdH+qJFBN10I26NQs0OR9Qzcm2sHpIsizojJiaG+++/nx07dhAYGMiCBQsYNWoU69evZ8uWLURGRjJ9+vTS7VNTU/nnn3945513GDFiBI8++ig7duxg27ZtbN68maSkJF599VWWL1/Oxo0biYqK4u233zbxFYpaE/c37FoMFz4Kfo3NjkY4saSPPkK5uREycaLZoYh6Sq6N50+GjhN1RkREBN27dwegV69exMXFsX37dp5//nnS0tLIyspi6NChpdsPHz4cpRRdunShUaNGdOnSBYBOnToRFxfHkSNH2LlzJwMGDACgoKCA/v371/rrErXMaoVfnwX/ptD/frOjEU4sP/YA6Yt/Ivi223Bt2NDscEQ9JdfG8yfJsqgzPDw8Sh+7uLiQm5vL+PHjWbhwId26dWPmzJmsWLHilO0tFkuZfS0WC0VFRbi4uDBkyBDmzJlTa69BOICt8+DYZrj2U3D3Njsa4cSSPvwQ5eFByMQ7zA5F1GNybTx/0gxD1GmZmZmEhYVRWFjIrFmzzmrffv368ffff7Nv3z4AsrOz2bt3b02EKRxFQTb8/go06QldbjA7GqchHfxOlb9/Pxk//0zwzWNxDQkxOxwhypBr49mRZFnUaf/973/p27cvAwYMoEOHDme1b8OGDZk5cyZjxoyha9eu9O/fn927d9dQpMIhrJkKmfEwdDJYpHisKungd6qkadOweHkRfIfUKgvHI9fGs6O0Aw+2HxUVpaOjo80Oo17atWsXkZGRZochzpP8Hc9CxjH4oCe0HQKjvzrvwymlNmito6ohMqchZbYhb+9eDoy8hpA77yT0sUfNDkdUIylT64aK/o6nK7Ol6kQIIQD+eBWsRXDZJLMjEU4uadqHWLy9CZ4w3uxQhBDVwCGTZWn/JoSoVce2wOZZ0PduCG5ldjTCieXt2UPmr78SdOstuAYFmR2OEKIaOGSyLO3fhBC1Rmv49TnwCoKBT5gdjXBySVOnYvH1JcRuAgchhHNzyGRZCCFqzZ5fIG4VXPwseAWaHY1TkruBhrydO8n8bTnBt92Gi1T2CFFnSLIshKi/igpg2QvQoB30Gm92NE5L7gYaTkydhsXfn+DbbjU7FCFENZJkWQhRf616C1L2w5D/goub2dEIJ5a7fQdZf/xB8PjbcPH3NzscIUQ1kmRZCFE/xa6Av96ErjdBu6Fn3FyI00n64AMsAQEE3yq1ykLUNZIsizpr8uTJppw3Ojqahx56yJRziyrKPA4LJhrNL65+G5QyOyLhxHK3biXrr78ImTABF19fs8MR4ozk+nh2JFkWdZZZhUFUVBTvv/++KecWVVBcZCTKBdkw+ktw9zE7IuHkTnwwFZfAQILGjTM7FCGqRK6PZ8fV7ACE43t58Q52xmdU6zE7NvHnpeGdTrvNV199xZQpU1BK0bVrV1xcXLj66qu5/vrrAfD19SUrK4tjx45x4403kpGRQVFRER999BE///wzubm5dO/enU6dOjFr1izefvttZsyYAcDEiRN55JFHiIuLY9iwYfTr1481a9bQu3dvJkyYwEsvvURiYiKzZs2iT58+FcaXnZ3Ngw8+yPbt2yksLGTSpEmMHDmSFStWMGXKFH766ScmTZrEoUOHiI2N5dChQzzyyCM89NBDZGdnM3r0aI4cOUJxcTEvvPACN954I+Hh4URHR9OgQQOio6N54oknWLFiBZMmTeLAgQOlx3nnnXf4999/WbJkCU2bNmXx4sW4uUmb2yr56w1j9ItrPoJQmYlLnJ+cTZvIXrWKho8/houvfPGqT8y6NoJcH2v7+ijJsnBIO3bs4NVXX2XNmjU0aNCAlJQUHnvssQq3nT17NkOHDuW5556juLiYnJwcBg4cyNSpU9m8eTMAGzZs4IsvvmDt2rVorenbty8XXXQRQUFB7Nu3j++++44ZM2bQu3dvZs+ezerVq1m0aBGTJ09m4cKFFZ73tdde45JLLmHGjBmkpaXRp08fLrvsslO22717N3/++SeZmZm0b9+ee++9l6VLl9KkSRN+/vlnAKoy5Nb+/fv5888/2blzJ/3792fBggX873//49prr+Xnn3/mmmuuqdJ7W6/t+x1WToHu46D7WLOjqTOUUsOB4W3atDE7lFqX9MFUXIKDCR4rnydRO+T6eKqavj5KsizOqCrfcqvbH3/8wQ033ECDBg0ACA4OrnTb3r17c/vtt1NYWMg111xD9+7dT9lm9erVXHvttfj4GDU/o0aNYtWqVYwYMYKIiAi6dOkCQKdOnbj00ktRStGlSxfi4uIqPe+yZctYtGgRU6ZMASAvL49Dhw6dst1VV12Fh4cHHh4ehIaGkpCQQJcuXXj88cd56qmnuPrqqxk4cOAZ35MrrrgCNzc3unTpQnFxMcOGDQM4Y5zCJiMevr/TqE2+8v/MjqZO0VovBhZHRUXdaXYstSlnwway16wh9MknsfhIrXJ9Y8a1EeT6WJGavj5Km2XhNFxdXbFarQBYrVYKCgoAGDRoECtXrqRp06aMHz+er7766qyO6+HhUfrYYrGUPrdYLBQVFVW6n9aaBQsWsHnzZjZv3syhQ4eIjDz1tr798V1cXCgqKqJdu3Zs3LiRLl268Pzzz/PKK6+c8hrz8vIqPI7FYsHNzQ1l65R2pjgFRjvl+XdAYR7c8CW4e5sdkagDTnwwFZcGDQgaO8bsUEQ9J9fHmr0+SrIsHNIll1zCd999R3JyMgApKSmEh4ezYcMGABYtWkRhYSEABw8epFGjRtx5551MnDiRjRs3AuDm5la6zcCBA1m4cCE5OTlkZ2fzww8/VOnb6ukMHTqUDz74AK01AJs2baryvvHx8Xh7ezNu3DiefPLJ0pjtX+OCBQvOKz5h58/X4NAaGP4uNGxndjSiDshet46cf/8lZOIdWLy8zA5H1CNyfaz966M0wxAOqVOnTjz33HNcdNFFuLi40KNHD958801GjhxJt27dGDZsWOktoxUrVvB///d/uLm54evrW/rN+a677qJr16707NmTWbNmMX78+NLOCBMnTqRHjx7ndXvmhRde4JFHHqFr165YrVYiIiL46aefqrTvtm3bePLJJ0u/BX/00UcAvPTSS9xxxx288MILDB48+JxjE3ZifoPVb0PP26DraLOjEXVE0gdTcWnYgKCbbjI7FFHPyPWx9q+PqiTrd0RRUVE6Ojra7DDqpV27dlV4y0Q4l3r/d0w/Ah8PBP8mMHE5uNVeDaBSaoPWOqrWTugA6kuZnf3vWg6NH0+jZ58l+NZbzA5H1KJ6X6bWERX9HU9XZkszDCFE3VRcBPNvh+ICo51yLSbKou7SWnPigw9wDQ0l8Ea5UyFEfSDNMIQ4gy+++IL33nuvzLIBAwYwbdo0kyISVfLvNDi8FkZ9Dg3q35Bmombk/PMPuRs20OiF57HYdU4Soj6qL9fHWkuWlVKtgOeAAK319bV1XiHO14QJE5gwYYLZYYizkXIA/nwdOlwNXW8wOxpRR2itOfH+B7g2bkzgDfK5EqK+XB+r1AxDKTVDKZWolNpebvkwpdQepdQ+pdTTpzuG1jpWa33H+QQrhBBnpDX89AhYXGU8ZVGtslf/Te7mzTS4524s7u5mhyOEqCVVrVmeCUwFSgfoU0q5ANOAIcARYL1SahHgArxebv/btdaJ5x2tEEKcyZa5ELsCrpxidOwTohqUtlVuEkbgqFFmhyOEqEVVSpa11iuVUuHlFvcB9mmtYwGUUnOBkVrr14GrqzVKIYSoiuwk+PVZaN4XouRGVm2pD9NdZ69cSd7WrTR+5WWU1CoLUa+cz2gYTYHDds+P2JZVSCkVopT6GOihlHrmNNvdpZSKVkpFnzhx4jzCE6J2fPzxx2c9K5KoIUufgfxMGP4+WGSwn9qitV6stb4rICDA7FBqhFGrPBW3Zs0IvPZas8MRwinUpWtjrXXw01onA/dUYbtPgU/BGLOzpuMS4nzdc88ZP9aiNuxbDtu+hYuegtAOZkcj6pCsP1eQt307Ya+9inJzMzscIZxCXbo2nk/Vy1Ggud3zZrZlQlSLuLg4IiMjufPOO+nUqROXX345ubm5DB48mJKJD5KSkggPDwdg5syZXHPNNQwZMoTw8HCmTp3K22+/TY8ePejXrx8pKSkADB48mIcffpju3bvTuXNn1q1bh9VqpW3btpTczbBarbRp0wb7uxv79+9n2LBh9OrVi4EDB7J7924AJk2axJQpU0qP/dRTT9GnTx/atWvHqlWrANixYwd9+vShe/fudO3alZiYGOLi4ujcuXPp8adMmcKkSZNKj/Poo48SFRVFZGQk69evZ9SoUbRt25bnn3++5t50Z1WQDT89Cg3awcDHzY5G1CFaa05M/QC3Fi0IGDHC7HCEkGujCdfG86lZXg+0VUpFYCTJNwFjqyOo+tD+zakseRqOb6veYzbuAle8ccbNYmJimDNnDp999hmjR48+43zw27dvZ9OmTeTl5dGmTRvefPNNNm3axKOPPspXX33FI488AkBOTg6bN29m5cqV3H777Wzfvp1x48Yxa9YsHnnkEZYvX063bt1o2LBh6bHvuusuPv74Y9q2bcvatWu57777+OOPP06JoaioiHXr1vHLL7/w8ssvs3z5cj7++GMefvhhbr75ZgoKCiguLiYhIeG0r8Xd3Z3o6Gjee+89Ro4cyYYNGwgODqZ169Y8+uijhISEnPH9qzf+nAxph2DCUnCVsW9F9cn6/Xfyd+4i7PXXpVZZlCXXRqB+XBurlCwrpeYAg4EGSqkjwEta6+lKqQeAXzFGwJihtd5RHUFprRcDi6Oiou6sjuMJ5xUREUH37t0B6NWr1xnnqr/44ovx8/PDz8+PgIAAhg8fDkCXLl3YunVr6XZjxowBYNCgQWRkZJCWlsbtt9/OyJEjeeSRR5gxY0aZsSOzsrJYs2YNN9iNrZqfn19hDKNsPeXt4+3fvz+vvfYaR44cKf0WfCYjbLVYXbp0oVOnToSFhQHQqlUrDh8+LMlyifhN8O+H0GsCtOxvdjSiDtFWKyc+mIp7y5YEDJd+68JxyLWxdq+NVR0NY0wly38BfqnWiITjqcK33JriYTdDlouLC7m5ubi6umK1WgHIy8urdHuLxVL63GKxUFRUVLpOKVVmP6UUzZs3p1GjRvzxxx+sW7eOWbNmla63Wq0EBgayefPmKsfs4uJSes6xY8fSt29ffv75Z6688ko++eQT2rVrV/o6Tvda7F9HRa+lXisugkUPgU8oXDbJ7GhEHZP523Ly9+yhyf/eRLnKhLeiHLk21ptro0N2F1dKDVdKfZqenm52KMIBhYeHs2HDBgDmz59/TseYN28eAKtXryYgIICSXvwTJ05k3Lhx3HDDDbi4uJRu7+/vT0REBN999x1gtGPcsmVLlc8XGxtLq1ateOihhxg5ciRbt26lUaNGJCYmkpycTH5+Pj/99NM5vZZ67d9pcHwrXPk/8Ao0OxpRh2irlaSpU3GPiMD/qqvMDkeIM5JrY81xyGS5rg9DJM7PE088wUcffUSPHj1ISko6p2N4enrSo0cP7rnnHqZPn166fMSIEWRlZZXeZpo4cWJph4lZs2Yxffp0unXrRqdOnfjxxx+rfL5vv/2Wzp070717d7Zv386tt96Km5sbL774In369GHIkCF06CAjOJyVkimt218FkdLxSlSvzF9/JT8mhgb334+ySw6EcFRybaw5SmvHHZ0tKipKl/wxRO3atWsXkZGRZodRIwYPHsyUKVOIioo6ZV10dDSPPvpoaU9dZ1dn/45aw9fXwJEN8MA6h5ypTym1QWt96oesDqsrZbYuLiZ2xEhQ0OrHHyVZFqXqbJmKXBtPV2ZLIywhbN544w0++uijMu2xhIOSKa1FDcpYspSC/ftp+s7bkiiLek+ujQ5as2w3dNydMTExZodTL9Xlb8/1SZ38O6Ydgk8GGWMqT1jqsDP1Sc2yc9LFxcRePRzl6krEjwtRDvr5Euaok2VqPXS2NcsOWQpIm2UhRIUKcmDuzWC1wjUfOWyiLJxXxs8/U3DgAA0eeEASZSEEIM0whBDOQmtY/LAxCcDYeRDS2uyIRB2ji4o4MW0aHh064DfkMrPDEUI4CPnaLIRwDv9+CNu+hYufg3ZDzY5G1EHpi3+i8OAhGj5wv9QqCyFKSWnghIpSUjg4YQK5O6plwkQhHF/sX7DsBehwNQx83Oxo6gWlVCul1HSl1LkN2OpkdGEhSR9+iEfHSHwvvdTscIQQDsQhk2WZlOT0Ur+ZRc6/a7F4eZkdSr22cOFCdu7cWfr8xRdfZPny5SZGVEelHYL5EyCkDVz7sbRTPg9KqRlKqUSl1PZyy4cppfYopfYppZ4G0FrHaq3vMCfS2pe+aBGFhw/T8IEHT5nFTAhxdura9dEhrzrSwa9y1pwcUmfNwvfSS/Bo1crscOqEc50es3xh8Morr3DZZdLOsVqVdOgrLoKbZoOHn9kRObuZwDD7BUopF2AacAXQERijlOpY+6GZx5qfT9KHH+HZuTO+Fw82OxwhHIZcHw0OmSyLyqXNn09xejohd9T9Cp+3336bzp0707lzZ959913i4uKIjIzkzjvvpFOnTlx++eXk5uYCxmDqTz31FH369KFdu3alA6e/88473H777QBs27aNzp07k5OTw6RJk7jlllsYMGAAt9xyCzNnzuSBBx4oPffVV1/NihUrAPD19eW5556jW7du9OvXj4SEBNasWcOiRYt48skn6d69O/v372f8+PGlU4yGh4fzzDPP0L17d6Kioti4cSNDhw6ldevWfPzxx6Xn+b//+z969+5N165deemll2rjbXUe9h36rvsMGrQxOyKnp7VeCaSUW9wH2GerSS4A5gIjq3pMpdRdSqlopVT0iRMnqjHa2pP00UcUHj1K6BOPS62ycApyfaxdMhqGE9GFhSTPnIlXVC+8e/SotfO+ue5NdqfsrtZjdgjuwFN9nqp0/YYNG/jiiy9Yu3YtWmv69u3LRRddRExMDHPmzOGzzz5j9OjRLFiwgHHjxgHGN+B169bxyy+/8PLLL7N8+XIefvhhBg8ezA8//MBrr73GJ598gre3NwA7d+5k9erVeHl5MXPmzEpjyc7Opl+/frz22mv85z//4bPPPuP5559nxIgRXH311Vx//fUV7teiRQs2b97Mo48+yvjx4/n777/Jy8ujc+fO3HPPPSxbtoyYmBjWrVuH1poRI0awcuVKBg0adO5vbF1S2qHvedM69BUUWckrKsbf082U89eSpsBhu+dHgL5KqRDgNaCHUuoZrfXrFe2stf4U+BSMcZZrOtjqlrd3L8mfTyfgmmvw6dfP7HCEEzHj2ghyfTSDJMtOJGPJEorij9H4xRfNDqXGrV69mmuvvRYfHx8ARo0axapVq4iIiKB79+4A9OrVi7i4uNJ9Ro0adcpyi8XCzJkz6dq1K3fffTcDBgwo3X7EiBF4VaHdt7u7O1dffXXpsX/77bcqvYYRI0YA0KVLF7KysvDz88PPzw8PDw/S0tJYtmwZy5Yto4fti09WVhYxMTGSLIPDdOhbvCWelxbtYNEDA2jV0Ne0OMygtU4G7jE7jpqkrVaOv/AiLn5+hD71H7PDEaJK5PpY+yRZdhJaa5I/n45H2zb41vKH5UzfcmuTh4dH6WMXF5fS20z261xcXMq0s4qJicHX15f4+PgyxyopaABcXV2xWq2lz/Py8kofu7m5ld6aLX/sqsRqsVjKxG2xWCgqKkJrzTPPPMPdd99dpePVGw7SoU9rzWerYmka6EVEA58z7+C8jgLN7Z43sy2rMrtZV6szrhqXOncuuVu20OR/b+IaFGR2OMLJONK1EeT6WJMcss2yjIZxquxVq8jfu5fgO+6oF+N/Dhw4kIULF5KTk0N2djY//PADAwcOPOvjpKen89BDD7Fy5UqSk5NL20yVFx4ezubNm7FarRw+fJh169ad8dh+fn5kZmaedUwlhg4dyowZM8jKygLg6NGjJCYmnvPx6gQH6tC3el8Su49nMnFgRF1vx7oeaKuUilBKuQM3AYvO5gDO2Cm7MCGBE2+9jc8FF+A/fLjZ4QhRZXJ9rH0OWbOstV4MLI6KirrT7FgcRfJnn+MaFkbAVVeZHUqt6NmzJ+PHj6dPnz4ATJw4kaBzqPl59NFHuf/++2nXrh3Tp0/n4osvrvA2zoABA4iIiKBjx45ERkbSs2fPMx77pptu4s477+T999+vtJA5ncsvv5xdu3bRv39/wOgo8c033xAaGnrWx6oTys/QZ3KHvs9WHaChnwcjujcxNY7qpJSaAwwGGiiljgAvaa2nK6UeAH4FXIAZWus6P4h7wquvoouLafzypLr+ZUjUMXJ9rH1Ka8ftjxEVFaWjo6PNDsN0uVu2EHfjTYQ+/RQh48fXyjl37dpFZGRkrZxL1Byn+jv+Mw1+fdbo0HfRk6aGsvt4BsPeXcWTQ9tz/8XnlrQrpTZoraOqOTSHZNcM486YmBizwzmjzOXLOfLAg4Q+8TghEyeaHY5wIk5VpopKVfR3PF2ZXffv59cByZ9PxxIQQNANN5gdihA1I2a5Q3ToK/H5qgN4ublwc98WZofiFJypGUZxVhbH//sqHh06EHzbbWaHI4RwApIsO7j82ANkLl9O0JibsPjU6U5Gor46thW+uw0adXSIGfoSMvL4cfNRRkc1I9Db3dRYRPU78c67FCUmEvbfV1BudXpIQCFENZFk2cGlfPEFyt2d4FtuMTsUIapf+hGYPRo8A2Dsdw4xQ9+Xa+IotmpuvzDC7FBENcvdvJnU2bMJGjcOry5dzA5HCOEkHDJZltEwDIWJiaQvXEjAqGtxDQkxOxwhqldeOswaDQXZcPN34B9mdkRk5xcxa+0hhnZqTMsQuZNTVc5QZuvCQo698CKujRrR8OGHzQ5HCOFEHDJZdqb2bzUp9euv0cXFhEyYYHYoQlSv4kL49jZI2gOjv4JGncyOCIDvog+TnlvInYNamR2KU3GGMjt5xhfkx8TQ+MUXcfGVL0JCiKpzyKHjhNEJJXXOXPyGXo57C+lkJOoQreGnRyD2Txg5DVpfbHZEABRbNdP/PkCvlkH0bCETVNQlBXFxJE2bht/Qofhd4hifNyGE83DImmUBafPmYc3KIuQOGdZI1DErp8Cmb2DQf6DHOLOjKfXrjuMcTsnlzoFSq1yXaK05NulllIcHjZ571uxwhBBOSJJlB2QtKCBl5pd49++HV2fHuD0tTs/X1/ec9tu8eTO//PJLNUfjwLbMgz9fha43wcWOk7horfl0ZSwtQ7wZ0rGR2eE4HUdus5y+8Edy/v2X0Mcfx62+TvgjhEnqyrVRkmUHlLF4MUUnTshg+Xa01mXmpq8NVZ3j/nw4WoFQow6sgh/vh/CBMOIDcKBZ0zYcTGXz4TQmXhiBi8Vx4nIWjtpmuSglhcQ33sCrZ08CR8s49aLukWtj7ZA2yw5GW60kfz4dj46R+FxwgdnhAHB88mTyd+2u1mN6RHag8bOnr1mMi4tj6NCh9O3blw0bNtCnTx+2bdtGbm4u119/PS+//DLr16/n9ddf5/vvv+fHH3/kpptuIj09HavVSseOHYmNja3w2IMHD6Zbt2789ddfFBUVMWPGDPr06cOkSZPYv38/sbGxtGjRgtdff53bb7+dpKQkGjZsyBdffEGLFi04cOAAY8eOJSsri5EjR5Yed8WKFUyZMoWffvoJgAceeICoqCjGjx/P+vXrefjhh8nOzsbDw4PffvuNF198kdzcXFavXs0zzzzDjTfeWH1vsiM5sQfm3QzBreDGr8HVscYv/mxVLIHeblzfq7nZoYhqlPDGGxTn5BD2yssok8fvFnWPXBvrz7VRkmUHk/XnnxQcOECTt6agHKjmzSwxMTF8+eWX9OvXj5SUFIKDgykuLubSSy9l69at9OjRg82bNwOwatUqOnfuzPr16ykqKqJv376nPXZOTg6bN29m5cqV3H777Wzfvh2AnTt3snr1ary8vBg+fDi33XYbt912GzNmzOChhx5i4cKFPPzww9x7773ceuutTJs27Yyvo6CggBtvvJF58+bRu3dvMjIy8Pb25pVXXiE6OpqpU6ee93vlsDIT4JvrwcXDGCLOy7E6z8UlZbNsZwL3D26Dl7uL2eGIapK1+m8yFi2mwX334dHm3KYsF8JRybWxdkmy7EC01iR/9jluzZrhP3So2eGUOtO33JrUsmVL+vXrB8C3337Lp59+SlFREceOHWPnzp107dqV1q1bs2vXLtatW8djjz3GypUrKS4uZuDAgac99pgxYwAYNGgQGRkZpKWlATBixAi8vLwA+Oeff/j+++8BuOWWW/jPf/4DwN9//82CBQtKlz/11FOnPdeePXsICwujd+/eAPj7+5/Du+GECrJhzo2QkwTjf4aglmZHdIrpqw/gZrFw6wWOF5s4N9bcXI5PmoR7RAQhd99ldjiijpJrY/25NjrkfSlH7ixSk3I3bCB382aCJ4xHucr3GAAf2xTfBw4cYMqUKfz+++9s3bqVq666iry8PMD4h16yZAlubm5cdtllrF69mtWrV5+xQChfc1/y3KeK04pXVPPv6upapv1YSYz1krUY5t8Bx7bA9TOgaU+zIzpFanYB3204zDU9mhDq52l2OE7L0crspGnTKDxyhMYvT8Li4WF2OEJUO7k21i6HTJYdtbNITUv+fDouQUEEjhpldigOJyMjAx8fHwICAkhISGDJkiWl6wYOHMi7775L//79adiwIcnJyezZs4fOnTuf9pjz5s0DYPXq1QQEBFDR5+2CCy5g7ty5AMyaNau0kBkwYECZ5SVatmzJzp07yc/PJy0tjd9//x2A9u3bc+zYMdavXw9AZmYmRUVF+Pn5kZmZea5vi+PSGpY+DXuXwBX/g/ZXmB3RKU5k5jP+i3UUFFlluLjz5Ehldt6uXSR/MZPAG67Hp08fs8MRokbJtbF2OGSyXB/l7d1L1ooVBN0yDovtNoc4qVu3bvTo0YMOHTowduxYBgwYULqub9++JCQkMGjQIAC6du1Kly5dztjm29PTkx49enDPPfcwffr0Crf54IMP+OKLL+jatStff/017733HgDvvfce06ZNo0uXLhw9erR0++bNmzN69Gg6d+7M6NGj6dGjBwDu7u7MmzePBx98kG7dujFkyBDy8vK4+OKL2blzJ927dy8toOqEfz+EdZ9C/wegz51mR3OKfYmZXPvh3+xNyOLTW6Jo28jP7JBENdDFxRx74UVcgoIIfeIJs8MRosbJtbF2KK212TFUKioqSkdHR5sdRq2If+ppMpYto80fv+MaZH4HqF27dhEZGWl2GDVm8ODBTJkyhaioKLNDqVGm/B23zIMf7oLIEXDDl+BgoxD8G5vMXV9F4+7qwozxUXRtFlgj51FKbdBa1+0PWDlml9kpX31FwuTXafr2W/hfeaVpcYi6S66NdUNFf8fTldnSMNYBFB47RvrPPxM0doxDJMpCnLPdv8DCe42xlEd95nCJ8sJNR3ly/hZahvjwxfjeNA/2NjskUU0K4+NJfPc9fC4ahN8VjtfsRwjhvCRZdgApM78ErQm57TazQ6lz7r//fv7+++8yyx5++GFWrFhhTkB1Wexf8N14COsGY+aAm+N0mNNaM/WPfbz12176tQrmk3FRBHi7mR2WqCZaa46//ApoTdiLL8qwm0KcgVwbz44kyyYrTk8n9bvv8L/qStyaNjU7nDK01k5/0anKOI91Va02sToSDXPGGJOOjFsAHo7TBriw2MpzP2zj2+gjXNujKW9c1wUPVxlPuToppYYDw9uYNJ5x5q+/kvXXX4Q+/ZTDlaOi7pFro3M7l2ujY90jrYdS58xB5+QQcodjTW3t6elJcnJy7SZcotporUlOTsbTsxZqdxN2wjfXgW9DuOUH8A6u+XNWUWZeIbfPXM+30Ud46JI2vD26myTKNcDM0TCK09M5/upreHbqRPC4cbV+flG/yLXRuZ3rtVFqlk1kzcsj5auv8Rk0EM/27cwOp4xmzZpx5MgRTpw4YXYo4hx5enrSrFmzmj1JSix8fQ24ecGtP4J/WM2e7yyk5xZy4yf/sC8xi/9d35XRUTKVdV2U+NbbFKem0uLTT2R8elHj5Nro/M7l2igli4nSFy6kOCWFkImOVasM4ObmRkREhNlhCEeWEQ9fXQPFBTBhCQSFmx1RGZMW7SAmMYsZ43tzUbuGZocjakBOdDRp335L8O2349mxo9nhiHpAro31kzTDMIkuLiZ5xhd4duuKt22aRyGcRnYyfH0t5CQbbZRDHWsopZ+3HuOHTUd58JI2kijXUdaCAo69+BJuTZrQ8IH7zQ5HCFGHSc2ySTKXLaPw0CFCn3jc6TsKiHomLwNmXQcpB4xEuWkvsyMqIyEjj+cWbqNb80Duv9icDmei5iV/+hkFsbE0//QTLN4yBKAQouY4ZM2yUmq4UurT9PR0s0OpEVprkj+fjnt4OH6XXmp2OEJUXWGuMerF8W0w+iuIGGh2RGVorXly/lbyCot5Z3Q33FwcsogT5yl//36SP/kE/6uuwtc2O5kQQtQUh7ySmNmzujbk/PsveTt2EHz7BJSL9MwXTqK4EL69DQ7+Ddd+Au2HmR3RKb5Ze4iVe0/w3JWRtGroa3Y49UZtVnBoq5VjL72E8vam0TNP1/j5hBDCIZPlui75s89xadiAgJEjzQ5FiKqxFsMP90DMr3DVW9DlerMjOkXsiSwm/7yLQe0aMq5fS7PDqVdqs4Ijbf58cqM30Og/T+LaoEGNn08IISRZrmV5O3eSvWYNwbfeisXDw+xwhDgzreGXJ2D7fLj0Jeh9h9kRnaKo2Mqj327B3dXC/13fVfoB1FGFiYkk/t8UvPv0IWDUKLPDEULUE9LBr5Ylfz4di48PQTfeaHYoQlTN7y9D9AwY8AgMfMzsaCr04Yr9bDmcxtSxPWjk7zjTbIvqlfD66+j8fBq/PEm+EAkhao3ULNeigiNHyFi6lMCbbsTF39/scIQ4s1Vvw+p3oNcEuGyS2dFUaOuRNN77PYaR3ZtwddcmZocjakjmn3+SuWQpDe69Bw8Z51YIUYskWa5FKTO+ABcXgm+91exQhDiz9dONWuXO1xvtlB2wJi+3oJhH520m1M+DV0Z0NjscUUOs2dkcf+W/uLdpTcgdjtcMSAhRt0kzjFpSlJJC2vffEzBiOG6NGpkdjhCnt/tn+PlxaDsUrv0YLI45asubS3ez/0Q2syb2JcDbzexwRA058f77FB07RsvZs1Hu7maHI4SoZ6RmuZakfjMLnZcntSLC8SXshO/vgibdYfSX4OKYSeiqmBPMXBPHhAHhDGgjoyLUVbnbtpHy9TcEjrkJ7549zA5HCFEPSbJcC6w5OaTOmoXvpZfi0aqV2eEIUbmcFJg7Btx94KbZ4OZldkQVSs8p5MnvttIm1JenhnUwOxxRQ3RhIcdeeBHXkBBCH3PMzqVCiLpPmmHUgrT58ylOTydkotQqCwdWXATfjYeMeBj/M/g7Zmc5rTXPLdxGUlY+n90ahaebYzYREecv5auvyN+9m6bvv4eLn5/Z4Qgh6impWa5hurCQ5Jkz8YrqhXcPuYUoHNiy5+HAX3D1O9C8j9nRVKjYqnl6wTZ+2nqMR4e0o0uzujnLp4CCw4c58cFUfC+9FL8hQ8wORwhRj0myXMMyliyhKP6YtFUWjm3TN7D2I+h7L/QYZ3Y0FSoosvLQ3E3Miz7MQ5e25b7Brc0OSdhU93TXWmuOT3oZZbHQ+IXnZUxlIYSpJFmuQVprkj+fjkfbNvhedJHZ4QhRscPr4KdHIeIiuPxVs6OpUF5hMXd/Hc3PW4/x7JUdeGxIO0mgHEh1T3ed8dNPZP/9Nw0fewy3xo2r5ZhCCHGupM1yDcpetYr8vXsJe/11lEW+lwgHlBEP88YZ7ZNvmAkujlckZOYVMvHLaNbFpTD52i6M7dvC7JBENZq3/hDFVgj2cSPYx4OgomyKX3sdz65dCRpzk9nhCSGEJMs1Kfmzz3Ft3JiAq640OxQhTlWYC3PHQkE23PojeAebHdEpUrMLGP/FOrbHZ/Dujd0Z2b2p2SGJavbe8hji0/NKnz+6cS6XpKdzX4MJpE3+gyBvN0J8PAiyJdPB5X97uxPkY2zj5W5uZ8+CIitpOQWk5hSSkl1AWk4BKTkFpOUUkpZTQCN/Tzo28adTWICMCy6EE5FkuYbkbtlCzvr1hD79lAyiLxyP1rD4YYjfZAwRFxppdkSnSMzI45bp6ziQnM0n43pxWUeZzKcu+uOJwaTmFJCSXUDmmn8IWBjN0StHc/WwQSRnF5SuO5CUzYaDaaTmFFBs1RUey9PNUjax9q4kwbb9DvByw8VScXOevMJi0nIKSc0pIDXblgDnFJCWfTIBTrHFZ2xTSFZ+UaWv08PVQn6RtfR500AvI3Fu4k/HMH86NQ2gSYCnNC8SwgFJslxDkj+fjsXfn8DrbzA7FCFOteYD2DoPLn4OOlxldjSnOJKaw7jP15KYmc/M8b25QCYdqbM83VwIC/CikYci9rN3oEULLpn8DJd5ela4vdWqycwrIjk7n9ScApKzjGQ1OdtIakt+Gwl21mmTWKUg0MuNYB93grzdySsqJjXbSJBzCoorjdnXw5UgHzeCvI39WjXwIch2DOO3m63G21gW6O2Gp5sLJzLz2XUsgx3xGew8lsGO+HSW70pA23L/QG83OoaVJM/+dAwLoHVDH1xdpBmfEGaqtWRZKXUNcBXgD0zXWi+rrXPXtrw9e8lcvpyQu+/CxdfH7HCEKCtmOSx/CTqOhEFPmh3NKfafyGLc52vJzi/im4l96dkiyOyQRC1I+uhjCg8eosWM6VgqSZQBLBZFgLfbWTVjKKklTs7OJzW75LeRUKfYaq5Tswvx83SlXSM/grzdCfYxktxgb3cCbc+DvN0I9HbH3fXckteGfh409GvIoHYNS5flFBSx61gmO49lsDM+nZ3xGXz978HSWmh3VwsdGvuV1kB3bBJAZJgf3u5S1yVEbanSf5tSagZwNZCote5st3wY8B7gAnyutX6jsmNorRcCC5VSQcAUoE4my7qggPinn8YlKIjg224zOxwhykraB/Nvh9COcM1HRtWaA9kRn86t09ehFMy9qz8dm/ibHZKoBXl79pI8fToBI0fic8EF1X58TzcXGge40Dig8iTcLN7urvRqGUSvlie/FBYVW4lNymaHLXneEZ/BL9uOM2fdYcD4t41o4GPUQDcJKG3O0cDXw6yXIUSdVtWvpjOBqcBXJQuUUi7ANGAIcARYr5RahJE4v15u/9u11om2x8/b9quTTnzwAfm7dtHsw2m4BkmNmHAgeekw5yZjxIubZhtTWjuQDQdTGf/FOvw8XPlmYl9aNfQ1OyRRC3RxMcdefAEXX19Cn37K7HAcgquLhXaN/GjXyI9rbXNZaa2JT8+zJc9GEr3pUBo/bT1Wul+on4dRA93ElkSH+dMi2BtLJe2yhRBVU6VkWWu9UikVXm5xH2Cf1joWQCk1FxiptX4doxa6DGX0WngDWKK13ljZuZRSdwF3AbRo4VxDROWsX0/y59MJvOEG/C65xOxwhDjJWgwLJkLqAWPki6CWZkdUxuqYJO76OppQPw9m3dmPpoFeZodUqrC4EDcXGbmgpqTOnUvelq00efMNqWA4DaUUTQO9aBroxRC7zq7pOYWl7Z+NphwZrIxJKu0E6evhSmSYX2ny3LGJP20b+eLhKtPEC1FV59PoqSlw2O75EaDvabZ/ELgMCFBKtdFaf1zRRlrrT4FPAaKioiru8uyAijMziX/qadxaNKeR1I4IR/PHfyFmGVz1FoRfaHY0Zfy64zgPzt5Eq4Y+fHVHH0L9HOdW+e8Hf+edje/w2ZDPCPMNMzucOsdaUEDyx5/gc0F//EeMMDscpxTg7Ub/1iH0bx1SuiyvsJiYhCx2Hks3OhPGZ/Bd9GGybZ0WXS2Kto38SpPnTk38iQzzJ8BLvhQKUZFa6yGgtX4feL+2zlfbEl59lcKEBMJnfYPFx7Fub4t6btt8WP0O9BoPUY417fr3G4/w5PytdG0WwBfjexPo7TjDLP51+C+eWPkEnUI64e8hbadrgsXdnfC5cwBkyLRq5OnmQpdmAXRpdnJGRatVczAlp0w76JUxJ1iw8UjpNs2DvU62g7aNyNHYX4azE+J8kuWjQHO7581sy86bUmo4MLxNmzbVcbgal7F0Kek/LqLBfffh1b272eEIcVL8JvjxfmhxAVzxfw7Voe/LNXG8tGgHA9qE8OktUfh4OE7v/jVH1/DoikdpH9Sejy77CB83+QJcU9yaykQztcFiUUQ08CGigQ9Xd21SujwxM680eS5pxvHrjoTS9cE+7mVqoDuG+dOqoW+l41MLURedz9VpPdBWKRWBkSTfBIytjqC01ouBxVFRUXdWx/FqUmFCAsdemoRnly40uPces8MR4qSsRJh7M/g0hNFfgatj1NpqrZn25z6mLNvLkI6N+GBMDzzdHKf95Lpj63joz4doFdCKT4Z8gp+7n9khCVFjQv08CW3vyeD2oaXLsvKL2H3MNhb0UeP3zL/jKCg2hrPzdLPQvrHdhCpN/OnQ2N/0GRSFqClVHTpuDjAYaKCUOgK8pLWerpR6APgVYwSMGVrrHTUWqQPSVivHnnkWXVBAk/+9iXKT9l7CQRTlw7xbICcF7vgVfBueeZ9aoLXmjSW7+WRlLNf2aMr/ru+KmwNNuLAxYSMP/PEAzf2a8+nlnxLgEXDmnYSoY3w9XIkKDyYqPLh0WWGxlf0nsk7WQsdn8NOWeGavPQSARUGrhr6lyXPJiBzBPo7xJV2I81HV0TDGVLL8F+CXao3IiaR+M4vsNWtoPOklPCIizA5HCIPW8MsTcPhfuH4GhHUzOyIAiq2a5xduY866w9zavyWThndyqCGttp7Yyn2/30cj70Z8dvlnBHsGn3mnOkwp5QN8CBQAK7TWs0wOSZjIzcVCh8ZGDfKonsYyrTVH03JLk+cd8RlsOJjKoi3xpfs19ve0S56NWQmbB3tJO2jhVBynkaAdZ2iznL9vH4lvvYXvRRcReOONZocjxEnrP4eNX8HAx6HzdWZHAxi1Uo/O28xPW49x/8WteeLy9g51sdyRvIN7fruHYM9gPr/8cxp41c3ptc9ygqlRwHyt9WKl1DxAkmVRhlKKZkHeNAvyZminxqXLU7MLTpnWe8XeE6XD2fl5uBJp1wa6YxN/2ob6nfPMiELUNIdMlh29zbIuKODok//B4u1N2Kv/daiLvqjnDqyEJU9Buyvg4ufNjgYwhrG6b9ZG/tidyNNXdOCei1qbHVIZe1L2cPdvd+Pn7sf0y6fTyKfRmXdyXjOp+gRTzYBtts2KayyiJU/D8W1n3k44jSDgAtsPAIFgDdDkFBSTXVBETn4R2UnF5MQXY9WaTGAT4OXugo+7K94ett/uLrhaJIEW56BxF7ii0kmlz5pDJsuO7sQHU41Z+qZNxbWhY7QFFYLE3UY75ZA2MOpTcICLTGZeIXd8Gc36uBQmX9uFsX0da6KhQxmHuOu3u/B08eTzoZ/X+bGUz2aCKYzEuRmwGaj0w+TME0mJ2mNRCl8PV3w9XMHWZ1ajySu02hJoI5FOzSngRNbJKRY8XC34eLji4+6Ct7srPh6uuLkoFFJJJWqPJMtnKSc6muTPPyfg+uvwu/RSs8MRwpB+FL4ZBS7uMHYeeJo/LnBKdgHjv1jHzvgM3r2xOyO7O9YQYcXWYp5d/SxF1iK+HPYlzf2an3mnuqmyCabeB6Yqpa4CFle283lPJFWNtT/CuSjAy/ZTQmvNicx8dsSfnJVwR3wGBxNySrdp4OtOx5KxoG3NOcJDfByqD4SoWxwyWXbUNsvFWVnE/+cp3Jo3p/Ezz5gdjhCG3FT45jrIy4AJP0Ow+Z1Nj6fnccv0tRxKyeGTW3pxaaTjNW2YtWsWW05sYfKFkwkPCDc7HIejtc4GJpgdh6hflFKE+nsS6u/JxR1ODmeXmVfIrmOZZSZVmb4/lsJi4/uZt7sLkWH2w9kF0LaRr0MNSymcl0Mmy47aZjnh1dcoPH6cljJLn3AUhbkwZwyk7Ieb5zvEyBc74zO46+toUrMLmDmhT5lpeB3FwYyDvL/pfS5qdhFXt7ra7HDMdt4TTDlqBYeoO/w83egTEUyfiJOj1BQUWYlJzCwdjWNnfAbfbzzKV/kHAWNa7zahvnS0S6A7NpFpvcXZc8hk2RFlLP2V9IULaXDfvXj36GF2OEJAcRHMvwMO/Qs3fAGtLjI7In7cfJSnFmwlwMuNOXf1o2uzQLNDOoVVW3nx7xdxd3Hnxf4vSgfdaphgylErOETd5u5qoVOTADo1KTut96GUnNJROHbGZ7A6JonvN578/tcsyKt0GLtOTWRab3FmkixXQWFCIsdfesk2S9+9ZocjhDGW8s+PwZ6fjWmsO11rajhFxVbeWLKbz1cfoHd4ENNu7kmon6epMVVmzu45bEzcyH8H/JdQ79Az71CHyARToq6zWBThDXwIb+DDlV1Odtg9kZlfmkDviM9gV3wGy3YmoG2t7Eum9bYfEzqigUzrLQwOmSw70i09bbVy7Nlnsebn0+RNmaVPOIgVr8PGL42xlPveZWooyVn5PDB7E//EJnNb/5Y8d1VHhx0v9XDGYd7d8C4XNr2Qka1Hmh1OraupCaYcqcwWoiIN/Ty4yK8hF7U7OYJV+Wm9dxxL54ty03p3aOzPhW0aMHFgBIHeMhthfaW0PvvOy7UlKipKR0dHmxpDytffkPDaazR+6UWCxlR4nRGidq3/HH5+HHqMgxFTwcRbh9uOpHP319EkZxcw+douXNermWmxnIlVW7nj1zvYnbKbH0b+QGOfxmfe6TwopTZoraNq9CQOxhHKbCHOR2GxlX2JJ6f13h6fzvq4FHw9XLnnotZMGBCOt7tD1jOK83S6Mlv+4qeRv28fiVOm4HPRIAJvusnscISAnT/Cz09Au2Fw9XumJsrfRR/muYXbaejrwYJ7L6Bz04Az72SieXvmEZ0QzcsXvFzjibIQwjm5uViIDPMnMsyf63oZy/Ycz+T/ft3D//26h5lr4njo0rbc1Ls5bi6OeQdNVD/5S1dCFxRw9D/GLH1NXn1VGv4L88WthgUToVlvuP4LcDHnu25BkZUXFm7nyflb6R0exOIHL3T4RPlI5hHe2fAOFzS5gGvbmNu+WwjhXNo39uPz26JYcG9/IkJ8eGHhdi57+y8WbYnHanXcu/Oi+kiyXIkTU6eRv3MXYf99RWbpE+Y7vt0YIi4owph0xN3blDASM/MY+9m/fP3vQe4a1IovJ/Qh2Mex2/FZtZWX1ryERVmY1H+SfPGtAUqp4UqpT9PT080ORYga06tlMPPu7scX43vj5ebCQ3M2cfUHq1mxJxFHbtIqzp8kyxXI2bDBmKXvulH4XXaZ2eGI+i71oDHpiLsvjFsA3sFn3qcGbDiYytXvr2ZHfAYfjOnBs1dG4uoEtyHn753PuuPreDzq8To/nbVZtNaLtdZ3BQQ49h0GIc6XUoqLO4Tyy0MDee+m7mTlFzH+i/Xc9Om/bDyUanZ4ooY45JXOzFqK0ln6mjal0TPP1vr5hSgjO9mYxroo10iUA2t/SmatNbPWHuSmT//B082FH+6/gOHdmtR6HOciPiuet6Lfom9YX65ve73Z4Qgh6giLRTGye1OWP3YRr4zsxP4T2Yz6cA13fRVNTEKm2eGJauaQybKZtRQJr02m8Ngxmrz5Ji6+MkufMFFBNsy+AdIOw5i50KhjrYeQV1jM0wu28dwP2xnQpgGLH7iQDo39az2Oc6G15qU1L6HRvHzBy9L8QghR7dxdLdzaP5y/nhzME5e345/9yQx9dyVPfLeFo2m5ZocnqomMhmEnY9ky0n/4gZB778G7p8zSJ0xUXAjf3gbxm2D019DygloPIT4tl3u/2cCWI+k8eEkbHrmsnVMN0D9181T+PfYvz/d9nqa+Tc0Op06TcZZFfefj4coDl7Tl5r4t+XDFPr785yCLNsdzS/+W3H9xG4fv2yFOT8ZZtilMSOTAiBG4NW9O+JzZMvmIMI/WsPBe2DIHhr8HvcbX6umPpuWyYMMRvlwTR36RlbdGd2NoJ+caau2HmB94cc2LjGo7yrROfTLOshD1V3xaLu8tj+G7DYfxdnflzoGtmDgwAh8PqaN0VDLO8hlorTn23HPGLH3/+58kysJcy18yEuXBz9ZaopxXWMxvOxP4Nvowq/cloTUMaBPCyyM60ybUt1ZiqC5r4tfwyj+v0D+sP8/3e16aXwghal2TQC/evL4rdw6KYMqve3ln+V6++ieOBy9pw5i+LfBwdTE7RHEWJFkGUmfNJnv1ahq9+AIerSLMDkfUZ/98CH+/B1G3w0X/qfHTbT+aznfRh1m4OZ703EKaBnrx0CVtub5XM5oHmzM83fnYm7qXx1c8TkRgBG8Nfgs3i3zxFUKYp02oHx/f0ovNh9N4c8luJi3eyeerD/DYkHaM7N7UqZq21Wf1PlnO37+fxP/7P3wGDZTprIW5ts2HX5+ByOFw5ZQam50vLaeAHzfH8230YXbEZ+DuamFop8aMjmrGgNYNsDhp4Z2Yk8j9v9+Pl6sXH176IX7ufmaHVG9Im2UhTq9780Bm39mX1fuSeHPpbh77dgufrozlyaHtuaRDqNwBc3AO2WbZruC9MyYmpsbOowsKiLtpDIXx8UQs+hG30NAaO5cQp7X/D5g1Gpr3gXHfg5tntR6+2Kr5e18S30YfZtmOBAqKrXRu6s/oqOaM6NaEQG/n7nySU5jD+KXjicuI48thXxIZEml2SNJmWQhRIatV88v2Y7y1bC8HkrLp3yqEt2/sRliAl9mh1WtO12ZZa70YWBwVFXVnTZ7nxLQPydu5k6YfvC+JsjBP/CaYdws0aAc3za7WRPlQcg7zNxxm/oYjxKfnEeDlxti+LbghqhmdmtSNCSSKrEU8ufJJ9qTu4YNLPnCIRFkIISpjsSiu7tqEoZ0aM3f9Yd74ZRdXvreKKTd049LIRmaHJyrgkMlybcjZuJHkzz4jYNQo/IcMMTscUV+lxMKsG8AryJh0xCvwvA+ZV1jM0u3H+Tb6MGv2J6MUDGzbkGeviuSyyEZ4utWdjiVaa95Y9wYrj6zkhX4vMKjZILNDEkKIKnFzsXBLv5YMaB3CA7M3cceX0dxxYQRPDeuAu6tDToNRb9XLZLl0lr4mTWj0rMzSJ0ySlQhfXwvWYqPphf/5TcV8PD2Pj//az4KNR8jMK6J5sBePD2nHqF7NaBpYN2/vfbXzK+btmceEThMY3X602eEIIcRZa9XQl+/vu4DXf9nF9NUHWB+XwgdjetAyRCZGcxT1MllOmPw6hfHxtPzma5mlT5gjLwO+uQ4yE+C2xdCw3Tkf6nh6Hh+t2MecdYexas3VXcMY3bs5/SJCnLazXlUsi1vGlOgpDGk5hEd6PWJ2OKKe01qTWZhJUk4SJ3JPkJSbRFJuEidyTnAi9wQpeSmEeofSMaQjHUM60j6oPd5uzjfijKgZnm4uvDyyM/1bh/Cf+Vu5+v3VvH5dF67u2sTs0AT1MFnOWLaM9O+/J+Tuu/Hu2dPscER9VJQP88ZBwg4YMwea9z6nw5RPkm+IasZ9g9s45ZBvZ2tz4maeXf0s3Rp2Y/KFk7EouWVppro8GkaxtZjU/NTSpNc+CU7KLZsY5xfnn7K/h4sHDbwaEOwZzN9H/2bR/kUAWJSFCP+I0uS5Y0hHOgR3kAS6nhvWOYxOTQJ4aO4mHpi9iTX7k3nx6o51qvmcM3LI0TBKVHfP6sLERA6MGIlb06bGLH3uzj0CgHBCVissuAN2fA/XfATdx571IUqaW8xedwirVXN9r2bcf3H9SJLBmHTkyb+eJMAjgG+u/IZgz2CzQ6qQjIbhPNLy0lhxZAWHMg6RnJdcJhFOyUvBqq2n7OPv7k8DrwY09GpIA2/bb68GZZY18GqAn5tf6bBgWmsScxLZmbyTnSk7jd/JO0nKTQJAoYgIKJtARwZHSgJdDxUWW5mybA+f/BVLh8Z+TB3bgzahMhxmTXK60TBqgjFL3/NY8/Jo8n//k0RZ1L6EHcaEIzu+h8smnXWiXN+TZKu2MmP7DD7Y9AGtAlrx/iXvO2yiLBxfZkEmfxz6g6VxS/k3/l+KdBEWZSHEM6Q06Y0MiTyZ/JYkwt7GYw8Xj7M+p1KKRj6NaOTTiItbXFy6vDSBtv2sPbaWn2J/MvZBER4QbiTPwbYEOiQSHzdpQliXublYeOaKSPq3CuHxb7cw/IO/eWVkJ67v1UzGZDaBQybLNXFLL3X2bLJXraLRC8/j0apVtR1XiNNKjTMmG9m+ABJ3gnKBCx+FAY9U+RAJGXl8tKL+JslgJDbPrX6OPw//yRXhVzDpgklS2ybOWk5hDisOr2Bp3FJWH11NobWQpr5NubXTrQwLH0a7oHa4WGr/dneodyih3qEMbj64dNmJnBNlEuj1x9bzc+zPgJFAt/RveUoNtK+7c01NL85scPtQfnl4II/M3cyT87eyZn8y/72mM74eDpm+1Vl1qhlGsVXz9IKtXNujKf1bh5R++8qPjeXAtaPw7tOH5p9+It/KRM3KTIAdP8D2+XBkvbGseV/ocgN0vAZ8G1bpMPZJcrFVc31PI0luEVK/ksR9qft4dMWjHM48zONRjzMucpxT/A9LMwzHkFeUx6qjq1h6YCkrj6wkrziPUO9QhoYPZVj4MLo06OIUnyeApNwkdibvZEfyjtIkOjEnETiZQEeGRNIppJMk0HVMsVUz9Y99vPf7XlqG+DB1bI86M1a+ozhdmV2nkuXYE1mM/uRfkrLy6dosgLsHtWZou2AO33wzhUePyix9oubkpsGuxUaCfGAlaCs06gxdrofO10FgiyofSpLkk5YeWMqLa17E29Wbtwa/Ra9GvcwOqcokWTZPQXEBa+LXsDRuKX8e+pOcohyCPYO5vOXlDIsYRo/QHnWmU+jpEmigtAa6JIHuENxBpoJ3Yv/GJvPw3E2kZhfy/NWR3NKvpdN82XN09SZZBmNChu83HuXTlfuJS87hoQPLuWLLUkLfeYeQK4bVUKSiXirMhb1LjWYWMcuguACCwo0a5M7XQ2iHszpcYkYeH/21n9lrD1Fk1VzXsykPXNy2XibJhdZC3tnwDl/v/JruDbvz1uC3CPV2ri+6kizXriJrEeuOrWNJ3BJ+P/Q7mQWZBHgEcFmLyxgWMYyoRlG4WurHreuSBNr+JyEnoXR9S/+Wpe2fS9pASwLtPJKz8nniuy38uecEQzs14n/XdSPA283ssJxevUqWSxRbNSu+X07jFx5mefNefD3oViYMiGBc35byoRLnrrgQYlcYCfLun6AgC3wbQadRRpLctCec5bd8SZLLSspN4om/nmBDwgbGdhjLE1FP4ObifP+zkizXvGJrMRsTN7LkwBKWH1xOan4qvm6+XNLiEoaFD6Nfk364WZzvs1MTknOTyybQKTs5nn28dH0LvxZl20CHROLv7m9ixOJ0rFbN9NUHeHPpbhr5e/LB2B70bBFkdlhOrX4my1nZHLj2WrBaSXx3Oh+vT2Dl3hP4uLswtm8L7riwFY0DPKs5YlEnWa1w+F8jQd65EHKSwTMAIkcYzSzCB8I5dApKzMjj479imbX2oCTJNpsTN/P4isfJKMjgxf4vMrz1cLNDOmf1KVm265R9Z0xMTI2ey6qtbD2xlSUHlrDs4DKScpPwcvVicLPBDIsYxoCmA85ppIr6KCUv5ZQa6GPZx0rXN/drfkonwgAPaSfrSDYdSuXBOZs4np7HE0Pbc9fAVnV6MqqaVC+T5fjnniP9h4W0/PorvHsZ7Rx3xKfz6cpYftp6DIuCkd2bcs9FrWTsQnEqreH4Ntj2HWz/HjKOgKsXtL/CSJDbXAau53ZBLp8kj+rRlAcuaVPvpzadu3sub65/kzCfMN4Z/A7tg9ubHdJ5qU/JcomaqlnWWrMzeSdL45ayNG4px7OP425xZ1CzQQyLGMagZoPwcq2bU7rXtpS8FHYl7yqTQMdnx5eub+bbrEwC3TGkoyTQJkvPLeSZ77fyy7bjBHq7EdUymD4RQfQOD6Zz0wDcXOpG+/yaVu+S5czlyznywIOE3HUXoY89esr6wyk5fL4qlnnRh8krtHJZZCPuHdyKXi1lzNZ6L3m/Mczbtu8gaS9YXKH1pUaC3P5K8Kh6z3KtNSnZBRxKyeFQSg6HU3LYfyKbX7YdkyS5nBnbZ/DOhne4qNlFTB44uU7c/pVk+fxordmbupdf435ladxSDmcextXiyoAmAxgaPpSLm18sIz3UktS8VCOBtptI5WjW0dL1TX2blkmeO4V0kgS6lmmt+XXHcf7cfYJ1cSkcSMoGwMvNhZ4tA+kdHkyfiGB6NA/Cy11mA6xIvUqWi06cIHbESNzCwgifO+e0k48kZ+Xz1T8H+fKfONJyColqGcQ9F7Xmkg6hchujPsk4ZkwUsu07iN9kLGs5wEiQI0eCT0ilu+YVFnM0Lbc0GT6UnFMmOc4uKC6zfUM/Dy5q15AHLm5DeANJkgEW7F3ApH8mMSx8GG8MfMOUcW5rgiTL5yY2PZZfDxgJcmx6LC7Khb5hfRkWPoxLWlwiSZiDSMtLK5M8nzaBtnUmDPQMNC/geiYxM4/ouFTWHUhh3YEUdh3PQGtwc1F0aRpA74hg+oQHE9UyWPpx2dSbZFlrzeG77yZn7Toivl+AR+vWVdovp6CIeesP8/mqAxxNy6VtqC93X9SaEd2a4O4qty/qpJwU2LXIaIcctxrQENbN6KTXaRQENAWMz9SJrHwOp+RwOCW3NBEuSYaPZ+Rh/y/k6WahRbA3LYK9aW77XfLTLMhbvtGXs/zgch7/63H6h/Xng0s+cMqOfJWRZLnqDmceNmqQDyxlT+oeFIqoxlEMCx/GZS0vk5kanYQk0I4rPbeQjQdTWXsghfVxKWw9kkZhsUYpaN/Ijz4RwfRsEURkmD+tGvrUy6Yb9SZZzt+3jwPX30DoE08QPO7msz5fYbGVn7bG88lfsew+nklYgCd3XBjBTX1ayGw5dUFBNuxZYiTI+5aDtRBC2lAYOYqjza5ivw4rkwgfsiXIuYVla4cb+3uWTYZDvEqfN/T1kDEvq2jtsbXcu/xeIkMi+WzIZ3VuRj5Jls/Mqq1MWDqBjYkbAejWsBtXRFzBkJZDnG6oQFGx9Pz0UzoRHsk6Urq+iU+TU9pAB3nKqA41La+wmE2H0lgfZ9Q8bzyUSo7tTqi7i4W2jXzpGOZPpO2nY5h/na+BrjfJMkDh0aO4NmlyXgmL1poVe0/w8Yr9rD2Qgr+nK7f2D2f8gHAa+Eova2eiM4+Tt2sZ1r3L8IxbjktRLpnuoUT7XswvXMhfGWEkZhWU2cfb3aVMjXCLEG+aBxnJcLMgLzzdpHb4fO1I2sHtv95OE98mzBw2s07eWpdkuWrejn6bIM8ghoYPpYlvkxqKTDiS9Px0dqWU7UR4OPNw6fown7DSCVTCA8KJ8I+ghX8L6cRZgwqLrcSeyGbXsQx2Hstgl+0nye762DTQi8gwv9LkOTLMn5Yh3nWmgqheJcvVbdOhVD75K5Zfdx7H3cXC9b2acdegVtIpq5ZprckpKCY1p4C0nELjJ7eA1JxC0nOM32k5haRn59IwYwcds/8lqiCaSGIBSNCBLC/uxaLiC1hPe8ICfGge7FVhk4lgH/c688/viGLTYxm/ZDzebt58dcVXdbYGUZJlIaouoyDjlFE4DmUeKrNNY5/GhPuH09K/JREBEaWPw3zC6kxfB0eTmJnHzvgMdh3LLE2kY09kYbWljg183RnQpgED2jRgYNsGhAU47xcap0uWa3PMzqrafyKLz1bG8v3GoxRZrVzRJYx7BrWmS7O6VyNW03ILio1EN9tIeEuS39ScAtJzC0nNLiAtt5A0W2KcmlNIem4BhcUVf1aDyWCI+3Yudd1CP70Zf52JFQuHfDpzKGQASY0vorhhJxr6e9IyxIemgV7SFt0kx7KOcevSWykoLuDrK76mhX/VpwF3NpIsC3F+cgpzOJR5iLiMOOLS44jLiONg+kHiMuLIKswq3c7d4k4L/xaE+4cTHmAk0OH+4UQERNTJu1ZmyyssZm9CJjviM1gbm8zqfckkZeUD0LqhDwPbNmRAmwb0axWMn6fzNN1wumS5hCMWvIkZecz4O45Z/x4kM7+IAW1CuOei1lzYpkG9q43MLyouk+gaj41ENzWngHS75em5Jx/nF1krPaanm4VAL3cCvd0I9HYjyLvksTuBXsbzAC8XmuXuoVHCSgKOrsD12CYUGnwaQpsh0HYItL4YvKTdmyNJyUvhtiW3kZSbxBfDvqBD8NlNB+5sJFkWomZorUnOSz6ZQGccLH18JPMIRbqodNtAj8AySXSEfwThAeE092uOu0vlo2WJqtNas/t4Jn/vS2JVTBJrDySTV2jFxaLo0TyQC9s24MI2DejaLNChK6okWa4BGXmFzF57iBmrD5CYmU+nJv7cc1FrrujcGFcn60VaUGQlPdcu0a2kZrf0sa3ZQ/mOb/bcXSylCa99olv63NuNIG83ArzcCfJxK02QK20PnJMC+/+AmN+Mznk5SYCCZlHQ9nIjQW7cDSzO9d7XF9mF2dzx6x3sS9vHx5d9TFTjup9DSrIsRO0rtBZyNPOokUBnxHEg/UDp46TcpNLtLMpCE58mtAywJdD+4bQMMGqkG3k3qneVX9Upv6iYDQdT+XtfEqtjkth6NB2tQSlo4OtBkwBPGgd4EhbgRZPAsr9D/TxMy6EkWa5B+UXF/LDxKJ+ujCU2KZsWwd7cOTCCG6Ka13pHsKJiW9JbLtFNK2neUL69b7ZR45uVX1TpMV0tqkzCa5/oljy2rwkO9HYnyNsNLzeX8ytstIbjWyFmGcQshyPrQFvBK9iYPa/tEGOykNOMgSwcQ0FxAfctv4/ohGjevfhdBjcfbHZItaI+JcuO2HROiPKyCrI4mHGQAxkHSmujSxLp3KLc0u28XL1Km3K09G9JVOMo+oX1MzFy55aWU8A/+5PZk5DJsbQ84tNzOZaex7G03FPmInCxKHqHBzGmTwuGdW6Mh+vZ5VEHkrL5Nvowt/ZvedbtpyVZrgVWq2bZzgQ+/ms/mw+n4efpSpC3Oy4WhVJgUQoXZTx2sSgsSmFRYLE9LllnUap0nzLbKVVmnQYy84rskuICMvMqT3otCruEt+LkN8CW6Nonv74errX3DTsvHfb/Cft+MxLkrOPG8iY9bM0rLoemPUE6cjiNYmsxT/z1BMsPLWfyhZMZ3nq42SHVmvqULJdwpjJbiBJaaxJyEso05yhpJx2fHY9VW7mo2UU81fspmvs3NzvcOkNrTWZ+0ckEOi2PQyk5/LwtnsMpuQT7uHN9r2aM6dOCiNNM4pVTUMQv247z7frDrItLwcWieOfG7ozodnaj69SfZLmoAOaOgfZXQOfrTGmzqrVm3YEUFm4+Sl6hlWKrxqo1WlP62PgBq9YUW8uu0xqKS7axGttVtA4Nfl5utuYN9rW8bgT5uBNQrtmDn4er481KqDUk7rLVHv8Gh/8FaxF4BkDrS4zkuM1l4Fs3R0uo67TWTPpnEt/HfM9TvZ9iXMdxZodUqyRZFsL55RfnM2fXHD7a8hFF1iImdJ7AHV3ukGHsapDVqlm9L4nZaw/x264Eiq2aC1qHMKZPC4Z2aoy7qwWtNVuOpDNv/WEWb4knK7+I8BBvRvduzvU9mxHq73nW560/yXLKAZg7FhJ3gouHkTR3v9lIvFxkUhGHkJ8FB/46mSBn2GZ3atTFaFrR9nJo1lv+XnXAOxveYcb2GdzV9S4e7PGg2eHUOkmWhag7EnMSeXvD2/wc+zNhPmH8p/d/uLTFpdK2uYYlZuTxbfRh5qw7zNG0XEJ83BnWuTEbDqay+3gmnm4WruwSxo1RzekTEXxef4/6kyyDUVt5bAtsng3bvoPcFPBtBF1HQ7ex0KhjzQQrKqY1JMXYkuNlcHCNMXOeux+0Hnyy9thfJiOoK7TWzNg+g3c3vsvodqN5vt/z9fKCIsmyEHVP9PFoJq+bTExqDP3D+vNM32eICIgwO6w6r9iqWRVzgtlrD/H77kQ6NfFndFRzRnRvgn81DU9Xv5Jle0UFRoK2eTbE/Grc4g/rDt3HQufrpXNYTSnIgbhVJ2uP0w4ayxtGnqw9bt4XXGXYnrrmSOYRXlv7GquPrmZo+FDeHPhmvZ0sQJJlIeqmImsR8/bMY9qmaeQW53JLx1u4u+vd+LjJZGW1obDYilsNjJhRf5Nle9lJRk3z5tnGKAsWN2g31Eic214OLs4zcLZDSt5vG9btNziwCorzwc0bWg0+OXpFYN2dgKK+K7QW8vXOr/lo80dYlIUHezzImA5j6m2iDJIsC1HXJecm897G9/hh3w+EeoXyeNTjXBFxRb28k1YXSLJc3vHtsGUObJ0H2SfAuwF0ucFInMO6Vv/56qLCPDi42hi1ImYZpOw3loe0tdUeD4GWA8DVw9w4RY3bemIrL//zMntT9zK4+WCe6/scjX0amx2W6SRZFqJ+2HJiC6/9+xq7UnYR1SiKZ/o+Q7ugdmaHJc6SJMuVKS6Efb/DltmwZwkUF0CjzkbS3OUGGYWhvNSDtmHdfoMDK6EwB1w9IXygbWKQyyC4ldlRilqSWZDJ+xvfZ96eeTT0bsizfZ/l0haXmh2Ww5BkWYj6o9hazPf7vue9je+RVZDFmA5juLf7vfi7+5sdmqgiSZarIicFti8wmmnEbwTlYtSOdh8L7YbVzxrSogI49M/JtsdJe4zlQeG25PhyCL8Q3GQInfpEa83yQ8t5Y+0bnMg9wdjIsTzQ/QF83X3NDs2hSLIsRP2TlpfGB5s+4Lu93xHkGcSjvR5lROsRWJTMLuvoJFk+W4m7jdrmrd9C5jFjvObO10P3MdCkpzFnY12VfvRk7XHsCijIAhd3o0lFybTSIW3q9nsgKnUs6xiT105mxZEVdAjuwEv9X6Jzg85mh+WQJFkWov7ambyT19a+xtYTW+nWsBvP9n2WjiEyGpcjk2T5XFmLIfZPo7Z5989QlAcNO0C3MdD1RvAPMy+26lJcCIfXGbXH+5ZDwnZjeUDzkyNXhA8ED6k1rM+KrEXM2jWLaZunAXB/9/u5OfJmXC0yHnZlJFkWon6zaiuL9i/inQ3vkJqXyg3tbuChng8R4BFgdmiiApIsV4fcNNjxg9Ex8PBaUBZjspNuY6DDVc7VFCHzuJEYx/xmTC+dnw4WV2jR/2SC3LCD1B4LAHYk7eDlf15mV8ouBjUbxHN9n6OJr4yLfSaSLAshADIKMvho80fM2T0HP3c/Hur5EKPajKrXowU5IkmWq1vSPiNp3jIXMo6ARwB0vtaYLbBZb8dLMq3FcCTa1rximTFpC4BfmG1Yt8uNId48pSOCOCm7MJupm6Yye/dsQjxDeLrP0wxpOUSGRaoiSZaFEPb2pOzh9XWvsyFhAx1DOvJs32fp1rCb2WEJG4dIlpVSkcDDQAPgd631R2fax+ELXqsV4lbC5jmwa5ExOkRwa6Ntc9cbjaYMZiUW2UnGSB8xy2D/75CbanRabN7nZO1xo86Ol9gL0xVaC1m0bxEfbfmIxJxERrcfzcM9H8bP3c/s0JyKJMtCiPK01iw5sIS3ot8iMTeRa9tcy8M9HybESyZJM9t5J8tKqRnA1UCi1rqz3fJhwHuAC/C51vqNKhzLAnyltR53pm2dquDNz4SdPxrtmw/+bSxz9TKGn/NrbEy57dsI/Gy/fRuffOzTEM73dozVCsc2GU0rYpbB0Y2ANo7dxjbuceuLjc6KQlSgJEn+bNtnHM06SpcGXXiqz1NS83GO6kKyrJRqBTwHBGitrz/T9k5VZgthouzCbD7Z8glf7/waLzcvnox6kmvaXCN37kxUHcnyICALI8ntbFvmAuwFhgBHgPXAGIzE+fVyh7hda52olBoB3At8rbWefabzOm3Bm3LAGLc54yhkJRhthLMSIes45KWfur2yGEmtb2jZJLrM40ZG0m3fNjonBfb/YZs5bznkJAEKmkWdHLmicTewyJA1onIVJcn3druXC5teKAX3eTA7Wa7mSo75kiwLUf1i02J5+Z+X2Zi4kT6N+/Bi/xdp6d/S7LDqpWpphqGUCgd+skuW+wOTtNZDbc+fAdBal0+UKzrWz1rrqypZdxdwF0CLFi16HTx4sErxOY3CXFvinGCXSJc8TjAS6qxE40cXn7q/h7+ROLt5GSNXaCt4BZ9se9z6EvCR2znizCRJrlkOkCxXSyWHbT9JloWoIVZtZUHMAt6Jfof84nzu7X4vt3W6DTeLm9mh1SunK7PPZ9ynpsBhu+dHgL6nCWIwMArwAH6pbDut9afAp2AUvOcRn2Ny84KglsbP6ViLISe5XBJd8jjBqKEe+ISRIDftef7NOES9UVGS/Fzf5yRJrmO01ittlRz2+gD7tNaxAEqpucBIWyXH1edynnIVHOcesBD1lEVZuKHdDQxuNpjX173OexvfY8mBJUzqP4kuDbuYHZ7g/JLls6K1XgGsqK3zOT2Li61ZRig0ln8Wcf4kSRacfSVHCPAa0EMp9UxFdw7rfAWHELWkoXdD3h78Nr8f+p3J/05m3JJxjO0wlgd7PIi3m7fZ4dVr55MsHwWa2z1vZlsmhHAgkiSLc6W1TgbuMTsOIeqTS1tcSp/GfXhv43t8s+sbfj/0Oy/0e4GBzQaaHVq9dT49v9YDbZVSEUopd+AmYFF1BKWUGq6U+jQ9vYLOcEKIKim0FrJg7wKG/zCcSf9MItgzmA8v/ZBZV85iYLOBkijXTzVSySFlthDVy8/dj+f7Pc9XV3yFl6sX9/1+H/9Z+R+Sc5PNDq1eqlKyrJSaA/wDtFdKHVFK3aG1LgIeAH4FdgHfaq13VEdQWuvFWuu7AgJkSkghzpYkyeI0aqSSQ8psIWpGj9AefDf8O+7rdh+/HfyNkT+OZOG+hTjyhHJ1UZWaYWitx1Sy/BdO01lPCFF7cotyWbx/MTO2z5DmFqKkkmMw0EApdQR4SWs9XSlVUsnhAsyorkoOIUTNcHdx597u9zI0fCiT/pnEC3+/wE+xP/FSv5do7t/8zAcQ580hp7tWSg0Hhrdp0+bOmJgYs8MRwqEdzTrKvN3zWBCzgIyCDBkCzkGYPXRcbZIyW4jaYdVW5u+dz9sb3qbYWsy93e/l1o634mqptfEa6iyHmO76XMiYnUJUTGtNdEI0s3bN4s/Df6JQXNriUm6OvJkeoT0kSXYA9SlZLiFlthC1IyE7gclrJ/PH4T/oENyBSRdMolNIJ7PDcmo1Nc6yEKKW5Rbl8kvsL8zaPYuY1BgCPQK5vfPt3Nj+Rhr7NDY7PCGEELWgkU8j3rvkPZYfXM7ktZMZ+/NYxkWO455u9+Dn7md2eHWOJMtCOIFjWceYu2cuC2IWkJ6fTvug9rxywStcEXEFnq6eZocn6jm7ZhhmhyJEvXJZy8voE9aHdze8y1c7v+LbPd9yefjlXNvmWno16iV3GauJQzbDkPZvQpxsajF712z+OPwHYIy/ObbDWCkEnYA0wxBC1KadyTv5bu93LDmwhOzCbFr6t+SaNtcwsvVIGno3NDs8hydtloVwInlFefxy4Bdm7ZrF3tS9BHgEcF3b67ip/U2E+YaZHZ6oIkmWhRBmyCnM4beDv/F9zPdsTNyIi3JhYNOBXNv2WgY2G4ibxc3sEB2StFkWwgkczz7O3N1zmR8zn/T8dNoFtWNS/0lc2epKvFy9zA5PCCGEE/B282Zkm5GMbDOSuPQ4ftj3A4v2L2LFkRU08GrA8NbDGdVmFOEB4WaH6jSkZlkIE2mt2Zi4kVm7ZvHHoT/QaC5pfgljI8cS1ShKmlo4sfpUsyxN54RwbEXWIlYdWcX3+75n1ZFVFOtieob25Nq213J5y8vxdvM2O0TTSTMMIRxMfnE+v8T+wuzds9mdsht/d3+ua2c0tWji28Ts8EQ1qE/Jcgkps4VwfCdyTrBo/yIW7ltIXEYcPm4+DAsfRr8m/fBz88PX3Rc/Nz983Hzwc/fDy9WrXlTcOF2yLLUUoi6yaiuHMg6xaP8i5u+dT2p+Km0C23Bz5M1c1eoqaWpRx0iyLIRwZCV3Nr+P+Z7fDv5GblFuhdu5KBd83HzwdfPF190XXzdf/NyNpLpHwx5c1eoqfN19azn66ud0yXIJKXiFs0rPT2dv6l5iUmOM32kx7EvdR05RDhZlYXCzwdwceTO9G/euF9/Y6yNJloUQziKnMIf4rHiyCrPILMgs/Z1dmF36PKsgi8zCTLIKssguzCY1P5Xj2cfxcvXiqlZXcWP7G+kQ3MHsl3LOpIOfEDWksLiQ2PTY0oS4JEFOzEks3cbf3Z92Qe0Y2WYkbYPackGTC2jq29TEqIUQQoiTvN28aRN0duOka63ZnrSdeXvmsXj/YubvnU/Xhl25sf2NXN7y8jo1B4DULAtRBVprjmcfL02IS5LiuPQ4inQRAK4WV1oFtKJdUDvaBrU1fge2JdQ7VGqP66H6VLMsTeeEqN/S89NZtH8R3+75lriMOAI8AhjZeiSj24+mpX9Ls8OrEmmGIcRZyCzIZF/aPvamGLXFManGT2ZhZuk2YT5hpUlx20AjMW4Z0FLGrxSl6lOyXELKbCHqN60164+vZ96eefxx6A+KdBH9wvoxuv1oBjcf7NDXSKdrhiFTp4raUGgt5GD6wTLNJ2JSY4jPji/dxtfNl7ZBbbmy1ZW0DWxL26C2tAlqg7+7v4mRCyGEEI5HKUWfsD70CevDiZwT/LDvB+bvnc9jKx4j2DOYq1pdxYjWI2gf1N6p7rhKzbKo0/KL88ksyCSzIJOjWUdPdrhLjSE2PZZCayEArsqV8IBwo5Y4uF1pbXFjn8ZO9Q8tHIfULAshBBRbi1l1dBU/7vuRFUdWUGQtom1QW0a0GsGVra4k1DvU7BABJ6xZFgKModayC7PJKsgioyCjTA/dkp/yz0uWZRRkkFWQRYG14JTjhnqHGh3tml5QmhRHBETg7uJuwqsUQggh6i4XiwuDmw9mcPPBpOWl8WvcryyKXcRbG97inY3v0D+sP8NbD+eSFpecMoRqdmE2celxxKbHGj9psRzPOc7I1iO5qcNNWJSlVl6D1CyLGlNYXGgkrWdIaDMLMskstFtXcHLoGs3pP59erl6lYz6WjPvo7+ZvDKpuW1YyyHqodyjtgtoR4BFQS++AqM+kZlkIISoXlx7H4tjF/LT/J+Kz4/F29WZIyyH4uvsSm2Ykxwk5CaXbuypXmvs3x9PFk10pu+gZ2pOXL3i52qbtlg5+4qxprckpyjl9gmuX5FaU9OYX55/2HBZlKZvo2j22X+bvfmryW5IYO3JnAVG/1adkWUbDEEKcK6u2siFhA4v3L2bZwWVYtZVWAa2ICIigVUAr43FgBM39muNmcUNrzaL9i3hz/ZsUFBdwf/f7uaXjLbhazq+xhCTL9VChtfCUBLaiJLey5gxZhVlYtfW05/Bw8ahSQmv/3P7H29Vb2gOLOqs+JcslpMwWQpyPYmsxFmWpUm5wIucE//33v/x5+E86hXTilQGv0C6o3TmfW9osV5NiazHFupgiaxHFuphiazFFuuiU58VWYxurtpY+L9bGj9V6clmF67W19HgVPS75XaSLyC4wZtbJKDxZ21syw05l01aWUKgyU1b6ufvR2LsxbQPbnmzO4O5fZhv7537uftLGVwghhBDVxsXiUuVtG3o35L2L3+PXuF+ZvHYyN/50I3d1vYuJnSfi5lK9d50dMlk+16Hj0vPTeXPdmycT2JLk1i4hLVlefllVEuAztZ+tLS7KBVeLKz5uPmUS2FDv0FMS2sqaOPi4+dRaw3ghhBBCiOqmlGJYxDD6hPXhjXVv8OHmD1l+cDmvXfhatU697ZDJstZ6MbA4KirqzrPZr8haxMbEjbhaXHFRLrhYXHBVrmWeu1vccXV1xcXiUpp0VvS7ZL2bxa30cUXHc1XGsU7Zv+SxxQWLspRuV7KuwsenWe+qXLEoS5VvTwghhBBC1AfBnsH8b9D/GBY+jNfWvkZOYU61Ht8hk+VzFeIVwtLrlpodhhBCCCGEqGWXtLiEC5teWO3NROU+vBBCCCGEqBNqoj+VJMtCCCGEEEJUQpJlIYQQ50UpNVwp9Wl6errZoQghRLWTZFkIIcR50Vov1lrfFRAgs2MKIeoeSZaFEEIIIYSohCTLQgghhBBCVMIhk2Vp/yaEEEIIIRyBQybL0v5NCCGEEEI4AqW1Y0zhXBGl1Ang4DnsGgA4QrV0TcdRXcc/n+Ocy75ns09Vt63Kdg2ApCqe15nJ5792j1PZ/i211g3P47hOR8rsWjt+TX1ma3N7KbNPks9/7R7r7MtsrXWd+wE+NTuG2oijuo5/Psc5l33PZp+qbluV7YBosz8TtfEjn//aPY6jvN/O/OMo76F8Zmtveymzq/9z4ehxVOfxaztvcchmGNVgsdkB2NR0HNV1/PM5zrnsezb7VHVbR/mbOwJHeS/qw+e/OvYXjvMeyme29rZ3lL+5I3CU98JZPv/ne6yz3tehm2EIUZ2UUtFa6yiz4xBCCHFmUmYLR1FXa5aFqMinZgcghBCiyqTMFg5BapaFEEIIIYSohNQsCyGEEEIIUQlJloUQQgghhKiEJMtCCCGEEEJUQpJlUW8ppVoppaYrpeabHYsQQojTkzJbmEWSZVGnKKVmKKUSlVLbyy0fppTao5Tap5R6GkBrHau1vsOcSIUQQkiZLZyBJMuirpkJDLNfoJRyAaYBVwAdgTFKqY61H5oQQohyZiJltnBwkiyLOkVrvRJIKbe4D7DPVitRAMwFRtZ6cEIIIcqQMls4A0mWRX3QFDhs9/wI0FQpFaKU+hjooZR6xpzQhBBClCNltnAormYHIIRZtNbJwD1mxyGEEOLMpMwWZpGaZVEfHAWa2z1vZlsmhBDC8UiZLRyKJMuiPlgPtFVKRSil3IGbgEUmxySEEKJiUmYLhyLJsqhTlFJzgH+A9kqpI0qpO7TWRcADwK/ALuBbrfUOM+MUQgghZbZwDkprbXYMQgghhBBCOCSpWRZCCCGEEKISkiwLIYQQQghRCUmWhRBCCCGEqIQky0IIIYQQQlRCkmUhhBBCCCEqIcmyEEIIIYQQlZBkWdQrSimtlHrL7vkTSqlJJoYkhBCiElJmC0cgybKob/KBUUqpBmYHIoQQ4oykzBamk2RZ1DdFwKfAo2YHIoQQ4oykzBamk2RZ1EfTgJuVUgFmByKEEOKMpMwWppJkWdQ7WusM4CvgIbNjEUIIcXpSZguzSbIs6qt3gTsAH5PjEEIIcWbvImW2MIkky6Je0lqnAN9iFL5CCCEcmJTZwkySLIv67C1AelgLIYRzkDJbmEJprc2OQQghhBBCCIckNctCCCGEEEJUQpJlIYQQQgghKiHJshBCCCGEEJWQZFkIIYQQQohKSLIshBBCCCFEJSRZFkIIIYQQohKSLAshhBBCCFEJSZaFEEIIIYSoxP8DihSj6+y/91AAAAAASUVORK5CYII=\n", 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\n", 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" ] diff --git a/_unittests/ut_onnxrt/test_onnx_profiling.py b/_unittests/ut_onnxrt/test_onnx_profiling.py index 8baf64dbd..bf6809e59 100644 --- a/_unittests/ut_onnxrt/test_onnx_profiling.py +++ b/_unittests/ut_onnxrt/test_onnx_profiling.py @@ -29,7 +29,9 @@ def test_profile_onnxruntime1(self): node_def2 = helper.make_node('Add', ['X', 'Zt'], ['Z'], name='Z') graph_def = helper.make_graph( [node_def, node_def2], 'test-model', [X, Y], [Z]) - model_def = helper.make_model(graph_def, producer_name='onnx-example') + model_def = helper.make_model( + graph_def, producer_name='onnx-example', + ir_version=6, producer_version='0.1') model_def = insert_node( model_def, node='Z', op_type='Cast', to=TensorProto.INT64, # pylint: disable=E1101 name='castop') diff --git a/_unittests/ut_testing/test_einsum.py b/_unittests/ut_testing/test_einsum.py index 6ffd198c2..0e9d14813 100644 --- a/_unittests/ut_testing/test_einsum.py +++ b/_unittests/ut_testing/test_einsum.py @@ -176,6 +176,15 @@ def fct(): self.assertIn("numpy_extended_dot", out) self.assertEqualArray(exp, res) + def test_decompose_einsum_equation_py(self): + m1 = numpy.arange(0, 8).astype(numpy.float32).reshape((2, 2, 2)) + m2 = numpy.arange(0, 4).astype(numpy.float32).reshape((2, 2)) + seq = decompose_einsum_equation( + "bac,ch->ah", (2, 2, 2), (2, 2)) + res1 = apply_einsum_sequence(seq, m1, m2) + res2 = apply_einsum_sequence(seq, m1, m2, matmul_impl='py') + self.assertEqualArray(res1, res2) + def test_einsum_sub_op(self): self.assertRaise(lambda: EinsumSubOp(2, "er", (2, 2)), ValueError) self.assertRaise(lambda: EinsumSubOp(2, "expand_dims"), RuntimeError) diff --git a/_unittests/ut_testing/test_einsum_generic_dot.py b/_unittests/ut_testing/test_einsum_generic_dot.py index 866d4185d..6fddee2c0 100644 --- a/_unittests/ut_testing/test_einsum_generic_dot.py +++ b/_unittests/ut_testing/test_einsum_generic_dot.py @@ -1404,7 +1404,7 @@ def common_test(self, sh1, sh2, axes, left, right): m1 = numpy.empty(sh1).ravel() m1 = numpy.arange(len(m1)).reshape(sh1).astype(numpy.float64) + 10 m2 = numpy.empty(sh2).ravel() - m2 = numpy.arange(len(m2)).reshape(sh2).astype(numpy.float64) + 10000 + m2 = numpy.arange(len(m2)).reshape(sh2).astype(numpy.float64) + 1000 try: exp = numpy_extended_dot(m1, m2, axes, left, right) @@ -1432,7 +1432,7 @@ def common_test(self, sh1, sh2, axes, left, right): try: self.assertEqualArray(exp, dot) except AssertionError: - raise AssertionError( + raise AssertionError( # pylint: disable=W0707 "shape1=%r shape2=%r\naxes=%r left=%r right=%r\n" "m1=%r\nm2=%r\nexp=\n%r\ndot=\n%r" "\n-----\n%s" % (m1.shape, m2.shape, axes, left, right, diff --git a/mlprodict/testing/einsum_impl.py b/mlprodict/testing/einsum_impl.py index 07811ed82..f3df26be1 100644 --- a/mlprodict/testing/einsum_impl.py +++ b/mlprodict/testing/einsum_impl.py @@ -129,14 +129,18 @@ def decompose_einsum_equation(equation, *shapes, strategy="simple", verbose=Fals raise ValueError("Unknown strategy %r." % strategy) -def apply_einsum_sequence(seq, *inputs, verbose=False): +def apply_einsum_sequence(seq, *inputs, verbose=False, **kwargs): """ Applies a sequence of operations on a list of inputs. The sequence of operations is produced by function @see fn decompose_einsum_equation. :param seq: sequence of operations - :param inputs: inputs: + :param inputs: inputs + :param kwargs: additional parameters, + see :meth:`apply_sequence + `. :return: output .. runpython:: @@ -156,7 +160,7 @@ def apply_einsum_sequence(seq, *inputs, verbose=False): See notebook :ref:`einsumdecompositionrst`. """ - return seq.apply_sequence(*inputs, verbose=verbose) + return seq.apply_sequence(*inputs, verbose=verbose, **kwargs) def _basic_verification(lengths, shapes, equation): diff --git a/mlprodict/testing/einsum_impl_classes.py b/mlprodict/testing/einsum_impl_classes.py index 0fa2d33e8..ee28fcb82 100644 --- a/mlprodict/testing/einsum_impl_classes.py +++ b/mlprodict/testing/einsum_impl_classes.py @@ -5,7 +5,8 @@ import numpy from .einsum_impl_ext import ( numpy_extended_dot, numpy_diagonal, - _numpy_extended_dot_equation) + _numpy_extended_dot_equation, + numpy_extended_dot_python) class EinsumSubOp: @@ -222,13 +223,13 @@ def _get_data(self, data, key): raise TypeError( "Unexpected input type %r." % type(key)) - def _apply_id(self, data, verbose=False): + def _apply_id(self, data, verbose=False, **kwargs): self._check_inputs_(1) inp = self.inputs[0] output = self._get_data(data, inp) return output - def _apply_diagonal(self, data, verbose=False): + def _apply_diagonal(self, data, verbose=False, **kwargs): self._check_inputs_(1) inp = self.inputs[0] m = self._get_data(data, inp) @@ -244,7 +245,7 @@ def _apply_diagonal(self, data, verbose=False): output = numpy_diagonal(m, axis=diag0[0], axes=diag0[1]) return output - def _apply_expand_dims(self, data, verbose=False): + def _apply_expand_dims(self, data, verbose=False, **kwargs): self._check_inputs_(1) inp = self.inputs[0] m = self._get_data(data, inp) @@ -254,7 +255,7 @@ def _apply_expand_dims(self, data, verbose=False): output = numpy.expand_dims(m, self.kwargs['axis'][0]) return output - def _apply_transpose(self, data, verbose=False): + def _apply_transpose(self, data, verbose=False, **kwargs): self._check_inputs_(1, True) inp = self.inputs[0] m = self._get_data(data, inp) @@ -266,7 +267,7 @@ def _apply_transpose(self, data, verbose=False): self._check_shape_(output) return output - def _apply_matmul(self, data, verbose=False): + def _apply_matmul(self, data, verbose=False, **kwargs): self._check_inputs_(2) inp1 = self.inputs[0] inp2 = self.inputs[1] @@ -282,12 +283,16 @@ def _apply_matmul(self, data, verbose=False): print("- %s, shapes=%r @ %r axes=%r left=%r right=%r" % ( self.name, m1.shape, m2.shape, axes, left, right)) - output = numpy_extended_dot(m1, m2, axes, left, right, - verbose=verbose) + if kwargs.get('matmul_impl', None) == 'py': + output = numpy_extended_dot_python(m1, m2, axes, left, right, + verbose=verbose) + else: + output = numpy_extended_dot(m1, m2, axes, left, right, + verbose=verbose) self._check_shape_(output) return output - def _apply_reduce_sum(self, data, verbose=False): + def _apply_reduce_sum(self, data, verbose=False, **kwargs): self._check_inputs_(1) inp = self.inputs[0] m = self._get_data(data, inp) @@ -300,7 +305,7 @@ def _apply_reduce_sum(self, data, verbose=False): self._check_shape_(output) return output - def _apply_squeeze(self, data, verbose=False): + def _apply_squeeze(self, data, verbose=False, **kwargs): self._check_inputs_(1) inp = self.inputs[0] m = self._get_data(data, inp) @@ -313,11 +318,20 @@ def _apply_squeeze(self, data, verbose=False): output = numpy.squeeze(output, axis=a) return output - def apply(self, data, verbose=False): + def apply(self, data, verbose=False, **kwargs): """ Applies one operator on the data. :param data: dictionary storing the results + :param verbose: prints out intermediate results + :param kwargs: additional parameters, see + methods `_apply*` + :return: output + + Known additional paramaters: + * 'matmul_impl': if None calls :epkg:`numpy:einsum` through + @see fn numpy_extended_dot (default) or 'py' to call + @see fn numpy_extended_dot_python instead. """ if verbose: print() @@ -328,7 +342,7 @@ def apply(self, data, verbose=False): if meth is None: raise NotImplementedError( "apply not implemented for %r." % self.name) - output = meth(data, verbose) + output = meth(data, verbose, **kwargs) data[id(self)] = output if verbose: @@ -476,11 +490,15 @@ def d2sd(d): rows.append("}") return "\n".join(rows) - def apply_sequence(self, *inputs, verbose=False): + def apply_sequence(self, *inputs, verbose=False, **kwargs): """ Applies a sequence of operations on a list of inputs. :param inputs: inputs: + :param verbose: prints out intermediate results + :param kwargs: additional parameters, + see :meth:`apply + `. :return: output """ if verbose: @@ -488,7 +506,7 @@ def apply_sequence(self, *inputs, verbose=False): data = {i: inp for i, inp in enumerate(inputs)} last = None for op in self: - last = op.apply(data, verbose=verbose) + last = op.apply(data, verbose=verbose, **kwargs) if last is None: raise RuntimeError( "Sequence of operations is empty.") diff --git a/mlprodict/testing/einsum_impl_ext.py b/mlprodict/testing/einsum_impl_ext.py index a1e1f84bf..fbe975a53 100644 --- a/mlprodict/testing/einsum_impl_ext.py +++ b/mlprodict/testing/einsum_impl_ext.py @@ -233,6 +233,9 @@ def numpy_extended_dot_python(m1, m2, axes, left, right, verbose=False): This implementation is not efficient but shows how to implement this operation without :epkg:`numpy:einsum`. """ + def dispb(c): + return "".join("o" if b else "." for b in c) + if m1.dtype != m2.dtype: raise TypeError( "Both matrices should share the same dtype %r != %r." @@ -309,18 +312,34 @@ def intermediate(l1, l2, l3): names, kind, cols, common, broadcast, pos = intermediate(l1, l2, l3) if any(broadcast): + if verbose: + print("GENERICDOT: before broadcast %s,%s->%s or %s" % ( + "".join(l1), "".join(l2), "".join(l3), + _numpy_extended_dot_equation( + len(m1.shape), len(m1.shape), axes, left, right))) + print("GENERICDOT: names=%s kind=%r common=%s broadcast=%s" % ( + "".join(names), kind.tolist(), + dispb(common), dispb(broadcast))) + for i in range(len(broadcast)): # pylint: disable=C0200 if broadcast[i] and not (kind[i] & 3) == 3: raise RuntimeError( "Broadcast should only happen on common axes, " "axes=%r left=%r right=%r shape1=%r shape2=%r." "" % (axes, left, right, m1.shape, m2.shape)) + if not broadcast[i]: + continue # We split letters. p = cols[names[i]] dim = (m1.shape[p], m2.shape[p]) let = [l1[p], l2[p], l3[p]] inp = 1 if dim[0] == 1 else 0 + if verbose: + print("GENERICDOT: name=%s dim=%r let=%r inp=%r p=%r" % ( + names[i], dim, let, inp, p)) + print(" B0 l1=%r, l2=%r l3=%r" % (l1, l2, l3)) if (kind[i] & 4) > 0: + # Summation axis is part of the output. if let[inp].lower() == let[inp]: let[inp] = let[inp].upper() else: @@ -330,28 +349,40 @@ def intermediate(l1, l2, l3): l2[p] = let[inp] else: l1[p] = let[inp] + if verbose: + print(" B1 l1=%r, l2=%r l3=%r" % (l1, l2, l3)) else: - raise NotImplementedError() + # Summation axis is not part of the output. + if let[inp].lower() == let[inp]: + let[inp] = let[inp].upper() + else: + let[inp] = let[inp].lower() + if inp == 1: + l2[p] = let[inp] + else: + l1[p] = let[inp] + if verbose: + print(" B2 l1=%r, l2=%r l3=%r" % (l1, l2, l3)) names, kind, cols, common, broadcast, pos = intermediate(l1, l2, l3) - indices = [0 for n in names] - pl1 = [names.index(c) for c in l1] - pl2 = [names.index(c) for c in l2] - limits = [m1.shape[pos[n]] if (kind[n] & 1) == 1 else m2.shape[pos[n]] - for n in range(len(names))] - plo = [-1 if c not in names else names.index(c) for c in l3] + indices = numpy.array([0 for n in names], dtype=numpy.int64) + pl1 = numpy.array([names.index(c) for c in l1], dtype=numpy.int64) + pl2 = numpy.array([names.index(c) for c in l2], dtype=numpy.int64) + limits = numpy.array( + [m1.shape[pos[n]] if (kind[n] & 1) == 1 else m2.shape[pos[n]] + for n in range(len(names))], dtype=numpy.int64) + plo = numpy.array( + [-1 if c not in names else names.index(c) for c in l3], dtype=numpy.int64) if verbose: - def dispb(c): - return "".join("o" if b else "." for b in c) - print("GENERICDOT: %s,%s->%s or %s" % ( "".join(l1), "".join(l2), "".join(l3), _numpy_extended_dot_equation( len(m1.shape), len(m1.shape), axes, left, right))) print("GENERICDOT: shape1=%r shape2=%r shape=%r" % ( m1.shape, m2.shape, res.shape)) + print("GENERICDOT: axes=%r left=%r right=%r" % (axes, left, right)) print("GENERICDOT: pl1=%r pl2=%r plo=%r" % (pl1, pl2, plo)) print("GENERICDOT: names=%s kind=%r common=%s broadcast=%s" % ( "".join(names), kind.tolist(), @@ -362,15 +393,17 @@ def dispb(c): while indices[0] < limits[0]: + # The function spends most of its time is these three lines. t1 = tuple(indices[n] for n in pl1) t2 = tuple(indices[n] for n in pl2) to = tuple(0 if n == -1 else indices[n] for n in plo) - c = m1[tuple(t1)] * m2[tuple(t2)] + + c = m1[t1] * m2[t2] if verbose: print(" %r x %r -> %r v=%r I=%r" % (t1, t2, to, c, indices)) - res[tuple(to)] += c + res[to] += c last = len(indices) - 1 indices[last] += 1 From 60650d9be458ab73d1ece01b4d4515c313574f0a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Mon, 26 Apr 2021 01:53:27 +0200 Subject: [PATCH 12/33] update unit tests --- _unittests/ut_onnxrt/test_onnx_inference.py | 6 ++++-- _unittests/ut_onnxrt/test_onnx_tools.py | 3 ++- _unittests/ut_onnxrt/test_onnxrt_runtime_empty.py | 3 ++- _unittests/ut_onnxrt/test_onnxrt_simple.py | 3 ++- _unittests/ut_testing/test_experimental.py | 3 ++- 5 files changed, 12 insertions(+), 6 deletions(-) diff --git a/_unittests/ut_onnxrt/test_onnx_inference.py b/_unittests/ut_onnxrt/test_onnx_inference.py index fc554d62d..546ffaac8 100644 --- a/_unittests/ut_onnxrt/test_onnx_inference.py +++ b/_unittests/ut_onnxrt/test_onnx_inference.py @@ -32,7 +32,8 @@ def test_onnx_inference_name_confusion(self): node_def2 = helper.make_node('Add', ['X', 'Zt'], ['Z'], name='Z') graph_def = helper.make_graph( [node_def, node_def2], 'test-model', [X, Y], [Z]) - model_def = helper.make_model(graph_def, producer_name='onnx-example') + model_def = helper.make_model( + graph_def, producer_name='mlprodict', ir_version=6, producer_version='0.1') oinf = OnnxInference(model_def) X = numpy.random.randn(4, 2).astype( # pylint: disable=E1101 @@ -55,7 +56,8 @@ def test_onnx_inference_name_confusion_input(self): node_def2 = helper.make_node('Add', ['X', 'Zt'], ['Z'], name='Z') graph_def = helper.make_graph( [node_def, node_def2], 'test-model', [X, Y], [Z]) - model_def = helper.make_model(graph_def, producer_name='onnx-example') + model_def = helper.make_model( + graph_def, producer_name='mlprodict', ir_version=6, producer_version='0.1') oinf = OnnxInference(model_def) X = numpy.random.randn(4, 2).astype( # pylint: disable=E1101 diff --git a/_unittests/ut_onnxrt/test_onnx_tools.py b/_unittests/ut_onnxrt/test_onnx_tools.py index ab398679c..16b1c9d95 100644 --- a/_unittests/ut_onnxrt/test_onnx_tools.py +++ b/_unittests/ut_onnxrt/test_onnx_tools.py @@ -29,7 +29,8 @@ def test_onnx_inference_name_confusion(self): node_def2 = helper.make_node('Add', ['X', 'Zt'], ['Z'], name='Z') graph_def = helper.make_graph( [node_def, node_def2], 'test-model', [X, Y], [Z]) - model_def = helper.make_model(graph_def, producer_name='onnx-example') + model_def = helper.make_model( + graph_def, producer_name='mlprodict', ir_version=6, producer_version='0.1') model_def = insert_node( model_def, node='Z', op_type='Cast', to=TensorProto.INT64, # pylint: disable=E1101 name='castop') diff --git a/_unittests/ut_onnxrt/test_onnxrt_runtime_empty.py b/_unittests/ut_onnxrt/test_onnxrt_runtime_empty.py index 29262e7b3..49e76bc69 100644 --- a/_unittests/ut_onnxrt/test_onnxrt_runtime_empty.py +++ b/_unittests/ut_onnxrt/test_onnxrt_runtime_empty.py @@ -54,7 +54,8 @@ def test_onnxt_runtime_empty_unknown(self): 'AddUnknown', ['X', 'Zt'], ['Z'], name='Z') graph_def = helper.make_graph( [node_def, node_def2], 'test-model', [X, Y], [Z]) - model_def = helper.make_model(graph_def, producer_name='onnx-example') + model_def = helper.make_model( + graph_def, producer_name='mlprodict', ir_version=6, producer_version='0.1') oinf = OnnxInference(model_def, runtime='empty') self.assertNotEmpty(oinf) dot = oinf.to_dot() diff --git a/_unittests/ut_onnxrt/test_onnxrt_simple.py b/_unittests/ut_onnxrt/test_onnxrt_simple.py index 943952b0d..7da3ab466 100644 --- a/_unittests/ut_onnxrt/test_onnxrt_simple.py +++ b/_unittests/ut_onnxrt/test_onnxrt_simple.py @@ -389,7 +389,8 @@ def test_blofat16(self): [make_tensor_value_info("X", TensorProto.FLOAT16, [3]), # pylint: disable=E1101 make_tensor_value_info("Y", TensorProto.FLOAT16, [3])], # pylint: disable=E1101 [make_tensor_value_info("Z", TensorProto.FLOAT16, [3])]) # pylint: disable=E1101 - model_proto = make_model(graph) + model_proto = make_model( + graph, producer_name='mlprodict', ir_version=6, producer_version='0.1') oinf = OnnxInference(model_proto) x_val = [1, 2, -3] diff --git a/_unittests/ut_testing/test_experimental.py b/_unittests/ut_testing/test_experimental.py index c1b728787..6c44f2db5 100644 --- a/_unittests/ut_testing/test_experimental.py +++ b/_unittests/ut_testing/test_experimental.py @@ -27,7 +27,8 @@ def ort_path_pad(self, x, pads): npads = numpy.array(pads, dtype=numpy.int64) op = helper.make_node('Pad', ['X', 'P'], ['Y']) graph = helper.make_graph([op], 'graph', [X, P], [Y]) - model = helper.make_model(graph, producer_name='model') + model = helper.make_model( + graph, producer_name='mlprodict', ir_version=6, producer_version='0.1') op_set = model.opset_import[0] # pylint: disable=E1101 op_set.version = get_opset_number_from_onnx() sess = InferenceSession(model.SerializeToString()) From 84b36181a88b6a879701fbcad8a16397a45bca13 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Mon, 26 Apr 2021 17:40:20 +0200 Subject: [PATCH 13/33] decompose matmul into smaller pieces --- _doc/notebooks/einsum_decomposition.ipynb | 176 ++++++--- _unittests/ut_testing/test_einsum.py | 47 ++- .../ut_testing/test_einsum_generic_dot.py | 32 +- mlprodict/testing/einsum_impl.py | 106 +++++- mlprodict/testing/einsum_impl_classes.py | 147 ++++++- mlprodict/testing/einsum_impl_ext.py | 358 +++++++++++++----- 6 files changed, 671 insertions(+), 195 deletions(-) diff --git a/_doc/notebooks/einsum_decomposition.ipynb b/_doc/notebooks/einsum_decomposition.ipynb index d2409df2f..892573a7e 100644 --- a/_doc/notebooks/einsum_decomposition.ipynb +++ b/_doc/notebooks/einsum_decomposition.ipynb @@ -285,16 +285,16 @@ { "data": { "text/html": [ - "
\n", + "
\n", "" ], "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -342,13 +342,59 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## onnxruntime" + "## operator matmul\n", + "\n", + "This operator can be used to represent either a multiplication, either a matrix multiplication but it applies only on arrays with the same number of dimensions. It can be broken into multiplication of matrix multiplication." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "seq_broken = decompose_einsum_equation(\"bac,cd,def->ebc\", m1.shape, m2.shape, m3.shape, strategy='numpy')\n", + "RenderJsDot(seq_broken.to_dot(size=7))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Operator *transpose_mm* is a regular transposition, it takes two inputs but only tranposes the first input before returning it. Operator *batch_dot* is a matrix multiplication. It is left that way on purpose as it may be implemented with function dot or gemm. The operator distinguishes between 3 kind of axes: batch axes, kept axes, sum(mation) axes. It then reshapes both input matrices with 3D tensors, batch axis, row axis, column axis to use function [numpy.dot](https://numpy.org/doc/stable/reference/generated/numpy.dot.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## onnxruntime" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, "outputs": [], "source": [ "import onnx\n", @@ -379,7 +425,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -392,7 +438,7 @@ " [12114390., 13179168.]]], dtype=float32)" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -412,14 +458,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:11<00:00, 1.13it/s]\n" + "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:11<00:00, 1.15it/s]\n" ] }, { @@ -456,62 +502,62 @@ " \n", " \n", " \n", - " 37\n", - " 0.027626\n", - " 0.000687\n", - " 0.027092\n", - " 0.029385\n", + " 50\n", + " 0.005723\n", + " 0.000071\n", + " 0.005598\n", + " 0.005849\n", " 10\n", " 10\n", - " 0.276258\n", - " custom_einsum\n", + " 0.057234\n", + " onnxruntime\n", " 50\n", " \n", " \n", - " 38\n", - " 0.006592\n", - " 0.000820\n", - " 0.005656\n", - " 0.008152\n", + " 51\n", + " 0.256746\n", + " 0.004340\n", + " 0.248917\n", + " 0.265715\n", " 10\n", " 10\n", - " 0.065923\n", - " onnxruntime\n", - " 50\n", + " 2.567464\n", + " numpy.einsum\n", + " 55\n", " \n", " \n", - " 39\n", - " 0.271303\n", - " 0.009298\n", - " 0.258212\n", - " 0.287778\n", + " 52\n", + " 0.052522\n", + " 0.000903\n", + " 0.051411\n", + " 0.054281\n", " 10\n", " 10\n", - " 2.713027\n", - " numpy.einsum\n", + " 0.525222\n", + " custom_einsum\n", " 55\n", " \n", " \n", - " 40\n", - " 0.052446\n", - " 0.001392\n", - " 0.050480\n", - " 0.055732\n", + " 53\n", + " 0.025694\n", + " 0.003825\n", + " 0.023930\n", + " 0.037133\n", " 10\n", " 10\n", - " 0.524461\n", - " custom_einsum\n", + " 0.256936\n", + " tr/resh/dot\n", " 55\n", " \n", " \n", - " 41\n", - " 0.009457\n", - " 0.000586\n", - " 0.008699\n", - " 0.010656\n", + " 54\n", + " 0.008572\n", + " 0.000565\n", + " 0.008283\n", + " 0.010209\n", " 10\n", " 10\n", - " 0.094571\n", + " 0.085718\n", " onnxruntime\n", " 55\n", " \n", @@ -521,21 +567,21 @@ ], "text/plain": [ " average deviation min_exec max_exec repeat number total \\\n", - "37 0.027626 0.000687 0.027092 0.029385 10 10 0.276258 \n", - "38 0.006592 0.000820 0.005656 0.008152 10 10 0.065923 \n", - "39 0.271303 0.009298 0.258212 0.287778 10 10 2.713027 \n", - "40 0.052446 0.001392 0.050480 0.055732 10 10 0.524461 \n", - "41 0.009457 0.000586 0.008699 0.010656 10 10 0.094571 \n", + "50 0.005723 0.000071 0.005598 0.005849 10 10 0.057234 \n", + "51 0.256746 0.004340 0.248917 0.265715 10 10 2.567464 \n", + "52 0.052522 0.000903 0.051411 0.054281 10 10 0.525222 \n", + "53 0.025694 0.003825 0.023930 0.037133 10 10 0.256936 \n", + "54 0.008572 0.000565 0.008283 0.010209 10 10 0.085718 \n", "\n", " name N \n", - "37 custom_einsum 50 \n", - "38 onnxruntime 50 \n", - "39 numpy.einsum 55 \n", - "40 custom_einsum 55 \n", - "41 onnxruntime 55 " + "50 onnxruntime 50 \n", + "51 numpy.einsum 55 \n", + "52 custom_einsum 55 \n", + "53 tr/resh/dot 55 \n", + "54 onnxruntime 55 " ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -598,6 +644,13 @@ " res[\"N\"] = N\n", " results.append(res) \n", "\n", + " res = measure_time(lambda x: apply_einsum_sequence(seq, *x, matmul_impl=\"pyf\"),\n", + " [m1, m2, m3],\n", + " repeat=10, number=10)\n", + " res['name'] = \"tr/resh/dot\"\n", + " res[\"N\"] = N\n", + " results.append(res) \n", + "\n", " res = measure_time(lambda x: sess.run(None, {'X': x[0], 'Y': x[1], 'Z': x[2]}),\n", " [m1.astype(numpy.float32), m2.astype(numpy.float32),\n", " m3.astype(numpy.float32)],\n", @@ -613,12 +666,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -645,9 +698,16 @@ "ax[1].set_title(\"Benchmark einsum function\\n(ratio, baseline=numpy)\");" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Version `tr/resh/dot` is an implementation based on the decomposition of a simplified einsum into a sequence of transpose, reshape, (batch_)dot or mul operations." + ] + }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [] diff --git a/_unittests/ut_testing/test_einsum.py b/_unittests/ut_testing/test_einsum.py index 0e9d14813..a3ed93875 100644 --- a/_unittests/ut_testing/test_einsum.py +++ b/_unittests/ut_testing/test_einsum.py @@ -177,12 +177,31 @@ def fct(): self.assertEqualArray(exp, res) def test_decompose_einsum_equation_py(self): + m1 = numpy.arange(0, 24).astype(numpy.float32).reshape((2, 3, 4)) + m2 = numpy.arange(0, 20).astype(numpy.float32).reshape((4, 5)) + verbose = False + for strat in ['numpy', 'simple']: + with self.subTest(strategy=strat): + seq = decompose_einsum_equation( + "bac,ch->ah", (2, 3, 4), (4, 5), strategy=strat, + verbose=verbose) + res1 = apply_einsum_sequence(seq, m1, m2, verbose=verbose) + res2 = apply_einsum_sequence( + seq, m1, m2, matmul_impl='py', verbose=verbose) + if strat == 'simple': + self.assertRaise( + lambda: apply_einsum_sequence( + seq, m1, m2, matmul_impl='py2'), # pylint: disable=W0640 + ValueError) + self.assertEqualArray(res1, res2) + + def test_decompose_einsum_equation_pyf(self): m1 = numpy.arange(0, 8).astype(numpy.float32).reshape((2, 2, 2)) m2 = numpy.arange(0, 4).astype(numpy.float32).reshape((2, 2)) seq = decompose_einsum_equation( "bac,ch->ah", (2, 2, 2), (2, 2)) res1 = apply_einsum_sequence(seq, m1, m2) - res2 = apply_einsum_sequence(seq, m1, m2, matmul_impl='py') + res2 = apply_einsum_sequence(seq, m1, m2, matmul_impl='pyf') self.assertEqualArray(res1, res2) def test_einsum_sub_op(self): @@ -214,18 +233,20 @@ def test_case_1_iii_ii_i_j(self): res = apply_einsum_sequence(seq, m1, verbose=verbose) self.assertEqualArray(exp, res) - def common_test_case_2(self, equation, verbose=False): + def common_test_case_2(self, equation, verbose=False, strategy='simple'): m1 = numpy.arange(2 * 2 * 2).reshape((2, 2, 2)) + 10 m2 = numpy.arange(4).reshape((2, 2)) + 100 exp = numpy.einsum(equation, m1, m2) seq = decompose_einsum_equation( - equation, m1.shape, m2.shape, verbose=verbose) + equation, m1.shape, m2.shape, verbose=verbose, strategy=strategy) res = apply_einsum_sequence(seq, m1, m2, verbose=verbose) self.assertEqualArray(exp, res) def test_case_2_A(self): - self.common_test_case_2('abc,cd->abc') + for strat in ['simple', 'numpy']: + with self.subTest(strategy=strat): + self.common_test_case_2('abc,cd->abc', strategy=strat) def test_many_2(self): m1 = numpy.arange(2 * 2 * 2).reshape((2, 2, 2)) + 10 @@ -310,9 +331,16 @@ def optimize_compare(self, equation, operands=None, verbose=False): shapes = [m.shape for m in inputs] - seq = decompose_einsum_equation(equation, *shapes, verbose=verbose) - got = apply_einsum_sequence(seq, *inputs, verbose=verbose) - self.assertEqualArray(exp, got, decimal=6) + with self.subTest(strategy='numpy'): + seq = decompose_einsum_equation( + equation, *shapes, verbose=verbose, strategy='numpy') + got = apply_einsum_sequence(seq, *inputs, verbose=verbose) + self.assertEqualArray(exp, got, decimal=6) + with self.subTest(strategy='simple'): + seq = decompose_einsum_equation( + equation, *shapes, verbose=verbose) + got = apply_einsum_sequence(seq, *inputs, verbose=verbose) + self.assertEqualArray(exp, got, decimal=6) def test_numpy_test_hadamard_like_products(self): # Hadamard outer products @@ -345,11 +373,11 @@ def test_np_test_expand(self): def test_np_test_edge_cases1(self): # Difficult edge cases for optimization + self.optimize_compare('efc,dbc,acf,fd->abe', verbose=False) self.optimize_compare( 'eac->ace', operands=[numpy.arange(24).reshape((2, 3, 4))]) self.optimize_compare('eac->ace') self.optimize_compare('bd,db,eac->ace') - self.optimize_compare('efc,dbc,acf,fd->abe') self.optimize_compare('ba,ac,da->bcd') def test_np_test_edge_cases2(self): @@ -412,13 +440,13 @@ def np_test_inner_product(self): self.optimize_compare('abc,cba') def test_np_test_random_cases_difficult(self): + self.optimize_compare('db,bc,cfc->d', verbose=False) self.optimize_compare('cac,c,h->h') self.optimize_compare('cfc,c,h->h') self.optimize_compare('cfc,c,d->d') self.optimize_compare('c,cfc,d->d') self.optimize_compare('d,c,cfc->d') self.optimize_compare('d,bc,cfc->d') - self.optimize_compare('db,bc,cfc->d') self.optimize_compare('adb,bc,cfc->d') self.optimize_compare('adb,bc,fa,cfc->d') self.optimize_compare('ecb,fef,bad,ed->ac') @@ -437,4 +465,5 @@ def test_np_test_edge_cases_duplicate_indices(self): if __name__ == "__main__": + # TestEinsum().test_np_test_random_cases_difficult() unittest.main() diff --git a/_unittests/ut_testing/test_einsum_generic_dot.py b/_unittests/ut_testing/test_einsum_generic_dot.py index 6fddee2c0..0bd65521f 100644 --- a/_unittests/ut_testing/test_einsum_generic_dot.py +++ b/_unittests/ut_testing/test_einsum_generic_dot.py @@ -7,7 +7,8 @@ import numpy from pyquickhelper.pycode import ExtTestCase from mlprodict.testing.einsum_impl_ext import ( - numpy_extended_dot, numpy_extended_dot_python) + numpy_extended_dot, numpy_extended_dot_python, + numpy_extended_dot_matrix) confs = [ @@ -1390,16 +1391,27 @@ class TestEinsumGenericdot(ExtTestCase): - def test_generic_dot(self): + def test_generic_dot_python(self): for i, conf in enumerate(confs): - with self.subTest(i=i, conf=conf): + with self.subTest(i=i, total=len(confs), conf=conf): r = self.common_test(conf["shape1"], conf["shape2"], - conf["axes"], conf["left"], conf["right"]) + conf["axes"], conf["left"], conf["right"], + numpy_extended_dot_python) if not r: print(i, conf) - def common_test(self, sh1, sh2, axes, left, right): + def test_generic_dot_matrix(self): + + for i, conf in enumerate(confs): + with self.subTest(i=i, total=len(confs), conf=conf): + r = self.common_test(conf["shape1"], conf["shape2"], + conf["axes"], conf["left"], conf["right"], + numpy_extended_dot_matrix) + if not r: + print(i, conf) + + def common_test(self, sh1, sh2, axes, left, right, fct): m1 = numpy.empty(sh1).ravel() m1 = numpy.arange(len(m1)).reshape(sh1).astype(numpy.float64) + 10 @@ -1411,10 +1423,9 @@ def common_test(self, sh1, sh2, axes, left, right): except ValueError: return False try: - dot = numpy_extended_dot_python(m1, m2, axes, left, right) + dot = fct(m1, m2, axes, left, right) except (IndexError, NotImplementedError, ValueError): - dot = numpy_extended_dot_python( - m1, m2, axes, left, right, verbose=True) + dot = fct(m1, m2, axes, left, right, verbose=True) try: self.assertEqualArray(exp, dot) @@ -1427,8 +1438,7 @@ def common_test(self, sh1, sh2, axes, left, right): f = io.StringIO() with redirect_stdout(f): exp = numpy_extended_dot(m1, m2, axes, left, right) - dot = numpy_extended_dot_python( - m1, m2, axes, left, right, verbose=True) + dot = fct(m1, m2, axes, left, right, verbose=True) try: self.assertEqualArray(exp, dot) except AssertionError: @@ -1442,5 +1452,5 @@ def common_test(self, sh1, sh2, axes, left, right): if __name__ == "__main__": - # TestEinsumGenericdot().test_generic_dot() + # TestEinsumGenericdot().test_generic_dot_matrix() unittest.main() diff --git a/mlprodict/testing/einsum_impl.py b/mlprodict/testing/einsum_impl.py index f3df26be1..4179ede8a 100644 --- a/mlprodict/testing/einsum_impl.py +++ b/mlprodict/testing/einsum_impl.py @@ -124,8 +124,9 @@ def decompose_einsum_equation(equation, *shapes, strategy="simple", verbose=Fals if not isinstance(sh, tuple): raise TypeError( "All shapes must be tuples for %r is not." % sh) - if strategy == "simple": - return _decompose_einsum_equation_simple(equation, *shapes, verbose=verbose) + if strategy in ("simple", "numpy"): + return _decompose_einsum_equation_simple( + equation, *shapes, verbose=verbose, keep_matmul=strategy == 'simple') raise ValueError("Unknown strategy %r." % strategy) @@ -234,9 +235,92 @@ def _apply_squeeze_transpose(op, row_last, row_output): yield op -def _decompose_einsum_equation_simple(equation, *shapes, verbose=False): +def _apply_einsum_matmul(fd, op1, op2, axes, left, right, ndim, + keep_matmul, verbose=False): + """ + Decomposes the generic matrix multiplication into numpy operations + if *keep_matmul* is False. + """ + if keep_matmul: + if verbose: + print(" -- MATMUL -> matmul axes=%r left=%r right=%r" + "" % (axes, left, right)) + yield EinsumSubOp(fd, 'matmul', op1, op2, + axes=axes, left=left, right=right, ndim=ndim) + + elif len(axes) == 0: + if verbose: + print(" -- MATMUL -> mul axes=%r left=%r right=%r" + "" % (axes, left, right)) + yield EinsumSubOp(fd, 'mul', op1, op2) + + elif (len(set(axes) & set(left)) == 0 and + len(set(axes) & set(right)) == 0): + + # No intersection between axes and right: matrix multiplication + if verbose: + print(" -- MATMUL -> batch_dot axes=%r left=%r right=%r" + "" % (axes, left, right)) + + all_axes = set(left) | set(right) | set(axes) + common_axes = list(set(left) & set(right)) + for i in range(ndim): + if i not in all_axes: + common_axes.append(i) + common_axes.sort() + + # Transpose + i_axes = [(-1 if i in common_axes + else (1 if i in axes else 0), i) + for i in range(ndim)] + i_axes.sort() + perm = [_[1] for _ in i_axes] + perm_left = [i for i in range(len(perm)) if perm[i] in left] + perm_right = [i for i in range(len(perm)) if perm[i] in right] + op1 = EinsumSubOp(fd, 'transpose_mm', op1, op2, perm=tuple(perm)) + yield op1 + op2 = EinsumSubOp(fd, 'transpose', op2, perm=tuple(perm)) + yield op2 + + # Reshape + all_axes = list(range(0, ndim)) + new_axes = all_axes[-len(axes):] + new_common_axes = all_axes[:len(common_axes)] + not_in_both = [] + for i in range(0, ndim): + if i not in left and i not in right and i not in common_axes: + not_in_both.append(i) + + op = EinsumSubOp(fd, 'batch_dot', op1, op2, + batch_axes=tuple(new_common_axes), + keep_axes=None, sum_axes=tuple(new_axes), + left=tuple(perm_left), right=tuple(perm_right), + ndim=ndim) + yield op + + # Transpose again, reverse perm + rev_perm = perm.copy() + for i, p in enumerate(perm): + rev_perm[p] = i + op_unused = EinsumSubOp(fd, 'transpose_mm', op1, + op, perm=tuple(rev_perm)) + yield op_unused + op = EinsumSubOp(fd, 'transpose', op, perm=tuple(rev_perm)) + yield op + else: + raise NotImplementedError( + "axes and right or left have axes in common, " + "axes=%r left=%r right=%r ndim=%r." % ( + axes, left, right, ndim)) + + +def _decompose_einsum_equation_simple(equation, *shapes, verbose=False, + keep_matmul=True): """ Applies strategy simple of function @see fn decompose_einsum_equation. + + :param keep_matmul: break matmul operator into numpy operations + or keep is it is """ letters, mat, lengths, duplicates = analyse_einsum_equation(equation) if len(letters) != mat.shape[1]: @@ -322,12 +406,16 @@ def _decompose_einsum_equation_simple(equation, *shapes, verbose=False): if verbose: print(" -- MATMUL common_dims=%r" % common_dims) print(rows) - op = EinsumSubOp(fd, 'matmul', graph.last_op, op, - axes=tuple(common_dims), - left=tuple(left), right=tuple(right), - ndim=rows.shape[1]) - op.compute_output_row(rows[0, :], rows[1, :], verbose=verbose) - marked = graph.append(op) + for iop in _apply_einsum_matmul(fd, graph.last_op, op, + axes=tuple(common_dims), + left=tuple(left), + right=tuple(right), + ndim=rows.shape[1], + keep_matmul=keep_matmul, + verbose=verbose): + op = iop + op.compute_output_row(rows[0, :], rows[1, :], verbose=verbose) + marked = graph.append(op) # End graph.mark(i, marked) diff --git a/mlprodict/testing/einsum_impl_classes.py b/mlprodict/testing/einsum_impl_classes.py index ee28fcb82..8b77b30e7 100644 --- a/mlprodict/testing/einsum_impl_classes.py +++ b/mlprodict/testing/einsum_impl_classes.py @@ -6,19 +6,22 @@ from .einsum_impl_ext import ( numpy_extended_dot, numpy_diagonal, _numpy_extended_dot_equation, - numpy_extended_dot_python) + numpy_extended_dot_python, + numpy_extended_dot_matrix) class EinsumSubOp: """ Defines a sub operation used in Einsum decomposition. - :param name: name (reshape, transpose, reduce_sum, matmul, id) + :param name: name (reshape, transpose, reduce_sum, matmul, id, + squeeze, diagonal, mul, batch_dot) :param inputs: inputs :param kwargs: arguments """ _allowed = {'expand_dims', 'transpose', 'reduce_sum', 'matmul', 'id', - 'squeeze', 'diagonal'} + 'squeeze', 'diagonal', 'mul', 'batch_dot', + 'transpose_mm'} def __init__(self, full_dim, name, *inputs, **kwargs): self.full_dim = full_dim @@ -85,10 +88,12 @@ def dot_label(self): return "~" + eq return None - def _check_arg_(self, name, typ): + def _check_arg_(self, name, typ, empty=False): if name not in self.kwargs: raise RuntimeError( "Parameter %r not found for operator %r." % (name, self.name)) + if empty and self.kwargs[name] is None: + return if not isinstance(self.kwargs[name], typ): raise TypeError( "Unexpected type %r for parameter %r and parameter %r." @@ -118,6 +123,11 @@ def _compute_output_row_transpose(self, row, row2=None, verbose=False): row[i] = cpy[p] self._check_row_(row, verbose=verbose) + def _compute_output_row_transpose_mm(self, row, row2=None, verbose=False): + if row2 is None: + raise RuntimeError("transpose_mm expects a second input.") + self._compute_output_row_transpose(row2, row2=None, verbose=verbose) + def _compute_output_row_expand_dims(self, row, row2=None, verbose=False): self._check_arg_('axis', tuple) if row[self.kwargs['axis'][1]] != -1: @@ -153,6 +163,40 @@ def _compute_output_row_matmul(self, row, row2=None, verbose=False): row2[a] = -1 self._check_row_(row2, verbose=verbose) + def _compute_output_row_batch_dot(self, row, row2=None, verbose=False): + self._check_arg_('batch_axes', tuple) + self._check_arg_('keep_axes', tuple, empty=True) + self._check_arg_('sum_axes', tuple) + self._check_arg_('left', tuple) + self._check_arg_('right', tuple) + self._check_arg_('ndim', int) + if row2 is None: + raise RuntimeError("batch_dot expects two inputs.") + if verbose: + batch_axes = self.kwargs['batch_axes'] + keep_axes = self.kwargs['keep_axes'] + sum_axes = self.kwargs['sum_axes'] + left = self.kwargs['left'] + right = self.kwargs['right'] + ndim = self.kwargs['ndim'] + print(" BATCH_DOT %r @ %r batch_axes=%r keep_axes=%r sum_axes=%r " + "left=%r right=%r eq=%r" % ( + row, row2, batch_axes, keep_axes, sum_axes, left, right, + _numpy_extended_dot_equation(ndim, ndim, sum_axes, left, right))) + row2[:] = numpy.maximum(row, row2) + for a in self.kwargs['sum_axes']: + if a not in self.kwargs['right']: + row2[a] = -1 + self._check_row_(row2, verbose=verbose) + + def _compute_output_row_mul(self, row, row2=None, verbose=False): + if row2 is None: + raise RuntimeError("mul expects two inputs.") + if verbose: + print(" MUL %r @ %r" % (row, row2)) + row2[:] = numpy.maximum(row, row2) + self._check_row_(row2, verbose=verbose) + def _compute_output_row_squeeze(self, row, row2=None, verbose=False): self._check_arg_('axes', tuple) for a in self.kwargs['axes']: @@ -267,6 +311,18 @@ def _apply_transpose(self, data, verbose=False, **kwargs): self._check_shape_(output) return output + def _apply_transpose_mm(self, data, verbose=False, **kwargs): + self._check_inputs_(2, True) + inp = self.inputs[0] + m = self._get_data(data, inp) + self._check_shape_(m) + if verbose: + print("- %s, shape=%r perm=%r" % ( + self.name, m.shape, self.kwargs['perm'])) + output = numpy.transpose(m, self.kwargs['perm']) + self._check_shape_(output) + return output + def _apply_matmul(self, data, verbose=False, **kwargs): self._check_inputs_(2) inp1 = self.inputs[0] @@ -283,12 +339,88 @@ def _apply_matmul(self, data, verbose=False, **kwargs): print("- %s, shapes=%r @ %r axes=%r left=%r right=%r" % ( self.name, m1.shape, m2.shape, axes, left, right)) - if kwargs.get('matmul_impl', None) == 'py': + impl = kwargs.get('matmul_impl', None) + if impl == 'pyf': + output = numpy_extended_dot_matrix(m1, m2, axes, left, right, + verbose=verbose) + elif impl == 'py': output = numpy_extended_dot_python(m1, m2, axes, left, right, verbose=verbose) - else: + elif impl is None: output = numpy_extended_dot(m1, m2, axes, left, right, verbose=verbose) + else: + raise ValueError( + "Unknown implementation of numpy_extended_dot ({}).".format(impl)) + self._check_shape_(output) + return output + + def _apply_mul(self, data, verbose=False, **kwargs): + self._check_inputs_(2) + inp1 = self.inputs[0] + inp2 = self.inputs[1] + m1 = self._get_data(data, inp1) + m2 = self._get_data(data, inp2) + self._check_shape_(m1) + self._check_shape_(m2) + + if verbose: + print("- %s, shapes=%r @ %r" % (self.name, m1.shape, m2.shape)) + + output = m1 * m2 + self._check_shape_(output) + return output + + def _apply_batch_dot(self, data, verbose=False, **kwargs): + self._check_inputs_(2) + inp1 = self.inputs[0] + inp2 = self.inputs[1] + m1 = self._get_data(data, inp1) + m2 = self._get_data(data, inp2) + self._check_shape_(m1) + self._check_shape_(m2) + batch_axes = self.kwargs['batch_axes'] + keep_axes = self.kwargs['keep_axes'] + sum_axes = self.kwargs['sum_axes'] + left = self.kwargs['left'] + right = self.kwargs['right'] + + if verbose: + print("- %s, shapes=%r @ %r batch_axes=%r keep_axes=%r " + "sum_axes=%r" % ( + self.name, m1.shape, m2.shape, batch_axes, keep_axes, sum_axes)) + + if len(m1.shape) != len(m2.shape): + raise RuntimeError( + "batch_dot only work with two tensors with the same number " + "of dimensions not %r @ %r." % (m1.shape, m2.shape)) + + dim0 = int(numpy.prod([m1.shape[i] for i in batch_axes])) + dimb = int(-1 if keep_axes is None else numpy.prod( + [m1.shape[i] for i in keep_axes])) + dim1 = int(numpy.prod([m1.shape[i] for i in sum_axes])) + dim2 = int(numpy.prod([m2.shape[i] for i in sum_axes])) + + m1sh = m1.reshape((dim0, dimb, dim1)) + m2sh = m2.reshape((dim0, dimb, dim2)) + dot = m1sh @ numpy.transpose(m2sh, (0, 2, 1)) + + # new shape + taken = set(batch_axes) | set(sum_axes) + ax = [i for i in range(len(m1.shape)) if i not in taken] + new_shape = ([m1.shape[i] for i in batch_axes] + + [m1.shape[i] for i in left] + + [m2.shape[i] for i in right]) + while len(new_shape) < len(m1.shape): + new_shape.append(1) + + if verbose: + print("- %s, shapes=%r @ %r -> %r" % ( + self.name, m1sh.shape, m2sh.shape, dot.shape)) + print("- %s, batch_axes=%r ax=%r new_shape=%r left=%r right=%r" % ( + self.name, batch_axes, ax, new_shape, left, right)) + + output = dot.reshape(tuple(new_shape)) self._check_shape_(output) return output @@ -335,7 +467,8 @@ def apply(self, data, verbose=False, **kwargs): """ if verbose: print() - print("apply %r." % self.name) + print("apply %r (%s)." % ( + self.name, ", ".join(map(lambda s: str(id(s)), self.inputs)))) method_name = "_apply_%s" % self.name meth = getattr(self, method_name, None) diff --git a/mlprodict/testing/einsum_impl_ext.py b/mlprodict/testing/einsum_impl_ext.py index fbe975a53..8dce0f076 100644 --- a/mlprodict/testing/einsum_impl_ext.py +++ b/mlprodict/testing/einsum_impl_ext.py @@ -116,6 +116,29 @@ def _check_(axs, n): return eq +def _common_check_numpy_extended_dot(m1, m2, axes, left, right): + """ + Common verifications for all implementations of + @see fn numpy_extended_dot. + """ + if m1.dtype != m2.dtype: + raise TypeError( + "Both matrices should share the same dtype %r != %r." + "" % (m1.dtype, m2.dtype)) + m1_dim = len(m1.shape) + m2_dim = len(m2.shape) + if m1_dim != m2_dim: + raise RuntimeError( + "Matrices m1 and m2 must have the same number of dimensions, " + "m1=%r, m2=%r." % (m1_dim, m2_dim)) + total = set(axes) | set(left) | set(right) + if len(total) > m1_dim: + raise ValueError( + "Whole set of involved axes should be inferior to the number " + "of dimensions: %r = {%r} | {%r} | {%r} has more than %d elements" + "." % (total, axes, left, right, m1_dim)) + + def numpy_extended_dot(m1, m2, axes, left, right, verbose=False): """ Extended version of a matrix multiplication (:epkg:`numpy:dot`) @@ -208,10 +231,7 @@ def numpy_extended_dot(m1, m2, axes, left, right, verbose=False): The current implementation still uses :epkg:`numpy:einsum` but this should be replaced. """ - if m1.dtype != m2.dtype: - raise TypeError( - "Both matrices should share the same dtype %r != %r." - "" % (m1.dtype, m2.dtype)) + _common_check_numpy_extended_dot(m1, m2, axes, left, right) eq = _numpy_extended_dot_equation( len(m1.shape), len(m2.shape), axes, left, right) if verbose: @@ -227,31 +247,13 @@ def numpy_extended_dot(m1, m2, axes, left, right, verbose=False): return output.reshape(tuple(new_shape)) -def numpy_extended_dot_python(m1, m2, axes, left, right, verbose=False): +def numpy_extended_dot_ouput_shape(m1, m2, axes, left, right): """ - Implementation of @see fn numpy_extended_dot in pure python. - This implementation is not efficient but shows how to - implement this operation without :epkg:`numpy:einsum`. + Computes the output shape of results produced by function + @see fn numpy_extended_dot or @see fn numpy_extended_dot_python. """ - def dispb(c): - return "".join("o" if b else "." for b in c) - - if m1.dtype != m2.dtype: - raise TypeError( - "Both matrices should share the same dtype %r != %r." - "" % (m1.dtype, m2.dtype)) + _common_check_numpy_extended_dot(m1, m2, axes, left, right) m1_dim = len(m1.shape) - m2_dim = len(m2.shape) - if m1_dim != m2_dim: - raise RuntimeError( - "Matrices m1 and m2 must have the same number of dimensions, " - "m1=%r, m2=%r." % (m1_dim, m2_dim)) - total = set(axes) | set(left) | set(right) - if len(total) > m1_dim: - raise ValueError( - "Whole set of involved axes should be inferior to the number " - "of dimensions: %r = {%r} | {%r} | {%r} has more than %d elements" - "." % (total, axes, left, right, m1_dim)) new_shape = numpy.full(m1_dim, 1, dtype=numpy.int64) for i in left: @@ -263,11 +265,10 @@ def dispb(c): "Matrices should the same dimension for dimension %d, " "shapes=%r @ %r." % (i, m1.shape, m2.shape)) new_shape[i] = m2.shape[i] + return new_shape - # output shapes - res = numpy.full(tuple(new_shape), 0, dtype=m1.dtype) - # indices +def _numpy_extended_dot_python_l1l2l3(m1_dim, axes, left, right): l1 = [chr(i + 97) for i in range(m1_dim)] l2 = [chr(i + 97) for i in range(m1_dim)] l3 = [chr(i + 97) for i in range(m1_dim)] @@ -284,87 +285,125 @@ def dispb(c): l3[a] = "-" else: l3[a] = l3[a].lower() + return l1, l2, l3 - def intermediate(l1, l2, l3): - names = list(sorted(set(l1 + l2))) - kind = numpy.zeros(len(names), dtype=numpy.int64) - cols = {} - for i, n in enumerate(names): - if n in l1: - kind[i] += 1 - cols[n] = l1.index(n) - if n in l2: - kind[i] += 2 - cols[n] = l2.index(n) - if n in l3: - kind[i] += 4 +def _numpy_extended_dot_python_intermediate(m1_shape, m2_shape, l1, l2, l3): + names = list(sorted(set(l1 + l2))) + kind = numpy.zeros(len(names), dtype=numpy.int64) + cols = {} - pos = numpy.zeros(len(names), dtype=numpy.int64) - for j in range(0, pos.shape[0]): - pos[j] = cols[names[j]] - common = [(kind[i] & 3) == 3 for i in range(len(kind))] - broadcast = [common[i] and m1.shape[pos[i]] != m2.shape[pos[i]] - for i in range(len(common))] + for i, n in enumerate(names): + if n in l1: + kind[i] += 1 + cols[n] = l1.index(n) + if n in l2: + kind[i] += 2 + cols[n] = l2.index(n) + if n in l3: + kind[i] += 4 - return names, kind, cols, common, broadcast, pos + pos = numpy.zeros(len(names), dtype=numpy.int64) + for j in range(0, pos.shape[0]): + pos[j] = cols[names[j]] + common = [(kind[i] & 3) == 3 for i in range(len(kind))] + broadcast = [common[i] and m1_shape[pos[i]] != m2_shape[pos[i]] + for i in range(len(common))] - names, kind, cols, common, broadcast, pos = intermediate(l1, l2, l3) + return names, kind, cols, common, broadcast, pos - if any(broadcast): + +def _numpy_extended_dot_python_update_broadcast( + m1, m2, axes, left, right, l1, l2, l3, names, broadcast, cols, + kind, common, verbose=False): + + def dispb(c): + return "".join("o" if b else "." for b in c) + + if verbose: + print("GENERICDOT: before broadcast %s,%s->%s or %s" % ( + "".join(l1), "".join(l2), "".join(l3), + _numpy_extended_dot_equation( + len(m1.shape), len(m1.shape), axes, left, right))) + print("GENERICDOT: names=%s kind=%r common=%s broadcast=%s" % ( + "".join(names), kind.tolist(), + dispb(common), dispb(broadcast))) + + for i in range(len(broadcast)): # pylint: disable=C0200 + if broadcast[i] and not (kind[i] & 3) == 3: + raise RuntimeError( + "Broadcast should only happen on common axes, " + "axes=%r left=%r right=%r shape1=%r shape2=%r." + "" % (axes, left, right, m1.shape, m2.shape)) + if not broadcast[i]: + continue + # We split letters. + p = cols[names[i]] + dim = (m1.shape[p], m2.shape[p]) + let = [l1[p], l2[p], l3[p]] + inp = 1 if dim[0] == 1 else 0 if verbose: - print("GENERICDOT: before broadcast %s,%s->%s or %s" % ( - "".join(l1), "".join(l2), "".join(l3), - _numpy_extended_dot_equation( - len(m1.shape), len(m1.shape), axes, left, right))) - print("GENERICDOT: names=%s kind=%r common=%s broadcast=%s" % ( - "".join(names), kind.tolist(), - dispb(common), dispb(broadcast))) - - for i in range(len(broadcast)): # pylint: disable=C0200 - if broadcast[i] and not (kind[i] & 3) == 3: - raise RuntimeError( - "Broadcast should only happen on common axes, " - "axes=%r left=%r right=%r shape1=%r shape2=%r." - "" % (axes, left, right, m1.shape, m2.shape)) - if not broadcast[i]: - continue - # We split letters. - p = cols[names[i]] - dim = (m1.shape[p], m2.shape[p]) - let = [l1[p], l2[p], l3[p]] - inp = 1 if dim[0] == 1 else 0 + print("GENERICDOT: name=%s dim=%r let=%r inp=%r p=%r" % ( + names[i], dim, let, inp, p)) + print(" B0 l1=%r, l2=%r l3=%r" % (l1, l2, l3)) + if (kind[i] & 4) > 0: + # Summation axis is part of the output. + if let[inp].lower() == let[inp]: + let[inp] = let[inp].upper() + else: + let[inp] = let[inp].lower() + l3[p] = let[inp] + if inp == 1: + l2[p] = let[inp] + else: + l1[p] = let[inp] if verbose: - print("GENERICDOT: name=%s dim=%r let=%r inp=%r p=%r" % ( - names[i], dim, let, inp, p)) - print(" B0 l1=%r, l2=%r l3=%r" % (l1, l2, l3)) - if (kind[i] & 4) > 0: - # Summation axis is part of the output. - if let[inp].lower() == let[inp]: - let[inp] = let[inp].upper() - else: - let[inp] = let[inp].lower() - l3[p] = let[inp] - if inp == 1: - l2[p] = let[inp] - else: - l1[p] = let[inp] - if verbose: - print(" B1 l1=%r, l2=%r l3=%r" % (l1, l2, l3)) + print(" B1 l1=%r, l2=%r l3=%r" % (l1, l2, l3)) + else: + # Summation axis is not part of the output. + if let[inp].lower() == let[inp]: + let[inp] = let[inp].upper() + else: + let[inp] = let[inp].lower() + if inp == 1: + l2[p] = let[inp] else: - # Summation axis is not part of the output. - if let[inp].lower() == let[inp]: - let[inp] = let[inp].upper() - else: - let[inp] = let[inp].lower() - if inp == 1: - l2[p] = let[inp] - else: - l1[p] = let[inp] - if verbose: - print(" B2 l1=%r, l2=%r l3=%r" % (l1, l2, l3)) - - names, kind, cols, common, broadcast, pos = intermediate(l1, l2, l3) + l1[p] = let[inp] + if verbose: + print(" B2 l1=%r, l2=%r l3=%r" % (l1, l2, l3)) + + return l1, l2, l3 + + +def numpy_extended_dot_python(m1, m2, axes, left, right, verbose=False): + """ + Implementation of @see fn numpy_extended_dot in pure python. + This implementation is not efficient but shows how to + implement this operation without :epkg:`numpy:einsum`. + """ + def dispb(c): + return "".join("o" if b else "." for b in c) + + new_shape = numpy_extended_dot_ouput_shape(m1, m2, axes, left, right) + m1_dim = len(m1.shape) + + # output result + res = numpy.full(tuple(new_shape), 0, dtype=m1.dtype) + + # indices + l1, l2, l3 = _numpy_extended_dot_python_l1l2l3(m1_dim, axes, left, right) + names, kind, cols, common, broadcast, pos = ( + _numpy_extended_dot_python_intermediate( + m1.shape, m2.shape, l1, l2, l3)) + + if any(broadcast): + l1, l2, l3 = _numpy_extended_dot_python_update_broadcast( + m1, m2, axes, left, right, l1, l2, l3, names, broadcast, cols, + kind, common, verbose=verbose) + + names, kind, cols, common, broadcast, pos = ( + _numpy_extended_dot_python_intermediate( + m1.shape, m2.shape, l1, l2, l3)) indices = numpy.array([0 for n in names], dtype=numpy.int64) pl1 = numpy.array([names.index(c) for c in l1], dtype=numpy.int64) @@ -415,3 +454,120 @@ def intermediate(l1, l2, l3): indices[i - 1] += 1 return res + + +def numpy_extended_dot_matrix(m1, m2, axes, left, right, verbose=False): + """ + Implementation of @see fn numpy_extended_dot using dot product + and not a custom python implementation like + @see fn numpy_extended_dot_python. + """ + _common_check_numpy_extended_dot(m1, m2, axes, left, right) + + if len(axes) == 0: + # Simple multiplication + if verbose: + print("GENERICDOT: Mul %r @ %r" % (m1.shape, m2.shape)) + res = m1 * m2 + return res + + if (len(set(axes) & set(left)) == 0 and + len(set(axes) & set(right)) == 0): + + # No intersection between axes and right: matrix multiplication + + common_axes = sorted(set(left) & set(right)) + + # Transpose + i_axes = [(-1 if i in common_axes + else (1 if i in axes else 0), i) + for i in range(len(m1.shape))] + i_axes.sort() + perm = [_[1] for _ in i_axes] + trm1 = numpy.transpose(m1, axes=perm) + trm2 = numpy.transpose(m2, axes=perm) + all_axes = list(range(0, len(m1.shape))) + new_axes = all_axes[-len(axes):] + new_common_axes = all_axes[:len(common_axes)] + final_shape = numpy_extended_dot_ouput_shape(m1, m2, axes, left, right) + + if verbose: + print("GENERICDOT: MatMul %r @ %r -> %r -- %s" % ( + m1.shape, m2.shape, final_shape, + _numpy_extended_dot_equation( + len(m1.shape), len(m1.shape), axes, left, right))) + print("GENERICDOT: axes=%r left=%r right=%r" % (axes, left, right)) + print("GENERICDOT: perm=%r common_axes=%r" % (perm, common_axes)) + + # Reshape + dim0 = int(numpy.prod([trm1.shape[i] for i in new_common_axes])) + dim0b = int(numpy.prod([trm2.shape[i] for i in new_common_axes])) + dim1 = numpy.prod([trm1.shape[i] for i in new_axes]) + dim2 = numpy.prod([trm2.shape[i] for i in new_axes]) + if dim1 != dim2: + raise RuntimeError( + "Summation axis do not have the same length %d != %d, " + "shape1=%r shape2=%r axes=%r left=%r right=%r." % ( + dim1, dim2, m1.shape, m2.shape, axes, left, right)) + if dim0 != dim0b: + raise RuntimeError( + "Looping axis do not have the same length %d != %d, " + "shape1=%r shape2=%r axes=%r left=%r right=%r." % ( + dim0, dim0b, m1.shape, m2.shape, axes, left, right)) + + shm1 = trm1.reshape((dim0, -1, dim1)) + shm2 = trm2.reshape((dim0, -1, dim2)) + + if verbose: + print("GENERICDOT: Reshape %r @ %r -> %r @ %r" % ( + (dim0, -1, dim1), (dim0, -1, dim2), shm1.shape, shm2.shape)) + + # Multiplication (this should be done in a different way. + res = shm1 @ numpy.transpose(shm2, axes=(0, 2, 1)) + + if verbose: + print("GENERICDOT: Shape after multiplication %s" % (res.shape, )) + + # Transpose again + not_in_both = [] + for i in range(0, len(m1.shape)): + if i not in left and i not in right: + not_in_both.append(i) + ordered_axes = (common_axes + + list(i for i in left if i not in right) + + list(i for i in right if i not in left) + + not_in_both) + current_shape = ([m1.shape[i] for i in sorted(common_axes)] + + [m1.shape[i] for i in sorted(left) if i not in common_axes] + + [m2.shape[i] for i in sorted(right) if i not in common_axes] + + [1 for i in not_in_both]) + + if verbose: + print("GENERICDOT: current_shape=%r final_shape=%r" % ( + current_shape, final_shape)) + + if len(current_shape) != len(final_shape): + raise RuntimeError( + "Shapes mismatch %r > %r, " + "shape1=%r shape2=%r axes=%r left=%r right=%r." % ( + current_shape, final_shape, + m1.shape, m2.shape, axes, left, right)) + + res = res.reshape(current_shape) + + perm = [(a, i) for i, a in enumerate(ordered_axes)] + perm.sort() + perm = [p[1] for p in perm] + + if verbose: + print("GENERICDOT: ordered_axes=%r perm=%r" % ( + ordered_axes, perm)) + + return numpy.transpose(res, axes=perm) + + else: + raise RuntimeError( + "shape1=%r shape2=%r axes=%r left=%r right=%r final_shape=%r eq=%s." % ( + m1.shape, m2.shape, axes, left, right, final_shape, + _numpy_extended_dot_equation( + len(m1.shape), len(m1.shape), axes, left, right))) From 8dd3a65d167db7c4517efc6e91b8c9543c9372c8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Wed, 28 Apr 2021 00:38:16 +0200 Subject: [PATCH 14/33] update, still fixes to do --- _unittests/ut_testing/test_einsum.py | 7 +- .../ut_testing/test_einsum_generic_dot.py | 62 +++++++- mlprodict/testing/einsum_impl.py | 36 +++-- mlprodict/testing/einsum_impl_classes.py | 23 ++- mlprodict/testing/einsum_impl_ext.py | 132 ++++++++++++------ 5 files changed, 200 insertions(+), 60 deletions(-) diff --git a/_unittests/ut_testing/test_einsum.py b/_unittests/ut_testing/test_einsum.py index a3ed93875..63d816814 100644 --- a/_unittests/ut_testing/test_einsum.py +++ b/_unittests/ut_testing/test_einsum.py @@ -244,9 +244,10 @@ def common_test_case_2(self, equation, verbose=False, strategy='simple'): self.assertEqualArray(exp, res) def test_case_2_A(self): - for strat in ['simple', 'numpy']: + for strat in ['numpy', 'simple']: with self.subTest(strategy=strat): - self.common_test_case_2('abc,cd->abc', strategy=strat) + self.common_test_case_2( + 'abc,cd->abc', strategy=strat, verbose=True) def test_many_2(self): m1 = numpy.arange(2 * 2 * 2).reshape((2, 2, 2)) + 10 @@ -465,5 +466,5 @@ def test_np_test_edge_cases_duplicate_indices(self): if __name__ == "__main__": - # TestEinsum().test_np_test_random_cases_difficult() + # TestEinsum().test_case_2_A() unittest.main() diff --git a/_unittests/ut_testing/test_einsum_generic_dot.py b/_unittests/ut_testing/test_einsum_generic_dot.py index 0bd65521f..77ba25b62 100644 --- a/_unittests/ut_testing/test_einsum_generic_dot.py +++ b/_unittests/ut_testing/test_einsum_generic_dot.py @@ -1386,6 +1386,8 @@ axes=(), left=(0, 1), right=(0, 1, 2)), dict(shape1=(2, 1), shape2=(1, 2), axes=(), left=(0,), right=(1,)), dict(shape1=(2, 2), shape2=(2, 2), axes=(), left=(0, 1), right=(0, 1)), + dict(shape1=(2, 2, 1), shape2=(1, 2, 2), + axes=[], left=[0, 2], right=[1, 2]), ] @@ -1411,7 +1413,55 @@ def test_generic_dot_matrix(self): if not r: print(i, conf) - def common_test(self, sh1, sh2, axes, left, right, fct): + def test_generic_dot_matrix_conf1(self): + + conf = dict(shape1=(2, 2, 1), shape2=(1, 2, 2), + axes=[2], left=[0], right=[1, 2]) + self.common_test(conf["shape1"], conf["shape2"], + conf["axes"], conf["left"], conf["right"], + numpy_extended_dot_python) + self.common_test(conf["shape1"], conf["shape2"], + conf["axes"], conf["left"], conf["right"], + numpy_extended_dot_matrix, + verbose=False) + + def test_generic_dot_matrix_conf2(self): + + conf = dict(shape1=(2, 2, 1), shape2=(1, 2, 2), + axes=[], left=[0, 2], right=[1, 2]) + self.common_test(conf["shape1"], conf["shape2"], + conf["axes"], conf["left"], conf["right"], + numpy_extended_dot_python) + self.common_test(conf["shape1"], conf["shape2"], + conf["axes"], conf["left"], conf["right"], + numpy_extended_dot_matrix, + verbose=False) + + def test_generic_dot_matrix_conf3(self): + + conf = dict(shape1=(2, 2, 2), shape2=(2, 2, 2), + axes=[2], left=[0], right=[1]) + self.common_test(conf["shape1"], conf["shape2"], + conf["axes"], conf["left"], conf["right"], + numpy_extended_dot_python) + self.common_test(conf["shape1"], conf["shape2"], + conf["axes"], conf["left"], conf["right"], + numpy_extended_dot_matrix, + verbose=False) + + def test_generic_dot_matrix_conf0(self): + + conf = dict(shape1=(1, 5, 4, 1), shape2=(1, 1, 4, 6), + axes=(2,), left=(0, 1), right=(3,)) + self.common_test(conf["shape1"], conf["shape2"], + conf["axes"], conf["left"], conf["right"], + numpy_extended_dot_python) + self.common_test(conf["shape1"], conf["shape2"], + conf["axes"], conf["left"], conf["right"], + numpy_extended_dot_matrix, + verbose=False) + + def common_test(self, sh1, sh2, axes, left, right, fct, verbose=False): m1 = numpy.empty(sh1).ravel() m1 = numpy.arange(len(m1)).reshape(sh1).astype(numpy.float64) + 10 @@ -1419,11 +1469,12 @@ def common_test(self, sh1, sh2, axes, left, right, fct): m2 = numpy.arange(len(m2)).reshape(sh2).astype(numpy.float64) + 1000 try: - exp = numpy_extended_dot(m1, m2, axes, left, right) + exp = numpy_extended_dot( + m1, m2, axes, left, right, verbose=verbose) except ValueError: return False try: - dot = fct(m1, m2, axes, left, right) + dot = fct(m1, m2, axes, left, right, verbose=verbose) except (IndexError, NotImplementedError, ValueError): dot = fct(m1, m2, axes, left, right, verbose=True) @@ -1437,7 +1488,8 @@ def common_test(self, sh1, sh2, axes, left, right, fct): f = io.StringIO() with redirect_stdout(f): - exp = numpy_extended_dot(m1, m2, axes, left, right) + exp = numpy_extended_dot( + m1, m2, axes, left, right, verbose=verbose) dot = fct(m1, m2, axes, left, right, verbose=True) try: self.assertEqualArray(exp, dot) @@ -1452,5 +1504,5 @@ def common_test(self, sh1, sh2, axes, left, right, fct): if __name__ == "__main__": - # TestEinsumGenericdot().test_generic_dot_matrix() + # TestEinsumGenericdot().test_generic_dot_matrix_conf2() unittest.main() diff --git a/mlprodict/testing/einsum_impl.py b/mlprodict/testing/einsum_impl.py index 4179ede8a..2e2ccea52 100644 --- a/mlprodict/testing/einsum_impl.py +++ b/mlprodict/testing/einsum_impl.py @@ -236,7 +236,7 @@ def _apply_squeeze_transpose(op, row_last, row_output): def _apply_einsum_matmul(fd, op1, op2, axes, left, right, ndim, - keep_matmul, verbose=False): + keep_matmul, row1, row2, verbose=False): """ Decomposes the generic matrix multiplication into numpy operations if *keep_matmul* is False. @@ -248,7 +248,7 @@ def _apply_einsum_matmul(fd, op1, op2, axes, left, right, ndim, yield EinsumSubOp(fd, 'matmul', op1, op2, axes=axes, left=left, right=right, ndim=ndim) - elif len(axes) == 0: + elif len(axes) == 0 and len(set(left) & set(right)) == 0: if verbose: print(" -- MATMUL -> mul axes=%r left=%r right=%r" "" % (axes, left, right)) @@ -269,6 +269,26 @@ def _apply_einsum_matmul(fd, op1, op2, axes, left, right, ndim, common_axes.append(i) common_axes.sort() + # ReduceSum* + has_dim = set(i for i in range(len(row1)) if row1[i] >= 0) + right_no_left = (set(right) & has_dim) - (set(right) & (set(left) | set(axes))) + if right_no_left: + if verbose: + print(' -- MATMUL reduce1 has_dim=%r axes=%r' % (has_dim, right_no_left)) + op1 = EinsumSubOp(fd, 'reduce_sum_mm', op1, op2, + axes=tuple(sorted(right_no_left))) + yield op1 + + has_dim = set(i for i in range(len(row2)) if row2[i] >= 0) + left_no_right = (set(left) & has_dim) - (set(left) & (set(right) | set(axes))) + if left_no_right: + if verbose: + print(' -- MATMUL reduce2 has_dim=%r axes=%r' % (has_dim, left_no_right)) + op2 = EinsumSubOp(fd, 'reduce_sum', op2, + axes=tuple(sorted(left_no_right))) + yield op2 + + # Transpose i_axes = [(-1 if i in common_axes else (1 if i in axes else 0), i) @@ -406,13 +426,11 @@ def _decompose_einsum_equation_simple(equation, *shapes, verbose=False, if verbose: print(" -- MATMUL common_dims=%r" % common_dims) print(rows) - for iop in _apply_einsum_matmul(fd, graph.last_op, op, - axes=tuple(common_dims), - left=tuple(left), - right=tuple(right), - ndim=rows.shape[1], - keep_matmul=keep_matmul, - verbose=verbose): + for iop in _apply_einsum_matmul( + fd, graph.last_op, op, axes=tuple(common_dims), + left=tuple(left), right=tuple(right), + ndim=rows.shape[1], keep_matmul=keep_matmul, + row1=rows[0, :], row2=rows[1, :], verbose=verbose): op = iop op.compute_output_row(rows[0, :], rows[1, :], verbose=verbose) marked = graph.append(op) diff --git a/mlprodict/testing/einsum_impl_classes.py b/mlprodict/testing/einsum_impl_classes.py index 8b77b30e7..c6359ea2d 100644 --- a/mlprodict/testing/einsum_impl_classes.py +++ b/mlprodict/testing/einsum_impl_classes.py @@ -18,10 +18,14 @@ class EinsumSubOp: squeeze, diagonal, mul, batch_dot) :param inputs: inputs :param kwargs: arguments + + Operator suffixed by `_mm` (*transpose_mm*, *reduce_sum_mm*) + are equivalent to the same operator without the suffix + but takes two inputs and only changes the first one. """ _allowed = {'expand_dims', 'transpose', 'reduce_sum', 'matmul', 'id', 'squeeze', 'diagonal', 'mul', 'batch_dot', - 'transpose_mm'} + 'transpose_mm', 'reduce_sum_mm'} def __init__(self, full_dim, name, *inputs, **kwargs): self.full_dim = full_dim @@ -142,6 +146,11 @@ def _compute_output_row_reduce_sum(self, row, row2=None, verbose=False): row[a] = -1 self._check_row_(row, verbose=verbose) + def _compute_output_row_reduce_sum_mm(self, row, row2=None, verbose=False): + if row2 is None: + raise RuntimeError("reduce_sum_mm expects a second input.") + self._compute_output_row_reduce_sum(row2, row2=None, verbose=verbose) + def _compute_output_row_matmul(self, row, row2=None, verbose=False): self._check_arg_('axes', tuple) self._check_arg_('left', tuple) @@ -437,6 +446,18 @@ def _apply_reduce_sum(self, data, verbose=False, **kwargs): self._check_shape_(output) return output + def _apply_reduce_sum_mm(self, data, verbose=False, **kwargs): + self._check_inputs_(2, True) + inp = self.inputs[0] + m = self._get_data(data, inp) + self._check_shape_(m) + if verbose: + print("- %s, shape=%r axes=%r" % ( + self.name, m.shape, self.kwargs['axes'])) + output = numpy.sum(m, self.kwargs['axes']) + self._check_shape_(output) + return output + def _apply_squeeze(self, data, verbose=False, **kwargs): self._check_inputs_(1) inp = self.inputs[0] diff --git a/mlprodict/testing/einsum_impl_ext.py b/mlprodict/testing/einsum_impl_ext.py index 8dce0f076..f1df5f05c 100644 --- a/mlprodict/testing/einsum_impl_ext.py +++ b/mlprodict/testing/einsum_impl_ext.py @@ -89,12 +89,6 @@ def _check_(axs, n): _check_(left, m1_dim) _check_(right, m1_dim) - for a in axes: - if a in left and a in right: - raise RuntimeError( - "One axis belongs to every set (axes, left, right). " - "axes=%r, left=%r, right=%r." % (axes, left, right)) - l1 = [chr(i + 97) for i in range(m1_dim)] l2 = [chr(i + 97) for i in range(m1_dim)] l3 = [chr(i + 97) for i in range(m1_dim)] @@ -262,7 +256,7 @@ def numpy_extended_dot_ouput_shape(m1, m2, axes, left, right): if (i in left and m1.shape[i] != m2.shape[i] and m1.shape[i] != 1 and m2.shape[i] != 1): raise RuntimeError( - "Matrices should the same dimension for dimension %d, " + "Matrices should have the same dimension for dimension %d, " "shapes=%r @ %r." % (i, m1.shape, m2.shape)) new_shape[i] = m2.shape[i] return new_shape @@ -321,11 +315,11 @@ def dispb(c): return "".join("o" if b else "." for b in c) if verbose: - print("GENERICDOT: before broadcast %s,%s->%s or %s" % ( + print("[GENERICDOT] before broadcast %s,%s->%s or %s" % ( "".join(l1), "".join(l2), "".join(l3), _numpy_extended_dot_equation( len(m1.shape), len(m1.shape), axes, left, right))) - print("GENERICDOT: names=%s kind=%r common=%s broadcast=%s" % ( + print("[GENERICDOT] names=%s kind=%r common=%s broadcast=%s" % ( "".join(names), kind.tolist(), dispb(common), dispb(broadcast))) @@ -343,7 +337,7 @@ def dispb(c): let = [l1[p], l2[p], l3[p]] inp = 1 if dim[0] == 1 else 0 if verbose: - print("GENERICDOT: name=%s dim=%r let=%r inp=%r p=%r" % ( + print("[GENERICDOT] name=%s dim=%r let=%r inp=%r p=%r" % ( names[i], dim, let, inp, p)) print(" B0 l1=%r, l2=%r l3=%r" % (l1, l2, l3)) if (kind[i] & 4) > 0: @@ -415,20 +409,20 @@ def dispb(c): [-1 if c not in names else names.index(c) for c in l3], dtype=numpy.int64) if verbose: - print("GENERICDOT: %s,%s->%s or %s" % ( + print("[GENERICDOT] %s,%s->%s or %s" % ( "".join(l1), "".join(l2), "".join(l3), _numpy_extended_dot_equation( len(m1.shape), len(m1.shape), axes, left, right))) - print("GENERICDOT: shape1=%r shape2=%r shape=%r" % ( + print("[GENERICDOT] shape1=%r shape2=%r shape=%r" % ( m1.shape, m2.shape, res.shape)) - print("GENERICDOT: axes=%r left=%r right=%r" % (axes, left, right)) - print("GENERICDOT: pl1=%r pl2=%r plo=%r" % (pl1, pl2, plo)) - print("GENERICDOT: names=%s kind=%r common=%s broadcast=%s" % ( + print("[GENERICDOT] axes=%r left=%r right=%r" % (axes, left, right)) + print("[GENERICDOT] pl1=%r pl2=%r plo=%r" % (pl1, pl2, plo)) + print("[GENERICDOT] names=%s kind=%r common=%s broadcast=%s" % ( "".join(names), kind.tolist(), dispb(common), dispb(broadcast))) - print("GENERICDOT: pos=%r" % pos.tolist()) - print("GENERICDOT: cols=%r" % cols) - print("GENERICDOT: limits=%r" % limits) + print("[GENERICDOT] pos=%r" % pos.tolist()) + print("[GENERICDOT] cols=%r" % cols) + print("[GENERICDOT] limits=%r" % limits) while indices[0] < limits[0]: @@ -458,57 +452,95 @@ def dispb(c): def numpy_extended_dot_matrix(m1, m2, axes, left, right, verbose=False): """ - Implementation of @see fn numpy_extended_dot using dot product - and not a custom python implementation like + Implementation of @see fn numpy_extended_dot using dot product, + multiplication, transpose and reduction + but not a custom python implementation like @see fn numpy_extended_dot_python. """ _common_check_numpy_extended_dot(m1, m2, axes, left, right) - if len(axes) == 0: + if verbose: + print("[GENERICDOT] shape1=%r shape2=%r axes=%r " + "left=%r right=%r -- %s" % ( + m1.shape, m2.shape, axes, left, right, + _numpy_extended_dot_equation( + len(m1.shape), len(m1.shape), axes, left, right))) + + if len(axes) == 0 and len(set(left) & set(right)) == 0: # Simple multiplication - if verbose: - print("GENERICDOT: Mul %r @ %r" % (m1.shape, m2.shape)) res = m1 * m2 + if verbose: + print("[GENERICDOT] Mul %r @ %r -> %r" % ( + m1.shape, m2.shape, res.shape)) return res if (len(set(axes) & set(left)) == 0 and len(set(axes) & set(right)) == 0): # No intersection between axes and right: matrix multiplication + # ReduceSum + right_no_left = set(right) - (set(right) & (set(left) | set(axes))) + if right_no_left: + red1 = m1.sum(axis=tuple(sorted(right_no_left)), keepdims=True) + if verbose: + print("[GENERICDOT] reducesumL=%r, %r -> %r" % ( + right_no_left, m1.shape, red1.shape)) + else: + red1 = m1 - common_axes = sorted(set(left) & set(right)) + left_no_right = set(left) - (set(left) & (set(right) | set(axes))) + if left_no_right: + red2 = m2.sum(axis=tuple(sorted(left_no_right)), keepdims=True) + if verbose: + print("[GENERICDOT] reducesumR=%r, %r -> %r" % ( + left_no_right, m2.shape, red2.shape)) + else: + red2 = m2 # Transpose + common_axes = sorted(set(left) & set(right)) i_axes = [(-1 if i in common_axes else (1 if i in axes else 0), i) for i in range(len(m1.shape))] i_axes.sort() perm = [_[1] for _ in i_axes] - trm1 = numpy.transpose(m1, axes=perm) - trm2 = numpy.transpose(m2, axes=perm) - all_axes = list(range(0, len(m1.shape))) - new_axes = all_axes[-len(axes):] - new_common_axes = all_axes[:len(common_axes)] + trm1 = numpy.transpose(red1, axes=perm) + trm2 = numpy.transpose(red2, axes=perm) + if verbose: + print("[GENERICDOT] transposeL=%r, %r -> %r" % ( + perm, red1.shape, trm1.shape)) + print("[GENERICDOT] transposeR=%r, %r -> %r" % ( + perm, red2.shape, trm2.shape)) final_shape = numpy_extended_dot_ouput_shape(m1, m2, axes, left, right) + perm_left = [i for i in range(len(perm)) if perm[i] in left] + perm_right = [i for i in range(len(perm)) if perm[i] in right] if verbose: - print("GENERICDOT: MatMul %r @ %r -> %r -- %s" % ( + print("[GENERICDOT] MatMul %r @ %r -> %r -- %s" % ( m1.shape, m2.shape, final_shape, _numpy_extended_dot_equation( len(m1.shape), len(m1.shape), axes, left, right))) - print("GENERICDOT: axes=%r left=%r right=%r" % (axes, left, right)) - print("GENERICDOT: perm=%r common_axes=%r" % (perm, common_axes)) + print("[GENERICDOT] axes=%r left=%r right=%r" % + (axes, left, right)) + print("[GENERICDOT] perm=%r perm_left=%r " + "perm_right=%r" % ( + perm, perm_left, perm_right)) # Reshape - dim0 = int(numpy.prod([trm1.shape[i] for i in new_common_axes])) - dim0b = int(numpy.prod([trm2.shape[i] for i in new_common_axes])) + dim0 = int(numpy.prod([trm1.shape[i] for i in perm_left])) + dim0b = int(numpy.prod([trm2.shape[i] for i in perm_right])) + all_axes = list(range(0, len(m1.shape))) + new_axes = all_axes[-len(axes):] dim1 = numpy.prod([trm1.shape[i] for i in new_axes]) dim2 = numpy.prod([trm2.shape[i] for i in new_axes]) if dim1 != dim2: raise RuntimeError( "Summation axis do not have the same length %d != %d, " - "shape1=%r shape2=%r axes=%r left=%r right=%r." % ( - dim1, dim2, m1.shape, m2.shape, axes, left, right)) + "shape1=%r shape2=%r trshape1=%r trshape2=%r " + "axes=%r left=%r right=%r (new_axes=%r)" + "." % (dim1, dim2, m1.shape, m2.shape, + trm1.shape, trm2.shape, + axes, left, right, new_axes)) if dim0 != dim0b: raise RuntimeError( "Looping axis do not have the same length %d != %d, " @@ -519,14 +551,14 @@ def numpy_extended_dot_matrix(m1, m2, axes, left, right, verbose=False): shm2 = trm2.reshape((dim0, -1, dim2)) if verbose: - print("GENERICDOT: Reshape %r @ %r -> %r @ %r" % ( + print("[GENERICDOT] Reshape %r @ %r -> %r @ %r" % ( (dim0, -1, dim1), (dim0, -1, dim2), shm1.shape, shm2.shape)) # Multiplication (this should be done in a different way. res = shm1 @ numpy.transpose(shm2, axes=(0, 2, 1)) if verbose: - print("GENERICDOT: Shape after multiplication %s" % (res.shape, )) + print("[GENERICDOT] Shape after multiplication %s" % (res.shape, )) # Transpose again not_in_both = [] @@ -543,7 +575,7 @@ def numpy_extended_dot_matrix(m1, m2, axes, left, right, verbose=False): [1 for i in not_in_both]) if verbose: - print("GENERICDOT: current_shape=%r final_shape=%r" % ( + print("[GENERICDOT] current_shape=%r final_shape=%r" % ( current_shape, final_shape)) if len(current_shape) != len(final_shape): @@ -560,14 +592,30 @@ def numpy_extended_dot_matrix(m1, m2, axes, left, right, verbose=False): perm = [p[1] for p in perm] if verbose: - print("GENERICDOT: ordered_axes=%r perm=%r" % ( + print("[GENERICDOT] ordered_axes=%r perm=%r" % ( ordered_axes, perm)) return numpy.transpose(res, axes=perm) else: + # Multiplication and Matrix multiplication at the same time. + l_axes = set(left) & set(axes) + r_axes = set(right) & set(axes) + if r_axes and not l_axes: + new_axes = list(a for a in axes if a not in right) + new_left = list(sorted(set(left) | r_axes)) + if verbose: + eq1 = _numpy_extended_dot_equation( + len(m1.shape), len(m1.shape), axes, left, right) + eq2 = _numpy_extended_dot_equation( + len(m1.shape), len(m1.shape), new_axes, new_left, right) + print("[GENERICDOT] replace left %r by %r axes %r by %r, " + "eq %r by %r" % ( + left, new_left, axes, new_axes, eq1, eq2)) + return numpy_extended_dot_matrix(m1, m2, new_axes, new_left, right, + verbose=verbose) raise RuntimeError( - "shape1=%r shape2=%r axes=%r left=%r right=%r final_shape=%r eq=%s." % ( - m1.shape, m2.shape, axes, left, right, final_shape, + "shape1=%r shape2=%r axes=%r left=%r right=%r eq=%s." % ( + m1.shape, m2.shape, axes, left, right, _numpy_extended_dot_equation( len(m1.shape), len(m1.shape), axes, left, right))) From 32e356d72c5337fdc0571dfe1661bb1bd71e9269 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Wed, 28 Apr 2021 17:16:02 +0200 Subject: [PATCH 15/33] Fix matrix multiplication --- .../ut_testing/test_einsum_generic_dot.py | 3 +- mlprodict/testing/einsum_impl.py | 13 ++-- mlprodict/testing/einsum_impl_ext.py | 77 ++++++++++--------- 3 files changed, 50 insertions(+), 43 deletions(-) diff --git a/_unittests/ut_testing/test_einsum_generic_dot.py b/_unittests/ut_testing/test_einsum_generic_dot.py index 77ba25b62..0f2a0f4dc 100644 --- a/_unittests/ut_testing/test_einsum_generic_dot.py +++ b/_unittests/ut_testing/test_einsum_generic_dot.py @@ -1476,7 +1476,7 @@ def common_test(self, sh1, sh2, axes, left, right, fct, verbose=False): try: dot = fct(m1, m2, axes, left, right, verbose=verbose) except (IndexError, NotImplementedError, ValueError): - dot = fct(m1, m2, axes, left, right, verbose=True) + dot = fct(m1, m2, axes, left, right, verbose=not verbose) try: self.assertEqualArray(exp, dot) @@ -1504,5 +1504,4 @@ def common_test(self, sh1, sh2, axes, left, right, fct, verbose=False): if __name__ == "__main__": - # TestEinsumGenericdot().test_generic_dot_matrix_conf2() unittest.main() diff --git a/mlprodict/testing/einsum_impl.py b/mlprodict/testing/einsum_impl.py index 2e2ccea52..011947203 100644 --- a/mlprodict/testing/einsum_impl.py +++ b/mlprodict/testing/einsum_impl.py @@ -271,23 +271,26 @@ def _apply_einsum_matmul(fd, op1, op2, axes, left, right, ndim, # ReduceSum* has_dim = set(i for i in range(len(row1)) if row1[i] >= 0) - right_no_left = (set(right) & has_dim) - (set(right) & (set(left) | set(axes))) + right_no_left = (set(right) & has_dim) - \ + (set(right) & (set(left) | set(axes))) if right_no_left: if verbose: - print(' -- MATMUL reduce1 has_dim=%r axes=%r' % (has_dim, right_no_left)) + print(' -- MATMUL reduce1 has_dim=%r axes=%r' % + (has_dim, right_no_left)) op1 = EinsumSubOp(fd, 'reduce_sum_mm', op1, op2, axes=tuple(sorted(right_no_left))) yield op1 has_dim = set(i for i in range(len(row2)) if row2[i] >= 0) - left_no_right = (set(left) & has_dim) - (set(left) & (set(right) | set(axes))) + left_no_right = (set(left) & has_dim) - \ + (set(left) & (set(right) | set(axes))) if left_no_right: if verbose: - print(' -- MATMUL reduce2 has_dim=%r axes=%r' % (has_dim, left_no_right)) + print(' -- MATMUL reduce2 has_dim=%r axes=%r' % + (has_dim, left_no_right)) op2 = EinsumSubOp(fd, 'reduce_sum', op2, axes=tuple(sorted(left_no_right))) yield op2 - # Transpose i_axes = [(-1 if i in common_axes diff --git a/mlprodict/testing/einsum_impl_ext.py b/mlprodict/testing/einsum_impl_ext.py index f1df5f05c..8f9df145c 100644 --- a/mlprodict/testing/einsum_impl_ext.py +++ b/mlprodict/testing/einsum_impl_ext.py @@ -462,9 +462,9 @@ def numpy_extended_dot_matrix(m1, m2, axes, left, right, verbose=False): if verbose: print("[GENERICDOT] shape1=%r shape2=%r axes=%r " "left=%r right=%r -- %s" % ( - m1.shape, m2.shape, axes, left, right, - _numpy_extended_dot_equation( - len(m1.shape), len(m1.shape), axes, left, right))) + m1.shape, m2.shape, axes, left, right, + _numpy_extended_dot_equation( + len(m1.shape), len(m1.shape), axes, left, right))) if len(axes) == 0 and len(set(left) & set(right)) == 0: # Simple multiplication @@ -511,9 +511,12 @@ def numpy_extended_dot_matrix(m1, m2, axes, left, right, verbose=False): perm, red1.shape, trm1.shape)) print("[GENERICDOT] transposeR=%r, %r -> %r" % ( perm, red2.shape, trm2.shape)) - final_shape = numpy_extended_dot_ouput_shape(m1, m2, axes, left, right) + final_shape = numpy_extended_dot_ouput_shape( + m1, m2, axes, left, right) perm_left = [i for i in range(len(perm)) if perm[i] in left] perm_right = [i for i in range(len(perm)) if perm[i] in right] + perm_common_axes = [i for i in range(len(perm)) + if perm[i] in common_axes] if verbose: print("[GENERICDOT] MatMul %r @ %r -> %r -- %s" % ( @@ -523,39 +526,37 @@ def numpy_extended_dot_matrix(m1, m2, axes, left, right, verbose=False): print("[GENERICDOT] axes=%r left=%r right=%r" % (axes, left, right)) print("[GENERICDOT] perm=%r perm_left=%r " - "perm_right=%r" % ( - perm, perm_left, perm_right)) + "perm_right=%r perm_common_axes=%r" % ( + perm, perm_left, perm_right, perm_common_axes)) # Reshape - dim0 = int(numpy.prod([trm1.shape[i] for i in perm_left])) - dim0b = int(numpy.prod([trm2.shape[i] for i in perm_right])) - all_axes = list(range(0, len(m1.shape))) - new_axes = all_axes[-len(axes):] - dim1 = numpy.prod([trm1.shape[i] for i in new_axes]) - dim2 = numpy.prod([trm2.shape[i] for i in new_axes]) + dim0 = int(numpy.prod([trm1.shape[i] for i in perm_common_axes])) + dim0b = int(numpy.prod([trm2.shape[i] for i in perm_common_axes])) + if len(axes) > 0: + all_axes = list(range(0, len(m1.shape))) + new_axes = all_axes[-len(axes):] + else: + new_axes = [] + dim1 = int(numpy.prod([trm1.shape[i] for i in new_axes])) + dim2 = int(numpy.prod([trm2.shape[i] for i in new_axes])) if dim1 != dim2: raise RuntimeError( "Summation axis do not have the same length %d != %d, " - "shape1=%r shape2=%r trshape1=%r trshape2=%r " - "axes=%r left=%r right=%r (new_axes=%r)" - "." % (dim1, dim2, m1.shape, m2.shape, - trm1.shape, trm2.shape, - axes, left, right, new_axes)) - if dim0 != dim0b: - raise RuntimeError( - "Looping axis do not have the same length %d != %d, " - "shape1=%r shape2=%r axes=%r left=%r right=%r." % ( - dim0, dim0b, m1.shape, m2.shape, axes, left, right)) - - shm1 = trm1.reshape((dim0, -1, dim1)) - shm2 = trm2.reshape((dim0, -1, dim2)) + "trshape1=%r trshape2=%r " + "p_axes=%r p_left=%r p_right=%r p_common=%r" + "." % (dim1, dim2, trm1.shape, trm2.shape, + new_axes, perm_left, perm_right, perm_common_axes)) + else: + shm1 = trm1.reshape((dim0, -1, dim1)) + shm2 = trm2.reshape((dim0b, -1, dim2)) - if verbose: - print("[GENERICDOT] Reshape %r @ %r -> %r @ %r" % ( - (dim0, -1, dim1), (dim0, -1, dim2), shm1.shape, shm2.shape)) + if verbose: + print("[GENERICDOT] Reshape %r @ %r -> %r @ %r" % ( + (dim0, -1, dim1), (dim0, -1, dim2), shm1.shape, shm2.shape)) + print("[GENERICDOT] matmul") - # Multiplication (this should be done in a different way. - res = shm1 @ numpy.transpose(shm2, axes=(0, 2, 1)) + # Multiplication (this should be done in a different way. + res = shm1 @ numpy.transpose(shm2, axes=(0, 2, 1)) if verbose: print("[GENERICDOT] Shape after multiplication %s" % (res.shape, )) @@ -569,14 +570,18 @@ def numpy_extended_dot_matrix(m1, m2, axes, left, right, verbose=False): list(i for i in left if i not in right) + list(i for i in right if i not in left) + not_in_both) - current_shape = ([m1.shape[i] for i in sorted(common_axes)] + - [m1.shape[i] for i in sorted(left) if i not in common_axes] + - [m2.shape[i] for i in sorted(right) if i not in common_axes] + - [1 for i in not_in_both]) + + perm_not_in_both = [i for i in range(len(perm)) + if perm[i] in not_in_both] + current_shape = ([max(trm1.shape[i], trm2.shape[i]) + for i in sorted(perm_common_axes)] + + [trm1.shape[i] for i in sorted(perm_left) if i not in perm_common_axes] + + [trm2.shape[i] for i in sorted(perm_right) if i not in perm_common_axes] + + [1 for i in perm_not_in_both]) if verbose: - print("[GENERICDOT] current_shape=%r final_shape=%r" % ( - current_shape, final_shape)) + print("[GENERICDOT] current_shape=%r final_shape=%r " + "last_shape=%r" % (current_shape, final_shape, res.shape)) if len(current_shape) != len(final_shape): raise RuntimeError( From 2035baeb3094e593c5e27e0bdb7bf66a9529a13f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Thu, 29 Apr 2021 00:37:59 +0200 Subject: [PATCH 16/33] fix decomposition of einsum --- _unittests/ut_testing/test_einsum.py | 75 ++++++------ mlprodict/testing/einsum_impl.py | 18 ++- mlprodict/testing/einsum_impl_classes.py | 141 +++++++++++++++-------- 3 files changed, 140 insertions(+), 94 deletions(-) diff --git a/_unittests/ut_testing/test_einsum.py b/_unittests/ut_testing/test_einsum.py index 63d816814..c00c4acfb 100644 --- a/_unittests/ut_testing/test_einsum.py +++ b/_unittests/ut_testing/test_einsum.py @@ -247,7 +247,7 @@ def test_case_2_A(self): for strat in ['numpy', 'simple']: with self.subTest(strategy=strat): self.common_test_case_2( - 'abc,cd->abc', strategy=strat, verbose=True) + 'abc,cd->abc', strategy=strat, verbose=False) def test_many_2(self): m1 = numpy.arange(2 * 2 * 2).reshape((2, 2, 2)) + 10 @@ -312,36 +312,38 @@ def test_many_3(self): # core/tests/test_einsum.py. def optimize_compare(self, equation, operands=None, verbose=False): - if operands is not None: - inputs = operands - else: - eqs = equation.split("->")[0].split(",") - inputs = [] - for d, eq in enumerate(eqs): - i = numpy.arange(2 ** len(eq)).reshape( - (2,) * len(eq)).astype(numpy.float32) - inputs.append(i + numpy.array([3 ** d], dtype=numpy.float32)) - - exp = numpy.einsum(equation, *inputs) - if verbose: - print("###### equation", equation) - path = numpy.einsum_path(equation, *inputs, optimize=False) - print(path[1]) - path = numpy.einsum_path(equation, *inputs) - print(path[1]) - - shapes = [m.shape for m in inputs] - - with self.subTest(strategy='numpy'): - seq = decompose_einsum_equation( - equation, *shapes, verbose=verbose, strategy='numpy') - got = apply_einsum_sequence(seq, *inputs, verbose=verbose) - self.assertEqualArray(exp, got, decimal=6) - with self.subTest(strategy='simple'): - seq = decompose_einsum_equation( - equation, *shapes, verbose=verbose) - got = apply_einsum_sequence(seq, *inputs, verbose=verbose) - self.assertEqualArray(exp, got, decimal=6) + with self.subTest(equation=equation): + if operands is not None: + inputs = operands + else: + eqs = equation.split("->")[0].split(",") + inputs = [] + for d, eq in enumerate(eqs): + i = numpy.arange(2 ** len(eq)).reshape( + (2,) * len(eq)).astype(numpy.float32) + inputs.append( + i + numpy.array([3 ** d], dtype=numpy.float32)) + + exp = numpy.einsum(equation, *inputs) + if verbose: + print("###### equation", equation) + path = numpy.einsum_path(equation, *inputs, optimize=False) + print(path[1]) + path = numpy.einsum_path(equation, *inputs) + print(path[1]) + + shapes = [m.shape for m in inputs] + + with self.subTest(strategy='numpy'): + seq = decompose_einsum_equation( + equation, *shapes, verbose=verbose, strategy='numpy') + got = apply_einsum_sequence(seq, *inputs, verbose=verbose) + self.assertEqualArray(exp, got, decimal=6) + with self.subTest(strategy='simple'): + seq = decompose_einsum_equation( + equation, *shapes, verbose=verbose) + got = apply_einsum_sequence(seq, *inputs, verbose=verbose) + self.assertEqualArray(exp, got, decimal=6) def test_numpy_test_hadamard_like_products(self): # Hadamard outer products @@ -350,8 +352,8 @@ def test_numpy_test_hadamard_like_products(self): def test_np_test_np_test_collapse(self): # Inner products - self.optimize_compare('ab,ab,c->c') self.optimize_compare('ab,ab,cd,cd->ac') + self.optimize_compare('ab,ab,c->c') self.optimize_compare('ab,ab,cd,cd->cd') # self.optimize_compare('ab,ab,c->') # self.optimize_compare('ab,ab,cd,cd->') @@ -404,12 +406,10 @@ def test_np_test_combined_views_mapping(self): def test_np_test_broadcasting_dot_cases1(self): # Ensures broadcasting cases are not mistaken for GEMM - a = numpy.random.rand(1, 5, 4) b = numpy.random.rand(4, 6) c = numpy.random.rand(5, 6) d = numpy.random.rand(10) - self.optimize_compare('ijk,kl,jl,i->i', operands=[a, b, c, d]) e = numpy.random.rand(1, 1, 5, 4) @@ -418,8 +418,9 @@ def test_np_test_broadcasting_dot_cases1(self): def test_np_test_broadcasting_dot_cases2(self): # Edge case found in gh-11308 - g = numpy.arange(64).reshape(2, 4, 8) - self.optimize_compare('obk,ijk->ioj', operands=[g, g]) + f = numpy.arange(7 * 55).reshape(7, 11, 5) + g = numpy.arange(30).reshape(2, 3, 5) + self.optimize_compare('obk,ijk->ioj', operands=[f, g]) def np_test_complex(self): # Long test cases @@ -466,5 +467,5 @@ def test_np_test_edge_cases_duplicate_indices(self): if __name__ == "__main__": - # TestEinsum().test_case_2_A() + # TestEinsum().test_np_test_broadcasting_dot_cases1() unittest.main() diff --git a/mlprodict/testing/einsum_impl.py b/mlprodict/testing/einsum_impl.py index 011947203..274ec2e47 100644 --- a/mlprodict/testing/einsum_impl.py +++ b/mlprodict/testing/einsum_impl.py @@ -307,7 +307,7 @@ def _apply_einsum_matmul(fd, op1, op2, axes, left, right, ndim, # Reshape all_axes = list(range(0, ndim)) - new_axes = all_axes[-len(axes):] + new_axes = all_axes[-len(axes):] if len(axes) > 0 else [] new_common_axes = all_axes[:len(common_axes)] not_in_both = [] for i in range(0, ndim): @@ -321,10 +321,15 @@ def _apply_einsum_matmul(fd, op1, op2, axes, left, right, ndim, ndim=ndim) yield op - # Transpose again, reverse perm - rev_perm = perm.copy() - for i, p in enumerate(perm): - rev_perm[p] = i + # Transpose again + ordered_axes = (common_axes + + list(i for i in left if i not in right) + + list(i for i in right if i not in left) + + not_in_both) + rev_perm = [(a, i) for i, a in enumerate(ordered_axes)] + rev_perm.sort() + rev_perm = [p[1] for p in rev_perm] + op_unused = EinsumSubOp(fd, 'transpose_mm', op1, op, perm=tuple(rev_perm)) yield op_unused @@ -435,7 +440,8 @@ def _decompose_einsum_equation_simple(equation, *shapes, verbose=False, ndim=rows.shape[1], keep_matmul=keep_matmul, row1=rows[0, :], row2=rows[1, :], verbose=verbose): op = iop - op.compute_output_row(rows[0, :], rows[1, :], verbose=verbose) + op.compute_output_row(rows[0, :], rows[1, :], + ab=True, verbose=verbose) marked = graph.append(op) # End diff --git a/mlprodict/testing/einsum_impl_classes.py b/mlprodict/testing/einsum_impl_classes.py index c6359ea2d..631cdd93e 100644 --- a/mlprodict/testing/einsum_impl_classes.py +++ b/mlprodict/testing/einsum_impl_classes.py @@ -109,14 +109,22 @@ def _check_row_(self, row, inp=False, verbose=False): """ if verbose: if inp: - print() - print('<-' if inp else '->', self.name, row, self.kwargs) + print('<<' if inp else '>>', self.name, row, self.kwargs) + else: + print('<<' if inp else '>>', self.name, row) - def _compute_output_row_id(self, row, row2=None, verbose=False): + def _compute_output_row_id(self, row, row2=None, ab=False, verbose=False): + if ab: + raise RuntimeError("ab option not allowed.") + self._check_row_(row, True, verbose=verbose) row[:] = row2[:] self._check_row_(row, verbose=verbose) - def _compute_output_row_transpose(self, row, row2=None, verbose=False): + def _compute_output_row_transpose(self, row, row2=None, ab=False, verbose=False): + if ab: + self._compute_output_row_transpose(row2, verbose=verbose) + return + self._check_row_(row, True, verbose=verbose) self._check_arg_('perm', tuple) if len(self.kwargs['perm']) != len(row): raise RuntimeError( @@ -127,12 +135,18 @@ def _compute_output_row_transpose(self, row, row2=None, verbose=False): row[i] = cpy[p] self._check_row_(row, verbose=verbose) - def _compute_output_row_transpose_mm(self, row, row2=None, verbose=False): + def _compute_output_row_transpose_mm(self, row, row2=None, ab=False, verbose=False): + if not ab: + raise RuntimeError("ab must be True.") + self._check_row_(row, True, verbose=verbose) if row2 is None: raise RuntimeError("transpose_mm expects a second input.") - self._compute_output_row_transpose(row2, row2=None, verbose=verbose) + self._compute_output_row_transpose(row, row2=None, verbose=verbose) - def _compute_output_row_expand_dims(self, row, row2=None, verbose=False): + def _compute_output_row_expand_dims(self, row, row2=None, ab=False, verbose=False): + if ab: + raise RuntimeError("ab option not allowed.") + self._check_row_(row, True, verbose=verbose) self._check_arg_('axis', tuple) if row[self.kwargs['axis'][1]] != -1: raise RuntimeError( @@ -140,18 +154,63 @@ def _compute_output_row_expand_dims(self, row, row2=None, verbose=False): row, self.kwargs['axis'])) self._check_row_(row, verbose=verbose) - def _compute_output_row_reduce_sum(self, row, row2=None, verbose=False): + def _compute_output_row_reduce_sum(self, row, row2=None, ab=False, verbose=False): + if ab: + raise RuntimeError("ab option not allowed.") + self._check_row_(row, True, verbose=verbose) self._check_arg_('axes', tuple) for a in self.kwargs['axes']: row[a] = -1 self._check_row_(row, verbose=verbose) - def _compute_output_row_reduce_sum_mm(self, row, row2=None, verbose=False): + def _compute_output_row_reduce_sum_mm(self, row, row2=None, ab=False, verbose=False): + if not ab: + raise RuntimeError("ab must be true.") + self._check_row_(row2, True, verbose=verbose) if row2 is None: raise RuntimeError("reduce_sum_mm expects a second input.") self._compute_output_row_reduce_sum(row2, row2=None, verbose=verbose) - def _compute_output_row_matmul(self, row, row2=None, verbose=False): + def _compute_output_row_squeeze(self, row, row2=None, ab=False, verbose=False): + if ab: + raise RuntimeError("ab option not allowed.") + self._check_row_(row, True, verbose=verbose) + self._check_arg_('axes', tuple) + for a in self.kwargs['axes']: + row[a] = -1 + self._check_row_(row, verbose=verbose) + + def _compute_output_row_diagonal(self, row, row2=None, ab=False, verbose=False): + if ab: + raise RuntimeError("ab option not allowed.") + self._check_row_(row, True, verbose=verbose) + self._check_arg_('diag', list) + to_remove = [] + for choice, choices in self.kwargs['diag']: + for ch in choices: + if ch != choice: + to_remove.append(ch) + for i in range(len(row)): # pylint: disable=C0200 + if row[i] in choices: + if row[i] != choice: + row[i] = choice + to_remove.sort() + for r in to_remove: + for i in range(len(row)): # pylint: disable=C0200 + if row[i] == r: + raise RuntimeError( + "Unexpected result r=%r row=%r to_remove=%r " + "diag=%r." % ( + r, row, to_remove, self.kwargs['diag'])) + if row[i] > r: + row[i] -= 1 + self._check_row_(row, verbose=verbose) + + def _compute_output_row_matmul(self, row, row2=None, ab=False, verbose=False): + if not ab: + raise RuntimeError("ab must be True.") + self._check_row_(row, True, verbose=verbose) + self._check_row_(row2, True, verbose=verbose) self._check_arg_('axes', tuple) self._check_arg_('left', tuple) self._check_arg_('right', tuple) @@ -172,7 +231,11 @@ def _compute_output_row_matmul(self, row, row2=None, verbose=False): row2[a] = -1 self._check_row_(row2, verbose=verbose) - def _compute_output_row_batch_dot(self, row, row2=None, verbose=False): + def _compute_output_row_batch_dot(self, row, row2=None, ab=False, verbose=False): + if not ab: + raise RuntimeError("ab must be True.") + self._check_row_(row, True, verbose=verbose) + self._check_row_(row2, True, verbose=verbose) self._check_arg_('batch_axes', tuple) self._check_arg_('keep_axes', tuple, empty=True) self._check_arg_('sum_axes', tuple) @@ -188,9 +251,9 @@ def _compute_output_row_batch_dot(self, row, row2=None, verbose=False): left = self.kwargs['left'] right = self.kwargs['right'] ndim = self.kwargs['ndim'] - print(" BATCH_DOT %r @ %r batch_axes=%r keep_axes=%r sum_axes=%r " + print(" BATCH_DOT batch_axes=%r keep_axes=%r sum_axes=%r " "left=%r right=%r eq=%r" % ( - row, row2, batch_axes, keep_axes, sum_axes, left, right, + batch_axes, keep_axes, sum_axes, left, right, _numpy_extended_dot_equation(ndim, ndim, sum_axes, left, right))) row2[:] = numpy.maximum(row, row2) for a in self.kwargs['sum_axes']: @@ -198,7 +261,11 @@ def _compute_output_row_batch_dot(self, row, row2=None, verbose=False): row2[a] = -1 self._check_row_(row2, verbose=verbose) - def _compute_output_row_mul(self, row, row2=None, verbose=False): + def _compute_output_row_mul(self, row, row2=None, ab=False, verbose=False): + if not ab: + raise RuntimeError("ab must be True.") + self._check_row_(row, True, verbose=verbose) + self._check_row_(row2, True, verbose=verbose) if row2 is None: raise RuntimeError("mul expects two inputs.") if verbose: @@ -206,47 +273,18 @@ def _compute_output_row_mul(self, row, row2=None, verbose=False): row2[:] = numpy.maximum(row, row2) self._check_row_(row2, verbose=verbose) - def _compute_output_row_squeeze(self, row, row2=None, verbose=False): - self._check_arg_('axes', tuple) - for a in self.kwargs['axes']: - row[a] = -1 - self._check_row_(row, verbose=verbose) - - def _compute_output_row_diagonal(self, row, row2=None, verbose=False): - self._check_arg_('diag', list) - to_remove = [] - for choice, choices in self.kwargs['diag']: - for ch in choices: - if ch != choice: - to_remove.append(ch) - for i in range(len(row)): # pylint: disable=C0200 - if row[i] in choices: - if row[i] != choice: - row[i] = choice - to_remove.sort() - for r in to_remove: - for i in range(len(row)): # pylint: disable=C0200 - if row[i] == r: - raise RuntimeError( - "Unexpected result r=%r row=%r to_remove=%r " - "diag=%r." % ( - r, row, to_remove, self.kwargs['diag'])) - if row[i] > r: - row[i] -= 1 - self._check_row_(row, verbose=verbose) - - def compute_output_row(self, row, row2=None, verbose=False): + def compute_output_row(self, row, row2=None, ab=False, verbose=False): """ Updates *row* based on the operator. """ - self._check_row_(row, True, verbose=verbose) - method_name = "_compute_output_row_%s" % self.name meth = getattr(self, method_name, None) if meth is None: raise NotImplementedError( "compute_output_row not implemented for %r." % self.name) - meth(row, row2=row2, verbose=verbose) + if verbose and ab: + print(" -- called as a binary operator") + meth(row, row2=row2, ab=ab, verbose=verbose) def _check_inputs_(self, n_expected, check_dim=False): if len(self.inputs) != n_expected: @@ -405,21 +443,22 @@ def _apply_batch_dot(self, data, verbose=False, **kwargs): "of dimensions not %r @ %r." % (m1.shape, m2.shape)) dim0 = int(numpy.prod([m1.shape[i] for i in batch_axes])) + dim0b = int(numpy.prod([m2.shape[i] for i in batch_axes])) dimb = int(-1 if keep_axes is None else numpy.prod( [m1.shape[i] for i in keep_axes])) dim1 = int(numpy.prod([m1.shape[i] for i in sum_axes])) dim2 = int(numpy.prod([m2.shape[i] for i in sum_axes])) m1sh = m1.reshape((dim0, dimb, dim1)) - m2sh = m2.reshape((dim0, dimb, dim2)) + m2sh = m2.reshape((dim0b, dimb, dim2)) dot = m1sh @ numpy.transpose(m2sh, (0, 2, 1)) # new shape taken = set(batch_axes) | set(sum_axes) ax = [i for i in range(len(m1.shape)) if i not in taken] - new_shape = ([m1.shape[i] for i in batch_axes] + - [m1.shape[i] for i in left] + - [m2.shape[i] for i in right]) + new_shape = ([max(m1.shape[i], m2.shape[i]) for i in batch_axes] + + [m1.shape[i] for i in left if i not in batch_axes] + + [m2.shape[i] for i in right if i not in batch_axes]) while len(new_shape) < len(m1.shape): new_shape.append(1) From 1b660ddd2a253b0ff756a8c767655546aa0c4423 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Thu, 29 Apr 2021 09:54:24 +0200 Subject: [PATCH 17/33] documentation --- _doc/sphinxdoc/source/api/testing.rst | 10 +++++ mlprodict/testing/einsum_impl_ext.py | 63 ++++++++++++++++++++++++++- 2 files changed, 72 insertions(+), 1 deletion(-) diff --git a/_doc/sphinxdoc/source/api/testing.rst b/_doc/sphinxdoc/source/api/testing.rst index 028b8197e..c8dcc42cb 100644 --- a/_doc/sphinxdoc/source/api/testing.rst +++ b/_doc/sphinxdoc/source/api/testing.rst @@ -28,6 +28,16 @@ Einsum .. autosignature:: mlprodict.testing.experimental_c.custom_einsum_double +.. autosignature:: mlprodict.testing.einsum_impl_ext.numpy_diagonal + +.. autosignature:: mlprodict.testing.einsum_impl_ext.numpy_extended_dot + +.. autosignature:: mlprodict.testing.einsum_impl_ext.numpy_extended_dot_python + +.. autosignature:: mlprodict.testing.einsum_impl_ext.numpy_extended_dot_matrix + +.. autosignature:: mlprodict.testing.einsum_impl_ext.numpy_extended_dot_ouput_shape + Pad ^^^ diff --git a/mlprodict/testing/einsum_impl_ext.py b/mlprodict/testing/einsum_impl_ext.py index 8f9df145c..d9552d7e1 100644 --- a/mlprodict/testing/einsum_impl_ext.py +++ b/mlprodict/testing/einsum_impl_ext.py @@ -58,7 +58,8 @@ def numpy_diagonal(m, axis, axes): def _numpy_extended_dot_equation(m1_dim, m2_dim, axes, left, right): """ Returns the equation equivalent to an extended version - of a matrix multiplication (see @see fn numpy_extended_dot). + of an aligned matrix multiplication + (see @see fn numpy_extended_dot). :param m1: number of dimensions of the first matrix :param m2: number of dimensions of the second matrix @@ -67,6 +68,26 @@ def _numpy_extended_dot_equation(m1_dim, m2_dim, axes, left, right): :param left: left axes :param right: right axes :return: equation + + .. runpython:: + :showcode: + + import numpy + from mlprodict.testing.einsum_impl_ext import ( + numpy_extended_dot_python, _numpy_extended_dot_equation) + + a = numpy.arange(6).reshape((3, 2, 1)) + b = numpy.arange(12).reshape((3, 1, 4)) + + print(numpy_extended_dot_python( + a, b, axes=(0, ), left=(1,), right=(2,))) + + # Equivalent einsum equation + print('equation', _numpy_extended_dot_equation( + len(a.shape), len(a.shape), axes=(0, ), left=(1,), right=(2,))) + + # Same einsum computation written in a different way. + print(numpy.einsum('kix,kxj->xij', a, b)) """ if m1_dim != m2_dim: raise RuntimeError( @@ -374,6 +395,26 @@ def numpy_extended_dot_python(m1, m2, axes, left, right, verbose=False): Implementation of @see fn numpy_extended_dot in pure python. This implementation is not efficient but shows how to implement this operation without :epkg:`numpy:einsum`. + + .. runpython:: + :showcode: + + import numpy + from mlprodict.testing.einsum_impl_ext import ( + numpy_extended_dot_python, _numpy_extended_dot_equation) + + a = numpy.arange(6).reshape((3, 2, 1)) + b = numpy.arange(12).reshape((3, 1, 4)) + + print(numpy_extended_dot_python( + a, b, axes=(0, ), left=(1,), right=(2,))) + + # Equivalent einsum equation + print('equation', _numpy_extended_dot_equation( + len(a.shape), len(a.shape), axes=(0, ), left=(1,), right=(2,))) + + # Same einsum computation written in a different way. + print(numpy.einsum('kix,kxj->xij', a, b)) """ def dispb(c): return "".join("o" if b else "." for b in c) @@ -456,6 +497,26 @@ def numpy_extended_dot_matrix(m1, m2, axes, left, right, verbose=False): multiplication, transpose and reduction but not a custom python implementation like @see fn numpy_extended_dot_python. + + .. runpython:: + :showcode: + + import numpy + from mlprodict.testing.einsum_impl_ext import ( + numpy_extended_dot_matrix, _numpy_extended_dot_equation) + + a = numpy.arange(6).reshape((3, 2, 1)) + b = numpy.arange(12).reshape((3, 1, 4)) + + print(numpy_extended_dot_matrix( + a, b, axes=(0, ), left=(1,), right=(2,))) + + # Equivalent einsum equation + print('equation', _numpy_extended_dot_equation( + len(a.shape), len(a.shape), axes=(0, ), left=(1,), right=(2,))) + + # Same einsum computation written in a different way. + print(numpy.einsum('kix,kxj->xij', a, b)) """ _common_check_numpy_extended_dot(m1, m2, axes, left, right) From f0efa4764872a275af602fc8c30f9d480970a1ba Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Thu, 29 Apr 2021 10:48:00 +0200 Subject: [PATCH 18/33] remove useless transpose --- .travis.yml | 9 +----- appveyor.yml | 14 -------- mlprodict/testing/einsum_impl.py | 41 ++++++++++++++++-------- mlprodict/testing/einsum_impl_classes.py | 7 +++- mlprodict/testing/einsum_impl_ext.py | 14 +++++--- 5 files changed, 44 insertions(+), 41 deletions(-) diff --git a/.travis.yml b/.travis.yml index 00d49b8d5..ad20bfb28 100644 --- a/.travis.yml +++ b/.travis.yml @@ -9,14 +9,7 @@ matrix: before_install: - sudo apt-get install libgeos-dev libproj-dev proj-data graphviz libblas-dev liblapack-dev - - wget https://apt.llvm.org/llvm.sh - - chmod +x llvm.sh - - sudo ./llvm.sh 10 - - ls /usr/bin/llvm* - - export LLVM_CONFIG=/usr/bin/llvm-config - # - sudo ln -s /usr/bin/llvm-config-10 /usr/bin/llvm-config - - sudo apt-get -y install graphviz - # onnx + - sudo apt-get install graphviz - sudo apt-get install protobuf-compiler libprotoc-dev cmake - git clone -b master --single-branch https://github.com/onnx/onnx.git --recursive - cd onnx diff --git a/appveyor.yml b/appveyor.yml index 112234eeb..d8022a688 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -11,21 +11,7 @@ init: install: - "%PYTHON%\\python -m pip install --upgrade pip" - pip install wheel - # for many packages - "%PYTHON%\\Scripts\\pip install -r requirements-win.txt" - # install precompiled versions not available on pypi - - "%PYTHON%\\Scripts\\pymy_install3 llvmlite numba" - # onnx - #- git clone -b master --single-branch https://github.com/onnx/onnx.git --recursive - #- cd onnx - #- set ONNX_ML=1 - #- set ONNX_BUILD_TESTS=1 - #- set ONNXIFI_DUMMY_BACKEND=1 - #- python setup.py bdist_wheel - #- dir dist - #- python setup.py install - #- cd .. - # other dependencies - "%PYTHON%\\Scripts\\pip install -r requirements.txt" build: off diff --git a/mlprodict/testing/einsum_impl.py b/mlprodict/testing/einsum_impl.py index 274ec2e47..4e4cf82b2 100644 --- a/mlprodict/testing/einsum_impl.py +++ b/mlprodict/testing/einsum_impl.py @@ -164,6 +164,16 @@ def apply_einsum_sequence(seq, *inputs, verbose=False, **kwargs): return seq.apply_sequence(*inputs, verbose=verbose, **kwargs) +def is_transpose_identity(perm): + """ + Tells if the permutation *perm* does nothing (itentity). + + :param perm: permutation + :return: boolean + """ + return list(perm) == list(range(len(perm))) + + def _basic_verification(lengths, shapes, equation): if len(lengths) - 1 != len(shapes): raise ValueError( @@ -204,8 +214,9 @@ def _apply_transpose_reshape(op, row): continue new_perm[perm[p][1]] = i p += 1 - op = EinsumSubOp(len(row), 'transpose', op, perm=tuple(new_perm)) - yield op + if not is_transpose_identity(new_perm): + op = EinsumSubOp(len(row), 'transpose', op, perm=tuple(new_perm)) + yield op def _apply_squeeze_transpose(op, row_last, row_output): @@ -228,8 +239,10 @@ def _apply_squeeze_transpose(op, row_last, row_output): new_perm[i] = perm[p][1] p += 1 perm = [p[1] for p in perm] - op = EinsumSubOp(len(row_last), 'transpose', op, perm=tuple(new_perm)) - yield op + if not is_transpose_identity(new_perm): + op = EinsumSubOp(len(row_last), 'transpose', op, + perm=tuple(new_perm)) + yield op if len(sq) > 0: op = EinsumSubOp(len(row_last), 'squeeze', op, axes=tuple(sq)) yield op @@ -300,10 +313,11 @@ def _apply_einsum_matmul(fd, op1, op2, axes, left, right, ndim, perm = [_[1] for _ in i_axes] perm_left = [i for i in range(len(perm)) if perm[i] in left] perm_right = [i for i in range(len(perm)) if perm[i] in right] - op1 = EinsumSubOp(fd, 'transpose_mm', op1, op2, perm=tuple(perm)) - yield op1 - op2 = EinsumSubOp(fd, 'transpose', op2, perm=tuple(perm)) - yield op2 + if not is_transpose_identity(perm): + op1 = EinsumSubOp(fd, 'transpose_mm', op1, op2, perm=tuple(perm)) + yield op1 + op2 = EinsumSubOp(fd, 'transpose', op2, perm=tuple(perm)) + yield op2 # Reshape all_axes = list(range(0, ndim)) @@ -330,11 +344,12 @@ def _apply_einsum_matmul(fd, op1, op2, axes, left, right, ndim, rev_perm.sort() rev_perm = [p[1] for p in rev_perm] - op_unused = EinsumSubOp(fd, 'transpose_mm', op1, - op, perm=tuple(rev_perm)) - yield op_unused - op = EinsumSubOp(fd, 'transpose', op, perm=tuple(rev_perm)) - yield op + if not is_transpose_identity(rev_perm): + op_unused = EinsumSubOp(fd, 'transpose_mm', op1, + op, perm=tuple(rev_perm)) + yield op_unused + op = EinsumSubOp(fd, 'transpose', op, perm=tuple(rev_perm)) + yield op else: raise NotImplementedError( "axes and right or left have axes in common, " diff --git a/mlprodict/testing/einsum_impl_classes.py b/mlprodict/testing/einsum_impl_classes.py index 631cdd93e..37a7735aa 100644 --- a/mlprodict/testing/einsum_impl_classes.py +++ b/mlprodict/testing/einsum_impl_classes.py @@ -58,6 +58,10 @@ def _check_(self): raise RuntimeError( "perm has duplicated values %r (name=%r)." "" % (perm, self.name)) + if False and list(perm) == list(range(len(perm))): + raise ValueError( + "Transpose = identity perm=%r. It must be removed." + "" % perm) elif self.name == 'matmul': self._check_arg_('axes', tuple) self._check_arg_('left', tuple) @@ -130,8 +134,9 @@ def _compute_output_row_transpose(self, row, row2=None, ab=False, verbose=False) raise RuntimeError( "Unexpected permutation %r (row=%r)." "" % (self.kwargs['perm'], row)) + perm = self.kwargs['perm'] cpy = row.copy() - for i, p in enumerate(self.kwargs['perm']): + for i, p in enumerate(perm): row[i] = cpy[p] self._check_row_(row, verbose=verbose) diff --git a/mlprodict/testing/einsum_impl_ext.py b/mlprodict/testing/einsum_impl_ext.py index d9552d7e1..8e54515b7 100644 --- a/mlprodict/testing/einsum_impl_ext.py +++ b/mlprodict/testing/einsum_impl_ext.py @@ -87,7 +87,7 @@ def _numpy_extended_dot_equation(m1_dim, m2_dim, axes, left, right): len(a.shape), len(a.shape), axes=(0, ), left=(1,), right=(2,))) # Same einsum computation written in a different way. - print(numpy.einsum('kix,kxj->xij', a, b)) + print(numpy.einsum('kix,kxj->xij', a, b)) """ if m1_dim != m2_dim: raise RuntimeError( @@ -447,7 +447,8 @@ def dispb(c): [m1.shape[pos[n]] if (kind[n] & 1) == 1 else m2.shape[pos[n]] for n in range(len(names))], dtype=numpy.int64) plo = numpy.array( - [-1 if c not in names else names.index(c) for c in l3], dtype=numpy.int64) + [-1 if c not in names else names.index(c) for c in l3], + dtype=numpy.int64) if verbose: print("[GENERICDOT] %s,%s->%s or %s" % ( @@ -613,7 +614,8 @@ def numpy_extended_dot_matrix(m1, m2, axes, left, right, verbose=False): if verbose: print("[GENERICDOT] Reshape %r @ %r -> %r @ %r" % ( - (dim0, -1, dim1), (dim0, -1, dim2), shm1.shape, shm2.shape)) + (dim0, -1, dim1), (dim0, -1, dim2), + shm1.shape, shm2.shape)) print("[GENERICDOT] matmul") # Multiplication (this should be done in a different way. @@ -636,8 +638,10 @@ def numpy_extended_dot_matrix(m1, m2, axes, left, right, verbose=False): if perm[i] in not_in_both] current_shape = ([max(trm1.shape[i], trm2.shape[i]) for i in sorted(perm_common_axes)] + - [trm1.shape[i] for i in sorted(perm_left) if i not in perm_common_axes] + - [trm2.shape[i] for i in sorted(perm_right) if i not in perm_common_axes] + + [trm1.shape[i] for i in sorted(perm_left) + if i not in perm_common_axes] + + [trm2.shape[i] for i in sorted(perm_right) + if i not in perm_common_axes] + [1 for i in perm_not_in_both]) if verbose: From 222bad2eb4ffdc53aa204ddadc05b6bad7f7d815 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Thu, 29 Apr 2021 11:19:01 +0200 Subject: [PATCH 19/33] merge expand_dims node into a single one --- _unittests/ut_testing/test_einsum.py | 2 +- mlprodict/testing/einsum_impl.py | 5 ++--- mlprodict/testing/einsum_impl_classes.py | 26 ++++++++++++++++-------- 3 files changed, 20 insertions(+), 13 deletions(-) diff --git a/_unittests/ut_testing/test_einsum.py b/_unittests/ut_testing/test_einsum.py index c00c4acfb..a8df617ea 100644 --- a/_unittests/ut_testing/test_einsum.py +++ b/_unittests/ut_testing/test_einsum.py @@ -467,5 +467,5 @@ def test_np_test_edge_cases_duplicate_indices(self): if __name__ == "__main__": - # TestEinsum().test_np_test_broadcasting_dot_cases1() + # TestEinsum().test_case_2_A() unittest.main() diff --git a/mlprodict/testing/einsum_impl.py b/mlprodict/testing/einsum_impl.py index 4e4cf82b2..449763229 100644 --- a/mlprodict/testing/einsum_impl.py +++ b/mlprodict/testing/einsum_impl.py @@ -203,9 +203,8 @@ def _apply_transpose_reshape(op, row): else: p += 1 perm.append((r, i)) - for a in reversed(axes): - op = EinsumSubOp(len(row), 'expand_dims', op, axis=a) - yield op + op = EinsumSubOp(len(row), 'expand_dims', op, axes=tuple(axes)) + yield op perm.sort() p = 0 new_perm = numpy.arange(len(row)) diff --git a/mlprodict/testing/einsum_impl_classes.py b/mlprodict/testing/einsum_impl_classes.py index 37a7735aa..c987a87b4 100644 --- a/mlprodict/testing/einsum_impl_classes.py +++ b/mlprodict/testing/einsum_impl_classes.py @@ -58,7 +58,7 @@ def _check_(self): raise RuntimeError( "perm has duplicated values %r (name=%r)." "" % (perm, self.name)) - if False and list(perm) == list(range(len(perm))): + if list(perm) == list(range(len(perm))): raise ValueError( "Transpose = identity perm=%r. It must be removed." "" % perm) @@ -152,11 +152,17 @@ def _compute_output_row_expand_dims(self, row, row2=None, ab=False, verbose=Fals if ab: raise RuntimeError("ab option not allowed.") self._check_row_(row, True, verbose=verbose) - self._check_arg_('axis', tuple) - if row[self.kwargs['axis'][1]] != -1: - raise RuntimeError( - "Dimension should be -1 in row %r axis=%r." % ( - row, self.kwargs['axis'])) + self._check_arg_('axes', tuple) + axes = self.kwargs['axes'] + for axis in axes: + if not isinstance(axis, tuple): + raise TypeError( + "Parameter axes of expand_dims should be a tuple of " + "tuple, axes=%r." % axes) + if row[axis[1]] != -1: + raise RuntimeError( + "Dimension should be -1 in row %r axis=%r." % ( + row, self.kwargs['axis'])) self._check_row_(row, verbose=verbose) def _compute_output_row_reduce_sum(self, row, row2=None, ab=False, verbose=False): @@ -346,9 +352,11 @@ def _apply_expand_dims(self, data, verbose=False, **kwargs): inp = self.inputs[0] m = self._get_data(data, inp) if verbose: - print("- %s, shape=%r axis=%r" % ( - self.name, m.shape, self.kwargs['axis'])) - output = numpy.expand_dims(m, self.kwargs['axis'][0]) + print("- %s, shape=%r axes=%r" % ( + self.name, m.shape, self.kwargs['axes'])) + output = m + for axis in reversed(self.kwargs['axes']): + output = numpy.expand_dims(output, axis[0]) return output def _apply_transpose(self, data, verbose=False, **kwargs): From 801366d65c04d8ba6dd3acdc9e2167fb506c5f48 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Thu, 29 Apr 2021 17:19:29 +0200 Subject: [PATCH 20/33] simplifies output graph --- _doc/notebooks/einsum_decomposition.ipynb | 85 ++++++++++----------- _unittests/ut_testing/test_einsum.py | 70 +++++++++--------- mlprodict/testing/einsum_impl.py | 19 ++++- mlprodict/testing/einsum_impl_classes.py | 90 ++++++++++++++++++++++- mlprodict/testing/einsum_impl_ext.py | 3 +- 5 files changed, 185 insertions(+), 82 deletions(-) diff --git a/_doc/notebooks/einsum_decomposition.ipynb b/_doc/notebooks/einsum_decomposition.ipynb index 892573a7e..06950144d 100644 --- a/_doc/notebooks/einsum_decomposition.ipynb +++ b/_doc/notebooks/einsum_decomposition.ipynb @@ -285,16 +285,16 @@ { "data": { "text/html": [ - "
\n", + "
\n", "" ], "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -355,16 +355,16 @@ { "data": { "text/html": [ - "
\n", + "
\n", "" ], "text/plain": [ - "" + "" ] }, "execution_count": 10, @@ -373,7 +373,8 @@ } ], "source": [ - "seq_broken = decompose_einsum_equation(\"bac,cd,def->ebc\", m1.shape, m2.shape, m3.shape, strategy='numpy')\n", + "seq_broken = decompose_einsum_equation(\"bac,cd,def->ebc\", m1.shape, m2.shape, m3.shape, \n", + " strategy='numpy', clean=True)\n", "RenderJsDot(seq_broken.to_dot(size=7))" ] }, @@ -465,7 +466,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:11<00:00, 1.15it/s]\n" + "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:11<00:00, 1.11it/s]\n" ] }, { @@ -503,61 +504,61 @@ " \n", " \n", " 50\n", - " 0.005723\n", - " 0.000071\n", - " 0.005598\n", - " 0.005849\n", + " 0.004894\n", + " 0.000222\n", + " 0.004628\n", + " 0.005283\n", " 10\n", " 10\n", - " 0.057234\n", + " 0.048943\n", " onnxruntime\n", " 50\n", " \n", " \n", " 51\n", - " 0.256746\n", - " 0.004340\n", - " 0.248917\n", - " 0.265715\n", + " 0.263068\n", + " 0.001402\n", + " 0.261303\n", + " 0.265483\n", " 10\n", " 10\n", - " 2.567464\n", + " 2.630676\n", " numpy.einsum\n", " 55\n", " \n", " \n", " 52\n", - " 0.052522\n", - " 0.000903\n", - " 0.051411\n", - " 0.054281\n", + " 0.053470\n", + " 0.001030\n", + " 0.051849\n", + " 0.054855\n", " 10\n", " 10\n", - " 0.525222\n", + " 0.534695\n", " custom_einsum\n", " 55\n", " \n", " \n", " 53\n", - " 0.025694\n", - " 0.003825\n", - " 0.023930\n", - " 0.037133\n", + " 0.045314\n", + " 0.004231\n", + " 0.041232\n", + " 0.053182\n", " 10\n", " 10\n", - " 0.256936\n", + " 0.453135\n", " tr/resh/dot\n", " 55\n", " \n", " \n", " 54\n", - " 0.008572\n", - " 0.000565\n", - " 0.008283\n", - " 0.010209\n", + " 0.008238\n", + " 0.000564\n", + " 0.007009\n", + " 0.008989\n", " 10\n", " 10\n", - " 0.085718\n", + " 0.082384\n", " onnxruntime\n", " 55\n", " \n", @@ -567,11 +568,11 @@ ], "text/plain": [ " average deviation min_exec max_exec repeat number total \\\n", - "50 0.005723 0.000071 0.005598 0.005849 10 10 0.057234 \n", - "51 0.256746 0.004340 0.248917 0.265715 10 10 2.567464 \n", - "52 0.052522 0.000903 0.051411 0.054281 10 10 0.525222 \n", - "53 0.025694 0.003825 0.023930 0.037133 10 10 0.256936 \n", - "54 0.008572 0.000565 0.008283 0.010209 10 10 0.085718 \n", + "50 0.004894 0.000222 0.004628 0.005283 10 10 0.048943 \n", + "51 0.263068 0.001402 0.261303 0.265483 10 10 2.630676 \n", + "52 0.053470 0.001030 0.051849 0.054855 10 10 0.534695 \n", + "53 0.045314 0.004231 0.041232 0.053182 10 10 0.453135 \n", + "54 0.008238 0.000564 0.007009 0.008989 10 10 0.082384 \n", "\n", " name N \n", "50 onnxruntime 50 \n", @@ -617,7 +618,7 @@ " \n", " if seq is None:\n", " seq = decompose_einsum_equation(\n", - " equation, m1.shape, m2.shape, m3.shape)\n", + " equation, m1.shape, m2.shape, m3.shape, clean=True)\n", " if sess is None:\n", " model = make_model(equation)\n", " sess = InferenceSession(model.SerializeToString())\n", @@ -671,7 +672,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] diff --git a/_unittests/ut_testing/test_einsum.py b/_unittests/ut_testing/test_einsum.py index a8df617ea..d3cc6a201 100644 --- a/_unittests/ut_testing/test_einsum.py +++ b/_unittests/ut_testing/test_einsum.py @@ -160,7 +160,7 @@ def fct(): dot = seq.to_dot() print(dot) red = dot.split('red') - self.assertEqual(len(red), 4) + self.assertEqual(len(red), 5) res = apply_einsum_sequence(seq, m1, m2, verbose=True) print("########################## END") return res @@ -312,38 +312,42 @@ def test_many_3(self): # core/tests/test_einsum.py. def optimize_compare(self, equation, operands=None, verbose=False): - with self.subTest(equation=equation): - if operands is not None: - inputs = operands - else: - eqs = equation.split("->")[0].split(",") - inputs = [] - for d, eq in enumerate(eqs): - i = numpy.arange(2 ** len(eq)).reshape( - (2,) * len(eq)).astype(numpy.float32) - inputs.append( - i + numpy.array([3 ** d], dtype=numpy.float32)) - - exp = numpy.einsum(equation, *inputs) - if verbose: - print("###### equation", equation) - path = numpy.einsum_path(equation, *inputs, optimize=False) - print(path[1]) - path = numpy.einsum_path(equation, *inputs) - print(path[1]) - - shapes = [m.shape for m in inputs] - - with self.subTest(strategy='numpy'): - seq = decompose_einsum_equation( - equation, *shapes, verbose=verbose, strategy='numpy') - got = apply_einsum_sequence(seq, *inputs, verbose=verbose) - self.assertEqualArray(exp, got, decimal=6) - with self.subTest(strategy='simple'): - seq = decompose_einsum_equation( - equation, *shapes, verbose=verbose) - got = apply_einsum_sequence(seq, *inputs, verbose=verbose) - self.assertEqualArray(exp, got, decimal=6) + for clean in [False, True]: + with self.subTest(equation=equation): + if operands is not None: + inputs = operands + else: + eqs = equation.split("->")[0].split(",") + inputs = [] + for d, eq in enumerate(eqs): + i = numpy.arange(2 ** len(eq)).reshape( + (2,) * len(eq)).astype(numpy.float32) + inputs.append( + i + numpy.array([3 ** d], dtype=numpy.float32)) + + exp = numpy.einsum(equation, *inputs) + if verbose: + print("###### equation", equation) + path = numpy.einsum_path(equation, *inputs, optimize=False) + print(path[1]) + path = numpy.einsum_path(equation, *inputs) + print(path[1]) + + shapes = [m.shape for m in inputs] + + with self.subTest(strategy='numpy'): + seq = decompose_einsum_equation( + equation, *shapes, verbose=verbose, + strategy='numpy', clean=clean) + got = apply_einsum_sequence( + seq, *inputs, verbose=verbose) + self.assertEqualArray(exp, got, decimal=6) + with self.subTest(strategy='simple'): + seq = decompose_einsum_equation( + equation, *shapes, clean=clean, verbose=verbose) + got = apply_einsum_sequence( + seq, *inputs, verbose=verbose) + self.assertEqualArray(exp, got, decimal=6) def test_numpy_test_hadamard_like_products(self): # Hadamard outer products diff --git a/mlprodict/testing/einsum_impl.py b/mlprodict/testing/einsum_impl.py index 449763229..96cde87f6 100644 --- a/mlprodict/testing/einsum_impl.py +++ b/mlprodict/testing/einsum_impl.py @@ -1,6 +1,7 @@ """ @file -@brief Function to dig into Einsum computation. +@brief Main functions decomposing einsum computation into +more simple functions. """ import numpy from .einsum_impl_classes import EinsumSubOp, GraphEinsumSubOp @@ -76,7 +77,8 @@ def analyse_einsum_equation(equation): return "".join(letters), mat, lengths, duplicates -def decompose_einsum_equation(equation, *shapes, strategy="simple", verbose=False): +def decompose_einsum_equation(equation, *shapes, strategy="simple", + clean=False, verbose=False): """ Decomposes an equation used in :epkg:`numpy:einsum` knowing the input shapes. It returns a sequence of operations @@ -86,6 +88,7 @@ def decompose_einsum_equation(equation, *shapes, strategy="simple", verbose=Fals :param shapes: sequence of input shapes :param strategy: there are different way to decompose the equation, this parameters defines the way to do it (see below) + :param clean: clean the unnecessary node in the graph :param verbose: verbosity :return: instance of @see cl GraphEinsumSubOp @@ -125,9 +128,17 @@ def decompose_einsum_equation(equation, *shapes, strategy="simple", verbose=Fals raise TypeError( "All shapes must be tuples for %r is not." % sh) if strategy in ("simple", "numpy"): - return _decompose_einsum_equation_simple( + graph = _decompose_einsum_equation_simple( equation, *shapes, verbose=verbose, keep_matmul=strategy == 'simple') - raise ValueError("Unknown strategy %r." % strategy) + else: + raise ValueError("Unknown strategy %r." % strategy) + + # Last step: clean unused nodes. + graph.mark_last_node() + if clean: + graph.simplify_mm_nodes(verbose=verbose) + graph.clean_unused_nodes(verbose=verbose) + return graph def apply_einsum_sequence(seq, *inputs, verbose=False, **kwargs): diff --git a/mlprodict/testing/einsum_impl_classes.py b/mlprodict/testing/einsum_impl_classes.py index c987a87b4..5cdafa932 100644 --- a/mlprodict/testing/einsum_impl_classes.py +++ b/mlprodict/testing/einsum_impl_classes.py @@ -1,6 +1,7 @@ """ @file -@brief Function to dig into Einsum computation. +@brief Classes representing the sequence of matrix operations to +implement einsum computation. """ import numpy from .einsum_impl_ext import ( @@ -180,7 +181,7 @@ def _compute_output_row_reduce_sum_mm(self, row, row2=None, ab=False, verbose=Fa self._check_row_(row2, True, verbose=verbose) if row2 is None: raise RuntimeError("reduce_sum_mm expects a second input.") - self._compute_output_row_reduce_sum(row2, row2=None, verbose=verbose) + self._compute_output_row_reduce_sum(row, row2=None, verbose=verbose) def _compute_output_row_squeeze(self, row, row2=None, ab=False, verbose=False): if ab: @@ -566,6 +567,7 @@ def __init__(self, letters, mat, lengths, duplicates): self._nodes = {} self._mark = {} self._ops = [] + self._inputs = {} self.last_op = None self.last_added_op = None self.metadata = dict( @@ -584,6 +586,7 @@ def append(self, op): raise RuntimeError("Key %d already added." % op) self._nodes[op] = op self.last_added_op = op + self._inputs[op] = op return None if isinstance(op, EinsumSubOp): if op in self._nodes: @@ -595,6 +598,14 @@ def append(self, op): return op raise TypeError("Unexpected type %r." % type(op)) + def mark_last_node(self): + """ + Marks the last node as the final output. + """ + if self.last_added_op is None: + raise RuntimeError("last_added_op is None.") + self.mark(-1, self.last_added_op) + def mark(self, i, op): """ Marks one input or result as an intermediate result @@ -604,6 +615,9 @@ def mark(self, i, op): """ if not isinstance(i, int): raise TypeError("i must an integer not %r." % type(i)) + if i != -1 and i not in self._inputs: + raise RuntimeError( + "Input %d was not registered in %r." % (i, self._inputs)) if isinstance(op, EinsumSubOp): if id(op) not in self._nodes: raise RuntimeError( @@ -717,3 +731,75 @@ def apply_sequence(self, *inputs, verbose=False, **kwargs): raise RuntimeError( "Sequence of operations is empty.") return last + + def clean_unused_nodes(self, verbose=False): + """ + Cleans nodes with unused outputs. + + :param verbose: display intermediate information + """ + + def iteration(it): + # Walks through all nodes. + is_used = {} + for node in self._ops: + if not isinstance(node, EinsumSubOp): + continue + if id(node) not in is_used: + is_used[id(node)] = [] + for inp in node.inputs: + if not isinstance(inp, EinsumSubOp): + continue + idn = id(inp) + if idn not in is_used: + is_used[idn] = [] + is_used[idn].append(id(node)) + + # Remove unused nodes. + removed = [] + for k, v in is_used.items(): + if len(v) == 0: + removed.append(k) + removed = set(removed) + i_rem = [] + for i, op in enumerate(self._ops): + if not isinstance(op, EinsumSubOp): + continue + if id(op) in removed and id(op) not in self._mark: + i_rem.append((i, id(op))) + for i, idn in reversed(i_rem): + if verbose: + print("[GraphEinsumSubOp.clean_nodes] remove node " + "i=%d: %d - id=%d" % (it, i, idn)) + del self._ops[i] + del self._nodes[idn] + return len(i_rem) > 0 + + it = 1 + while iteration(it): + it += 1 + + self.last_op = None + self.last_added_op = None + + def simplify_mm_nodes(self, verbose=False): + """ + Node name suffixed by `mm` are an artifact to keep + the graph consistent while building it. They can + now be replaced by the equivalent node without suffix `mm`. + + :param verbose: display intermediate information + """ + for op in self: + if not isinstance(op, EinsumSubOp): + continue + if op.name.endswith('_mm'): + if verbose: + print("[GraphEinsumSubOp.simplify_mm_nodes] node %r" + " - id=%d" % (op.name, id(op))) + if len(op.inputs) != 2: + raise RuntimeError( + "Expecting 2 inputs for node %r not %r id=%r." % ( + op.name, len(op.inputs), id(op))) + op.name = op.name[:-3] + op.inputs = op.inputs[:1] diff --git a/mlprodict/testing/einsum_impl_ext.py b/mlprodict/testing/einsum_impl_ext.py index 8e54515b7..d3271c2a4 100644 --- a/mlprodict/testing/einsum_impl_ext.py +++ b/mlprodict/testing/einsum_impl_ext.py @@ -1,6 +1,7 @@ """ @file -@brief Function to dig into Einsum computation. +@brief Functions implemented einsum computation for two +matrices having the same dimensions. """ import numpy From 218f03141fe6f9f4b12e5b5b13f9835020658979 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Fri, 30 Apr 2021 02:10:29 +0200 Subject: [PATCH 21/33] add onnx export to einsum --- _doc/notebooks/einsum_decomposition.ipynb | 176 ++++++---- _unittests/ut_testing/test_einsum.py | 71 +++- mlprodict/onnxrt/doc/nb_helper.py | 6 +- mlprodict/onnxrt/onnx_inference_exports.py | 5 +- mlprodict/onnxrt/ops_cpu/_op_numpy_helper.py | 47 +++ mlprodict/onnxrt/ops_cpu/op_matmul.py | 4 +- mlprodict/testing/einsum_impl.py | 36 +- mlprodict/testing/einsum_impl_classes.py | 345 ++++++++++++++++++- 8 files changed, 608 insertions(+), 82 deletions(-) diff --git a/_doc/notebooks/einsum_decomposition.ipynb b/_doc/notebooks/einsum_decomposition.ipynb index 06950144d..99ae47cf5 100644 --- a/_doc/notebooks/einsum_decomposition.ipynb +++ b/_doc/notebooks/einsum_decomposition.ipynb @@ -152,6 +152,15 @@ "add_notebook_menu()" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext mlprodict" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -163,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -176,7 +185,7 @@ " [12114390., 13179168.]]], dtype=float32)" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -202,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -215,7 +224,7 @@ " [12114390., 13179168.]]])" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -248,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -259,7 +268,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -270,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -279,25 +288,25 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
\n", + "
\n", "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -316,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -329,7 +338,7 @@ " [12114390., 13179168.]]], dtype=float32)" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -349,33 +358,33 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
\n", + "
\n", "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "seq_broken = decompose_einsum_equation(\"bac,cd,def->ebc\", m1.shape, m2.shape, m3.shape, \n", + "seq_clean = decompose_einsum_equation(\"bac,cd,def->ebc\", m1.shape, m2.shape, m3.shape, \n", " strategy='numpy', clean=True)\n", - "RenderJsDot(seq_broken.to_dot(size=7))" + "RenderJsDot(seq_clean.to_dot(size=7))" ] }, { @@ -385,6 +394,47 @@ "Operator *transpose_mm* is a regular transposition, it takes two inputs but only tranposes the first input before returning it. Operator *batch_dot* is a matrix multiplication. It is left that way on purpose as it may be implemented with function dot or gemm. The operator distinguishes between 3 kind of axes: batch axes, kept axes, sum(mation) axes. It then reshapes both input matrices with 3D tensors, batch axis, row axis, column axis to use function [numpy.dot](https://numpy.org/doc/stable/reference/generated/numpy.dot.html)." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ONNX\n", + "\n", + "The previous graph can be converted into ONNX." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "onx = seq_clean.to_onnx(\"Y\", \"X1\", \"X2\", \"X3\", dtype=numpy.float32)\n", + "with open(\"einsum.onnx\", \"wb\") as f:\n", + " f.write(onx.SerializeToString())\n", + "%onnxview onx " + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -394,7 +444,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -426,7 +476,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -439,7 +489,7 @@ " [12114390., 13179168.]]], dtype=float32)" ] }, - "execution_count": 12, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -459,14 +509,16 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:11<00:00, 1.11it/s]\n" + "C:\\xavierdupre\\__home_\\github_fork\\scikit-learn\\sklearn\\experimental\\enable_hist_gradient_boosting.py:16: UserWarning: Since version 1.0, it is not needed to import enable_hist_gradient_boosting anymore. HistGradientBoostingClassifier and HistGradientBoostingRegressor are now stable and can be normally imported from sklearn.ensemble.\n", + " warnings.warn(\n", + "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:13<00:00, 1.02s/it]\n" ] }, { @@ -504,61 +556,61 @@ " \n", " \n", " 50\n", - " 0.004894\n", - " 0.000222\n", - " 0.004628\n", - " 0.005283\n", + " 0.005348\n", + " 0.000432\n", + " 0.004913\n", + " 0.006119\n", " 10\n", " 10\n", - " 0.048943\n", + " 0.053479\n", " onnxruntime\n", " 50\n", " \n", " \n", " 51\n", - " 0.263068\n", - " 0.001402\n", - " 0.261303\n", - " 0.265483\n", + " 0.274089\n", + " 0.015949\n", + " 0.250878\n", + " 0.304891\n", " 10\n", " 10\n", - " 2.630676\n", + " 2.740892\n", " numpy.einsum\n", " 55\n", " \n", " \n", " 52\n", - " 0.053470\n", - " 0.001030\n", - " 0.051849\n", - " 0.054855\n", + " 0.054787\n", + " 0.005735\n", + " 0.049863\n", + " 0.065422\n", " 10\n", " 10\n", - " 0.534695\n", + " 0.547868\n", " custom_einsum\n", " 55\n", " \n", " \n", " 53\n", - " 0.045314\n", - " 0.004231\n", - " 0.041232\n", - " 0.053182\n", + " 0.050210\n", + " 0.002667\n", + " 0.043978\n", + " 0.054084\n", " 10\n", " 10\n", - " 0.453135\n", + " 0.502098\n", " tr/resh/dot\n", " 55\n", " \n", " \n", " 54\n", - " 0.008238\n", - " 0.000564\n", - " 0.007009\n", - " 0.008989\n", + " 0.008228\n", + " 0.000470\n", + " 0.007381\n", + " 0.008970\n", " 10\n", " 10\n", - " 0.082384\n", + " 0.082277\n", " onnxruntime\n", " 55\n", " \n", @@ -568,11 +620,11 @@ ], "text/plain": [ " average deviation min_exec max_exec repeat number total \\\n", - "50 0.004894 0.000222 0.004628 0.005283 10 10 0.048943 \n", - "51 0.263068 0.001402 0.261303 0.265483 10 10 2.630676 \n", - "52 0.053470 0.001030 0.051849 0.054855 10 10 0.534695 \n", - "53 0.045314 0.004231 0.041232 0.053182 10 10 0.453135 \n", - "54 0.008238 0.000564 0.007009 0.008989 10 10 0.082384 \n", + "50 0.005348 0.000432 0.004913 0.006119 10 10 0.053479 \n", + "51 0.274089 0.015949 0.250878 0.304891 10 10 2.740892 \n", + "52 0.054787 0.005735 0.049863 0.065422 10 10 0.547868 \n", + "53 0.050210 0.002667 0.043978 0.054084 10 10 0.502098 \n", + "54 0.008228 0.000470 0.007381 0.008970 10 10 0.082277 \n", "\n", " name N \n", "50 onnxruntime 50 \n", @@ -582,7 +634,7 @@ "54 onnxruntime 55 " ] }, - "execution_count": 13, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -667,12 +719,12 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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faD01XwHxQohZ5fy0Xvz9KP7YjYpT/DU4Ca2uMQQ4V+znXVbBaL3JAEgpM9F61i3fx2v9PiiKcmNS0DoYLF1xTBRCuAghvjGXUKQDmwEv89Wk6/FDS6rOWTx3jtKPkSWJLratPeAnhNALIaaZyx7S0RLFwn2C1sN6F3BOCPG3EKKb+fkGwPOWPbtox6+i0q/rKO1Y1ABYbtHmUcAIBJax3bLsoyzKez4o/v4Wvg+/Aq2EEA3ReqrTpJQ7LdYt/L1JLUdsShmpZLmaklIapZTL0P74bwUS0f7wWkspvcxfnlK7GfB6EoFcoHHlRazVVKN9sp4OBJqT/t/QEtpCsoRNU9Au+88TQnS/xi6i0S7zeVl8OUsp/y1vrFLKz6WUHdF6TpoBL5oXZaF9KClUaaNWFN+X+WToX/rqV4gG6ouSbwgq6T22dAHtRFO4X1e0y6OxZdy3oig35gDa8cZS8b/X59FKArpIKT3QSsbg8nH0Wn/fiWi9qw0snqtP+f62LUdgqm9uLxEYBQwF7kAr8wi1jEtKuUtKORStRGMF2hUp0I5V7xY7brtIKa9VQlcW0cCdxdp1klLGQtFweqV9vVLGfVTG+aD4+3sBiu5TWoJW0jcGrfTRUksgSkqZXgExKMWoZLmaEpqhaPW2R6WUJmA2Wg1wgHmdukKI69aSmredC3wshAg29xB0K0fNWFk5AI5o5QEGIcSdaHVZ1yWl3AQ8CCwTQnQuZbWZwMvCfFOJ0G5kube8QQohOgkhuphvnMhC+yBRWL+9Dxhu7t1pglb7XVlOoPUUDzTH8hra+1cWO4GLwDQhhKsQwsnig8YloJ4o4UZDs5+AR4QQ4ebfgfeAHVLKqBt+JYqilMVvaDW21+KO1jGSKrQb1t4stvwSWj3yVcxX/JYA7woh3IV2A/RzQPGb5kKvsf/RQohW5vtm3gZ+MbfrDuShXYVyQTtuYG7XQQjxoBDCU2o1tulcPqbOBiaZj7nCfLwaKIQo3sNeXjPNr7OBOQZ/8zmz8L1wu8bXe6W2eqV9wF1CCB/zFd5nbjJmgNfN55fWaLXkluMnf49WRjeEq5PlXmiliEolUMly9bNKCJGJdrB5F3hYSnnYvOwltBvVtpsvg21A64EoixeAg8AutPKFD6jg3w8pZQbazQxL0HqLR6HdlFbW7f9Au7FkVbGbAguXL0eLe5H59R9Cu2muvDzQDuApaJfBkoCPzMs+QavRuwR8x5U3sFQoc8nJFOBbtJ6fLKBME7SYT16D0eoUz5u3u8+8+C/gMBAnhEgsYdsNwOtoVwEuol1xuP9mXouiKGXyPVry5XyNdT4FnNF6c7ejldtZ+gwYIbSRMj7nak+iHUvOoN0H8iNaZwmYy7e4dk/zArRa3Ti08r3CG9S+t9j2CFfefA5ab2iU+dg8Ca3zAyllJDABrfQtBe0cNvYa+y+rz9DOL+uFEBnmeLpUQLuWFgD70UpO1nNlYnuj/kZ7D/4Epksp1xcukFL+g/YhY4+U8lyx7R7gJsbnVq5NSHm9K7KKoiiKolQFIcR7QLyU8lMr7Ps1tBpplXRVMXNv/lnA/lr3mggh/gJ+lFJ+a/HcYGCMlHJkadspN0cly4qiKIqiKFZUlmRZCNEJbTz+EPOVWqWKqDIMRVEURVEUGyaE+A6ttPIZlShXPdWzrCiKoiiKoiilUD3LiqIoiqIoilIKlSwriqIoiqIoSilKmrDAZvj5+cnQ0FBrh6EoilJuu3fvTpRSlnUSmWrNfDf+YHd39wnNmhWfU0NRFMX2XeuYbdM1yxERETIyMtLaYSiKopSbEGK3lDLC2nFUJXXMVhSlurrWMVuVYSiKoiiKoihKKVSyrCiKoiiKoiilUMmyoiiKoiiKopTCJm/wK7xZpEmTJtYOpdYqKCggJiaG3Nxca4ei3CAnJyfq1auHvb29tUNRajh1zFZqC3VurP5u5NyobvBTSnT27Fnc3d3x9fVFCGHtcJRyklKSlJRERkYGDRs2tHY4tZK6wU9Rah51bqzernVuVDf4KeWWm5urDgbVmBACX19f1fuhKIpSgdS5sXq70XOjSpaVUqmDQfWmfn4VICcVDi8HG74Cp1QP6evWY0hJsXYYSgVQx9bq7UZ+fipZVhRFKc2mafDzI5B4wtqRKNVY3pkzxD73HEkzZ1o7FEVRboBKlhVFUUpy6TDsnAURj4B/c2tHo1Rj8f/7GJ2TE74TJ1o7FEVRboBKlpUaISoqipYtWzJhwgRat25Nv379yMnJYfbs2XTq1Il27dpxzz33kJ2dDcDYsWOZPHkyXbt2pVGjRmzatIlx48bRsmVLxo4dW9Tu+vXr6datGx06dODee+8lMzPTSq9QqVJSwm8vgpMn3Pa6taOxeUKIwUKIWWlpadYOxeZkR0aS+eef+E4Yj52vr7XDUWoZdW6sGCpZVmqMkydP8vjjj3P48GG8vLxYunQpw4cPZ9euXezfv5+WLVsyZ86covVTUlLYtm0bn3zyCUOGDOHZZ5/l8OHDHDx4kH379pGYmMg777zDhg0b2LNnDxEREXz88cdWfIVKlTn4C5z7B25/A1x8rB2NzZNSrpJSTvT09LR2KDZFSsmlDz/CLjAQn4cftnY4Si2lzo03zybHWVaUG9GwYUPCw8MB6NixI1FRURw6dIjXXnuN1NRUMjMz6d+/f9H6gwcPRghBmzZtCAwMpE2bNgC0bt2aqKgoYmJiOHLkCN27dwcgPz+fbt26VfnrUqpYbjqsfw2C20OHh6wdjVKNZaxdS+6BAwS9+y46Z2drh6PUUurcePNUsqzUGI6OjkXf6/V6cnJyGDt2LCtWrKBdu3bMnz+fTZs2XbW+Tqe7YludTofBYECv19O3b19++umnKnsNig3Y/CFkxsH9C0Gnt3Y0SjVlys8n/uNPcGzWDM9hQ60djlKLqXPjzVNlGEqNlpGRQVBQEAUFBSxcuLBc23bt2pV//vmHU6dOAZCVlcWJE2pUhBot/hhsnwHtx0C9WjWfiFLBUn78kYKYGAJefBGhVx+6FNuizo3lo5JlpUb773//S5cuXejevTstWrQo17b+/v7Mnz+fBx54gLZt29KtWzeOHTtWSZEqViclrP0POLjCHVOtHY1SjRnT0kicMRPX7t1x63GrtcNRlKuoc2P5qOmulRIdPXqUli1bWjsM5Sapn2M5HF4OP4+Fu6ZD5wk33Zya7rr2uvThRyTPm0fDFctxaq6GHaxJ1DG1Zijp51jtprtWwxApilKl8rNg3atQpw1EjLN2NEo1lh8TQ8qCBXgOG6YSZUWpIWwyWVbDECmKUqU2T4f0WK1XWd3UV26qg+OyhE8+Bb0e/6efsnYoiqJUEJtMlhVFUapM4in49wto9wDU72rtaKol1cGhyTl4iPQ1a/B5+GHs69SxdjiKolQQlSwrilJ7SQm/vwT2znDHW9aORqnGpJTEf/gheh8ffCeMt3Y4iqJUIJUsK4pSex1aCqc2QO+XwT3Q2tEo1Vjmxk1k79qF3+NT0Lu5WTscRVEqkEqWFUWpnS4dgZVPQUiXChn9Qqm9pMFA/PTpOISG4j1ypLXDURSlgqkZ/BRFqX1yUmHxaHB0g3u/A729tSNSqrHUX5aSf+YM9b78AmGvfpcUpaZRPctKjfXee+9ZZb+RkZE89ZS6E95mmUywfBKkntMSZY8ga0ekVGPGzCwSvvwS544dcbv9dmuHoyhlos6P5aOSZaXGstbBICIigs8//9wq+1bKYMt0OLEW+r8PDbpZOxqlmkueOwdjYiKB/3kRIYS1w1GUMlHnx/JRZRjKdb216jBHLqRXaJutgj14c3Dra67z/fffM336dIQQtG3bFr1ez6BBgxgxYgQAbm5uZGZmcvHiRe677z7S09MxGAzMmDGDNWvWkJOTQ3h4OK1bt2bhwoV8/PHHzJ07F4Dx48fzzDPPEBUVxYABA+jatSv//vsvnTp14pFHHuHNN98kPj6ehQsX0rlz5xLjy8rK4sknn+TQoUMUFBQwdepUhg4dyqZNm5g+fTqrV69m6tSpnD9/njNnznD+/HmeeeYZnnrqKbKyshg5ciQxMTEYjUZef/117rvvPkJDQ4mMjMTPz4/IyEheeOEFNm3axNSpUzl79mxRO5988gnbt29n7dq11K1bl1WrVmGvLv9e34n1sPE9aHu/qlNWblrBpXiS5s3H/c4BOLdrZ+1wlCpmrXMjqPNjVZ8fVbKs2KTDhw/zzjvv8O+//+Ln50dycjLPPfdciev++OOP9O/fn1dffRWj0Uh2djY9evTgyy+/ZN++fQDs3r2befPmsWPHDqSUdOnShV69euHt7c2pU6f4+eefmTt3Lp06deLHH39k69atrFy5kvfee48VK1aUuN93332X2267jblz55Kamkrnzp254447rlrv2LFjbNy4kYyMDJo3b87kyZP5/fffCQ4OZs2aNQCUZTKH06dPs3HjRo4cOUK3bt1YunQpH374IXfffTdr1qxh2LBhZXpva63kM7BsPASGwaBPQPUCKjcp4YvPkQYDAaUcmxSlMqjz49Uq+/yokmXlusryKbei/fXXX9x77734+fkB4OPjU+q6nTp1Yty4cRQUFDBs2DDCw8OvWmfr1q3cfffduLq6AjB8+HC2bNnCkCFDaNiwIW3atAGgdevW3H777QghaNOmDVFRUaXud/369axcuZLp06cDkJuby/nz569ab+DAgTg6OuLo6EhAQACXLl2iTZs2PP/887z00ksMGjSIHj16XPc9ufPOO7G3t6dNmzYYjUYGDBgAcN04FSA/GxaPAQTctwAcXKwdkVLN5Z44Qdqy5fiMGY1DSIi1w1GswBrnRlDnx5JU9vlR1Swr1YadnR0mkwkAk8lEfn4+AD179mTz5s3UrVuXsWPH8v3335erXUdHx6LvdTpd0WOdTofBYCh1OyklS5cuZd++fezbt4/z58/TsmXLa7av1+sxGAw0a9aMPXv20KZNG1577TXefvvtq15jbm5uie3odDrs7e2L6iOvF2etJyWsegouHYZ75oBPQ2tHVOPUxumu46dPR+fmhu+kSdYORVHU+bGSz48qWVZs0m233cbPP/9MUlISAMnJyYSGhrJ7924AVq5cSUFBAQDnzp0jMDCQCRMmMH78ePbs2QOAvb190To9evRgxYoVZGdnk5WVxfLly8v0afVa+vfvzxdffIGUEoC9e/eWedsLFy7g4uLC6NGjefHFF4titnyNS5cuvan4FLMd38DBn+G2V6Hp1ZcBlZtX26a7ztq2jazNW/B77DHsvL2tHY5Sy6jzY9WfH1UZhmKTWrduzauvvkqvXr3Q6/W0b9+eDz74gKFDh9KuXTsGDBhQdMlo06ZNfPTRR9jb2+Pm5lb0yXnixIm0bduWDh06sHDhQsaOHVt0M8L48eNp3779TV2eef3113nmmWdo27YtJpOJhg0bsnr16jJte/DgQV588cWiT8EzZswA4M033+TRRx/l9ddfp3fv3jccm2IW9Q+sfxWaD4Rbn7d2NEoNIE0mLn30EfbBwXiPftDa4Si1kDo/Vv35URRm/bYoIiJCRkZGWjuMWuno0aMlXjJRqpda/XPMTIAZt4CTB0z4C5yqttdTCLFbShlRpTu1stpwzE779VcuvPR/BH/0EZ6DB1k7HKWK1epjag1S0s/xWsds1bOsKErNtO5lyEmBh1ZUeaKs1Eym3FziP/0Mp9at8Rh4l7XDURSlilRZsiyEaAS8CnhKKUdU1X4V5WbNmzePzz777IrnunfvzldffWWliJTrOrlBq1Pu9RIEWueOdaXmSf5+AYaLFwmeNg2hU7f8KEptOT+WKVkWQswFBgHxUsowi+cHAJ8BeuBbKeW00tqQUp4BHhVC/HJzIStK1XrkkUd45JFHrB2GUlb5WbDmWfBtCreq8W+VimFITiZp1izcevfGtUvJEzEoSm1TW86PZe1Zng98CRSNOSKE0ANfAX2BGGCXEGIlWuL8frHtx0kp4286WkVRlOvZNA1Sz8PYNWDvZO1olBoi8esZmLKzCXhB3SiqKLVNmZJlKeVmIURosac7A6fMPcYIIRYBQ6WU76P1QiuKolStiwdg21fQfgyE3mrtaJQaIj8qipRFi/C6914cmzSxdjiKolSxmym6qgtEWzyOMT9XIiGErxBiJtBeCPHyNdabKISIFEJEJiQk3ER4iqLUKiajNvmIiw/0fdva0Sg1SPzHnyAcHPB/4nFrh6IoihVU2Q1+Usok4LpTHUkpZwGzQBuGqLLjUpSbNXPmTFxcXHjooYesHUrttnMWXNirzdLnUvr0r4pSHtl79pKxfj1+Tz6Bnb+/tcNRlGqjJp0bbyZZjgVCLB7XMz+nKLXKJDXdrfWlRsOf/4UmfSHsHmtHo9QQUkriP/wQO39/fGvBTUyKUpFq0rnxZsowdgFNhRANhRAOwP3AyooISggxWAgxKy0trSKaU6qpqKgoWrZsyYQJE2jdujX9+vUjJyeH3r17UzjxQWJiIqGhoQDMnz+fYcOG0bdvX0JDQ/nyyy/5+OOPad++PV27diU5ORmA3r178/TTTxMeHk5YWBg7d+7EZDLRtGlTCkt/TCYTTZo0wbIU6PTp0wwYMICOHTvSo0cPjh07BsDUqVOZPn16UdsvvfQSnTt3plmzZmzZsgWAw4cP07lzZ8LDw2nbti0nT54kKiqKsLCiwWWYPn06U6dOLWrn2WefJSIigpYtW7Jr1y6GDx9O06ZNee211yrvTa+OpITfXgAkDPwfCGHtiJQaImP9H+Ts24ffU0+ic3GxdjiKAqhzozXOjWUdOu4noDfgJ4SIAd6UUs4RQjwBrEMbAWOulPJwRQQlpVwFrIqIiJhQEe0pN2nt/0HcwYpts04buLPUkQaLnDx5kp9++onZs2czcuTI684Hf+jQIfbu3Utubi5NmjThgw8+YO/evTz77LN8//33PPPMMwBkZ2ezb98+Nm/ezLhx4zh06BCjR49m4cKFPPPMM2zYsIF27drhb3HZdeLEicycOZOmTZuyY8cOpkyZwl9//XVVDAaDgZ07d/Lbb7/x1ltvsWHDBmbOnMnTTz/Ngw8+SH5+PkajkUuXLl3ztTg4OBAZGclnn33G0KFD2b17Nz4+PjRu3Jhnn30WX1/f675/tcKRX+HE79DvHfBuYO1olBpC5ucT/7//4di0CV7Dh1s7HMUWqXMjUDvOjWUdDeOBUp7/DfitQiNSFAsNGzYkPDwcgI4dO153rvo+ffrg7u6Ou7s7np6eDB48GIA2bdpw4MCBovUeeED7le7Zsyfp6emkpqYybtw4hg4dyjPPPMPcuXOvGDsyMzOTf//9l3vvvbfouby8vBJjGG4+sVrG261bN959911iYmKKPgVfz5AhQ4pib926NUFBQQA0atSI6OholSwD5KTC2v9AnbbQZbK1o1FqkJRFiyk4f56Qb2Yi9Hprh6MoV1Dnxqo9N9rkdNdCiMHA4CZqiB7bUIZPuZXF0dGx6Hu9Xk9OTg52dnaYTCYAcnNzS11fp9MVPdbpdBgMhqJlotileiEEISEhBAYG8tdff7Fz504WLlxYtNxkMuHl5cW+ffvKHLNery/a56hRo+jSpQtr1qzhrrvu4ptvvqFZs2ZFr+Nar8XydZT0Wmq1P9+CrAQYtRj0Nnk4q7Zq86yrxvR0Er/+GpduXXHt2dPa4Si2Sp0ba8250Sbn65RSrpJSTvT09LR2KIoNCg0NZffu3QD88suNTQi5ePFiALZu3YqnpyeFv2vjx49n9OjR3HvvvegtepM8PDxo2LAhP//8M6Dd+LN///4y7+/MmTM0atSIp556iqFDh3LgwAECAwOJj48nKSmJvLw8Vq9efUOvpdY6tw0i52o9ysHtrR1NtSCEmCuEiBdCHCr2/AAhxHEhxCkhxP+BNuuqlPJR60RqXUmzZ2NMSyPwxRevSh4UxVapc2PlsclkWVGu5YUXXmDGjBm0b9+exMTEG2rDycmJ9u3bM2nSJObMmVP0/JAhQ8jMzCy6zDR+/PiiGyYWLlzInDlzaNeuHa1bt+bXX38t8/6WLFlCWFgY4eHhHDp0iIceegh7e3veeOMNOnfuTN++fWnRosUNvZZayZAPq58BzxDo84q1o6lO5gMDLJ+wmI31TqAV8IAQolXVh2YbCi5cIPm77/EcMhinVrX2bVCqIXVurERSSpv96tixo1Ss48iRI9YOodL06tVL7tq1q8Rlu3btkrfeemsVR1R5auzPcdOHUr7pIeXx360dSamASGkDx9HiX0AocMjicTdgncXjl4GXLR7/Uta2a8IxO/Y//5FH27SV+bGx1g5FsUE19pgq1bnxWsdsm+xZVkPHKdYwbdo07rnnHt5//31rh6JcS0wkbP4QWg2DZv2tHU1NUOJsrLVx1tXcI0dIW7kKn4cfwj442NrhKIpNUOdGEFoybZsiIiJkYTe/UrWOHj1Ky5YtrR2GcpNq3M8xMx6+6aXdzDfxb5ueqU8IsVtKGWHtOIoTQoQCq6WUYebHI4ABUsrx5sdjgC5SyifK23Z1PmZLKTn/yDjyjh2j8R/r0bu7WzskxQbVuGNqLVXSz/Fax2yb7FlWFEW5irEAljwMOSlw30KbTpSrmZuejbUmXA3M2ryZ7O3b8ZsyRSXKiqJcQSXLiqJUD+tfg/P/wpDPIaittaOpSW56NlZZzUcwkgYD8dOnY9+gPt7332ftcBRFsTEqWa6GjKmpRE95nNzjJ6wdiqJUjf2LYcdM6DoF2o60djTVlnk21m1AcyFEjBDiUSmlASicjfUosERW0Gys1UXKT4vIO3mKgOefRzg4WDscRVFsjE2O4q8mJbm25AU/kPnXX/g//bS1Q1GUyndxP6x6ChrcCn3ftnY01ZpUs7FexZCURMLnn+N6yy249+1r7XAURbFBNtmzXN0v6VUmY2YmyQsW4HbH7Tg1b2btcGq1FStWcOTIkaLHb7zxBhs2bLBiRDVQdjIsHg0uvnDvfNDbWzsipQTVuWY5/uOPMeXkEPjaq2oCEkWpIDXt/GiTybJSupQff8KUno7fY5OsHUqNcaPTYxY/GLz99tvccccdFRWWYjLCL+MgIw5GLgA3/yoPIT49lzUHLlJgNF1/5VqsunZw5OzfT9rSZfg8/BCOjRpZOxxFsTnq/KhRyXI1YsrOJnnePFx79MC5TZi1w6l0H3/8MWFhYYSFhfHpp58SFRVFy5YtmTBhAq1bt6Zfv37k5OQA0Lt3b1566SU6d+5Ms2bN2LJlCwCffPIJ48aNA+DgwYOEhYWRnZ3N1KlTGTNmDN27d2fMmDHMnz+fJ564PFLWoEGD2LRpEwBubm68+uqrtGvXjq5du3Lp0iX+/fdfVq5cyYsvvkh4eDinT59m7NixRVOMhoaG8vLLLxMeHk5ERAR79uyhf//+NG7cmJkzZxbt56OPPqJTp060bduWN998syre1urjz7fhzEYY+D+o19EqIfyw4zxP/LSHC6k5Vtm/Unmk0Ujc2//Fzt8fv8lTrB2OopSLOj9WLZusWVZKlvrzzxhTUvCbXLW9yh/s/IBjyccqtM0WPi14qfNLpS7fvXs38+bNY8eOHUgp6dKlC7169eLkyZP89NNPzJ49m5EjR7J06VJGjx4NaJ+Ad+7cyW+//cZbb73Fhg0bePrpp+nduzfLly/n3Xff5ZtvvsHFxQWAI0eOsHXrVpydnZk/f36psWRlZdG1a1feffdd/vOf/zB79mxee+01hgwZwqBBgxgxYkSJ29WvX599+/bx7LPPMnbsWP755x9yc3MJCwtj0qRJrF+/npMnT7Jz506klAwZMoTNmzfTs2fPG39ja4rDy+GfT6HjI9DhIauEkG8w8eOO8/RpHkADX1erxFBdVMf7TFKXLiX38GGCP/oIvZv6+SrlZ41zI6jzozXYZLJcHQ+8lc2Ul0fSnLm4dOmCS4cO1g6n0m3dupW7774bV1ftJDZ8+HC2bNlCw4YNCQ8PB6Bjx45ERUUVbTN8+PCrntfpdMyfP5+2bdvy2GOP0b1796L1hwwZgrOz83VjcXBwYNCgQUVt//HHH2V6DUOGDAGgTZs2ZGZm4u7ujru7O46OjqSmprJ+/XrWr19P+/btAcjMzOTkyZMqWb50BFY8DvU6w50fWC2MtYcukpiZx0PdGlgthupCSrkKWBURETHB2rGUhTE1lYSPP8E5oiMegwZaOxxFKRd1fqx6NpksV7cDb1VIW7YMQ3w8wR9WffJwvU+5VcnR0bHoe71eX3SZyXKZXq+/os7q5MmTuLm5ceHChSvaKjzQANjZ2WEyXa5Lzc3NLfre3t6+6Maf4m2XJVadTndF3DqdDoPBgJSSl19+mccee6xM7dUKOamw+EFwdIOR34Od43U3qSwLtp0j1NeFnk2rvlZaqVwJn3+OMT2dOq+/rm7qU26YLZ0bQZ0fK5OqWa4GZEEBibNn4xwejkuXLtYOp0r06NGDFStWkJ2dTVZWFsuXL6dHjx7lbictLY2nnnqKzZs3k5SUVFQzVVxoaCj79u3DZDIRHR3Nzp07r9u2u7s7GRkZ5Y6pUP/+/Zk7dy6ZmZkAxMbGEh8ff8PtVXsmEyx/DFLPw73fgUeQ1UI5FJtG5LkUHuwYwrFtF60Wh1Lxco8cIWXRYrxHjcKpeXNrh6Mo5abOj1XPJnuWlSulrVyF4cJFgt58s9b0gnTo0IGxY8fSuXNnAMaPH4+3t3e523n22Wd5/PHHadasGXPmzKFPnz4lXsbp3r07DRs2pFWrVrRs2ZIOZSh1uf/++5kwYQKff/55qQeZa+nXrx9Hjx6lW7dugHajxA8//EBAQEC526oR/v4ATvwOd02HBt2sGsqCbefw0utx357CpphoAht64BvsZtWYbFl1KZ2TUhL333fQe3nh/9ST1g5HUW6IOj9WPSGltMqOyyIiIkJGRkZaOwyrkkYjp++6C52rKw2XLq2yZPno0aO0bNmySvalVJ5q83M8+AssfRTajYJhX4MVPxSmZufT+50/eTjfBadcE/3Hh9EovPylGEKI3VLKiEoI0WbZ+jE77ddfufDS/xH07jt43XOPtcNRqqFqc0xVrqmkn+O1jtmqDMPGpa/9nYJz5/GbNKnW9CortcypP2H5JG2GvkGfWDVRBljydxT3pNrjlCcZNKXdDSXKiu0xZmZy6aPpOLVti+fdd1s7HEVRqhFVhmHDpMlE0jczcWjSGPdqPJi3opQqdg8sHgP+LeCBH8HeyarhJMdlkbomBg90DHs6nKAmXlaNR6k4iV9+hTEpiZAZXyN0qp9IUZSys8kjRnWeOrUiZfz5J3knT+H32CR1cFdqnqTTsPBecPWF0b+Ak3Vnf0uKzWTJh7sRRkm9e0JVolyD5J08SfKCBXiNGIFzmzbWDkdRlGrGJjOw6jp1akWSUpI0Yyb2DerjcecAa4ejKBUrIw4WmC+Fj1kB7nWsGk78uXSWf7yHnAIj6wMlQ3qHWjWe6saWOziklMS9+x46Nzf8n3vW2uEoilIN2WSyrEDWli3kHjmC38SJCDtVLaPUILlp8MMIyEqEB38G38ZWDefCyVRWfLIXnb2e+U45DLy1PvZ6dWgsD1vu4MhYt47s7dvxf/op7G5gxABFURR1RrBBUkoSv56BXXAQnoMHWzscRak4Bbmw6EFIOAr3LYC61p2N8sLJFFZ9vg9XT0cudPQgyx5Gda5v1ZiUimPKzubStA9wbNkS7/vus3Y4iqJUUypZtkHZO3aSs28ffhMmIBwcrB2OolQMkxGWTYCoLTBsJjS53arhpF7K5rcZB3H3dWLAU21ZfPgCd7YJIsDDujcZKhUn8ZtZGOLiqPP6awi93trhKIpSTalk2QYlzpyJnb8/nua53BXb5+Z2YxNW7Nu3j99++62Co7FBUsJvL8DRldD/fWh7r1XDyc0sYPWX+9HpBQMfb8e6U4lk5Bp4uFsDq8alVJz8qCiS587Fc+gQXMowiYKiKBWvppwbVbJsY7L37CV7+3Z8Hh2HzmK+9NpOSnnF3PRVoaxz3N8MWzsgVJq/P4TIudD9Geg2xaqhGAtM/DbzAJkpedw1uS0efk58vy2KlkEedGygalprAiklce+9h3BwwP/5560djqJUGnVurBrqzjEbkzhzBnpvb7xHjrR2KEXi3nuPvKPHKrRNx5YtqPPKK9dcJyoqiv79+9OlSxd2795N586dOXjwIDk5OYwYMYK33nqLXbt28f7777Ns2TJ+/fVX7r//ftLS0jCZTLRq1YozZ86U2Hbv3r1p164df//9NwaDgblz59K5c2emTp3K6dOnOXPmDPXr1+f9999n3LhxJCYm4u/vz7x586hfvz5nz55l1KhRZGZmMnTo0KJ2N23axPTp01m9ejUATzzxBBEREYwdO5Zdu3bx9NNPk5WVhaOjI3/88QdvvPEGOTk5bN26lZdffpn7amJdZeRc2PSeNjvfHVOtGoqUkr9+OMrFU2n0G9+aOo082Xk2mWNxGUwb3kZN/HODbma6660nEwn1c6Get0uFxZO5cRNZm7cQ8NJL2NfW6eOVSqfOjbXn3KiSZRuSc+gwWZu34P/ss+hcKu7EUZ2dPHmS7777jq5du5KcnIyPjw9Go5Hbb7+dAwcO0L59e/bt2wfAli1bCAsLY9euXRgMBrp06XLNtrOzs9m3bx+bN29m3LhxHDp0CIAjR46wdetWnJ2dGTx4MA8//DAPP/wwc+fO5amnnmLFihU8/fTTTJ48mYceeoivvvrquq8jPz+f++67j8WLF9OpUyfS09NxcXHh7bffJjIyki+//PKm3yubdGQlrHkemvaHIZ9bfXa+yN+iOLHjEl2GNKRpRCAA322LwsPJjqHhda0aW3UmpVwFrIqIiJhQnu1yC4w8//M+TBLmje1EWN2bH03DlJfHpffew6FxY3xGP3jT7SmKLVLnxqplk8nyzfRSVGdJ38xE5+GB94OjrB3KFa73KbcyNWjQgK5duwKwZMkSZs2ahcFg4OLFixw5coS2bdvSuHFjjh49ys6dO3nuuefYvHkzRqORHj16XLPtBx54AICePXuSnp5OamoqAEOGDMHZ2RmAbdu2sWzZMgDGjBnDf/7zHwD++ecfli5dWvT8Sy+9dM19HT9+nKCgIDp16gSAh4fHDbwb1UzUVlg6Hup2hHvng97equGc2BnHzlVnadG1Dh3vDAXgYloO6w7F8Uj3UJwd1A1gVc3JXs+CR7swdu5O7vtmGzNGd6Rns5ubXjxpzhwKYmKoP28uwt66v3NKzabOjbXn3GiTNcu2PGZnZck9cYKMPzbgM3o0+hssiK+JXF1dATh79izTp0/nzz//5MCBAwwcOJDc3FxA+4Neu3Yt9vb23HHHHWzdupWtW7de94BQ/JJ74ePCfV5PSZfs7ezsrqgfK4yx1ok7CD89AN6hMGoJOFj3SsmFU6n8+f1Rgpt60Xt0C4QQRCVm8eC3O9AJwZiuoVaNrzZrFujO8se7U9/XlXHzd/FzZPQNt5UfE0vSN7NwHzAA127dKjBKRbEt6txYtWwyWa6Nkr6Zhc7FBe8xo60dik1KT0/H1dUVT09PLl26xNq1a4uW9ejRg08//ZRu3brh7+9PUlISx48fJyws7JptLl68GICtW7fi6elJSR/ObrnlFhYtWgTAwoULiw4y3bt3v+L5Qg0aNODIkSPk5eWRmprKn3/+CUDz5s25ePEiu3btAiAjIwODwYC7uzsZGRk3+rbYprRYbdIRR3cYswxcfKwaTmp8NmtnHMTD15k7J7VBb6djx5kkhn39D8lZ+Sx4tDP1fVXZkzUFejix5LGudG3ky4u/HOCzDSeRUpa7nfgPPgCdjsCX/lMJUSqK7VHnxqqhkmUbkHf2LOlr1+I96gE1w1Qp2rVrR/v27WnRogWjRo2ie/fuRcu6dOnCpUuX6NmzJwBt27alTZvr36zl5ORE+/btmTRpEnPmzClxnS+++IJ58+bRtm1bFixYwGeffQbAZ599xldffUWbNm2IjY0tWj8kJISRI0cSFhbGyJEjad++PQAODg4sXryYJ598knbt2tG3b19yc3Pp06cPR44cITw8vOgAVa3lZ8FP92v/PvgLeNazaji5WQWs+eoAAAMfb4uTqz0/R0Yzes4OfFwdWDGlO10a+Vo1RkXj7mTP3LGdGN6hLp9sOMH/LT1IgbHsd/lnbv2HjD/+wG/SJOyDgioxUkWxHercWDXEjXx6ryoREREyMjLS2mFUuguvvEr6mjU0+XMDdn5+1g4HgKNHj9KyZUtrh1FpevfuzfTp04mIiLB2KJWqSn+OJhP8/DAcWw0PLIZm/apmv6WFYzSx8rN9XDyTxtBn2lOnkScfrT/OjE2n6d7El69HdcTTpfJqWoUQu6WUNfsXrJiKOGZLKfnkjxN8/tcpejXz5+sHO+DqeO3ba2R+PmeGDkOajDRatQqdmsxJqSTq3FgzlPRzvNYx2yZv8KtN8mNiSVu5Eu8HHrCZRFlRbsim97RJR/q9a/VEGeDUnnhiT6Ry20Mt8K7vzpSFe/j9cBwPdK7P20NbY69XF9ZswZbFJzCZJB6+znj4O+Hh58zjPRoT5OXMaysOcd+sbcwd24kA99JnVkxesID8s2cJmfWNSpQVRalwKlm2sqQ53yKEwPfRcdYOpUZ6/PHH+eeff6547umnn2bTpk3WCaimOvAzbP4I2o+Gbo9bOxqklOzfEI1XoAverbwZ+c02Dl1I47WBLXn01oZqPOUKdjMjGCVfzCLhfAZ52VdOdODoascrbt4cPJXJW+/8y4heoTQJ9cLD3wk3Hyf05g87BZcukfjV17jddhtu5svNtsRoMJGRlEtGSi6OznZ4+jvjWIlXNBSlLNS5sXxUsmxFBZfiSftlKZ7Dh2Nfp461w6mRyjLOo3KTYiLh18ehQXcY+InVx1IGuHg6jfhzGTS9sz7Dvv6X9NwCZo+J4I5WgdYOrUa60XGWAYY+o9Uu5mYVkJGUS1pCDulJOaQn5pKemEPbAhOZybkcWRnFEfM2QoCbtxMe/k7YnT6IPrA3DYY8StzZNDx8nXF2t6+yD0QmkyQrNY8Mi5jTk7R/M5JyyUzNg2LVjo6udnj6OePp74xngAsehd/7O+Pi6aA+zCmVTp0by0cly1aUPHcu0mTCd8J4a4eiKDcmLQYWjQL3OjByAdjZxiXw/RuisXfW80LkKVxd7Pll0i20CrbN8TsVjZOrPU6u9vjXd79q2bmELKZ8u5Ps1DwmdWxAqJMj6Yk5pEQlkJDlQX6DgZxZEQfEAWDnqMfDVyvp8PRzxt3PCU8/ZzzM39uXY0xtKSU5GQWkJ+WQkZirJfJJuWQkaslxRnIuJqNFNizAzcsRd18n6jb3LorDzceJ/BwDaQk52geChGwuRaVzak8C0nR5ezt7HR7mxNnD39kiqXa+okddUZSqo5JlKzEkJ5OyeDGegwfjUM+6IwYoyg3Jz9LGUs7Phod+BVfbGFUiLSGHM/sTOOevR+h1LJtyC0GeztYOS7kJDfxd+eGp7oz/PpL/23uG1wa2YtxdzTk7/BVMmZmELPuVzGzISMwlLTFH69019/LGHEvGkH/lqBouHg54+Dnj4edk/tcZd18tmS3sGc4o7CFOysWQZ7xieyc3ezx8nfCv707jDv64+5rb8nXG3ccJvX3ZE1qjUes5T4vXkui0xBzSzQl19JFkDAWXYxc6gbuPozmRdtES6QBzYu3njL2jmlhHUSqDSpatJHn+d8i8PHwnTrR2KIpSfiYTLH8MLh3SRr4IsJ27ww9sjAYBa3IzmTqyrUqUawhvVwcWju/CM4v28d/VR3BetZT2J05Q94vPcfRyw9ELfIOvntCpqGc40VzekZBb9P3FU2mc3HWJ4oNC2Tvp8fDVktCQFj64FybVvk64+zrh4FRxp069Xoenvwue/leP9S2lJDst/3JvdGIOafHZpCXkcGr3JfKyrqzzdvFwKCrn8Cj2r5Nr1ZWmKEpNo5JlKzCmppKycCEedw7AsVFDa4djk1JTU/nxxx+ZMmVKqetMmjSJMWPGXDGuZHlNnToVNzc3XnjhhTLvIyoqikGDBnHo0KFS2923bx8XLlzgrrvuuuHYbNqm9+DoKpsZ+aJQXo6BI1svcNzBRHgzX0Z0VFdtahInez1fPdiBDxdto+l7CznXqA2hvfpccxshBC4eDrh4OFCn0dWTKxgNJjJTtB5kR2c7PHydcXS1s4nEUgiBq5cjrl6OBDf1ump5XnZBUSKtlXZo/8YcTyFze9wV6zqYby60rI8uTKbdvBwROuu/XuX61LnROmwyWb6ZO6urg+QfFmLKysL3scesHYrNSk1N5euvv77qgGAwGLCz035tt2/fftVNCpbLK0JJ+yiLffv2ERkZWe0OCGVSNPLFGJsY+cLSka2xGPJN7PUy8MPd1x98X6l+9DrBw4fWkCINPNPwTubM3cnshyLwcrmxenm9Xek9u7bO0cWegAb2BDS4uh7fkG8kvbAsxSKhTozJ4Oz+hCvqrPV2Oq2MpLBGOuByUu3h61yushKlcqlzo3XYZLJ8M3dW2zpjZibJCxbgdvvtODVvbu1wbNb//d//cfr0acLDw7G3t8fJyQlvb2+OHTvGiRMnOHr0KM2aNUOv19O7d2/Cw8PZunUrDzzwAL179+a5554jMzMTPz8/5s+fT1BQEJ9//jkzZ87Ezs6OVq1aFU3JeeTIEXr37s358+d55plneOqppwCu2Mfu3bsZN04b3q9fv8s9qbm5uUyePJnIyEjs7Oz4+OOP6d69O2+88QY5OTls3bqVl19+mfvuu6/q38TKcMXIFx/bxMgXhUxGEzvWneO83sjoO5vSwNfV2iEplSBn/37Sli3Df/yjvNyvP88t3s89M/5l/iOdCfGpfglvZbFz0OMT7IpP8NV/ByaT1OqkCxPpeK1WOi0hhwsnUimwrNEW4ObtaP5A4XxV77SDs02mETWWOjdah/otr2IpP/2EKS0Nv0nVp1d5y5ITJEZnVmibfiFu9BjZrNTl06ZN49ChQ+zbt49NmzYxcOBADh06RMOGWtnK2rVrGTBgQNH6+fn5REZGUlBQQK9evfj111/x9/dn8eLFvPrqq8ydO5dp06Zx9uxZHB0dSU1NLdr22LFjbNy4kYyMDJo3b87kyZOxt7e/Yh+PPPIIX375JT179uTFF18s2varr75CCMHBgwc5duwY/fr148SJE7z99ttERkby5ZdfVuj7ZlVpMdoNfTY28kWhQzviMGYaiA9x4N1bVXlTTSSNRuLe/i92AQH4TprMIDdX/N0cmfB9JHd//S/zxnaiTb2rSy2UK+l0oujGRlpcuaywxrtwxA7Lmw7P7k8gJ6PgivWd3e0v90IXlXdoiXVVDuFnDercWHvOjSpZrkKmnByS583H9dZbcW7TxtrhVCudO3cuOhgArFu3jnnz5hU9Lvx0evz4cQ4dOkTfvn0BMBqNBAUFAdC2bVsefPBBhg0bxrBhw4q2HThwII6Ojjg6OhIQEMClS5eoV69e0T5SU1NJTU2lp3nCgzFjxrB27VoAtm7dypNPPglAixYtaNCgASdOnKi8N8Ja8rPgp/uhIAceXmkzI19Y2vDrKXJ0Jp57qAN2anitGil16VJyDx8mePp09G5aj2mXRr4snXwLY+ft4r5Z2/jqwQ70aR5g5UirL8sa76DGV3/wyM81XFEfnZao9UyXdLOkvaP+il5oD4taaTdvR3Tq7/SmqXNj1VDJchVK/flnjMnJ+E2eZO1QyuVan3Kriqvr5UuJ2dnZpKamEhwcfNVyKSWtW7dm27ZtV7WxZs0aNm/ezKpVq3j33Xc5ePAgAI6OjkXr6PV6DAbDFfuw/KRdKxWNfHHY5ka+KPTH5nM4phkwtfGiTYiXtcNRKoExNZWEjz/BJSICj4FX1js2DXRn+ZRbeGT+LsZ/F8l7d4dxX6f6Voq0ZnNwssM/xB3/kKvHwy6crVCrj84uSqpT4rI4dygJo+HyMHg6ndDGvzbXSVv2Snv4OWFXjrGwrUWdG1Mr6VXZHpUsVxFTfj5J387BpVMnXDp2tHY4Ns/d3Z2MjIwSl23cuJE+fUq+A7558+YkJCSwbds2unXrRkFBASdOnKBly5ZER0fTp08fbr31VhYtWkRmZumXzyz34eXlhZeXF1u3buXWW29l4cKFRev16NGDhQsXctttt3HixAnOnz9P8+bNOXnyZKnxVzsb37XJkS8K5RYY+fPX0wTqYOJDba0dTq1UFTdlJ3z+OcaMDAJff63ES/sBHk4sfqwbUxbu4aWlB4lNzeXZO5rW6DIAW6O30+EV6IJXoAtw5dUnaZJkpuZpPdKJV47eEXcmnfycK4fBc/VyxLeuK00jAmnU3r9Ch+urztS50TrUb18VSVu2HEN8PMEfTLN2KNWCr68v3bt3JywsDGdnZwIDL09TvHbtWkaMGFHidg4ODvzyyy889dRTpKWlYTAYeOaZZ2jWrBmjR48mLS0NKSVPPfUUXl5epe6/+D7mzZvHuHHjEEJccRPDlClTmDx5Mm3atMHOzo758+fj6OhInz59mDZtGuHh4dXqJoarHPgZtky3yZEvCn2x8ih1syR1OgXg4W5bddS1RWXflJ175Agpixbj/eCD17wx2s3RjjkPR/DKsoN8/udJLqbm8N7wNtiry/1Wp02o4oS7jzazoSUpJXlZ5tkNE7NJi9cS6Qun0/jzu6P8vegEjdv707xrHeo1867Vw9ypc6N1CFl8NHYbEhERISMjI60dxk2TBQWcHnAndn5+NFj0U7Xo6Th69CgtW9re5XaADh06sGPHDuzt7av1PqrCTf0cYyJh3l1QLwLGrLC5G/oAjlxI56MPt9Mxz46H370Fdx8na4dURAixW0oZYe04qlJlHLOllJwb9SD5587R+Pe16D2uP225lJJPN5zksz9P0rOZP18/2AE3R9U3VN1IKYk7ncaxHXGciownP8eAm7cjzbvUoXnXOnjXqfoRb9S5seaeG691zFZHjyqQtnoNBbGxpV4+VMpnz549NWIfNs3GR74AMJokr/68n555ekLD/WwqUVYqTvrKleTs3UvQu++WKVEG7Sa1Z/s2I9jLiVeWH2LkzG3Mf6QTAR7qd6Q6EUIQ1MSLoCZe9Li3KWcPJHJ8exx71p1j9+/nCGzoQfMudWjaKRAn1+qdvFUEdW6sPCpZrmTSaCTpm29wbNkSt169rB2OolxffpaWKNvwyBcA8/45i4jKwkE60KlfqLXDUSqBMTOTSx9Nx6ldWzzvHlbu7e/rVJ9ADyemLNzD3V//y3fjOtEk4Oob0xTbZ+egp2lEIE0jAslKy+PEzksc336RzYtOsPWXkzRs40fzrnWoH+aLXpXdKBVMJcuVLGPdOvKjoqj72WfVrldZSlntYlYuu6ESK5MJlk+CuIMwyjZHvgCITs7m43UnGG9ypE5jDwIblq3HUaleEr/8CmNSEiEzZiB0N5YA9W4ewJLHujF23i6Gf/0vsx+KoEsj2/wAqJSNq6cj7fvWp33f+iREZ3B8WxwndsVxem8Czu72NO0USIuuQfiFuFXKOUydG6u3Gzk3qo9flUiaTCTOmIlD48a4973D2uGUi5OTE0lJSTeWcClWJ6UkKSkJJ6dyXnb++wM4uhL6vg3N+ldOcDcp32Di5WUHaZwvcMyThN8eYu2QlEqQd/IkyQsW4HXvvTi3CbuptsLqerJ8yi34uzsyZs5OVh+4UEFRKtbmH+LOrSOb8vC07gyc0pbgpt4c2hzLkvd2sei/O9mz/hxZaXkVtj91bqzebvTcqHqWK1HmX3+Rd/IkwR99eMO9ItZSr149YmJiSEhIsHYoyg1ycnKiXr16Zd/g0DL4exqEPwi3PFl5gd2ElKx8Jv2wmx1nk3nZ2RsXB2gY7m/tsJQKJqUk7t330Lm54f/sMxXSZoiPC0sn38KE7yN54se9xKXl8uitDVUPYQ2h1+sIbetHaFs/crMKOLU7nmPbLrJt2Wm2Lz9NSCsfmnetQ6N2/jc1hrM6N1Z/5T43opLlSiOlJHHGTOzr18fjzjutHU652dvbXzErkFLDXdgLK6ZASBcY9AnYYAJxNjGLcfN3EZuSw4d9mpOw/Dxt722KrhYPI1VTZaxbR/b27dR58w3svL2vv0EZebk4sODRLjy3ZB/vrDlKTEoOrw9qhV79DtUoTq72hPWsS1jPuqReyubY9osc3xHHH3OO4OCkp0nHAJp3DSKoiWe5Pyypc2PtpJLlSpK1dSu5hw8T9M5/EXbqbVZsWPpF7YY+Vz+47wewc7z+NlVs2+kkJv2wG71OsHB8Z5L/vEiqk56WtwRZOzSlgpmys7k07QMcW7bEa+TICm/fyV7Plw904F3Po8zZepa4tFw+vT8cJ3vbnzFOKT+vQBe6Dm1Ml8GNiD2ZyvHtFzkRGc+Rfy7i4edE865BNO9SB09/Z2uHqtgwlcVVAikliV/PwC4oCM8hQ6wdjqKUriAHFo2C3DR4dD24BVg7oqssiYzm1eUHqe/jwvTeLTi7NIqLp9Jo37c+Ds7qEFbTJH4zC0NcHHU//h9CXzkJrE4neH1QK4K9nHlnzREe/HYHsx+KwMfV9oZIVCqG0AnqNfemXnNvet5v5MzeeI5tj2PXmrPsWn2WoCaetOgaROOOATiq44pSjPqNqATZ27eTs3evNq6ygzr4KjZKSlj5JFzYo/Uo12lj7YiuYDJJPlp/nBmbTtM/yJv+0pl/Zx/BxdOBnvc3o1WPYGuHqFSw/KgokufOxXPoUFw6dKj0/T16a0OCPJ14ZvE+Rsz4l6dub0ojf1ca+bupSUxqMHtHvdaj3DWIjORcTuyM49i2ODb+cIzNi0/QqJ0fzbsFEdLSR5V5KUAVJstCiGHAQMADmCOlXF9V+65KUkriP/4Eu6AgvEqZdlJRbMLWj+Hgz3Db69BysLWjuUJOvpHnluxj5/5LPO7sicvRXJJdDXQb3pg2vethfxM36Ci2SUpJ3HvvIRwcCHjh+Srb711tgvB3d+SxBbt5ZvG+oucD3B2LEudGfq409nejkb8r9bxdVI1zDeLu40THAaF06N+A+KgMjm2/yMldlzgZGY+LpwPNOteh9a3BeAW6WDtUxYrKlCwLIeYCg4B4KWWYxfMDgM8APfCtlHJaaW1IKVcAK4QQ3sB0oEYmyxnr1pN78CBB772HztH2aj8VBYBja+DPtyFsBPSousSkLOLTc3lq9i58zuUwLt8J+wIj4YMaEn57iCq7qMEyN24ia/MWAv7vJez8q3aEk06hPmx/+XbOJWVxOiGLM4mZnEnI4kxCJmsOXCQtp6BoXQe9jga+LjTyL0ygtSS6sZ8bni61ZxY5KSUGk6TAaKLAIMk3migwmjAYL39f+JVvkFc+NkoKDFc+NlguM5ooMJhwc7JjYJsgmgZW/kQyQggCG2pjtt86oilRh7TZAg/8Gc3BjTHcOrIprXsEq9FTailRlrEChRA9gUzg+8JkWQihB04AfYEYYBfwAFri/H6xJsZJKePN2/0PWCilvO6ciRERETIyMrLsr8bKpMHAmUGDEfZ2NFyxotLq7RTlpsQdgjn9wL85PPIb2NvOjS17jyfy3ez9NMoEOzsd4X1C6NC/AU5u1S8JEULsllJGWDuOqnSjx2xTXh5nBg5CODnSaPlyhL3t/LyllCRn5XMmUUuezyRcTqjPJ2VjMF0+h/q6Omi90X5ul3ul/V2p7+OCfRlmlZNSUmCUFkmkSXtclFiWviy/+HLD5ccGyyT0BhLYAssE2HD5cWVx0Ouw1wtyCoyYJLQO9uDu9nUZ0i64yqcsz0rL46/vjnL+SDJNOwXS+8HmODipD+010bWO2WX6iUspNwshQos93Rk4JaU8Y97JImColPJ9tF7o4kEIYBqwtiyJcnWUunQZ+VFR1Pv6K5UoK7YpM0Eb+cLJA+7/0WYS5dysApb/cIT4vYk0AkIiAuh3bzNcPdXVGWsRQrgCXwP5wCYp5cLK2lfSnDkUxMRQf/48m0qUQetx9HVzxNfNkU6hPlcsKzCaiE7O1nqhi3qjs/jz2CUWR+YXrWenE4T4uOBopytKPktKYAuMlTPRhRBgr9cVJaH2ep322K7YY/Nzro525uWXl2nLzY/tLj+2K77MvPyKx1e1VbxdHfbm5XY6UdR7m5CRx+oDF1i+N5Z31hzlvd+OcmtTf+5uH0z/1nVwcaj8pNXV05FBT7Rj9+/n2LnqDInRGfSfEIZvXbdK37diO27mN60uEG3xOAboco31nwTuADyFEE2klDNLWkkIMRGYCFC/fv2bCK9qmXJySPzqK5zbt8etTx9rh6MoVzPkwZIxkBUPj6wFD+sPu2YySY5siWXzslMY84zEeeoYP6k9jRt6WTu0GqmcJXXDgV+klKuEEIuBSkmW82NiSfpmFu53DsC1a9fK2EWlsdfrzL3HbkDgFcvScgqKeqLPJGYSlZhNgdFkTiS1hNGujAns5USypAT28nIHy2TUvB87nUBvkYBWJ/7ujjzSvSGPdG/IqfhMVuyNZfneWJ5dvB8Xh0P0b12Hu9vX5ZbGvtiVoef+RgmdIOKuUOo09mT9nMP8Mi2SXqOa06Kb9Y+hStWosmsJUsrPgc/LsN4sYBZol/QqO66KkvzDDxji47XhjqrhQUmp4aSE1c/B+W0wYi7UrfyRBq4n9kQKWxafICk2i/N6Ixlhbkyf2KlKeotqsfnAl8D3hU+YS+q+wqKkTgixEqgHHDSvZqysgOI/+AB0OgL/85/K2oVVeDrb076+N+3rV9ykKrVZkwA3XujfnOf6NiPyXArL98ayxtzr7O/uyJB2wdzdvi6tgz0q7Rxcr7k3973aiT/mHObP745y4WQqPe5vpm44rgVu5qwUC4RYPK5nfq7WMaalkTT7W9x69cIlolaVKCrVxbavYN8P0OslCLvHqqGkJ+Xw79LTnN4Tj8FJxxqXPMK7BfH58LaV2juklK+kDi1xrgfsAyrlByPz8yH+MH6dnLD/fXxl7EKpYXRov7CdgXfrS1Kz80nIzCd1Zz6ZO+GAvR4/N0f83BxwtKv4JNYVGOIl2BXcjch/uxG/9wD9m67C2zmlwvel3IQ6beDOUsecKLebSZZ3AU2FEA3RkuT7gVEVEZQQYjAwuEmTJhXRXKVLmj0bU0YG/s89Z+1QFOVqJ9bDH69DyyHQ6/+sFkZBvpG9686xZ/15AGLrOrAkM40ptzfh2b7N1BUZ6ymtpO5z4EshxEBgVWkb30zpnHBwoN7YTsiLB8obs6KgEwIfV0d8XB0xmEwkZeaTmJlHdEo20SnZeDjZ4efmiI+rA3a6ivu8pxOSLiH/Usf9AhtO38XPh0bTp9F6mvoer7B9KLalrEPH/QT0BvyEEDHAm1LKOUKIJ4B1aHVuc6WUhysiKCnlKmBVRETEhIporzIVxMWRvOAHPIcMxql5M2uHoyhXij8GSx+FwNZw90yowBNGWUkpObU7nn+XniIzJY/64X78kJ3GroQ0/js8jAe7NKjymMrKYDKQkZ+Bt1Ptu5QupcwCHinDejdXOnfnNNTHJOVm2aFVjQcC55OyWbFPq28+eyELBzsdfVsGcnf7uvRs5o+DXcUcBxsAI5NzWf/tYdafGsTFuo/RfURT9PbqCllNU9bRMB4o5fnfgN8qNKJqJvGrr5EmE35PPmntUBTlStnJ8NP9YOcE9/8EDq5VHkJCdAZbl5zkwslUfOu50faexjy78SgX03KZOboj/VrXqfKYymPV6VVM2zmNnwb9RCPPRtYOp7KokjqlRqnv68JTtzflyduasD8mjeV7Ylh14CJrDl7E28Wewe2CGda+Lu1DvG76ipa7jxPDnm/P9uWn2bchmriz6QyYGIaHn22MNKRUDHUnzU3IO3OW1GXL8B41Cod69awdjqJcZiyAJQ9BeiyMXQNeIdffpgLlZOaz49czHNl6AUcXe3qNao4p1JVx3+/CYJL8OKELHRv4XL8hKyowFfDNgW9o4NGAhh4NrR1OZbrpkrrqVjqn1A5CCMJDvAgP8eK1Qa3YfCKB5XtjWbwrmu+3nSPU14Vh7esyLLwuoX433pmg1+voPqIpQU28+Ov7oyx+dxe3P9ySRuFVO7mOUnlsMlmuLgfehE8/RefoiN+kx6wdiqJcae1LELUF7v4GQjpX6q7ycwwkxmSSEJ1BYnQGiTGZJF/IQkpo07senQY1ZEdsKpO/3Y63iwOLxnWmSYDtj1G68tRKYjNjeeX2V2pMPXVlldRVp9I5pXay1+u4vWUgt7cMJCO3gLWH4lixN5bP/jzJpxtO0qG+F3e3r8ugtsF4uzrc0D4ahfvjV8+N32cdYu3Mg4TfEULXuxujVzcuV3tlmsHPWmx5Br+cAweIGnkffk88gf8Tj1s7HEW5bOds+O0F6P4M9H2rQpvOTs8vSooTzmeSGJ1BWkJO0XJnd3v8Q9zxC3GjWZc6+Aa7sXR3DC8tPUDTQHfmP9KJwCqegetGFBgLGLh8IH7Ofiy8a+ENJctqBj9FsX0XUnNYuf8Cy/fEcvxSBnY6Qe/mAQzvUJfbWgTgZF/+ETWMBSb++eUkB/+OpU4jD/qND8Pdx/aPe7XdTc/gp1xJSkn8/z5G7+ODz9ix1g5HUS47s0nrVW52J9z+xk01lZ6UQ3yUOTGO1hLj7PTLs5J5+DnhF+JOi2518Atxxz/EHRdPh6LEUkrJ15tO8eHvx7mlsS8zx3TEw8m2ZmcrzfJTy7mYdZE3u71ZY3qVK1N1uRqoKMUFezkzqVdjHuvZiKMXM1i+N4Zf911gw9FLuDvZMbBNECM7hdChHONl6+119HygOUFNvdi44BhL3t3FHeNa0aC1byW+EqUyqZ7lG5C59R+ix48n8JVX8HlojLXDURTN2c3aVNaeITD+D3B0v6FmjEYT25afZv8GbTQxoRP4BLkUJcR+IW741XPD0aX0xNdokry96jDfbTvHkHbBfHRv20oZ87Qy5BnzGLhsIEGuQXx/5/c3nCyrnmVFqZ6MJsm/pxNZvjeW3w/FkZ1vpHdzf17o15ywup7laiv1Uja/zzpI0oUsOg5oQOdBDdGpsgybVO16lm25l0KaTMR//D/s69bF6/77rB2OomiOroJfxoFPIxiz7IYT5YzkXNbNPsSls+mE9axLy+5B+AS7YleOS5FZeQaeX7Kf3w/HMaFHQ16+syU6XfXpnV16YimXsi/xzq3vqF5lRamF9DpBj6b+9Gjqz3+HGvh+2zlm/n2aQV9sZWDbIJ7r24zG/mW778Ir0IV7Xopgy+IT7F57jrjTafR9tDWuno6V/CqUiqR6lsspbc0aLjz/AsEffoDnkCHWDkdRYM8CWPUU1O0Io5aAy42NMhF1MJEN849gMkpuG9OSJh0Dyt3GmYRMJv2wm1PxmbxyV0vG96hew63lGnK5a9ld1Peoz7z+824qWVY9y4pSc6TlFPDtljPM2XqW3AIjIzrW46nbm1LP26XMbRzbdpG/fzyOvbMd/R5tTb3mtW/8dltW7XqWbZXMzyfhs89xbN4cj0GDrB2OosA/n8Efb0Dj2+C+H25oLGWj0cTOlWfYs+48fiFu9B8fhldg2U8AhdYfjuP5Jfux0wu+G9eZHk2r37BJP5/4mYScBD7o+YHqVS4HW74aqCgVwdPZnuf7NefhW0L5euNpfth+jhV7LzCqS30e79MEf/fr9xS36BaEf3131s0+xMpP99J5cCM6DmiAqEZX3morVThTDqlLl1Jw/jz+zz6DsMJMaIpSREotSf7jDWg9HB5YfEOJcmZKHr9+spc9687Tukcw9/ynY7kTZaNJ8uHvx5i4YDcN/V1Z9eSt1TJRzi7I5tuD39K5Tmc61elk7XCqFSnlKinlRE/P8tVzKkp14+fmyBuDW7Hxxd7c3b4uC7afo+eHG/lo3THSsguuu71vXTdG/F8ETSIC2bHyDKu/3E9OZv51t1OsyyZ7lm2xl8KUnU3C11/jHNERt169rB2OUpsZDbD6Gdi7ACLGwV3TQVf+m+fOH07ij3lHMBSY6PtoK5p1Kv9seslZ+Ty9aC9bTiZyf6cQpg5pfUNDLdmCJceXkJybzJTwKdYORVEUG1fXy5kPRrRlYq9GfPLHCb7aeJoF287xWK/GPNI9FBeH0tMrByc7+o5rRXBTL7YuOcnCN7bTomsQrXsG412n6mdaVa5P1SyXUeLMmSR8+hkNfvoRl/btrR2OUlsV5MLSR+HYauj5H+jzCpSzXMBkNLFz9Vl2rz2Hb11X+k8Iu6ED9IGYVCb/sIeEzDzeHtKa+zvXL3cbtiK7IJsBSwfQwqcFs/rNqpA2Vc2yotQehy+k8b/1J/jrWDx+bo48eVsT7u8cct1RgBJjMtnzexSn9yZgMkrqNvOidc+6NAr3R2+nrmBXJVWzfJMMKSkkfTsHt9tvV4myYj15GbBolDZE3IBp0HVyuZvISstj/beHuXAylZbdg+hxXzPsHcrfE7x413le//Uw/m6O/DKpG23reZW7DVvy47EfSclLUb3KN8gWrwYqSlVqHezJ3LGdiIxK5sN1x3lz5WFmbT7DM3c05e72dbErZbg4v3pu9BsfRnZ6Pkf/vcDhLRdY/+1hnN3tadk9mNa3BuPh51zFr0Yprsb1LK8/HEev5v4VOqbrpWkfkPz99zT6dQWOTZtWWLuKUmZZibBwBFw8AMO+hnb3l7uJ6GPJ/DHnMAV5RnqNak6LrkHlbiO3wMjUlYdZtCuaHk39+Oz+9vjc4NSwtiIzP5MBywbQxq8NM+6YUWHtqp5lRamdpJRsOZnIR+uOczA2jcb+rjzfrzkDWte57jCa0iQ5fySZQ5tjOXcwEQk0aO1LWM+61A/zrVbDcFY3taZn+XhcBo/9sJvezfyZMbpjhdROFly4QMqPP+I5dKhKlBXrSI2GBXdDWjTc/yM0H1CuzU0mSeSas+z6LQrvOq4MezYMn+Dyl13EpuYw+YfdHIhJ4/E+jXmub3P0NeDAvfDoQtLy0ng8XE1bryjKzRNC0LOZPz2a+rHucBzT159gysI9hNX14IV+zenVzL/U0XaETtAgzJcGYb5kJOdyZOsFjvxzgTVfH8DN25HWPYJp2T1YjdNcxWpcz/JPO8/zyvKD3NrEj9kPRdx0wnzhlVdJX72axr+vxT44+KbaUpRySziuJcp5GTBqMTS4pVybZ6fn88fcw8QcS6F51zr0eqA59o7l/5vYejKRJ3/ag8Eo+d/IdvRrXf6bAW1Ren46A5YOoGNAR764/YsKbVv1LCuKAtqIQSv2xvLJhhPEpOTQOdSHFwc0p1No2cbENxpNRB1I5NDfscQcS0GnEzQM96N1z7rUa+athp6rINWuZ/lm6t8e6FwfO53gP0sPMG7+Lr59OOKad6VeS96pU6StWIHPQw+pRFmperG74YcRoLODsWsgqG35Nj+ewvo5h8nLMXDbQy1oeUv5f4ellMz4+zTT1x2nSYAbM0d3pFEZZ66ydVJKPtj5AZn5mUwOL3/9t6IoSlnodYJ7OtZjcLtgFu86z+d/neLemdvKPIW2Xq+jcfsAGrcPIPVSNoe3xHJ020VO70nAM8CZsJ51adE1CCc3+yp6RbVPjetZLrR8bwzPL9lPRKgPc8d2ws2x/Alz9ONPkL1jB43/WI+dt5ppR6lCZzbBoge12fjGrADfxmXeVJoku3+PYueqs3gGuDBgYhi+dcuf4KbnFvDCkv2sP3KJwe2C+eCeNjf8wdMW/XziZ97e9jaT202ulBv7alPPskUHx4STJ09aOxxFsWk5+Ua+2xbFjE2nScspYEi7YN4Y3Ao/t7KXVhgKjJzek8Chv2OJO5OG3k5Hk44BdBrUEE9/dUPgjbjWMbvGJssAK/df4NnF+wgP8WL+I51wdyr7p67svXs598Ao/J9+Cr/JqtdJqUJHVmrDw/k2gdHLwKNsN+JJKclOz+ev745y/kgyTTsF0vvB5jg4lS/BNZkk649c4oPfjxGdnM0rd7Xkke6hNWpGu8OJhxmzdgyd63Tmq9u/Qn8D41RfT21KlgupMgxFKbu0nAJmbz7DrM1ncHOy4+2hrRnYJqjcx9rEmEwOb4nl+PY47Bx0DH4yHP/67pUUdc1Va5NlgLUHL/LkT3sJq+vJd+M64+l8/YRZSsn5MQ+RFxVFk/Xr0LmUf+pfRbkhu7/TJhypG4Hp/kXkSXdyMgrIzconJ6OAnMwCcjOLfZ9ZQG5mATkZBRgNJvR2Onrc15RWtwaX66CbbzDx675YZv59mtMJWTTwdeGjEe3o3LBsdXXVRWpuKvetvg+JZPGgxXg7Vc5VI5UsK4pSFsfjMnjxl/0ciEnjzrA6vD00rEzTZxeXEpfFys/3kZdl4M7JbQhpUbOO3ZWtVifLoA0n9/iPe2hRx4MFj3bGy+XaQ11l/v030Y9NIvCN1/EZNeqm96/UTlJKTCaJscCE0WDCWCAxGkzkZZuT26Ik15zwnjtKTlwsuXZ1yNEFkJttgFL+PB2c9Di5O+DsZo+zm732vas9Tu72hIb5lWu0i6w8A4t2RfPtljNcTMulVZAHk3s35s6wOqWODVpdmaSJKX9OYefFnXx/5/eE+YVV2r5UsqwoSlkZjCZmbTnDp3+cxNVRz1tDwxjctvy9zJkpeaz6Yh+p8dncMbYVTSMCKynimqfWJMv5OQYWvrkdr0AXPAOc8QpwwStA+35vSgZTftpHkwA3fhjfpdSxYaXJxNm7h2PKyaHxmtUIe9spmJcmSVpCDgnRGaTEZSNN5p+d0IaqKfyb0v4V5ue5/McmQJT2vHkbUfi9ednlNsVV65mburyu7nL7RduU+Lz2v6IwC9suIcYr17Pc9+XXW9bXJaXEUGDCZDBhKDBhNFgksgZTyd+bE1xDqcvN7ZSw3GAwlZrsWhI6gZN9Ls7GOJzdHXEKDcPZwxEnN3uc3RzMybDF9272FTKzU0pWPvP/jeK7bVGkZhfQtZEPk3s3oWdTvxpVcmFpxv4ZfL3va17v+jojm4+s1H2pZFlRlPI6eSmDF345wP7oVPq3DuS/w8IIcHcqVxu5WQX8NuMAF0+n0fO+ZrTpXa+Soq1Zas1oGIYCE/XDfEmLzybqQCI5GQUWjcILbm6cPpXH+//9h2E9GlA3xAPPAGc8/JyLko/01avJO36c4P9Nt2qibCwwkXwxi4ToDBKjM0mMziAxJpOCPOPllQRlSsaUG6OzE9jZ6dDb69Dbmb+u+F5g7+iA3k6gt9dhZ6dDZ15+9XaiaHsHJzuczb3CTi4Cxz9fQOz7ATqNhzs/Al3l9uZeSM3h2y1n+WnneXIKjPRtFcjk3o3pUL9m38T6T+w/zNg3g8GNBnNvs3utHY6iKMpVmga6s3RSN+ZsPcv//jhBv08289aQ1gxpV/ayOidXe4Y8Fc66bw+zedEJstPz6Ty4YY3tBKkKNapnubi8HANp8dmkxmeTFp9Danw2MefSSb6UjZO8/EsjdAJ3Xye8/Jxg23rc9Nk0evlJvAJdcfd1qvQZc/JzDCTGZJoT4wwSojNJuZiFyaj9bOwc9fjXc8Ovnht+9d3xD3HHJ8gVvf3lpEpKiZSAlFr+LLUvqf0PKbV1oPB587olrFfY3uXH8trPy+IxXG7PvBttXZNFG4XPy+KPLeI3b1MUq+Xrsnhc/HUV9bgXe11CR1HCWmoSbK/Tkl+9rvLHrizI1W7kO7Yaer0EvV++3JVfCU7FZzDz7zOs2BsLwNDwukzq1YimgTX/RpALmRcYuXokAS4BLLxrIc52lX+3eG3qWVajYShKxTsVn8mLv+xn7/lU+rYK5N1hYQR4lL2X2WQ0senH4xz95yKtbg2m1wPN0NWw0rqKVGvKMMpq+5kkJs/dRWNHR17s3hgyDaReyibpWAzpaSaMdpd/GXV2Ak8/ZzwDLEs7tMduXo7lTqiy0vJIjL4yMU5PyCla7uxuj1+IO/4hbuZ/3fH0d1aDjtc0uemwaBREbYEBH0DXSZW2q73nU5ix6TTrj1zCyV7H/Z3qM75HQ+p5144bV/ON+Ty89mGi0qNYNGgRDTwaVMl+a1OyXEiVYShKxTKaJHO3nmX6+uM42euZOqQVw8LrlrmXWErJjpVn2L32HA3b+dFvfGvsKmB245pIJcsl2BWVzNi5O/Fzd+SnCV0JtDNxul8/HJo2xf+zmaQl5JAan6P1TF/SeqXTEnIwFpiK2rCz1+FpTpy9LJPpQBec3exJS8i5qsc4Jz2/aHsPP6erEmMXTwd1qaSmy0qEH+6BS4dg2AxoW/G1s1JKtpxMZMam02w7k4Snsz0P3xLK2FtCS63Xr6ne2f4Oi48v5tPen3J7g9urbL8qWVYUpaKcTsjkP78cYPe5FO5oGcC7d7chsBy9zAc2RrNlyUmCm3hx1+Q2OLrYzv1YtkIly6XYcz6Fh+fsxMvVnvniAPlzviF0yWKc25Y8U5o0STJT8y6XdVzKNpd55JCekIPJdPm9FDpRVA4gdAKfIJeihNgvRCupUL+stVBqNCwYBmkxMPJ7aNa/Qps3miRrD11kxqbTHL6QTh0PJ8b3aMgDnevjegMT81R3q06v4pWtr/BI60d4LuK5Kt23SpYVRalIRpNk3j9n+WjdcRztdLwxuDX3dCh7L/PJXZfYMP8I3nVcGPxkOK5e5R+eriZTyfI1HIhJZcqXG/hi9Tu4du9Os2++uqF2TEYTGcm5Rb3RmSl5eAW44Bfihk+wq7rsUdulRsPOb2D399rjUYuhQbcKaVpKyaHYdFYfuMDqAxeJTc2hkZ8rk3o1Zmj7YBztat/vnpSSZSeXMW3nNFr7tebbft9ip6vaDwsqWVYUpTKcTcziP7/sZ1dUCn2a+/P+8LbU8SxbL3P00WTWzjyIk5t2E6BXYO0oxyuLajcaRlVqW8+Lb3SHwJjPf7xu4ZPELBr6lX2M2kI6vQ5Pfxc8/V2gtW8lRKpUS9E7YdtXcHSV9rjVEO1GPv/mN9WslJLjlzJYvf8iqw9cICopGzudoEdTP14b2JJ+reugr6V17jEZMUzdNpUdF3cQERjBR70+qvJEWVEUpbI09HNl8cRufLctig9+P0bfT/7m9UGtuLdjvev2Moe09GHYc+1Z/eV+ln60m0FPtCMw1KOKIq++an3Pcn5MLGfuvBPZ7y4e9LodO53gxwldaRLgVqn7VWowYwEc+RW2z4DYSHD0hI4PQ+eJ4BVyU02fTshk9f6LrDpwgVPxmegE3NLYj8Htgujfus51J9ypyYwmIz8d+4nP936OQPB8xPOMaDYCnbDO3d+qZ1lRlMoWlZjFf5YeYOfZZHo18+fVgS1pVoYRjlIvZbPy833kZBZw12NtCGmlZvtTZRjXcOGll0j/fR2N16/jLC6Mmr0DkPw4oWuZfuEUpUh2Muz5DnbOhvRY8GkMXSdDuwfA8cY/fJ1PymaVucTi6MV0hIDOoT4MahfMnWF18HNTdWdn0s7w5j9vsi9hH93rdufNrm8S5BZk1ZhUslw7GU1G0vPTSclLITU3ldS8y18OOgc8HD3wdPDEw9EDDwfzl6MHjnr1d6zcGJNJsmD7OaatPUZOgZG29TwZ0bEeQ9oFX7MDJSstj1Wf7yclLovbx7akWac6VRi17al2yXJVjdmZe/w4Z4fdje+j4wh44QVAG9dw1OztGEySheO70DJIXZ5QriPxpNaLvP8nKMiGhj2h6+PQtN8NTzByITWHNQe0Eov9MWkAdKjvxaC2wQxsG1Suu6BrMoPJwPzD85mxbwZOdk681PklBjcabBMjytSmZLmmjrNsNBlJy0/Tkt3cVFLyUkjLSyMlN+VyEmz5fF4K6XnpyBuYLcpJ71SUOF/xr/n74gm2p6Nn0TJ7nbpZXIGkzDxW7r/AL7tjOHwhHQe9jjtaBTCiYz16NvXHroQxlvNyDPz29QEunEwl4q5Qwu8IqbWDD1S7ZLlQZfdSRD82iey9e2myfh16L6+i588mZjFq9nZyCoz88GgXwup6VloMSjUlJZzZBNu/hpPrQe8AbUZqPcl1wm6oyfiMXH47cJHVBy4SeS4FgDZ1PRnUNoiBbYNqzbjIZXU8+Tiv//M6R5OPckf9O3i166v4OftZO6witSlZLnQjx+z/2/J/JGQnXJ6yXpvgHoFACFH0fNFzFs8LLKa2t1hmuX3h98CV21m2aV4vKz9L6xE2J8LXSnwd9Y54OXrh7eSNp6Mn3o7eeDl64eXkpT1v8djbUVsn35hPen669pWXTlp+Gul56UXPpeWllbg825B9zffQ2c75cvJcQjJ9xWOL5NvdwR29rvbdAFwbHLmQztI9MazYG0tSVj5+bo4M71CXezrUo3mdK6+aGwqMbFxwjBM7L2HvpCesR13a3RGCq2ftutqhkuUSZEdGcm70GPyfew6/iROuWn4+KZsHZm8nI7eABY92oV2IV6XEoVQzBTlw8GetJzn+CLj6a9NUR4wDt4ByN5eclc/aQxdZvf8i288mISW0qOPOoLZBDGobTOgN3Gxa00kpmXlgJrP2z8LD0YNXu7xKv9B+1g7rKipZLpsX/36R+Ox4JPLybJ7m/4pm4aRw1k55xXpF61osK1zXchvL9Qq/v6JN87/uDu5lSny9nLyqZBbIQgWmAjLyMy4n03nXTq4tn8s15l6zbTd7t1J7r4v3clsm3O4O7la7H0ApuwKjiU3HE/hldzR/Ho3HYJJFZRqD2wbjbTHufmJMBnt+P8ep3fEIvaBFtyDa962PV0Dt6KhRyXIxUkrOjXqQgthYGq/7HZ1zyQe96ORsRn27ndSsAr57tDMd6ntXeCxKNZERB7vmQOQcyE6CwDDoOgXajAC78n36TsspYN3hOFYfuMg/pxIxmiSN/F0Z3DaYwe2CaBKgauVLI6VkeuR0vj/yPXc2vJNXOr+Cl5OXtcMqkUqWFVtQ1Jt9rZ7sUpLvfFN+qe0KBO4O7tftvS6pdMTV3tUmSqVqm7KWaaQlZLP3j2iO/XsRk9FE4w4BdOjfAP/6NfvcpJLlYjL++ouYKY9T56238L7v2rOnXUjNYdTs7SRm5jPvkU50ClV3jNYmpth9mLZ/jf7wMjAZyA69g/jWj5Lk15kcg4mcfCM5BUZyC4zkFpjIKTCSk1/4WFuWU2Aqei4r38Ch2DQKjJL6Pi5FPcgtg9zVyaMMvtz7Jd8c+IYHWjzAy51ftun3TCXLSnUmpSTXmFv25Nri+fS8dAzSUGrbeqEv6sW3LB0pnnQHugbSMbCjuvmxEpRUpnF7iwBa1/WgdbAHLep4QK6RA39Fc/DvWApyjdRv5UOH/g0IbuZl08feG6WSZQvSaOTssGHI/AIarV6FsL9+IXtcWi6jZm8nLj2XuWM70bWRGke5ukjLLuD4pQyOX8ogIT2XXIsEN6fASF5hQpuvJbW5BUby8/PpXLCT+02r6SKOkiUdWWLszXfGfkTJso2w4GSvw9lej5O9/vK/Dnqc7HW0CvJgUNtg2tbzrJEHnMry7cFv+WzPZwxvOpw3u71p85eAVbKs1FZSSnIMOWUqE7nqufx0TNJU1JaznTPdg7vTp34fetTtgbeTusJbkQrLNJbujmHH2SRSsgsAEAIa+rrSMtiDVr5uBMQXkLY/mbzMAgIbetChfwMatvVD1KDx/FWybCF1+QouvvwydT/9BI8BA8q8XXxGLg/O3kF0SjZzHu5E9ya2cyORArkFRk7FZ3I8TkuMj8dpX3HpV9brOZuTVi2B1RUls84Oejz1efTOXk/v1GX45ceS5lCH/cH3carecPQuXto2FttaJsGWibGjnQ5dDTqA2IKFRxcybec07mp4F+/d+l61uClJJcuKUn4maSKrIIv0/HTOpJ5hU/QmNkVvIj4nHp3QEe4fzm31b6N3SG8aeDSwdrg1ipSSuPRcDsemc+RiOocvpHHkYjrRyTkA2EnoqnOkfbYepzyJvbcDXYY2ol3XYCtHXjFUsmxmysvj9J13YufjS+jPS8rdq5eYmcfob3dwNjGLWQ9F0KuZf4XFppSN0SQ5l5TF8bgMjsVlcMKcGEclZWEy/yo76HU0CXCjeR137StQ+zfI06nkn3nKOdjxDexdAHnpENJFG9WixWDQq5nfrG3piaVM3TaV2+vfzke9Pqo2w2SpZFlRKoaUkiNJR9gYvZGN0Rs5kXICgEaejegd0ps+IX1o49emWnyIro7Scgo4ejGdIxfSOXwhnaMX0hDR2XTKscPfpCPZ1472QxvSt0MwjnY39zPIyzFgLDDh5GZf5Z1OKlk2S5o/n/hpH1B/3lxcu3W7oTaSs/IZ/e0OTsVnMnNMB25rEVhh8SmXSSm5lJ7Hsbh0Tly6nBifvJRJnkG7RCcENPBxsUiIPWhex51QX5cSx5MstgM4v10b+u3YahA6aDVMu2mvXsfKf4FKmaw+s5pXtrzCLXVv4fM+n+Ogrz4zFKpkWVEqR2xmLJuiN7ExeiO743ZjkAZ8nHzoHdKb3vV60zW4a5WOVlIb5RmMHI1N58/lJ7E/lkGugH+8TLTpUod7OtQjPKR8dc1Gg4l9G84TuSYKQ4EJBDi72ePs7oCLh4P2r7sDzh72uHo50qidPw7OFduZpZJlwJiRwem+/XBq1Yr6c+fcVFup2fmMmbOTY3HpfDWqA/1a1+5Zb26WZV3x8bh0TsRlcvxSBmk5BUXrBLg7XtFL3LyOO00D3HF2KOenWEM+HFmhJckX9oKTF0Q8Ap0mgGfdCn1dys3ZcG4DL/z9Ah0DO/LV7V/hZFe9JmJRybKiVL70/HS2xmxlU/QmtsRuIbMgEye9E12Du9InpA896/W0qfHXa6L48xms+vYgufG5nHA0st4xn6BAV+7pUI9h7etS1+vaH1xijiWzedEJUuKyadjOj5CWPmSn55OdkU9Oej7Z6fnkZOSTnVGAIc8IgJu3I71Ht6BB6yvvIYtOzub9tUd55a6W5Z6bQCXLQPxnn5E0Yyahv/yCc1jrm24vLaeAh+fu5FBsGp8/0J672lh3at3qwLKuuLC3uHhdsbujHc3ruNOsjjst6rjTLFBLkC3Hgiw3kxHSouHgL7DrW8i4CL5NzVNR3w8OaixjW7M5ZjNPb3ya1r6tmdV3Fi721W+cT5UsK0rVKjAWEHkpko3RG9kUvYmLWRcRCNr6t6VPSB/6hPShoWdDdWN1JTAaTez5/RyRv0Uh7XUcCtLzW0oaQkC3Rr4M71CPzqE+2OmF9qXTkZ9ZwN6VZzizOwEPPyd63NeM0DbX/mBTkGck/lw6f/94nJS4bFp0q0P3EU2xc9Iz758oPv7jBELAZ/e3p2+r8l35r/XJsiExkVN9++HWuxf1PvmkAiLTZOQWMHbeLvZFp/LpfeEMblczitxvlmVdcdHNdpcyiEq8sq64cYAbLcpaV3w9UkJWIiSdKvZ1GpLPgDFPW69RH+j2ODS+/YanolYq146LO3j8z8dp5NmIb/t/i4dD9ZxyvjYlyzV1umul+pJScjzluFbnfH4jR5OPAtDAowG96/Wmd0hvwgPCsdOp+1IqUmJMJn9+d4TE6EzqtvXlXANHlh25yLmky7NQCgnt8/XcmmOPHtjpaGCHkwGjDux1OtrW8+TeiHoMbBuMm2PJPx9DgZHINVHsWX8eexc7/vEysSkzkztaBvDW0LDr9maXpNolyxV94I17+7+kLFlC49WrcAgNven2LGXlGXhk/i4io5L538h23N2+XoW2b8tuvK7YjVBf1+vXFZckNx2ST2tJcGEyXPhvXtrl9XT24NMIfJuAb2Pt35AuENCigl69Uhn2xe9j4h8TqetWl7n951brYaJqU7JcSPUsK7YqLiuOv6P/ZmP0RnbE7cBgMuDl6EXPej3pE9KHW4JvqZZXsGyR0Whi77pz7FoThaOLHT0faE6arx1nE7PJvZhFxj8JmFLyEUFO6Dr6YHS1w2CUGE3aXAV/HYvndEIWzvZ67moTxMiIenRu6HNVR1pmnoFPlxyiYHsiAUYdrk09GDmxDS7uNzYud7VLlgtVxIE3//x5Tt81EK8R9xA0dWrFBFZMdr6B8d9Fsu1MEh/c05aRESGVsh9rSsspuJwQx13uLa6cuuI8SIm6uoc46RRkXrJYUYBnyOVkuOirsfa8GsmiWjmcdJjx68bj6+zL/AHzq32doUqWFcU2ZeZn8s+Ff9gUvYnNMZtJz0/HQedAl6Au2k2CIb0JcAmwdpjVXmJMJn99f5SE8xk0iQjA3kHP0X8v4ubtyK33NqVRe/8SryRLKdlzPpVfdkezav9FMvMMNPB1YUSHetzTsR7BXs6sPxzHmysPE5eey+hO9emvc+bgH9E4utjR475mNOkYUO6r1LU6WY59/gUy/vyTxuvWYR9Yeb/8OflGJi6IZMvJRN67uw2jutSvtH1VppLqik9cyuBiWgXXFZuMkBZTrJfY/JV6HiwGpcfVX0uCfRpfmRj7NAR7dcdzTXA46TCP/fEYbvZuzB8wnzqu1f+mWZUsK4rtKzAVsC9+H3+d/4uN0RuJzYwFIMw3jD71+9A7pDdNvZqqOucbpPUyn2fXmrMgod3tIUQMDMXBqWydWdn5Bn4/FMfPkTFsO5OEENA0wI0TlzJpUced94a3oUN97QpkUqyWnMefy6DPmBa06l6+0thamyznHjnC2eH34DtxIgHPPVuBkZWyvwIjk3/YzcbjCbw1pDWjuzZAb6OTUxTWFVveaHetuuJmgebEuI47wWWtKy5rHTGAg1vJPcQ+jcHZq1LeA8U2RMZF8sRfT+Dp4Mm3/b8lxL1mXJlRybKiVC9SSk6nni66QfBA4gEA6rnVo39ofwY0HEBz7+Yqcb4BqZe0mmWvwBsvdTmflM0ve2L4+3g8A8KCGN+jIfbFyjlNRhOHt1ygxS1B2JfzqnatTZbPT5hIzoEDNPljPXqPqrlJKM9g5PGFe9lw9BJ2OkEdTyfqejlT19uZeoX/ertQ18uZIC+nmx7A+3oK64oLh2U7HpfJ8UvpFVtXnJdRQg1xaXXEDa+sIy78cgvUAlFqlS0xW3h207MEuwUzq++sGtGjXEgly4pSvSVkJ7ApZhMbzm1gx8UdGKWRBh4N6B/an/6h/VWPcw1TK5PlrB07Of/wwwS8+CK+j46r4MiuLd9gYtX+C5xJzCQ2JYfY1BxiUnK4lJ5b1GsLWm7o7+Z4RQJdmFTX89a+d3Eoe91tpdYVl1hHfMZcRxxnsaJlHXHjYnXE9VUdsVJkXdQ6/m/L/9HUqykz+87Ex8nH2iFVKJUsK0rNkZKbwobzG1gXtY5dcbswSRONPBsVJc6NvRpbO0TlJtW6ZFlKSdR992OIj6fx72vROdnGZAYFRhNxabnEmBPo2JQcYlKyte9Tc7iQmkOB8cqfh7eLPXW9nanrdWVC7efmwLmk7CuGZyteV9ys2LBs160rNpkgPaaEHuIS6ohd/K5MhFUdsVIOy04u461tbxHuH86Xt3+Ju4O7tUOqcCpZVpSaKTEnkT/P/cnvUb+z+9JuJJImXk20Uo3QAYR6hlo7ROUG1LpkOX39emKfepqgd/6L14gRlRBZ5TCZJAmZecSkZF+RUF9OrHPIKTBesU2564ot64iTi5VMJJ2+so7Y3rWEOuIm4NsInKvvkF6KdX1/+Hs+ivyI7nW780nvT2rstLQqWVaUmi8hO4H159azPmo9e+L3ANDcuzkDGg6gf4P+hHjUjHswaoNalSxLg4Ezg4eAEDRa+SvCruZc9pdSkpJdQGxKDomZeYT4OJdeV3yjdcQ+Fsmxex1VR6xUGCklM/bPYMb+GfRt0JcPenyAvd7e2mFVGpUsK0rtEpcVxx/n/uD3qN85kKDdHNjKt1VRqUZdt7pWjlC5lmsds2tOJmmWtmIF+WfPUveLz2tUogwghMDH1QGfwlIKQz4klzDSxFV1xFyuI257r6ojVqqcSZr4aNdH/HD0B4Y1Gcab3d5UM2cpilKj1HGtw5hWYxjTagwXMi+wPmo966LW8cnuT/hk9ye08WtTlDjXpJuZa4Ma1bNsys3ldP8B2NUJJHTRItu8S1VKKMiGvEzIz4T8LPNXCY/zSlueCblp2jjFV9QR+5ZcR+zdEBzUzESKdRhNRqZum8qKUysY3XI0L3Z6EZ2o+VONq55lRVEAojOiixLnwmm32/m3Y0DoAPo26Euga6CVI1SgFpVh5J88ROxjDxFwex1cQ91uYs8V8J5IE+RnX5ngFia+ZW1f6MHRDRzcwcHV4ssNHN3BO9SifKIRuNSs0QSU6q/AWMBLW17ij3N/MLndZCa3m2ybH2IrgUqWFUUp7lz6OdZFrWNd1DpOpJxAILgl+BZeiHiBJt5NrB1erVZryjAcgoMIHemGEFmQmXVzjd30CV1oia1H3WJJrtuVj4v+tXje0Zwc6x1UzbBSbeUYcnh207P8E/sPL0a8yEOtH7J2SIqiKFbVwKMBE9tOZGLbiZxJO8PvZ3/nh6M/cO+qexnVchST203GzeFmOvuUylCjepYVRbENGfkZPPHnE+xL2MfUblO5u+nd1g6pyqmeZUVRyiI5N5nP9nzGspPL8HP24/mI5xnYcGCtuQpnK2pNz7KiKBUnIz+DtLw0sg3ZZBdkl/pvVkEWOYacy88ZsonOiCYxJ5EPe35I/9D+1n4pyg0SQjQCXgU8pZTVZxxORalGfJx8eOuWtxjRdATv7niXl7e8zC8nfuGVLq/QzLuZtcNTUMmyoijFXMi8wJd7v2T1mdXI69TXCwQu9i642Lngau+Ks50zLvYuNPduzlu3vMUtwbdUUdRKcUKIucAgIF5KGWbx/ADgM0APfCulnFZaG1LKM8CjQohfKjteRant2vi3YeFdC1l2ahmf7fmMkatGcn+L+5kSPgUPBw9rh1erqWRZURQA0vLSmH1gNj8e+xGBYHSr0TT3bl6UDJf0r5O+lMlvFFswH/gS+L7wCSGEHvgK6AvEALuEECvREuf3i20/TkoZXzWhKooCoNfpubfZvfSt35cv9n7Bj0d/ZO3ZtTzX8TkGNx5cK0YSskUqWVaUWi7PmMePR39k9sHZZOZnMqTxEJ5o/4QaB7Sak1JuFkKEFnu6M3DK3GOMEGIRMFRK+T5aL3S5CSEmAhMB6tevf+MBK4pSxMvJi9e7vc7wZsN5b/t7vPbPa0WlGS19W1o7vFqnyj6iCCFaCiFmCiF+EUJMrqr9KopSMqPJyK+nfmXQ8kF8vPtjwv3D+WXIL7xz6zsqUa656gLRFo9jzM+VSAjhK4SYCbQXQrxc0jpSyllSyggpZYS/v3/FRqsotVxr39YsuGsBb9/yNuczznP/mvt5Z/s7pFnOxKtUujL1LFdQ7dtRYJIQQod2WXDGzQSuKMqNkVLyz4V/+Hj3x5xMOUlr39a82/1dOgd1tnZoio2RUiYBk6wdh6LUZjqh4+6md3Nb/dv4at9XLD6+mPVR63mm4zMMazJMlWZUgbK+w/OBAZZPWNS+3Qm0Ah4QQrQSQrQRQqwu9hVg3mYIsAb4rcJegaIoZXY46TAT1k9g8obJ5BTk8FHPj/hx4I8qUa49YoEQi8f1zM/dFCHEYCHErLQ01dulKJXF09GTV7q8wpJBS2jo2ZA3/32T0b+N5nDiYWuHVuOVeZxlc+3b6sKeZSFEN2CqlLK/+fHLAObat+u1tUZKObCUZZb1bx3PnTtXpvgURSlddEY0X+z5grVRa/F29Oaxdo8xstlI7PX21g6txrKFcZZLOG7bASeA29GS5F3AKCllhZxt1TjLilI1pJSsPrOa/0X+j+TcZO5pdg9Pt38aLycva4dWbVXWOMsl1b51uUYQvYHhgCPX6FmWUs4CZoF24L2J+BSl1kvJTWHWgVksOr4IO2HHhDYTGBc2Ts0QVQsIIX4CegN+QogY4E0p5RwhxBPAOrTyubkVlSgrilJ1hBAMbjyY3iG9+Xrf1/x07Cf+OPcH73Z/l14hvawdXo1TZaNhSCk3AZuqan+KUpvlGHL44cgPzD00l2xDNnc3uZsp4VMIcAmwdmhKFZFSPlDK879RwaVwQojBwOAmTZpUZLOKolyHu4M7L3V+ieFNh/Pq1ld5dtOzfH7b59xa91Zrh1aj3ExVeKXUvoGqf1OUG2U0GVl2chmDlg3i872fE1EngmVDljH1lqkqUVYqjZRylZRyoqenp7VDUZRaqal3U2b3m01jr8Y8s/EZdlzcYe2QapSbSZZ3AU2FEA2FEA7A/cDKighKHXgVpXyklGyO2cyIVSN48983qeNWh/kD5vPFbV/Q2KuxtcNTFEVRKpmnoyez+s4ixD2EJ/96kr3xe60dUo1RpmTZXPu2DWguhIgRQjwqpTQAhbVvR4ElqvZNUare4cTDPLr+UR7/83EKTAV83PtjfrjzBzoGdrR2aIqiKEoV8nbyZna/2QS6BDJlwxQ1UkYFKfNoGNag7qxWlNJZjnDh4+TDpHaTGNFsBPY6NcKFLbCF0TCqikXN8oSTJ09aOxxFqfXisuIY+/tYMvIzmNt/Ls19mls7JJt3rWO2TY5krWqWFaV0qbmpfLDzA4asGMLG6I1MbDuRNXev4YEWD6hEWbEKVTqnKLaljmsd5vSfg7OdMxP/mMjp1NPWDqlas8lkWR14FeVquYZc5hycw13L7uLHYz8ytPFQ1gz///buPbbNqw7j+HOcxIlzsdPGbps1adqsqEnHEJRQyrSNXcrWrisDJDYKmgRqG0CswBhiMIY0IW1I46LuAlqLiqpq2tC0wtRsEyswxICNjW7wB2sYZVOTJesldzd3Jz78kcZJmjiXNs15bX8/qmX79Xve/uy8PXl6fHz8nHZ9aBdLwQEAJlheuFz7btwnn/Fp5+Gdaow2ui4pZXkyLAMYMxwf1qG3D2nrM1u1+43dWrd0nQ5uPcgKFwCAaVUEK/TLT/xSQ/EhbT+8Xc3d87JoWcYhLAMe9nLzy7rt2dv0/b9+XyV5Jdp3wz49ev2jWr2I9WzhHUydA7xr9aLV2nvDXvXEerTjhR061XPKdUkpx5NhmY4Xme4/7f9R7eFaffkPX1Z3rFsPXv2gntjyhNaXrnddGjAJU+cAb6taXKU9G/eoY6BDOw7vUGtfq+uSUoonwzIdLzLVie4Tuucv9+jWult1tP2ovvOR7+jQpw5p86rN8hlP/nMFAKSAyyOX6xfX/0Knek+p9ve16uzvdF1SyuC3L+AB0cGofvb6z3Tzb2/WC8df0Jfe/yU9/5nndfva2+XP8rsuDwCQBtYtXaeHr3tYDV0Nqv19raKDUdclpQTCMuDQ4PCgDrx5QDf95ibt//d+bVq1Sc9++lnd+eE7FfQHXZcHAEgzG0o3aPe1u3Ws85i++oevqifW47okzyMsAw7EbVzPv/O8PvnMJ/XjIz/WZSWX6amtT+n+K+9XaWGp6/KAOeFzJkBquarsKv3k6p/ozdY39bU/fk19Q32uS/I0T4ZlOl6ks9dOvKZtz23T3X+5W4U5hdqzcY/2fGKPqhZXuS4NOC98zgRIPddXXK8fXfUj/fP0P/WNF7+hgeEB1yV5VrbrAqZira2TVFdTU7PTdS3AXFhr1TnQqZa+FrX2tup032m19rWqpbdFLX0tau5u1tG2o1pWsEwPXPmAtlRu4YN7AAAnNq/arMHhQd37t3t108GbtHrRalUEKyZcLim4RFm+LNelOuXJsAx4TdzG1dHfMRJ8+1oS4fd07+kJ21r7WhWLxya1L8wpVCQ/okggom99+Fv6fPXnlZuV6+CZAAAw5pbVt6ggp0CHGw6rMdqourfr1B3rTjye7ctWeVH5SHguqlBFqEIrgytVEaxQJBCRMcZh9QuDsIyMNhwfVsdARyL8Jrtu62vTkB2a1D7oDyoSiCiSH1HN0hqF88NaEliicH5YkUAkcTuQHXDw7AAAmNnGio3aWLFR0sg7pG39bWqMNqoh2qDj0eNqjDbqePS4XnnvlQnTNYL+oKpLqrV28VpVl1SrenG1VgRXpN07poRlpBVrrfqG+tQ10KXOgU51DnSqa6BLXQNdautvmzg9ordVbf1tGrbDk45TnFucGAmuLK7UkvwlCgfCWpK/RJFAROFAWOFAWHnZeQ6eJQAAF4cxJvE7bt3SdRMei9u4TvWc0vHocTVEG/RWx1uqb6vX4/WPJ95Vzc/OV9XiKq0tGQvQq0KrlO1L3cjpycqNMVslbV29mq/0zWSxeCwRdEeDb3QgOiEET3U91TQISTIyWpS3KDESvGbRmokBeHRUOBBWTlbOAj9bIHXRZwOZwWd8Ki0sVWlhqT52yccS22PxmN7pfEdH246qvr1e9W31OnjsoPrqR1bZyM3K1Y0rb9S9G+5NyXdajbXWdQ1J1dTU2CNHjrguAxcobuPqjnWrq3/caO/guBDcf3b0d3Bi6J1u7cdsX7aKc4tVnFusUG4ocTuYG5y0PeQPqThv5H6OjxCMhWGMed1aW+O6joVEnw1g1HB8WA3RBh1tP6o3Tr2hp//7tKpLqvXwtQ9racFS1+VNMl2f7cmRZXhX/1D/hEA7fppDstHf6GB0yqkO0shob5G/KBFwS/JKdGnoUoVyQxNC8Ln3A9mBjPhQAQAAqSjLl6XK4kpVFlfq5sqbdXXZ1br7pbu17blteuS6R3RZ+DLXJc4aYTlDDcWHFB2MjgXf/nNGe6cIwV0DXdOuwxjIDoyN5uaGtKZgjUL+cSE37+zor39s9LfIX5TxS9IAAJDurim/Rgc2H9CuF3fpi7/7ou6/8n7dsPIG12XNCmE5xVlr1RPrmXYOb2LaQ//Y9jOxM0mPmW2yJ0xnWF64XGtL1k6c2jDFNUuhAQCAZNYsXqMntjyhb/7pm7rrz3fpjq47VPuBWs+/U0xY9rjeWK+au5vVdKZJTd1Niev3ut9Te3+7ogPRKZc0G1WUUzQWaPNCWhFckXRqw+jtgpwCz5+4AAAg9YQDYe27cZ/ue/k+PfqvR/V219v64RU/nLfVpYbjI9M+5/Nda8KyY3EbV0tvSyIIv3vm3bFQfKZJbf1tE/YvyClQeVG5VhSt0AeXfHDsg22jUxvyxkJv0B9M6aVaAABA+snNytUDVz6gS4sv1UNvPKTmM8166LqHFA6E53wsa60azzTq7+/9Xa+efFWvnXxND171oK5YfsW81evJJJVuyxD1xnonBODxI8TNZ5o1GB9M7OszPi3LX6ayojJdU36NyorKVFZYlrgO5YYY9QUAACnNGKMdl+/QyuBK3fPXexIf/KtaXDVj25beFr168tVEQD7Zc1KStKxgma4tv1bFecXzW2s6LR3X0d+hu/58l6SRVRaMjEb+nL2tkR/OdNunfexsSJ2uzej24fiwTvScmHJ0uDCnUOVF5ZOCcFlRmUoLSlnjF0gDmbR03LgBjp3Hjh1zXQ6AFFPfVq9dL+5SdDCqj5d9XEPxIcXiscR1LB5TbHjkuifWo6buJklSKDek9cvWa0PpBn209KNaUbTivAcUM2rpuLiNa/Q/AFZW1lpZjd0f+TN5+3RtEo/N0Gb8bZ/xqbSgdNLocHlRuYL+IKPDANKGtbZOUl1NTc1O17UASD3VJdV6csuT+sHLP1B9e71yfDmJS7YvW/4svwpyCpTjy5E/y6/PrvmsNpRuUNXiqgX5au20CsuL8hZp/6b9rssAAADAHETyI3ps42Ouy5jSxY/jAAAAQIoiLAMAAABJEJYBAACAJAjLAAAAQBKeDMvGmK3GmL1dXV2uSwEAAEAG82RYttbWWWtrQ6GQ61IAAACQwTwZlgEAAAAvICwDAAAASXj6666NMS2SGs6jaUiSFyY8X+w65uv4F3Kc82k7lzaz3Xc2+4Ultc7y701lnP8Le5xk7SustZELOG7KGP26a0m3STqf77vmnF3Y48y1/cXYnz57DOf/wh5r7n22tTbtLpL2uq5hIeqYr+NfyHHOp+1c2sx239nsJ+mI63NiIS6c/wt7HK+83ql88cpryDm7cPvTZ8//eeH1Oubz+AudW9J1Gkad6wLOuth1zNfxL+Q459N2Lm1mu69XfuZe4JXXIhPO//loD++8hpyzC7e/V37mXuCV1yJVzv8LPdac23p6GgYwn4wxR6y1Na7rAADMjD4bXpGuI8vAVPa6LgAAMGv02fAERpYBAACAJBhZBgAAAJIgLAMAAABJEJYBAACAJAjLyFjGmEpjzD5jzNOuawEATI8+G64QlpFWjDG/MsacNsb8+5ztm4wxbxlj/meM+a4kWWvfsdZud1MpAIA+G6mAsIx0s1/SpvEbjDFZkn4uabOktZK2GWPWLnxpAIBz7Bd9NjyOsIy0Yq19SVL7OZvXS/rf2VGJQUm/lnTLghcHAJiAPhupgLCMTLBc0rvj7jdJWm6MKTHGPCbpQ8aY77kpDQBwDvpseEq26wIAV6y1bZK+4roOAMDM6LPhCiPLyATNksrH3S87uw0A4D302fAUwjIywT8kvc8Ys8oY45f0OUmHHNcEAJgafTY8hbCMtGKMeVLSK5LWGGOajDHbrbVDku6Q9IKkeklPWWvfdFknAIA+G6nBWGtd1wAAAAB4EiPLAAAAQBKEZQAAACAJwjIAAACQBGEZAAAASIKwDAAAACRBWAYAAACSICwjoxhjrDHmp+Puf9sYc5/DkgAASdBnwwsIy8g0A5I+Y4wJuy4EADAj+mw4R1hGphmStFfSna4LAQDMiD4bzhGWkYl+LukLxpiQ60IAADOiz4ZThGVkHGttVNIBSV93XQsAYHr02XCNsIxMtVvSdkkFjusAAMxst+iz4QhhGRnJWtsu6SmNdL4AAA+jz4ZLhGVksp9K4hPWAJAa6LPhhLHWuq4BAAAA8CRGlgEAAIAkCMsAAABAEoRlAAAAIAnCMgAAAJAEYRkAAABIgrAMAAAAJEFYBgAAAJIgLAMAAABJ/B/VHA2hxQjfYQAAAABJRU5ErkJggg==\n", + "image/png": 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\n", 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" ] @@ -708,7 +760,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [] diff --git a/_unittests/ut_testing/test_einsum.py b/_unittests/ut_testing/test_einsum.py index d3cc6a201..2578c86a0 100644 --- a/_unittests/ut_testing/test_einsum.py +++ b/_unittests/ut_testing/test_einsum.py @@ -6,12 +6,15 @@ from contextlib import redirect_stdout import itertools import numpy +from onnxruntime import ( + InferenceSession, GraphOptimizationLevel, SessionOptions) from pyquickhelper.pycode import ExtTestCase from mlprodict.testing.einsum_impl_ext import ( numpy_diagonal, numpy_extended_dot, numpy_extended_dot_python) from mlprodict.testing.einsum_impl import ( analyse_einsum_equation, decompose_einsum_equation, EinsumSubOp, apply_einsum_sequence) +from mlprodict.onnxrt import OnnxInference class TestEinsum(ExtTestCase): @@ -180,11 +183,13 @@ def test_decompose_einsum_equation_py(self): m1 = numpy.arange(0, 24).astype(numpy.float32).reshape((2, 3, 4)) m2 = numpy.arange(0, 20).astype(numpy.float32).reshape((4, 5)) verbose = False - for strat in ['numpy', 'simple']: + for strat, opname in [('numpy', 'batch_dot'), + ('simple', 'matmul')]: with self.subTest(strategy=strat): seq = decompose_einsum_equation( "bac,ch->ah", (2, 3, 4), (4, 5), strategy=strat, verbose=verbose) + self.assertIn(opname, seq.to_dot()) res1 = apply_einsum_sequence(seq, m1, m2, verbose=verbose) res2 = apply_einsum_sequence( seq, m1, m2, matmul_impl='py', verbose=verbose) @@ -195,6 +200,68 @@ def test_decompose_einsum_equation_py(self): ValueError) self.assertEqualArray(res1, res2) + def test_decompose_einsum_equation_onnx(self): + m1 = numpy.arange(0, 24).astype(numpy.float32).reshape((2, 3, 4)) + m2 = numpy.arange(0, 20).astype(numpy.float32).reshape((4, 5)) + verbose = False + for strat, opname in [('numpy', 'batch_dot')]: # pylint: disable=W0612 + with self.subTest(strategy=strat): + seq = decompose_einsum_equation( + "bac,ch->ah", (2, 3, 4), (4, 5), strategy=strat, + verbose=verbose) + res1 = apply_einsum_sequence(seq, m1, m2, verbose=verbose) + self.assertRaise( + lambda: seq.to_onnx( # pylint: disable=W0640 + "Y", "X1", "X2", dtype=numpy.float32), + NotImplementedError) + seq.simplify_mm_nodes() + seq.clean_unused_nodes() + onx = seq.to_onnx("Y", "X1", "X2", dtype=numpy.float32) + + oinf = OnnxInference(onx) + oxres = oinf.run({'X1': m1.astype(numpy.float32), + 'X2': m2.astype(numpy.float32)}) + res2 = oxres['Y'] + self.assertEqualArray(res1, res2) + + oinf = OnnxInference(onx, runtime="onnxruntime1") + oxres = oinf.run({'X1': m1.astype(numpy.float32), + 'X2': m2.astype(numpy.float32)}) + res2 = oxres['Y'] + self.assertEqualArray(res1, res2) + + def test_decompose_einsum_equation_onnx2(self): + m1 = numpy.arange(0, 24).astype(numpy.float32).reshape((2, 3, 4)) + m2 = numpy.arange(0, 20).astype(numpy.float32).reshape((4, 5)) + m3 = numpy.arange(0, 77 * 5).astype(numpy.float32).reshape((5, 7, 11)) + verbose = False + for strat, opname in [('numpy', 'batch_dot')]: # pylint: disable=W0612 + with self.subTest(strategy=strat): + seq = decompose_einsum_equation( + "bac,cd,def->ebc", (2, 3, 4), (4, 5), (5, 7, 11), + strategy=strat, verbose=verbose) + res1 = apply_einsum_sequence(seq, m1, m2, m3, verbose=verbose) + # verbose=verbose) + seq.simplify_mm_nodes() + seq.clean_unused_nodes() + onx = seq.to_onnx("Y", "X1", "X2", "X3", dtype=numpy.float32) + + oinf = OnnxInference(onx) + oxres = oinf.run({'X1': m1.astype(numpy.float32), + 'X2': m2.astype(numpy.float32), + 'X3': m3.astype(numpy.float32)}) + res2 = oxres['Y'] + self.assertEqualArray(res1, res2) + + so = SessionOptions() + so.graph_optimization_level = GraphOptimizationLevel.ORT_DISABLE_ALL + oinf = InferenceSession(onx.SerializeToString(), so) + oxres = oinf.run(None, {'X1': m1.astype(numpy.float32), + 'X2': m2.astype(numpy.float32), + 'X3': m3.astype(numpy.float32)}) + res2 = oxres[0] + self.assertEqualArray(res1, res2) + def test_decompose_einsum_equation_pyf(self): m1 = numpy.arange(0, 8).astype(numpy.float32).reshape((2, 2, 2)) m2 = numpy.arange(0, 4).astype(numpy.float32).reshape((2, 2)) @@ -471,5 +538,5 @@ def test_np_test_edge_cases_duplicate_indices(self): if __name__ == "__main__": - # TestEinsum().test_case_2_A() + # TestEinsum().test_decompose_einsum_equation_onnx2() unittest.main() diff --git a/mlprodict/onnxrt/doc/nb_helper.py b/mlprodict/onnxrt/doc/nb_helper.py index 6629c5104..f76397e43 100644 --- a/mlprodict/onnxrt/doc/nb_helper.py +++ b/mlprodict/onnxrt/doc/nb_helper.py @@ -10,7 +10,7 @@ def onnxview(graph, recursive=False, local=False, add_rt_shapes=False, - runtime='python'): + runtime='python', size=None): """ Displays an :epkg:`ONNX` graph into a notebook. @@ -23,12 +23,14 @@ def onnxview(graph, recursive=False, local=False, add_rt_shapes=False, the runtime has to be `'python'` :param runtime: the view fails if a runtime does not implement a specific node unless *runtime* is `'empty'` + :param size: graph size .. versionchanged:: 0.6 Parameter *runtime* was added. """ sess = OnnxInference(graph, skip_run=not add_rt_shapes, runtime=runtime) - dot = sess.to_dot(recursive=recursive, add_rt_shapes=add_rt_shapes) + dot = sess.to_dot(recursive=recursive, + add_rt_shapes=add_rt_shapes, size=size) return RenderJsDot(dot, local=local) diff --git a/mlprodict/onnxrt/onnx_inference_exports.py b/mlprodict/onnxrt/onnx_inference_exports.py index 2e2417b2a..6ff142996 100644 --- a/mlprodict/onnxrt/onnx_inference_exports.py +++ b/mlprodict/onnxrt/onnx_inference_exports.py @@ -47,7 +47,7 @@ def to_dot(self, recursive=False, prefix='', # pylint: disable=R0914 'nodesep': '0.05', 'width': '0.5', 'height': '0.1', - 'size': '5', + 'size': '7', } One example: @@ -99,7 +99,7 @@ def dot_label(text): 'nodesep': '0.05', 'width': '0.5', 'height': '0.1', - 'size': '5', + 'size': '7', } options.update(params) @@ -201,6 +201,7 @@ def dot_label(text): iname += 1 dobj['name'] = name node.name = name + fill_names[name] = node atts = [] if 'atts' in dobj: diff --git a/mlprodict/onnxrt/ops_cpu/_op_numpy_helper.py b/mlprodict/onnxrt/ops_cpu/_op_numpy_helper.py index b8c3a2ee6..e2f1e0556 100644 --- a/mlprodict/onnxrt/ops_cpu/_op_numpy_helper.py +++ b/mlprodict/onnxrt/ops_cpu/_op_numpy_helper.py @@ -50,3 +50,50 @@ def _numpy_dot_inplace_right(a, b): except ValueError: # pragma no cover return numpy.dot(a, b) return numpy.dot(a, b) + + +def numpy_matmul_inplace(inplaces, a, b): + """ + Implements a matmul product, deals with inplace information. + """ + if inplaces.get(0, False) and hasattr(a, 'flags'): + return _numpy_matmul_inplace_left(a, b) + if inplaces.get(1, False) and hasattr(b, 'flags'): + return _numpy_matmul_inplace_right(a, b) + return numpy.matmul(a, b) + + +def _numpy_matmul_inplace_left(a, b): + "Subpart of @see fn numpy_matmul_inplace." + if a.flags['F_CONTIGUOUS']: + if len(b.shape) == len(a.shape) == 2 and b.shape[1] <= a.shape[1]: + try: + numpy.matmul(a, b, out=a[:, :b.shape[1]]) + return a[:, :b.shape[1]] + except ValueError: + return numpy.matmul(a, b) + if len(b.shape) == 1: + try: + numpy.matmul(a, b.reshape(b.shape[0], 1), out=a[:, :1]) + return a[:, :1].reshape(a.shape[0]) + except ValueError: # pragma no cover + return numpy.matmul(a, b) + return numpy.matmul(a, b) + + +def _numpy_matmul_inplace_right(a, b): + "Subpart of @see fn numpy_matmul_inplace." + if b.flags['C_CONTIGUOUS']: + if len(b.shape) == len(a.shape) == 2 and a.shape[0] <= b.shape[0]: + try: + numpy.matmul(a, b, out=b[:a.shape[0], :]) + return b[:a.shape[0], :] + except ValueError: # pragma no cover + return numpy.matmul(a, b) + if len(a.shape) == 1: + try: + numpy.matmul(a, b, out=b[:1, :]) + return b[:1, :] + except ValueError: # pragma no cover + return numpy.matmul(a, b) + return numpy.matmul(a, b) diff --git a/mlprodict/onnxrt/ops_cpu/op_matmul.py b/mlprodict/onnxrt/ops_cpu/op_matmul.py index 2e329afc7..d80a34533 100644 --- a/mlprodict/onnxrt/ops_cpu/op_matmul.py +++ b/mlprodict/onnxrt/ops_cpu/op_matmul.py @@ -5,7 +5,7 @@ @brief Runtime operator. """ from ._op import OpRunBinaryNum -from ._op_numpy_helper import numpy_dot_inplace +from ._op_numpy_helper import numpy_matmul_inplace class MatMul(OpRunBinaryNum): @@ -14,7 +14,7 @@ def __init__(self, onnx_node, desc=None, **options): OpRunBinaryNum.__init__(self, onnx_node, desc=desc, **options) def _run(self, a, b): # pylint: disable=W0221 - return (numpy_dot_inplace(self.inplaces, a, b), ) + return (numpy_matmul_inplace(self.inplaces, a, b), ) def to_python(self, inputs): return "import numpy", "return %s @ %s" % tuple(inputs) diff --git a/mlprodict/testing/einsum_impl.py b/mlprodict/testing/einsum_impl.py index 96cde87f6..d98544c47 100644 --- a/mlprodict/testing/einsum_impl.py +++ b/mlprodict/testing/einsum_impl.py @@ -93,7 +93,14 @@ def decompose_einsum_equation(equation, *shapes, strategy="simple", :return: instance of @see cl GraphEinsumSubOp About *strategy*: - * `'simple'`: align all dimensions in the alphabetical order + * `'simple'`: align all dimensions in the alphabetical order, + some generic matrix multiplication remains implemented with + :epkg:`numpy:einsum` but only with two matrices aligned on + the same dimension (see @see fn numpy_extended_dot) + * `'numpy'`: same as `simple` but the decomposition does not use + :epkg:`numpy:einsum` anymore but only multiplication or + matrix multiplication merged into a single operator called + *batch_dot* (see @see fn numpy_extended_dot_matrix) Available operations: *expand_dims*, *transpose*, *matmul*, *reduce_sum*, *id*, *squeeze*, *diagonal*. It analyses an equation and produces a graph @@ -128,8 +135,10 @@ def decompose_einsum_equation(equation, *shapes, strategy="simple", raise TypeError( "All shapes must be tuples for %r is not." % sh) if strategy in ("simple", "numpy"): + op_matmul = {'simple': 'matmul', + 'numpy': 'batch_dot'} graph = _decompose_einsum_equation_simple( - equation, *shapes, verbose=verbose, keep_matmul=strategy == 'simple') + equation, *shapes, verbose=verbose, op_matmul=op_matmul[strategy]) else: raise ValueError("Unknown strategy %r." % strategy) @@ -259,12 +268,17 @@ def _apply_squeeze_transpose(op, row_last, row_output): def _apply_einsum_matmul(fd, op1, op2, axes, left, right, ndim, - keep_matmul, row1, row2, verbose=False): + op_matmul, row1, row2, verbose=False): """ Decomposes the generic matrix multiplication into numpy operations - if *keep_matmul* is False. + depending on the operator to use for matrix multiplication + *op_matmul* (see @see fn decompose_einsum_equation). """ - if keep_matmul: + allowed = {'matmul', 'batch_dot', 'dot'} + if op_matmul not in allowed: + raise ValueError( + "Unknown operator op_matmul=%r not in %r." % (op_matmul, allowed)) + if op_matmul == 'matmul': if verbose: print(" -- MATMUL -> matmul axes=%r left=%r right=%r" "" % (axes, left, right)) @@ -368,12 +382,14 @@ def _apply_einsum_matmul(fd, op1, op2, axes, left, right, ndim, def _decompose_einsum_equation_simple(equation, *shapes, verbose=False, - keep_matmul=True): + op_matmul='matmul'): """ - Applies strategy simple of function @see fn decompose_einsum_equation. + Applies strategy `simple`, `numpy` + defined in by function @see fn decompose_einsum_equation. - :param keep_matmul: break matmul operator into numpy operations - or keep is it is + :param op_matmul: which operator to use for matrix multiplication, + a single operator *matmul*, or *batch_dot* with *transposes*, + *reduce_sum*, or just *dot* """ letters, mat, lengths, duplicates = analyse_einsum_equation(equation) if len(letters) != mat.shape[1]: @@ -462,7 +478,7 @@ def _decompose_einsum_equation_simple(equation, *shapes, verbose=False, for iop in _apply_einsum_matmul( fd, graph.last_op, op, axes=tuple(common_dims), left=tuple(left), right=tuple(right), - ndim=rows.shape[1], keep_matmul=keep_matmul, + ndim=rows.shape[1], op_matmul=op_matmul, row1=rows[0, :], row2=rows[1, :], verbose=verbose): op = iop op.compute_output_row(rows[0, :], rows[1, :], diff --git a/mlprodict/testing/einsum_impl_classes.py b/mlprodict/testing/einsum_impl_classes.py index 5cdafa932..fb0b4615f 100644 --- a/mlprodict/testing/einsum_impl_classes.py +++ b/mlprodict/testing/einsum_impl_classes.py @@ -1,9 +1,14 @@ +# pylint: disable=C0302 """ @file @brief Classes representing the sequence of matrix operations to implement einsum computation. """ import numpy +from onnx import helper, numpy_helper +from ..tools.onnx2py_helper import guess_proto_dtype +from ..tools.asv_options_helper import ( + get_opset_number_from_onnx, get_ir_version_from_onnx) from .einsum_impl_ext import ( numpy_extended_dot, numpy_diagonal, _numpy_extended_dot_equation, @@ -11,6 +16,22 @@ numpy_extended_dot_matrix) +def single_axes(axes): + """ + *axes* contains positive values, then it is the position + of this axis in the original matrix, otherwise it is -1 + meaning this axis is an added single dimension to align + all the dimensions based on the einsum equation. + + :param axes: axes described above + :return: list of integer in set `{1, 2}`, 1 for + a single axis, 2 otherwise + """ + if axes is None: + return axes + return [(1 if a == -1 else 2) for a in axes] + + class EinsumSubOp: """ Defines a sub operation used in Einsum decomposition. @@ -33,6 +54,7 @@ def __init__(self, full_dim, name, *inputs, **kwargs): self.name = name self.inputs = inputs self.kwargs = kwargs + self._info = {} if name not in EinsumSubOp._allowed: raise ValueError( "Unexpected name %r. It should be in %r." @@ -296,7 +318,21 @@ def compute_output_row(self, row, row2=None, ab=False, verbose=False): "compute_output_row not implemented for %r." % self.name) if verbose and ab: print(" -- called as a binary operator") + self.add_info(i_row=single_axes(row), i_row2=single_axes(row2)) meth(row, row2=row2, ab=ab, verbose=verbose) + self.add_info(o_row=single_axes(row), o_row2=single_axes(row2)) + + def add_info(self, **kwargs): + """ + Adds information to the node. + + :param kwargs: dictionary + """ + for k, v in kwargs.items(): + if k in self._info: + raise KeyError( + "Key %r already added (operator %r)." % (k, self.name)) + self._info[k] = v def _check_inputs_(self, n_expected, check_dim=False): if len(self.inputs) != n_expected: @@ -468,8 +504,6 @@ def _apply_batch_dot(self, data, verbose=False, **kwargs): dot = m1sh @ numpy.transpose(m2sh, (0, 2, 1)) # new shape - taken = set(batch_axes) | set(sum_axes) - ax = [i for i in range(len(m1.shape)) if i not in taken] new_shape = ([max(m1.shape[i], m2.shape[i]) for i in batch_axes] + [m1.shape[i] for i in left if i not in batch_axes] + [m2.shape[i] for i in right if i not in batch_axes]) @@ -477,6 +511,8 @@ def _apply_batch_dot(self, data, verbose=False, **kwargs): new_shape.append(1) if verbose: + taken = set(batch_axes) | set(sum_axes) + ax = [i for i in range(len(m1.shape)) if i not in taken] print("- %s, shapes=%r @ %r -> %r" % ( self.name, m1sh.shape, m2sh.shape, dot.shape)) print("- %s, batch_axes=%r ax=%r new_shape=%r left=%r right=%r" % ( @@ -556,11 +592,256 @@ def apply(self, data, verbose=False, **kwargs): print("+ %s, shape=%r -- %d" % (self.name, output.shape, id(self))) return output + def _onnx_name(self): + return 'einsum%d_%s' % (id(self), self.name[:2]) + + def _check_onnx_opset_(self, opset, limit): + if opset is not None and opset < limit: + raise RuntimeError( + "Opset (%r) must be >= %r for operator %r." + "" % (opset, limit, self.name)) + + def _to_onnx_id(self, names, opset, verbose=False, **kwargs): + self._check_inputs_(1) + inp = self.inputs[0] + name = self._get_data(names, inp) + yield helper.make_node('Identity', [name], [self._onnx_name()]) + + def _to_onnx_expand_dims(self, names, opset, verbose=False, **kwargs): + self._check_inputs_(1) + self._check_onnx_opset_(opset, 11) + inp = self.inputs[0] + name = self._get_data(names, inp) + axes = self.kwargs['axes'] + name_axes = name + '_axes' + yield numpy_helper.from_array( + numpy.array([a[1] for a in axes], dtype=numpy.int64), name=name_axes) + yield helper.make_node( + 'Unsqueeze', [name, name_axes], [self._onnx_name()]) + + def _to_onnx_squeeze(self, names, opset, verbose=False, **kwargs): + self._check_inputs_(1) + self._check_onnx_opset_(opset, 11) + inp = self.inputs[0] + name = self._get_data(names, inp) + axes = self.kwargs['axes'] + name_axes = name + '_axes' + yield numpy_helper.from_array( + numpy.array(axes, dtype=numpy.int64), name=name_axes) + yield helper.make_node( + 'Squeeze', [name, name_axes], [self._onnx_name()]) + + def _to_onnx_transpose(self, names, opset, verbose=False, **kwargs): + self._check_inputs_(1) + inp = self.inputs[0] + name = self._get_data(names, inp) + perm = self.kwargs['perm'] + yield helper.make_node( + 'Transpose', [name], [self._onnx_name()], perm=perm) + + def _to_onnx_reduce_sum(self, names, opset, verbose=False, **kwargs): + self._check_inputs_(1) + self._check_onnx_opset_(opset, 11) + inp = self.inputs[0] + name = self._get_data(names, inp) + axes = self.kwargs['axes'] + name_axes = self._onnx_name() + '_axes' + yield numpy_helper.from_array( + numpy.array(axes, dtype=numpy.int64), name=name_axes) + yield helper.make_node( + 'ReduceSum', [name, name_axes], [self._onnx_name()], keepdims=1) + + def _to_onnx_batch_dot(self, names, opset, verbose=False, **kwargs): + self._check_inputs_(2) + self._check_onnx_opset_(opset, 13) + inp1, inp2 = self.inputs[:2] # pylint: disable=W0632 + name1 = self._get_data(names, inp1) + name2 = self._get_data(names, inp2) + + batch_axes = self.kwargs['batch_axes'] + keep_axes = self.kwargs['keep_axes'] + sum_axes = self.kwargs['sum_axes'] + left = self.kwargs['left'] + right = self.kwargs['right'] + root = self._onnx_name() + + name_shape1 = root + "_shape1" + name_shape2 = root + "_shape2" + yield helper.make_node('Shape', [name1], [name_shape1]) + yield helper.make_node('Shape', [name2], [name_shape2]) + + name_batch_axes = root + "_batch_axes" + yield numpy_helper.from_array( + numpy.array(batch_axes, dtype=numpy.int64), name=name_batch_axes) + name_sum_axes = root + "_sum_axes" + yield numpy_helper.from_array( + numpy.array(sum_axes, dtype=numpy.int64), name=name_sum_axes) + + # dim0 = int(numpy.prod([m1.shape[i] for i in batch_axes])) + # dim0b = int(numpy.prod([m2.shape[i] for i in batch_axes])) + name_dim0 = root + "_dim0" + yield helper.make_node( + 'Gather', [name_shape1, name_batch_axes], [name_dim0 + 'g']) + name_dim0b = root + "_dim0b" + yield helper.make_node( + 'Gather', [name_shape2, name_batch_axes], [name_dim0b + 'g']) + + yield helper.make_node( + 'ReduceProd', [name_dim0 + 'g'], [name_dim0], keepdims=1) + yield helper.make_node( + 'ReduceProd', [name_dim0b + 'g'], [name_dim0b], keepdims=1) + + # dimb = int(-1 if keep_axes is None else numpy.prod( + # [m1.shape[i] for i in keep_axes])) + if keep_axes in (-1, None): + name_dimb = root + "__1" + yield numpy_helper.from_array( + numpy.array([-1], dtype=numpy.int64), name=name_dimb) + else: + name_keep_axes = root + "_keep_axes" + name_dimb = root + "_dimb" + yield numpy_helper.from_array( + numpy.array(keep_axes, dtype=numpy.int64), name=name_keep_axes) + yield helper.make_node( + 'Gather', [name_shape1, name_keep_axes], [name_dimb + 'g']) + yield helper.make_node( + 'ReduceProd', [name_dimb + 'g'], [name_dimb], keepdims=1) + + # dim1 = int(numpy.prod([m1.shape[i] for i in sum_axes])) + # dim2 = int(numpy.prod([m2.shape[i] for i in sum_axes])) + name_dim1 = root + "_dim1" + yield helper.make_node( + 'Gather', [name_shape1, name_sum_axes], [name_dim1 + 'g']) + name_dim2 = root + "_dim2" + yield helper.make_node( + 'Gather', [name_shape2, name_sum_axes], [name_dim2 + 'g']) + + yield helper.make_node( + 'ReduceProd', [name_dim1 + 'g'], [name_dim1], keepdims=1) + yield helper.make_node( + 'ReduceProd', [name_dim2 + 'g'], [name_dim2], keepdims=1) + + # *shape1, *shape2 + name_agg_shape1 = root + "_resh1" + name_agg_shape2 = root + "_resh2" + yield helper.make_node('Concat', [name_dim0, name_dimb, name_dim1], + [name_agg_shape1], axis=0) + yield helper.make_node('Concat', [name_dim0b, name_dimb, name_dim2], + [name_agg_shape2], axis=0) + + # m1sh = m1.reshape((dim0, dimb, dim1)) + # m2sh = m2.reshape((dim0b, dimb, dim2)) + name_agg1 = root + "_aresh1" + name_agg2 = root + "_aresh2" + yield helper.make_node('Reshape', [name1, name_agg_shape1], [name_agg1]) + yield helper.make_node('Reshape', [name2, name_agg_shape2], [name_agg2]) + + # dot = m1sh @ numpy.transpose(m2sh, (0, 2, 1)) + name_agg2_tr = root + "_aresh2_tr" + yield helper.make_node( + 'Transpose', [name_agg2], [name_agg2_tr], perm=[0, 2, 1]) + name_dot = root + "_dot" + yield helper.make_node( + 'MatMul', [name_agg1, name_agg2_tr], [name_dot]) + + # new_shape = ([max(m1.shape[i], m2.shape[i]) for i in batch_axes] + + # [m1.shape[i] for i in left if i not in batch_axes] + + # [m2.shape[i] for i in right if i not in batch_axes]) + name_max_dim = root + "_max_dim" + yield helper.make_node( + 'Max', [name_dim0 + 'g', name_dim0b + 'g'], [name_max_dim]) + + left_set = list(sorted(set(left) - (set(batch_axes) & set(left)))) + name_left_set = root + "_left_set" + yield numpy_helper.from_array( + numpy.array(left_set, dtype=numpy.int64), name=name_left_set) + name_left_dim = root + "_left_dim" + yield helper.make_node( + 'Gather', [name_shape1, name_left_set], [name_left_dim]) + + right_set = list(sorted(set(right) - (set(batch_axes) & set(right)))) + name_right_set = root + "_right_set" + yield numpy_helper.from_array( + numpy.array(right_set, dtype=numpy.int64), name=name_right_set) + name_right_dim = root + "_right_dim" + yield helper.make_node( + 'Gather', [name_shape2, name_right_set], [name_right_dim]) + + name_new_shape = root + '_new_shape' + diff = ( + self.full_dim - + (len(batch_axes) + len(left_set) + len(right_set))) + if diff > 0: + names_ones = root + "_ones" + yield numpy_helper.from_array( + numpy.array([1 for i in range(diff)], dtype=numpy.int64), + name=names_ones) + yield helper.make_node( + 'Concat', [name_max_dim, name_left_dim, + name_right_dim, names_ones], + [name_new_shape], axis=0) + else: + yield helper.make_node( + 'Concat', [name_max_dim, name_left_dim, name_right_dim], + [name_new_shape], axis=0) + + name_final = root + '_final' + yield helper.make_node( + 'Reshape', [name_dot, name_new_shape], [name_final]) + + def to_onnx(self, names, opset=None, verbose=False, **kwargs): + """ + Converts this node into ONNX. Enumerates all ONNX node + which participate to the conversion. The last one + is the final output. + + :param names: dictionary where to find already converted name + :param opset: opset + :param verbose: prints out intermediate results + :param kwargs: additional parameter for the conversion + :return: output + """ + if opset is None: + opset = get_opset_number_from_onnx() + if verbose: + print() + print("to_onnx %r (%s) opset=%r." % ( + self.name, + ", ".join(map(lambda s: str(id(s)), self.inputs)), + opset)) + + method_name = "_to_onnx_%s" % self.name + meth = getattr(self, method_name, None) + if meth is None: + if self.name.endswith("_mm"): + raise NotImplementedError( + "to_onnx not implemented for %r." + "You should call method simplify_mm_nodes " + "to remove it." % self.name) + raise NotImplementedError( + "to_onnx not implemented for %r." % self.name) + for node in meth(names, verbose=verbose, opset=opset, **kwargs): + if hasattr(node, 'output'): + names[id(self)] = node.output[0] + if verbose: + print("+ OP %r -- (%s - %d)" % + (node.output[0], self.name, id(self))) + elif verbose: + # Initializer + print("+ CT %r -- (%s - %d)" % + (node.name, self.name, id(self))) + yield node + class GraphEinsumSubOp: """ Class gathering all nodes produced to explicit einsum operators. + + :param letters: list of distinct letters + :param mat: matrix, see @see fn analyse_einsum_equation + :param lengths: lengths of every input + :param duplicates: see @see fn analyse_einsum_equation """ def __init__(self, letters, mat, lengths, duplicates): @@ -803,3 +1084,63 @@ def simplify_mm_nodes(self, verbose=False): op.name, len(op.inputs), id(op))) op.name = op.name[:-3] op.inputs = op.inputs[:1] + + def to_onnx(self, output, *inputs, dtype=None, verbose=False, + opset=None, **kwargs): + """ + Converts the graph into ONNX. + + :param output: output name + :param inputs: input names + :param dtype: type used for all operators + :param opset: desired opset, None for the last one + :param verbose: display intermediate operators + :param kwargs: additional parameter to use when building + the ONNX graph + :return: ONNX graph + """ + # inputs + if opset is None: + opset = get_opset_number_from_onnx() + if verbose: + print("[GraphEinsumSubOp.to_onnx] %r -> %s opset=%r " + "dtype=%r" % (inputs, output, opset, dtype)) + onx_inputs = [] + proto = guess_proto_dtype( + numpy.float32 if dtype is None else dtype) + lengths = self.metadata['lengths'] + for inp, le in zip(inputs, lengths): + onx_inputs.append(helper.make_tensor_value_info( + inp, proto, [None for i in range(le)])) + + # output + onx_output = helper.make_tensor_value_info( + output, proto, [None for i in range(lengths[-1])]) + + # nodes + names = {i: name for i, name in enumerate(inputs)} + nodes = [] + inits = [] + for op in self: + for onx_node in op.to_onnx(names, verbose=verbose, opset=opset): + if hasattr(onx_node, 'output'): + nodes.append(onx_node) + else: + inits.append(onx_node) + + # last node + last_node = nodes[-1] + nodes.append(helper.make_node( + 'Identity', [last_node.output[0]], [output])) + + # Builds the graph + model = helper.make_model( + opset_imports=[helper.make_operatorsetid('', opset)], + ir_version=kwargs.get('ir_version', get_ir_version_from_onnx()), + producer_name=kwargs.get('producer_name', 'mlprodict'), + producer_version=kwargs.get('producer_version', "0.0.dev"), + graph=helper.make_graph( + name=kwargs.get('name', 'einsum'), + inputs=onx_inputs, outputs=[onx_output], + initializer=inits, nodes=nodes)) + return model From 50747a3869875523abb348a124bd0fa1a8708883 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Fri, 30 Apr 2021 09:23:08 +0200 Subject: [PATCH 22/33] fix issue with sparse --- mlprodict/onnxrt/ops_cpu/_op_numpy_helper.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/mlprodict/onnxrt/ops_cpu/_op_numpy_helper.py b/mlprodict/onnxrt/ops_cpu/_op_numpy_helper.py index e2f1e0556..3ad0219fb 100644 --- a/mlprodict/onnxrt/ops_cpu/_op_numpy_helper.py +++ b/mlprodict/onnxrt/ops_cpu/_op_numpy_helper.py @@ -3,6 +3,7 @@ @brief numpy redundant functions. """ import numpy +from scipy.sparse.coo import coo_matrix def numpy_dot_inplace(inplaces, a, b): @@ -56,6 +57,8 @@ def numpy_matmul_inplace(inplaces, a, b): """ Implements a matmul product, deals with inplace information. """ + if isinstance(a, coo_matrix) or isinstance(b, coo_matrix): + return numpy.dot(a, b) if inplaces.get(0, False) and hasattr(a, 'flags'): return _numpy_matmul_inplace_left(a, b) if inplaces.get(1, False) and hasattr(b, 'flags'): From 41f627317dd7e09f9c38883e5f4cf8a443b58ccb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Fri, 30 Apr 2021 10:03:11 +0200 Subject: [PATCH 23/33] fix matmul computation --- mlprodict/onnxrt/ops_cpu/_op_numpy_helper.py | 47 +++----------------- mlprodict/onnxrt/ops_cpu/op_transpose.py | 4 ++ 2 files changed, 11 insertions(+), 40 deletions(-) diff --git a/mlprodict/onnxrt/ops_cpu/_op_numpy_helper.py b/mlprodict/onnxrt/ops_cpu/_op_numpy_helper.py index 3ad0219fb..5c1dc116e 100644 --- a/mlprodict/onnxrt/ops_cpu/_op_numpy_helper.py +++ b/mlprodict/onnxrt/ops_cpu/_op_numpy_helper.py @@ -9,6 +9,7 @@ def numpy_dot_inplace(inplaces, a, b): """ Implements a dot product, deals with inplace information. + See :epkg:`numpy:dot`. """ if inplaces.get(0, False) and hasattr(a, 'flags'): return _numpy_dot_inplace_left(a, b) @@ -56,47 +57,13 @@ def _numpy_dot_inplace_right(a, b): def numpy_matmul_inplace(inplaces, a, b): """ Implements a matmul product, deals with inplace information. + See :epkg:`numpy:matmul`. + Inplace computation does not work well as modifying one of the + container modifies the results. This part still needs to be + improves. """ if isinstance(a, coo_matrix) or isinstance(b, coo_matrix): return numpy.dot(a, b) - if inplaces.get(0, False) and hasattr(a, 'flags'): - return _numpy_matmul_inplace_left(a, b) - if inplaces.get(1, False) and hasattr(b, 'flags'): - return _numpy_matmul_inplace_right(a, b) - return numpy.matmul(a, b) - - -def _numpy_matmul_inplace_left(a, b): - "Subpart of @see fn numpy_matmul_inplace." - if a.flags['F_CONTIGUOUS']: - if len(b.shape) == len(a.shape) == 2 and b.shape[1] <= a.shape[1]: - try: - numpy.matmul(a, b, out=a[:, :b.shape[1]]) - return a[:, :b.shape[1]] - except ValueError: - return numpy.matmul(a, b) - if len(b.shape) == 1: - try: - numpy.matmul(a, b.reshape(b.shape[0], 1), out=a[:, :1]) - return a[:, :1].reshape(a.shape[0]) - except ValueError: # pragma no cover - return numpy.matmul(a, b) - return numpy.matmul(a, b) - - -def _numpy_matmul_inplace_right(a, b): - "Subpart of @see fn numpy_matmul_inplace." - if b.flags['C_CONTIGUOUS']: - if len(b.shape) == len(a.shape) == 2 and a.shape[0] <= b.shape[0]: - try: - numpy.matmul(a, b, out=b[:a.shape[0], :]) - return b[:a.shape[0], :] - except ValueError: # pragma no cover - return numpy.matmul(a, b) - if len(a.shape) == 1: - try: - numpy.matmul(a, b, out=b[:1, :]) - return b[:1, :] - except ValueError: # pragma no cover - return numpy.matmul(a, b) + if len(a.shape) <= 2 and len(b.shape) <= 2: + return numpy_dot_inplace(inplaces, a, b) return numpy.matmul(a, b) diff --git a/mlprodict/onnxrt/ops_cpu/op_transpose.py b/mlprodict/onnxrt/ops_cpu/op_transpose.py index 859b2d5e2..f4f8db062 100644 --- a/mlprodict/onnxrt/ops_cpu/op_transpose.py +++ b/mlprodict/onnxrt/ops_cpu/op_transpose.py @@ -21,6 +21,10 @@ def __init__(self, onnx_node, desc=None, **options): def _run(self, data): # pylint: disable=W0221 if self.perm_ is None: return (numpy.transpose(data), ) + if len(self.perm_) != len(data.shape): + raise RuntimeError( + "Inconsistent permutation %r with shape %r." % ( + self.perm_, data.shape)) return (numpy.transpose(data, axes=self.perm_), ) def _infer_shapes(self, x): # pylint: disable=W0221 From 9f3885cc85392c5839a98572e016fbfbbaabb8dc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Fri, 30 Apr 2021 10:25:12 +0200 Subject: [PATCH 24/33] fix issue with latest version of scikit-learn --- _unittests/ut_onnxrt/test_onnxrt_switch_types.py | 15 --------------- 1 file changed, 15 deletions(-) diff --git a/_unittests/ut_onnxrt/test_onnxrt_switch_types.py b/_unittests/ut_onnxrt/test_onnxrt_switch_types.py index 8fbfdd30b..3954a8823 100644 --- a/_unittests/ut_onnxrt/test_onnxrt_switch_types.py +++ b/_unittests/ut_onnxrt/test_onnxrt_switch_types.py @@ -81,11 +81,6 @@ def test_onnxt_iris_gaussian_process_exp_sine_squared_12(self): res = oinf.switch_initializers_dtype(clr) last = res[-1] self.assertEqual(last[0], 'pass2') - _linv = 0 - for a in enumerate_fitted_arrays(clr): - if "_K_inv" in a[-2]: - _linv += 1 - self.assertEqual(_linv, 1) res = oinf.run({'X': X_test.astype(numpy.float64)}) ym3, std3 = res['GPmean'], res['GPcovstd'] self.assertEqualArray(ym3, ym2) @@ -123,11 +118,6 @@ def test_onnxt_iris_gaussian_process_exp_sine_squared_13(self): res = oinf.switch_initializers_dtype(clr) last = res[-1] self.assertEqual(last[0], 'pass2') - _linv = 0 - for a in enumerate_fitted_arrays(clr): - if "_K_inv" in a[-2]: - _linv += 1 - self.assertEqual(_linv, 1) res = oinf.run({'X': X_test.astype(numpy.float64)}) ym3, std3 = res['GPmean'], res['GPcovstd'] self.assertEqualArray(ym3, ym2) @@ -166,11 +156,6 @@ def test_onnxt_iris_gaussian_process_dot_product(self): res = oinf.switch_initializers_dtype(clr) last = res[-1] self.assertEqual(last[0], 'pass2') - _linv = 0 - for a in enumerate_fitted_arrays(clr): - if "_K_inv" in a[-2]: - _linv += 1 - self.assertEqual(_linv, 1) res = oinf.run({'X': X_test}) ym3, std3 = res['GPmean'], res['GPcovstd'] self.assertEqualArray(ym3, ym2, decimal=5) From 662727f009e787ee7ea7ab728ef7c1bb8f02c0e4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Fri, 30 Apr 2021 14:52:35 +0200 Subject: [PATCH 25/33] finalize conversion to onnx --- _doc/notebooks/einsum_decomposition.ipynb | 186 ++++++++++++-------- _unittests/ut_testing/test_einsum.py | 63 ++++++- mlprodict/testing/einsum_impl_classes.py | 201 +++++++++++++++------- 3 files changed, 314 insertions(+), 136 deletions(-) diff --git a/_doc/notebooks/einsum_decomposition.ipynb b/_doc/notebooks/einsum_decomposition.ipynb index 99ae47cf5..61a262fbb 100644 --- a/_doc/notebooks/einsum_decomposition.ipynb +++ b/_doc/notebooks/einsum_decomposition.ipynb @@ -294,16 +294,16 @@ { "data": { "text/html": [ - "
\n", + "
\n", "" ], "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -364,16 +364,16 @@ { "data": { "text/html": [ - "
\n", + "
\n", "" ], "text/plain": [ - "" + "" ] }, "execution_count": 11, @@ -411,16 +411,16 @@ { "data": { "text/html": [ - "
\n", + "
\n", "" ], "text/plain": [ - "" + "" ] }, "execution_count": 12, @@ -430,11 +430,39 @@ ], "source": [ "onx = seq_clean.to_onnx(\"Y\", \"X1\", \"X2\", \"X3\", dtype=numpy.float32)\n", - "with open(\"einsum.onnx\", \"wb\") as f:\n", - " f.write(onx.SerializeToString())\n", + "# with open(\"einsum.onnx\", \"wb\") as f:\n", + "# f.write(onx.SerializeToString())\n", "%onnxview onx " ] }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[ 8866198., 9864696.],\n", + " [12090270., 13152928.]],\n", + "\n", + " [[ 8883886., 9884376.],\n", + " [12114390., 13179168.]]], dtype=float32)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from onnxruntime import InferenceSession\n", + "sess = InferenceSession(onx.SerializeToString())\n", + "sess.run(None, {'X1': m1.astype(numpy.float32), \n", + " 'X2': m2.astype(numpy.float32), \n", + " 'X3': m3.astype(numpy.float32)})[0]" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -444,7 +472,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -476,7 +504,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -489,7 +517,7 @@ " [12114390., 13179168.]]], dtype=float32)" ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -509,16 +537,14 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\xavierdupre\\__home_\\github_fork\\scikit-learn\\sklearn\\experimental\\enable_hist_gradient_boosting.py:16: UserWarning: Since version 1.0, it is not needed to import enable_hist_gradient_boosting anymore. HistGradientBoostingClassifier and HistGradientBoostingRegressor are now stable and can be normally imported from sklearn.ensemble.\n", - " warnings.warn(\n", - "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:13<00:00, 1.02s/it]\n" + "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:12<00:00, 1.05it/s]\n" ] }, { @@ -555,63 +581,63 @@ " \n", " \n", " \n", - " 50\n", - " 0.005348\n", - " 0.000432\n", - " 0.004913\n", - " 0.006119\n", + " 63\n", + " 0.267888\n", + " 0.002161\n", + " 0.264015\n", + " 0.270904\n", " 10\n", " 10\n", - " 0.053479\n", - " onnxruntime\n", - " 50\n", + " 2.678881\n", + " numpy.einsum\n", + " 55\n", " \n", " \n", - " 51\n", - " 0.274089\n", - " 0.015949\n", - " 0.250878\n", - " 0.304891\n", + " 64\n", + " 0.052419\n", + " 0.000910\n", + " 0.051563\n", + " 0.054143\n", " 10\n", " 10\n", - " 2.740892\n", - " numpy.einsum\n", + " 0.524191\n", + " custom_einsum\n", " 55\n", " \n", " \n", - " 52\n", - " 0.054787\n", - " 0.005735\n", - " 0.049863\n", - " 0.065422\n", + " 65\n", + " 0.041699\n", + " 0.000276\n", + " 0.041196\n", + " 0.042327\n", " 10\n", " 10\n", - " 0.547868\n", - " custom_einsum\n", + " 0.416995\n", + " dec-matmul\n", " 55\n", " \n", " \n", - " 53\n", - " 0.050210\n", - " 0.002667\n", - " 0.043978\n", - " 0.054084\n", + " 66\n", + " 0.007084\n", + " 0.000175\n", + " 0.006942\n", + " 0.007584\n", " 10\n", " 10\n", - " 0.502098\n", - " tr/resh/dot\n", + " 0.070836\n", + " ort-einsum\n", " 55\n", " \n", " \n", - " 54\n", - " 0.008228\n", - " 0.000470\n", - " 0.007381\n", - " 0.008970\n", + " 67\n", + " 0.015473\n", + " 0.000276\n", + " 0.015132\n", + " 0.015853\n", " 10\n", " 10\n", - " 0.082277\n", - " onnxruntime\n", + " 0.154734\n", + " ort-matmul\n", " 55\n", " \n", " \n", @@ -620,21 +646,21 @@ ], "text/plain": [ " average deviation min_exec max_exec repeat number total \\\n", - "50 0.005348 0.000432 0.004913 0.006119 10 10 0.053479 \n", - "51 0.274089 0.015949 0.250878 0.304891 10 10 2.740892 \n", - "52 0.054787 0.005735 0.049863 0.065422 10 10 0.547868 \n", - "53 0.050210 0.002667 0.043978 0.054084 10 10 0.502098 \n", - "54 0.008228 0.000470 0.007381 0.008970 10 10 0.082277 \n", + "63 0.267888 0.002161 0.264015 0.270904 10 10 2.678881 \n", + "64 0.052419 0.000910 0.051563 0.054143 10 10 0.524191 \n", + "65 0.041699 0.000276 0.041196 0.042327 10 10 0.416995 \n", + "66 0.007084 0.000175 0.006942 0.007584 10 10 0.070836 \n", + "67 0.015473 0.000276 0.015132 0.015853 10 10 0.154734 \n", "\n", " name N \n", - "50 onnxruntime 50 \n", - "51 numpy.einsum 55 \n", - "52 custom_einsum 55 \n", - "53 tr/resh/dot 55 \n", - "54 onnxruntime 55 " + "63 numpy.einsum 55 \n", + "64 custom_einsum 55 \n", + "65 dec-matmul 55 \n", + "66 ort-einsum 55 \n", + "67 ort-matmul 55 " ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -660,6 +686,7 @@ "\n", "equation = \"bac,cd,def->ebc\"\n", "sess = None\n", + "sess2 = None\n", "seq = None \n", "\n", "results = []\n", @@ -674,6 +701,9 @@ " if sess is None:\n", " model = make_model(equation)\n", " sess = InferenceSession(model.SerializeToString())\n", + " if sess2 is None:\n", + " onx = seq_clean.to_onnx(\"Y\", \"X1\", \"X2\", \"X3\", dtype=numpy.float32)\n", + " sess2 = InferenceSession(onx.SerializeToString())\n", "\n", " res = measure_time(lambda x: numpy.einsum(equation, *x, optimize=True),\n", " [m1, m2, m3],\n", @@ -700,7 +730,7 @@ " res = measure_time(lambda x: apply_einsum_sequence(seq, *x, matmul_impl=\"pyf\"),\n", " [m1, m2, m3],\n", " repeat=10, number=10)\n", - " res['name'] = \"tr/resh/dot\"\n", + " res['name'] = \"dec-matmul\"\n", " res[\"N\"] = N\n", " results.append(res) \n", "\n", @@ -708,7 +738,15 @@ " [m1.astype(numpy.float32), m2.astype(numpy.float32),\n", " m3.astype(numpy.float32)],\n", " repeat=10, number=10)\n", - " res['name'] = \"onnxruntime\"\n", + " res['name'] = \"ort-einsum\"\n", + " res[\"N\"] = N\n", + " results.append(res) \n", + "\n", + " res = measure_time(lambda x: sess2.run(None, {'X1': x[0], 'X2': x[1], 'X3': x[2]}),\n", + " [m1.astype(numpy.float32), m2.astype(numpy.float32),\n", + " m3.astype(numpy.float32)],\n", + " repeat=10, number=10)\n", + " res['name'] = \"ort-matmul\"\n", " res[\"N\"] = N\n", " results.append(res) \n", " \n", @@ -719,12 +757,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -755,12 +793,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Version `tr/resh/dot` is an implementation based on the decomposition of a simplified einsum into a sequence of transpose, reshape, (batch_)dot or mul operations." + "Version `dec-matmul` is an implementation based on the decomposition of a simplified einsum into a sequence of transpose, reshape, (batch_)dot or mul operations. This decomposition is converted into ONNX and executed with *onnxruntime*, version `ort-matmul`. Both version are faster than the numpy optimized version. The ONNX graph may contain consecutive transpose which should be merged." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [] diff --git a/_unittests/ut_testing/test_einsum.py b/_unittests/ut_testing/test_einsum.py index 2578c86a0..e36d6f77d 100644 --- a/_unittests/ut_testing/test_einsum.py +++ b/_unittests/ut_testing/test_einsum.py @@ -1,5 +1,5 @@ """ -@brief test log(time=6s) +@brief test log(time=8s) """ import unittest import io @@ -241,7 +241,6 @@ def test_decompose_einsum_equation_onnx2(self): "bac,cd,def->ebc", (2, 3, 4), (4, 5), (5, 7, 11), strategy=strat, verbose=verbose) res1 = apply_einsum_sequence(seq, m1, m2, m3, verbose=verbose) - # verbose=verbose) seq.simplify_mm_nodes() seq.clean_unused_nodes() onx = seq.to_onnx("Y", "X1", "X2", "X3", dtype=numpy.float32) @@ -253,6 +252,13 @@ def test_decompose_einsum_equation_onnx2(self): res2 = oxres['Y'] self.assertEqualArray(res1, res2) + oinf = OnnxInference(onx, runtime="onnxruntime2") + oxres = oinf.run({'X1': m1.astype(numpy.float32), + 'X2': m2.astype(numpy.float32), + 'X3': m3.astype(numpy.float32)}) + res2 = oxres['Y'] + self.assertEqualArray(res1, res2) + so = SessionOptions() so.graph_optimization_level = GraphOptimizationLevel.ORT_DISABLE_ALL oinf = InferenceSession(onx.SerializeToString(), so) @@ -340,11 +346,25 @@ def test_many_2(self): for i, (eq, exp) in enumerate(res): with self.subTest(equation=eq, index=i, total=len(res)): + verbose = 12 if eq == ',abc,cd->abd' else 0 seq = decompose_einsum_equation( eq, m1.shape, m2.shape) res = apply_einsum_sequence(seq, m1, m2) self.assertEqualArray(exp, res) + seq = decompose_einsum_equation( + eq, m1.shape, m2.shape, strategy='numpy', clean=True) + if verbose: + print("#########", eq) + res = apply_einsum_sequence(seq, m1, m2, verbose=verbose) + self.assertEqualArray(exp, res) + onx = seq.to_onnx('Y', 'X1', 'X2', dtype=numpy.float32) + oinf = OnnxInference(onx) + res2 = oinf.run({'X1': m1.astype(numpy.float32), + 'X2': m2.astype(numpy.float32)}, + verbose=verbose, fLOG=print) + self.assertEqualArray(exp, res2['Y']) + def test_many_3(self): m1 = numpy.arange(2 * 2 * 2).reshape((2, 2, 2)) + 10 m2 = numpy.arange(4).reshape((2, 2)) + 100 @@ -370,11 +390,27 @@ def test_many_3(self): for i, (eq, exp) in enumerate(res): with self.subTest(equation=eq, index=i, total=len(res)): + verbose = 12 if eq == ',abc,cd,def->abd' else 0 seq = decompose_einsum_equation( eq, m1.shape, m2.shape, m3.shape) res = apply_einsum_sequence(seq, m1, m2, m3) self.assertEqualArray(exp, res) + if verbose: + print("#########", eq) + seq = decompose_einsum_equation( + eq, m1.shape, m2.shape, m3.shape, + strategy='numpy', clean=True) + res = apply_einsum_sequence(seq, m1, m2, m3, verbose=verbose) + self.assertEqualArray(exp, res) + onx = seq.to_onnx('Y', 'X1', 'X2', 'X3', dtype=numpy.float32) + oinf = OnnxInference(onx) + res2 = oinf.run({'X1': m1.astype(numpy.float32), + 'X2': m2.astype(numpy.float32), + 'X3': m3.astype(numpy.float32)}, + verbose=verbose, fLOG=print) + self.assertEqualArray(exp, res2['Y']) + # Taken from https://github.com/numpy/numpy/blob/main/numpy/ # core/tests/test_einsum.py. @@ -401,14 +437,33 @@ def optimize_compare(self, equation, operands=None, verbose=False): print(path[1]) shapes = [m.shape for m in inputs] + vv = 12 if equation == ",a,ab,abc->abc" else verbose with self.subTest(strategy='numpy'): seq = decompose_einsum_equation( equation, *shapes, verbose=verbose, strategy='numpy', clean=clean) got = apply_einsum_sequence( - seq, *inputs, verbose=verbose) + seq, *inputs, verbose=vv) self.assertEqualArray(exp, got, decimal=6) + + if clean: + with self.subTest(strategy='onnx'): + inps = ['X%d' % (i + 1) for i in range(len(inputs))] + try: + onx = seq.to_onnx('Y', *inps, dtype=numpy.float32) + except NotImplementedError as e: + if "diagonal" in str(e): + onx = None + else: + raise e + if onx is not None: + oinf = OnnxInference(onx) + inps = {n: v.astype(numpy.float32) + for n, v in zip(inps, inputs)} + got = oinf.run(inps, verbose=vv, fLOG=print)['Y'] + self.assertEqualArray(exp, got, decimal=6) + with self.subTest(strategy='simple'): seq = decompose_einsum_equation( equation, *shapes, clean=clean, verbose=verbose) @@ -538,5 +593,5 @@ def test_np_test_edge_cases_duplicate_indices(self): if __name__ == "__main__": - # TestEinsum().test_decompose_einsum_equation_onnx2() + # TestEinsum().test_many_3() unittest.main() diff --git a/mlprodict/testing/einsum_impl_classes.py b/mlprodict/testing/einsum_impl_classes.py index fb0b4615f..7179d8634 100644 --- a/mlprodict/testing/einsum_impl_classes.py +++ b/mlprodict/testing/einsum_impl_classes.py @@ -499,6 +499,11 @@ def _apply_batch_dot(self, data, verbose=False, **kwargs): dim1 = int(numpy.prod([m1.shape[i] for i in sum_axes])) dim2 = int(numpy.prod([m2.shape[i] for i in sum_axes])) + if verbose: + print("- %s, reshape=%r into %r" % ( + self.name, m1.shape, (dim0, dimb, dim1))) + print("- %s, reshape=%r into %r" % ( + self.name, m2.shape, (dim0b, dimb, dim2))) m1sh = m1.reshape((dim0, dimb, dim1)) m2sh = m2.reshape((dim0b, dimb, dim2)) dot = m1sh @ numpy.transpose(m2sh, (0, 2, 1)) @@ -651,7 +656,15 @@ def _to_onnx_reduce_sum(self, names, opset, verbose=False, **kwargs): yield helper.make_node( 'ReduceSum', [name, name_axes], [self._onnx_name()], keepdims=1) - def _to_onnx_batch_dot(self, names, opset, verbose=False, **kwargs): + def _to_onnx_mul(self, data, verbose=False, **kwargs): + self._check_inputs_(2) + inp1 = self.inputs[0] + inp2 = self.inputs[1] + m1 = self._get_data(data, inp1) + m2 = self._get_data(data, inp2) + yield helper.make_node('Mul', [m1, m2], [self._onnx_name()]) + + def _to_onnx_batch_dot(self, names, opset, verbose=False, **kwargs): # pylint: disable=R0914 self._check_inputs_(2) self._check_onnx_opset_(opset, 13) inp1, inp2 = self.inputs[:2] # pylint: disable=W0632 @@ -665,69 +678,137 @@ def _to_onnx_batch_dot(self, names, opset, verbose=False, **kwargs): right = self.kwargs['right'] root = self._onnx_name() + name_one = root + "_1" + name_zero = root + "_0" + yield numpy_helper.from_array( + numpy.array([1], dtype=numpy.int64), name=name_one) + yield numpy_helper.from_array( + numpy.array([0], dtype=numpy.int64), name=name_zero) + name_shape1 = root + "_shape1" name_shape2 = root + "_shape2" + concat_left = [] + concat_right = [] yield helper.make_node('Shape', [name1], [name_shape1]) yield helper.make_node('Shape', [name2], [name_shape2]) - name_batch_axes = root + "_batch_axes" - yield numpy_helper.from_array( - numpy.array(batch_axes, dtype=numpy.int64), name=name_batch_axes) - name_sum_axes = root + "_sum_axes" - yield numpy_helper.from_array( - numpy.array(sum_axes, dtype=numpy.int64), name=name_sum_axes) + if len(batch_axes) > 0: + name_batch_axes = root + "_batch_axes" + yield numpy_helper.from_array( + numpy.array(batch_axes, dtype=numpy.int64), name=name_batch_axes) + + if len(sum_axes) > 0: + name_sum_axes = root + "_sum_axes" + yield numpy_helper.from_array( + numpy.array(sum_axes, dtype=numpy.int64), name=name_sum_axes) # dim0 = int(numpy.prod([m1.shape[i] for i in batch_axes])) # dim0b = int(numpy.prod([m2.shape[i] for i in batch_axes])) - name_dim0 = root + "_dim0" - yield helper.make_node( - 'Gather', [name_shape1, name_batch_axes], [name_dim0 + 'g']) - name_dim0b = root + "_dim0b" - yield helper.make_node( - 'Gather', [name_shape2, name_batch_axes], [name_dim0b + 'g']) - - yield helper.make_node( - 'ReduceProd', [name_dim0 + 'g'], [name_dim0], keepdims=1) - yield helper.make_node( - 'ReduceProd', [name_dim0b + 'g'], [name_dim0b], keepdims=1) + if len(batch_axes) > 1: + name_dim0 = root + "_dim0" + name_dim0b = root + "_dim0b" + name_dim0g = name_dim0 + 'g' + name_dim0bg = name_dim0b + 'g' + concat_left.append(name_dim0) + concat_right.append(name_dim0b) + yield helper.make_node( + 'Gather', [name_shape1, name_batch_axes], [name_dim0g]) + yield helper.make_node( + 'Gather', [name_shape2, name_batch_axes], [name_dim0bg]) + yield helper.make_node( + 'ReduceProd', [name_dim0g], [name_dim0], keepdims=1) + yield helper.make_node( + 'ReduceProd', [name_dim0bg], [name_dim0b], keepdims=1) + elif len(batch_axes) == 1: + name_dim0g = root + "_dim0g" + name_dim0bg = root + "_dim0bg" + name_dim0 = name_dim0g + name_dim0b = name_dim0bg + concat_left.append(name_dim0) + concat_right.append(name_dim0b) + yield helper.make_node( + 'Gather', [name_shape1, name_batch_axes], [name_dim0g]) + yield helper.make_node( + 'Gather', [name_shape2, name_batch_axes], [name_dim0bg]) + else: + name_dim0 = name_one + name_dim0b = name_one + concat_left.append(name_dim0) + concat_right.append(name_dim0b) # dimb = int(-1 if keep_axes is None else numpy.prod( # [m1.shape[i] for i in keep_axes])) - if keep_axes in (-1, None): + if keep_axes in (-1, None) or len(keep_axes) == 0: name_dimb = root + "__1" + concat_left.append(name_dimb) + concat_right.append(name_dimb) yield numpy_helper.from_array( numpy.array([-1], dtype=numpy.int64), name=name_dimb) + elif len(keep_axes) == 1: + name_keep_axes = root + "_keep_axes" + name_dimb = root + "_dimb" + name_dimbg = name_dimb + concat_left.append(name_dimb) + concat_right.append(name_dimb) + yield numpy_helper.from_array( + numpy.array(keep_axes, dtype=numpy.int64), name=name_keep_axes) + yield helper.make_node( + 'Gather', [name_shape1, name_keep_axes], [name_dimbg]) else: name_keep_axes = root + "_keep_axes" name_dimb = root + "_dimb" + name_dimbg = name_dimb + 'g' + concat_left.append(name_dimb) + concat_right.append(name_dimb) yield numpy_helper.from_array( numpy.array(keep_axes, dtype=numpy.int64), name=name_keep_axes) yield helper.make_node( - 'Gather', [name_shape1, name_keep_axes], [name_dimb + 'g']) + 'Gather', [name_shape1, name_keep_axes], [name_dimbg]) yield helper.make_node( - 'ReduceProd', [name_dimb + 'g'], [name_dimb], keepdims=1) + 'ReduceProd', [name_dimbg], [name_dimb], keepdims=1) # dim1 = int(numpy.prod([m1.shape[i] for i in sum_axes])) # dim2 = int(numpy.prod([m2.shape[i] for i in sum_axes])) - name_dim1 = root + "_dim1" - yield helper.make_node( - 'Gather', [name_shape1, name_sum_axes], [name_dim1 + 'g']) - name_dim2 = root + "_dim2" - yield helper.make_node( - 'Gather', [name_shape2, name_sum_axes], [name_dim2 + 'g']) - yield helper.make_node( - 'ReduceProd', [name_dim1 + 'g'], [name_dim1], keepdims=1) - yield helper.make_node( - 'ReduceProd', [name_dim2 + 'g'], [name_dim2], keepdims=1) + if len(sum_axes) == 0: + name_dim1 = name_one + name_dim2 = name_one + concat_left.append(name_dim1) + concat_right.append(name_dim2) + elif len(sum_axes) == 1: + name_dim1 = root + "_dim1" + name_dim2 = root + "_dim2" + name_dim1g = name_dim1 + name_dim2g = name_dim2 + concat_left.append(name_dim1) + concat_right.append(name_dim2) + yield helper.make_node( + 'Gather', [name_shape1, name_sum_axes], [name_dim1g]) + yield helper.make_node( + 'Gather', [name_shape2, name_sum_axes], [name_dim2g]) + else: + name_dim1 = root + "_dim1" + name_dim2 = root + "_dim2" + name_dim1g = name_dim1 + 'g' + name_dim2g = name_dim2 + 'g' + concat_left.append(name_dim1) + concat_right.append(name_dim2) + yield helper.make_node( + 'Gather', [name_shape1, name_sum_axes], [name_dim1g]) + yield helper.make_node( + 'Gather', [name_shape2, name_sum_axes], [name_dim2g]) + yield helper.make_node( + 'ReduceProd', [name_dim1g], [name_dim1], keepdims=1) + yield helper.make_node( + 'ReduceProd', [name_dim2g], [name_dim2], keepdims=1) # *shape1, *shape2 name_agg_shape1 = root + "_resh1" name_agg_shape2 = root + "_resh2" - yield helper.make_node('Concat', [name_dim0, name_dimb, name_dim1], - [name_agg_shape1], axis=0) - yield helper.make_node('Concat', [name_dim0b, name_dimb, name_dim2], - [name_agg_shape2], axis=0) + yield helper.make_node( + 'Concat', concat_left, [name_agg_shape1], axis=0) + yield helper.make_node( + 'Concat', concat_right, [name_agg_shape2], axis=0) # m1sh = m1.reshape((dim0, dimb, dim1)) # m2sh = m2.reshape((dim0b, dimb, dim2)) @@ -740,6 +821,7 @@ def _to_onnx_batch_dot(self, names, opset, verbose=False, **kwargs): name_agg2_tr = root + "_aresh2_tr" yield helper.make_node( 'Transpose', [name_agg2], [name_agg2_tr], perm=[0, 2, 1]) + name_dot = root + "_dot" yield helper.make_node( 'MatMul', [name_agg1, name_agg2_tr], [name_dot]) @@ -747,25 +829,32 @@ def _to_onnx_batch_dot(self, names, opset, verbose=False, **kwargs): # new_shape = ([max(m1.shape[i], m2.shape[i]) for i in batch_axes] + # [m1.shape[i] for i in left if i not in batch_axes] + # [m2.shape[i] for i in right if i not in batch_axes]) - name_max_dim = root + "_max_dim" - yield helper.make_node( - 'Max', [name_dim0 + 'g', name_dim0b + 'g'], [name_max_dim]) + concat_final = [] + if len(batch_axes) > 0: + name_max_dim = root + "_max_dim" + concat_final.append(name_max_dim) + yield helper.make_node( + 'Max', [name_dim0g, name_dim0bg], [name_max_dim]) left_set = list(sorted(set(left) - (set(batch_axes) & set(left)))) - name_left_set = root + "_left_set" - yield numpy_helper.from_array( - numpy.array(left_set, dtype=numpy.int64), name=name_left_set) - name_left_dim = root + "_left_dim" - yield helper.make_node( - 'Gather', [name_shape1, name_left_set], [name_left_dim]) + if len(left_set) > 0: + name_left_dim = root + "_left_dim" + name_left_set = root + "_left_set" + yield numpy_helper.from_array( + numpy.array(left_set, dtype=numpy.int64), name=name_left_set) + yield helper.make_node( + 'Gather', [name_shape1, name_left_set], [name_left_dim]) + concat_final.append(name_left_dim) right_set = list(sorted(set(right) - (set(batch_axes) & set(right)))) - name_right_set = root + "_right_set" - yield numpy_helper.from_array( - numpy.array(right_set, dtype=numpy.int64), name=name_right_set) - name_right_dim = root + "_right_dim" - yield helper.make_node( - 'Gather', [name_shape2, name_right_set], [name_right_dim]) + if len(right_set) > 0: + name_right_dim = root + "_right_dim" + name_right_set = root + "_right_set" + yield numpy_helper.from_array( + numpy.array(right_set, dtype=numpy.int64), name=name_right_set) + yield helper.make_node( + 'Gather', [name_shape2, name_right_set], [name_right_dim]) + concat_final.append(name_right_dim) name_new_shape = root + '_new_shape' diff = ( @@ -776,14 +865,10 @@ def _to_onnx_batch_dot(self, names, opset, verbose=False, **kwargs): yield numpy_helper.from_array( numpy.array([1 for i in range(diff)], dtype=numpy.int64), name=names_ones) - yield helper.make_node( - 'Concat', [name_max_dim, name_left_dim, - name_right_dim, names_ones], - [name_new_shape], axis=0) - else: - yield helper.make_node( - 'Concat', [name_max_dim, name_left_dim, name_right_dim], - [name_new_shape], axis=0) + concat_final.append(names_ones) + + yield helper.make_node( + 'Concat', concat_final, [name_new_shape], axis=0) name_final = root + '_final' yield helper.make_node( From 91ec8935840838f466c71a459b3267fe3015fbf6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Fri, 30 Apr 2021 20:05:51 +0200 Subject: [PATCH 26/33] Update test_einsum.py --- _unittests/ut_testing/test_einsum.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_unittests/ut_testing/test_einsum.py b/_unittests/ut_testing/test_einsum.py index e36d6f77d..27495d6e2 100644 --- a/_unittests/ut_testing/test_einsum.py +++ b/_unittests/ut_testing/test_einsum.py @@ -462,7 +462,7 @@ def optimize_compare(self, equation, operands=None, verbose=False): inps = {n: v.astype(numpy.float32) for n, v in zip(inps, inputs)} got = oinf.run(inps, verbose=vv, fLOG=print)['Y'] - self.assertEqualArray(exp, got, decimal=6) + self.assertEqualArray(exp, got, decimal=5) with self.subTest(strategy='simple'): seq = decompose_einsum_equation( From b28dd2d61e6a3f769df5355153456392862d9530 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Sun, 2 May 2021 00:03:33 +0200 Subject: [PATCH 27/33] remove duplicate transpose --- _unittests/ut_testing/test_einsum.py | 55 ++++- .../ut_testing/test_einsum_generic_dot.py | 42 +++- mlprodict/testing/einsum_impl.py | 43 ++-- mlprodict/testing/einsum_impl_classes.py | 232 ++++++++++++++---- mlprodict/testing/einsum_impl_ext.py | 8 +- 5 files changed, 300 insertions(+), 80 deletions(-) diff --git a/_unittests/ut_testing/test_einsum.py b/_unittests/ut_testing/test_einsum.py index 27495d6e2..31e22f585 100644 --- a/_unittests/ut_testing/test_einsum.py +++ b/_unittests/ut_testing/test_einsum.py @@ -137,8 +137,6 @@ def test_decompose_einsum_equation_exc(self): lambda: decompose_einsum_equation("abc,ch->ah", (2, 2, 2), (2, 2), strategy="donotexist"), ValueError) - self.assertRaise( - lambda: decompose_einsum_equation("abc,ch->ah"), ValueError) self.assertRaise( lambda: decompose_einsum_equation("abc,ch->ah", (2, 2, 2), (2, 2), "donotexist"), @@ -179,6 +177,26 @@ def fct(): self.assertIn("numpy_extended_dot", out) self.assertEqualArray(exp, res) + def test_decompose_einsum_equation_py_noshape(self): + m1 = numpy.arange(0, 24).astype(numpy.float32).reshape((2, 3, 4)) + m2 = numpy.arange(0, 20).astype(numpy.float32).reshape((4, 5)) + verbose = False + for strat, opname in [('numpy', 'batch_dot'), + ('simple', 'matmul')]: + with self.subTest(strategy=strat): + seq = decompose_einsum_equation( + "bac,ch->ah", strategy=strat, verbose=verbose) + self.assertIn(opname, seq.to_dot()) + res1 = apply_einsum_sequence(seq, m1, m2, verbose=verbose) + res2 = apply_einsum_sequence( + seq, m1, m2, matmul_impl='py', verbose=verbose) + if strat == 'simple': + self.assertRaise( + lambda: apply_einsum_sequence( + seq, m1, m2, matmul_impl='py2'), # pylint: disable=W0640 + ValueError) + self.assertEqualArray(res1, res2) + def test_decompose_einsum_equation_py(self): m1 = numpy.arange(0, 24).astype(numpy.float32).reshape((2, 3, 4)) m2 = numpy.arange(0, 20).astype(numpy.float32).reshape((4, 5)) @@ -346,16 +364,21 @@ def test_many_2(self): for i, (eq, exp) in enumerate(res): with self.subTest(equation=eq, index=i, total=len(res)): - verbose = 12 if eq == ',abc,cd->abd' else 0 + verbose = 12 if eq == ',abc,dc->acd' else 0 + if verbose: + print('\n########################################clean=False') + print("#########0", eq) seq = decompose_einsum_equation( - eq, m1.shape, m2.shape) - res = apply_einsum_sequence(seq, m1, m2) + eq, m1.shape, m2.shape, verbose=verbose) + res = apply_einsum_sequence(seq, m1, m2, verbose=verbose) self.assertEqualArray(exp, res) - seq = decompose_einsum_equation( - eq, m1.shape, m2.shape, strategy='numpy', clean=True) if verbose: - print("#########", eq) + print('\n########################################clean=True') + print("#########1", eq) + seq = decompose_einsum_equation( + eq, m1.shape, m2.shape, strategy='numpy', + clean=True, verbose=verbose) res = apply_einsum_sequence(seq, m1, m2, verbose=verbose) self.assertEqualArray(exp, res) onx = seq.to_onnx('Y', 'X1', 'X2', dtype=numpy.float32) @@ -591,7 +614,21 @@ def test_np_test_edge_cases_duplicate_indices(self): self.optimize_compare('baa,dcf,af,cde->be') self.optimize_compare('fff,fae,bef,def->abd') + def test_exc(self): + self.assertRaise( + lambda: EinsumSubOp(2, 'transpose', 0, perm=(1, 1)), + RuntimeError) + self.assertRaise( + lambda: EinsumSubOp(2, 'transpose', 0, perm=(0, 1)), + ValueError) + self.assertRaise( + lambda: EinsumSubOp(2, 'matmul', 0, 1, + axes=(0, 1), left=(0, 1), right=(0, 1)), + RuntimeError) + r = repr(EinsumSubOp(2, 'transpose', 0, perm=(1, 0))) + self.assertIn("EinsumSubOp('transpose', 0, perm=(1, 0))", r) + if __name__ == "__main__": - # TestEinsum().test_many_3() + # TestEinsum().test_many_2() unittest.main() diff --git a/_unittests/ut_testing/test_einsum_generic_dot.py b/_unittests/ut_testing/test_einsum_generic_dot.py index 0f2a0f4dc..3a49a66ab 100644 --- a/_unittests/ut_testing/test_einsum_generic_dot.py +++ b/_unittests/ut_testing/test_einsum_generic_dot.py @@ -8,7 +8,9 @@ from pyquickhelper.pycode import ExtTestCase from mlprodict.testing.einsum_impl_ext import ( numpy_extended_dot, numpy_extended_dot_python, - numpy_extended_dot_matrix) + numpy_extended_dot_matrix, numpy_diagonal, + _numpy_extended_dot_equation, + _common_check_numpy_extended_dot) confs = [ @@ -1502,6 +1504,44 @@ def common_test(self, sh1, sh2, axes, left, right, fct, verbose=False): exp.ravel(), dot.ravel(), f.getvalue())) return True + def test_exc(self): + self.assertRaise( + lambda: numpy_diagonal(None, 6, (8, 9)), + RuntimeError) + self.assertRaise( + lambda: _numpy_extended_dot_equation(4, 5, None, None, None), + RuntimeError) + self.assertRaise( + lambda: _numpy_extended_dot_equation(1, 1, (4, 5), (6, 7), (8, 9)), + ValueError) + self.assertRaise( + lambda: _numpy_extended_dot_equation( + 10, 10, (-4, 5), (6, 7), (8, 9)), + ValueError) + self.assertRaise( + lambda: _common_check_numpy_extended_dot( + numpy.array([5], dtype=numpy.float32), + numpy.array([5], dtype=numpy.int64), + None, None, None), + TypeError) + + def test_verbose(self): + conf = dict(shape1=(1, 5, 4, 1), shape2=(1, 1, 4, 6), + axes=(2,), left=(0, 1), right=(3,)) + sh1 = conf['shape1'] + sh2 = conf['shape2'] + axes = conf['axes'] + left = conf['left'] + right = conf['right'] + m1 = numpy.empty(sh1).ravel() + m1 = numpy.arange(len(m1)).reshape(sh1).astype(numpy.float64) + 10 + m2 = numpy.empty(sh2).ravel() + m2 = numpy.arange(len(m2)).reshape(sh2).astype(numpy.float64) + 1000 + self.capture( + lambda: numpy_extended_dot_python(m1, m2, axes, left, right)) + self.capture( + lambda: numpy_extended_dot_matrix(m1, m2, axes, left, right)) + if __name__ == "__main__": unittest.main() diff --git a/mlprodict/testing/einsum_impl.py b/mlprodict/testing/einsum_impl.py index d98544c47..12a4f9272 100644 --- a/mlprodict/testing/einsum_impl.py +++ b/mlprodict/testing/einsum_impl.py @@ -110,8 +110,7 @@ def decompose_einsum_equation(equation, *shapes, strategy="simple", :showcode: from mlprodict.testing.einsum_impl import decompose_einsum_equation - seq = decompose_einsum_equation( - "bac,cd,def->ebc", (2, 2, 2), (2, 2), (2, 2, 2)) + seq = decompose_einsum_equation("bac,cd,def->ebc") for op in seq: print(op) @@ -128,12 +127,11 @@ def decompose_einsum_equation(equation, *shapes, strategy="simple", See notebook :ref:`einsumdecompositionrst`. """ - if len(shapes) == 0: - raise ValueError("No input shapes.") - for sh in shapes: - if not isinstance(sh, tuple): - raise TypeError( - "All shapes must be tuples for %r is not." % sh) + if len(shapes) > 0: + for sh in shapes: + if not isinstance(sh, tuple): + raise TypeError( + "All shapes must be tuples for %r is not." % sh) if strategy in ("simple", "numpy"): op_matmul = {'simple': 'matmul', 'numpy': 'batch_dot'} @@ -146,6 +144,7 @@ def decompose_einsum_equation(equation, *shapes, strategy="simple", graph.mark_last_node() if clean: graph.simplify_mm_nodes(verbose=verbose) + graph.remove_duplicate_transpose(verbose=verbose) graph.clean_unused_nodes(verbose=verbose) return graph @@ -167,16 +166,16 @@ def apply_einsum_sequence(seq, *inputs, verbose=False, **kwargs): .. runpython:: :showcode: + import numpy from mlprodict.testing.einsum_impl import ( decompose_einsum_equation, apply_einsum_sequence) m1 = numpy.arange(2 * 2 * 2).reshape((2, 2, 2)) + 10 m2 = numpy.arange(4).reshape((2, 2)) + 100 - m3 = numpy.arange(2 * 2).reshape((2, 2)) + 1000 + m3 = numpy.arange(8).reshape((2, 2, 2)) + 1000 - seq = decompose_einsum_equation( - "bac,cd,def->ebc", (2, 2, 2), (2, 2), (2, 2, 2)) - res = apply_einsum_sequence(seq, m1, m2, verbose=verbose) + seq = decompose_einsum_equation("bac,cd,def->ebc") + res = apply_einsum_sequence(seq, m1, m2, m3) print(res) See notebook :ref:`einsumdecompositionrst`. @@ -276,17 +275,17 @@ def _apply_einsum_matmul(fd, op1, op2, axes, left, right, ndim, """ allowed = {'matmul', 'batch_dot', 'dot'} if op_matmul not in allowed: - raise ValueError( + raise ValueError( # pragma: no cover "Unknown operator op_matmul=%r not in %r." % (op_matmul, allowed)) if op_matmul == 'matmul': - if verbose: + if verbose: # pragma: no cover print(" -- MATMUL -> matmul axes=%r left=%r right=%r" "" % (axes, left, right)) yield EinsumSubOp(fd, 'matmul', op1, op2, axes=axes, left=left, right=right, ndim=ndim) elif len(axes) == 0 and len(set(left) & set(right)) == 0: - if verbose: + if verbose: # pragma: no cover print(" -- MATMUL -> mul axes=%r left=%r right=%r" "" % (axes, left, right)) yield EinsumSubOp(fd, 'mul', op1, op2) @@ -295,7 +294,7 @@ def _apply_einsum_matmul(fd, op1, op2, axes, left, right, ndim, len(set(axes) & set(right)) == 0): # No intersection between axes and right: matrix multiplication - if verbose: + if verbose: # pragma: no cover print(" -- MATMUL -> batch_dot axes=%r left=%r right=%r" "" % (axes, left, right)) @@ -311,7 +310,7 @@ def _apply_einsum_matmul(fd, op1, op2, axes, left, right, ndim, right_no_left = (set(right) & has_dim) - \ (set(right) & (set(left) | set(axes))) if right_no_left: - if verbose: + if verbose: # pragma: no cover print(' -- MATMUL reduce1 has_dim=%r axes=%r' % (has_dim, right_no_left)) op1 = EinsumSubOp(fd, 'reduce_sum_mm', op1, op2, @@ -322,7 +321,7 @@ def _apply_einsum_matmul(fd, op1, op2, axes, left, right, ndim, left_no_right = (set(left) & has_dim) - \ (set(left) & (set(right) | set(axes))) if left_no_right: - if verbose: + if verbose: # pragma: no cover print(' -- MATMUL reduce2 has_dim=%r axes=%r' % (has_dim, left_no_right)) op2 = EinsumSubOp(fd, 'reduce_sum', op2, @@ -375,7 +374,7 @@ def _apply_einsum_matmul(fd, op1, op2, axes, left, right, ndim, op = EinsumSubOp(fd, 'transpose', op, perm=tuple(rev_perm)) yield op else: - raise NotImplementedError( + raise NotImplementedError( # pragma: no cover "axes and right or left have axes in common, " "axes=%r left=%r right=%r ndim=%r." % ( axes, left, right, ndim)) @@ -396,6 +395,8 @@ def _decompose_einsum_equation_simple(equation, *shapes, verbose=False, raise RuntimeError( # pragma: no cover "Unexpected number of letters %r, shape=%r." % ( letters, mat.shape)) + if len(shapes) == 0: + shapes = [(2, ) * le for le in lengths[:-1]] _basic_verification(lengths, shapes, equation) # last_row, current_row (row = shape) @@ -501,11 +502,11 @@ def _decompose_einsum_equation_simple(equation, *shapes, verbose=False, if rows[0, d] > 0 and rows[1, d] == -1: red.append(d) elif rows[0, d] == -1 and rows[1, d] >= 0: - raise RuntimeError( + raise RuntimeError( # pragma: no cover "Issue in equation %r, variable %d, last_result is %r, " "output is %r." % (equation, d, rows[0, :], rows[1, :])) if len(red) > 0: - if verbose: + if verbose: # pragma: no cover print("-- REDUCE2 axes=%r" % red) print(mat) op = EinsumSubOp(fd, 'reduce_sum', op, axes=tuple(red)) diff --git a/mlprodict/testing/einsum_impl_classes.py b/mlprodict/testing/einsum_impl_classes.py index 7179d8634..895cad73c 100644 --- a/mlprodict/testing/einsum_impl_classes.py +++ b/mlprodict/testing/einsum_impl_classes.py @@ -78,13 +78,13 @@ def _check_(self): self._check_arg_('perm', tuple) perm = self.kwargs['perm'] if len(perm) != len(set(perm)): - raise RuntimeError( + raise RuntimeError( # pragma: no cover "perm has duplicated values %r (name=%r)." "" % (perm, self.name)) if list(perm) == list(range(len(perm))): - raise ValueError( - "Transpose = identity perm=%r. It must be removed." - "" % perm) + raise ValueError( # pragma: no cover + "Transpose = identity perm={}. It must be removed." + "".format(perm)) elif self.name == 'matmul': self._check_arg_('axes', tuple) self._check_arg_('left', tuple) @@ -94,7 +94,7 @@ def _check_(self): right = self.kwargs['right'] for a in axes: if a in left and a in right: - raise RuntimeError( + raise RuntimeError( # pragma: no cover "One axis belongs to every set (axes, left, right). " "axes=%r, left=%r, right=%r." % (axes, left, right)) @@ -121,12 +121,12 @@ def dot_label(self): def _check_arg_(self, name, typ, empty=False): if name not in self.kwargs: - raise RuntimeError( + raise RuntimeError( # pragma: no cover "Parameter %r not found for operator %r." % (name, self.name)) if empty and self.kwargs[name] is None: return if not isinstance(self.kwargs[name], typ): - raise TypeError( + raise TypeError( # pragma: no cover "Unexpected type %r for parameter %r and parameter %r." "" % (type(self.kwargs[name]), name, self.name)) @@ -154,7 +154,7 @@ def _compute_output_row_transpose(self, row, row2=None, ab=False, verbose=False) self._check_row_(row, True, verbose=verbose) self._check_arg_('perm', tuple) if len(self.kwargs['perm']) != len(row): - raise RuntimeError( + raise RuntimeError( # pragma: no cover "Unexpected permutation %r (row=%r)." "" % (self.kwargs['perm'], row)) perm = self.kwargs['perm'] @@ -165,32 +165,33 @@ def _compute_output_row_transpose(self, row, row2=None, ab=False, verbose=False) def _compute_output_row_transpose_mm(self, row, row2=None, ab=False, verbose=False): if not ab: - raise RuntimeError("ab must be True.") + raise RuntimeError("ab must be True.") # pragma: no cover self._check_row_(row, True, verbose=verbose) if row2 is None: - raise RuntimeError("transpose_mm expects a second input.") + raise RuntimeError( # pragma: no cover + "transpose_mm expects a second input.") self._compute_output_row_transpose(row, row2=None, verbose=verbose) def _compute_output_row_expand_dims(self, row, row2=None, ab=False, verbose=False): if ab: - raise RuntimeError("ab option not allowed.") + raise RuntimeError("ab option not allowed.") # pragma: no cover self._check_row_(row, True, verbose=verbose) self._check_arg_('axes', tuple) axes = self.kwargs['axes'] for axis in axes: if not isinstance(axis, tuple): - raise TypeError( + raise TypeError( # pragma: no cover "Parameter axes of expand_dims should be a tuple of " "tuple, axes=%r." % axes) if row[axis[1]] != -1: - raise RuntimeError( + raise RuntimeError( # pragma: no cover "Dimension should be -1 in row %r axis=%r." % ( row, self.kwargs['axis'])) self._check_row_(row, verbose=verbose) def _compute_output_row_reduce_sum(self, row, row2=None, ab=False, verbose=False): if ab: - raise RuntimeError("ab option not allowed.") + raise RuntimeError("ab option not allowed.") # pragma: no cover self._check_row_(row, True, verbose=verbose) self._check_arg_('axes', tuple) for a in self.kwargs['axes']: @@ -199,15 +200,16 @@ def _compute_output_row_reduce_sum(self, row, row2=None, ab=False, verbose=False def _compute_output_row_reduce_sum_mm(self, row, row2=None, ab=False, verbose=False): if not ab: - raise RuntimeError("ab must be true.") + raise RuntimeError("ab must be true.") # pragma: no cover self._check_row_(row2, True, verbose=verbose) if row2 is None: - raise RuntimeError("reduce_sum_mm expects a second input.") + raise RuntimeError( # pragma: no cover + "reduce_sum_mm expects a second input.") self._compute_output_row_reduce_sum(row, row2=None, verbose=verbose) def _compute_output_row_squeeze(self, row, row2=None, ab=False, verbose=False): if ab: - raise RuntimeError("ab option not allowed.") + raise RuntimeError("ab option not allowed.") # pragma: no cover self._check_row_(row, True, verbose=verbose) self._check_arg_('axes', tuple) for a in self.kwargs['axes']: @@ -216,7 +218,7 @@ def _compute_output_row_squeeze(self, row, row2=None, ab=False, verbose=False): def _compute_output_row_diagonal(self, row, row2=None, ab=False, verbose=False): if ab: - raise RuntimeError("ab option not allowed.") + raise RuntimeError("ab option not allowed.") # pragma: no cover self._check_row_(row, True, verbose=verbose) self._check_arg_('diag', list) to_remove = [] @@ -232,7 +234,7 @@ def _compute_output_row_diagonal(self, row, row2=None, ab=False, verbose=False): for r in to_remove: for i in range(len(row)): # pylint: disable=C0200 if row[i] == r: - raise RuntimeError( + raise RuntimeError( # pragma: no cover "Unexpected result r=%r row=%r to_remove=%r " "diag=%r." % ( r, row, to_remove, self.kwargs['diag'])) @@ -242,7 +244,7 @@ def _compute_output_row_diagonal(self, row, row2=None, ab=False, verbose=False): def _compute_output_row_matmul(self, row, row2=None, ab=False, verbose=False): if not ab: - raise RuntimeError("ab must be True.") + raise RuntimeError("ab must be True.") # pragma: no cover self._check_row_(row, True, verbose=verbose) self._check_row_(row2, True, verbose=verbose) self._check_arg_('axes', tuple) @@ -250,7 +252,8 @@ def _compute_output_row_matmul(self, row, row2=None, ab=False, verbose=False): self._check_arg_('right', tuple) self._check_arg_('ndim', int) if row2 is None: - raise RuntimeError("matmul expects two inputs.") + raise RuntimeError( + "matmul expects two inputs.") # pragma: no cover if verbose: ndim = self.kwargs['ndim'] axes = self.kwargs['axes'] @@ -267,7 +270,7 @@ def _compute_output_row_matmul(self, row, row2=None, ab=False, verbose=False): def _compute_output_row_batch_dot(self, row, row2=None, ab=False, verbose=False): if not ab: - raise RuntimeError("ab must be True.") + raise RuntimeError("ab must be True.") # pragma: no cover self._check_row_(row, True, verbose=verbose) self._check_row_(row2, True, verbose=verbose) self._check_arg_('batch_axes', tuple) @@ -277,7 +280,8 @@ def _compute_output_row_batch_dot(self, row, row2=None, ab=False, verbose=False) self._check_arg_('right', tuple) self._check_arg_('ndim', int) if row2 is None: - raise RuntimeError("batch_dot expects two inputs.") + raise RuntimeError( + "batch_dot expects two inputs.") # pragma: no cover if verbose: batch_axes = self.kwargs['batch_axes'] keep_axes = self.kwargs['keep_axes'] @@ -297,11 +301,11 @@ def _compute_output_row_batch_dot(self, row, row2=None, ab=False, verbose=False) def _compute_output_row_mul(self, row, row2=None, ab=False, verbose=False): if not ab: - raise RuntimeError("ab must be True.") + raise RuntimeError("ab must be True.") # pragma: no cover self._check_row_(row, True, verbose=verbose) self._check_row_(row2, True, verbose=verbose) if row2 is None: - raise RuntimeError("mul expects two inputs.") + raise RuntimeError("mul expects two inputs.") # pragma: no cover if verbose: print(" MUL %r @ %r" % (row, row2)) row2[:] = numpy.maximum(row, row2) @@ -314,7 +318,7 @@ def compute_output_row(self, row, row2=None, ab=False, verbose=False): method_name = "_compute_output_row_%s" % self.name meth = getattr(self, method_name, None) if meth is None: - raise NotImplementedError( + raise NotImplementedError( # pragma: no cover "compute_output_row not implemented for %r." % self.name) if verbose and ab: print(" -- called as a binary operator") @@ -336,30 +340,30 @@ def add_info(self, **kwargs): def _check_inputs_(self, n_expected, check_dim=False): if len(self.inputs) != n_expected: - raise RuntimeError( + raise RuntimeError( # pragma: no cover "Number of inputs must be %d not %d for operator %r." "" % (n_expected, len(self.inputs), self.name)) def _check_shape_(self, m): if len(m.shape) != self.full_dim: - raise RuntimeError( + raise RuntimeError( # pragma: no cover "Number of dimensions %r is different from expected value " "%d." % (m.shape, self.full_dim)) def _get_data(self, data, key): if isinstance(key, int): if key not in data: - raise RuntimeError( + raise RuntimeError( # pragma: no cover "Unable to find key %d in %r." % ( key, list(sorted(data)))) return data[key] if isinstance(key, EinsumSubOp): if id(key) not in data: - raise RuntimeError( + raise RuntimeError( # pragma: no cover "Unable to find key %d in %r." % ( id(key), list(sorted(data)))) return data[id(key)] - raise TypeError( + raise TypeError( # pragma: no cover "Unexpected input type %r." % type(key)) def _apply_id(self, data, verbose=False, **kwargs): @@ -377,7 +381,7 @@ def _apply_diagonal(self, data, verbose=False, **kwargs): self.name, m.shape, self.kwargs['diag'])) diag = self.kwargs['diag'] if len(diag) != 1: - raise NotImplementedError( + raise NotImplementedError( # pragma: no cover "Not implemented with more than one duplicated indice " "%r." % diag) diag0 = diag[0] @@ -488,7 +492,7 @@ def _apply_batch_dot(self, data, verbose=False, **kwargs): self.name, m1.shape, m2.shape, batch_axes, keep_axes, sum_axes)) if len(m1.shape) != len(m2.shape): - raise RuntimeError( + raise RuntimeError( # pragma: no cover "batch_dot only work with two tensors with the same number " "of dimensions not %r @ %r." % (m1.shape, m2.shape)) @@ -588,7 +592,7 @@ def apply(self, data, verbose=False, **kwargs): method_name = "_apply_%s" % self.name meth = getattr(self, method_name, None) if meth is None: - raise NotImplementedError( + raise NotImplementedError( # pragma: no cover "apply not implemented for %r." % self.name) output = meth(data, verbose, **kwargs) @@ -602,7 +606,7 @@ def _onnx_name(self): def _check_onnx_opset_(self, opset, limit): if opset is not None and opset < limit: - raise RuntimeError( + raise RuntimeError( # pragma: no cover "Opset (%r) must be >= %r for operator %r." "" % (opset, limit, self.name)) @@ -949,27 +953,29 @@ def append(self, op): """ if isinstance(op, int): if op in self._nodes: - raise RuntimeError("Key %d already added." % op) + raise RuntimeError( # pragma: no cover + "Key %d already added." % op) self._nodes[op] = op self.last_added_op = op self._inputs[op] = op return None if isinstance(op, EinsumSubOp): if op in self._nodes: - raise RuntimeError( + raise RuntimeError( # pragma: no cover "Key %d already added, op=%r." % (id(op), op)) self._nodes[id(op)] = op self._ops.append(op) self.last_added_op = op return op - raise TypeError("Unexpected type %r." % type(op)) + raise TypeError( # pragma: no cover + "Unexpected type %r." % type(op)) def mark_last_node(self): """ Marks the last node as the final output. """ if self.last_added_op is None: - raise RuntimeError("last_added_op is None.") + raise RuntimeError("last_added_op is None.") # pragma: no cover self.mark(-1, self.last_added_op) def mark(self, i, op): @@ -980,19 +986,21 @@ def mark(self, i, op): :param op: integer (an input) or an instance of @see cl EinsumSubOp. """ if not isinstance(i, int): - raise TypeError("i must an integer not %r." % type(i)) + raise TypeError( # pragma: no cover + "i must an integer not %r." % type(i)) if i != -1 and i not in self._inputs: - raise RuntimeError( + raise RuntimeError( # pragma: no cover "Input %d was not registered in %r." % (i, self._inputs)) if isinstance(op, EinsumSubOp): if id(op) not in self._nodes: - raise RuntimeError( + raise RuntimeError( # pragma: no cover "Key %d not found, op=%r." % (id(op), op)) self._mark[i] = op self._mark[id(op)] = i self.last_op = op else: - raise TypeError("Unexpected type %r." % type(i)) + raise TypeError( # pragma: no cover + "Unexpected type %r." % type(i)) def __iter__(self): "Iterates on nodes." @@ -1094,7 +1102,7 @@ def apply_sequence(self, *inputs, verbose=False, **kwargs): for op in self: last = op.apply(data, verbose=verbose, **kwargs) if last is None: - raise RuntimeError( + raise RuntimeError( # pragma: no cover "Sequence of operations is empty.") return last @@ -1164,12 +1172,146 @@ def simplify_mm_nodes(self, verbose=False): print("[GraphEinsumSubOp.simplify_mm_nodes] node %r" " - id=%d" % (op.name, id(op))) if len(op.inputs) != 2: - raise RuntimeError( + raise RuntimeError( # pragma: no cover "Expecting 2 inputs for node %r not %r id=%r." % ( op.name, len(op.inputs), id(op))) op.name = op.name[:-3] op.inputs = op.inputs[:1] + def _get_forward_nodes(self): + """ + Returns the forward nodes. + """ + forward = {} + for op in self: + if isinstance(op, int): + continue + for inp in op.inputs: + key = inp if isinstance(inp, int) else id(inp) + if key in forward: + forward[key].append(op) + else: + forward[key] = [op] + return forward + + def _replace_node_sequence(self, added, deleted): + """ + Removes a sequence of nodes. The method does not check + that the graph remains consistent. + """ + forward = self._get_forward_nodes() + key = id(deleted[-1]) + if key not in forward: + raise RuntimeError( + "key %r missing in all forward nodes." % key) + + # deletion + mark_input = None + for d in deleted: + del self._nodes[id(d)] + if id(d) in self._mark: + del self._mark[id(d)] + dels = [] + for k, v in self._mark.items(): + if id(v) == id(d): + mark_input = k + dels.append(k) + if len(dels) != 1: + raise RuntimeError( + "Input %d has more than one marked operator " + "(%r)." % (id(d), dels)) + del self._mark[dels[0]] + + dels = set(id(o) for o in deleted) + rem = [] + for i, op in enumerate(self._ops): + if id(op) in dels: + rem.append(i) + if len(rem) != len(deleted): + raise RuntimeError( + "Mismatched length %r, %r, len=%r." % ( + rem, dels, len(deleted))) + for i in reversed(rem): + del self._ops[i] + self.last_add_op = None + + # insertion + if added is not None: + self._ops.insert(rem[0], added) + self._nodes[id(added)] = added + for op in forward[key]: + new_inputs = list(op.inputs) + for i in range(len(op.inputs)): + if id(op.inputs[i]) == key: + new_inputs[i] = added + op.inputs = tuple(new_inputs) + if mark_input is not None: + self.mark(mark_input, added) + else: + inps = deleted[0].inputs + if len(inps) != 1: + raise RuntimeError( + "More than one input. Call another method.") + inp = inps[0] + for op in forward[key]: + new_inputs = list(op.inputs) + for i in range(len(op.inputs)): + if id(op.inputs[i]) == key: + new_inputs[i] = inp + op.inputs = tuple(new_inputs) + if mark_input is not None: + self.mark(mark_input, inp) + + def remove_duplicate_transpose(self, verbose=False): + """ + Removes consecutive transpose by merging them. + + :param verbose: display intermediate information + """ + modif = 1 + while modif > 0: + modif = 0 + candidates = [] + forward = self._get_forward_nodes() + for op in self: + if op.name == "transpose": + inp = op.inputs[0] + if (isinstance(inp, EinsumSubOp) and + inp.name == 'transpose' and + len(forward[id(inp)]) == 1): + candidates.append(op) + + if len(candidates) > 0: + modif = 1 + # Not efficient to take the first one and to + # start again but the graph should not be too big. + cand = candidates[0] + op2 = cand + op1 = cand.inputs[0] + perm1 = op1.kwargs['perm'] + perm2 = op2.kwargs['perm'] + if len(perm1) != len(perm2): + raise RuntimeError( + "Transposition should have the same length " + "%r, %r." % (perm1, perm2)) + perm = list(perm1) + for i in range(len(perm)): # pylint: disable=C0200 + perm[i] = perm1[perm2[i]] + if list(range(len(perm))) == perm: + # identity, everything needs to be removed + new_op = None + else: + new_op = op2.__class__( + op2.full_dim, op2.name, op1.inputs[0], + perm=tuple(perm)) + self._replace_node_sequence(new_op, [op1, op2]) + if verbose: + print("[GraphEinsumSubOp.remove_duplicate_transpose] remove nodes %r" + " - id=%d,%d + %d perm1=%r perm2=%r -> perm=%r" % ( + op2.name, id(op1), id(op2), + id(new_op) if new_op is not None else -1, + perm1, perm2, perm)) + def to_onnx(self, output, *inputs, dtype=None, verbose=False, opset=None, **kwargs): """ diff --git a/mlprodict/testing/einsum_impl_ext.py b/mlprodict/testing/einsum_impl_ext.py index d3271c2a4..b0775fc4a 100644 --- a/mlprodict/testing/einsum_impl_ext.py +++ b/mlprodict/testing/einsum_impl_ext.py @@ -277,7 +277,7 @@ def numpy_extended_dot_ouput_shape(m1, m2, axes, left, right): for i in right: if (i in left and m1.shape[i] != m2.shape[i] and m1.shape[i] != 1 and m2.shape[i] != 1): - raise RuntimeError( + raise RuntimeError( # pragma: no cover "Matrices should have the same dimension for dimension %d, " "shapes=%r @ %r." % (i, m1.shape, m2.shape)) new_shape[i] = m2.shape[i] @@ -347,7 +347,7 @@ def dispb(c): for i in range(len(broadcast)): # pylint: disable=C0200 if broadcast[i] and not (kind[i] & 3) == 3: - raise RuntimeError( + raise RuntimeError( # pragma: no cover "Broadcast should only happen on common axes, " "axes=%r left=%r right=%r shape1=%r shape2=%r." "" % (axes, left, right, m1.shape, m2.shape)) @@ -603,7 +603,7 @@ def numpy_extended_dot_matrix(m1, m2, axes, left, right, verbose=False): dim1 = int(numpy.prod([trm1.shape[i] for i in new_axes])) dim2 = int(numpy.prod([trm2.shape[i] for i in new_axes])) if dim1 != dim2: - raise RuntimeError( + raise RuntimeError( # pragma: no cover "Summation axis do not have the same length %d != %d, " "trshape1=%r trshape2=%r " "p_axes=%r p_left=%r p_right=%r p_common=%r" @@ -685,7 +685,7 @@ def numpy_extended_dot_matrix(m1, m2, axes, left, right, verbose=False): left, new_left, axes, new_axes, eq1, eq2)) return numpy_extended_dot_matrix(m1, m2, new_axes, new_left, right, verbose=verbose) - raise RuntimeError( + raise RuntimeError( # pragma: no cover "shape1=%r shape2=%r axes=%r left=%r right=%r eq=%s." % ( m1.shape, m2.shape, axes, left, right, _numpy_extended_dot_equation( From 93aca50cefaf920c919b689a7ffa4ac2b49c53dd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Sun, 2 May 2021 00:03:44 +0200 Subject: [PATCH 28/33] code coverage --- _doc/notebooks/einsum_decomposition.ipynb | 1058 +++++++++++++++-- _unittests/ut_onnx_conv/test_conv_helpers.py | 21 + .../test_onnxrt_runtime_lightgbm.py | 9 + _unittests/ut_testing/test_experimental.py | 69 +- _unittests/ut_testing/test_verify_code.py | 95 ++ mlprodict/onnx_conv/convert.py | 11 +- mlprodict/onnx_conv/parsers/parse_lightgbm.py | 3 +- mlprodict/testing/experimental.py | 22 +- mlprodict/testing/verify_code.py | 8 +- 9 files changed, 1167 insertions(+), 129 deletions(-) create mode 100644 _unittests/ut_onnx_conv/test_conv_helpers.py diff --git a/_doc/notebooks/einsum_decomposition.ipynb b/_doc/notebooks/einsum_decomposition.ipynb index 61a262fbb..cfe71d05a 100644 --- a/_doc/notebooks/einsum_decomposition.ipynb +++ b/_doc/notebooks/einsum_decomposition.ipynb @@ -252,7 +252,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Decomposition of bac,cd,def->ebc" + "### Decomposition of bac,cd,def->ebc" ] }, { @@ -283,7 +283,7 @@ "metadata": {}, "outputs": [], "source": [ - "seq = decompose_einsum_equation(\"bac,cd,def->ebc\", m1.shape, m2.shape, m3.shape)" + "seq = decompose_einsum_equation(\"bac,cd,def->ebc\")" ] }, { @@ -294,16 +294,16 @@ { "data": { "text/html": [ - "
\n", + "
\n", "" ], "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -351,7 +351,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## operator matmul\n", + "### operator matmul\n", "\n", "This operator can be used to represent either a multiplication, either a matrix multiplication but it applies only on arrays with the same number of dimensions. It can be broken into multiplication of matrix multiplication." ] @@ -364,16 +364,16 @@ { "data": { "text/html": [ - "
\n", + "
\n", "" ], "text/plain": [ - "" + "" ] }, "execution_count": 11, @@ -382,8 +382,7 @@ } ], "source": [ - "seq_clean = decompose_einsum_equation(\"bac,cd,def->ebc\", m1.shape, m2.shape, m3.shape, \n", - " strategy='numpy', clean=True)\n", + "seq_clean = decompose_einsum_equation(\"bac,cd,def->ebc\", strategy='numpy', clean=True)\n", "RenderJsDot(seq_clean.to_dot(size=7))" ] }, @@ -398,7 +397,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## ONNX\n", + "### ONNX\n", "\n", "The previous graph can be converted into ONNX." ] @@ -411,16 +410,16 @@ { "data": { "text/html": [ - "
\n", + "
\n", "" ], "text/plain": [ - "" + "" ] }, "execution_count": 12, @@ -467,7 +466,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## onnxruntime" + "### onnxruntime" ] }, { @@ -481,7 +480,7 @@ "from onnxruntime import InferenceSession\n", "\n", "\n", - "def make_model(equation):\n", + "def make_model1(equation):\n", " model = helper.make_model(\n", " opset_imports=[helper.make_operatorsetid('', 13)],\n", " graph=helper.make_graph(\n", @@ -498,7 +497,7 @@ " return model\n", "\n", "\n", - "model = make_model(\"bac,cd,def->ebc\")\n", + "model = make_model1(\"bac,cd,def->ebc\")\n", "sess = InferenceSession(model.SerializeToString())" ] }, @@ -523,7 +522,9 @@ } ], "source": [ - "sess.run(None, {'X': m1, 'Y': m2, 'Z': m3})[0]" + "sess.run(None, {'X': m1.astype(numpy.float32), \n", + " 'Y': m2.astype(numpy.float32), \n", + " 'Z': m3.astype(numpy.float32)})[0]" ] }, { @@ -544,7 +545,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:12<00:00, 1.05it/s]\n" + "C:\\xavierdupre\\__home_\\github_fork\\scikit-learn\\sklearn\\experimental\\enable_hist_gradient_boosting.py:16: UserWarning: Since version 1.0, it is not needed to import enable_hist_gradient_boosting anymore. HistGradientBoostingClassifier and HistGradientBoostingRegressor are now stable and can be normally imported from sklearn.ensemble.\n", + " warnings.warn(\n", + "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 14/14 [00:19<00:00, 1.38s/it]\n" ] }, { @@ -581,64 +584,64 @@ " \n", " \n", " \n", - " 63\n", - " 0.267888\n", - " 0.002161\n", - " 0.264015\n", - " 0.270904\n", + " 82\n", + " 0.062646\n", + " 0.000681\n", + " 0.062002\n", + " 0.063933\n", " 10\n", " 10\n", - " 2.678881\n", - " numpy.einsum\n", - " 55\n", + " 0.626458\n", + " custom_einsum\n", + " 60\n", " \n", " \n", - " 64\n", - " 0.052419\n", - " 0.000910\n", - " 0.051563\n", - " 0.054143\n", + " 83\n", + " 0.048764\n", + " 0.001461\n", + " 0.047808\n", + " 0.052906\n", " 10\n", " 10\n", - " 0.524191\n", - " custom_einsum\n", - " 55\n", + " 0.487644\n", + " dec-matmul\n", + " 60\n", " \n", " \n", - " 65\n", - " 0.041699\n", - " 0.000276\n", - " 0.041196\n", - " 0.042327\n", + " 84\n", + " 0.040966\n", + " 0.000602\n", + " 0.040169\n", + " 0.041773\n", " 10\n", " 10\n", - " 0.416995\n", - " dec-matmul\n", - " 55\n", + " 0.409658\n", + " dec-batch_dot\n", + " 60\n", " \n", " \n", - " 66\n", - " 0.007084\n", - " 0.000175\n", - " 0.006942\n", - " 0.007584\n", + " 85\n", + " 0.010761\n", + " 0.000982\n", + " 0.009832\n", + " 0.012314\n", " 10\n", " 10\n", - " 0.070836\n", + " 0.107609\n", " ort-einsum\n", - " 55\n", + " 60\n", " \n", " \n", - " 67\n", - " 0.015473\n", - " 0.000276\n", - " 0.015132\n", + " 86\n", + " 0.015497\n", + " 0.000365\n", + " 0.014887\n", " 0.015853\n", " 10\n", " 10\n", - " 0.154734\n", + " 0.154967\n", " ort-matmul\n", - " 55\n", + " 60\n", " \n", " \n", "\n", @@ -646,18 +649,18 @@ ], "text/plain": [ " average deviation min_exec max_exec repeat number total \\\n", - "63 0.267888 0.002161 0.264015 0.270904 10 10 2.678881 \n", - "64 0.052419 0.000910 0.051563 0.054143 10 10 0.524191 \n", - "65 0.041699 0.000276 0.041196 0.042327 10 10 0.416995 \n", - "66 0.007084 0.000175 0.006942 0.007584 10 10 0.070836 \n", - "67 0.015473 0.000276 0.015132 0.015853 10 10 0.154734 \n", + "82 0.062646 0.000681 0.062002 0.063933 10 10 0.626458 \n", + "83 0.048764 0.001461 0.047808 0.052906 10 10 0.487644 \n", + "84 0.040966 0.000602 0.040169 0.041773 10 10 0.409658 \n", + "85 0.010761 0.000982 0.009832 0.012314 10 10 0.107609 \n", + "86 0.015497 0.000365 0.014887 0.015853 10 10 0.154967 \n", "\n", " name N \n", - "63 numpy.einsum 55 \n", - "64 custom_einsum 55 \n", - "65 dec-matmul 55 \n", - "66 ort-einsum 55 \n", - "67 ort-matmul 55 " + "82 custom_einsum 60 \n", + "83 dec-matmul 60 \n", + "84 dec-batch_dot 60 \n", + "85 ort-einsum 60 \n", + "86 ort-matmul 60 " ] }, "execution_count": 16, @@ -688,26 +691,29 @@ "sess = None\n", "sess2 = None\n", "seq = None \n", + "seq2 = None \n", "\n", "results = []\n", - "for N in tqdm([2, 3, 4, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55]):\n", + "for N in tqdm([2, 3, 4, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60]):\n", " m1 = numpy.random.randn(N, N, N)\n", " m2 = numpy.random.randn(N, N)\n", " m3 = numpy.random.randn(N, N, N)\n", " \n", " if seq is None:\n", - " seq = decompose_einsum_equation(\n", - " equation, m1.shape, m2.shape, m3.shape, clean=True)\n", + " seq = decompose_einsum_equation(equation, clean=True)\n", + " if seq2 is None:\n", + " seq2 = decompose_einsum_equation(equation, clean=True, strategy='numpy')\n", " if sess is None:\n", - " model = make_model(equation)\n", + " model = make_model1(equation)\n", " sess = InferenceSession(model.SerializeToString())\n", " if sess2 is None:\n", - " onx = seq_clean.to_onnx(\"Y\", \"X1\", \"X2\", \"X3\", dtype=numpy.float32)\n", + " onx = seq2.to_onnx(\"Y\", \"X1\", \"X2\", \"X3\", dtype=numpy.float32)\n", " sess2 = InferenceSession(onx.SerializeToString())\n", "\n", " res = measure_time(lambda x: numpy.einsum(equation, *x, optimize=True),\n", " [m1, m2, m3],\n", " repeat=10, number=10)\n", + "\n", " res['name'] = \"numpy.einsum\"\n", " res[\"N\"] = N\n", " results.append(res)\n", @@ -723,6 +729,7 @@ " res = measure_time(lambda x: apply_einsum_sequence(seq, *x),\n", " [m1, m2, m3],\n", " repeat=10, number=10)\n", + "\n", " res['name'] = \"custom_einsum\"\n", " res[\"N\"] = N\n", " results.append(res) \n", @@ -734,6 +741,13 @@ " res[\"N\"] = N\n", " results.append(res) \n", "\n", + " res = measure_time(lambda x: apply_einsum_sequence(seq2, *x, matmul_impl=\"pyf\"),\n", + " [m1, m2, m3],\n", + " repeat=10, number=10)\n", + " res['name'] = \"dec-batch_dot\"\n", + " res[\"N\"] = N\n", + " results.append(res) \n", + "\n", " res = measure_time(lambda x: sess.run(None, {'X': x[0], 'Y': x[1], 'Z': x[2]}),\n", " [m1.astype(numpy.float32), m2.astype(numpy.float32),\n", " m3.astype(numpy.float32)],\n", @@ -762,9 +776,9 @@ "outputs": [ { "data": { - "image/png": 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\n", 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pSxO9rLyxPOH7QGsdk0uMtYEZWc7/CpYksHpBzklrvQHLk8OZQKRSak4Bnx7d+Hnc+NqdwnPjOTgrSzv3msDpG77v/KqGpTYHAK11PJaarayfY25/D0IIkR9XsTwQy+q6e4BSylUp9bW1CVossAnwttZe58UHS6H4dJb3TpPzPSE7Z2/Y1gT4KKWMSqmp1mZjsVgK+teOCZYajnuB00qpv5VSHazv1wZeyFqzguV6ndlUOA85XXtrA8uz7PMwkAFUzud+83OM/Cjo/e/Gz/fa57ASaKqUqoOlpihGa70zy7rX/m6iCxCbsDOS7Ihcaa0ztNa/YLmY3QFEYbmQNNNae1t/vLRlMIO8RAHJQL2ii9jSpwjLk57pQGVr0rYWS0Jyjc5m06tYmk3NV0p1yuUQZ7E0G/DO8uOitd5W0Fi11p9rrVtjeZLXEHjJuigBS1J5TZGNmnbjsaw394o5r36ds0AtlX0H3+w+46zCsdw4rx3XDUtzi/P5PLYQQuTHPizX16xuvD69gKVJVTuttSeWJsbw330jt+tZFJbajdpZ3qtFwa5lWUc8rWXdXxTwCNAf6IGlmZxv1ri01ru01v2xNHFbgaUGHCzX5vduuE+5aq1za3KdH2eB3jfs11lrfR4yh+PO6ee1fB6jKO5/N36+4ZDZL/knLE3Ah2NpKp9VEyBMax1bCDEIG5FkR+RKWfTH0t/ksNbaDMzF0gemknWd6kqpPPtSWLedB3yilKpmfWLVoQBtiPPLEXDC0rwqXSnVG0s73TxprYOAocAvSqm2Oaw2G3hVWTuJKkvH1AcLGqRSqo1Sqp21I2QClkTwWv+lUGCQ9WljfSx9n4rKMSw1NfdZY3kdy+eXHzuBC8BUpZSbUso5S6IYAdRQ2QyUYPUDMFIpFWD9G3gf2KG1DrvlMxFCiJutxdLHJDceWB7kRStLh/u3blgegaU/zk2sLQx+At5TSnkoy4A1zwM3dvr3zeX4w5RSTa39ZN8Gfrbu1wNIwVLr7YrlOol1v45KqaFKKS9t6WMSy3/3kLnAWOs9Rlmvz/cppW6s4Sqo2dbzrG2NoaK1jHDts3DP5ef9HPd6vVDgXqVUeWuLkgm3GTPAG9b7aTMsfamyzp+zCEuz637cnOx0xdJ0XZRgkuyInKxSSsVjuXi+BzymtT5oXTYJS0f77dZq9fVYnojlx4vAfmAXluZf0yjkv0OtdRyWzok/YamteQRLp/r8bv8nlo6iq24Y1ODa8uVY4l5iPf8DWDr9F5QnlhvSVSzV6peBj6zLPsXSZjsCWMj1HVILlbXJ3jjg/7A8iUwA8jXBqvVm3BdLu/Uz1u0esi7eABwELiqlorLZdj3wBpZauAtYavwevp1zEUKIbCzCUnh2yWWdzwAXLLUp27E0z85qBvCAsozU9jk3exbLtfMUln6f32N5uAfW5r7kXtPzLZa+KhexNPe+1sF+UZZtD3H9YEFgqY0Is96LxmJ5WIfWOhgYjaWp9FUs9+wRuRw/v2ZguZ/+oZSKs8bTrhD2m9W3wF4sTfb+4PrE5Fb9jeUz+AuYrrX+49oCrfVWLEnibq316Ru2G8JtzM8k7IPSOq+WJkIIIYQQJZdS6n0gUmv9mQ2O/TqWPkJSaC5m1tq0fwFTbn1LlVIbgO+11v+X5b2+wHCt9eCcthMlgyQ7QgghhBCi1MlPsqOUaoNl/rma1pYhopSRZmxCCCGEEKLMUUotxNIUf4IkOqWX1OwIIYQQQgghSiWp2RFCCCGEEEKUSpLsCCGEEEIIIUql7CYCtBs+Pj7a19fX1mEIIUSZFhISEqW1zu9Es2WK3KeEEML2crtP2XWy4+vrS3BwsK3DEEKIMk0pdePcE8JK7lNCCGF7ud2npBmbEEIIIYQQolSSZEcIIYQoIKVUX6XUnJiYGFuHIoQQIheS7AghhBAFpLVepbUe4+XlZetQhBBC5MKu++yI0i0tLY1z586RnJxs61DELXJ2dqZGjRqYTCZbhyKEEELkSsodJd+tlDsk2RE2c+7cOTw8PPD19UUpZetwRAFprbl8+TLnzp2jTp06tg5HCCGEyJWUO0q2Wy13SDM2YTPJyclUqFBBLjgllFKKChUqyBMyIYQQJYKUO0q2Wy132GWyIx0/yw654JRs8v0JIYQoSeS+VbLdyvdnl8mOdPwUQgghhBBC3C67THaEEEIIIYQQ4nZJsiNEPoWFhdGkSRNGjx5Ns2bNuOeee0hKSmLu3Lm0adOGFi1acP/995OYmAjAiBEjeOqpp2jfvj1169YlKCiIUaNG0aRJE0aMGJG53z/++IMOHTrQqlUrHnzwQeLj4210hkKI/JLm1kKIoibljsIhyY4QBXD8+HGefvppDh48iLe3N8uWLWPQoEHs2rWLvXv30qRJE7755pvM9a9evco///zDp59+Sr9+/Zg4cSIHDx5k//79hIaGEhUVxbvvvsv69evZvXs3gYGBfPLJJzY8QyFEfkhzayFEcZByx+2ToaeFKIA6deoQEBAAQOvWrQkLC+PAgQO8/vrrREdHEx8fT8+ePTPX79u3L0op/P39qVy5Mv7+/gA0a9aMsLAwzp07x6FDh+jUqRMAqampdOjQodjPS5ReKaf+xdG3Nsogz7aEfUq/nISxnDPKIB3HhbiRlDtunyQ7QhSAk5NT5u9Go5GkpCRGjBjBihUraNGiBQsWLCAoKOim9Q0Gw3XbGgwG0tPTMRqN3H333fzwww/Fdg6i7Eg9d46wIUPwHjCAyq++YutwhLiJTjdzac5+nOp4Uv7hxrYORwi7I+WO2yeP+oS4TXFxcVStWpW0tDQWL15coG3bt2/P1q1bOXHiBAAJCQkcO3asKMIUZYw5JYVz48eD2Uy5oY/YOhwhspWwO4KMmBRcW1W2dShClBhS7igYSXaEuE3vvPMO7dq1o1OnTjRuXLAnkxUrVmTBggUMGTKE5s2b06FDB44cOVJEkYqyJOLdd0k5dJhq06bhWKuWrcMR4iY6w0zcxrOYanrg1MDb1uEIUWJIuaNglNba1jHkKDAwUAcHB9s6DFFEDh8+TJMmTWwdhrhN8j3an+hly7jwv9ep8OSTVJo44bb3p5QK0VoH3n5kpY/cp25dQvBFrv58nAojmuHSuLytwxFlgNyvSofsvsfc7lNSsyOEEKVI8qFDXJzyNq4d2lNx/LO2DkeIbOkMTezGs5iqu+PcqJytwxFClGKS7AghRCmRERPDufHPYSxfnuoff4wyGm0dkhDZStwbScblZDzvrIVSMgqbEKLo2GWyI5O1CSFEwWizmfCXJ5EWEUGNzz7Fobw0CypKcp+6ddqsidtwFlNVN5ybyt+pEKJo2WWyI5O1CSFEwVyeM4f4v/+m8iuTcLHOySCKjtynbl3SvkukRyXhIbU6QohiYJfJjhBCiPyL37qVSzM+x7NPH8o9IsNMC/ulzZrYDWdwqOyKS7MKtg5HCFEGSLIjhBAlWFp4OOEvvIhT/XpUfXuKPCkXdi3pQBTpkUmWvjoG+VsVQhQ9SXaEEKKE0qmpnJswEZ2WRvUZn2NwdbV1SELkyNJX5wwOFV1w8fexdThCiDJCkh0hbtP7779vk+MGBwczfvx4mxxb2IeYX38led8+qr77Dk5169g6HCFylXzoMmkXE6VWR4jbIGWOgpNkR4jbZKsLT2BgIJ9//rlNji1sT2vNlW+/w6lRIzx69bJ1OELkSmtN7F9ncPBxwaV5RVuHI0SJJWWOgnOwdQBCAExZdZBD4bGFus+m1Tx5q2+zPNdbtGgR06dPRylF8+bNMRqN9OnThwceeAAAd3d34uPjuXDhAg899BCxsbGkp6cza9Ys1qxZQ1JSEgEBATRr1ozFixfzySefMG/ePACeeOIJJkyYQFhYGL169aJ9+/Zs27aNNm3aMHLkSN566y0iIyNZvHgxbdu2zTa+hIQEnn32WQ4cOEBaWhqTJ0+mf//+BAUFMX36dFavXs3kyZM5c+YMp06d4syZM0yYMIHx48eTkJDA4MGDOXfuHBkZGbzxxhs89NBD+Pr6EhwcjI+PD8HBwbz44osEBQUxefJk/v3338z9fPrpp2zfvp1169ZRvXp1Vq1ahclkKrwvSdyyxF27SDl6lKrvviP9dITdSz58hbQLCZR7sCHKKH+vwvZsVe6QMkfxlzkk2RFl2sGDB3n33XfZtm0bPj4+XLlyheeffz7bdb///nt69uzJ//73PzIyMkhMTKRz5858+eWXhIaGAhASEsL8+fPZsWMHWmvatWtH165dKVeuHCdOnGDp0qXMmzePNm3a8P3337NlyxZ+/fVX3n//fVasWJHtcd977z3uvPNO5s2bR3R0NG3btqVHjx43rXfkyBE2btxIXFwcjRo14qmnnuK3336jWrVqrFmzBoD8zAly8uRJNm7cyKFDh+jQoQPLli3jww8/ZODAgaxZs4YBAwbk67MVRevqt99i9PbGs08fW4ciRK60tozAZizvjGuA1OqIskvKHDcrjjKHJDvCLuSnBqYobNiwgQcffBAfH0tn2fK5TMTYpk0bRo0aRVpaGgMGDCAgm7lMtmzZwsCBA3FzcwNg0KBBbN68mX79+lGnTh38/f0BaNasGXfddRdKKfz9/QkLC8vxuH/88Qe//vor06dPByA5OZkzZ87ctN59992Hk5MTTk5OVKpUiYiICPz9/XnhhReYNGkSffr0oXPnznl+Jr1798ZkMuHv709GRga9rE2k8opTFJ/Uc+eJ+2sDFZ54AoOzs63DESJXyceuknYunnL3N0AZpfW8sA+2KHdImeNmxVHmkKuOEDdwcHDAbDYDYDabSU1NBaBLly5s2rSJ6tWrM2LECBYtWlSg/To5OWX+bjAYMl8bDAbS09Nz3E5rzbJlywgNDSU0NJQzZ87QpEmTXPdvNBpJT0+nYcOG7N69G39/f15//XXefvvtm84xOTk52/0YDAZMJlNmE6m84hTF5+oP34NSlBvysK1DESJXWmvi1p/B6O2Ea8tKtg5HCLsjZY6iL3NIsiPKtDvvvJOlS5dy+fJlAK5cuYKvry8hISEA/Prrr6SlpQFw+vRpKleuzOjRo3niiSfYvXs3ACaTKXOdzp07s2LFChITE0lISGD58uX5erKRm549e/LFF1+gtQZgz549+d42PDwcV1dXhg0bxksvvZQZc9ZzXLZs2W3FJ4qXOTGR6KU/43H33ZiqVrV1OGWWUqqvUmpOfppplGUpJ6JJPRuHR/eaKAcpcoiyTcoctilzSDM2UaY1a9aM//3vf3Tt2hWj0UjLli2ZNm0a/fv3p0WLFvTq1SuzejgoKIiPPvoIk8mEu7t75lOWMWPG0Lx5c1q1asXixYsZMWJEZse/J554gpYtW95WVewbb7zBhAkTaN68OWazmTp16rB69ep8bbt//35eeumlzCcms2bNAuCtt97i8ccf54033qBbt263HJsofjG/rsIcG0v5R4fbOpQyTWu9ClgVGBg42tax2CutNbHrz2D0csStdWVbhyOEzUmZwzZlDnUtc7NHgYGBOjg42NZhiCJy+PDhbKtGRcki32Px0Vpzqm9fDI5O+C77udhGYVNKhWitA4vlYCWM3Kdylnwymqi5+/HuXw/3DtVsHY4Qcr8qJbL7HnO7T0mdshBClBCJ//xD6omTlBs+XIabFnYv7q8zGDwccQusYutQhBBlmDRjE8JOzJ8/nxkzZlz3XqdOnZg5c6aNIhL25sq332GsUAHP++61dShC5CrlVAwpp2Lw6lMXZZLnqkLYm7JU5pBkRwg7MXLkSEaOHGnrMISdSj1zhvigIHyeGovB0dHW4QiRq9gNZzC4m3BrK7U6QtijslTmkMctQghRAlxd/D0YjXg/JMNNC/uWcjqWlBPReHSpgcHRaOtwhBBlnF0mOzKkpxBC/CcjPoHoZcvw7NkTU2WZq0TYt9i/zmBwc8CtvQyNLoSwPbtMdrTWq7TWY7y8vGwdihBC2FzMyhWY4+NluGlh91LPxpFy7CrunaVWRwhhH+wy2RFCCGGhzWaufrcY5+bNcWnRwtbhCJGr2L/OYHB1wL2D1OoIIeyDJDtCZDF58mSmT59erPuIjo7mq6++ynO9bt26cavzeeRn2/fff/+W9i2KVsLWraT++y/lhw+zdShC5Cr1fDzJR67gfkd1DE4y/pEQeZEyR/GQZEcIG8vvhaeoSbJjn658+y3Gij549uxp61CEyFXsX2dQzg64d5QJRIWwV2WxzCGPXoR9WPcKXNxfuPus4g+9p+a52nvvvcfChQupVKkSNWvWpHXr1pw8eZKnn36aS5cu4erqyty5c2ncuDERERGMHTuWU6dOATBr1iw6dux40z737t1Lhw4diIqK4uWXX2b06NHEx8fTv39/rl69SlpaGu+++y79+/fnlVde4eTJkwQEBHD33Xfz0UcfMW3aNL777jsMBgO9e/dm6lTLeSxdupRx48YRHR3NN998Q+fOnbM9p6SkJEaOHMnevXtp3LgxSUlJmct++OEH3n//fbTW3HfffUybNo1XXnmFpKQkAgICaNasGYsXL76VT1wUspR//yVh02Z8nn0GJcNNCzuWGh5P8qHLePaohcFZihaiBLBRuUPKHMVf5pArkijTQkJCWLJkCaGhoaSnp9OqVStat27NmDFjmD17Ng0aNGDHjh2MGzeODRs2MH78eLp27cry5cvJyMggPj4+2/3u27eP7du3k5CQQMuWLbnvvvuoVKkSy5cvx9PTk6ioKNq3b0+/fv2YOnUqBw4cIDQ0FIB169axcuVKduzYgaurK1euXMncb3p6Ojt37mTt2rVMmTKF9evXZ3v8WbNm4erqyuHDh9m3bx+tWrUCIDw8nEmTJhESEkK5cuW45557WLFiBVOnTuXLL7/MjEHYh6vfLUaZTJR76CFbhyJEruI2nkU5GaVWR4hcSJnDNmUOSXaEfchHDUxR2Lx5MwMHDsTV1RWAfv36kZyczLZt23jwwQcz10tJSQFgw4YNLFq0CACj0UhOIwb2798fFxcXXFxc6N69Ozt37uS+++7jtddeY9OmTRgMBs6fP09ERMRN265fv56RI0dmxlS+fPnMZYMGDQKgdevWhIWF5XhemzZtYvz48QA0b96c5s2bA7Br1y66detGxYoVARg6dCibNm1iwIABeX5WonhlxMURs3w5nvfei4OPj63DESJHaRcTSNofhcedNTG4mmwdjhD5Y4Nyh5Q5bFPmkGRHiBuYzWa8vb3z/cRh5syZzJ07F4C1a9cCoJS6bh2lFIsXL+bSpUuEhIRgMpnw9fUlOTm5QLE5OTkBloteenp6gbYVJcvlOXMwJyZSToabFnYuduNZlKMR907VbR2KECWOlDmKngxQIMq0Ll26sGLFCpKSkoiLi2PVqlW4urpSp04dli5dCoDWmr179wJw1113MWvWLAAyMjKIiYnh6aefJjQ0lNDQUKpVszThWLlyJcnJyVy+fJmgoCDatGlDTEwMlSpVwmQysXHjRk6fPg2Ah4cHcXFxmTHdfffdzJ8/n8TERIDrqpQLcl7ff/89AAcOHGDfvn0AtG3blr///puoqCgyMjL44Ycf6Nq1KwAmk4m0tLQCH0sUvuRjx7g8fwFe9w/CpVkzW4cjsiGTX1ukRSaStO8S7h2rYnSTWh0hciNlDtuUOSTZEWVaq1ateOihh2jRogW9e/emTZs2ACxevJhvvvmGFi1a0KxZM1auXAnAjBkz2LhxI/7+/rRu3ZpDhw5lu9/mzZvTvXt32rdvzxtvvEG1atUYOnQowcHB+Pv7s2jRIho3bgxAhQoV6NSpE35+frz00kv06tWLfv36ERgYSEBAwC0NS/nUU08RHx9PkyZNePPNN2ndujUAVatWZerUqXTv3p0WLVrQunVr+vfvD8CYMWNo3rw5Q4cOLfDxROHRZjMXp7yN0d2dSi++aOtwRA5k8muLuI1nUQ4G3O+QWh0h8iJlDtuUOZTWusgPcqsCAwP1rY7xLezf4cOHadKkia3DELdJvsfCFf3zz1x4/Q2qvvce3vcPsnU4ACilQrTWgbaOwx6V5ftUWlQSER8H4965Ot731rV1OELkSe5XpUN232Nu9ymp2RFCCDuRfuUKkR9NxyWwNV4DB9g6HCFyFbfxLBgNeHSuYetQhBAiRzJAgRAl2O+//86kSZOue69OnTosX77cRhGJ2xH50XQyEhKo+tZbKIM8ixL2K/1yEol7InDvUA2jh8wBJURZUFLLHJLsCFGC9ezZk549e9o6DFEIEnbuJGb5ciqMHo1Tgwa2DkeIXMUFnQODwqOr1OoIUVaU1DKHPDoUQggb06mpXJzyNqbq1fEZ95StwxEiV+lXk0kIicCtTRWMnk62DkcIIXIlNTtCCGFjl+cvIPXkSWrMnoXBxcXW4QiRq7igs6DAo1tNW4cihBB5kpodIYSwodSzZ4n66is87r4bj27dbB2OELlKj04hITgCt8DKOHhJrY4Qwv5JsiNEFpMnT76lMeYL04IFCwgPDy+0/XXr1o2yOjSuvdNac/Gdd1BGI5X/95qtwxEiT3F/nwUttTpCFAYpcxQPSXaEsDOFfeER9ivujz9J2LQZn/HPYqpSxdbhCJGrjNgUEnZdxK11ZRzKOds6HCFEISgLZQ5JdkSZ995779GwYUPuuOMOjh49CsDJkyfp1asXrVu3pnPnzhw5cgSAiIgIBg4cSIsWLWjRogXbtm27aX+TJ0/mscceo3PnztSuXZtffvmFl19+GX9/f3r16kVaWhoAb7/9Nm3atMHPz48xY8agtebnn38mODiYoUOHEhAQQFJSEr6+vrz66qsEBAQQGBjI7t276dmzJ/Xq1WP27NkABAUF0adPn8wYnnnmGRYsWFDEn5y4HRnxCUS89x5OjRtTftgwW4cjRJ7i/j4HZo1HNxmBTYhbJWWO4icDFAi7MG3nNI5cOVKo+2xcvjGT2k7KdZ2QkBCWLFlCaGgo6enptGrVitatWzNmzBhmz55NgwYN2LFjB+PGjWPDhg2MHz+erl27snz5cjIyMoiPj892vydPnmTjxo0cOnSIDh06sGzZMj788EMGDhzImjVrGDBgAM888wxvvvkmAMOHD2f16tU88MADfPnll0yfPp3AwP8mAq5VqxahoaFMnDiRESNGsHXrVpKTk/Hz82Ps2LGF96GJYhP1xeekX7pEjS8+RznIpVjYt4y4VOJ3XMS1ZWUcKsggGqLks0W5Q8octiF3WFGmbd68mYEDB+Lq6gpAv379SE5OZtu2bTz44IOZ66WkpACwYcMGFi1aBIDRaMTLyyvb/fbu3RuTyYS/vz8ZGRn06tULAH9/f8LCwgDYuHEjH374IYmJiVy5coVmzZrRt2/fbPfXr1+/zO3j4+Px8PDAw8MDJycnoqOjb/tzEMUr+dAhrnz7Hd4PDcalRQtbhyNEnuI2n4MMMx7dpa+OELdKyhy2IcmOsAt51cAUJ7PZjLe3N6Ghoflaf+bMmcydOxeAtWvXAuDkZBmlyGAwYDKZUEplvk5PTyc5OZlx48YRHBxMzZo1mTx5MsnJyTkeI+v+rv2edX8ODg6YzebM93Pbl7AtnZHBhbcmYyxXjkoTJ9o6HCHylBGfSsI/F3ANqITJR2p1ROlgL+UOKXMUPemzI8q0Ll26sGLFCpKSkoiLi2PVqlW4urpSp04dli5dClhGzNq7dy8Ad911F7NmzQIgIyODmJgYnn76aUJDQwkNDaVatWr5Ou61C4OPjw/x8fH8/PPPmcs8PDyIi4sr0HnUrl2bQ4cOkZKSQnR0NH/99VeBthfFJ3b1apL376fypJcx5vCUTgh7Er/lPDpdanWEuF1S5rANSXZEmdaqVSseeughWrRoQe/evWnTpg0Aixcv5ptvvqFFixY0a9aMlStXAjBjxgw2btyIv78/rVu35tChQ7d0XG9vb0aPHo2fnx89e/bMPC7AiBEjGDt2bGZnwfyoWbMmgwcPxs/Pj8GDB9OyZctbiksULZ2RQdTsr3Fq1AjPHJoPCGFPMhLSiN92AZfmFTFVcrV1OEKUaFLmsA2ltS6eAylVF/gf4KW1fiA/2wQGBmp7G6tbFJ7Dhw/TpEkTW4chbpN8j/kXu3Yt559/geqffYqntU11SaCUCtFaB+a9ZtlT2u9TMX+EEbfhLJUntsJU2c3W4QhxW+R+VTpk9z3mdp/KV82OUmqeUipSKXXghvd7KaWOKqVOKKVeyW0fWutTWuvH83M8IYQobbTZTNSs2TjWq4fHPffYOhwh8mROTCN+azgu/j6S6AghSqz8NmNbAFz3GFIpZQRmAr2BpsAQpVRTpZS/Umr1DT+VCjVqIYQoYeI3bCDl+HF8nhyDMkgLYmH/4reFo1MypK+OEKJEy9dobFrrTUop3xvebguc0FqfAlBKLQH6a60/APoghBACsHQ4jfpqFqZatfC8915bhyNEnszJ6cRtCce5aQUcq7nbOhwhhLhlt/N4sTpwNsvrc9b3sqWUqqCUmg20VEq9mst6Y5RSwUqp4EuXLt1GeEIIYR8SNm0i+dAhS62OTCAqSoD4beHo5HQ876pl61CEEOK2FNtdV2t9Gchz2lWt9RxgDlg6fhZ1XEIIUZQya3WqVcPLOlGbEPbMnJJB/JbzODcuj2N1qdURQpRst1Ozcx7I2pC3hvU9IYQQVonbt5O0dy8VxoxGmUy2DkeIPCWGRGBOTMfjTumrI4Qo+W4n2dkFNFBK1VFKOQIPA78WTlhCiKxmz57NokWLbB2GuAVRX83CoVIlvAYOtHUoIg9KqbpKqW+UUj/nvXbppM2a+G3hONb0wKmWp63DEULYSGkqd+SrGZtS6gegG+CjlDoHvKW1/kYp9QzwO2AE5mmtDxZZpEKUYWPH5tkCVNihxOBgEnftovJrr2JwcrJ1OGWSUmoelkFzIrXWflne7wXMwHL/+j+t9VTrgDuPl+VkJ+VENOlRSZR/qJGtQxFC2FBpKnfkq2ZHaz1Ea11Va23SWtfQWn9jfX+t1rqh1rqe1vq9wgpKKdVXKTUnJiamsHZZZhXXpLElVVhYGE2aNGH06NE0a9aMe+65h6SkJLp168a1iQKjoqLw9fUFYMGCBQwYMIC7774bX19fvvzySz755BNatmxJ+/btuXLlCgDdunXjueeeIyAgAD8/P3bu3InZbKZBgwZcG3jDbDZTv359sg7EcfLkSXr16kXr1q3p3LkzR44cAWDy5MlMnz49c9+TJk2ibdu2NGzYkM2bNwNw8OBB2rZtS0BAAM2bN+f48eOEhYXh55dZvmP69OlMnjw5cz8TJ04kMDCQJk2asGvXLgYNGkSDBg14/fXXi+5DL0OivpqFsUIFvB980NahlGULyOfUCcUfmv2J33oeg4cJF38fW4ciRKkk5Y7iL3fY5bBAWutVwKrAwMDRto6lJNMZmsjZe3FvWwW3NlVsHU6uLr7/PimHjxTqPp2aNKbKa6/lud7x48f54YcfmDt3LoMHD2bZsmW5rn/gwAH27NlDcnIy9evXZ9q0aezZs4eJEyeyaNEiJkyYAEBiYiKhoaFs2rSJUaNGceDAAYYNG8bixYuZMGEC69evp0WLFlSsWDFz32PGjGH27Nk0aNCAHTt2MG7cODZs2HBTDOnp6ezcuZO1a9cyZcoU1q9fz+zZs3nuuecYOnQoqampZGRkEBERkeu5ODo6EhwczIwZM+jfvz8hISGUL1+eevXqMXHiRCpUqJDn5yeyl7R3LwnbtlHppRcxuLjYOpwyqyBTJwCH8rNPpdQYYAxArVqlZ7SytKgkko9exbNHLZSDzAUlSjcpd1iUhXKHXSY7onAk7o0k7Wwchq41bB2KXatTpw4BAQEAtG7dmrCwsFzX7969Ox4eHnh4eODl5UXfvn0B8Pf3Z9++fZnrDRkyBIAuXboQGxtLdHQ0o0aNon///kyYMIF58+YxcuTIzPXj4+PZtm0bD2apBUhJSck2hkGDBt0Ub4cOHXjvvfc4d+5c5pOSvPSzjg7m7+9Ps2bNqFq1KgB169bl7NmzkuzchqhZszF6eeH90MO2DkXcLLupE9oppSoA72GdIsE6b9xNSuuooQn/hINR4dauqq1DEaJUk3JH8ZY7JNkppXSGJm7DWUxV3HBuav8F1vw8CSkqTln6UhiNRpKSknBwcMBsNgOQnJyc4/oGgyHztcFgID09PXOZUuq67ZRS1KxZk8qVK7NhwwZ27tzJ4sWLM5ebzWa8vb0JDQ3Nd8xGozHzmI888gjt2rVjzZo13HvvvXz99dc0bNgw8zxyO5es55HduYiCST50iPigICo+Nx6ju5utwxH5lN8pEkojc0o6CcERuPr7YPRwtHU4QhQ5KXeUnXKH1FOXUon7LpEelWRpjmBQeW8gruPr60tISAgAP/98a32Vf/zxRwC2bNmCl5cXXl5eADzxxBMMGzaMBx98EKPRmLm+p6cnderUYenSpYClv9XevXvzfbxTp05Rt25dxo8fT//+/dm3bx+VK1cmMjKSy5cvk5KSwurVq2/pXETBRM2ajcHDg3LDhtk6FJE9mTrhBokhkeiUDNw75Tg3uBCiCEm5o+jYZbIjAxTcHm3WxP11BlMV1xJRq2OPXnzxRWbNmkXLli2Jioq6pX04OzvTsmVLxo4dyzfffJP5fr9+/YiPj8+sSn7iiScyOyUuXryYb775hhYtWtCsWTNWrlyZ7+P99NNP+Pn5ERAQwIEDB3j00UcxmUy8+eabtG3blrvvvpvGjRvf0rmI/Es5fpy4P/+k/PBhGD08bB2OyJ5MnZCFNmvi/wnHVNMDx5ryNyuELUi5o+goex6tKzAwUF/7MkT+Je6J5MqPRyk/tAmudjyizuHDh2nSpImtwygS3bp1Y/r06QQGBt60LDg4mIkTJ2aOZlLSlebv8Vacf+FF4jdupN5f63EoV87W4RQKpVSI1vrmP+YSIOvUCUAE/02dcC/wGf9NnVCgEUWVUn2BvvXr1x99/Pjxwg26mCUfu0rUvAOUf6gRri0r2TocIYpMab5flfVyR273KemzU8posyZ2g6VWx6WZ1OrYm6lTpzJr1qzr2syK0iPl33+JXbeOCqNGlppEp6TTWg/J4f21wNrb2G+pGTU0flu4DDctRCkl5Q5JdkqdpH2XSL+URPmhjaWvjg0FBQVl+/4rr7zCK6+8UrzBiGJz+es5KEdHymcZ7UYIe5YWlUTykSt43CXDTQtRkkm5I2dyZStFtFkT+9cZHCq74tJMntAJUZxSz50jZtUqyj00GAcZsluUENeGm3aX4aaFEKWUJDulyLVaHc+7ZAQ2IYrb5TlzUQYD5Uc9butQRDEoDQPpXBtu2sXfB6OnDDcthCid7DLZKQ03keJ2ra+OQ2VXXPykVkeI4pR24QLRy5fj9cD9mCpLB++yQGu9Sms95trQriVR4m7rcNMdq9k6FCGEKDJ2meyUhptIcUvaf4n0SKnVEcIWIqd/DIDPE0/YOBIh8kebNfHbLMNNO9XytHU4QghRZOwy2REFY+mrcxaHSlKrU1Tef//9Am8THBzM+PHjiyAaYU9if/+D2DVr8HlqLKbqMiGjKBlSTkSTfilJanWEsENS5ihckuyUAkn7o0iPTJRanSKgtcZsNt/ShScwMJDPP/+8CKIS9iL9yhUuTpmCc9Om+Iy2zxGIV+0N53J8iq3DEHYmfls4BneTXc/FJkRZI2WOoiHJTgmXOQJbJVeZI+EWffLJJ/j5+eHn58dnn31GWFgYjRo14tFHH8XPz4/HH3+cpKQkAgICGDp06E3bJyQkMGrUKNq2bUvLli0zZx8OCgqiT58+AEyePJlRo0bRrVs36tatm3lBSkhI4L777qNFixb4+fnx448/AuDr65s5g3JwcDDdunXL3M9jjz1G586dqV27Nr/88gsvv/wy/v7+9OrVi7S0tKL+uEQWF99+h4y4OKpO/QBlMtk6nJtsPBLJ+CV7+GLDCVuHIuxIelQSyUev4Nauqgw3LUQxkzJH8ZN5dkq4pAOWWp3yQxqV6FqdzT8dI+psfKHu06emO50HN8x1nZCQEObPn8+OHTvQWtOuXTu6du3K8ePHWbhwIe3btwdg6dKlhIaGZruP9957jzvvvJN58+YRHR1N27Zt6dGjx03rHTlyhI0bNxIXF0ejRo146qmn+O2336hWrRpr1qwBID+Dcpw8eZKNGzdy6NAhOnTowLJly/jwww8ZOHAga9asYcCAAXnuQ9y+2HXriPvtNypOnIhzw9z/zmzhRGQc43/YQ5Mqnrzcq5Gtwyl1lFJ9gb7169e3dSgFFv9POCgZblqUbbYod0iZwzbs8pGOjMaWP//V6rjg4l/R1uGUSFu2bGHgwIG4ubnh7u7OoEGD2Lx5M7Vr18686OTljz/+YOrUqQQEBNCtWzeSk5M5c+bMTevdd999ODk54ePjQ6VKlYiIiMDf358///yTSZMmsXnzZvIzKEfv3r0xmUz4+/uTkZFBr169APD39ycsLKxA5y9uTXpUFBenvI2zvz8VHh9l63BuEp2YyuMLg3EyGZn7WCCujvJcq7CV1IF0Moebbi7DTQtR3KTMYRt2eQfUWq8CVgUGBtpnI3g7kXQgivSIRMo/XLJrdYA8a2CKm5ubW47LZs6cydy5cwFYu3YtWmuWLVtGo0bXPz2PiIi47rWTk1Pm70ajkfT0dBo2bMju3btZu3Ytr7/+OnfddRdvvvkmDg4OmM1mAJKTk7Pdj8FgwGQyoZTKfJ2enn6LZyzyS2vNxSlTMCcmUm3qBygH+7qMpmWYefr73VyITuaHMe2p7u1i65CEHZHhpoWwsKdyh5Q5ipZd1uyIvGXW6lR0waW51Orcqs6dO7NixQoSExNJSEhg+fLldO7c+ab1TCZTZtvUp59+mtDQUEJDQ6lWrRo9e/bkiy++QGsNwJ49e/J9/PDwcFxdXRk2bBgvvfQSu3fvBiztZ0NCQgBYtmzZ7Z6mKESxq9cQ9+d6Kj73HE716tk6nJu8s/oQW09c5v1B/rSuXc7W4Qg7kjncdA13GW5aCBuQModtSLJTQiUdtNTqyAhst6dVq1aMGDGCtm3b0q5dO5544gnKlbu5gDhmzBiaN2+ebWfBN954g7S0NJo3b06zZs1444038n38/fv307ZtWwICApgyZQqvv/46AG+99RbPPfccgYGBGI3GWz9BUajSIiO5+O67uAQEUH7EY7YO5yaLd5xm0T+nGdOlLg+0rmHrcISdyRxuupMMkS6ELUiZwzbUtczQHgUGBurg4GBbh2F3tFkT+fludIam8sTWJTbZOXz4ME2aNLF1GOI2lZXvUWvNuXFPk7BtG3VWLMepTh1bh3Sdf05eZvg3O+jcwIf/e6wNxkK8LiilQrTWgYW2w1KkJN2nohYcJPVcHFVfaSujsIkyqazcr0q77L7H3O5TcrUrgZIOXibtYiKed0qtjhDFJWblSuI3bqTS8xPtLtE5czmRcYtD8PVxY8aQloWa6IjslbSBdGS4aSFEWSVXvBJGmzVxf53BwccFlxbSV0eI4pAWEUHEe+/jEtiacsOH2zqc68Qlp/HEol2YNfzfo4F4OtvffD+lUUkbjU2GmxZClFWS7JQwyYcuk3YxQfrqCFFMtNZceOMNdHo61d57D2Wwn8tmhlkz8cdQTl5KYNbQVvj65Dyijyi7zCkZMty0EKLMsp+7dhYlrXlAcdFmTex6qdURojjF/PILCZs2U+mFF3CsXdvW4WQymzWvLNvH+sORvNW3KR3r+2S73uLDizkXd66YoxP2JHF3hAw3LYQos+wy2SlpzQOKy7VaHQ+p1RGiWKSFhxPxwVRc27al3CNDbB1OJrNZ89ry/SwNOcdzdzXg0Q6+2a638OBCpu6cypIjS4o3QGE3sg437VjTw9bhCCFEsbPLZEfcLHNeHR8XXGVeHSGKnNaaC6+/gTabqfq+/TRfM5s1r688wJJdZ3n2zvpM6NEg2/V+OvoT04Onc0/te5jQekLxBinsRtbhpq9NBiiEEGWJfdy9RZ6SD18m7UICHnfWRBnlhlXc3n///SLb94IFCwgPDy+0/XXr1o2SMhSuPYv+aSkJ27ZR+eWXcKxhH3PWaK1589cDfL/jDOO61eP5uxtmW4BddXIV725/ly41ujC181QcDA42iFbYg/ht4RjcTbj6Z9/MUQhhf6TMUbgk2SkBtLb21angjGuLSrYOp0zRWmM2m0vUhUfcvtRz54mcNg23jh3wfughW4cDWP4WJ/96kO+2n+HJrnV5qWejbBOdP0//yetbX6dtlbZ80u0TTEYZna0olIS+pTLctBAli5Q5ioZc/UqA5ENXrLU6taRWpwh88skn+Pn54efnx2effUZYWBiNGjXi0Ucfxc/Pj8cff5ykpCQCAgKync148uTJPPbYY3Tu3JnatWvzyy+/8PLLL+Pv70+vXr1IS0sD4O2336ZNmzb4+fkxZswYtNb8/PPPBAcHM3ToUAICAkhKSsLX15dXX32VgIAAAgMD2b17Nz179qRevXrMnj0bgKCgIPr06ZMZwzPPPMOCBQuK5fMq7bTZzIXXXwelqPrOO3bR9EdrzdurD7Hwn9OM7lyHV3o1zjauTec28fKml2nu05zP7/wcJ6OTDaItG0pC39L/hpuuYutQhBBWUuYoftK2wc5prYn967SlVieg9NbqbFwwh8jTpwp1n5Vq16X7iDG5rhMSEsL8+fPZsWMHWmvatWtH165dOX78OAsXLqR9+/YALF26lNDQ0Bz3c/LkSTZu3MihQ4fo0KEDy5Yt48MPP2TgwIGsWbOGAQMG8Mwzz/Dmm28CMHz4cFavXs0DDzzAl19+yfTp0wkM/G/i31q1ahEaGsrEiRMZMWIEW7duJTk5GT8/P8aOHXv7H47IUfSyZSRu306Vt6dgql7d1uGgtebdNYeZvzWMUZ3q8Nq9TbJNdHZe2MnzQc/TwLsBM3vMxNXkaoNohb3IHG7a3wejpyS9QtzIFuUOKXPYhtTs2Lnkw1dIC5danaKyZcsWBg4ciJubG+7u7gwaNIjNmzdTu3btzItOfvTu3RuTyYS/vz8ZGRn06tULAH9/f8LCwgDYuHEj7dq1w9/fnw0bNnDw4MEc99evX7/M7du1a4eHhwcVK1bEycmJ6OjoWz5fkbv0qCgiP5qOa2Ag3g8+aOtw0FrzwbojfLPlX0Z09OWNPtknOqGRoTyz4RlqetTk67u/xtPR87+F6anFGLGwF5nDTXeS4aaFsBdS5rANqdmxY5a+OqcxlvJaHSDPGpji5uaW8+SMM2fOZO7cuQCsXbsWACcny5NTg8GAyWTKLJAaDAbS09NJTk5m3LhxBAcHU7NmTSZPnkxycnKOx8i6v2u/Z92fg4MDZrM58/3c9iXyL2LqNHRSElXenmLz5mtaa6b9dpQ5m07xaIfavNW3abYxHb58mHHrx1HRpSJz7p5DOedy/y1MiYd5PaHlcGhf8p/OifzRWoabFiIv9lTukDJH0ZKaHTt2rVbHs7vU6hSVzp07s2LFChITE0lISGD58uV07tz5pvVMJlNmO9inn36a0NBQQkNDqVYtf09Nr10YfHx8iI+P5+eff85c5uHhQVxcXIHirl27NocOHSIlJYXo6Gj++uuvAm0vbha/eQuxq1dTYcwYnOrWtXU4LN93kAUnplDH71vOOX/CqN9HMeK3ETy27jEeW/cYw9cOZ9jaYTz+x+O4O7rzf/f8HxVdswxLrzX8+ixEHoKKjWx3IqLYZQ433bGazZN2IcR/pMxhG3ZZs6OU6gv0rV+/vq1DsRlLX50zGMs749qydNfq2FKrVq0YMWIEbdu2BeCJJ56gXLlyN603ZswYmjdvTqtWrVi8eHGBj+Pt7c3o0aPx8/OjSpUqtGnTJnPZiBEjGDt2LC4uLvzzzz/52l/NmjUZPHgwfn5+1KlTh5YtWxY4JvEfc1ISF6dMwdHXlwpP2sfTvi/3zsDkfoSa5f3JMGeglEKhMBgMGDCAAoWiXZV2TGg9garuVa/fwfav4OAvcNdbUK+7bU5C2ET8Vutw0zInmxB2RcoctqG01raOIUeBgYHa3sfuLipJhy9zeeEhyj3QALfA0jmSzuHDh2nSpImtwxC3qTR8j5Eff8LluXOptXAhbu3a2jocQi4cYMQfQ2jkNICfH36n4DsI2woL+0Kj3vDQd3CbT/eVUiFa68C81yx77O0+lX45iYvTg/HoXhOve3xtHY4QdqU03K9E9t9jbvcpacZmh67NqyO1OkIUveSjx7g8fz5egwbZRaID8M626egMFya0GV3wjWMvwNIRUL4ODJh124mOKFni/7lgGW66fdW8VxZCiDJAkh07lHz0Kmnn4/HsXhNllK9IiKKizWYuvvUWRg8PKr30oq3DAWDXxV2cjA/BNfEeOtWtWbCN01Php0chNQEeWgzOnnlvI26JPU4qak7JIGHXRRluWgghspCStJ3JHIGtvDOuraRWR4iiFP3jjySFhlL5lUk4ZNNuurhprflw5yeY07x4oOFDBe9c/vtrcG4nDJgJlRoXTZACsM9JRRP3WIeb7ijDTQshxDWS7NiZ5KNXSTsntTpCFLW0yEgiP/4E1w7t8bTOMWBrG85s4MjVA6Re6sEDLesUbOO9S2DXXOjwDDQbWDQBCrultSZ+q3W46Voy3LQQQlwjpWk7klmrU85JanWEKGIR73+ATk2l6ltv2cXwvOnmdD7f8zkOGZVp4tGduhXd87/xxf2wagL4doYeU4osRmG/ZLhpIYTIniQ7diT52LVanVpSqyNEEYoLCiLut9/wGfcUjr6+tg4HgFUnV3Eq5hRxF+5mQECt/G+YdBV+HAYu5eCBeWC0yxkFRBGL3ybDTQshRHakRG0nMkdgk1odIYqUOTGRiLffwbF+PSqMGmXrcABIyUhhZuhMfEz1Mcc3o0+LfI6kFXcRlo2GmPMweBG4y7WjLEq/nETykSu4ta2CcpDbuhBCZCWPAO1EyrGrpJ2Nw3tQfblZiWy5u7sTHx9f4O1CQ0MJDw/n3nvvLYKoSp5LX84kLTyc2ou/Qzk62jocAJYcWUJEYgRuVx7kjgaVqOThfPNKWkP0GTi9DU5vtfx75aRl2X0fQ802N28jyoTCGm46w5zBiegT7I7czZnYMwxuNJg6XgXsOyaEKDVKS7lDkh07kFmr4+2EW6vKtg6nzNJao7XGYCi+ZDM9PR0Hh6L9bxgaGkpwcLDdXHRsKfnwYa4sXIj34MG4tm5t63AAiEuNY+7+ufiVa8s/h2vwctcsI2lFnYCwzdYEZxvEnrO87+wNtTtC4Eio0wWqtrBJ7ML2zCkZJATf2nDTKRkpHIg6wJ7IPYREhLA3ci9xaXEAGJWRFSdWMK3LNLrU6FIUoQtR5km5o3hIsmMHUo5Hk3o2Du+BZbdWJ3rVSVLDEwp1n47V3PDuWy/XdcLCwujZsyft2rUjJCSEtm3bsn//fpKSknjggQeYMmUKu3bt4oMPPuCXX35h5cqVPPzww8TExGA2m2natCmnTp3Kdt/dunWjRYsW/P3336SnpzNv3jzatm3L5MmTOXnyJKdOnaJWrVp88MEHjBo1iqioKCpWrMj8+fOpVasW//77L4888gjx8fH0798/c79BQUFMnz6d1atXA/DMM88QGBjIiBEj2LVrF8899xwJCQk4OTnx559/8uabb5KUlMSWLVt49dVXeeihhwrvQy5BdEYGF954E2O5clR64Xlbh5Np/oH5xKTEEGAagLPJwD3NqlgWhG2BBfdZfnerBL6doPYES5JTsQkU481R2K/EPRHo5PwNNx2TEkNoZCi7I3ezJ3IPB6IOkGZOA6CeVz161elFy0otaVW5FQrFhI0TeOavZxjfajyP+z0uAx+IUkXKHWWn3GGXyY5Sqi/Qt379+rYOpchljsDm7YRba6nVsYXjx4+zcOFC2rdvz5UrVyhfvjwZGRncdddd7Nu3j5YtWxIaGgrA5s2b8fPzY9euXaSnp9OuXbtc952YmEhoaCibNm1i1KhRHDhwAIBDhw6xZcsWXFxc6Nu3L4899hiPPfYY8+bNY/z48axYsYLnnnuOp556ikcffZSZM2fmeR6pqak89NBD/Pjjj7Rp04bY2FhcXV15++23CQ4O5ssvv7ztz6oku/r9DyQfOEC1j6djtJO5US4lXuK7w9/Rs3Yv/trkxD1NK+LuZL0sn1gPBgd4ahv4NAQpaIobaK2J35bzcNMX4i+wO3I3uyN2sztyNyeiTwDgYHCgWYVmDGsyjFaVWxFQMQBvZ++btl/YeyFvbXuLGbtncPjyYd7p9A6uJteiPi0hSj0pdxQvu0x2tNargFWBgYGjbR1LUUs5Hk3qmbJdqwPk+SSkKNWuXZv27dsD8NNPPzFnzhzS09O5cOEChw4donnz5tSrV4/Dhw+zc+dOnn/+eTZt2kRGRgadO3fOdd9DhgwBoEuXLsTGxhIdHQ1Av379cHFxAeCff/7hl19+AWD48OG8/PLLAGzdupVly5Zlvj9p0qRcj3X06FGqVq1KmzaWvhuenp638GmUTmkXL3Lp009xu+MOPO2kWh3g631fk5aRRmvPIfyceIEBLbM8nQ/bCtVaQcVGtgtQ2LWUE9GkRyZR7sGGaDTHrx5nT8QeS4ITuZuLCRcBcDO5EVAxgN51etOyUkv8fPxwcXDJc/8uDi5M6zyNJuWb8NnuzwiLDWNG9xnU8KhR1KcmRJGTckfZKXfYZbJTVmTW6nhJrY4tubm5AfDvv/8yffp0du3aRbly5RgxYgTJycmA5aKxbt06TCYTPXr0YMSIEWRkZPDRRx/luu8bm31ce33tmHnJrtmIg4MDZrM58/W1GEXOIt57D202U2WyfcypA3Am9gzLji3j/ob3s/UIlHdzpHMD67DBqQkQvhs6PmvbIEWObN0CITUjlfMbD4NzOq9eeZfgJSHEpVr621R0qUiryq0Y0WwErSu3poF3A4wG4y0dRynFSL+RNCzXkJc2vcSQNUOY3nU67arm/nRZCJEzKXcUr7JblWAHrtXqeHSvWaZrdexFbGwsbm5ueHl5ERERwbp16zKXde7cmc8++4wOHTpQsWJFLl++zNGjR/Hz88t1nz/++CMAW7ZswcvLC69smk917NiRJUuWALB48eLMpzadOnW67v1rateuzaFDh0hJSSE6Opq//voLgEaNGnHhwgV27doFQFxcHOnp6Xh4eBAXF3erH0uJF/fXX8T9uR6fp8fhWMN+nkh/GfolJqOJoY1G8eehCPo0r4rp2vxaZ3eCOR1qd7JtkCJHWutVWusx2f2fLgqxqbFsOreJGbtn8Ni6xxiwoA+mU2ksc/uDsITT3FP7Ht674z3WDlrLXw/+xfSu0xnaZCiNyze+5UQnq07VO7HkviVUcK7Ak38+ybeHvkVrXQhnJkTZJeWO4iE1OzaitSb2rzOWWp1AqdWxBy1atKBly5Y0btyYmjVr0qnTfwXNdu3aERERQZcullGJmjdvzsWLF/OsJXB2dqZly5akpaUxb968bNf54osvGDlyJB999FFmR0GAGTNm8MgjjzBt2rTrOgrWrFmTwYMH4+fnR506dWjZsiUAjo6O/Pjjjzz77LMkJSXh4uLC+vXr6d69O1OnTiUgIMAuOgoWp4z4BC6+8y5OjRpRYcQIW4eT6fDlw6z7dx2j/UcTcspMSrqZ/gHV/1vh9FZQBqgpT8/LqosJFzP72uyJ3MPxq8fRaByUA00rNOV5nkAZFKMem8DLlYrnHlLLsxaL71vMa5tf48NdH3LkyhHe7PAmTsaCjQInhLCQckfxUPb8ZCYwMFAHBwfbOowikXz8KlHfHMB7QD3c2+c9ik5pdPjwYZo0aWLrMIpMt27dmD59OoGBgbYOpUjZ8/d48f33ufrtd/j+8D0uAQG2DifT2PVj2X9pP+vuX8fT3x7m9OVE/n6p2383sfn3QloijAmyaZzXKKVCtNal+w/5FhXGfcqszZyKPpXZ12ZPxB7CE8IBcHVwJaBSgGWUtEqt8K/oj1OGIxc+2IFzo/JUGNK4ME6jwPF+vfdrvtr7FX4V/Pi0+6dUcatS7HEIUVD2fL8qDGW53JHbfUpqdmwgc14dL0fcAuUGIURRSNp/gKvfLabckIftKtHZe2kvW89v5YXWL5Cc7MjWE1E83b3+f4lOWjKcC4a2pX58ljLvl+O/sPHMRvZc2kNMSgwAFZwr0KpyKx5t9igtK7WkYbmGOBiuv1XHB4fne7jpomBQBp4KeIpG5Rvx6uZXeXj1w3za/VNaVmppk3iEECI3kuzYQMqJaFJPx+Ldv5701SkFnn76abZu3Xrde8899xxBQUG2CUhgTknhwuuv41ChAhUnTrR1ONf5M+xPTAYTDzZ6kB+2h2PWXN+E7XwwZKSA7x22C1IUi5CIEMJiw7ir1l2ZNTc1PWrm2kwlc7jp6tkPN12c7qx1J9/f9z3jN4xn1O+jeK3dazzY8EGbxiREWSDljoKRZKeYZfbV8XTErY3U6pQG+RmLXhSvyI+mk3L0KDW/no3Rw7YFwhv9fe5v2lZpi5vJjZWh4fhX96J+Jff/VgjbCiio1cFmMYriMaXjlJtqbfKSdbhpexhZsJ53Pb6/73smbZ7E2/+8zZHLR3il7SuYjCZbhyZEqSXljoKRaoVilvpvDKlhsTICm5U99xkTebPH7y9uw0aufvcd5R97FPeuXW0dznX+jfmXsNgwutXsxslL8ew/H0P/gBuaIp3eAlX8wMXbJjGK4lPQRAcgfls4BjcTri0qFkFEt8bLyYuZd85klN8ofjr2E0/88QRRSVG2DkuIbNnjfUvk3618f1LaLmYJuyJQzkYZgQ3LiCGXL1+WC08JpbXm8uXLODs72zqUTGkRkVx47TWcmjSh4gsv2DqcmwSdDQKga42urNxzHoOCfi2yJDvpqXB2lww5LbKVfjmJ5CNXcGtXxe4elhkNRia2nsiHXT7k0OVDPLz6YQ5GHbR1WEJcR8odJdutljukGVsxMienk3QgCtdWlVCm25/3oKSrUaMG586d49KlS7YORdwiZ2dnatjJ3DU6I4PwSZMwp6RQ/ePpGBwdbR3STYLOBtG4fGOquFVhRegROtX3oZJnlot2+G5IT5JkR2Qr/p8LoBTu7araOpQc9a7TG19PX57b+ByP/fYYb3V4i771+to6LCEAKXeUBrdS7pBkpxgl7ruETjPj2lpqdQBMJhN16tSxdRiilLj8zTwSt2+n6rvv4FS3rq3DucnV5KuEXgplTPMx7D4TzZkriYy/q8H1K4VtsfwryY64gTklg4Tgi7j4VcDoZd/z2jSp0IQlfZbwQtALvLblNY5cOcLE1hNvqdmeEIVJyh1lk33Vg5dyicEROFRywbGmfXWYFqKkS9q7l0uff45H71543X+/rcPJ1ubzmzFrM91qdGNl6HmcTQZ6NrvhwcfpbVCxCbhVsE2Qwm4l7om0DDfdqXreK9uB8s7lmXPPHIY0HsKiQ4t4av1TRCdH2zosIUQZJMlOMUm7lEjqmTjcWlexixF0hCgtMuLjOf/Ci5gqVaLqlCl2+/8r6GwQlVwqUd+7Eav3XaBHk8p4OGcZsSojHc7uAF+p1RHXsww3fd4uhpsuCJPBxGvtXuPtjm8TEhHCw2se5tjVY7YOSwhRxkiyU0wSQyLAAK4tK9k6FCFKDa01FydPIe3CBapNn47R09PWIWUrNSOVree30qVmF/48FMmVhFQGBNzwhP7CXkiNlyZsZUk+O0lfG27avWM1u03mczOwwUDm95pPakYqw9YO48/Tf9o6JCFEGSLJTjHQZk3C7kicG5bH6Gl/naaFKKliVq4kdvVqfJ4eh2sr+529fdfFXSSmJ9KlWlc++eMYDSu7073xDQ8+Tkt/nTJn/WRYOgL+3Zxr4pM53HRz+xluuqBaVGzBkj5LaFCuAc8HPc/nuz/HrM22DksIUQZIslMMko9fxRybKgMTCFGIUsPCiHj7HVwDA/F58klbh5OroLNBuDi4cCa8OqeiEnipZ2OMhhue0IdthQoNwEOuE2WGyRVOboSFfWBmO9g+G5Kir1vluuGmTSX7ll3JtRLze85nYP2BzN0/l/EbxhOXGmfrsIQQpZxdDo2ilOoL9K1fv76tQykUiSERGFwdcGlS3tahCFEq6NRUzr/wIphMVPvoQ5TRfody11oTdC6INpXbMXPDaQJrl6NHkxtqdcwZcOYfaDbQNkGKAiuU+1S3SdBpPBz4BYK/gd8mwV9TwP8BCHwcqgWUiOGmC8LR6MiUjlNoUqEJH+78kEfWPMLnd35OHa/SN0KW1hqzNmPG/N/v2owmy+9aY8acWcuV3ft57Se35VprGldojKejfTbxFaI42GWyo7VeBawKDAwcbetYbpc5MY2kg5dxb1fV7iaBE6Kkipwxg+SDB6n+xeeYqtp3IfDo1aNcTLhIY6f7iYxL4auhrW7ud3FxP6TEgu8dtglSFFih3adMLtByqOUnPNSS9Oz/GXYvwly1AwnnXsWlqf0PN10QSimGNB5Cfe/6vBD0Ao+seYSevj0BckwErhXi81pekITgumX5TEgKEpO98HT0ZEzzMQxpPARHozSlF2WPXSY7pUni3kuQoaUJmxCFJH7LVq58Mw/vIQ/jeffdtg4nT0Fng1Aogvb40KNJJQJ9s6nhPb3V8q/01ynbqgVAvy/g7ndg7xISgw6j0wy4h02A39pC4CjwKR0tHgDaVGnDkj5L+N+W//H3ub8xYEAphUEZMCgDiiy/K5X5WimFAUP2v2fZzsHgkO3ynI6T1/Jrv994nJziyGs/+V43h32jyPOYKRkpfHfoO6YHT+eHIz/wbMtn6V2nNwYlD19F2SHJThFLCI7AVNUNx+rutg5FiBIv/fJlwl95BacG9ak8aZKtw8mXoLNBlHeoz5kkF17q2Tj7lcK2Qjlf8CoZc6iIIubijW73JPHbdmPyScSxVg3Y+TVsnwl1ukKbx6HRvWA05b2vfNJak5JuJiElncTUDAwGRXVvl0Lbf06quVdjfq/5RX6csuyO6newLXwbn4Z8yiubX2HhwYW8EPgC7aq2s3VoQhQLSXaKUNrFBNLOx+PVx/5mcxeipNFmM+Gvvoo5Lo5q877B4Oxs65DyFJkYycHLB0m/1ItBrWrQqEo2c6SYzXBmGzS6r/gDFHYr5WQ06ZGJlHuwIar1AoiLgD2L0CELUD89itm9CvHNhhLV8GFiHSuRmJpOYkoGCamWZCUxNYPElHQSUjMsy6z/JqRkfZ2RmdwkpqZjvmFAuP/d24TRXeT+VRp0rNaR9lXbs+bUGr7Y8wVP/PEEd1S/g4mtJ9KwXENbhydEkZJkpwglBEeAUcncOkIUgqvffkvCps1UfvMNnBuWjJvz3+f+BsCc2JSJd+cQ86XDkHRVJhMtg/4+dolTl+JvSkQSUjMYcCqJmgZ4dusxYoIOk5SaQUJKc5JTp9GZ3QyLWU/X7Z/guv1T1ptb811GD7aam6FvGGTV0WjA1cmIm6MDLo5G3ByNuDo6UNXLhKujA67W125Oxiyvjfx+MIIP1h2mYRUPujYsuUNei/8YlIG+9fpyj+89fH/4e+bum8uDqx6kf73+PB3wNJXdpLm9KJ0k2SkiOsNMYmgkzo3LY3QrvKYGQpRFyYcOETH9Y9zvuotyQ4bYOpx8W31iPebU8gxv1TbnJkFh0l+nrPp+x2l+PxgBgNGgcHW0JCW1HIw0ijfyl5fC28ORak4OmUmKq6MRN6fGhDkOJyE9nMbnlnHnmV/olbKLFE9f4vyGk958CC5elXB1NGIy3lrfjPuaV2XQV9t49vvd/PrMHfj6uBXmqQsbcjI6MdJvZOYQ4D8c+YF1/65jeNPhjPQbiYdjNjXQQpRgSudzBmdbCAwM1MHBwbYO45YkHbzM5W8PUeHRprg0rWDrcIQoscwJCfx7/wOYk5Kos2I5DuXK2TqkfElMS6T9953QMR3Y9PhnlHPLYRSknx6F87th4oHiDbAAlFIhWutAW8dhj27nPnU1IRUAVycjjkZD5ih90WtOEb/1PFUntc3fKGzpKXBoJez6Bs5uB6MT+A2yDF9dIxBuHP0vn85eSaTfl1uo4O7E8nEd8XCWB3el0bm4c3yx5wvW/ruWck7leLLFkwxuOBhTIfYJE6Ko5XafkuE4ikhCSAQGdxPOjWRuHSFux8X33yf19GmqffhhiUl0AL7bux5NOn0b9Mg50dEaTm+D2h2LNzhhF8q5OVLOzREnB2NmomNOzSBhVwQufj75H27awQmaD4bHf4entkHLYXB4FXzTA77uAiELIDWhwPHVLO/KzKGt+DcqgYk/7sV8Y6ceUSrU8KjBtC7TWNJnCQ3KNWDqzqn0X9mf38N+x54fiAuRX5LsFIGM+FSSj1zBtVUllPHWnqgJISB27Vpilv1ChSfH4Naura3DyTetNQv3rgWzM692vzfnFaOOQcIlacImMiXuiUQnp+Pesdqt7aByM+jzCbxwBO77BLQZVj0HHzeGtS9B5JEC7a5jPR/euK8J6w9H8Nlfx28tJlEiNKvQjP+75//46q6vcDI68eLfLzJs7TBCIkJsHZoQt0X67BSBxD2XwKxxk7l1hLhlqefOc+HNt3AJCKDi00/bOpwC+ePQRaLZSzOvtnjkNmpc2BbLv0U4mejm7xdQt3U7qjdqUmTHEIVDa0381nBM1d1xrH2bM947eViGqA4cBWd3WiYrDVkAO+dYkuvAUdCkHzjkPcnkYx19ORgey+d/HadJFQ96+9v3RL6lUkYapMRZf2Kz/J7da+t7ybGWGr2a7aDjs+CZ9/emlKJzjc50rNaRX0/+ypd7vmTEbyPoXrM7E1pPoK6XjM4nSh5JdgqZ1prEkIuYarhjqiwdOoW4FTo9nfAXXwSg2vTpKFPJaTueYda8/9fvGLziGd48l1odsEwm6l4FyhdNAeLf0BB2rvwZR1c3SXZKgMzhph9omNms7bYpBbXaWX56vg97voOQ+bDscXCrCC2HQ+BI8K6Vyy4U7w7048SleF5Yupc6Fd1oXOU2k7GyIj3VmnzEZJ+QZH2dHJtz8pKelPexlMGS5Dp5Wv/1AJML7JgNu+Zavus7JuT6XV9jNBgZ2GAgver04rtD3/HNgW8YtHIQ9ze4n6cCnsLHxef2PxshiokkO4UsLTyBtIuJeA+oZ+tQhCixLs2cSVJoKNU/+RjHGiVros1lu89xIS0EFwx0qZlLjY3WlpHYfDvdcgfy3KSnprJh3mzKVatBYJ8Bhb5/Ufjit13A4OaAa4siGurZzcdS2O04Hk5usNT2bP0MtnwKDXtaBjSofxcYjDdt6uRgZPaw1vT9YgujFwXz69N35NwXraTT2jLoQ0ouyce195LzqGXJSMn7eMoIzp7XJyrulaBCvSzvZUlgMn9ueM/RLftryZV/Ld/z7kWweyG0eBjueN6y/zy4OLgwuvlo7m94P1/v/Zqfjv7EqlOrGNlsJI81ewxXk2vBP18hipkkO4UsIfgiOChcm8u8BELciqQDB7n89Ry8Bg3C8948akbsTHJaBp/+eQz3yscIqNIaLyevnFe+cgriLxZZf51dvy4jOuICD7z+LkaHklMzVlalX0km+fBlPLrVRJmKuDutwQANelh+os9aCsC7F8H3D1qe+rceaakFcL/+PlbZ05mvh7fmoa+38/T3u1k0qi0Otzi0dZHQGtIS89e866b3b1jfnJb38QymLEmKNfnwrJZNIpJLouLsCQ7ORfLAI1P5OtB3BnR5CbZ+bvm+Q78Hvweg8wtQqXHeu3Auz6vtXmVok6F8tvszvtr7FT8e/ZFxAeMY1GAQDgYpTgr7JX+dhUinm0naewmXphUwuErhQoiC0mYzF99+G2P58lR+9RVbh1Ngi/4J42LiedyN4XSvOSz3lU9b59cpgv460REX2bliKY06dKa2f0Ch718Uvvh/wkGBW/ti7g/jXRPufB26ToIjqy3DV/81Bf56GxzdweQMDi7Wf51paXJhc1XF/jMpHP2yHM1qVbYU1k0u1n9dM9f97718/JuRmnsNSXJsDsnLDevrjLzP2cH55sTDu2b+ak+cPMDZy/KvQz5Hy7MXXjXg3g8tCc4/X8CuebB/KTTtB51fhKrN89xFLc9afNLtE/Ze2ssnwZ/wzvZ3+O7wd0xoNYHuNbsXXvNLIQqRJDuFKOnQZcyJ6bgFVrF1KEKUSNE//0zyvn1U+3AaRo+SNbFdTFIaMzeepHHds5wDutXolvsGYVvB1Qd8GhZqHFprNi74GmU00vXRxwt136JoZB1u2iG/w00XNqMJmg20/Fw6CgeXQ1K0pa9IWvJ1/1Z2TsbsHk/i5YskJB/GTaVBejKkJeUv2bhVJtebEw+3OvlLUpytTcEc3fM1KEOp5lEZ7nkXOk2E7V9ZBq04tBIa9rbU/tRonecuWlRswYJeC9h4diOfhnzKcxufo1WlVjwf+DwtKrYohpMQIv8k2SlEiSERGL0ccarvbetQhChx0q9e5dLHn+AaGIhn3762DqfAZv99ktjkNJr6HMcpox41PWvmvsHprZb5dQr5SejJ4B2c2r2LrsMfx6O8dCIuCW57uOnCVrERdMu9ZrVihplH5+0kOOwqPz7Znpa1rHNgZaRZkp5ryU9+/zU6Zt/EK7M/igcYpchSqNwqwF1vWEZq2znHkvj8351Q705L0pPH/F9KKe6sdSddanThl+O/8FXoVwxbO4y7a9/NhFYTqOWZ90AIQhQHuXIUkozYFJKPXbW0tzZINa4QBXXp08/IiI+n8ptvlLimEBdjkpm35V/ube7Ftqt7eazZY7lvcPU0xJy1FDIKUVpKMhsXzsGnZm1a9ip5CWNZpLUmfls4pmputz/cdDFyMBqY+Ugr+s3cwpPfhrDq2Tuo7OlsqSEymoCScy5lnos3dH0Z2j8FwfNg2xcwv7elP2GXl6But1wfyjgYHBjcaDB96vZhwcEFLDi4gI1nNjK40WCebPEk5Z1lcnVhW3bUs7BkS9gdCRpcZW4dIQosad8+opcupfzw4Tg3LNxmXUUtJimNJ78LQWvo4BdFuk6nW81uuW90rb9OIQ9OsGP5T8ReiuSuUU9hdJBnWSVByskY0iMSce9YvcQl+eXcHJn7aCDxKemM/S6ElPQibMImip6TB3R6Dp7bB72mWUZx+3YAfHM3HPvdMgBELlxNrowLGMeagWsY2GAgPx79kXt/uZe5++aSlJ+hs4UoIpLsFALL3DoRONb2xOTjYutwhChRdEYGF6e8jUPFivg884ytwymQmMQ0hn+zg0PhMcwc2op9V7dR3rk8/j7+uW8YthVcykGlpoUWy5Xwc+z69Readu5OjaZ+hbZfUbTit4UX7XDTRaxxFU8+frAFe85E8/ryA+g8CsSiBHB0hfZj4blQ6PMpxEfA94Ph6y5w6Fcwm3PdvKJrRd7s8Ca/9PuFtlXa8vmez+nzSx+WH19OhlkSYlH8JNkpBKln40i/lIRboNTqCFFQ0UuXknzwIJUmvYzRveRMxBudmMrQb7Zz5EIcs4e1plvj8mw5v4UuNbpgzGaekuuc3gK1OlqGAC4EWms2zP8ak5MTXYaNKpR9iqJ3bbhpt7ZVi3646SLU278q4++sz9KQcyzcFmbrcERhcXCCwFHw7G7o/xWkJsBPw2FWR9j/M+SRuNT1rsvnd37Ogl4LqOJWhTe3vckDqx5g87nNkhSLYlVyr652JDE4AmUy4NJcOgMLURDpV64Q+elnuLZrV6Lm1LmakMrQ/9vBsYvxfD28NXc1qcyeiD3EpcblPQpbzHm4GmaZTLSQHNu+ldP79tDpoWG4eZcrtP2KohW/3UbDTReBCT0a0qNJZd5Zc5htJ6NsHY4oTEYTtBwKz+yC+7+xvLfscfiyDexZbBmUIhetK7fmu3u/4+OuH5OSkcK4v8Yx+o/RHLx8sBiCF0KSndtmTs0gce8lXPx9MDhJG3khCiLy448xJyRQpQQNSnAlIZVH/m8HxyPjmfNoa7o3rgTAxrMbcTQ40qFah5w3TomH5U8CyjLiUSFITUokaNFcKvnWo8U9JSdhFKBMRtxaV7HdcNOFyGBQfPpQC+r4uPH04t2cvZJo65BEYTMYwf8BeGobDP4WHN1g5Tj4opVlYIP0lBw3VUpxj+89rOy/klfbvsqxq8cYsnoI7+94n4S0hGI8CVEWSbJzm5IPXkanZMjABEIUUOKePcQs+4UKIx7DqV49W4eTL5fjU3hk7nZOXYrn/x4NpFsjS6Kjtebvc3/Trmo7XE2u2W+cHAPf3W8ZnGDg11CpSaHE9M+yJcRfucxdjz+FIa/mc8KueN1dm3L3N7B1GIXGw9nE3EcDyTBrRi8KJjE13dYhiaJgMFgmIn1yEzyyFNwrw+qJMCMAts+G1JwTXZPRxCNNHmHNoDUMaTyEJUeW0H9Ff4LOBhVX9KIMkmTnNiWERGAs54RTHS9bhyJEiaHT07n49js4VKmCz1NP2TqcfImKT+GRuTv4NyqBbx5rQ5eG/3UoPxVzirNxZ3MehS3xCizqD+eD4YH50OKhQonp9P5Qdq9diV/3e6jWsHGh7LMsU0q5KaUWKqXmKqWG2jqekqiOjxtfPNKKYxFxvLh0r/TNKM2Ugob3wON/wqMroXxd+G0SzGgOWz6DlLgcN/Vw9ODVdq/y7b3f4uHowbMbnuWFoBe4lHip+OIXZYYkO7chPTqZlJPRuLWuLHPrCFEAV5f8SMrhw1R+5RUMbvY/KMGlOEuNzukrCcwb0YY7GvzXPy8mJYZpO6ehUHSp0eXmjeMvwcK+EHEQHvoOmg0olJiObP2bXz6YTPlqNegydESh7LM0UkrNU0pFKqUO3PB+L6XUUaXUCaXUtRk0BwE/a61HA/2KPdhSomvDikzq1Zi1+y8yc+MJW4cjippSlrl4Rq6Bkeugij+sfws+84e/P4Sk6Bw3bVGxBT/1+YnxLccTdDaI/iv68/OxnzHr3Ed8E6IgJNm5DYkh1rl1WkkTNiHyKz0qikszZuDWsSMePe+xdTh5ioxLZsjc7Zy9ksS8EW3oVP+/ROfolaM8vPphdkXs4s0Ob1LFrcr1G8degAX3weWT8MiP0Kh3ocS0e92vrPn8I6o1bMxDU6bh4pH9BI5aa/74vwOcCIkslOOWUAuAXlnfUEoZgZlAb6ApMEQp1RSoAZy1riZj5N6GMV3q0j+gGh//eYz1hyJsHY4oLrU7wvDl8MQGqNkeNr5nSXr+egcSLme7icloYnTz0Szrt4zGFRoz5Z8pjPp9FKdiThVz8KK0KrYe9UqpAcB9WKZV/kZr/UdxHbsoaLMmISQCp7peOJR3tnU4QpQYkR9Nx5ycTOXXX7f7QQkiYy2JzoWYZOaPbEP7uhUyl605tYbJ2ybj6ejJ/J7zCagUcP3G0WdhUT+Ij4RhP4PvHbcdj9aaLT8sZOfKn6nfpgP3jX8JB0fHHNePCIvleHAk1RqW3RHatNablFK+N7zdFjihtT4FoJRaAvQHzmFJeEIphoeBF99/n5TDR4r6MDYzQWvuDo8lesssjlT3wsUkfcrKFkdI7QExZ2HtfDAsAo8q4FkdjDdftxTwJnA5qSZn40LY80lfItyrUsWtKgbs+14hbp9Tk8ZUee21Itl3vi7mBWwGkC2t9Qpr04CxQOE0WLeh1LBYMq4k4ypz6wiRb4nBwcSsXEmFUaNwqlvH1uHkKiI2mYfnWBKdBSPbZiY6aeY0pu2cxiubX6Fphab82PfHmxOdK6dg/r2WJ5nDlxdKopORns7vs2awc+XPNO/Ri77Pv5JrogNw4O/zmJyNNGwr16kbVOe/GhywJDnVgV+A+5VSs4BVOW2slBqjlApWSgVfunTrfQyOX77IvvRE/s1I5pI5jSRtLlV9XAxK0bCyBwalOHoxjnRz6Tk3kU+OblCxMVRvBa4VIDYczgVbrpEZN4/epgAfFx/8fPwo5+zN+fjzHIo6SHxafPHHLkqN/NbsLAC+BBZdeyNLM4C7sdwodimlfgWMwAc3bD9Ka32tHcXr1u1KtISQCJSTERc/mVtHiPzIHJSgWlV8nhxj63BydTHGUqMTGZvMolFtCfQtD0BUUhQv/v0iIREhDGsyjOcDn8dkMF2/8aVjlhqd9BR47FeoFnDb8aSlJLP6s2mc2r2Ljg8Opf39D+dZK5YUn8qJ4EiadqqKo7MMi58fWusEYGQ+1psDzAEIDAy85RJ8fF1fTlw4gznjv1HLHJyc8K5clXJVquFdtZrl96rV8K5SFfdyFey+NjQ70f9e4ZG52+lU34d5I9pglD6uZdflk7DlE9i7BIwn4eHFOQ7DXx/YdG4T725/l4sJJxjcaDDPtXoOD0eP4o1ZlHj5ugMWpBmA1voDoM+N+1CWK/RUYJ3WendOx1JKjQHGANSqVSs/4RU7c0oGSfsv4dqiEgZHqZYXIj+uLl5MyrFjVP/icwyuOQzPbAeuJKTy8Jx/iIpPZdHjbWld25LohEaG8kLQC8SmxvJB5w/oU/emy5xlEIJF/S2/j1gDlZvedjxJcbEsnzaFiyeOc/foZ2jeo1feGwGHt10gI91Ms67VbzuGUug8UDPL6xrW94pV76efp+fY54i7fImrF8KJvniBqxfDib4YTtS5M5wM2VkqEqG2dcozpX8z/rf8AB/+foRXexfOsOuiBKpQD/rPhC4vw5Kh8P1D8OACaHxftqt3qdGFFf1X8MWeL/j+yPdsPLOR19q/xl217ireuEWJdjuP+7JrBtAul/WfBXoAXkqp+lrr2dmtVFhPzIpS0v5L6FQzrq0r2ToUIUqEtMhILn3+BW5dOuPRo4etw8mR2ax54adQwqOT+WFMO1rXLo/WmqXHlvLBzg+o7FqZ7+79jkblG928cXgofDsAHFwsNTo+tz9/SmxUJMvee5OYSxH0feFVGrTJZcLSLLRZc3DTeao18KZCNffbjqMU2gU0UErVwZLkPAw8YotADEYjXpWq4FWpCrS4fpk5I6NAiVC5ylXxttNEaGi72hwKj+Xrv0/RtKon/QMkCb9dqelmYpLSiElK5WpiGtGJaUQnphKTZPn9qvX3yp7ONK/hhX91L3wruGGwh5q1crVhxCr47gH4cTgMmmOZsDQbriZXJrWdxH117+OtbW8xYeMEetTqwavtXqWSq5TDRN6KrW2D1vpz4PPiOl5RSgiJwMHHBcfa2Y+AJIS4XuRH09GpqVT53/9sXujKzZzNp9h49BLv9G9G69rlSclI4b3t77H8xHI6Ve/EtM7T8HLKZk6ts7ssE4Y6e1kSnfK33x8pJjKCJW++RFpqCg/87x1qNPHL97ZnDl0hNiqZ9gNKxmStRUkp9QPQDfBRSp0D3tJaf6OUegb4HUvT63la64MF3G9foG/9+vULO+RMuSZC5gziokpWIvRW32Yci4jj5Z/3Ua+iO37VZX46gJT0DGIS04hO+i9hsfyeanmdlGZdbn1tXSchNecBAw0KvF0d8XR24M9DEaSkW4Zy9nBywK+6lyX5qeFFixre1CjnYpvrsks5eHQFfP8wLHsC0hKh1aM5ru7n48eSPktYeHAhs/fOZvuK7UxsPZEHGj6AQcngwiJnt5Ps2EUzgOKWfjmJ1H9j8ezpa9eFNiHsRcKOncSuWoXPuKdwrF3b1uHkKDjsCh/9fpT7/KsyrH1tLsRfYGLQRA5ePsiTzZ/kqRZPYTRk02z19D+w+AFwqwiPrQLvmjevU0Baa/6Y8wWpyUk8/PZHVKzlW6Dt9/99DldPR+oGVMx75VJOaz0kh/fXAmtvY7+rgFWBgYGjb3Uft8NgyEcidPEC0RfCbykRKl+tBm7ehTuKn6ODga+Gtqb/l1sYsyiYX5+9Ax93p0I9hi0lp2Vk1qpkl7BEJ1prYRKuJTCWdRJzSVocDApvVxNeLia8XR2p4ulMoyoelHN1xNvFZFlm/b2cq6P1tQl3R4fMGpz0DDPHI+PZfy6Gfeej2X8uhvlbw0jNsCRA3q4m/K8lQNW9aV7Di6pezsVTxnHygKFL4afh8OuzkJoA7XOeaNpkMPGE/xPcU/se3v7nbd7Z/g5rTq3hrQ5vUde7btHHK0okld+RX6x9dlZrrf2srx2AY8BdWJKcXcAjBX06lpvAwEAdHBxcWLsrFDF/hBG38SxVX2mL0av0XKSFKAo6LY1TAweik5Kpu2Y1Bmf7HKb9SkIq932+GUcHA6uevYNDV0N4+e+XSTOn8f4d79O9VvfsN/x3s6XNuWc1S42OZ7VCiefg33/x21efctfj4wi4594CbRsblcS3b/xDYG9f2vUrnJu/UipEax1YKDsrZezxPpWbGxOh6Ihwrl4I5+rFC8REXPwvEVKKLo+MoE2/+ws9hv3nYnhg9jZa1PDmuyfa4ehgX0/lk9MyrAmKJTGJSbo+YfkvgUm1JjCWJmPJaTlPhGkyKrxcLMmItzVx+e93S8JSztWEt3UdL+v77k4ORZJ0pKabORYRx75zMew/H82+czHXjZjn4+6If3Uv/Gt409yaCFXyLMLrd3oKLHscDq+CO9+ALi/muYnWmpUnVzI9eDoJaQmM9h/NE/5P4JjNsNai9MvtPpWvmp2iagaQy/GKvHnArdBmTWJIJE4NykmiI0Q+XPn2O1JPnKTGV1/ZbaJzrZ/O5fhUvh5Zh6m73mL1qdXU9arLZ90/w9fLN/sNT26EH4ZY2p4/+it4FM7wzgnRVwlaOJdqjZrSIp+DEWR1cPN5lFI061w4iZcoXa6rEWre8rplWROh/et/Y9Pi+aSnpdLh/mwrx26Zfw0vPnygOc8tCeXt1Qd5d4B/oe4fLAXh5DRzZsISnZR6c1OxbBKW6MS0zCZf2XE0GvByNWUmJjXLu+JvTUz+S2CuT1jKuTri6mi0q9Ygjg4G/Kp7WZsSWgaDSk7L4MjFOPafi7YmQTH8few410YMr+zplFnz41/Di+bVvahQWDVzDk7wwAJY+TRseAdS4+GutyCXz0wpxYD6A+hcvTMf7vqQWXtn8VvYb0zuMJlWlVsVTlyiVMjvaGxF0gwgl+PZtHlATlJORpMRk4LXvfY9P4gQ9iAtIoKoL7/EvVs3PO7MoWbEDszZfIqgE//Ste0+nv/nNYzKyIhmIxjbYiyuphxGjTux3jKSUPl68OhKcC+85mIbF84lLSWZe8Y8izIU7Il3eloGh7ZeoE4LH9zL2WdyKexX1kSoll9zHGbNYNtPizGnp9Nx8LBCLaz3D6huGbBg0ymaVPVkaLvsm7hqrUlMzchsEnYtYbmWmMRkbSqWtV9LUhqpuSUtDobMhMXL1USt8q60qOGd2QzMO7taGFcTLib7SloKk7PJSEBNbwJqeme+l5iazuELsew9a0l+9p2L5q8jEVxrFFTd28VaA+SVOQiCt+st1qwYHWDALHB0hS2fQmoi9JoKeVwHK7hUYFqXafSp24d3t7/LY789xoMNH2RC6wl4OkrfalGMAxSUBgkhEShnB1yaVsh7ZSHKuMhp09AZGVR+/X+2DiVHm0+c49PgL/FquIU9MakMrD+QsS3GUsWtSs4bHf3N0r68YiMYvhLcCu96cDJkJ0e3baLj4KFUqFHwvj8nd18iOT4NPxluusjZawuEwmIwGOn11ASMDg5s/+VH0tPS6DJ0ZKEW9F/u1ZgjF+N4a+VBjkfEk5CSztXE65uNxSSmZfYtyY6zyXBdTUpdH/ebEpZyrqb/mpBZ33eRaSPyxdXRgda1y2cOwQ8Ql5zGwfBYax+gGPafi+a3gxczl9cq70rzGv/1AfKr7omHsym73d/MYID7PgGTK/zzpaUPT7/PIbv+kjfoXKMzy/svZ2boTL47/B1BZ4N4rd1r9KhtvyOAiuIhyU4+mZPSSTpwGbfAyiiTfbUvFsLeJPzzD7Fr1+Hz7DM41qhh63BukpaRxoIDP/BFyCwcfeK5o8adPB/4HHW98ujjcng1LB0BVfxg2C/gWj739QsgNSmR9d98hU/N2rTtn/0QrHk58Pc5vCu7UqNR4XYsFzez1xYIhUkZDNw9+hmMJhPBq34hIz2N7o+NKbSEx2hQfP5wS4Z9s4Mfd539rymYi4n6laxJi4u1L8sNCUs5V0e8XEw4myRpKW4eziba161A+7r/PeiJSUzjQHhMZh+g0LPRrN53IXN53YpuNL/WB6iGF82qeeLqmEMRVCm4513L4AVBH1hGaRs0B4x5J0yuJldeavMS99a9lynbpjAxaCJ31ryT19q9RmW3wmlqLEoeSXbyKXHfJUg349Za/rMIkRudmsrFd97FVKsWFZ54wtbhXMeszaz7dx1f7vmSc/HnMKfU5b2uHzOgace8Nz64wtKBtmoADFsGLt6FGtvmHxYSf+Uy/Sa+itEhn09Bs7h0Jo6Lp2K548EGpbaZjSh+ymDgzpFjMTqYCFmzgoy0NHo8Pq7ATSxz4uVqYtWzdxTKvoTteLma6FTfh071fTLfu5KQyn5rzc++czHs+PcKK0LDAcvQ2M1rePPhA81pWNnj5h0qBd1eAUc3+ON1SEuyTD5qyl/z3GYVmvH9fd/z7aFv+Sr0K/qv7M+HXT6kS40uhXG6ooSxy2THHpsHJIZE4FDZFVMNmaBPiNxcXriQ1FOnqDnnawxO9jGQh9aaLee3MGP3DI5ePUoFky+JZ0by1l0DGdA0H33w9v8Mv4yBGm0sw6Q6F2478PNHDhH6x1pa9epL1QbZTFiaDwc2ncfBZKBxh1ya4AlxC5RSdB3+OEaTiZ0rlpKRns49Tz6LIR9Ni0TZVd7Nka4NK9K14X99GiPjktl/Loa952L4fscZ+n+5lan3++c8yWzHZy1N2tY8D98Phoe/B6f8lcMcDA6M9BtJj9o9eCHoBZ7b+ByfdvuUbjW7FcLZiZLELttjaa1Xaa3HeHnZx4RjaZGJpJ6Jw611ZXliKkQu0sLDifpqFu497sK9i308Qdt3aR+jfh/FuL/GEZ8Wz5jGr3P2wJP0rNuV4R18897B3iXwy2io1cFSo1PIiU56Whp/zPkCjwo+dHp4+C3tIyUxjWM7L9KwbWWcXAteKyREXpRS3PHwo3R44BEOBq3nt5mfYs7IeX4YIbJTycOZu5pU5vm7G7Jm/B34VffkuSWhvLXyQM4DSrR5HAbMhrDN8N0gSI4p0DFretTk/3r+H43LNWZi0EQ2nNlQCGciShK7THbsTUJIBBjAtWUlW4cihF2LmDoNtKbKq6/aOhSOXjnKxI0TGbp2KKdiTvFq21dZeM8yvt/gQ3VvN6be3zzvhxe7v4XlY8G3Mwz9Kd9PFAti54qfuHL+LHePfgZHZ5db2seR7RdJTzXj19X++keVVkqpvkqpOTExBSt4lWRKKTo++Ah3PPwoh7cEsebzj8hIT897QyGyUdnTme9Ht+eJO+qw8J/TPDznHy7EJGW/csAQSzO287thYV9IuFygY3k6ejLnnjk0Ld+UF4JeYP3p9bd/AqLEkGQnDzpDk7g7AudG5TF63P5EVXGpcYRGhpLfyVyFKCniN28h7o8/8Bk7FlN124wGdjX5KosPL2bwqsE8sOoBtoVvY1zAONYNWsfDjYbwys8HuRyfysxHWuGZ1+hAwfPh12eg3p3wyI+WtuOFLOrsaXYsX0qTzt2pE9D6lvahtebA3+epXMeTirWyafsuioS9tUAoTu0GDqbbo09wbPsWVn06lfS0NFuHJEook9HA632aMvORVhy9GEefz7ew7URU9is37Q9DfoBLR2HBvRB3Mfv1cuDh6MHsu2fT1KcpL/79In+E/VEIZyBKAkl28pB8/CrmuLTbGpggMS2RtafW8uyGZ+n6Y1eGrxvO7L2zCzFKIWzLnJpKxLvv4li7NuVHjSzWY6dlpPHXmb94bsNz3Ln0TqbunArApDaT+P3+33mqxVO4mlyZu/kUG49e4vU+TfCvkUcBdedcWD0BGvS0tBE33VqNS27M5gz++PpznFxd6fborQ/kcP7oVaIjEvGX4aZFMWp93wDuHDWWk8Hb+fXj90hPTbV1SKIEu695VVY+cwfl3BwZ9s0Ovgo6gdmczUPhBnfD0J8h+izM6wXRZwp0HA9HD77u8TX+Pv68vOllfgv7rZDOQNgzuxygwJ4khkRgcHPAuXHBhphNTk9m8/nNrPt3HZvPbSY5I5lKrpV4uPHDRCVF8dXer6jgUoHBjQYXUeRCFJ8r8+aRevo0Nf/v/zA43n4NaF601hy6cohfT/zKun/XcTXlKhWcKzC08VD61e9Hw3INr1s/5PQVPvz9KPf6V2F4++wnLwTg4n748y04+Rc0us/SbMKhaM4n9Pe1XDh+lHufeQFXz1uvHTjw93mc3UzUay3NbEXxatmzD0YHB/6cO5PlH77NgJdex+Qkk9mKW1O/kjsrn+7EpGX7+PC3o+w+Hc3Hg1vg5XJDLXydzpbJnBffD/N6W373yf+AVu6O7sy+ezbj1o/jlU2voLWmd53ehXw2wp7YZbJjL6OxZSSkkXToMu7tq6Ic8q4ES81IZVv4Ntb9u46gs0EkpidS3rk8A+oPoFedXrSs1BKDMpBuTichLYH3drxHBZcK3FXrrqI/GSGKSOq580TN/hqPnj1xv6NTkR7rUuIl1pxaw8qTKzkRfQJHgyPda3WnX71+dKzWEQfDzZe0qwmpPPP9Hqp7u+TcTyfmHGx4D/b+AM5ecM970O7JfM3rcCtioyLZ8sNC6gS0pvEd3W59P5eTOLU3ioAeNXGQ+UaEDTS/qxdGBxO/z5rBL1MnM3DSW7fc90wINycHvhjSkta1y/HemsP0+3ILs4a2pmm1GwaGqdkGHlsN3w6E+daEp3LT/B/H5MasHrMY99c4Xtn8Chk6gz51+xTy2Qh7oey570hgYKAODg622fHjt54netUpKo1viWO17Dsmp5nT2HlhJ+v+XceGMxuIS4vDy8mLHrV60KtOLwIrB2ZbAEtMS2T0H6M5evUoc+6eQ6vKrYr6dIQoEmeffoaEbduot3YNpqpVC33/KRkpbDyzkZUnV7ItfBtmbaZ5xeb0r9efnr498XLKuVbEbNY8sSiYLcejWPZUx5ubryVFw5ZPYfssy+t2T0Ln58Gl6Cbl1FqzfNoUzh06wIiPv8Kz4q3VyGitWfV5KBdOxfLIW+3wKF90T9SVUiFa68AiO0AJZuv7lL04svVv1n75MVXrN2LQq5Nxci38Pm6ibAkOu8LT3+8mOjGN9wb680DrbAZguXQMFvWD9GTLRM/VC1aWSkxL5JkNzxASEcK7nd6lb72+hRS9KG653afssmbHXiTsjsRUze2mRCfDnEFIRAjrwtax/vR6olOicTe5c2etO+nl24v21dpjMuT+RNjV5MqXd33Jo+se5ZkNz7Co1yLql7OfeYWEyI8rixcT/9dfVHzh+UJNdLTW7L20l5UnV/L7v78TlxZHFbcqPO73OH3r9aWOVz7mxgHmbj7FhiORvN2/2fWJTnoK7PoGNn1oSXiaPwR3/g+8axXaOeRk/4bf+XdPMN1HjLnlRAcszdfOHr5K10caFWmiI7JnLy0Q7EXjTl0xODiwZsaH/Pzu69z/2js4u8u8dOLWBfqWZ/WznRn/wx5eXLqX3Weu8lbfpjg5ZKnFrtgQRq6DRf1hYT/LPGi1O+T7GK4mV2beNZNn/3qW/235H2Ztpn/9/kVwNsKWpGYnB6kXEoicsRvvvnVx71Qdszaz99Je1v27jj9P/0lUUhQuDi50q9mNXr696FS9E07Ggk+gGB4fzrC1wzAoA9/d+x1V3GRCQFEyXF3yIxcnT8a9e3dqfD4DZbr9Jl9aazae3ciM3TM4FXMKFwcXetTqQb/6/WhbpS0Glf8xVUJOX2Hw19vp2awyMx9pZWm+ZjbDoeWwfgpEn4a63eDut6Fqi9uOPT8iTp3ghzdfokYTPwa9OvmWJ2WMjkzkx3d3Uq2+N32ebVHk839JzU7OpGbneidDdrDqkw8oX6MWD/zvndvqjyYEQHqGmel/HGP23ydpXsOLr4a2okY51+tXijlvSXhizsGQ7y2jaBZAUnoS4zeMZ8eFHUzpOIWBDQYW4hmI4pDbfUqSnRxErzpJ/PYLxIz1ZO3F3/k97HciEiNwMjrRuXpnetXpRZcaXXBxuP22yUevHGXEbyOo5FqJRb0X5dosRwh7cPWnn7j45lu4d+1K9S8+L5RBCY5dPcaHuz5kx4Ud1PWqy4hmI7jH9x7cTAVvDnMsIo4hc7bj5uTA6vF3WIaZ/ncz/PkmhO+Gyn6WJKd+8fWXS4qL5btXJ6C1ZtgHn91yIdBs1iyfHsLVi4k8/EY73MsV/CFLQUmykzNJdm72b2gIv05/D+8qVXng9Xdx8y66ZqHieumpqcRdiSIuKoq4y5eIv3KZirXr4BvQ6pYfrtiL3w9e5MWf9mI0KmY83JKuDStev0L8Jfh2AEQdswwu0/i+Au0/OT2Z5zY+x7bwbUzuMJn7G95faLGLoifJTgForTkWdRSHL8I54HaS16t8joPBgTuq3UHP/2/vrqOjuto2Dv9G4u4OCQR3d/cWakApdaFK3VvqLm9b2q9K3aBAaWlpcadocHdC3D0Zn/P9MSEkJMEiM0mea62ssTNn9kAmZ+6z9352zBiGRQ27rC9fFxKXFse9K+6lY2BHZo2ahatWhqUIx5S3YAGpM17AY/AgIj/9tMZBJ0efw2e7PuP3Y7/j5ezFA10eYHKbyRccClqdY+mFTP16C2qVijn39KWlkggrX4GjS8E7Eoa/AJ2vh3o88CtWK3+8+yqJ+/dww6vvERrb+sJPqsaOpfFsWXiSUXe2p3Xv+ukJlrBTPQk7VUvYv4c/33sN74AgJr/4Jp7+AfZuUoNntVoozs2lMDuTwuwsCrIybddLg01hdhYl+XlVPtcrMIjOI8bSafjoBh0+T2UVc/8vOziSXsijI1rz0PBY1OpyPdslOfDrJEjZDdfNgk6TLmn/BouBR9Y8wsbkjbzU7yUmt55cu29A1BkJOxfhRN4JlsYvZemppYQl+/BS0r381mUNLXt2Yniz4fXS27I8fjlPrnuSIVFD+GjoR1UWNhDCnvL++JPUGTPwGDCAyM8+Re1y+b0KJouJ2Ydn89Werygxl3BD2xu4v8v9NfqsnQk6KpWK32+IoPmBz2HXL+DsaSs80Oe+Olkz50I2zZ/N5t9nM3LadLqMuvwSp1lJRcx/O46YLoGMubtjnQ9fO0PCTvUk7FQv6dB+/njnVTx8fZn84lt4BwZd+ElNlKIo6IsKSwNMaXgpd70gK5Pi3BysFkuF5zm5uuEVEIh3YBBeAYF4BQThVXrdOzAIdx8/Tu/dyZ4VS0jYvwe1RkNsr350GXUFUR061dvfkNqkM1qY8ec+/tiVzNA2Qcyc0hVf93In3QyFMPsGOL0RrvoEut96Sfs3WAw8tuYxNiRv4IU+LzCl7ZRafgeiLjS4sFNu4ufdx44dq7PXSShIsAWc+KUcyz2GChU9Q3vy6PGpBOR6Ev5cX1Sa+v1DMOfwHN7a+hYTW03k5X4vN8g/RKJxylu4kNTnnsejXz8iP/8Mtevl9T4qisK6pHX8b/v/OF1wmoERA3mq51O08G1Ro/adSE7jk29/pB+7uc77KM55x0HtBL3vhkFPgod9ziyf2r2DP955hQ6DhzPm/kcv+zNtMVuZ/852SgqMTH2pN26edb+e0RkSdqonYef8Uo4eYsFbL+Pq6cX1L72JT3DTnJdq0uspqCLA2K5nUZiVidloqPActUaLV0BApQDjFVAabAKDcHH3uOi/KTkpSexduZQDa1eiLy7CLzySLiPH0WHIiAZXTEJRFH7ZmsBriw4Q4u3KFzf1qFiExlgC826B4yth7DvQ9/5L2r/RYuSJtU+wNmktz/d5nqltp9byOxC1rcGFnTPq4iCSUpTCsvhlLI1fysHsgwB0DerK2JixjG4+Gn+LD6lvb8VzUCS+4y6u4lNt+2TnJ3y972vu73I/D3R9wC5tEKK8/EWLSHn6Gdz79iHqiy8uO+gcyz3G+3Hvszl1MzE+MTzV8ykGRQ66vEZZrZC2B06spuTQCpxS4nDCjFXjijp6gG2CarsJ4HeeRUTrWH5GOr889yhe/gFMfeN/NVpwccvCE+xYeporHuhMTOfAWmzlhUnYqZ6EnQtLO3GMBW++iJOrG5NfehO/0HB7N6lWWcxminKyy3pjCsoCTEZZmNEXFVZ8kkqFh6+fLcAEBOEVWLFXxisgCA8fX1Tqiy/KcrFMRgNHN//HnhW2hY21Ts606T+YLqPGERrbukGdZN2dmMcDv+wgq9jIa1d14Ibe5Spqmg3w+51w+B8Y/iIMfvKS9m2ymHhi3ROsSVzDs72f5aZ2N9Vy60VtavJhJ6Mkg+Xxy1kav5Q9mXsA6BDQgbHRYxkTPYYwz7MlcwvXJ5G/+BQhj/fAKdi9ul3WKUVReHnTy/x5/E9e7Psi17e53i7tEAIg/59/SXn6adx79SLqyy9Qu136MLBcfS6f7f6M+Ufn4+nkyQNdH+D6Ntdf+ryc/GQ4uQZOrIYTa0CXA8ARotmq6sKI8TcQ0Xk4ONl/zpvZaOS3l58mLy2Vm97+qEZf8NJO5vPH+zto0y+MEbe2q8VWXhwJO5XV1wiExiIj/iS/v/ECaq2WyS++SUBElL2bdFEUq5WSgvyyHpmyeTKlvTGF2ZkU5eXCOd+lXD08y3pfyvfEnAk2nv4BaLR1s2jxpciIP8meFYs5tGEtJoOe4OiWdBk1jrYDhzSYxWFzio088tsuNhzL4vqekbx2dUdczyyybDHDXw/A3rnQdrytl8f34n/3TBYTT61/ilUJq3iq51Pc2uHShsSJ+tMkw06OPocV8StYGr+UHek7UFBo7deasdFjGRs9lijvyr/siqKQPnMnamcNwdO71rD1NWO2mnl0zaNsSN7Ah0M+ZETz+qsaJcQZBUuWkPzEk7j36EHUV1+idr+0EwAmi4nfjvzGF3u+oMRUwvVtrueBLg/g6+p7cTswFsPpTaXhZjVkHrbd7xkCLYeTHtSfW9e6k40Pc+7uS6sQr0t7g3Vo+az/Y9+qZVz91IvE9uxz2fsxGS3MfWMbVrPCDS/2xtmt/ufySdipnvTsXLysxNPMf30GAJNfeIPAZtH2bRBgKCmuPNH/TLDJzqQoOwuL2VzhOVon5wo9MJWGmAUGNpigcIahpIRD/61lz4rFZCXE4+zmRrtBw+k6apxD/D9diMWq8PHKo3yy+jgdwr354qYeNAsoPV5ZrbBxJqx7D1QqGPwU9HsQtBc3FNhkNfHM+mdYcXoFT/Z8kts63FZ3b0RctiYZdib8OYH4gnhifGIYFz2OMTFjaOFz/jkBxqRCMj7dje+1sXj2qf2V4C+Vzqxj2vJpHM4+zKzRs+gR0sPeTRJNSMHSZSQ/8QRu3brS7KuvUHtcfBVCRVHYkLyB9+PeJ74gngHhA3iq11O09G154SdbrRD3tW3oQcIWsBhB6wrN+9uGprUcDsHtOZFVzNRZW7AqisMFnf1rVrDsy4/pfc1kBk2t2YFx/W9H2bc2iasf60ZkG/tUUZKwUz0JO5cmJyWJ+a89j9lsZtKM1wmJuYi/CZfpbBnmsz0xBef0yhh1ugrPUanVePoFnBNgKoYaNy/vBjXU61IoikLK0cPsWbGYo1v+w2IyEd6mPV1HjaNV34Foa2E9tbq0+nA6j/62G4CPpnRlRLuQsw/mJcDS52zHlsDWcOUHEDP4ovZrspp4dv2zLD+9nMd6PMadHe+sg9aLmmiSYWdT8iYC3AJo7Xfx409zFx6neHs64TP6oLbD2dOq5OnzuGXJLWTrs/lx7I+08mtl7yaJJqBgxQqSH3sct86diZo1C43nxQedE3kneC/uPTalbCLaO5qnej3FoIhBF/c5tFrg74dg968Q3AFiS8NNs34VqqidzCzihllbsFgV5tzTl9YOFHQy4k8y54UnCW/TjokzXqvR2haJh3P4e+ZuOg+PZND1l1+uuqYk7FRPws6ly0tLZd7rz2PUlTDp+dcvqxR7+TLMVVcwq7oMs5u3T5UBxisgCO/AIDx8/VBrGvZ6NLWlpCCfA+tWsXfFEvLSU3Hz8qbD0JF0GTkO31D7nxCuTmJOCff9soMDKQU8NDyWR0e2RlO+PPXR5bDkKciNh46TYMyb4HXhwhlmq5nnNzzPkvglPNL9EaZ1mlZ3b0JcsiYZdi6VYrKS8uZWXNv4ETC1bb285sVKKUrh5sU3o1Kp+GXcLxXmGAlR2wpXrSLpkUdx69iRqG++RnORVXry9Hl8vudz5h2Zh7uTOw90eYApbadc/LwcqwX+ehD2zIahz8HQZ6vc7FRWMTfM2ozZ4nhBR19UxC/PPYLFbOaWdz7G3cf3svdl0Jn57bWtaJ01TJnRC62z/b6ASdipnoSdy1OQmcG8155DV1jIdc+9SkSbs3PRFEVBV1hQrlLZ2Yn+Z4acFeVko1itFfbp7OZ2ToAJxDswuOy6Z0AgTs51vwhvY6NYrZzev4e9K5ZwfPsWFKuV6C7d6TxqHC2793bIcKg3WXjpr/3M257EoFaBfHxDN/w9yg1bM+ngv5nw30egcYZhz0Pve0Bz/hPdZquZGf/NYPGpxTzY9UHu7XJv3b4RcdEaXNixx8TPkr2Z5Mw+TOBdHXFt5XgLbh3JOcIdS+8gyD2In8b9VC/r/oimp3D1GpIeeQTXdu1o9u03aLwuHCRMVhPzjszj892fU2QqYnLryUzvOh0/10v4HFkt8Nd02DMHhj4PQ5+pcrPyQWf23X1pE+o4QUexWln4/uvE79nFlFfeIbx1zU6abF10ku3/xjPxmR6Extj38y5hp3oSdi5fYXYW819/nqKcHFr3HUBhTnZZz8y5ZZg1Wi2eZwJM+UBTOk/Gu7QMs6hbhTlZ7F+9gr2rllKUk42nfwCdho+h04jRePnXb5XIizE3LoEX/zpAoIczn93UnW7NzjkuZZ+AJU/bSlSHdLQNbWvW97z7tFgtvLjxRRadXMQ9ne/hwa4PNtphjQ1Jgws7Z9TnQSTzu/2Y00sIfaYXKrVj/tLGpcVx74p76RDQgVmjZ+GmbVgTIIVjK1y7lqSHHsa1TRuaffctGm/v825/Kv8UqxNWs/D4QuIL4ukX1o+nej116UMtrRZYeL+tWs6wF2DIU1VuFp9VzA2ztmC0WJnjYEEHYMsfc9k492eG33kf3caMr9G+DCUmfpqxmai2foy9t1MttfDySdipnoSdminKzeHvD96kMCuzQtWyMwHmzG13b586KcMsLo/VYuHkzjj2rFhM/J6dqNRqWvboQ5fRV9C8YxeH+r/an5zPfb/sIL1Az4wr2nFb/+iK4URR4NAi23yegiToehOMfBU8q18E12K18NqW1/jj2B9MaTOF53o/h6YGQ5ZFzUnYuQBLvoHUd7bhNSwKn9HRdf56NbE8fjlPrnuSIVFD+GjoR2jVjjG3SDRsRevXkzT9QVxataLZ99+h8anck2BVrBzIOsDqxNWsTljNyfyTAHQM6Mi9Xe5lSOSQSz+7ZbXAn/fBvnkw/AVblZwqlA86s+/uQ9vQ8wex+nZ0y38smvkubfsP5oqHnqzxWb5ti04S9288U17oTWCk/Rf7k7BTPQk7oqnLS0tl76ql7F+zAl1hAb6hYXQuXazU3dsxRqHkl5h4Yv5uVh7K4MpOYbwzsRNerucMsTYW2yq2bf4UnD1gxEvQ4w6oJsQoisJHOz/i+/3fMyZ6DG8NfAtnTf0t9iwqkrBzAQVrEilYFk/okz3RBjp+b8lvh3/jza1vMrHVRF7u97J0n4oaKdrwH0nTp+Mc25Lm332Hxte37DGT1URcWhyrE1azJnENGSUZaFQaeob2ZHjUcIY3G06ox2WuiG4xw8L7YN9820Fl0BNVbnY62xZ09CYLs+/uS7swxwk6VquFTfN+Zeuf8wiLbcPkF9/E6TIXXD3jTK9OZBs/xt1n/14dkLBzPhJ2hLAxm0wc27qRPSsWk3z4IBonJ1r3HUiXkeMIb9PO7t9VrFaFWRtO8v6yIzTzd+eLm7tXfeIs8wj8+wTEb4DwbrahbRHVV8P9Yf8PfLDjA/qG9eXjYR/j7mSfNRqbOgk756EoCukf7EDt6UTwfV3q9LVq0yc7P+HrfV9zX5f7mN51ur2bIxqo4k2bSLz/AZxjYmj2/Xdo/fwoMZXwX/J/rE5czfrE9RSaCnHTujEgfADDmw1ncOTgms8Zs5jhz3tg/wIY+QoMfKzKzRKyS7hh1mZ0Dhh0dEWFLP7kfeL37KTjsNGMuPM+tM41P6sX9+8pti06xfUzehEU5RhD9STsVCaLigpRvayEePasXMLB9asx6nQENoumy8hxtBs0DJdLXK+ttm09mc1Dc3ZRoDfxxjWdmNQjsvJGimI7Pi2bAUXp0PMOGP4iuPtXuc+FxxfyyqZXaB/Qns9GfHZpc1ZFrZCwcx6G0wVkfrEHv0mt8Oh5mWeo7UBRFF7e9DJ/Hv+TF/u+yPVtrrd3k0QDU7xlC4n33odz8+Z4ffkhG4r3sDphNZtTNmO0GvF18WVo1FCGRw2nX3g/XLU167EoYzHDH3fDgT9s46IHPlrlZuWDzq/T+tI+3HGCTkb8Sds8g+xsRtx5H51Hjq2V/Rp0Zn6esYnwVr5ccX/nWtlnbZCwU72aHKeObE2jKFePd6Ab3gFueAe64urpZPcz4ELUFqNex+GN69izfAkZ8SdwcnGl3cChdBl9BcHR51/7sC5lFhp4eM4uNp/MZkrPKF69ugOuTlUMV9MXwNq3YetX4OYLo16DLjdCFXOS1iSs4cl1TxLhFcGsUbMuf9SDuCwSds4jd8ExSvZkEDajL2qXhjW5zGw18+iaR1mftJ4Ph37IyOYj7d0k0UAUb91Gwj33UBLizdd3R7JZdwCrYiXcI5zhzWzD07oFd6v9OWEWEyyYBgcXwqjXYcDDVW6WmFPCDbO2UGw0M9vBgs7BDWtYMetTXD09mfDYczWuulZeWa/O870IauYYvTogYed8anKcWvrVPk7syqxwn5OLBu9AV7xKw493oFtpGLJdd2pgxykhwHaCNu3EUfasWMKRjesxm4xEtG3P6HsfwT88wi5tslgVPlpxlE/XHKddmDef39SdmMBqKvql7YN/n4TELRDVxza0LbTyMOPtadt5aPVDeDp78tWory64mL2oPU0z7OxfAG5+EDOk2sllVqOF1De34tYhAP/r29SgpfajM+uYtnwah7MP89Wor+gZKt9HRNUUReFwzmF2Lv+Zjm8tJN1H4dUbNYRFti0LOG382tTdWWWLCRbcBQf/gtFvQv8Hq9zscFoBd/2wnWKjmV+n9aFDuGNMcLWYzaz75Vt2LVlEZLuOjH/0GTx8a2+oglFn5qcZmwiL9eXKBxynVwck7JxPTU/KGfVmCrP1FGTpKMjSU5BdepmloyBbj9lgqbC9m5dThfDjHeiGV6ArPoFuePq5oNY4ThUsIaqiLyriwLpVbPnjNyxmM6Punk67gUPt1p41hzN4bN5uLBaF9yd3ZmzHatYytFptyyOseBF0ebZ1eYbPAJeKJ6YO5xzmvhX3YVEsfD7iczoFOcbcy8au6YUdRYHP+0HmIfAKg87X27odgyuegS3elUHu3CME3dMJlxa+tdNoO8jT53Hr0lvJKsnih3E/0NrPfiutC8ditprZlbGL1Qm2Cmreh5N5fq6FIj9XEt++h0GdxxPlFVX3DbGY4Pc74dDfMOYt6Ff1PLNlB9J4bO5uvFy1fHd7L4cJOsV5uSz66B2SDx+g+xVXM/imO9Boa7fXa/vieLb+fZLJz/UkuLnj9GSBhJ3zqcsRCLbFNU0UZOsoPBOEMm0hqCBLR2GOAcV69hiuUqvw9HM52yN0Tu+Qm5cMkROOoyArk8X/9z7Jhw/Scdhoht9xD04utTRc+hIl5ZYwffYu9iTmcdfAGJ4d1xan6k4clOTA6tdh+/e2Aga3/Gkb4lZOYkEid6+4mxx9Dh8P+5h+4f3q/k00cU0v7ACY9HB0Kez5DY4tB8UCYV2hy1ToNAk8Asn8ei/mXAOhT/Vs8AeAlKIUbll8C6jgl3G/EOZZzZkJ0aAoioJVsWJRLFgUy9nr1nK3y103K2asVisJhQmsTljNuqR15BnycFY7c52uPVd/sRfn4BBifv4Fp+Dg+nkTZiP8fgcc/gfGvA39HqjyfX66+jgfrDhKlyhfZt3SgxBv+xz0zpVy9BCLPnwbfXExo+99qE7OQDpyrw5I2Dkfe1Zjs1qsFOUaysLPmd6hwmwd+Vl6dAXGCttrndV4BbjhE+iK1zm9Q96Brji7ylIGon5ZLRY2zZ/N1oXzCIiIYvyjzxAY1dwubTGarby1+BA/bIqnR3M/Pr2xG2E+56nQe2QpzL0ZwjrbAo9rxZNzmSWZ3LvyXk7ln+KdQe8wJnpMHb+Dpq3BhZ1ar3JTlAn7f4fdsyFtL6i1mJtPJO3QLXiPiMB7VN2PqSzUm0jIKSExp4Qig4Vru0WgqeXFS4/mHuX2JbcT6B7IT2N/wtfVt1b339goioLBYkBn1qE369GZdejMOkrMJWXXz/yUf7z8j8lqKgsgVus5oaSKEHJuaLFaSx8rF2LODTeXy8vZiyGRQxjebDg9M73JuHc62sBAmv30E04hdgg6Y9+FvvdV2qTEaOap+Xv5d18q13WL4K3rOlU9UbSeKYrC3pVLWP39LLwCA7n6iRkENY+pk9faviSerX85Zq8OSNg5H0cuPW0yWs72CGVVHB5XkKXDpK84RM7Vw6msJ8g3xJ1OQyNx95Z1Q0Tdi9+7iyWffoBRp2P4nffScegou52E/mdvCs/8vhcXJw0zp3RlcOvqFxflyBKYewuEdSkNPBX/fucb8nlo9UPsztjNC31fkGJSdajBhZ0z6uQgkn4A9vxGwRYdBSUTCPV+DG3nodD1Rlsd9cv8cJktVlLz9STmlJBQ7ufM7dwSU4XtX7iyHdMG1X7IikuL474V99EuoB1fj/4aN63jrxt0PharpcqQcSaQVBdCdGYdOlPppaWK+8w69Bb9JYcJV40rblo33LRuuGpdcVI7oVap0ag0aNQaNCqN7Xa561qVttJ9lbYvvV1+e7VKjVatrX57lQa1uuL2Z7bzd/Wna3BXnNRO6PbuJeHOu9D4+9P8559wCgmpo/+tc5iNMP92OPIvjHsf+txTaZPkPB33/LSdg6kFPDeuLXcPauEQvaxmo5GV337OgbUrienagyseegpXz7pZ3NOot/XqhLbwYfx0xyx/L2Gneo4cds5HURQMxWYKsnXkZ+rOzhvK1pcNlXPzdGL0XR2IaCNldEXdK87LZfH/vU/C/r20HTCEUXdPx9nNPmWqT2QW8cAvOzmaUcjDw1vx8IhW1Z+gPvwvzLsVwrvDzQsqBR6dWceT655kfdJ6pnedzr2d73WI41xjI2HnHIpVIe39OLRuOoIifoZD/4BZBwGx0OUG6HwD+Faex5CvM1UbZpJzdZjLjZ3WqlVE+rkR5e9Os3I/Uf7ufLTiKJtOZLP8scFE+df+B3nF6RU8sfYJhkQO4aNhH9V+Ra1yyveOXPSP6WzYKAsupsrb6c16jFbjhRtRjlalrRBGzlwv+3E6e91V44q7k3vlbcr9nNmHu9YdV60ralXDmvyr27efhDvvROPrS/OffsQprJ6GN5oNMO82OLoErvgf9L670iZx8Tnc/8sODCYrn0ztxrC29dTbdAG6okIWvPkS6SeP0XfiDfSbNBV1NUVOasOOpfFsWXiSSc/2JCTa8Xp1QMLO+TTUsHMh2clFLJ21n/yMEnpPiKHH2GhUtTwaQYhzWa0Wtv05n03zZ+MbGsqVjzxDSExLu7RFZ7QwY+E+/tiZzMDYQD6+oSsBni5Vb3xoke3kXkQPW+A5p2iByWrilU2v8PeJv7mx7Y080/uZBvd9wtFJ2DmH/kQeWV/vw39KG9y7BdvqqB/8C+ueOahPbwQgzb83O3zHsErVl2N5kJBTQr6uYu+Mv4dzuTDjVhZmmvm7E+bjVu1ZgJQ8HaM+XEfPaH9+uKNXnST8uYfn8sbWN7iu1XW82PfFKntBKg3XMp0NIFX1hJx5Tvn9XE7vyJmgUV0IqSponC+InPlx17rjpHGq9X/Lhkp34AAJd9yJxtvbFnTCw+vnhc0G21muo0urDTpz4xJ4YeF+Iv3c+frWnsQG102vyaUyGQ38/saLpJ84yvhHnyW2V986fT2j3szPMzYTEuPN+Acds1cHJOycT2MNO2D7/Vw3+whHt6UT1c6PkXd0kGFtol4kHdzPv5+8h66wgKG33k2X0VfYpTdEURTmxiXy0t8H8Hd35tMbu9EzuuqFRTn4ty3wRPaCm3+vFHisipUPtn/ATwd/4oqYK3hj4Bs4qeU7S22RsFNKURTySkxkzzuC08kClgwJJj5fV9ZLk5qvJ0xJ51r1f1yn2UCMOh0dLux0H8ixsPGYmw0iMsCrNNS44eV6+b+k3288xauLDvLxDV25umvd1Jj/v13/x6y9sy75eeV7R6oKIdX1mpQPJVX2qpTeL2cz6p7+4EFO33EnGg8P29C1iHpax8Ckh3m32IqCXPkh9LqrwsNmi5U3/rVNAB3UKpBPp3bHx90x/thbrRb++ehdjm3bxPhHn6FNv0F1/po7l51m858nmPRMT0JiHLNXByTsnE9jDjtgO24e/C+FDXOP4eqhZfS0DoS3kmFtou6VFOSz9LMPObV7B6369Gf0vQ/j6mGfE2P7k/OZPnsnybk6nh3XlrsGxlQdvg4stFUejeoDN80Hl4rtVRSFb/d/y8c7P2ZgxEA+GPIB7k72GarX2DTJsLP5RDbHM4tsw8yyzw45MxvM/I0XKzDxHnoCPZ0rDDUru+7nRkjBPjR759hWetfng1d4aRnrqZXKWF8qi1Xhus83kpSrY9UTQ/B1r/2zZYqisPD4QtJL0s87VOvcYCNnGho23e7dJN57HyoPd5r/9BPOkZH188Imva0yzfEVMH4m9LyjwsN5JUYenL2L/45nMa20tKfWQdYEURSFNT/OYteSRQy9dRo9rrymzl/TqDfz8wubCW7uxYSHutb569WEhJ3Kar2QjoPLSipk2dcHyM8ooc/VLeg+urkMaxN1TrFa2f7Pn/z32094+gcy/tGnCYu1z7qIBXoTT83fw7ID6YzpEMJ7k7rg41bF96UDf8Lvd0GzvrbA41x5odLfj/7O61tep1NgJz4b8Rk+Lo6xzEJD1iTDzlWf/sfepHyctWqi/NzKwkzfYoVOe/MouT6WiA5BeLhcxHwWk94292DPb3Bsha2MdXg3W+jpOBE8Ai+rjQdTCpjw6X9M7B7Be5McdwiLaBgsBQVkzvyY3Dlz0IaF0vzHH3GOqoc1dKA06NwEx1fChI+hx+0VHj6WXsi0n7aTmqfnjWs7cn3PemrXRdq+6A/W/fId3a+4mmG3VR52VxfO9OpMfLoHoS0c+0AnYad6jb1npzyj3szaXw5zbHsGzTr4M/L29rh5ybA2UfdSjh7in4/fozg3h0E33k6PK6+x27C2b/87xTtLDhPu68aMK9sxtE0QLtpz5nXuXwALpkHzAXDj3CoDz8rTK3l6/dM0927O16O/JtDt8r5LCpsmGXaOZxTh6aIl2MsFdbmzTxlf7sFabCLk8R6X90EpyoR9822r6JaWsabVGFthg9ZjQFvN5LVqvLv0MF+sPcHsu/vQv6X8ootLp5hNFCycT/qH/4clLx+/sX0JmjQQjbcPaN3AybXcZemPk9vZ61qXy65CCIBJB7/dCCfWwFWfQPdbKzy86lA6j/y2G1cnDV/d0oMezR1rCMzhjev495P3ad13IOMfeRqVuu57m0wGCz+/sImgKC8mPNy1zl+vpiTsVK8phR2wfdk7sCGF/+Ydw9XTyTasLdbX3s0STYC+qIhlX87keNwWWnTvxdgHHsPNyz7Df7fH5/DwnF2k5OvxctUypkMo4zuHMSA28OxipPt+hz/uLg0888C58nC1LalbeHj1w4S4h/D16K8J9Qit53fSeDTJsFMVU5aO9P9tx3tsNN5Da+HMcvoBW+jZOw+K0sHNz9bT02XqRZex1pssjJm5HrVKxZJHBjnE+iLCjhQFjMVQkgUl2VCcbbssu51lW725JAuKszCk5pC2SU1Juguu/kZCe+bj5m+68OtUoCoNQKWhSOtyNgw5ld6uNjS5wsl1EP8fXPV/0P2Wcm9F4ct1J3lv2WE6hvsw69Ye51+gzQ4SD+xlwVsvERrbhkkzXkfrXD9nqXctT2DTH8cbRK8OSNg5n6YWds7ITChk2df7KcjW0/fqFnQb1UyGtYk6pygKu5b+w/pfvsXNx5crH36KyLYd7NIWk8XKxuNZ/LM3lWX70yg0mPFzd2JcpzDGdw6jT0wAmv2/w5/3QPRAmDq3ysCzM30nD6x6AF8XX74d8y0RnvU0x7aRkbBTKn9ZPIVrEwl7rjca70vrgTkvixlOrYXdc2yLJ5r1ENCqtIz1lCrLWJf337Esbv52Kw8Oi+XJMfYZiyrqiNUCutzSkJJdFlLKAktZgMk++2PWV70vtRO4B4B7AFZnP7K3G8jekIrKWUPQ5EH4XTkMlVeQbVilk7utKppZZxtiZtbZbpt0tv2fuTTrzz5e4bKqx/QVn2vSAQponGH8R9Dt5rKm6k0Wnlmwl792pzChSzjvTeyMm7NjBfmsxNP89tLTePj5c8Nr7+Hm6XXhJ9WCM706gVFeXNUAenVAws75NNWwA2DUmVnzy2GO78igeccARt7eHldPmfMp6l76yeP8M/Nd8jPTGXD9zfS+elK99MpXx2C2sP5oFov2pLDyUDolRgtBXi5c2SmM2zy3EL3+CVQthsDU32wnEc+xL3Mf9668Fw8nD74Z/Q3NvZvb4V00bBJ2KF1b591tOIV6EHhHx1rZZ5VKy1izZw6c3gioIGaQrben3VWVKnOc8fi83fy9O4V/Hx5Em9D6+dIlLoPVYgskRem2n+Jze12yK17X5QLVfMZcvMvCCx6BZ69XuB0I7v622y7eoFJRtGEDaa+/gSkhAe8JEwh5+im0QedZ4bmuKApYTKBYbT08pdLy9dzz83b2Jefz5Og2PDC0pcMtoFaYk8XsF55EsVq58fX/4R1Uf2v8bFt0krh/47nuqR6EtXT8Xh2QsHM+TTnsQOmwtvXJbJh/DHcvZ0ZP69hgfq9Fw2YoKWHF159yZNN6mnfuxrjpj+Pha/9h0jqjhdWHM1i0J4XVRzIwmq3c5bWFF0z/R1HEQDxvm4eqih6ewzmHuWf5PWjUGr4Z/Q0tfe2zvlBDJWEH0B/NJeu7/fjf2Bb3zvX0xTA33jbEbc8cyDlpO9ve7ipbRbfogRXm9+QUGxn54TqaB7iz4L7+FeYZiXpgKISijLMhpigDCtMq31ecaStQcS61tmJYqS6wlN0OAO2lDZkypaeT/vY7FC5dinN0NKEvv4RHv3619A9QO3Ym5HLvzzsoMZiZeUM3RrUPsXeTKjGUFDP35WfIy0jnhlffJTi6Rb29dn5mCXNe3UaLbkGMvss+Qy8uh4Sd6jX1sHNGZkIhS2ftozDHQN9rWtBtpAxrE3VPURT2rV7Omu+/wsXDg3EPPkHzTl3t3awyhXoTKw+ls2hPKkHHf+dtzVds13ZlY89PuKJbTKWT2yfyTjBt+TQsVguzRs+irX/NKv82JRJ2gOw5h9EfzSV8Rh9U2nru6lQUSNxmCz37/wBDvm3uQ/N+EDMEWgyB0M78uSeVx+bu4fWrO3BLv+j6bWNjZDHbwklRFaGlQpDJAFNx5eerteARDF4h4BkCnsGll+VuewTZgourT80m+Z+HYjaT++uvZH78CYrFQuB99+J/112o62l+ycX6fUcSz/+xj1AfV765rSetQxyvh9JiNvHH26+QdGg/1z77CtGdu9Xr6//z2R5SjuZx06t98fCtxaG0dUzCTvUk7Jxl0JlZ89MhTuzKJLpTACNub4+rhwxrE3UvMyGef2a+S05KEn2vm0K/iVNRaxxr6HReiZGjS7+g996XWGvtwr3Gx2ge4s/o9qH0jvGne3M/PF20nC44zbTl0yg2FTNr1Cw6BtbhaKRakJavJ8Tbxe4jOBpc2Knt9QusJSZS3tqKR69Q/K6OrXkDa8Kkh5Nr4ORa28TuzEO2+119UWIG8WNaNPNzWvLNY1MI85WFpipRFNuaR0UZFw4xJdlUOYTM1fdsWPEKrSbIhNgKTthxDDDY1sxJffU1DIcO4TF4EKEvvlh/5aQvksWq8M6SQ3y94RT9Wwbw2Y3d8fNwrCAGtjOASz79gEP/rWXsA4/RYciIen39U3uzWPz5XgZMiqXryGb1+to1JWGnehJ2KlIUhX1rk9m44Bju3s6MmdaxQRThEA2fSa9n1XdfcmDdSiLbdeSKh5/Ey98Bq9zu+BEWPUxS4CCe1jzFloQirAqoVdAh3Iee0X7Ehhn58dRzFJjy+HzE53QP6W7vVpdRFIXkomR2Zexi7r4N7Eg9xHXNHubNK0fZtV0NLuycUVsHkaItqeQtPE7wg11xjnSws82FaXBqvS34nFoH+YkA5GiC8O84ytbrEzMEvMPs3NA6ZjaUhpQzISa9Ys9L+RBjMVR+vsalYmg5X2/MJZYHtwdLfj4ZH35E3rx5aIODCXn+ebxGj7L7mZMzSoxmTmYWczyjiAU7k9hwLIvb+0cz48p2Z8tuOpgNs39g21+/M2DKLfS9bkq9vrbZaGH2q1txctFw/YxeaBz036g6EnaqJ2GnahmnC1j29X6Kcgz0u64lXUZEOczfL9G4HVi3ilXffoHW2Zmx0x+jRbde9m5SZdu/h38ehVZjKLrme3Yml7A9Podt8TnsSsjDYLai0ubjE/MtaPOZ2vxlpnQcTvMA93r/HJmtZo7kHGFXxq6yn0xdJgCKxQWVyoqpqC1vDXiP67rX0yLmVWjyYSf9s91gshD8SHfH/mOrKJBzkvXLfqfw0EpGuR3F2ZhneyywzdngEz0Q3Hzt2dKLY7XaJuiX73mprjdGn1f1PtwDqwkx5wSZOhxGVp8URSH/r7/IeO99LPn5+N98M4EPPYTGs/KCZPUhp9jI8YwijmcUcSKzqOx6cp6ubBsXrZpXrurA1N6O21uxe9m/rPruCzqPGMvIu6fX+9+BrYtOsv3feK55rBsRbew/gfZSSdipnoSd6hlKTKz+6TAnd2cS3TmQEbe1k2Ftol5kJyfy78x3yUyIp+eE6xh4w61otBexiHx92v4d/PMYtB4Lk38sK/RjNFvZn5JP3KkcNsbHs8v4Doo2C13SLfirO9Mr2o9e0f70ivanfZh3nczxNlqM/HjgR7ambmVv1l50ZtsxP8wjjG7B3XC3tuTndWp6hrWnZ7dt/HjgO4zxT/Dr7RPoGe1f6+25GE067JjSi0n/aCc+V7bAa1DDqF1usli56tON5BbpWHlTAJ7JG229Pqc3gakEVGoI73Z2vk9U3wrVsOqcseScnpf0cwJNuUurufLzndwrB5aqQoxHEGiazoHRcOIEaa++Rsm2bbh16ULoKy/j2q5dnb+uoiik5OvLgszxjCJOZBRxPLOInGJj2XauTmpaBnkSG+xZdhkb7El0gAfO9T0P7hIcj9vC3x+8RUy3Hlz95Av1Po47L6OE315reEUJypOwUz0JO+enKAp71ySxacFxPHxcGHN3R0Ji7LMQZGOkKAoWkxWj3oLJYLZd6i0Y9WZMBgsmg4WgZl4ERTnYqJZ6YDIaWPfTN+xZsYSwVm24+skXHKJaWwVx38K/j9u+z02dA86VT2xml+Rwx7K7OV1wko7a6cQntig74Rgb7Mndg2K4plsELtraObZZFSvPrn+WJfFLaOffjm7B3egW3I2uwV0J9Qhl68lsbv1uG21Dvfj17r4YrPmMWTAGirqiZE5h4fQBRPnX/zSMJh128hafpOi/FMKe743G0/HmEVRnT2Ie136+kZv6NOf1a0onp5mNkBRnCz4n10HydluY0LhAs76lPT9DIbwrqC/xl95qsZVLvpgQYyio/HyV2hZOzh0yVlWQcfZsFL0wtcWq05H1xZdkf/89and3gh9/HN/Jtb9mgMli5XR2SaVemhOZRZQYz1aY83V3IrZcmGkZ7ElskCcRvm4NrkpgytFDzH9tBoHNo7n+xbdwcq3HkwLYvoj8+9leUo7ncdMrDasoQXkSdqonYefipJ8qYNk3+ynOM9D/ulg6D4907JEWdchqORNOSkPJmYBiMJcGlfL3mzEaKgYYY+n9Z64r1gt/jwuJ8abj4AhiewSjdbA1z+rakc3/sfSLjwiMbMb1L7+Nk0v9HgcuaPds+Gs6RPaGm+bZRqqco8BYwP0r7udA9gHeGfQOnf2G8N/xLH7YGM/B1AICPV24Y0A0N/Vphq97zb7rfrjjQ77f/z2PdH+EaZ2mVXhsf3I+U2dtIcTHlXn39sO/dH7u21vfZu6RuVhOP0uYZxgL7u+Pl2v9nqxusmFHsSikvr0V52beBN7avhZbVj9eW3SQ7zed4vf7+tGjeRXdgoZCW2/Pmfk+6ftt97v42Ia6tRgCMYNtiz4WnjsP5pwgU5xpWy/lXC7epaEltNywsSpCjHvApQcsQeGaNaS/8Sam5GR8rrmG4KeeRBsQUKN9mixWjqQVVuipOZ5ZRHxWMeZyB8UwH9dKvTSxwZ4EeDg3ii8hOSnJzHnpKVzdPZj6+vu4+/jWextO7clk8Rf7GmRRgvIk7FRPws7F0xebWP3TIU7tySKmSyDDb20Yw9oURSnrJTGVCyJGgwWjznze0HK2t+VsOLGYqjjWVkGtVuHkqsHJVYOzqxYnFw3OrhqcXLU4u9gubY9pcHLRlj6mwdlFW/Y8jVbN6X3ZHNiQTG5aCS7uWtr2DaPD4HD8Qu0zPNoeTuzYysL336BV735MePRZuy5AWqUDC2HBNAjpADf/AR6VvwcUm4qZvmo6uzJ28Vr/17g69moURWHTiWxmrT/JuqOZuDlpmNIrijsHxNAs4NJ7V+YcnsNbW99iSpspzOgzo8J3gZOZRUz+cjOuThp+v78fYT5nF0dNLUrlij+uYFDoVfy7tg+DWwXyzW290NTjCdImG3Z0h7LJ/vEgAbe0x61Dzb5A2kOxwczoj9bj7qzh34cHXXioUFGmLfSc6fnJO131dmpt9ZP3PUNKK5QF28ouV7Hwlag5U0oKaW+9RdHKVTjHtiTs5Zdx71WzSZSnsoqZtz2R33ckkVloK+KgUato7u9u650p7aE501vj6eJg45drUW5aCvNfn4HZYGDqG//DLzS83ttgMlqY88pWnFwbZlGC8iTsVE/CzqVRFIU9qxLZ/McJPPxKh7VF1/6wNovJWjF06M1ne0yqCC1nHjed+5zSkFPd2tDncnKpOpxUCCquGttttzP3Vx1aNFp1rZ14UhSFlGN57F+fzMldmVgtChGtfekwOIIWXYPQOPBQ5Nqy49+FrP3pG3pfPYlBN95u7+ZUdnQ5zL0Z/FvArQtt38XOoTPreHj1w2xJ3cKLfV/k+jbXlz12OK2Ar9ef4u89yVisCuM6hnH34BZ0jfK9qJdflbCKx9Y8xpCoIcwcOhNNuRPYKXk6Jn+5Gb3Jwvz7+tEiyLPS81/47wWWxS/j7uhveeufRO4cEMNLE+qvo6HJhp3snw9iiC8g7PneqBroF43Vh9O584ftPDGqNQ+NaHVpT86Nt/X8qDQVg4yrr91LKjdVislEzk8/k/nZZ2C1EvjAAwTcfhuqy1wzR2+ysHR/Gr/FJbDlZA4atYphbYK5qms4bUO9HH4+TV3ITkpg/hsvYDGbmTTjdUJi7LMK9da/T7J9cTzXPN6NiNYONk78EknYqZ6EncuTdiqfZV/vpyTfyIBJsXQcElkaQswYdWd7Ryr2lFQMJ5WCSrltrZaL+26j0aqrDBpOLlqc3Sr2klTqXTkTWkrDipOzpkEspFpSYOTQphQObEihMFuPm5cT7QaE02FgON6BbhfeQQOlKAqrvv2cPSuWMPq+h+k0bLS9m1TZqfUw+wbbyJlb/wbfyktNGCwGHl/7OOuT1nNb+9uY3m06btqz/29p+Xp+2BTPr1tPU6g30zvan3sGt2B42+Bqh6LvztjNtOXTaO3Xmm/HfFthfznFRiZ/uYmMAgNz7ulLx4iqS8mfzD/JNQuvYVqnaeQkjeCHTfG8dW0nbuxTP6MammTYsRSbSH1rK579wvEdX38rpNeF6bN3suJAOkseHUTLKtK0aBhKdu4k7eVXMBw7huewYYTMmIFz5OUVzTiYUsDcuAT+3JVMgd5M8wB3ru8ZxaQekYR4O9h45HqUfuoEC958EbVGw6QX3iAwqrld2pGXUcKc17YS2z2YUXc2zKIE5UnYqZ6EncunLzax6sdDxO/NuujnqFScDRpVBI7zDu8qDStl1101DbrHtaYUq0LCoRz2r0vm9L4sFKBZ+wA6Dg6neafABjdH82JYzGb+fPdVEg/sZeLzr9OsY2d7N6myxG3wyyRw9YZb/4KAyifsTBYTb297m/lH5xPhGcFL/V6if3j/CtsUGcz8ti2B7zfGk5yno0WQB7f3j6Z7Mz9igz1xdbL13MTnx3PLklvwcvbi53E/E+AWUGEfN369hSNphfx8Vx96x5y/0trjax9nS8oWFl+7lEfmHGbj8Sx+urM3/WPrfr2jJhl2Cjcmk7/oJCGPdsepgY9LzSjUM/KDdbQL8+a3e/o2ivkUTYk5N5eMDz4g//cFaMPCCH1hBl4jLn1By0K9ib/3pDA3LpG9Sfk4a9WM6xjKlF5R9I0JaJQHpkuReuwIC95+CWdXdya/+AZ+YfapvqgoCv98upfUE3nc9GpfPHwaZlGC8iTsVFbbi183VYqicGRrGgVZ+srBpTSslA8nWqfaG9olzirM0XNwYwoH/0uhJN+Ip58L7QeG035AeIMtrFIdQ0kxc158iuLcHKa+8T/8w+23Nky1UvfAz9faph3cshBCqh4OFpcWx6ubX+V0wWmuankVT/Z8Ej/XiiMJTBYri/elMmv9SQ6k2ApMqVUQHehBTLDCIdWbWFV6/jfga/pGtUFbegJAb7Jwx/dxxMXnMOvWHgxvG3LBZh/IPsAN/9zAYz0eY1LsLUz8fBPpBXoWTh9Q5dC32tQkw076xztBrSLkoW613Cr7+G1bAs/+sY93J3ZiSq+GO9G5KVGsVvL//JOM9/+HpaiIgNtvI/CBB1C7X/w8KEVR2HE6l9/iEvl3byo6k4W2oV7c0CuKa7pF1LjqSmOReHAff777Gh4+vkx+8U28g4Lt1paTuzNZ8uU+Bk5uRZcRlYcgNEQSdqonPTuiMbFYrJzem83+9UkkHspFpVYR0yWQjoMiiGzr1yCG6V2M/Ix0Zr/wBM6ubkx943+4e1c9NMuuMg7DT1eDxQi3/GFbcqQKBouBr/Z8xff7v8fL2Yunez/NlTFXVjopoCgKJzKLOJxWyNG0Qg6mZbHD+BYmTSolp+/Gqm+Gs1ZNbJAnbUK9SMvXs+VUNjOndOXqruc5eZh9wjbPqPT17ll+D0dzj7J04lIy8q1c8/lGfNycuH9oS3zdnPDzcMbXzQkfdyd83Zxrbah9kws7iqJgTChEMVtxbelb+w2zA6tV4Yavt3A4tYBVTwwlyKtxnWlpbPRHjpL26qvodu7ErUcPQl9+CdfWrS/6+dlFBv7YmcxvcQmcyCzGw1nDVV0juKFXFJ0jfeTMZjnxu3fw1wdv4R0UzOQX3sDT337FSMoXJZgyoxfqRjJERsJO9STsiMYqL6OEgxtSOLQpFX2xCZ8gNzoMiqBt/1DcGtBSHtVJOXqIea89T2jL1kx64Q20Tg5YGTDnJPx4tW3h9Zvm25YZqcbR3KO8uulV9mbtZUD4AF7s9yIRnlWHFLPVzKNrHmVD8gbeHfgBEc69OJJeyNH0Qo6k2S6zi4y8OL4dt/SLrr598Rvhhyvgyg+gl61MdVxaHHcuu5MX+rzAlLZTiIvP4bbvtlVY4qI8D2cNvu7OTOgSzrPj2l7sv0wlTS7sNFbHM4q44uMNjOkYyv9NbRw9Vo2NtbiYzM8/J+eHH9F4eRH81JP4XHvtRZW5tFgV/juexdy4BFYcTMdkUejR3I8pvaK4slMYHo24etrlOh63hX9mvoN/RBSTXnjD7mfnzhQluPaJboS3athFCcqTsFM9OU6Jxs5ssnBiZyYHNiSTejwftVZFbPdgOgyOIKxlwz75dnjTev79+D3aDxrG2OmPO+Z7yU+y9fAUpNgWHm0xtNpNLVYLvx35jU92foKCwvSu07mp3U1o1We/PyiKwhtb3mDe0Xk83+d5pradWs2+lAuXjp49BY4utVXvfWQ3OHugKAo3L7mZbF02/1z7D1q1Fp3RQlaRgbwSE3k6I7klJvJLjOSVmMgtva9bMz9u6Xv582zPd5ySb0+1wGo0YsnJwZKTgzknF0tuues5OZhzc7CUXc9FKSnBpW1b3Lp1xb1rV9y6dcMpLOyCrxMb7Mn0YbF8tPIo13WLYFhb+w3VERUpikLRqlWkvfkW5tRUfCZNJPiJJ9D6XfgLb3KejvnbE5m/PYnkPB1+7k7c1i+aKb2iaBXS9Fa9vliHN65j8acfENIilonPvYarp32Ld+Sll7Bz+Wla9wlpVEFHCNG0aZ00tOkTSps+oWQnF3FgQwpHtqRydFs6/uEedBwcQes+obi4NbyvlG37DyYvNYWN837BLyyCvhNvsHeTKvOJhDuWwE/XwK/Xw/U/QptxVW6qUWu4qd1NjGg2gje2vMH/tv+PxacW82r/V2nrb+s1+Xb/t8w7Oo87O95ZbdCx7esCQSfzqC3otBoNx5bD1i9h0BOoVCru7nQ3D61+iCWnljCh5QTcnDVE+bsTdf76BnVGenaqYNXpKgQXc05pWCl/vTS4WHJysBYVVb0jtRqNnx9afz80fv5o/P3R+vuhcnJCd+AA+n37UQy29VC0ISG4de2KW9euuHfrikv79qirKEdsNFu58pMNlBgtLH9ssJztdwDGpGTS33iDorVrcWndmtBXXsa9e/fzP8dsZdWhdH6LS2T9sUwUBQa1CmRKryhGtQ/BRSsLtJ7P/jUrWPbVJ0S0ac+1z7yMyyXMg6oLZ4oSpJ3I48ZGUpSgPOnZqZ707IimyKg3c3x7BvvXJ5OZUIjWWU3rXiF0GBxBcPPaXzepLimKwtLPPuTghjVc+cjTtO0/2N5NqlpJDvwyEdL2wnWzoOPE826uKArLTi/jna3vkGfI49YOt9LcqzmvbH6FK2Ku4O1Bb6NW1WCo9aJHYfdseOwA/P0gJGyGR/aAmx9WxcrEv23tW3DVgpq9zkVq0j07iqJgLS4p19tSPrhU0fOSk4Oi01W9MycntL6+aPz90fj74RYZWRZgbGHGD62/LdRo/PzQ+Picd/iSYjKhP3wE3e7d6HbtQrd7N4XLlgGgcnLCtUMH3Lp1KwtBTiHBOGvVvH1dJyZ9uZkPVxzlxfH1t2CTqEgxGsn+4UeyPv8c1GqCn34a/1tuRnWecb8nMouYF5fIgp1JZBUZCfV25aFhsUzuGUWUvyzgejF2LfuH1d99SfPO3bj6yRk4udi31LZRb2bX8gQSDmQzcHKrRhd0hBDiXM6uWlu1toHhZJwuYP/6ZI5uS+fgxlSCm3vRYXAErXqG4OTi+CfuVCoVo+59mPzMDJZ+/hHegUGEt25n72ZV5u5vK0U95wZYMA1MOuh2c7Wbq1QqxkaPpV9YPz7c8SHf7/8egF6hvXh9wOs1CyDFWbBnDnSZAp5BMPxF+HIg/DcTRr2KWqVmWqdpPLvhWdYlrmNYs2GX/1q1oNH27CQ9+hi6PXuwZGejGI1VbqNycbGFFT+/sgCjLe2BKQsufqVhxt8ftZdXnY/nNGVklIaf3eh270Z/4EBZ+7XhYbh3tYWfH/I8+SJZw4KHBtM50rdO29QQKYqCYjSi6PVYDQYUg6H0uhHFoMeq19vuMxiw6g0oBn2F67ZLA1aDHuWc61aDHsVgxJydhSUzC69RIwl5/vlqhyLqjBYW70tlblwi2+Jz0KpVjGgXzA29mjG4ddCFu4pFmbi/F7D+1+9p2bMP4x991q4TSs0mCwfWp7BjaTy6QhMtuwcx+q4OjaYoQXnSs1M96dkRwsZQYuLI1jT2r08hN7UYZzctbfqG0mFQOAHhjr9GoK6wgNkvPIGhpISb3vwAn+BQezepasYSmHsTnFgNPW6H/g9XuRbPueLS4lhxegUPdnsQb+ca9r6tfRfWvgUPbIXg0qICC+6GQ4tsc3e8QjFbzUz4cwL+rv78csUvdf79ucEVKKiN9QsOvPIS5rw83PwD8AgMxC0oBKfAgHI9L/6oPdwdczJaOVajEcPBg5Ts3o1u9x50u3djTksDwKBxIik4mp7jBuHR3RaCtIF1v3DTpVIsltKwYAsc5a9b9QYUo8EWPs65fiZU2EJK6fUzQUSvx2o0lAaR0hBTPtgYDFCD322VqysqFxfULi6oXF1tly4uqFxdULuUPubmhveE8XgNHVrlPvYn5/NbXAJ/7Uqh0GAmJtCDKb2iuK57BMFeTXfhz8uhKAqbf5/D5t9n06bfIMY9+AQarX06pi0WK4c2prJ9cTzFeQYi2/rR56oWhLZwwNKltUTCTvUk7AhRkaIopB7PZ//6ZE7sysBqVgiL9aHjkAhadg1G4+S4J4RyUpKY/cITePoFMPX193Fxd9B1Gs0GWDYDdv4IFhO0vwoGPAoR5x9CXytMepjZEcK6ws2/n70/5yR82gu63wbjPwRg3pF5vL7ldb4d/S29w3rXabMaXNg5oyYHkZ+ffYSMUycq3Ofi4YGbpzeuXl64eXrh6uVdeulV7n5v3Ly9cfX0ws3Ly+5DZKpiSk1Ft3s3B1duJHVTHK0LUlBbzAA4RUXZhr2VFj9wad0aVemXQkVRwGQqCwRnezHKB4lzQkWF3o3yoaJ8EDknfOj1WMv1qmAyXf6b1WjKAkf58KFycbaFjnLho+y6a+l2Lq6oXUtDStn10ue6utruc3E+u+/SS5Wz82WH4Hydib93J/NbXCIHUgpw0aq5slMYU3pF0TvG3+HDtSNSFIX1v37P9kV/0GHISEbf9xBqdf0PjbBaFY5tS2PbP6coyNIT2sKHvle3IKJN4y9GIGGnehJ2hKiertDIoU2pHNiQTEGWHldPJ9r1D6PDoHB8ghxz6HbC/r0seOtFojp05tpnXrbbibWLUphmKwwQ9y0YCiBmsC30tBxetu5Nrdv5E/z9kG1I3bmV4f553BbAHowD/xYYLAbGLhhLrG8sX4/+um7aU6pJhp3MhHiKc7LRFRWiKyxEX1RQelmIrrCg9NJ2v7G6OTqA1sm56nDkdSYQnQ1GrmeCkrvHRZUarilFUbjn5x1sOZzCotGBeJ04jG73bkp278KSmQWAys0NtYtLWcDBar3s11M5OdmCQfnejUrho5pekDNBo8og4lJF+Ci97oh178+hKArbTuUwNy6Rf/elYjBbaR/mzdTeUVzVNQIfN8d/D45KsVpZ/cNX7F72L11GX8mIO+6tl89WxTYonNiVybZFJ8lNKyEwypM+V7WgeceAJhNeJexUT8KOEBemWBUSD+dwYH0Kp/ZmoVgVItr4EtnGn/BWPgRHe6N1cpz5PfvXrGDZlx/TZdQ4Rtz1gOP/rdcXwI7vYfPnUJQGoZ1soaf9NaCpxbCmKPB5X1A7wX0bKgeqwjT4uCu0Gw8TvwHg+/3f8+GOD5nSZgo9QnrQJagLYR5htf5v2iTDzqWwmE3oi4psIaiwEN05wahCOCosQFdke0ypJjioVGpcPD3L9RpVDEc+wSG07jewVs5Op+brGPnBOro39+OnO3ujUqlQFAVTcoptzs++vSgmEyrn0vDh6nr2+oV6QVxdUTk7n+3xqOcvmY5MURTSCvT8tTuFuXGJnMoqxstFy9XdwrmhVzM6RjTeIU31xWq1sPyr/+PA2pX0nHAdg2+6o14POIqicHp/Nlv/PklWYhF+oe70ntCClt2CGs0q4hdLwk71JOwIcWmKcg0c2pTCse0Z5KYWA6DWqgiJ9iYs1pfwWF9CW/rYvZT1htk/sO2v3xl66930uPJqu7blopkNsHcebPwYso+BbzPo95CtkIFzLfSkHVsJv06Ea76ErtWUrV75iq1QwX3/QWhHik3FPLv+WbakbkFv0QMQ5BZE56DOdAnqQuegzrQPaI+b1q1GTZOwUwcUqxWDrqQsHNkuz4ahCsGo3DYmg+0/uu/EqQy4/qZaacuPm+J5+e8DzJzSlWu6Vb1arrh4iqKQrzORkqcnrUBHSp6e1Hwdqfl6UstdN5htYbdXtB9TejXjyk5huDk7zpmphsxiNrPk0w84snkD/SZNpd+kG+s16CQfzWXLwpOknczHO9CVXuNjaN07FHUTCzlnSNipniMfp4RwdLoiI6nH80k9nkfK8XwyEwpRrAoqFQREepaFn7BYn3qvdKlYrSya+Q7Htm3mmqdeoGWPPvX6+jVitcKRxbBxJiTFgXsA9L4Xet9tq+p2uX66GjKPwCN7QVt5eRQAdLnwcRdo1g9unFt2t8lq4ljuMfZk7mFv5l72ZO4hsTARAK1Ky+Q2k3m+z/OX3TQJOw7EbDSy8pvPObBuJdc++zItuvWq8T4tVoWJX2wiIaeEVY8Pwc+jml9AgaIoFOjNpOXrScnXkZqnJy1fR0p+xUCjM1kqPE+jVhHi5UKYrxuhPq6E+7gS5uPG4NZBxAY7fpWZhkJRFDJOnWDjvF84tWs7g268nd5XT6q31zcbLWz8/Tj71yfj4eNMzytjaNc/DI22afdqStipXmM8TglhLyaDhbRT+aQes4Wf9JP5mE22E4s+QW6Et/K1BaBWPngHutV9hVyDnnmvPkd2UiJTXn2XkJgLVz1zKIpiW//mv5lwbBk4e8HUORAz6NL3lbYfvhwAI16GQY+ff9sNH8Cq1+DOZdCsb7Wb5ehz2Ju5l72Ze4nxiWFCywmX3q5SEnYcjMloYM4LT1KYlcnN78yslfKGh9MKGP/Jf1zTLYL/Te5SC61smIoMZlLzbOElLf+cXpl8Pal5OoqNFYOMWgXBXq6E+boSVhpiyi59XQn3cSPIy0VKRNehwuwsDv23loPrV5OdlIDGyYmht0yj65gr660NWUlFLP/2ALmpxXQd1Yw+E2LQSk8dIGHnfBrrcUoIR2AxW8lMKCTleF5ZD5ChxFaQyd3HubTXxxZ+AsI962SIcVFuDrNnPIGiWLnxzQ/w8ne8qrcXJf0A/H4n5J629bi0GHJpz//zfji40LaI6IV6h4zFtrk7AbFwx+K6K5ZQjoQdB5SXlsovzz2KT0goU197H61zzXtj3l92mM/WnGD2tD70j22gH8bzKDGaqx1SllraS1NoMFd4jkoFQZ4uFcLLmevhvq6E+rgR7OWCUyNcG8XRmfR6jsVt5uD61ZzetxsUhfA27ekweDit+w3E1aN+eswURWHf2iQ2LTiBs7uWkbe3o1n7gHp57YZCwk71GvNxSghHo1gVclKLy4a9pR7PoyjXAICzm5awlj6ExfoQ3sqP4OZetdYrn3n6FHNeehq/0HBuePVdnFwdr1LvRSnKgB+vgtxTMPU3aHmRi30WpMLMTtDzDrji/Yt7zravYfGTcNPv0GrU5bf5IknYcVDHt2/lr/dfp9OIMYy+56Ea709vsjB25noUYNmjg3F1oMomF6IzWkjN15UOL7P1wKQWlF7m60nJ01GgN1d6XqCnc1lPTHjpELOy696uhHi74tzEhyA5EsVqJfHgPg6uX83RLRsxGfT4BIfQfvBw2g0ahl9oeL22R1doZNVPhzi9L5vmnQIYfks73L1lGOi5JOxUr7Efp4RwZIqiUJitrxB+ctNKANA4qQmJ9ia8lS/hrX2JbONXo2FvJ3fFsfDd12nRozdXPfGcXZZAqBVFmba5NzknbEPaWg6/8HNWvQYbPoSHd4J/i4t7HbMRPu0Jrt5wz3qo4yJXEnYc2IY5P7Jt4XzG3PcIHYfVPPluOp7Fjd9s5YGhLXl6bNtaaGHN6U0W0gv05wwps/XEnBlulltSeS0efw/nisPKSoeU2ebMuBHi44KLtoH+sWlispMTObRhDQfXr6EwOxNnN3fa9BtI+8HDiWjbwS5lPRMP5rDyh4MYSsz0n9iSTkMjHb+8qJ1I2KleUzhOCdGQ6AptRQ9sQ9/ybEUPFOg2qhn9J8bWaN+7li5i9fdf0XPCdQy5+c5aarEdFGfZAk/WMZg6G2JHVr+tsRg+bA/RA+GGXy/tdfbMhT/vgYnfQqe6nX97vuOUA6+U1DQMmHIzacePsurbLwiOaUlw9EUm5mr0jw1kUo9IZq0/yYQu4bQL866lllbNaLaWBply82JK58qkFdgCTXaxsdLzfN2dykJM92a+ZT0x5QNNQ+qZEpXpCgs4vGk9B9evJu34UVQqNdFdujH4pttp2asvTs71W1nnDIvZypa/TrJ7RQJ+YR5MeLgrgZFSZEIIIRoDNy9nWnQLokW3IACMejMbFxxn14oEgqO9ie0RfNn77jZ2ArmpKWxf9Ad+oeF0Hjm2tppdvzwC4da/bYFnzo22EFPdULPds0GfB/0vYwRSp0m2inBr3oSoPqDWlv5oSn9Kb6tKb9fRCUfp2XEAJfl5/PzsI2icnLj5rZm4etbsi1dusZGRH64jyt+dBff3v+yJ9SaLLciUn9yfWq5qWUqenqwiQ6Xnebtqyw0pcyPcx9XWG+NrCzehPq64O0vObowsZhMnd23n4LrVnNwZh9ViJqhZNO2HjKDtgCF4+tWg5GUtyEsvYfm3B8hMKKTD4AgGTIrFSYoQXJD07FSvqRynhGjILGYrCz/cSVZyMZOf6Yl/uMdl78tqsbDwvdeI37uLITffSYchI2v8vc1uSnJKy0kfhim/QuvRFR+3WmxD0dz8YdrKywsjR5bAnBsuvF2XqXDtl5e+/1IyjK0BSDl6iLmvPEt01x5c8+QLNV7A86/dyTzy225evaoDt/WPrvS42WIlo9BQYbJ/yjlzZjKLDJz76+HlorWFGN9yIeacif8eLhJkmhJFUUg7cZSD61dzeNMG9IUFuPv40m7gUNoPHl7j3sraauPhzamsn3sMjVbF8Fva0aJrkL2b1WBI2KleUzpOCdGQFeUamPfWNlzcnZj8bE+ca7BoqaGkhIXvvUbSof1onJxo1bs/HYeOolnHzg1vAfaSHPj5Gsg4BNf/DG3K9VYd+gfm3gSTvoeO113e/hUFTq6FvARQLLYAZTWfvTxzX0gHaHv5FVgl7DQQO5csYs0PXzHwhlvpc+31NdqXoijc9n0cO+JzeGRkKzIKDLbemNJAk16gx3rOf727s6bC5P4zgSbM90wpZle8XJ1q1C7R8JmMBrJOx5N+8jjpp06QfPgAuanJaJyciO3Zl/ZDhhPduTtqjWP0mBhKTKz99QjHd2QQ0dqXkXe0x9OvgVbSsRMJO9VrascpIRqylGO5LPxoN9GdAhh3b6cal6pOP3WC/WtWcPi/teiLi/AOCqbDkBF0HDoK76DLHy5X73S58PO1trV0rv8J2l5hu/+7sZCfDA/vAo1jn8iWsNNAKIrC4v/7H0c2bWDijNdo3qlrjfaXmFPC2JnrKTZacHVSl/XAhHrbyi6Xn/gf5uOGt6tWJmiLCox6HRnxJ8k4dYL0k8fJOHWC7OREFKttkTdXL29CYlrSuu8AWvetv3LRFyv1eB7LvztASZ6R3lfF0G10c9SyXtIlk7BTvaZ2nBKioduzKpH/5h+j7zUt6DE2ulb2aTYaOR63mf1rV9qWUgCadexCx2GjaNWrX60sL1LndHmlgWcfTP4BvMLgm+Ew5m3o94C9W3dBEnYaEKNex+wZT1BSkM8t73yMV0DN1svJLS0O4OvuJEFGnJe+uIiMUyfJOGXrsck4dYKc1GTOjGV09/ElpEUsITEtCS699AoIcsjfK0VR2LU8gS0LT+AV4MrouzoSElO3xToas6YSdlQqVQtgBuCjKMpFlQ5qiscpIRoyRVFY8e0Bju/IYPxDXWp9XbWCzAz2r13JgXUrKcjMwMXDg3YDh9Jx6ChCWtSsGlyd0+fDz9dB6m4IamsbevbYAVv5aAcnYaeByU5O5NfnHycwqhlTXnkHjVaGjonaVVKQT0b8ybLemoxTJ8hLTy173DMgkJCY0mAT05KQmJZ4+jeMhTYVRWHLwpPsXHaa2J7BDLupbY3GZouGEXZUKtV3wHggQ1GUjuXuHwt8DGiAbxRFeeci9vW7hB0hGi+TwcLv726nON/A9c/1wjvQrdZfQ7FaSdi/l/1rV3Bs2yYsJhNB0S3oOHQU7QYOwc3LQQOEPh9+mQhJcbYKbKPfsHeLLoqEnQboyOb/+GfmO3QbO4Hhd9xr7+aIBqw4L5f0U8fJOHmC9NJem8KszLLHfYJDSgPN2XDj7uNrvwbXgKIobJx/nD2rE+kwKJwhU9vUeEy2aDBhZzBQBPx0JuyoVCoNcBQYBSQBccBUbMHn7XN2caeiKBmlz5OwI0Qjl5dRwvy3t+MT5MZ1T3ZHW4eVOfVFRRzauJb9a1aQceoEGq2W2F796DhsFM06dXG8BUr1BbDje+h+K7j52bs1F0XW2WmA2vQbSOqxq9nx71+EtW5LuwFD7N0k4eAsZjN5aalkJyeQeTq+bDhacW5O2TZ+YRGEt25HyJjxhLSIJTi6ZcMtmXkOxaqw/rej7F+fTOdhkQy8vpVDDrETdUNRlPUqlSr6nLt7A8cVRTkJoFKpfgOuVhTlbWy9QEKIJso32J1Rd7Tn38/3sm7OEYbf2q7Ojhmunp50GzOebmPGkxF/kv1rVnBowxqObN6Ad1AwXUZdQafhox2nt8fVGwY8Yu9W1BoJOw5s0I13kHbiGMu/+oSgZtEERjW3d5OEA7CYTeSlpZKVmEB2UgLZyYlkJ54mNzUFq8UMgEqlxj8ikuYduxBc2mMTFN0CF3d3O7e+blitCmt/Pcyhjal0G9WMfte1lKAjACKAxHK3k4A+1W2sUqkCgDeBbiqV6rnSUFTVdvcA9wA0a9as9lorhKhX0Z0D6XllNNv/jSckxoeOgyPq/DWDo1sw/I57GXzTHRzfvoW9K5awYfYPbJ4/mzYDBtOt9GSkqD0SdhyYRqtl/CPP8POzj/D3h29z05sfNtovq6Iyi9lEbkqyLcwkJZCdaAs2uanJWC0W20YqFb7BofhHRtGiR28CI5sRENkM/4hInFyaRnllq8XK6p8Oc2RrGj2viKb3hBgJOuKyKIqSDdx3EdvNAmaBbRhbXbdLCFF3el8ZQ0Z8IRvmHiUw0pPQFj718rpaZ2fa9h9M2/6DyUqIZ/fyfzmwfjUH1q4krHVbuo0ZT+u+A2Tedi2QOTsNQOLBfcx/fQatevVj/GPPyhe5RsZsMpGbmmwLNGd+EhPITUspK/GMSoVvSCgBpWHmzI9/eESTCTVVsVisrPzuIMd3ZNDnqhb0vCLa3k1qlBrCnB2A0mFs/5Sbs9MPeEVRlDGlt58DqK7H5nLIcUqIhk9fbGL+23FYTFaun9Ebd2/7lIrWFxdxYO0qdi//h7y0VNx9fOk8ciydR47Fy79m1XkbOylQ0AjE/b2A9b9+z9Bbp9Hjymvs3RxxGcxGI7mpyWQlJZCTlGAbhpacSF65UKNSqfENDSMgMqpCqPELj8DJ2cXO78CxWExWln2zn1N7suh/XSzdRstworrSgMOOFluBghFAMrYCBTcqinKgtl5TjlNCNA5ZSYUseHcHwdHeXP1oV9Qatd3aolitxO/dxe5l/3By13ZUKhWteven25jxRLTrICe9qyAFChqBnhOuI+XoIdb98h0hLWKJbNfxwk8SdmEyGmzDz8r31CQlkJeWhqKUhhq1Gt/QcAIiomjTdwD+kc0IjGyGX1hEw1h8zM7MJgtLv9rP6f3ZDJrSms7DIu3dJGFnKpVqDjAUCFSpVEnAy4qifKtSqR4ElmGrwPZdbQUdlUo1AZgQGytj64VoDAIjvRh6c1tWfn+QTX+eYOCkVnZri0qtJqZrD2K69iAvLZXdKxazf81yjm75j8Bm0XQbM552A4fi5Np0R3ZcCunZaUAMJcX8+vxjGPV6bnnnYzx8G0Y5wMbKZNCTU0WoyU9PrxBq/MIiKvfUhEWgdZJxuJfDZLSw+PO9JB3JZeiNbegwqO4nlDZ1DaVnxx7kOCVE47J+7lH2rUli9F0daNUrxN7NKWMy6Dm8cT27li4i8/QpXNw96H3NZHqOvxa1xsFKV9uBDGNrRDIT4pk94wlCY1sx+YU35Re8HuiKCslJSiQ7OZGclCRykhPJSU4kPzMDSj8/ao3GFmoiogiIKh9qwmVyYS0y6s38+9leUo/nMfzWdrTtF2bvJjUJEnaqJ8cpIRoXi9nKXx/tIjOxkEnP9CQgwrGWZ1AUhZQjh9j213xO7owjKLoFo+95iNCW9uuJcgQSdhqZgxvWsOTTD+g54TqG3HynvZvTKCiKQlFOti3QlP7YridRkp9Xtp3WyRm/8Aj8wyPxj4gisDTY+IaGo9HKqNC6ZNCZ+ef/9pAeX8DIO9rRuleovZvUZEjYqZ4cp4RofIrzDcx7Mw4nFw2Tn+uJi7tjnrQ8tnUTq77/kpK8PLpfMYEB19/SZIe2OcScHZVK1Q54BAgEVimK8kV9vXZj037QMFKOHGL7oj8Ib9WWVn3627tJDYbVYiEvPa1cmCn9SUnCqNOVbefi4YF/RBQtuvfCPyKKgIgo/COi8A4KcryVjpsAfbGJRZ/sJiuxiDHTOtCye7C9mySEEKKR8vBxYew9HVn44S5W/nCIK+7rhErteEUBWvXpT1THzmyY/QM7/v2LY9s2M3LadGK69rB30xzKRfXsqFSq77CtNp1xpspN6f1jgY+xTfz8RlGUdy5iX2rgJ0VRbr7QtnLGrHpmk4m5rzxDTnIiN701E/9wmbdQXlmRgDNhpnQYWl5aChazuWw7Tz9//EuDzJlAExAZhbuPr1Q7cRC6IiN/f7ybnNRixt7TiZjOUn6zvknPTmXlChTcfezYMXs3RwhRB/auSWLD3KP0uSqGnlfE2Ls555V0+AArvvo/clKSaDtgCMNuuxt3H197N6ve1HgYm0qlGgwUYQspZ0p6arCV9ByFbVXqOGAqtuBz7hoGdyqKkqFSqa4C7gd+VhRl9oVeV8LO+RVkZfDzs4/i6evHjW980CS7Li9mPo1KpcYnJKRioImIwj8iEhd3Dzu/A3E+JQVG/pq5i/xMHVfc14lmHQLs3aQmScJO9eQ4JUTjpSgKK384yNFt6Yyf3oXmHR37GGQ2mdi2cB5b/5yPs5sbQ265iw5DRjSJk7e1MmenNhdrU6lU/yqKcuWFtpODyIXF79nJgrdfpt3AoYyb/nij/IW+5Pk0FUJNJL6h4VLOuQEqyjXw18xdFOXqufKBzkS29bd3k5osCTvVk+OUEI2byWhhwXs7KMrRM/m5XvgEudm7SReUnZTA8lmfknLkIM06dmHk3dPxCw23d7PqVF3N2YkAEsvdTgL6nKcRQ4HrABdg8Xm2uwe4B6BZM1kk8EKiu3Sn/+Qb2TTvV8Jbt6Pr6Cvs3aTLJvNpxBmFOXoWfrQLXaGRCQ93JTzW195NEkII0QQ5OWsYd28n5r8dx5Kv9jHx6R44OTv2d42AyGbc8Mo77F21lPW//sBPTz5I30lT6Tn+2iZZTKne3rGiKGuBtRex3SxgFtjOmNVtqxqHvtdOIfXoYdb8MIuQFi0Ji21j7yad16XOp2k/eITMp2kC9MUmUo7mkXg4hxO7MrGYrFz1SFdCY3zs3TQhhBBNmE+QG6Pu7MA/n+1h3a9HGHF7O4f/HqJSq+ky6gpa9OjN6u++4r85P3Jk03rGTX+coOaOPf+ottUk7CQDUeVuR5beJ+qZSq1m3ENP8suzj7Low3e4+Z2ZuHvb/wvipc6nqdhTI/NpGjuz0ULqiXySDueSdDiHzIRCFAW0LhoiWvnS56oWBDXzsnczhahSuQIF9m6KEKIeNO8YQO/xMWxbdIqQGG86DY20d5Muipd/IFc/OYNj2zax8pvP+fX5xxgw5RZ6jL+myYyGqcmcHS22AgUjsIWcOOBGRVEO1FbjZCz0pUk/eZw5Lz1FZLuOXPfcK/XySyzzacTFslqsZCQUloabXNJO5GMxW1GrVYS08CayrT+Rbf0IifZGo1Xbu7miHJmzUz05TgnRdChWhcVf7CXhQA7XPN6NsAY2xLqkIJ8Vsz7leNxmItt1ZOwDj+ETHGLvZtWK2qjGNgcYim2NnHTgZUVRvlWpVFcAM7FVYPtOUZQ3a6nBUtLzMu1dtYwVs/6PvhNvYMD1F6zufdEudT5NwDnlnGU+TdOjKAq5aSUkHc4h6XAuyUfzMOpswxQDIj2JautHZFt/wmJ9cHZtemOIGxIJO9WTsCNE02IoMTH/7e2YjBYmPtUD70DHL1hQnqIoHFy/mtXffwnA8Dvuo/3g4Q4/LO9CaqUamz3IQeTSKYrCsi8/5sDalVz7zMu06N7rkp4v69OImijK1ZN0OJfE0oBTkm8EwDvQtaznJrKNH25e0pvXkEjYqZ4cp4RoerKTi/j9vR1YjBai2vnTtl8YMV0C0Tp44YLy8jPSWfr5RyQd2k+r3v0Zefd0h5gCcbkk7DQxJqOBOS88SWFWJje/MxOf4NBK28j6NKI2WK0K8XuzSDxkCzd56SUAuHk5EdnGryzgNLQzX6IiCTvVk+OUEE1TQZaOQ5tSObwllaIcA85uWmJ7BNO2byihLX0axIlfq9XCjn//YuNvP+Hi4cmY+x+hRbdLO0nuKCTsNEF5aan88tyj+ISEMujG2y9vPk1YBFonJ/u9CeHQSgqMrPz+AImHcnFy0RDe2rcs4ASEe6BSO/4fenFxJOxUT45TQjRtilUh+Wguh7ekcWJnBmajFZ9gN9r2DaVN3zC8/B1/wffM06dY/OkHZCXE02XUOIbcfFeDW6hewk4TdWLHVha+93rZbZlPI2pL8pFcln93AEOJmUHXt6Jt/zA0Gikq0FhJ2KlM5pYKIc5l1Js5sTOTI1tSST6aByqIaO1H236htOwWjJOL437XMptMbJz7M9v/+RO/0DDGTX+CsFaOvZRJeQ0u7MhBpPaknTiGUaeT+TSiVlitCjuXxrNt0Sl8gt0Ze09HAiI87d0sUcck7FRPTsoJIapSkKXjyNY0Dm9OpSBLj9ZFQ2z3INr2DSO8la/Djn5IPLiPJZ99SGFWJi7uHji7uePi7o6zmzvO7u64lF46u9mue/j60W7QUJxc7NsT1ODCzhk1OYjsXH4afaEJZzctzm5aXNxLL8+57eyicdhfOCEcSUmBkRXfHSDpcC6t+4QwZGobqaLWREjYqZ6EHSHE+SiKQurxfA5vSeX4jgxMegteAa606RtK276h+AS527uJlRhKitm9fDHFeTkYS3QYdSUYSopLL0tsl7oSzAYDAG36DeLKR5626wn18x2nGu03lYQDOaSfzMdssp5/QxU4u1YOQRVuu5YLR24aXNycbJfutkutk+N2SwpRG5KO5LLi2wMYdGaG3dKWdv3DpJdQCCGEuACVSkV4K1/CW/kyaEprTu6yDXPbvjie7f/GE90pgOG3tcPN03GqlLq4e9DnmskX3M5iNrN90R/899tPhLdpR/dxV9VD6y5dow071zzWDQCL2YpRZ8ZQYsaoL73UmTHozGfvP+d2Ua6e7GTbbaPOzIU6vzRa9dnw46qpthfpzG1PP1dZGV40CFarwo4l8cT9cwrfEHeueqSrDFsTQgghLoOTs4Y2fUJp0yeUolw9hzalsn1JPPPf2s64+zo1uO+GGq2W3ldPIvX4Edb9/C0hMbFEtG1v72ZV0miHsdUWRVEwGSwXDkmll8aSc+4rMVfZu9T3mhb0GBtd/29IiIskw9bEGTKMrXqOcJwSQjRc6acKWDprH7oiE8NubkubPpWXC3F0+uIifn3uMcxGAze/8zEevn713oYmOYyttqhUKpxdtTX6kmcxWyv0Ku1emciWhSdxctHQeVhULbZWiNohw9aEEEKIuhcS483k53qx7Ov9rPz+IBmnC+g/MbZBVTh19fBkwuPPMeeFJ/n3k/eZNON11BrHmeLhkP+SKpVqgkqlmpWfn2/vptQKjVaNm6czvsHuBDf3ZsTt7YjpEsiGucc4uDHF3s0ToozVqhD37yn+nrkLF3ctk5/tSfsB4RJ0hDhHYztOCSHsx93bmase7Urn4ZHsXZ3E3zN3U1JgtHezLklwdAtG3j2dxAN7+W/uz/ZuTgUOGXYURVmkKMo9Pj4+9m5KndBo1IyZ1pGo9v6s+eUwx+LS7d0kISgpMLLok91sW3SKVr1DmPRsT5mfI0Q1GvtxSghRvzQaNYOub83IO9qTHl/A/LfjSI8vsHezLkmHISPoPHIscX/9zrG4zfZuThmHDDtNgcZJzbj7OhHW0oeV3x/k1N4sezdJNGFJR3KZ+8Y20k7kM+yWtoy8vb3MzxFCCCHqWZs+oUx8qgcqlYo//7eTQ5sa1gigYbfdQ0iLViz97CNy0xyj7RJ27MjJWcP46V0IjPJk2az9JB7KsXeTRBNz7rC1STJsTQghhLCroGZeTH6+J2GxPqz+6TDr5hzBYr7AUioOQuvszFWPP4dao2HRB29hMujPu31JQT57Vy3l5M64OmuThB07c3bTMuHhrviGuLH4i72kHs+zd5NEE1F+2Frr3qEybE0IIYRwEG6ezkx4qAvdRjVj/7pk/vpoF8X5Bns366J4BwVzxUNPkpl4mpVff8a5lZ9L8vPYs2IJ81+fwZf33sKKWZ9yeOO6OmuPjFNxAK4eTlz1SDf+/GAn/3y6h6sf60Zwc297N0s0YkmHc1jx3UGMOjPDb21L235SbU0IIYRwJGqNmv4TYwlq7sXqnw4x7604xt3bidAWjj9XMKZrD/pNnMrm32cT3qY9sb36cmzrJo5u3UjSwf0oihW/sHB6Xz2J1n0HEtQ8ps7aIuvsOJDCHD1//m8nRoOZax/vLmfZRa2zWhW2L44n7t9T+IW4M+bujvJ7Ji5I1tmpXlM7Tgkh7CM7uYjFX+ylKNfAoCmt6TDI8YecK1Yrf7z7Kqf37rL17igK/uGRtO43kNZ9BxIY1bzW3sP5jlMOGXZUKtUEYEJsbOzdx44ds3dz6lV+Zgl//G8nKHDtE93xDXG3d5NEI1Gcb2DFdwdJPpJLmz6hDJ7aWooQiIsiYad6EnaEEPVFX2xixXcHSTiQTbsBYQy+oTVaJ8dZz6YqusICVn37Bf4RUbTuO4DAqOZ18joNLuyc0VQPIjkpxfz54U60Tmque6oHXv6u9m6SaOCSDuew/LuDmHRmBk9tLcPWxCWRsFO9pnqcEkLYh9WqsG3RSXYsOU1YrA9X3N8ZVw8nezfL7s53nJICBQ7IP9yDqx7uilFvaVAT0oTjsVoVtv1zir8+3o1rabW1dv0dv+tbCEcni4oKIexBrVbR9+qWjJ7WgfT4Av54fwcF2Tp7N8uhSdhxUEHNvJjwUBeKC4z8/fFu9EUmezdJNDDF+Qb+/ng3cf+cok0fqbYmRG2SRUWFEPbUqmcIVz3clZICIwve3UFmQqG9m+SwJOw4sNAWPlx5fyfyM3T8/cluDDqzvZskGojEwznMfTOO9JP5DL+1nSwSKoQQQjQyEa39uPbJ7qg1Kv78YCcJB7Pt3SSHJGHHwUW29WfsvR3JTiri30/3YDJY7N0k4WAMJSZST+RzYEMy/807xl8zd/H3mWFrz/WkXf8wezdRCCGEEHUgINyTSc/0xDvIjX8/3cuhTan2bpLDkVO9DUB0p0BG3dWB5d/sZ/EXe7lyemeHr74hap9BZyYnpZjc1GJyUorJSS0iJ7WE4ryzc7q0zmr8Qj3oMiKKPhNa4OQivydCCCFEY+bh68J1T3RnyVf7WP3TIYpy9fS8Ilrm55aSsNNAxPYIxmxsx6ofD7Hs6wOMvbcjGo10zDVGBp35bKBJKSYnzXZZIdQ4qfEL8yCyjR/+4R74h3ngH+6Bl78rKrX8cRNCCCGaEmc3LeMf7MKaXw6zbdEpinL0DLmxDWr5ruiYYafcOjv2bopDadsvDJPBwvrfjrLq+4OMvLMDavli22CVhZqynpoLhxq/MFuw8Q6QUCOEEEKIszRaNSNua4eXvyvbF8dTnG9k9LQOTX7OrkO+e0VRFgGLevbsebe92+JoOg2NxGS0sPmPE2idNQy7ua186XVwRp3ZFmRKw8yZgFOUWznURLTxLe2l8cQ/zAOvAFcJtEIIIYS4KCqVij5XtcDTz4V1s4+w8MNdjH+wC+7ezvZumt04ZNgR59d9dHNMegvbF8fj5KJh4PWtZFymA6gQalKLyU2pHGo0Tmr8Qt0Jb10aas4MPwtwk1AjhBBCiFrRYVAEHj4uLPtmPwve2874B7vgF+ph72ZVqaTAiIuHts6mZ0jYaaB6T4jBZLCwZ1UiTi4a+l7T0t5NavTMJgslBUZ0BSZKCo2U5BvITS85f6hp5Vs2p8YvzAPvQAk1QgghhKh70Z0Duebx7vz72R4WvL+DKx/oQlhLx1obrDjPwJ8f7iQ0xoeRd7Svk9eQsNNAqVQqBkyKxWS0sGPpaZxcNfQYG23vZjU4JkNpgCk0UlJgrHBdV2C0hZrS60Z95bLfGq0avzB3wmJ9KxQKkFAjhBBCCHsLifZm4tM9WPR/e1j4wU6adQygde8QojsH4uR8+RVbLSYrp/dnk3Iij3b9wi5r0fKSAiN/zdxFSb6RDoMjLrstFyJhpwFTqVQMmdoGs8HCloUncXLR0HlYlL2bZVeKopwNMKVhRVdgpPjM7fLBptCEuZp1i1zctbh7O+Pm5UxQlBdu3s64eznb7iu97ubthKefzKkRQgghhOPyCXJn4tM92LH0NMfi0onfm4WTi4YW3YJo3SuEyLZ+F1W1TbEqpBzL4+i2NE7sysRQYlvs/sD6ZIbd0pbWvUIvuk26QlvQKczRM+Ghuu1xkrDTwKnVKkbc1g6z0cqGucfQOmtoPyDc3s2qVYqiYNSZywUV03l7Y8wma5X7cfVwsgUVbydCor1LrzuXhZry1zVaKdUohKieVA0VQjQkbp7ODJzUiv7XxZ4NLDszObIlDTcvJ2J7htC6VwghMd6V5oFnJRVxdFsax+LSKco12IJS1yBa9w7BL8yDFd8dYMW3B0k/VUD/ibEXnHujLzLx18zd5GfqGD+9M+Gt/OryraNSFKVOX6AmevbsqWzfvt3ezWgQLCYri7/YS8KhHEbf2YFWvULs3aTzUhQFQ4m54nCx/LM9MWWXBUZ0hSYs5ioCjArcPJ0qhJWqemDcvZ1x9XKSdYmEuEwqlWqHoig97d0ORyTHKSFEQ2UxWTl9IJuj29KJ35eFxWTFO9CV1r1DadYhgJRjuRzdlk5OSjFqtYpmHfxp3TuU6C4Vh8BZLFY2LTjO3tVJhMX6MObujnj4uFT5moYSW9DJSSnmigc60ax9QK28l/MdpyTsNCImo4V//m8PaSfyGXtvR2K6BNXr6ytWBX2xqXSIWBXDxgpMZ3tgCo1YLZV/91RqlS3A+JwZKlbu8pzrrp5OMoRMiHogYad6cpwSQjQGRp2Zk7szORqXTtKhHM7Eg9AWPrTuHUJsz2DcPM9fvvpoXBprfj6Ms6uWMfd0JDzWt8LjBp2Zvz/eTVZSIePu7UR0p8Baa3+DCzvlhgfcfezYMXs3p0Ex6sz8NXMXWclFjJ/ehah2/jXan9WqoC8ynbcH5sxjuiITirXy75Naozrb8+JlG0ZWXW+Mq4eTrBskhIORsFM9CTtCiMampMBI8pFcQmK88Q50u6TnZicXseSrfRRm6ek/MZbOwyNRqVQY9WYWfbKHjPiCOjkh3+DCzhlyELk8+mITCz/cSX6mjqse7krYOcnaYrGiL6zcA1NVb4y+yERVvyIarRo3b6cqJu1X7oFxcdfKOkBCNGASdqonxykhhKjIoDOz6oeDnNqTRateIQyc3IplX+8n9UQ+Y6Z1oGX34Fp/zfMdp6RAQSPk6uHEVY90488PdvLPp3to3jGgtAfGhK7AiL7YVOXztE7qsoDiFeBGSIxPpcn7Z4KNs6tGAowQQgghhKjAxU3LuHs7sXP5abb+dZKTuzKxWqyMuqtugs6FSNhppNy9nbn60a4snbWf9NOFuHs54xdiW+TS3cupyt4YJxcJMEIIIYQQomZUahU9xkYT3Nyb/+Yfo8fY5rTqaZ/iWRJ2GjFPP1cmPSMjT4QQQgghRP2LaufP1Jf62LUNUotXCCGEEEII0ShJ2BFCCCGEEEI0ShJ2hBBCCCGEEI2ShB0hhBBCCCFEoyRhRwghhBBCCNEoSdgRQgghhBBCNEoSdoQQQgghhBCNkoQdIYQQQgghRKPkkGFHpVJNUKlUs/Lz8+3dFCGEEKISOU4JIUTD4JBhR1GURYqi3OPj42PvpgghhBCVyHFKCCEaBocMO0IIIYQQQghRUxJ2hBBCCCGEEI2ShB0hhBBCCCFEo6RSFMXebaiWSqXKBE7XYBc+gCPOHq3vdtXF69XGPmuyj8t57qU852K3DQSyLrEdjY2jfs6gftvmqJ+zmuznzPOaK4oSVAvtaHQa8XEK5PNT033IccqxOOpnraF/zmprvzX9rPlWe5xSFKXR/gCz7N0GR2hXXbxebeyzJvu4nOdeynMudltge33+Xzrij6N+zuq7bY76OavJfhz5/7ax/Djyv7F8fuQ41Zh+HPWz1tA/Z7W137r8rDX2YWyL7N2AatR3u+ri9WpjnzXZx+U891Ke46i/O47Ikf+t6rNtjvo5q8l+HPn/trFw5H9j+fzIcaoxcdR/r4b+Oaut/dbZZ82hh7EJ4ehUKtV2RVF62rsdQgghRFXkOCWausbesyNEXZtl7wYIIYQQ5yHHKdGkSc+OEEIIIYQQolGSnh0hhBBCCCFEoyRhRwghhBBCCNEoSdgRQgghhBBCNEoSdoSoJSqVqoVKpfpWpVL9bu+2CCGEEFWRY5VoaiTsCHEeKpXqO5VKlaFSqfafc/9YlUp1RKVSHVepVM8CKIpyUlGUu+zTUiGEEE2VHKuEqJ6EHSHO7wdgbPk7VCqVBvgMGAe0B6aqVKr29d80IYQQApBjlRDVkrAjxHkoirIeyDnn7t7A8dKzY0bgN+Dqem+cEEIIgRyrhDgfCTtCXLoIILHc7SQgQqVSBahUqi+BbiqV6jn7NE0IIYQA5FglBABaezdAiMZCUZRs4D57t0MIIYSojhyrRFMjPTtCXLpkIKrc7cjS+4QQQghHIccqIZCwI8TliANaqVSqGJVK5QzcAPxt5zYJIYQQ5cmxSggk7AhxXiqVag6wGWijUqmSVCrVXYqimIEHgWXAIWCeoigH7NlOIYQQTZccq4SonkpRFHu3QQghhBBCCCFqnfTsCCGEEEIIIRolCTtCCCGEEEKIRknCjhBCCCGEEKJRkrAjhBBCCCGEaJQk7AghhBBCCCEaJQk7QgghhBBCiEZJwo4QdUClUikqleqDcrefVKlUr9ixSUIIIUQZOU6JpkLCjhB1wwBcp1KpAu3dECGEEKIKcpwSTYKEHSHqhhmYBTxm74YIIYQQVZDjlGgSJOwIUXc+A25SqVQ+9m6IEEIIUQU5TolGT8KOEHVEUZQC4CfgYXu3RQghhDiXHKdEUyBhR4i6NRO4C/CwczuEEEKIqsxEjlOiEZOwI0QdUhQlB5iH7UAihBBCOBQ5TonGTsKOEHXvA0Cq3QghhHBUcpwSjZZKURR7t0EIIYQQQgghap307AghhBBCCCEaJQk7QgghhBBCiEZJwo4QQgghhBCiUZKwI4QQQgghhGiUJOwIIYQQQgghGiUJO0IIIYQQQohGScKOEEIIIYQQolGSsCOEEEIIIYRolP4fDrwxZA5z85EAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { @@ -782,7 +796,7 @@ "for c in piv2.columns:\n", " piv2[c] /= np\n", " \n", - "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n", + "fig, ax = plt.subplots(1, 2, figsize=(14, 6))\n", "piv.plot(logy=True, logx=True, ax=ax[0])\n", "ax[0].set_title(\"Benchmark einsum function\")\n", "piv2.plot(logy=True, logx=True, ax=ax[1])\n", @@ -796,10 +810,898 @@ "Version `dec-matmul` is an implementation based on the decomposition of a simplified einsum into a sequence of transpose, reshape, (batch_)dot or mul operations. This decomposition is converted into ONNX and executed with *onnxruntime*, version `ort-matmul`. Both version are faster than the numpy optimized version. The ONNX graph may contain consecutive transpose which should be merged." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Another example with `bsnh,btnh->bnts`\n", + "\n", + "Another case, more frequent in deep learning." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Decomposition of `bsnh,btnh->bnts`" + ] + }, { "cell_type": "code", "execution_count": 17, "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "seq2 = decompose_einsum_equation(\"bsnh,btnh->bnts\", strategy='numpy', clean=True)\n", + "RenderJsDot(seq_clean.to_dot(size=7))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ONNX version" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "onx2 = seq2.to_onnx(\"Y\", \"X1\", \"X2\", dtype=numpy.float32)\n", + "%onnxview onx2 " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Benchmark" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 7/7 [00:12<00:00, 1.81s/it]\n" + ] + }, + { + "data": { + "text/html": [ + "
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averagedeviationmin_execmax_execrepeatnumbertotalnameN
370.2142700.0083120.2062040.23255110102.142702custom_einsum40
380.1491430.0067860.1391580.15990510101.491428dec-matmul40
390.0989740.0035140.0966200.10594110100.989736dec-batch_dot40
400.0480140.0031820.0459560.05436010100.480141ort-einsum40
410.0631430.0033050.0597820.07050410100.631427ort-matmul40
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" + ], + "text/plain": [ + " average deviation min_exec max_exec repeat number total \\\n", + "37 0.214270 0.008312 0.206204 0.232551 10 10 2.142702 \n", + "38 0.149143 0.006786 0.139158 0.159905 10 10 1.491428 \n", + "39 0.098974 0.003514 0.096620 0.105941 10 10 0.989736 \n", + "40 0.048014 0.003182 0.045956 0.054360 10 10 0.480141 \n", + "41 0.063143 0.003305 0.059782 0.070504 10 10 0.631427 \n", + "\n", + " name N \n", + "37 custom_einsum 40 \n", + "38 dec-matmul 40 \n", + "39 dec-batch_dot 40 \n", + "40 ort-einsum 40 \n", + "41 ort-matmul 40 " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "equation = \"bsnh,btnh->bnts\"\n", + "sess = None\n", + "sess2 = None\n", + "seq = None \n", + "\n", + "\n", + "def make_model2(equation):\n", + " model = helper.make_model(\n", + " opset_imports=[helper.make_operatorsetid('', 13)],\n", + " graph=helper.make_graph(\n", + " name='einsum_test',\n", + " inputs=[helper.make_tensor_value_info(\"X\", onnx.TensorProto.FLOAT, None),\n", + " helper.make_tensor_value_info(\"Y\", onnx.TensorProto.FLOAT, None)],\n", + " outputs=[helper.make_tensor_value_info(\"A\", onnx.TensorProto.FLOAT, None)],\n", + " nodes=[\n", + " helper.make_node(\"Einsum\", [\"X\", \"Y\"], [\"A\"], equation=equation)\n", + " ]\n", + " )\n", + " )\n", + " return model\n", + "\n", + "\n", + "results = []\n", + "for N in tqdm([2, 3, 4, 10, 20, 30, 40]):\n", + " m1 = numpy.random.randn(10, N, N, N)\n", + " m2 = numpy.random.randn(10, N, N, N)\n", + " \n", + " if seq is None:\n", + " seq = decompose_einsum_equation(equation, clean=True)\n", + " if seq2 is None:\n", + " seq2 = decompose_einsum_equation(equation, clean=True, strategy='numpy')\n", + " if sess is None:\n", + " model = make_model2(equation)\n", + " sess = InferenceSession(model.SerializeToString())\n", + " if sess2 is None:\n", + " onx = seq2.to_onnx(\"Y\", \"X1\", \"X2\", dtype=numpy.float32)\n", + " sess2 = InferenceSession(onx.SerializeToString())\n", + "\n", + " res = measure_time(lambda x: numpy.einsum(equation, *x, optimize=True),\n", + " [m1, m2],\n", + " repeat=10, number=10)\n", + " \n", + " res['name'] = \"numpy.einsum\"\n", + " res[\"N\"] = N\n", + " results.append(res)\n", + "\n", + " res = measure_time(lambda x: apply_einsum_sequence(seq, *x),\n", + " [m1, m2],\n", + " repeat=10, number=10)\n", + " res['name'] = \"custom_einsum\"\n", + " res[\"N\"] = N\n", + " results.append(res) \n", + "\n", + " res = measure_time(lambda x: apply_einsum_sequence(seq, *x, matmul_impl=\"pyf\"),\n", + " [m1, m2],\n", + " repeat=10, number=10)\n", + " res['name'] = \"dec-matmul\"\n", + " res[\"N\"] = N\n", + " results.append(res) \n", + "\n", + " res = measure_time(lambda x: apply_einsum_sequence(seq2, *x, matmul_impl=\"pyf\"),\n", + " [m1, m2],\n", + " repeat=10, number=10)\n", + " res['name'] = \"dec-batch_dot\"\n", + " res[\"N\"] = N\n", + " results.append(res) \n", + "\n", + " res = measure_time(lambda x: sess.run(None, {'X': x[0], 'Y': x[1]}),\n", + " [m1.astype(numpy.float32), m2.astype(numpy.float32),\n", + " m3.astype(numpy.float32)],\n", + " repeat=10, number=10)\n", + " res['name'] = \"ort-einsum\"\n", + " res[\"N\"] = N\n", + " results.append(res) \n", + "\n", + " res = measure_time(lambda x: sess2.run(None, {'X1': x[0], 'X2': x[1]}),\n", + " [m1.astype(numpy.float32), m2.astype(numpy.float32),\n", + " m3.astype(numpy.float32)],\n", + " repeat=10, number=10)\n", + " res['name'] = \"ort-matmul\"\n", + " res[\"N\"] = N\n", + " results.append(res) \n", + " \n", + "\n", + "df = DataFrame(results)\n", + "df.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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averagedeviationmin_execmax_execrepeatnumbertotalnameNeq
7150.0237060.0010490.0228050.025733550.118531custom_einsum20thns,tbns->tnbh
7160.0164000.0000910.0162980.016568550.081999dec-matmul20thns,tbns->tnbh
7170.0110340.0000540.0109320.011090550.055171dec-batch_dot20thns,tbns->tnbh
7180.0034390.0001300.0033350.003659550.017194ort-einsum20thns,tbns->tnbh
7190.0048020.0000540.0047190.004864550.024009ort-matmul20thns,tbns->tnbh
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" + ], + "text/plain": [ + " average deviation min_exec max_exec repeat number total \\\n", + "715 0.023706 0.001049 0.022805 0.025733 5 5 0.118531 \n", + "716 0.016400 0.000091 0.016298 0.016568 5 5 0.081999 \n", + "717 0.011034 0.000054 0.010932 0.011090 5 5 0.055171 \n", + "718 0.003439 0.000130 0.003335 0.003659 5 5 0.017194 \n", + "719 0.004802 0.000054 0.004719 0.004864 5 5 0.024009 \n", + "\n", + " name N eq \n", + "715 custom_einsum 20 thns,tbns->tnbh \n", + "716 dec-matmul 20 thns,tbns->tnbh \n", + "717 dec-batch_dot 20 thns,tbns->tnbh \n", + "718 ort-einsum 20 thns,tbns->tnbh \n", + "719 ort-matmul 20 thns,tbns->tnbh " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from itertools import permutations\n", + "\n", + "N = 20\n", + "m1 = numpy.random.randn(N, N, N, N)\n", + "m2 = numpy.random.randn(N, N, N, N)\n", + "\n", + "results = []\n", + "for perm in tqdm(list(permutations(letters))):\n", + " replace = {d: c for c, d in zip(letters, perm)}\n", + " eq = equation\n", + " for k, v in replace.items():\n", + " eq = eq.replace(k, v.upper())\n", + " eq = eq.lower()\n", + " \n", + " seq = decompose_einsum_equation(eq, clean=True)\n", + " seq2 = decompose_einsum_equation(eq, clean=True, strategy='numpy')\n", + " model = make_model2(eq)\n", + " sess = InferenceSession(model.SerializeToString())\n", + " onx = seq2.to_onnx(\"Y\", \"X1\", \"X2\", dtype=numpy.float32)\n", + " sess2 = InferenceSession(onx.SerializeToString())\n", + " \n", + " res = measure_time(lambda x: numpy.einsum(eq, *x, optimize=True),\n", + " [m1, m2],\n", + " repeat=5, number=5)\n", + " \n", + " res['name'] = \"numpy.einsum\"\n", + " res[\"N\"] = N\n", + " res[\"eq\"] = eq\n", + " results.append(res)\n", + " \n", + " res = measure_time(lambda x: apply_einsum_sequence(seq, *x),\n", + " [m1, m2],\n", + " repeat=5, number=5)\n", + " res['name'] = \"custom_einsum\"\n", + " res[\"N\"] = N\n", + " res[\"eq\"] = eq\n", + " results.append(res) \n", + "\n", + " res = measure_time(lambda x: apply_einsum_sequence(seq, *x, matmul_impl=\"pyf\"),\n", + " [m1, m2],\n", + " repeat=5, number=5)\n", + " res['name'] = \"dec-matmul\"\n", + " res[\"N\"] = N\n", + " res[\"eq\"] = eq\n", + " results.append(res) \n", + "\n", + " res = measure_time(lambda x: apply_einsum_sequence(seq2, *x, matmul_impl=\"pyf\"),\n", + " [m1, m2],\n", + " repeat=5, number=5)\n", + " res['name'] = \"dec-batch_dot\"\n", + " res[\"N\"] = N\n", + " res[\"eq\"] = eq\n", + " results.append(res) \n", + "\n", + " res = measure_time(lambda x: sess.run(None, {'X': x[0], 'Y': x[1]}),\n", + " [m1.astype(numpy.float32), m2.astype(numpy.float32),\n", + " m3.astype(numpy.float32)],\n", + " repeat=5, number=5)\n", + " res['name'] = \"ort-einsum\"\n", + " res[\"N\"] = N\n", + " res[\"eq\"] = eq\n", + " results.append(res) \n", + "\n", + " res = measure_time(lambda x: sess2.run(None, {'X1': x[0], 'X2': x[1]}),\n", + " [m1.astype(numpy.float32), m2.astype(numpy.float32),\n", + " m3.astype(numpy.float32)],\n", + " repeat=5, number=5)\n", + " res['name'] = \"ort-matmul\"\n", + " res[\"N\"] = N\n", + " res[\"eq\"] = eq\n", + " results.append(res) \n", + " \n", + "\n", + "df = DataFrame(results)\n", + "df.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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averagedeviationmin_execmax_execrepeatnumbertotalnameNeq
00.0023310.0000390.0023060.002409550.011657ort-matmul20bhst,bnst->bsnh
10.0023490.0000520.0023150.002451550.011743ort-matmul20hnst,hbst->hsbn
20.0023650.0000590.0023100.002441550.011823ort-matmul20hsnt,hbnt->hnbs
30.0023660.0000410.0023280.002426550.011828ort-matmul20bhts,bnts->btnh
40.0023880.0000580.0023480.002500550.011941ort-matmul20bhnt,bsnt->bnsh
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" + ], + "text/plain": [ + " average deviation min_exec max_exec repeat number total \\\n", + "0 0.002331 0.000039 0.002306 0.002409 5 5 0.011657 \n", + "1 0.002349 0.000052 0.002315 0.002451 5 5 0.011743 \n", + "2 0.002365 0.000059 0.002310 0.002441 5 5 0.011823 \n", + "3 0.002366 0.000041 0.002328 0.002426 5 5 0.011828 \n", + "4 0.002388 0.000058 0.002348 0.002500 5 5 0.011941 \n", + "\n", + " name N eq \n", + "0 ort-matmul 20 bhst,bnst->bsnh \n", + "1 ort-matmul 20 hnst,hbst->hsbn \n", + "2 ort-matmul 20 hsnt,hbnt->hnbs \n", + "3 ort-matmul 20 bhts,bnts->btnh \n", + "4 ort-matmul 20 bhnt,bsnt->bnsh " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = df.sort_values(\"average\").reset_index(drop=True)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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averagedeviationmin_execmax_execrepeatnumbertotalnameNeq
7150.0318340.0050690.0270320.041379550.159171custom_einsum20hnbt,hsbt->hbsn
7160.0329100.0038530.0267870.038817550.164551custom_einsum20bsnh,btnh->bnts
7170.0329300.0130940.0233310.056991550.164648custom_einsum20shtb,sntb->stnh
7180.0329590.0028670.0275360.035396550.164794numpy.einsum20htbs,hnbs->hbnt
7190.0356080.0017950.0338230.038916550.178038numpy.einsum20hnbt,hsbt->hbsn
\n", + "
" + ], + "text/plain": [ + " average deviation min_exec max_exec repeat number total \\\n", + "715 0.031834 0.005069 0.027032 0.041379 5 5 0.159171 \n", + "716 0.032910 0.003853 0.026787 0.038817 5 5 0.164551 \n", + "717 0.032930 0.013094 0.023331 0.056991 5 5 0.164648 \n", + "718 0.032959 0.002867 0.027536 0.035396 5 5 0.164794 \n", + "719 0.035608 0.001795 0.033823 0.038916 5 5 0.178038 \n", + "\n", + " name N eq \n", + "715 custom_einsum 20 hnbt,hsbt->hbsn \n", + "716 custom_einsum 20 bsnh,btnh->bnts \n", + "717 custom_einsum 20 shtb,sntb->stnh \n", + "718 numpy.einsum 20 htbs,hnbs->hbnt \n", + "719 numpy.einsum 20 hnbt,hsbt->hbsn " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "piv = df.pivot(\"eq\", \"name\", \"average\").sort_values(\"numpy.einsum\")\n", + " \n", + "fig, ax = plt.subplots(1, 1, figsize=(14, 6))\n", + "piv.plot(logy=True, logx=True, ax=ax)\n", + "ax.set_title(\"Benchmark einsum function\");" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, "outputs": [], "source": [] } diff --git a/_unittests/ut_onnx_conv/test_conv_helpers.py b/_unittests/ut_onnx_conv/test_conv_helpers.py new file mode 100644 index 000000000..1540f0fbd --- /dev/null +++ b/_unittests/ut_onnx_conv/test_conv_helpers.py @@ -0,0 +1,21 @@ +""" +@brief test log(time=4s) +""" +import unittest +from pyquickhelper.pycode import ExtTestCase +from skl2onnx.common.data_types import FloatTensorType +from mlprodict.onnx_conv.convert import guess_schema_from_model + + +class TestConvHelpers(ExtTestCase): + + def test_guess_schema_from_model(self): + class A: + def __init__(self, sh): + pass + r = guess_schema_from_model(A, A, [('X', FloatTensorType())]) + self.assertEqual(r[0][0], 'X') + + +if __name__ == "__main__": + unittest.main() diff --git a/_unittests/ut_onnx_conv/test_onnxrt_runtime_lightgbm.py b/_unittests/ut_onnx_conv/test_onnxrt_runtime_lightgbm.py index e5b37ad6c..cbb8ac3a2 100644 --- a/_unittests/ut_onnx_conv/test_onnxrt_runtime_lightgbm.py +++ b/_unittests/ut_onnx_conv/test_onnxrt_runtime_lightgbm.py @@ -15,6 +15,7 @@ from mlprodict.onnxrt import OnnxInference from mlprodict.onnx_conv import register_converters, to_onnx from mlprodict.tools.asv_options_helper import get_ir_version_from_onnx +from mlprodict.onnx_conv.parsers.parse_lightgbm import WrappedLightGbmBooster class TestOnnxrtRuntimeLightGbm(ExtTestCase): @@ -24,6 +25,14 @@ def setUp(self): logger.disabled = True register_converters() + def test_missing(self): + r = WrappedLightGbmBooster._generate_classes( # pylint: disable=W0212 + dict(num_class=1)) + self.assertEqual(r.tolist(), [0, 1]) + r = WrappedLightGbmBooster._generate_classes( # pylint: disable=W0212 + dict(num_class=3)) + self.assertEqual(r.tolist(), [0, 1, 2]) + @skipif_circleci('stuck') @ignore_warnings((RuntimeWarning, UserWarning)) def test_onnxrt_python_lightgbm_categorical(self): diff --git a/_unittests/ut_testing/test_experimental.py b/_unittests/ut_testing/test_experimental.py index 6c44f2db5..ede6eb633 100644 --- a/_unittests/ut_testing/test_experimental.py +++ b/_unittests/ut_testing/test_experimental.py @@ -34,8 +34,8 @@ def ort_path_pad(self, x, pads): sess = InferenceSession(model.SerializeToString()) return numpy.squeeze(sess.run(['Y'], {'X': x, 'P': npads})) - def fct_test(self, custom_fct, fct, *inputs): - got = custom_fct(*inputs, debug=True) + def fct_test(self, custom_fct, fct, *inputs, verbose=True): + got = custom_fct(*inputs, verbose=verbose) exp = fct(*inputs) try: self.assertEqualArray(exp, got) @@ -48,33 +48,42 @@ def fct_test(self, custom_fct, fct, *inputs): "MISMATCH {}\n{}".format(inputs, "\n".join(rows))) from e def test_experimental_pad_positive(self): - arr = numpy.arange(6) + 10 - paddings = numpy.array([1, 1]).reshape((-1, 2)) * 2 - self.fct_test(custom_pad, numpy.pad, arr, paddings) - - arr = numpy.arange(6) + 10 - paddings = numpy.array([1, 1]).reshape((-1, 2)) - self.fct_test(custom_pad, numpy.pad, arr, paddings) - - arr = numpy.arange(6).reshape((2, -1)) + 10 - paddings = numpy.array([1, 1, 1, 1]).reshape((-1, 2)) * 2 - self.fct_test(custom_pad, numpy.pad, arr, paddings) - - arr = numpy.arange(6).reshape((2, -1)) + 10 - paddings = numpy.array([1, 1, 2, 2]).reshape((-1, 2)) - self.fct_test(custom_pad, numpy.pad, arr, paddings) - - arr = numpy.arange(6).reshape((2, -1)) + 10 - paddings = numpy.array([1, 1, 1, 1]).reshape((-1, 2)) - self.fct_test(custom_pad, numpy.pad, arr, paddings) - - arr = numpy.arange(6).reshape((1, 2, -1)) + 10 - paddings = numpy.array([1, 1, 1, 1, 1, 1]).reshape((-1, 2)) - self.fct_test(custom_pad, numpy.pad, arr, paddings) - - arr = numpy.arange(6).reshape((1, 2, -1)) + 10 - paddings = numpy.array([1, 1, 1, 1, 1, 1]).reshape((-1, 2)) * 2 - self.fct_test(custom_pad, numpy.pad, arr, paddings) + for verbose in [True, False]: + with self.subTest(verbose=verbose): + arr = numpy.arange(6) + 10 + paddings = numpy.array([1, 1]).reshape((-1, 2)) * 2 + self.fct_test(custom_pad, numpy.pad, arr, + paddings, verbose=verbose) + + arr = numpy.arange(6) + 10 + paddings = numpy.array([1, 1]).reshape((-1, 2)) + self.fct_test(custom_pad, numpy.pad, arr, + paddings, verbose=verbose) + + arr = numpy.arange(6).reshape((2, -1)) + 10 + paddings = numpy.array([1, 1, 1, 1]).reshape((-1, 2)) * 2 + self.fct_test(custom_pad, numpy.pad, arr, + paddings, verbose=verbose) + + arr = numpy.arange(6).reshape((2, -1)) + 10 + paddings = numpy.array([1, 1, 2, 2]).reshape((-1, 2)) + self.fct_test(custom_pad, numpy.pad, arr, + paddings, verbose=verbose) + + arr = numpy.arange(6).reshape((2, -1)) + 10 + paddings = numpy.array([1, 1, 1, 1]).reshape((-1, 2)) + self.fct_test(custom_pad, numpy.pad, arr, + paddings, verbose=verbose) + + arr = numpy.arange(6).reshape((1, 2, -1)) + 10 + paddings = numpy.array([1, 1, 1, 1, 1, 1]).reshape((-1, 2)) + self.fct_test(custom_pad, numpy.pad, arr, + paddings, verbose=verbose) + + arr = numpy.arange(6).reshape((1, 2, -1)) + 10 + paddings = numpy.array([1, 1, 1, 1, 1, 1]).reshape((-1, 2)) * 2 + self.fct_test(custom_pad, numpy.pad, arr, + paddings, verbose=verbose) def test_experimental_pad_552(self): arr = numpy.random.rand(2, 2, 2).astype(numpy.float32) @@ -138,6 +147,8 @@ def test_experimental_einsum(self): self.assertEqual(ein.shape, ein2.shape) self.assertEqualArray(ein, ein2) + self.capture(lambda: custom_einsum(eq, x, y, verbose=True)) + x = numpy.random.rand(1, 8, 3, 5) y = numpy.random.rand(1, 8, 3, 5) bady1 = numpy.random.rand(2, 8, 3, 5) diff --git a/_unittests/ut_testing/test_verify_code.py b/_unittests/ut_testing/test_verify_code.py index 7041c8243..b4dc372dc 100644 --- a/_unittests/ut_testing/test_verify_code.py +++ b/_unittests/ut_testing/test_verify_code.py @@ -55,6 +55,26 @@ def fct(a, b): return a * b ''' +source3 = ''' +def fct(a, b): + return a {} b +''' + +source4 = ''' +def fct(a, b): + return [0, 1, 2, 3][a: b] +''' + +source5 = ''' +def fct(a, b): + return lambda x: x * 2 +''' + +source6 = ''' +def fct(a, b): + return [x for x in [1, 2]] +''' + class TestVerifyCode(ExtTestCase): @@ -72,6 +92,81 @@ def test_verify_code2(self): text = res.print_node(node) self.assertIn('body=', text) + def test_verify_code_ops(self): + for op in ['**', 'and', '*', '/', '-', '+', 'or']: + with self.subTest(op=op): + _, res = verify_code(source3.format(op)) + self.assertIn('CodeNodeVisitor', str(res)) + tree = res.print_tree() + if 'BinOp' not in tree and 'BoolOp' not in tree: + raise AssertionError( + "Unable to find %r in\n%r" % (op, str(tree))) + self.assertIn('\n', tree) + rows = res.Rows + node = rows[0]['node'] + text = res.print_node(node) + self.assertIn('body=', text) + + def test_verify_code_cmp(self): + for op in ['<', '>', '==', '!=', '>=', '<=']: + with self.subTest(op=op): + _, res = verify_code(source3.format(op)) + self.assertIn('CodeNodeVisitor', str(res)) + tree = res.print_tree() + self.assertIn('Compare', tree) + self.assertIn('\n', tree) + rows = res.Rows + node = rows[0]['node'] + text = res.print_node(node) + self.assertIn('body=', text) + + def test_verify_code_slice(self): + _, res = verify_code(source4) + self.assertIn('CodeNodeVisitor', str(res)) + tree = res.print_tree() + self.assertIn('Slice', tree) + self.assertIn('\n', tree) + rows = res.Rows + node = rows[0]['node'] + text = res.print_node(node) + self.assertIn('body=', text) + + def test_verify_code_ops_in(self): + for op in ['in', 'not in']: + with self.subTest(op=op): + _, res = verify_code(source3.format(op)) + self.assertIn('CodeNodeVisitor', str(res)) + tree = res.print_tree() + self.assertIn('Compare', tree) + self.assertIn('\n', tree) + rows = res.Rows + node = rows[0]['node'] + text = res.print_node(node) + self.assertIn('body=', text) + + def test_verify_code_lambda(self): + _, res = verify_code(source5) + self.assertIn('CodeNodeVisitor', str(res)) + tree = res.print_tree() + self.assertIn('Lambda', tree) + self.assertIn('\n', tree) + rows = res.Rows + node = rows[0]['node'] + text = res.print_node(node) + self.assertIn('body=', text) + + def test_verify_code_gen(self): + _, res = verify_code(source6, exc=False) + self.assertIn('CodeNodeVisitor', str(res)) + tree = res.print_tree() + self.assertIn('comprehension', tree) + self.assertIn('\n', tree) + rows = res.Rows + node = rows[0]['node'] + text = res.print_node(node) + self.assertIn('body=', text) + if __name__ == "__main__": + TestVerifyCode().test_verify_code_gen() unittest.main() diff --git a/mlprodict/onnx_conv/convert.py b/mlprodict/onnx_conv/convert.py index a0cd3d76e..82526fe5f 100644 --- a/mlprodict/onnx_conv/convert.py +++ b/mlprodict/onnx_conv/convert.py @@ -181,24 +181,23 @@ def _cast_data(X, ct): raise RuntimeError( # pragma: no cover "More than one column but input is an array.") return {schema[0][0]: _cast_data(X, schema[0][1])} - elif isinstance(X, pandas.DataFrame): + if isinstance(X, pandas.DataFrame): if len(schema) != X.shape[1]: raise RuntimeError( # pragma: no cover "Mismatch between onnx columns {} and DataFrame columns {}" "".format(len(schema), X.shape[1])) return {sch[0]: _cast_data(X[c].values, sch[1]).reshape((-1, 1)) for sch, c in zip(schema, X.columns)} - else: - raise TypeError( # pragma: no cover - "Unexpected type {}, expecting an array or a dataframe." - "".format(type(X))) + raise TypeError( # pragma: no cover + "Unexpected type {}, expecting an array or a dataframe." + "".format(type(X))) def guess_schema_from_model(model, tensor_type=None, schema=None): """ Guesses initial types from a model. - @param X dataset (dataframe, array) + @param model model @param tensor_type if not None, replaces every *FloatTensorType* or *DoubleTensorType* by this one diff --git a/mlprodict/onnx_conv/parsers/parse_lightgbm.py b/mlprodict/onnx_conv/parsers/parse_lightgbm.py index bfa541ae7..1dbfe5dff 100644 --- a/mlprodict/onnx_conv/parsers/parse_lightgbm.py +++ b/mlprodict/onnx_conv/parsers/parse_lightgbm.py @@ -35,7 +35,8 @@ def __init__(self, booster): # Here `gbdt` is chosen for no reason. self.boosting_type = 'gbdt' - def _generate_classes(self, model_dict): + @staticmethod + def _generate_classes(model_dict): if model_dict['num_class'] == 1: return numpy.asarray([0, 1]) return numpy.arange(model_dict['num_class']) diff --git a/mlprodict/testing/experimental.py b/mlprodict/testing/experimental.py index 341c2127a..1e1a06879 100644 --- a/mlprodict/testing/experimental.py +++ b/mlprodict/testing/experimental.py @@ -6,7 +6,7 @@ import numpy -def custom_pad(arr, paddings, constant=0, debug=False): +def custom_pad(arr, paddings, constant=0, verbose=False): """ Implements function `pad Date: Mon, 3 May 2021 01:13:36 +0200 Subject: [PATCH 29/33] Add function to benchmark einsum decomposition --- _unittests/ut_cli/test_cli_einsum.py | 77 +++++++++ .../ut_testing/test_einsum_benchmark.py | 37 +++++ mlprodict/__main__.py | 5 +- mlprodict/cli/__init__.py | 1 + mlprodict/cli/einsum.py | 76 +++++++++ mlprodict/testing/bench_helper.py | 48 ++++++ mlprodict/testing/einsum_bench.py | 147 ++++++++++++++++++ 7 files changed, 390 insertions(+), 1 deletion(-) create mode 100644 _unittests/ut_cli/test_cli_einsum.py create mode 100644 _unittests/ut_testing/test_einsum_benchmark.py create mode 100644 mlprodict/cli/einsum.py create mode 100644 mlprodict/testing/bench_helper.py create mode 100644 mlprodict/testing/einsum_bench.py diff --git a/_unittests/ut_cli/test_cli_einsum.py b/_unittests/ut_cli/test_cli_einsum.py new file mode 100644 index 000000000..6b6a671e4 --- /dev/null +++ b/_unittests/ut_cli/test_cli_einsum.py @@ -0,0 +1,77 @@ +""" +@brief test tree node (time=4s) +""" +import os +import unittest +from pyquickhelper.loghelper import BufferedPrint +from pyquickhelper.pycode import ExtTestCase, get_temp_folder +from mlprodict.__main__ import main + + +class TestCliEinsum(ExtTestCase): + + def test_cli_einsum(self): + st = BufferedPrint() + main(args=["einsum_test", "--help"], fLOG=st.fprint) + res = str(st) + self.assertIn("verbose", res) + + def test_cli_excel(self): + temp = get_temp_folder(__file__, "temp_cli_excel") + name = os.path.join(temp, "res.xlsx") + st = BufferedPrint() + main(args=["einsum_test", "--equation", "abc,cd->ad", + "--output", name, "--shape", "5", + "--verbose", "0"], fLOG=st.fprint) + self.assertExists(name) + res = str(st) + self.assertIn("wrote", res) + + def test_cli_csv(self): + temp = get_temp_folder(__file__, "temp_cli_csv") + name = os.path.join(temp, "res.csv") + st = BufferedPrint() + main(args=["einsum_test", "--equation", "abc,cd->ad", + "--output", name, "--shape", "(5,5,5);(5,5)", + "--verbose", "0"], fLOG=st.fprint) + self.assertExists(name) + res = str(st) + self.assertIn("wrote", res) + + def test_cli_csv_n(self): + temp = get_temp_folder(__file__, "temp_cli_csvn") + name = os.path.join(temp, "res.csv") + st = BufferedPrint() + main(args=["einsum_test", "--equation", "abc,cd->ad", + "--output", name, "--shape", "5,5", + "--verbose", "0"], fLOG=st.fprint) + self.assertExists(name) + res = str(st) + self.assertIn("wrote", res) + + def test_cli_csv_rt(self): + temp = get_temp_folder(__file__, "temp_cli_csv_rt") + name = os.path.join(temp, "res.csv") + st = BufferedPrint() + main(args=["einsum_test", "--equation", "abc,cd->ad", + "--output", name, "--shape", "(5,5,5);(5,5)", + "--verbose", "0", "--runtime", "onnxruntime"], + fLOG=st.fprint) + self.assertExists(name) + res = str(st) + self.assertIn("wrote", res) + + def test_cli_csv_perm(self): + temp = get_temp_folder(__file__, "temp_cli_csv_perm") + name = os.path.join(temp, "res.csv") + st = BufferedPrint() + main(args=["einsum_test", "--equation", "abc,cd->ad", + "--output", name, "--shape", "(5,5,5);(5,5)", + "--verbose", "0", "--perm", "1"], fLOG=st.fprint) + self.assertExists(name) + res = str(st) + self.assertIn("wrote", res) + + +if __name__ == "__main__": + unittest.main() diff --git a/_unittests/ut_testing/test_einsum_benchmark.py b/_unittests/ut_testing/test_einsum_benchmark.py new file mode 100644 index 000000000..5c110fb9f --- /dev/null +++ b/_unittests/ut_testing/test_einsum_benchmark.py @@ -0,0 +1,37 @@ +""" +@brief test log(time=8s) +""" +import unittest +from pyquickhelper.pycode import ExtTestCase +from mlprodict.testing.einsum_bench import einsum_benchmark + + +class TestEinsumBenchmark(ExtTestCase): + + def test_benchmark1(self): + for rt in ['numpy', 'python', 'onnxruntime']: + with self.subTest(rt=rt): + res = list(einsum_benchmark(shape=5)) + self.assertEqual(len(res), 2) + + def test_benchmark2(self): + for rt in ['numpy', 'python', 'onnxruntime']: + with self.subTest(rt=rt): + res = list(einsum_benchmark(shape=[5, 6])) + self.assertEqual(len(res), 4) + + def test_benchmark1_shape(self): + for rt in ['numpy', 'python', 'onnxruntime']: + with self.subTest(rt=rt): + res = list(einsum_benchmark(shape=[(5, 5, 5), (5, 5)])) + self.assertEqual(len(res), 2) + + def test_benchmarkn(self): + for rt in ['numpy']: + with self.subTest(rt=rt): + res = list(einsum_benchmark(shape=5, perm=True)) + self.assertEqual(len(res), 48) + + +if __name__ == "__main__": + unittest.main() diff --git a/mlprodict/__main__.py b/mlprodict/__main__.py index 0072b638d..b38a90994 100644 --- a/mlprodict/__main__.py +++ b/mlprodict/__main__.py @@ -21,6 +21,7 @@ def main(args, fLOG=print): from .cli.asv_bench import asv_bench from .cli.asv2csv import asv2csv from .cli.replay import benchmark_replay + from .cli.einsum import einsum_test except ImportError: # pragma: no cover from mlprodict.cli.validate import validate_runtime from mlprodict.cli.convert_validate import convert_validate @@ -28,6 +29,7 @@ def main(args, fLOG=print): from mlprodict.cli.asv_bench import asv_bench from mlprodict.cli.asv2csv import asv2csv from mlprodict.cli.replay import benchmark_replay + from mlprodict.cli.einsum import einsum_test fcts = dict(validate_runtime=validate_runtime, convert_validate=convert_validate, @@ -35,7 +37,8 @@ def main(args, fLOG=print): onnx_stats=onnx_stats, asv_bench=asv_bench, asv2csv=asv2csv, - benchmark_replay=benchmark_replay) + benchmark_replay=benchmark_replay, + einsum_test=einsum_test) try: from pyquickhelper.cli import cli_main_helper except ImportError: # pragma: no cover diff --git a/mlprodict/cli/__init__.py b/mlprodict/cli/__init__.py index d9f059bb9..43fa491ba 100644 --- a/mlprodict/cli/__init__.py +++ b/mlprodict/cli/__init__.py @@ -3,5 +3,6 @@ @brief Shortcut to *cli*. """ from .convert_validate import convert_validate +from .einsum import einsum_test from .optimize import onnx_optim from .validate import validate_runtime diff --git a/mlprodict/cli/einsum.py b/mlprodict/cli/einsum.py new file mode 100644 index 000000000..6120eef52 --- /dev/null +++ b/mlprodict/cli/einsum.py @@ -0,0 +1,76 @@ +""" +@file +@brief Command line to check einsum scenarios. +""" +import os + + +def einsum_test(equation="abc,cd->abd", shape="30", perm=False, + runtime='python', verbose=1, fLOG=print, + output=None, number=5, repeat=5): + """ + Investigates whether or not the decomposing einsum is faster. + + :param equation: einsum equation to test + :param shape: an integer (all dimension gets the same size) or + a list of shapes in a string separated with `;`) or + a list of integer to try out multiple shapes, + example: `5`, `(5,5,5),(5,5)`, `5,6` + :param perm: check on permutation or all letter permutations + :param runtime: `'numpy'`, `'python'`, `'onnxruntime'` + :param verbose: verbose + :param fLOG: logging function + :param output: output file (usually a csv file or an excel file), + it requires pandas + :param number: usual parameter to measure a function + :param repeat: usual parameter to measure a function + + .. cmdref:: + :title: Investigates whether or not the decomposing einsum is faster. + :cmd: -m mlprodict einsum_test --help + :lid: l-cmd-einsum_test + + The command checks whether or not decomposing an einsum function + is faster than einsum implementation. + + Example:: + + python -m mlprodict einsum_test --equation="abc,cd->abd" --output=res.csv + """ + from ..testing.einsum_bench import einsum_benchmark # pylint: disable=E0402 + + perm = perm in ('True', '1', 1, True) + if "(" not in shape: + if "," in shape: + shape = list(map(int, shape.split(","))) + else: + shape = int(shape) + else: + shapes = shape.replace('(', '').replace(')', '').split(";") + shape = [] + for sh in shapes: + spl = sh.split(',') + shape.append(tuple(map(int, spl))) + verbose = int(verbose) + number = int(number) + repeat = int(repeat) + + res = einsum_benchmark(equation=equation, shape=shape, perm=perm, + runtime=runtime, use_tqdm=verbose > 0, + number=number, repeat=repeat) + if output not in ('', None): + import pandas + df = pandas.DataFrame(res) + ext = os.path.splitext(output)[-1] + if ext == '.csv': + df.to_csv(output, index=False) + fLOG('[einsum_test] wrote file %r.' % output) + elif ext == '.xlsx': + df.to_excel(output, index=False) + fLOG('[einsum_test] wrote file %r.' % output) + else: + raise ValueError( + "Unknown extension %r in file %r." % (ext, output)) + else: + for r in res: + fLOG(r) diff --git a/mlprodict/testing/bench_helper.py b/mlprodict/testing/bench_helper.py new file mode 100644 index 000000000..927be121a --- /dev/null +++ b/mlprodict/testing/bench_helper.py @@ -0,0 +1,48 @@ +""" +@file +@brief Helpers for benchmarks. +""" +from timeit import Timer +import numpy + + +def measure_time(stmt, *x, repeat=5, number=5, div_by_number=True, first_run=True): + """ + Measures a statement and returns the results as a dictionary. + + :param stmt: string + :param *x: inputs + :param repeat: average over *repeat* experiment + :param number: number of executions in one row + :param div_by_number: divide by the number of executions + :param first_run: if True, runs the function once before measuring + :return: dictionary + + See `Timer.repeat + `_ + for a better understanding of parameter *repeat* and *number*. + The function returns a duration corresponding to + *number* times the execution of the main statement. + """ + try: + stmt(*x) + except RuntimeError as e: # pragma: no cover + raise RuntimeError("{}-{}".format(type(x), x.dtype)) from e + + def fct(): + stmt(*x) + + if first_run: + fct() + tim = Timer(fct) + res = numpy.array(tim.repeat(repeat=repeat, number=number)) + total = numpy.sum(res) + if div_by_number: + res /= number + mean = numpy.mean(res) + dev = numpy.mean(res ** 2) + dev = max(0, (dev - mean**2)) ** 0.5 + mes = dict(average=mean, deviation=dev, min_exec=numpy.min(res), + max_exec=numpy.max(res), repeat=repeat, number=number, + total=total) + return mes diff --git a/mlprodict/testing/einsum_bench.py b/mlprodict/testing/einsum_bench.py new file mode 100644 index 000000000..fc994a348 --- /dev/null +++ b/mlprodict/testing/einsum_bench.py @@ -0,0 +1,147 @@ +""" +@file +@brief Function to measure the performance of einsum decomposition. +""" +from itertools import permutations +import numpy +from onnx import helper, TensorProto +from onnxruntime import InferenceSession +from ..onnxrt import OnnxInference +from .bench_helper import measure_time +from .einsum_impl import decompose_einsum_equation, apply_einsum_sequence + + +def _make_einsum_model(equation, opset=13): + inputs = equation.split('->')[0].split(',') + + model = helper.make_model( + opset_imports=[helper.make_operatorsetid('', opset)], + graph=helper.make_graph( + name='einsum_test', + inputs=[ + helper.make_tensor_value_info( + "X%d" % i, TensorProto.FLOAT, None) # pylint: disable=E1101 + for i in range(len(inputs))], + outputs=[ + helper.make_tensor_value_info( + "Y", TensorProto.FLOAT, None)], # pylint: disable=E1101 + nodes=[ + helper.make_node( + "Einsum", ["X%d" % i for i in range(len(inputs))], ["Y"], + equation=equation) + ] + ) + ) + return model + + +def _make_inputs(equation, shapes): + inputs = equation.split('->')[0].split(',') + dims = [len(i) for i in inputs] + + if isinstance(shapes, int): + N = shapes + shapes = [(N, ) * le for le in dims] + else: + if len(shapes) != len(inputs): + raise ValueError( + "Unexpected number of shapes %r with equation %r." + "" % (shapes, equation)) + inputs = [numpy.random.randn(*sh) for sh in shapes] + return [i.astype(numpy.float32) for i in inputs] + + +def einsum_benchmark(equation="abc,cd->abd", shape=30, perm=False, + runtime='python', use_tqdm=False, + number=5, repeat=5, opset=13): + """ + Investigates whether or not the decomposing einsum is faster. + + :param equation: einsum equation to test + :param shape: an integer (all dimension gets the same size) or + a list of shapes in a string separated with `;`) + :param perm: check on permutation or all letter permutations + :param runtime: numpy, python, onnxruntime + :param use_tqdm: show progress + :param output: output file (usually a csv file or an excel file), + it requires pandas + :param number: usual parameter to measure a function + :param repeat: usual parameter to measure a function + :param opset: target opset + :return: list of dictionaries as an iterator + """ + scenarios = [] + if (isinstance(shape, list) and + all(map(lambda t: isinstance(t, int), shape))): + shape_list = shape + else: + shape_list = [shape] + + if perm: + if equation.lower() != equation: + raise ValueError( + "Only equations with lower letters are allowed but equation %r " + "is not." % equation) + letters = list(sorted(set( + c for c in equation if "a" <= c < "z" or "A" <= c < "Z"))) + for p in permutations(letters): + replace = {d: c for c, d in zip(letters, p)} + eq = equation + for k, v in replace.items(): + eq = eq.replace(k, v.upper()) + eq = eq.lower() + for dec in ['einsum', 'dec']: + for sh in shape_list: + scenarios.append((eq, runtime, dec, sh)) + else: + for dec in ['einsum', 'dec']: + for sh in shape_list: + scenarios.append((equation, runtime, dec, sh)) + + if use_tqdm: + from tqdm import tqdm + loop = tqdm(scenarios) + else: + loop = scenarios + + for eq, rt, dec, sh in loop: + inputs = _make_inputs(equation, sh) + + if dec == 'dec': + seq = decompose_einsum_equation(eq, strategy='numpy', clean=True) + else: + seq = None + + if rt == 'numpy': + if dec == 'einsum': + fct = lambda *x, eq=eq: numpy.einsum(eq, *x, optimize=True) + else: + fct = lambda *x, seq=seq: apply_einsum_sequence(seq, *x) + elif rt == 'onnxruntime': + if dec == 'einsum': + onx = _make_einsum_model(equation, opset=opset) + else: + onx = seq.to_onnx('Y', *["X%d" % i for i in range(len(inputs))], + opset=opset) + sess = InferenceSession(onx.SerializeToString()) # pylint: disable=W0612 + fct = lambda *x, sess=sess: sess.run( + None, {"X%d" % i: v for i, v in enumerate(x)}) + elif rt == 'python': + if dec == 'einsum': + onx = _make_einsum_model(equation, opset=opset) + else: + onx = seq.to_onnx('Y', *["X%d" % i for i in range(len(inputs))], + opset=opset) + sess = OnnxInference(onx) + fct = lambda *x, sess=sess: sess.run( + {"X%d" % i: v for i, v in enumerate(x)}) + else: + raise ValueError("Unexpected runtime %r." % rt) + + res = measure_time(fct, *inputs, repeat=repeat, number=number) + res['rt'] = rt + res['dec'] = dec + res['eq'] = eq + res['shapes'] = ";".join( + map(str, [m.shape for m in inputs])).replace(' ', '') + yield res From 67910d599300bce205e1967c6ffe08d5037ef8af Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Mon, 3 May 2021 10:03:43 +0200 Subject: [PATCH 30/33] fix ir_version --- mlprodict/testing/einsum_bench.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/mlprodict/testing/einsum_bench.py b/mlprodict/testing/einsum_bench.py index fc994a348..b0d121ef7 100644 --- a/mlprodict/testing/einsum_bench.py +++ b/mlprodict/testing/einsum_bench.py @@ -6,6 +6,7 @@ import numpy from onnx import helper, TensorProto from onnxruntime import InferenceSession +from skl2onnx.common._topology import OPSET_TO_IR_VERSION from ..onnxrt import OnnxInference from .bench_helper import measure_time from .einsum_impl import decompose_einsum_equation, apply_einsum_sequence @@ -16,6 +17,9 @@ def _make_einsum_model(equation, opset=13): model = helper.make_model( opset_imports=[helper.make_operatorsetid('', opset)], + ir_version=OPSET_TO_IR_VERSION.get(opset, 7), + producer_name='mlprodict', + producer_version='0.1', graph=helper.make_graph( name='einsum_test', inputs=[ From b2f5a71575a3d94bc80f699ac71606e478268d31 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Mon, 3 May 2021 10:52:29 +0200 Subject: [PATCH 31/33] support initializers --- _doc/sphinxdoc/source/api/testing.rst | 2 ++ _unittests/ut_testing/test_einsum.py | 34 +++++++++++++++++++++++- mlprodict/testing/einsum_bench.py | 8 +++--- mlprodict/testing/einsum_impl_classes.py | 14 +++++++++- 4 files changed, 52 insertions(+), 6 deletions(-) diff --git a/_doc/sphinxdoc/source/api/testing.rst b/_doc/sphinxdoc/source/api/testing.rst index c8dcc42cb..f34e86d67 100644 --- a/_doc/sphinxdoc/source/api/testing.rst +++ b/_doc/sphinxdoc/source/api/testing.rst @@ -28,6 +28,8 @@ Einsum .. autosignature:: mlprodict.testing.experimental_c.custom_einsum_double +.. autosignature:: mlprodict.testing.einsum_bench.einsum_benchmark + .. autosignature:: mlprodict.testing.einsum_impl_ext.numpy_diagonal .. autosignature:: mlprodict.testing.einsum_impl_ext.numpy_extended_dot diff --git a/_unittests/ut_testing/test_einsum.py b/_unittests/ut_testing/test_einsum.py index 31e22f585..32dc25b81 100644 --- a/_unittests/ut_testing/test_einsum.py +++ b/_unittests/ut_testing/test_einsum.py @@ -6,6 +6,7 @@ from contextlib import redirect_stdout import itertools import numpy +from onnx import numpy_helper from onnxruntime import ( InferenceSession, GraphOptimizationLevel, SessionOptions) from pyquickhelper.pycode import ExtTestCase @@ -628,7 +629,38 @@ def test_exc(self): r = repr(EinsumSubOp(2, 'transpose', 0, perm=(1, 0))) self.assertIn("EinsumSubOp('transpose', 0, perm=(1, 0))", r) + def test_bid_nd_bin(self): + + def local_test(inp1, inp2): + exp = numpy.einsum('bid,nd->bin', inp1, inp2) + seq = decompose_einsum_equation( + 'bid,nd->bin', clean=True, strategy='numpy') + got = apply_einsum_sequence(seq, inp1, inp2) + self.assertEqualArray(exp, got, decimal=3) + + onx = seq.to_onnx('Y', 'X1', 'X2') + oinf = OnnxInference(onx) + got = oinf.run({'X1': inp1, 'X2': inp2})['Y'] + self.assertEqualArray(exp, got, decimal=3) + + onx = seq.to_onnx( + 'Y', 'X1', 'X2', + initializer=[numpy_helper.from_array(inp2, name="X2")]) + oinf = OnnxInference(onx) + got = oinf.run({'X1': inp1})['Y'] + self.assertEqualArray(exp, got, decimal=3) + + inp1 = numpy.arange(2 * 3 * 5).reshape((2, 3, 5)).astype(numpy.float32) + inp2 = numpy.arange(5 * 7).reshape((5, 7)).astype(numpy.float32) + local_test(inp1, inp2.T) + + inp1 = numpy.random.uniform(size=[4, 5, 7]).astype(numpy.float32) + inp2 = numpy.random.uniform(size=[7, 8]).astype(numpy.float32) + local_test(inp1, inp2.T) + + self.optimize_compare('bid,nd->bin') + if __name__ == "__main__": - # TestEinsum().test_many_2() + # TestEinsum().test_bid_nd_bin() unittest.main() diff --git a/mlprodict/testing/einsum_bench.py b/mlprodict/testing/einsum_bench.py index b0d121ef7..dbe212cb9 100644 --- a/mlprodict/testing/einsum_bench.py +++ b/mlprodict/testing/einsum_bench.py @@ -6,13 +6,13 @@ import numpy from onnx import helper, TensorProto from onnxruntime import InferenceSession -from skl2onnx.common._topology import OPSET_TO_IR_VERSION from ..onnxrt import OnnxInference from .bench_helper import measure_time from .einsum_impl import decompose_einsum_equation, apply_einsum_sequence def _make_einsum_model(equation, opset=13): + from skl2onnx.common._topology import OPSET_TO_IR_VERSION # pylint: disable=E0611,E0001 inputs = equation.split('->')[0].split(',') model = helper.make_model( @@ -127,7 +127,7 @@ def einsum_benchmark(equation="abc,cd->abd", shape=30, perm=False, else: onx = seq.to_onnx('Y', *["X%d" % i for i in range(len(inputs))], opset=opset) - sess = InferenceSession(onx.SerializeToString()) # pylint: disable=W0612 + sess = InferenceSession(onx.SerializeToString()) fct = lambda *x, sess=sess: sess.run( None, {"X%d" % i: v for i, v in enumerate(x)}) elif rt == 'python': @@ -136,8 +136,8 @@ def einsum_benchmark(equation="abc,cd->abd", shape=30, perm=False, else: onx = seq.to_onnx('Y', *["X%d" % i for i in range(len(inputs))], opset=opset) - sess = OnnxInference(onx) - fct = lambda *x, sess=sess: sess.run( + oinf = OnnxInference(onx) + fct = lambda *x, oinf=oinf: oinf.run( {"X%d" % i: v for i, v in enumerate(x)}) else: raise ValueError("Unexpected runtime %r." % rt) diff --git a/mlprodict/testing/einsum_impl_classes.py b/mlprodict/testing/einsum_impl_classes.py index 895cad73c..4a5ac2c9e 100644 --- a/mlprodict/testing/einsum_impl_classes.py +++ b/mlprodict/testing/einsum_impl_classes.py @@ -1323,8 +1323,18 @@ def to_onnx(self, output, *inputs, dtype=None, verbose=False, :param opset: desired opset, None for the last one :param verbose: display intermediate operators :param kwargs: additional parameter to use when building - the ONNX graph + the ONNX graph, list of supported parameters: + *name*, *ir_version*, *producer_name*, + *producer_version*, *initializer* :return: ONNX graph + + Not all graphs can be converted into ONNX. Only graphs produced + with `strategy='numpy'` can be converted otherwise the following + error shows up: + + :: + + NotImplementedError: to_onnx not implemented for 'matmul'. """ # inputs if opset is None: @@ -1348,6 +1358,8 @@ def to_onnx(self, output, *inputs, dtype=None, verbose=False, names = {i: name for i, name in enumerate(inputs)} nodes = [] inits = [] + if "initializer" in kwargs: + inits.extend(kwargs['initializer']) for op in self: for onx_node in op.to_onnx(names, verbose=verbose, opset=opset): if hasattr(onx_node, 'output'): From 7f0c756eeca3dcf028ff4677183560ae49db32ff Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Mon, 3 May 2021 11:21:22 +0200 Subject: [PATCH 32/33] lint --- mlprodict/testing/einsum_bench.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/mlprodict/testing/einsum_bench.py b/mlprodict/testing/einsum_bench.py index dbe212cb9..53997f8e9 100644 --- a/mlprodict/testing/einsum_bench.py +++ b/mlprodict/testing/einsum_bench.py @@ -127,8 +127,8 @@ def einsum_benchmark(equation="abc,cd->abd", shape=30, perm=False, else: onx = seq.to_onnx('Y', *["X%d" % i for i in range(len(inputs))], opset=opset) - sess = InferenceSession(onx.SerializeToString()) - fct = lambda *x, sess=sess: sess.run( + sess = InferenceSession(onx.SerializeToString()) # pylint: disable=W0612 + fct = lambda *x, se=sess: se.run( None, {"X%d" % i: v for i, v in enumerate(x)}) elif rt == 'python': if dec == 'einsum': @@ -136,8 +136,8 @@ def einsum_benchmark(equation="abc,cd->abd", shape=30, perm=False, else: onx = seq.to_onnx('Y', *["X%d" % i for i in range(len(inputs))], opset=opset) - oinf = OnnxInference(onx) - fct = lambda *x, oinf=oinf: oinf.run( + oinf = OnnxInference(onx) # pylint: disable=W0612 + fct = lambda *x, oi=oinf: oi.run( {"X%d" % i: v for i, v in enumerate(x)}) else: raise ValueError("Unexpected runtime %r." % rt) From 516cceefedf63c6bab81d0cffe845e56ae10380c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?xavier=20dupr=C3=A9?= Date: Mon, 3 May 2021 12:57:16 +0200 Subject: [PATCH 33/33] documentation --- mlprodict/testing/einsum_impl_ext.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlprodict/testing/einsum_impl_ext.py b/mlprodict/testing/einsum_impl_ext.py index b0775fc4a..df905d02f 100644 --- a/mlprodict/testing/einsum_impl_ext.py +++ b/mlprodict/testing/einsum_impl_ext.py @@ -241,7 +241,7 @@ def numpy_extended_dot(m1, m2, axes, left, right, verbose=False): m1 = numpy.arange(8).reshape((2, 2, 2)) m2 = m1 + 10 - dot = numpy_extended_dot(m1, m2, [1], [0], [2], verbose=True)) + dot = numpy_extended_dot(m1, m2, [1], [0], [2], verbose=True) print(dot) The current implementation still uses :epkg:`numpy:einsum`