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test_dynamic_shapes.py
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test_dynamic_shapes.py
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# Owner(s): ["oncall: jit"]
import contextlib
import copy
import itertools
import math
import operator
import unittest
import numpy as np
import sympy
import torch
import torch.fx
import torch.nn.functional as F
from torch import sym_int, SymBool, SymFloat, SymInt
from torch._C import _disabled_torch_function_impl
from torch.fx.experimental import sym_node
from torch.fx.experimental.proxy_tensor import make_fx
from torch.fx.experimental.sym_node import method_to_operator, SymNode, to_node
from torch.fx.experimental.symbolic_shapes import (
_constrain_range_for_size,
DimConstraints,
DimDynamic,
expect_true,
guard_bool,
guard_float,
guard_int,
GuardOnDataDependentSymNode,
hint_int,
is_symbolic,
ShapeEnv,
StatelessSymbolicContext,
statically_known_true,
)
from torch.testing._internal.common_utils import (
instantiate_parametrized_tests,
parametrize,
run_tests,
skipIfTorchDynamo,
TestCase,
)
from torch.utils import _pytree as pytree
from torch.utils._python_dispatch import TorchDispatchMode
from torch.utils._sympy.functions import (
FloorDiv,
IsNonOverlappingAndDenseIndicator,
Mod,
)
aten = torch.ops.aten
meta_funcs = {}
def register_meta(op):
def decorator(f):
def add_func(op):
meta_funcs[op] = f
pytree.tree_map_(add_func, op)
return f
return decorator
@register_meta([aten.add.Tensor, aten.sub.Tensor])
def binary_meta(a, b):
return a.new_empty(a.shape)
@register_meta(aten.cat.default)
def cat_meta(tensors, dim=0):
concat_length = 0
shape = tensors[0].shape
for tensor in tensors:
for idx, (common_length, length) in enumerate(zip(shape, tensor.shape)):
if idx == dim:
concat_length = concat_length + length
else:
assert length == common_length
new_shape = list(shape)
new_shape[dim] = concat_length
return tensors[0].new_empty(new_shape)
@register_meta([aten.narrow_copy.default])
def narrow_copy_symint_meta(a, dim, start, length, **kwargs):
shape = []
for i, x in enumerate(a.shape):
if i == dim:
shape.append(length)
else:
shape.append(x)
return a.new_empty(tuple(shape))
@register_meta([aten.expand.default])
def expand_symint_meta(a, size, implicit=False):
return a.new_empty(size)
def create_contiguous(shape):
strides = [1]
for dim in reversed(shape[:-1]):
strides.append(dim * strides[-1])
return list(reversed(strides))
class FakeSymbolicTensor(torch.Tensor):
@staticmethod
def __new__(
cls,
sym_shape,
sym_strides,
dtype,
layout,
requires_grad,
device,
storage_offset=0,
):
# TODO: this is wrong in general
sym_stride = create_contiguous(sym_shape)
r = torch.Tensor._make_wrapper_subclass(
cls,
sym_shape,
sym_stride,
storage_offset,
dtype=dtype,
layout=layout,
requires_grad=requires_grad,
device=device,
)
return r
__torch_function__ = _disabled_torch_function_impl
def new_empty(self, shape):
return FakeSymbolicTensor(
shape, None, self.dtype, self.layout, self.requires_grad, self.device
)
@classmethod
def __torch_dispatch__(cls, func_overload, types, args=(), kwargs=None):
if func_overload in meta_funcs:
return meta_funcs[func_overload](*args, **kwargs)
if func_overload == torch.ops.aten.new_empty.default:
self = args[0]
shape = args[1]
return FakeSymbolicTensor(
shape,
self.stride(),
self.dtype,
self.layout,
self.requires_grad,
self.device,
)
raise RuntimeError(f"operator {func_overload} not supported")
def create_symbolic_tensor(name, arg, shape_env, source=None, dynamic_dims=None):
from torch._dynamo.source import ConstantSource
if source is None:
source = ConstantSource(name)
constraint_dims = [None] * arg.dim()
if dynamic_dims is None:
dynamic_dims = [DimDynamic.DUCK] * arg.dim()
(
sym_shapes,
sym_strides,
sym_storage_offset,
) = shape_env.create_symbolic_sizes_strides_storage_offset(
arg,
source=source,
symbolic_context=StatelessSymbolicContext(
dynamic_sizes=dynamic_dims, constraint_sizes=constraint_dims
),
)
return FakeSymbolicTensor(
sym_shapes,
sym_strides,
arg.dtype,
arg.layout,
arg.requires_grad,
arg.device,
sym_storage_offset,
)
def create_symtype(cls, pytype, shape_env, val, duck=True):
from torch._dynamo.source import ConstantSource
symbol = shape_env.create_symbol(
val,
source=ConstantSource(f"__testing_only{len(shape_env.var_to_val)}"),
dynamic_dim=DimDynamic.DUCK if duck else DimDynamic.DYNAMIC,
constraint_dim=None,
)
return cls(SymNode(symbol, shape_env, pytype, hint=val))
# TODO: default duck to False
def create_symint(shape_env, i: int, duck=True) -> SymInt:
return create_symtype(SymInt, int, shape_env, i, duck=duck)
def create_symbool(shape_env, b: bool) -> SymBool:
return create_symtype(SymBool, bool, shape_env, b)
def create_symfloat(shape_env, f: float) -> SymFloat:
return create_symtype(SymFloat, float, shape_env, f)
@skipIfTorchDynamo(
"Creating ShapeEnv fails for confusing reasons (also we never expect dynamo to see code like this)"
)
class TestPySymInt(TestCase):
def test_arith_ops(self):
shape_env = ShapeEnv()
symints = []
for i in range(2, 5):
symints.append((i, create_symint(shape_env, i)))
ops = [
operator.add,
operator.sub,
operator.floordiv,
operator.mul,
operator.mod,
]
for op in ops:
for args in itertools.permutations(symints, 2):
if not isinstance(args[0][1], int) and (
(op != operator.mod or op != operator.floordiv) and args[1][0] != 0
):
self.assertTrue(
op(args[0][1], args[1][1]) == op(args[0][0], args[1][0])
)
def test_reverse_arith_ops(self):
shape_env = ShapeEnv()
a = create_symint(shape_env, 2)
self.assertTrue(5 // a == 5 // 2)
a = create_symint(shape_env, 2)
self.assertTrue(5 * a == 5 * 2)
def test_sympify_symint(self):
shape_env = ShapeEnv()
a = create_symint(shape_env, 2)
self.assertIs(sympy.sympify(a), a.node.expr)
b = create_symfloat(shape_env, 3.0)
self.assertIs(sympy.sympify(b), b.node.expr)
c = create_symbool(shape_env, True)
self.assertIs(sympy.sympify(c), c.node.expr)
def test_roundtrip(self):
shape_env = ShapeEnv()
x = create_symbolic_tensor("x", torch.randn(5, 4, 3), shape_env)
self.assertTrue(not isinstance(x.shape[0], SymNode))
self.assertTrue(isinstance(x.shape[0], SymInt))
self.assertTrue(x.shape[0] == 5)
self.assertTrue(x.shape[1] == 4)
self.assertTrue(x.shape[2], 3)
self.assertTrue(x.size()[0], 5)
self.assertTrue(x.size()[1], 4)
# Should be simplifiable to an integer.
# Ref: https://github.com/pytorch/pytorch/pull/107492
self.assertTrue(isinstance(x.size()[1], SymInt))
self.assertTrue(
isinstance(x.size()[1].node.maybe_as_int(), int)
) # due to guard above
self.assertTrue(x.size()[2] == 3)
self.assertTrue(x.size(0) == 5)
self.assertTrue(x.size(1) == 4)
self.assertTrue(x.size(2) == 3)
self.assertTrue(isinstance(x.size(2), SymInt))
self.assertTrue(isinstance(x.size(2).node.maybe_as_int(), int))
y = create_symbolic_tensor("y", torch.randn(5, 4, 3)[1:], shape_env)
self.assertTrue(isinstance(y.storage_offset(), SymInt))
self.assertTrue(y.storage_offset() == 12)
def test_binary(self):
shape_env = ShapeEnv()
x = create_symbolic_tensor("x", torch.randn(5, 4, 3), shape_env)
y = create_symbolic_tensor("y", torch.randn(5, 4, 3), shape_env)
z = x + y
self.assertTrue(z.shape[0] == 5)
self.assertTrue(z.shape[1] == 4)
self.assertTrue(z.shape[2] == 3)
# broadcasting
y = create_symbolic_tensor("y2", torch.randn(1, 4, 1), shape_env)
z = x + y
self.assertTrue(z.shape[0] == 5)
self.assertTrue(z.shape[1] == 4)
self.assertTrue(z.shape[2] == 3)
def test_symint_args(self):
shape_env = ShapeEnv()
x = create_symbolic_tensor("x", torch.randn(5, 4, 3), shape_env)
y = create_symbolic_tensor("y", torch.randn(5, 4, 1), shape_env)
LAST_DIM = 2
z = x.narrow_copy(LAST_DIM, 0, y.shape[LAST_DIM])
self.assertTrue(z.shape[2] == y.shape[2])
# arithmetic expr with two symints
z = x.narrow_copy(LAST_DIM, 0, x.shape[LAST_DIM] - y.shape[LAST_DIM])
self.assertTrue(z.shape[2] == 2)
# arithmetic expr with a symint and python int
z = x.narrow_copy(LAST_DIM, 0, x.shape[LAST_DIM] - 1)
self.assertTrue(z.shape[2] == 2)
def test_symint_vargs(self):
shape_env = ShapeEnv()
x = create_symbolic_tensor("x", torch.randn(5, 4, 3), shape_env)
y = create_symbolic_tensor("y", torch.randn(1, 4, 1), shape_env)
# varargs
z = y.expand(x.shape[0], y.shape[1], x.shape[2])
self.assertTrue(z.shape[0] == 5)
self.assertTrue(z.shape[1] == 4)
self.assertTrue(z.shape[2] == 3)
# shape list
z = y.expand((x.shape[0], y.shape[1], x.shape[2]))
self.assertTrue(z.shape[0] == 5)
self.assertTrue(z.shape[1] == 4)
self.assertTrue(z.shape[2] == 3)
# mixed python symints and ints
z = y.expand(x.shape[0], y.shape[1], 3)
self.assertTrue(z.shape[0] == 5)
self.assertTrue(z.shape[1] == 4)
self.assertTrue(z.shape[2] == 3)
# mixed python symints and ints in a list
z = y.expand((x.shape[0], y.shape[1], 3))
self.assertTrue(z.shape[0] == 5)
self.assertTrue(z.shape[1] == 4)
self.assertTrue(z.shape[2] == 3)
# mixed python symints and ints
z = y.expand(5, y.shape[1], x.shape[2])
self.assertTrue(z.shape[0] == 5)
self.assertTrue(z.shape[1] == 4)
self.assertTrue(z.shape[2] == 3)
# mixed python ints and symints in a list
z = y.expand((5, y.shape[1], x.shape[2]))
self.assertTrue(z.shape[0] == 5)
self.assertTrue(z.shape[1] == 4)
self.assertTrue(z.shape[2] == 3)
z = y.expand((y.shape[1],))
z = y.expand(y.shape[1])
def test_stride(self):
shape_env = ShapeEnv()
x = create_symbolic_tensor("x", torch.randn(5, 5), shape_env)
self.assertIsInstance(x.stride()[0], SymInt)
def test_size_expressions(self):
shape_env = ShapeEnv()
x = create_symbolic_tensor("x", torch.randn(5), shape_env)
expand_x = x.expand(x.shape[0], x.shape[0])
if expand_x.shape[0] > 3:
result = expand_x + expand_x
else:
result = expand_x + expand_x
gt_op, _bt = shape_env.guards[-1]
self.assertTrue(isinstance(gt_op, sympy.core.relational.StrictGreaterThan))
self.assertTrue(str(x.shape[0]), str(gt_op.args[0]))
self.assertTrue(str(expand_x.shape[1]), str(x.shape[0]))
self.assertTrue(str(expand_x.shape[1]), str(result.shape[0]))
def test_floordiv_static(self):
shape_env = ShapeEnv()
s0 = create_symint(shape_env, 8)
# This was extracted from
# python test/inductor/test_cuda_cpp_wrapper.py -k
# DynamicShapesCudaWrapperCudaTests.test_insignificant_strides_cuda_dynamic_shapes_cuda_wrapper
bool(s0 % 2 == 0)
bool(s0 % (s0 // 2) == 0)
bool(2 * (s0 // 2) == s0)
self.assertTrue(statically_known_true(s0 // (s0 // 2) == 2))
def test_numel(self):
shape_env = ShapeEnv()
x = create_symbolic_tensor("x", torch.randn(5), shape_env)
self.assertIsInstance(x.numel(), torch.SymInt)
self.assertIsInstance(torch.numel(x), torch.SymInt)
x = torch.rand(3, 3)
self.assertIsInstance(x.numel(), int)
self.assertIsInstance(torch.numel(x), int)
def test_int_to_float(self):
shape_env = ShapeEnv()
x = create_symbolic_tensor("x", torch.randn(5), shape_env)
r = torch.sym_float(x.shape[0])
self.assertIsInstance(r, torch.SymFloat, msg=type(r))
def test_aten_ops(self):
shape_env = ShapeEnv()
x = create_symbolic_tensor("x", torch.randn(5), shape_env)
torch.ops.aten.narrow_copy.default(x, 0, 0, x.shape[0])
shape_env = ShapeEnv()
x = create_symbolic_tensor("x2", torch.randn(5, 4, 3), shape_env)
torch.ops.aten.expand.default(x, [x.shape[0], x.shape[1], x.shape[2]])
def test_fx_trace_intlist(self):
class CustomModule(torch.nn.Module):
def forward(self, x):
bs, c, h, w = x.shape
return F.pad(x, (0, w % 2, 0, h % 2, 0, 0))
m = CustomModule()
x = torch.rand(1, 3, 4, 4)
# should not TypeError: pad(): argument 'pad' (position 2) must be
# tuple of ints, not tuple
torch.fx.symbolic_trace(m)
def test_meta_symint(self):
shape_env = ShapeEnv()
a0 = create_symint(shape_env, 2)
r = torch.empty(a0, device="meta")
self.assertIsInstance(r.shape[0], SymInt)
def test_guard_int(self):
shape_env = ShapeEnv()
a0 = create_symint(shape_env, 2)
self.assertEqual(guard_int(a0), 2)
self.assertExpectedInline(str(shape_env.guards[0][0]), """Eq(s0, 2)""")
def test_prefer_deferred_runtime_assertions_over_guards(self):
shape_env = ShapeEnv(prefer_deferred_runtime_asserts_over_guards=True)
s0 = create_symint(shape_env, 2)
self.assertEqual(guard_int(s0), 2)
self.assertExpectedInline(str(shape_env.guards[0][0]), """Eq(s0, 2)""")
shape_env = ShapeEnv(prefer_deferred_runtime_asserts_over_guards=True)
s0 = create_symint(shape_env, 2)
self.assertTrue(expect_true(s0 == 2))
self.assertEqual(len(shape_env.guards), 0)
self.assertExpectedInline(
str([ra.expr for ra in shape_env.deferred_runtime_asserts[None]]),
"""[Eq(s0, 2)]""",
)
def test_sym_int(self):
shape_env = ShapeEnv()
a0 = create_symint(shape_env, 5)
r = sym_int(a0)
self.assertEqual(r, 5)
self.assertIsInstance(r, torch.SymInt, msg=type(r))
self.assertExpectedInline(str(shape_env.guards[0][0]), """Eq(s0, 5)""")
a1 = create_symint(shape_env, 7)
r = sym_int(a1 / 2)
self.assertEqual(guard_int(r), 3)
self.assertIsInstance(r, torch.SymInt, msg=type(r))
self.assertExpectedInline(
str(shape_env.guards[1][0]), """Eq(TruncToInt(IntTrueDiv(s1, 2)), 3)"""
)
a3 = create_symint(shape_env, 3)
r = sym_int(2.0 * torch.sym_float(a3))
self.assertEqual(guard_int(r), 6)
self.assertIsInstance(r, torch.SymInt, msg=type(r))
self.assertExpectedInline(
str(shape_env.guards[2][0]), """Eq(TruncToInt(2.0*ToFloat(s2)), 6)"""
)
def test_sym_sqrt(self):
shape_env = ShapeEnv()
a0 = create_symint(shape_env, 4)
r = torch._sym_sqrt(a0)
self.assertEqual(r, 2)
self.assertIsInstance(r, torch.SymFloat, msg=type(r))
self.assertExpectedInline(
str(shape_env.guards[0][0]), """Eq(OpaqueUnaryFn_sqrt(s0), 2.0)"""
)
def test_sym_floor(self):
shape_env = ShapeEnv()
a0 = create_symint(shape_env, 5)
r = math.floor(a0 / 2)
self.assertEqual(r, 2)
self.assertIsInstance(r, torch.SymInt, msg=type(r))
self.assertExpectedInline(
str(shape_env.guards[0][0]),
"""Eq(FloorToInt(IntTrueDiv(s0, 2)), 2)""",
)
r = math.floor(3.0 * a0)
self.assertEqual(r, 15)
self.assertIsInstance(r, torch.SymInt, msg=type(r))
self.assertExpectedInline(
str(shape_env.guards[1][0]),
"""Eq(FloorToInt(3.0*ToFloat(s0)), 15)""",
)
def test_sym_trunc(self):
shape_env = ShapeEnv()
a0 = create_symint(shape_env, 5)
r = math.trunc(a0 / 2)
self.assertEqual(r, 2)
self.assertIsInstance(r, torch.SymInt, msg=type(r))
self.assertExpectedInline(
str(shape_env.guards[0][0]), """Eq(TruncToInt(IntTrueDiv(s0, 2)), 2)"""
)
r = torch.sym_int(torch.sym_sqrt(a0))
self.assertEqual(r, 2)
self.assertIsInstance(r, torch.SymInt, msg=type(r))
self.assertExpectedInline(
str(shape_env.guards[1][0]), """Eq(TruncToInt(OpaqueUnaryFn_sqrt(s0)), 2)"""
)
def test_sym_ceil(self):
shape_env = ShapeEnv()
a0 = create_symint(shape_env, 5)
r = math.ceil(a0 / 2)
self.assertEqual(r, 3)
self.assertIsInstance(r, torch.SymInt, msg=type(r))
self.assertExpectedInline(
str(shape_env.guards[0][0]),
"""Eq(CeilToInt(IntTrueDiv(s0, 2)), 3)""",
)
r1 = 3.0 * a0
r = math.floor(r1)
self.assertEqual(r, 15)
self.assertIsInstance(r, torch.SymInt, msg=type(r))
self.assertExpectedInline(
str(shape_env.guards[1][0]),
"""Eq(FloorToInt(3.0*ToFloat(s0)), 15)""",
)
def test_sym_ite(self):
shape_env = ShapeEnv()
t = create_symint(shape_env, 5)
f = create_symint(shape_env, 4)
b1 = True
r1 = torch.sym_ite(b1, t, f)
self.assertTrue(r1 is t)
b2 = False
r2 = torch.sym_ite(b2, t, f)
self.assertTrue(r2 is f)
b3 = t == 5
r3 = torch.sym_ite(b3, t, f)
self.assertEqual(len(shape_env.guards), 0)
self.assertEqual(r3, 5)
self.assertEqual(type(t), type(r3))
self.assertExpectedInline(
str(shape_env.guards[0][0]),
"""Eq(Piecewise((s0, Eq(s0, 5)), (s1, True)), 5)""",
)
b4 = f == 5
r4 = torch.sym_ite(b4, t, f)
self.assertEqual(len(shape_env.guards), 1)
self.assertEqual(r4, 4)
self.assertEqual(type(f), type(r4))
self.assertExpectedInline(
str(shape_env.guards[1][0]),
"""Eq(Piecewise((s0, Eq(s1, 5)), (s1, True)), 4)""",
)
def test_tracing_sym_ite(self):
def f(x):
b = x.shape[0] == 5
ret = torch.sym_ite(b, x.shape[0], x.shape[1])
return ret
gm = make_fx(f, tracing_mode="symbolic")(torch.ones(4, 5))
self.assertEqual(len(gm.shape_env.guards), 0)
self.assertExpectedInline(
gm.code.strip(),
"""\
def forward(self, x_1):
sym_size_int = torch.ops.aten.sym_size.int(x_1, 0)
eq = sym_size_int == 5
sym_size_int_1 = torch.ops.aten.sym_size.int(x_1, 1); x_1 = None
sym_ite = torch.sym_ite(eq, sym_size_int, sym_size_int_1); eq = sym_size_int = sym_size_int_1 = None
return sym_ite""",
)
r1 = gm(torch.ones(4, 5))
self.assertIsInstance(r1, int)
self.assertEqual(r1, 5)
r2 = gm(torch.ones(5, 4))
self.assertIsInstance(r2, int)
self.assertEqual(r2, 5)
def test_int_conversion(self):
shape_env = ShapeEnv()
a0 = create_symint(shape_env, 2)
int(a0)
self.assertExpectedInline(str(shape_env.guards[0][0]), """Eq(s0, 2)""")
def test_data_dependent_guard(self):
shape_env = ShapeEnv()
s0 = shape_env.create_unbacked_symint()
self.assertRaises(GuardOnDataDependentSymNode, lambda: bool(s0 == 0))
def test_data_dependent_guard_propagate_real_tensors(self):
shape_env = ShapeEnv()
s0 = shape_env.create_unbacked_symint()
shape_env.set_unbacked_var_to_val(s0.node.expr, 0)
self.assertEqual(bool(s0 == 0), True)
def test_expect_true_basic(self):
shape_env = ShapeEnv()
i0 = shape_env.create_unbacked_symint()
i0_sym = i0.node.expr
# This doesn't error
self.assertTrue(expect_true(i0 == 0))
# This generates a deferred runtime assert via replacement
self.assertEqual(shape_env.replacements[i0_sym], 0)
# After expecting true, guards now resolve given the runtime assert
bool(i0 == 0)
def test_expect_true_with_s0(self):
shape_env = ShapeEnv()
s0 = create_symint(shape_env, 5)
i0 = shape_env.create_unbacked_symint()
self.assertTrue(expect_true(i0 < s0))
self.assertExpectedInline(
str([ra.expr for ra in shape_env.deferred_runtime_asserts[i0.node.expr]]),
"""[u0 < s0]""",
)
self.assertTrue(i0 < s0)
self.assertTrue(i0 != s0)
self.assertFalse(i0 > s0)
self.assertFalse(i0 >= s0)
def test_expect_true_prefer_later(self):
shape_env = ShapeEnv()
i0 = shape_env.create_unbacked_symint()
i1 = shape_env.create_unbacked_symint()
i1_sym = i1.node.expr
self.assertTrue(expect_true(i0 + i1 == 10))
# Importantly, this is put in i1, not i0!
self.assertExpectedInline(
str([ra.expr for ra in shape_env.deferred_runtime_asserts[i1_sym]]),
"""[Eq(u0 + u1, 10)]""",
)
self.assertTrue(i0 + i1 == 10)
# NB: We currently don't support deriving that we can substitute
# i0 + i1 with 10; maybe we should, but this means our rewriting
# system is no longer confluent (it's probably OK though, because
# you're unlikely to get other equalities like this on the
# unbacked SymInts.)
def test_unbacked_substitution(self):
shape_env = ShapeEnv()
i0 = shape_env.create_unbacked_symint()
i1 = shape_env.create_unbacked_symint()
_constrain_range_for_size(i0)
_constrain_range_for_size(i1)
self.assertTrue(expect_true(i0 == i1 * 4))
self.assertExpectedInline(str(i0), """u0""")
i2 = shape_env.create_unbacked_symint()
i3 = shape_env.create_unbacked_symint()
_constrain_range_for_size(i2)
_constrain_range_for_size(i3)
self.assertTrue(expect_true(i2 * 4 == i3))
self.assertExpectedInline(str(i3), """u3""")
def test_avoid_unbacked_substitution(self):
shape_env = ShapeEnv()
i0 = shape_env.create_unbacked_symint()
_constrain_range_for_size(i0)
i1 = shape_env.create_unbacked_symint()
_constrain_range_for_size(i1)
self.assertTrue(expect_true(i0 == 10 - i1))
self.assertExpectedInline(str(i0), """u0""")
def test_expect_true_double_digits(self):
shape_env = ShapeEnv()
ia = [shape_env.create_unbacked_symint() for _ in range(11)] # allocate 10
self.assertEqual(str(ia[-1]), "u10")
self.assertTrue(expect_true(sum(ia) == 20))
self.assertEqual(len(shape_env.deferred_runtime_asserts[ia[-1].node.expr]), 1)
def test_expect_true_refine_range(self):
shape_env = ShapeEnv()
for i, rel in enumerate(
[lambda x: x > 4, lambda x: 4 < x, lambda x: x >= 5, lambda x: 5 <= x]
):
with self.subTest(f"i = {i}"):
i0 = shape_env.create_unbacked_symint()
self.assertTrue(expect_true(rel(i0)))
self.assertTrue(statically_known_true(i0 != 3))
self.assertTrue(statically_known_true(i0 != 4))
self.assertFalse(statically_known_true(i0 != 5))
self.assertFalse(statically_known_true(i0 != 6))
self.assertTrue(statically_known_true(i0 > 4))
self.assertTrue(statically_known_true(i0 >= 5))
for i, rel in enumerate(
[lambda x: x < 4, lambda x: 4 > x, lambda x: x <= 3, lambda x: 3 >= x]
):
with self.subTest(f"i = {i}"):
i0 = shape_env.create_unbacked_symint()
self.assertTrue(expect_true(rel(i0)))
self.assertFalse(statically_known_true(i0 != 2))
self.assertFalse(statically_known_true(i0 != 3))
self.assertTrue(statically_known_true(i0 != 4))
self.assertTrue(statically_known_true(i0 != 5))
self.assertTrue(statically_known_true(i0 < 4))
self.assertTrue(statically_known_true(i0 <= 5))
def test_guard_refine_range(self):
shape_env = ShapeEnv()
for i, rel in enumerate(
[lambda x: x > 4, lambda x: 4 < x, lambda x: x >= 5, lambda x: 5 <= x]
):
with self.subTest(f"i = {i}"):
i0 = create_symint(shape_env, 10, duck=False)
self.assertTrue(bool(rel(i0)))
self.assertTrue(statically_known_true(i0 != 3))
self.assertTrue(statically_known_true(i0 != 4))
self.assertFalse(statically_known_true(i0 != 5))
self.assertFalse(statically_known_true(i0 != 6))
self.assertTrue(statically_known_true(i0 > 4))
self.assertTrue(statically_known_true(i0 >= 5))
for i, rel in enumerate(
[lambda x: x > 4, lambda x: 4 < x, lambda x: x >= 5, lambda x: 5 <= x]
):
with self.subTest(f"i = {i}"):
i0 = create_symint(shape_env, 2, duck=False)
self.assertFalse(bool(rel(i0)))
self.assertFalse(statically_known_true(i0 != 3))
self.assertFalse(statically_known_true(i0 != 4))
self.assertTrue(statically_known_true(i0 != 5))
self.assertTrue(statically_known_true(i0 != 6))
self.assertTrue(statically_known_true(i0 <= 4))
self.assertTrue(statically_known_true(i0 < 5))
for i, rel in enumerate(
[lambda x: x < 4, lambda x: 4 > x, lambda x: x <= 3, lambda x: 3 >= x]
):
with self.subTest(f"i = {i}"):
i0 = create_symint(shape_env, 2, duck=False)
self.assertTrue(bool(rel(i0)))
self.assertFalse(statically_known_true(i0 != 2))
self.assertFalse(statically_known_true(i0 != 3))
self.assertTrue(statically_known_true(i0 != 4))
self.assertTrue(statically_known_true(i0 != 5))
self.assertTrue(statically_known_true(i0 < 4))
self.assertTrue(statically_known_true(i0 <= 3))
for i, rel in enumerate(
[lambda x: x < 4, lambda x: 4 > x, lambda x: x <= 3, lambda x: 3 >= x]
):
with self.subTest(f"i = {i}"):
i0 = create_symint(shape_env, 10, duck=False)
self.assertFalse(bool(rel(i0)))
self.assertTrue(statically_known_true(i0 != 2))
self.assertTrue(statically_known_true(i0 != 3))
self.assertFalse(statically_known_true(i0 != 4))
self.assertFalse(statically_known_true(i0 != 5))
self.assertTrue(statically_known_true(i0 >= 4))
self.assertTrue(statically_known_true(i0 > 3))
def test_non_overlapping_and_dense(self):
shape_env = ShapeEnv()
a0 = create_symint(shape_env, 5)
r = torch.empty_strided((a0, 7), (1, a0), device="meta")
self.assertTrue(torch.ops.aten.is_non_overlapping_and_dense.default(r))
def test_non_overlapping_and_dense_unbacked(self):
shape_env = ShapeEnv()
u0 = shape_env.create_unbacked_symint()
torch._check_is_size(u0)
cf = torch.ops.aten.is_non_overlapping_and_dense.default
self.assertEqual(IsNonOverlappingAndDenseIndicator(u0.node.expr, 2, 2, 1), 1)
self.assertEqual(IsNonOverlappingAndDenseIndicator(2, u0.node.expr, 1, 2), 1)
self.assertTrue(cf(torch.empty_strided((u0, 2), (2, 1), device="meta")))
self.assertTrue(cf(torch.empty_strided((2, u0), (1, 2), device="meta")))
self.assertEqual(IsNonOverlappingAndDenseIndicator(u0.node.expr, 1), 1)
self.assertEqual(IsNonOverlappingAndDenseIndicator(1, u0.node.expr), 1)
self.assertTrue(cf(torch.empty_strided((u0,), (1,), device="meta")))
self.assertTrue(cf(torch.empty_strided((1,), (u0,), device="meta")))
Max = torch.sym_max
# NB: This only works because we're able to determine this tensor is
# contiguous. transpose(0, 1) makes it stop working
self.assertTrue(
cf(
torch.empty_strided(
(2, 3, 1, u0),
(3 * Max(1, u0), Max(1, u0), Max(1, u0), 1),
device="meta",
)
)
)
def test_numpy_sym_max(self):
self.assertEqual(torch.sym_max(np.int64(10), 12), 12)
self.assertEqual(torch.sym_max(np.int64(12), 10), 12)
self.assertEqual(torch.sym_max(np.int64(10), 12.5), 12.5)
self.assertEqual(torch.sym_max(np.int64(14), 12.5), 14.0)
self.assertEqual(torch.sym_max(np.float64(14.0), 12), 14.0)
self.assertEqual(torch.sym_max(np.float64(14.0), 16), 16.0)
def test_numpy_sym_min(self):
self.assertEqual(torch.sym_min(np.int64(10), 12), 10)
self.assertEqual(torch.sym_min(np.int64(12), 10), 10)
self.assertEqual(torch.sym_min(np.int64(10), 12.5), 10.0)
self.assertEqual(torch.sym_min(np.int64(14), 12.5), 12.5)
self.assertEqual(torch.sym_min(np.float64(14.0), 12), 12.0)
self.assertEqual(torch.sym_min(np.float64(14.0), 16), 14.0)
def test_debug_has_internal_overlap_unbacked(self):
shape_env = ShapeEnv()
u0 = shape_env.create_unbacked_symint()
torch._check_is_size(u0)
cf = torch._debug_has_internal_overlap
self.assertEqual(cf(torch.empty_strided((u0, 2), (2, 1), device="meta")), 0)
self.assertEqual(cf(torch.empty_strided((2, u0), (1, 2), device="meta")), 0)
self.assertEqual(cf(torch.empty_strided((u0,), (1,), device="meta")), 0)
self.assertEqual(cf(torch.empty_strided((1,), (u0,), device="meta")), 0)
Max = torch.sym_max
self.assertEqual(
cf(
torch.empty_strided(
(2, 3, 1, u0),
(3 * Max(1, u0), Max(1, u0), Max(1, u0), 1),
device="meta",
)
),
0,
)
# Wobbling these to zero is OK too
self.assertEqual(cf(torch.empty_strided((u0, 2), (3, 1), device="meta")), 2)
self.assertEqual(cf(torch.empty_strided((2, u0), (1, 3), device="meta")), 2)
def test_specialize_zero_one(self):
shape_env = ShapeEnv(specialize_zero_one=True)
a0 = create_symint(shape_env, 5)
assert a0 != 1
self.assertEqual(len(shape_env.guards), 0)
shape_env = ShapeEnv(specialize_zero_one=False)
a0 = create_symint(shape_env, 5)
assert a0 != 1
self.assertEqual(len(shape_env.guards), 1)
def test_duck_shape(self):
shape_env = ShapeEnv(duck_shape=True)
a0 = create_symint(shape_env, 5)
a1 = create_symint(shape_env, 5)
assert a0 == a1
self.assertEqual(len(shape_env.guards), 0)
shape_env = ShapeEnv(duck_shape=False)
a0 = create_symint(shape_env, 5)
a1 = create_symint(shape_env, 5)
assert a0 == a1
self.assertEqual(len(shape_env.guards), 1)
def test_int_bool(self):
# See https://github.com/pytorch/pytorch/issues/95981
shape_env = ShapeEnv(duck_shape=True)
a0 = create_symint(shape_env, 5)
assert a0
self.assertEqual(len(shape_env.guards), 0)
def test_symint_as_scalar(self):
shape_env = ShapeEnv()
a0 = create_symint(shape_env, 2)
sym_int_encountered = False
class TestSymInt(TorchDispatchMode):
def __torch_dispatch__(self, func, types, args=(), kwargs=None):
assert func == torch.ops.aten.add.Tensor
nonlocal sym_int_encountered
# WARNING: do not do identity tests on the outer
# SymInt/SymFloat, they are NOT STABLE
sym_int_encountered = kwargs["alpha"].node is a0.node
kwargs["alpha"] = 0
return func(*args)
x = torch.rand([4, 4])
with TestSymInt():
y = torch.add(x, x, alpha=a0)
self.assertTrue(sym_int_encountered)
def test_deepcopy(self):
shape_env = ShapeEnv()
a0 = create_symint(shape_env, 2)
assert a0 < 4
new_shape_env = copy.deepcopy(shape_env)
self.assertEqual(len(new_shape_env.guards), 1)
def test_print_readable_with_symints(self):
def f(a, b):
dim0 = a.shape[0] + b.shape[0]
dim1 = a.shape[1] + b.shape[1]
d = a.new_empty(dim0, dim1)
d = torch.ops.aten.native_dropout(d, 0.5, train=True)
return d
fx_g = make_fx(f, tracing_mode="symbolic")(torch.randn(5, 3), torch.randn(4, 3))
out = fx_g.print_readable(print_output=False)
self.assertExpectedInline(
out.strip(),
"""\
class f(torch.nn.Module):
def forward(self, a_1: "f32[s0, s1]", b_1: "f32[s2, s1]"):
# No stacktrace found for following nodes
sym_size_int: "Sym(s0)" = torch.ops.aten.sym_size.int(a_1, 0)
sym_size_int_1: "Sym(s2)" = torch.ops.aten.sym_size.int(b_1, 0)
add: "Sym(s0 + s2)" = sym_size_int + sym_size_int_1; sym_size_int = sym_size_int_1 = None
sym_size_int_2: "Sym(s1)" = torch.ops.aten.sym_size.int(a_1, 1)
sym_size_int_3: "Sym(s1)" = torch.ops.aten.sym_size.int(b_1, 1); b_1 = None
add_1: "Sym(2*s1)" = sym_size_int_2 + sym_size_int_3; sym_size_int_2 = sym_size_int_3 = None
new_empty: "f32[s0 + s2, 2*s1]" = torch.ops.aten.new_empty.default(a_1, [add, add_1], pin_memory = False); a_1 = add = add_1 = None
native_dropout = torch.ops.aten.native_dropout.default(new_empty, 0.5, True); new_empty = None
getitem: "f32[s0 + s2, 2*s1]" = native_dropout[0]
getitem_1: "b8[s0 + s2, 2*s1]" = native_dropout[1]; native_dropout = None
return (getitem, getitem_1)""", # noqa: B950
)
def test_statically_known_true(self):
shape_env = ShapeEnv()
s2, s3, s4 = (create_symint(shape_env, i) for i in range(2, 5))
# Statically known true
self.assertTrue(statically_known_true(True))
self.assertTrue(statically_known_true(s2 == s2))
self.assertTrue(statically_known_true(s2 * s3 > s3))
self.assertTrue(statically_known_true(s3 * s4 > s4))
self.assertTrue(statically_known_true((s3 + s3) % 2 == 0))
# Statically known false
self.assertFalse(statically_known_true(False))
self.assertFalse(statically_known_true(s3 * s4 <= s4))
self.assertFalse(statically_known_true((s3 + s3) % 2 == 1))
# True for hints, but not known statically
self.assertFalse(statically_known_true(s2 + s2 == s4))
self.assertFalse(statically_known_true(s4 % s2 == 0))
self.assertFalse(statically_known_true(s2 != s3))
self.assertFalse(statically_known_true(s3 * s4 > s2))
# False for hints, but not known statically
self.assertFalse(statically_known_true(s2 == s3))
self.assertFalse(statically_known_true(s2 > s3))
self.assertFalse(statically_known_true(s3 + s3 == s4))
# No guards should be generated
self.assertEqual(len(shape_env.guards), 0)
def test_ephemeral_source_simplification(self):
from torch._dynamo.source import EphemeralSource
# For full robustness, ensure the ephemeral source symbols are simplified out regardless
# of construction order or check order.
for construct_ephemeral_first, x_first_in_check in itertools.product(
[False, True], [False, True]
):
shape_env = ShapeEnv()
shape = (5, 10)
dynamic_dims = [DimDynamic.DYNAMIC for _ in shape]
x = create_symbolic_tensor(
"x",
torch.randn(*shape),
shape_env,
source=(EphemeralSource() if construct_ephemeral_first else None),
dynamic_dims=dynamic_dims,
)
y = create_symbolic_tensor(
"y",
torch.randn(*shape),
shape_env,