/
prelude.py
1563 lines (1399 loc) · 62 KB
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prelude.py
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
# pylint: disable=no-else-return, unidiomatic-typecheck, invalid-name
"""A prelude containing useful global functions and ADT definitions."""
from tvm.ir import IRModule, TypeCall
from tvm.tir import Any
from tvm.relay.transform import ToANormalFormExpr
from .ty import GlobalTypeVar, TensorType, scalar_type
from .expr import Var, GlobalVar, If, const
from .function import Function
from .op.tensor import add, subtract, equal
from .adt import Constructor, TypeData, Clause, Match
from .adt import PatternConstructor, PatternVar, PatternWildcard
from . import op, transform
from .analysis import free_vars
def get_tensor_array_shape(expr, dtype, prelude):
"""Get the static shape of a tensor array if it has fixed rank shape.
By design, static ADT tensor in TVM has type name in the format
of static_tensor_dim0_dim1_..._dimN_t.
Parameters
----------
expr : Relay Expr
Input expression.
dtype : str
Data type.
prelude : Prelude
Tensor array prelude
Returns
-------
shape : tuple of (int, Any) or None
The output shape. None if input tensor array
has dynamic shape.
"""
mod = prelude.mod
mod["main"] = Function(free_vars(expr), expr)
mod = transform.InferType()(mod)
checked_type = mod["main"].body.checked_type
assert isinstance(checked_type, TypeCall), "Input must be a tensor array."
ta_type_str = checked_type.args[0].func.name_hint
static_ta_ty_start = "static_tensor_{}".format(dtype)
if ta_type_str.startswith(static_ta_ty_start):
shape_str = ta_type_str.replace("{}_".format(static_ta_ty_start), "").replace("_t", "")
shape = []
if "scalar" not in shape_str:
for dim_str in shape_str.split("_"):
if dim_str in ["?", "any"]:
shape.append(Any())
else:
shape.append(int(dim_str))
return tuple(shape)
return None
def _get_name_static(canonical, dtype, shape):
"""Get name for static shape tensor array op corresponding
to the canonical name"""
dim_names = []
for dim in shape:
if isinstance(dim, Any):
dim_names.append("any")
else:
dim_names.append(str(dim))
shape_str = "_".join(dim_names)
if len(shape_str) == 0:
shape_str = "scalar"
if canonical == "tensor_t":
return "static_tensor_{}_{}_t".format(dtype, shape_str)
return "{}_{}_{}".format(canonical, dtype, shape_str)
class StaticTensorArrayOps(object):
"""Contains tensor array related ops for fixed rank tensor array"""
def __init__(self, prelude, dtype, shape):
"""Create tensor array ops registry"""
self.prelude = prelude
self.dtype = dtype
self.shape = shape
self.list, self.cons, self.nil = self.prelude.mod.get_type("List")
def get_name(self, canonical):
"""Get name corresponding to the canonical name"""
return _get_name_static(canonical, self.dtype, self.shape)
def get_global_var(self, canonical):
"""Get global corresponding to the canonical name"""
return self.prelude.get_global_var_static(canonical, self.dtype, self.shape)
def get_type(self, canonical):
"""Get type corresponding to the canonical name"""
return self.prelude.get_type_static(canonical, self.dtype, self.shape)
def get_ctor(self, canonical):
"""Get ctor corresponding to the canonical name"""
return self.prelude.get_ctor_static("tensor_t", canonical, self.dtype, self.shape)
def define_tensor_adt(self):
"""Defines the static tensor ADT, which is the container for tensors
with fixed shapes."""
tensor_type_name = self.get_name("tensor_t")
# This is effectively functioning as a monomorphizer.
# TODO(@jroesch): we should add full shape polymoprhism
# and do monomorphization.
#
# Skip register if tensor type is already registered.
global_type_names = set()
for g_ty_var in self.prelude.mod.get_global_type_vars():
global_type_names.add(g_ty_var.name_hint)
if tensor_type_name in global_type_names:
self.tensor_type_var = self.get_type("tensor_t")
return
self.tensor_type_var = GlobalTypeVar(tensor_type_name)
tensor_type = TensorType(self.shape, self.dtype)
tensor_constructor_name = self.get_name("tensor_constructor")
tensor_nil_name = self.get_name("tensor_nil")
tensor_nil_case = Constructor(tensor_nil_name, [], self.tensor_type_var)
tensor_case = Constructor(tensor_constructor_name, [tensor_type], self.tensor_type_var)
self.prelude.mod[self.tensor_type_var] = TypeData(
self.tensor_type_var, [], [tensor_nil_case, tensor_case]
)
def define_tensor_array(self):
"""Defines a function to create a tensor array with size n.
tensor_array(n) : Tensor[(), int32] -> list[tensor_t]
"""
tensor_array_constructor_name = self.get_name("tensor_array")
tensor_array_constructor_var = self._create_global_var(tensor_array_constructor_name)
tensor_nil_var = self.get_ctor("tensor_nil")
tensor_type_var = self.get_ctor("tensor_t")
n = Var("x", scalar_type("int32"))
body = If(
equal(n, const(0)),
self.nil(),
self.cons(tensor_nil_var(), tensor_array_constructor_var(subtract(n, const(1)))),
)
self.prelude.mod[tensor_array_constructor_var] = Function(
[n], body, self.list(tensor_type_var()), []
)
def define_tensor_take(self):
"""Defines a function to return a range of tensor_t on axis 0.
tensor_take(t, lower, upper) :
tensor_t -> Tensor[(), int32] -> Tensor[(), int32] -> tensor_t
"""
# We don't register take for scalar tensor.
ndim = len(self.shape)
if ndim == 0:
return
take_name = self.get_name("tensor_take")
if self.is_cached(take_name):
return
take_var = GlobalVar(take_name)
origin_tensor_constructor = self.get_ctor("tensor_constructor")
output_shape = [
Any(),
] + list(self.shape[1:])
tensor_type_var, tensor_constructor, _ = self._get_adt_by_shape(output_shape)
t = Var("tensor", self.tensor_type_var())
lower = Var("lower", scalar_type("int32"))
upper = Var("upper", scalar_type("int32"))
tvar = Var("t")
case = Clause(
PatternConstructor(origin_tensor_constructor, [PatternVar(tvar)]),
tensor_constructor(op.take(tvar, op.arange(lower, upper, dtype="int32"), axis=0)),
)
self.prelude.mod[take_var] = Function(
[t, lower, upper], Match(t, [case], False), tensor_type_var(), []
)
def define_tensor_concatenate(self):
"""Defines a function to concatenate two tensor_t on axis 0.
tensor_concatenate(t) : tensor_t -> tensor_t -> tensor_t
"""
# We don't register concatenate for scalar tensor.
ndim = len(self.shape)
if ndim == 0:
return
concat_name = self.get_name("tensor_concatenate")
concat_var = GlobalVar(concat_name)
if self.is_cached(concat_name):
return
output_shape = [
Any(),
] + list(self.shape[1:])
tensor_type_var, tensor_constructor, _ = self._get_adt_by_shape(output_shape)
origin_tensor_constructor = self.get_ctor("tensor_constructor")
origin_tensor_type_var = self.tensor_type_var
x = Var("x", origin_tensor_type_var())
y = Var("y", origin_tensor_type_var())
t1 = Var("t1")
t2 = Var("t2")
case = Clause(
PatternConstructor(origin_tensor_constructor, [PatternVar(t1)]),
Match(
y,
[
Clause(
PatternConstructor(origin_tensor_constructor, [PatternVar(t2)]),
tensor_constructor(op.concatenate([t1, t2], axis=0)),
)
],
False,
),
)
self.prelude.mod[concat_var] = Function(
[x, y], Match(x, [case], False), tensor_type_var(), []
)
def define_tensor_expand_dims(self):
"""Defines a function to grow a tensor_t's rank by adding one dimension in front
of the original tensor_t.
tensor_expand_dims(t) : tensor_t -> tensor_t
"""
expand_dims_name = self.get_name("tensor_expand_dims")
expand_dims_var = self._create_global_var(expand_dims_name)
setattr(self.prelude, expand_dims_name, expand_dims_var)
origin_tensor_type_var = self.tensor_type_var
origin_tensor_constructor = self.get_ctor("tensor_constructor")
x = Var("x", origin_tensor_type_var())
# Note: we set the added axis to be Any() instead of 1 due to
# in stack op, we need to recursively concatenate.
tensor_type_var, tensor_constructor, _ = self._get_adt_by_shape(
[
Any(),
]
+ list(self.shape)
)
t = Var("t")
case = Clause(
PatternConstructor(origin_tensor_constructor, [PatternVar(t)]),
tensor_constructor(op.expand_dims(t, 0, 1)),
)
self.prelude.mod[expand_dims_var] = Function(
[x], Match(x, [case], False), tensor_type_var(), []
)
def define_tensor_array_read(self):
"""Defines a function to get the nth element of a list. Assume the list has at least one
element.
tensor_array_read(ta, n) : list[static_tensor_t] -> Tensor[(), int32] ->
Tensor[self.shape, self.dtype]
"""
read_name = self.get_name("tensor_array_read")
if self.is_cached(read_name):
return
read_var = GlobalVar(read_name)
tensor_array = Var("tensor_array", self.list(self.tensor_type_var()))
n = Var("x", scalar_type("int32"))
self.prelude.mod[read_var] = Function(
[tensor_array, n], self.prelude.nth(tensor_array, n), self.tensor_type_var(), []
)
def is_cached(self, name):
try:
self.prelude.mod.get_global_var(name)
return True
except ValueError:
return False
def define_tensor_array_write(self):
"""Defines a function to update a tensor array at index n with value v.
tensor_array_write(ta, n, v) :
list[static_tensor_t] -> Tensor[(), int32] -> Tensor[self.shape, self.dtype] ->
list[static_tensor_t]
"""
write_name = self.get_name("tensor_array_write")
if self.is_cached(write_name):
return
write_var = GlobalVar(write_name)
tensor_array = Var("tensor_array", self.list(self.tensor_type_var()))
n = Var("x", scalar_type("int32"))
v = Var("v", self.tensor_type_var())
self.prelude.mod[write_var] = Function(
[tensor_array, n, v],
self.prelude.update(tensor_array, n, v),
self.list(self.tensor_type_var()),
[],
)
def define_tensor_array_unstack(self):
"""Defines a function to unstack the values of a tensor_t in a tensor array.
tensor_array_unstack_tensor(t) : tensor_t -> list[tensor_t]
"""
ndim = len(self.shape)
# We don't register unstack for scalar tensor array
if ndim == 0:
return
helper_name = self.get_name("tensor_array_unstack_helper")
helper_var = self._create_global_var(helper_name)
setattr(self.prelude, helper_name, helper_var)
tensor = Var("t", TensorType(self.shape, self.dtype))
up = Var("up", scalar_type("int32"))
i = Var("i", scalar_type("int32"))
tensor_var = Var("tensor", TensorType(self.shape, self.dtype))
reduced_tensor_type_var, tensor_constructor, _ = self._get_adt_by_shape(self.shape[1:])
helper_body = If(
equal(i, up),
self.nil(),
self.cons(
tensor_constructor(op.take(tensor, i, axis=0)),
helper_var(add(i, const(1)), up, tensor),
),
)
self.prelude.mod[helper_var] = Function(
[i, up, tensor], helper_body, self.list(reduced_tensor_type_var()), []
)
unstack_name = self.get_name("tensor_array_unstack")
unstack_var = self._create_global_var(unstack_name)
setattr(self.prelude, unstack_name, unstack_var)
shape = op.shape_of(tensor_var)
unstack_length = op.take(shape, const(0))
self.prelude.mod[unstack_var] = Function(
[tensor_var],
helper_var(const(0), unstack_length, tensor_var),
self.list(reduced_tensor_type_var()),
[],
)
def define_tensor_array_scatter(self, indices_shape=None, force_update=False):
"""Defines a function to scatter the values of a tensor_t in indices of a tensor array.
tensor_array_scatter(ta, indices, value) :
list[tensor_t] -> Tensor[(Any), int32] -> tensor_t -> list[tensor_t]
Set static indices shape by specifying indices_shape.
Set force_update to get static indices shape operator.
"""
# When this operator has already been registered, only update
# when force_update is set. This should be used only when we need to
# redefine this op for static indices shape.
tensor_array_scatter_name = self.get_name("tensor_array_scatter")
if hasattr(self.prelude, tensor_array_scatter_name) and not force_update:
return
tensor_array_scatter_helper_name = self.get_name("tensor_array_scatter_helper")
tensor_array_scatter_helper_var = self._create_global_var(tensor_array_scatter_helper_name)
ta = Var("ta", self.list(self.tensor_type_var()))
current = Var("current", scalar_type("int32"))
limit = Var("limit", scalar_type("int32"))
indices_ = Var("indices_", TensorType(indices_shape or [Any()], "int32"))
values_ = Var("values_", self.list(self.tensor_type_var()))
write_var = self.get_global_var("tensor_array_write")
read_var = self.get_global_var("tensor_array_read")
helper_body = If(
equal(current, limit),
ta,
tensor_array_scatter_helper_var(
write_var(ta, op.take(indices_, current), read_var(values_, current)),
add(current, const(1)),
limit,
indices_,
values_,
),
)
self.prelude.mod[tensor_array_scatter_helper_var] = Function(
[ta, current, limit, indices_, values_],
helper_body,
self.list(self.tensor_type_var()),
[],
)
tensor_array_scatter_var = self._create_global_var(tensor_array_scatter_name)
setattr(self.prelude, tensor_array_scatter_name, tensor_array_scatter_var)
tensor_array = Var("tensor_array", self.list(self.tensor_type_var()))
indices = Var("indices", TensorType(indices_shape or [Any()], "int32"))
values = Var("values", self.list(self.tensor_type_var()))
if indices_shape is None:
indices_shape = op.shape_of(indices)
limit = op.take(indices_shape, const(0))
else:
limit = const(indices_shape[0])
body = tensor_array_scatter_helper_var(tensor_array, const(0), limit, indices, values)
self.prelude.mod[tensor_array_scatter_var] = Function(
[tensor_array, indices, values], body, self.list(self.tensor_type_var()), []
)
def define_tensor_array_split(self, value_shape=None, lengths_shape=None, force_update=False):
"""Defines a function to split the values of a tensor_t into a tensor array.
tensor_array_split(ta, value, lengths) :
list[tensor_t] -> tensor_t -> Tensor[(Any), int32] -> list[tensor_t]
Set static value and lengths shapes by specifying value_shape and lengths_shape.
Set force_update to get static value and lengths shape operator.
"""
# Skip scalar case
ndim = len(self.shape)
if ndim == 0:
return
# When this operator has already been registered, only update
# when force_update is set. This should be used only when we need to
# redefine this op for static value/indices shape.
split_name = self.get_name("tensor_array_split")
if self.is_cached(split_name):
if not force_update:
return
tensor_array_split_helper_var = self.get_global_var("ta_split_helper")
split_var = self.get_global_var("tensor_array_split")
else:
tensor_array_split_helper_name = self.get_name("ta_split_helper")
tensor_array_split_helper_var = GlobalVar(tensor_array_split_helper_name)
split_var = GlobalVar(split_name)
output_shape = [
Any(),
] + list(self.shape[1:])
output_tensor_type_var, _, output_ops = self._get_adt_by_shape(output_shape)
output_ops.define_tensor_array_write()
write_var = output_ops.get_global_var("tensor_array_write")
if value_shape is None:
value_type_var = self.tensor_type_var
take_var = self.get_global_var("tensor_take")
else:
value_type_var, _, value_adts = self._get_adt_by_shape(value_shape)
value_adts.define_tensor_take()
take_var = value_adts.get_global_var("tensor_take")
ta1 = Var("tensor_array", self.list(output_tensor_type_var()))
value1 = Var("value1", value_type_var())
offset1 = Var("offset1", scalar_type("int32"))
current1 = Var("current1", scalar_type("int32"))
limit1 = Var("limit1", scalar_type("int32"))
lengths1 = Var("lengths", TensorType(lengths_shape or [Any()], "int32"))
helper1_body = If(
equal(current1, limit1),
ta1,
write_var(
tensor_array_split_helper_var(
ta1,
value1,
add(offset1, op.take(lengths1, current1)),
add(current1, const(1)),
limit1,
lengths1,
),
current1,
take_var(value1, offset1, add(op.take(lengths1, current1), offset1)),
),
)
self.prelude.mod[tensor_array_split_helper_var] = Function(
[ta1, value1, offset1, current1, limit1, lengths1],
helper1_body,
self.list(output_tensor_type_var()),
[],
)
tensor_array = Var("tensor_array", self.list(output_tensor_type_var()))
value = Var("value", value_type_var())
lengths = Var("lengths", TensorType(lengths_shape or [Any()], "int32"))
if lengths_shape is None:
lengths_shape = op.shape_of(lengths)
lengths_limit = op.take(lengths_shape, const(0))
else:
lengths_limit = const(lengths_shape[0])
body = tensor_array_split_helper_var(
tensor_array, value, const(0), const(0), lengths_limit, lengths
)
self.prelude.mod[split_var] = Function(
[tensor_array, value, lengths], body, self.list(output_tensor_type_var()), []
)
def define_tensor_array_concat(self):
"""Defines a function to return the values in the tensor array as concatenated tensor_t.
tensor_array_concat(ta) : list[tensor_t] -> tensor_t
"""
# We don't register concat for scalar tensor array.
ndim = len(self.shape)
if ndim == 0:
return
concat_name = self.get_name("tensor_array_concat")
if self.is_cached(concat_name):
return
concat_var = GlobalVar(concat_name)
output_shape = [
Any(),
] + list(self.shape[1:])
tensor_type_var, _, output_ops = self._get_adt_by_shape(output_shape)
# Register tensor concatenate and get tensor_nil var for output shape
output_ops.define_tensor_concatenate()
tensor_concat_var = output_ops.get_global_var("tensor_concatenate")
tensor_nil_var = output_ops.get_ctor("tensor_nil")
tensor_array = Var("tensor_array", self.list(tensor_type_var()))
hd = Var("hd")
tl = Var("tl")
nil_case = Clause(PatternConstructor(self.nil), tensor_nil_var())
cons_case = Clause(
PatternConstructor(self.cons, [PatternVar(hd), PatternVar(tl)]),
Match(
tl,
[
Clause(PatternConstructor(self.nil), hd),
Clause(PatternWildcard(), tensor_concat_var(hd, concat_var(tl))),
],
False,
),
)
self.prelude.mod[concat_var] = Function(
[tensor_array], Match(tensor_array, [nil_case, cons_case], False), tensor_type_var(), []
)
def define_tensor_array_stack(self):
"""Defines a function to get the values in the tensor array as a stack tensor_t.
tensor_array_stack(l) : list[tensor_t] -> tensor_t
"""
stack_name = self.get_name("tensor_array_stack")
stack_var = self._create_global_var(stack_name)
setattr(self.prelude, stack_name, stack_var)
tensor_array = Var("tensor_array", self.list(self.tensor_type_var()))
expand_dims_var = self.get_global_var("tensor_expand_dims")
# Register tensor_concatenate for output_shape
output_shape = [
Any(),
] + list(self.shape)
_, _, output_ops = self._get_adt_by_shape(output_shape)
output_ops.define_tensor_concatenate()
concat_var = output_ops.get_global_var("tensor_concatenate")
tensor_array_expand_dims = self.prelude.map(expand_dims_var, tensor_array)
tensors = self.prelude.foldl(
concat_var,
self.prelude.hd(tensor_array_expand_dims),
self.prelude.tl(tensor_array_expand_dims),
)
output_tensor_type_var, _, _ = self._get_adt_by_shape(output_shape)
self.prelude.mod[stack_var] = Function(
[tensor_array], tensors, output_tensor_type_var(), []
)
def define_tensor_array_gather(self):
"""Defines a function to return the selected values in a tensor array as tensor_t.
tensor_array_gather(ta, indices) : list[tensor_t] -> Tensor[(Any), int32] -> tensor_t
"""
helper_name = self.get_name("tensor_array_gather_helper")
helper_var = self._create_global_var(helper_name)
output_shape = [
Any(),
] + list(self.shape)
output_tensor_type_var, _, _ = self._get_adt_by_shape(output_shape)
stack_var = self.get_global_var("tensor_array_stack")
read_var = self.get_global_var("tensor_array_read")
ta = Var("ta", self.list(self.tensor_type_var()))
accu = Var("accu", self.list(self.tensor_type_var()))
current = Var("current", scalar_type("int32"))
limit = Var("limit", scalar_type("int32"))
indices_ = Var("indices_", TensorType([Any()], "int32"))
helper_body = If(
equal(current, const(0)),
stack_var(accu),
helper_var(
ta,
self.cons(read_var(ta, op.take(indices_, subtract(current, const(1)))), accu),
subtract(current, const(1)),
limit,
indices_,
),
)
self.prelude.mod[helper_var] = Function(
[ta, accu, current, limit, indices_], helper_body, output_tensor_type_var(), []
)
gather_name = self.get_name("tensor_array_gather")
gather_var = self._create_global_var(gather_name)
tensor_array = Var("tensor_array", self.list(self.tensor_type_var()))
indices = Var("indices", TensorType([Any()], "int32"))
indices_shape = op.shape_of(indices)
limit = op.take(indices_shape, const(0))
body = helper_var(tensor_array, self.nil(), limit, limit, indices)
self.prelude.mod[gather_var] = Function(
[tensor_array, indices], body, output_tensor_type_var(), []
)
def define_tensor_get_data(self):
"""Defines a function to get a Tensor from tensor_t with given shape."""
tensor_get_data_name = self.get_name("tensor_get_data")
tensor_get_data_var = self._create_global_var(tensor_get_data_name)
tensor_constructor = self.get_ctor("tensor_constructor")
t = Var("tensor", self.tensor_type_var())
tvar = Var("t")
case = Clause(PatternConstructor(tensor_constructor, [PatternVar(tvar)]), tvar)
self.prelude.mod[tensor_get_data_var] = Function(
[t], Match(t, [case], False), TensorType(self.shape, self.dtype), []
)
def register(self):
"""Register all tensor array ops in Prelude"""
self.define_tensor_adt()
self.define_tensor_take()
self.define_tensor_concatenate()
self.define_tensor_expand_dims()
self.define_tensor_array()
self.define_tensor_array_read()
self.define_tensor_array_write()
self.define_tensor_array_unstack()
self.define_tensor_array_scatter()
self.define_tensor_array_split()
self.define_tensor_array_concat()
self.define_tensor_array_stack()
self.define_tensor_array_gather()
self.define_tensor_get_data()
def _get_adt_by_shape(self, shape):
"""Get ADT type and constructor with given shape."""
adt_ops = StaticTensorArrayOps(self.prelude, self.dtype, shape)
adt_ops.define_tensor_adt()
tensor_type_var = adt_ops.get_type("tensor_t")
tensor_constructor = adt_ops.get_ctor("tensor_constructor")
return tensor_type_var, tensor_constructor, adt_ops
def _create_global_var(self, name):
"""Create a GlobalVar if doesn't exist in prelude."""
global_var_name_set = set()
for g_var_name in self.prelude.mod.get_global_vars():
global_var_name_set.add(g_var_name.name_hint)
if name not in global_var_name_set:
gvar = GlobalVar(name)
else:
gvar = self.prelude.mod.get_global_var(name)
return gvar
class TensorArrayOps(object):
"""Contains tensor array related ops"""
def __init__(self, prelude, dtype):
"""Create tensor array ops registry"""
self.prelude = prelude
self.dtype = dtype
self.list, self.cons, self.nil = self.prelude.mod.get_type("List")
def get_name(self, canonical):
"""Get name corresponding to the canonical name"""
return self.prelude.get_name(canonical, self.dtype)
def get_global_var(self, canonical):
"""Get global corresponding to the canonical name"""
return self.prelude.get_global_var(canonical, self.dtype)
def get_type(self, canonical):
"""Get type corresponding to the canonical name"""
return self.prelude.get_type(canonical, self.dtype)
def get_ctor(self, canonical):
"""Get ctor corresponding to the canonical name"""
return self.prelude.get_ctor(self.tensor_type_var.name_hint, canonical, self.dtype)
def define_tensor_adt(self):
"""Defines the dynamic tensor ADT, which is the container for tensors
with variable shapes."""
tensor_type_name = self.get_name("tensor_t")
self.tensor_type_var = tensor_type_var = GlobalTypeVar(tensor_type_name)
tensor0_type = TensorType([], self.dtype)
tensor1_type = TensorType([Any()], self.dtype)
tensor2_type = TensorType([Any(), Any()], self.dtype)
tensor3_type = TensorType([Any(), Any(), Any()], self.dtype)
tensor4_type = TensorType([Any(), Any(), Any(), Any()], self.dtype)
tensor5_type = TensorType([Any(), Any(), Any(), Any(), Any()], self.dtype)
tensor6_type = TensorType([Any(), Any(), Any(), Any(), Any(), Any()], self.dtype)
tensor_nil_name = self.get_name("tensor_nil")
tensor0_name = self.get_name("tensor0")
tensor1_name = self.get_name("tensor1")
tensor2_name = self.get_name("tensor2")
tensor3_name = self.get_name("tensor3")
tensor4_name = self.get_name("tensor4")
tensor5_name = self.get_name("tensor5")
tensor6_name = self.get_name("tensor6")
tensor_nil_case = Constructor(tensor_nil_name, [], tensor_type_var)
tensor0_case = Constructor(tensor0_name, [tensor0_type], tensor_type_var)
tensor1_case = Constructor(tensor1_name, [tensor1_type], tensor_type_var)
tensor2_case = Constructor(tensor2_name, [tensor2_type], tensor_type_var)
tensor3_case = Constructor(tensor3_name, [tensor3_type], tensor_type_var)
tensor4_case = Constructor(tensor4_name, [tensor4_type], tensor_type_var)
tensor5_case = Constructor(tensor5_name, [tensor5_type], tensor_type_var)
tensor6_case = Constructor(tensor6_name, [tensor6_type], tensor_type_var)
self.prelude.mod[tensor_type_var] = TypeData(
tensor_type_var,
[],
[
tensor_nil_case,
tensor0_case,
tensor1_case,
tensor2_case,
tensor3_case,
tensor4_case,
tensor5_case,
tensor6_case,
],
)
def define_tensor_take(self):
"""Defines a function to return a range of tensor_t on axis 0.
tensor_take(t, lower, upper) :
tensor_t -> Tensor[(), int32] -> Tensor[(), int32] -> tensor_t
"""
take_name = self.get_name("tensor_take")
take_var = GlobalVar(take_name)
tensor_t = self.tensor_type_var
tensor1_var = self.get_ctor("tensor1")
tensor2_var = self.get_ctor("tensor2")
tensor3_var = self.get_ctor("tensor3")
tensor4_var = self.get_ctor("tensor4")
tensor5_var = self.get_ctor("tensor5")
tensor6_var = self.get_ctor("tensor6")
t = Var("tensor", tensor_t())
lower = Var("lower", scalar_type("int32"))
upper = Var("upper", scalar_type("int32"))
t1 = Var("t1")
t2 = Var("t2")
t3 = Var("t3")
t4 = Var("t4")
t5 = Var("t5")
t6 = Var("t6")
tensor1_case = Clause(
PatternConstructor(tensor1_var, [PatternVar(t1)]),
tensor1_var(op.take(t1, op.arange(lower, upper, dtype="int32"))),
)
tensor2_case = Clause(
PatternConstructor(tensor2_var, [PatternVar(t2)]),
tensor2_var(op.take(t2, op.arange(lower, upper, dtype="int32"), axis=0)),
)
tensor3_case = Clause(
PatternConstructor(tensor3_var, [PatternVar(t3)]),
tensor3_var(op.take(t3, op.arange(lower, upper, dtype="int32"), axis=0)),
)
tensor4_case = Clause(
PatternConstructor(tensor4_var, [PatternVar(t4)]),
tensor4_var(op.take(t4, op.arange(lower, upper, dtype="int32"), axis=0)),
)
tensor5_case = Clause(
PatternConstructor(tensor5_var, [PatternVar(t5)]),
tensor5_var(op.take(t5, op.arange(lower, upper, dtype="int32"), axis=0)),
)
tensor6_case = Clause(
PatternConstructor(tensor6_var, [PatternVar(t6)]),
tensor6_var(op.take(t6, op.arange(lower, upper, dtype="int32"), axis=0)),
)
self.prelude.mod[take_var] = Function(
[t, lower, upper],
Match(
t,
[
tensor1_case,
tensor2_case,
tensor3_case,
tensor4_case,
tensor5_case,
tensor6_case,
],
False,
),
tensor_t(),
[],
)
def define_tensor_expand_dims(self):
"""Defines a function to grow a tensor_t's rank by adding one dimension in front
of the original tensor_t.
tensor_expand_dims(t) : tensor_t -> tensor_t
"""
expand_dims_name = self.get_name("tensor_expand_dims")
expand_dims_var = GlobalVar(expand_dims_name)
tensor_type_var = self.tensor_type_var
x = Var("x", tensor_type_var())
t0 = Var("t0")
t1 = Var("t1")
t2 = Var("t2")
t3 = Var("t3")
t4 = Var("t4")
t5 = Var("t5")
tensor0_var = self.get_ctor("tensor0")
tensor1_var = self.get_ctor("tensor1")
tensor2_var = self.get_ctor("tensor2")
tensor3_var = self.get_ctor("tensor3")
tensor4_var = self.get_ctor("tensor4")
tensor5_var = self.get_ctor("tensor5")
tensor6_var = self.get_ctor("tensor6")
tensor0_case = Clause(
PatternConstructor(tensor0_var, [PatternVar(t0)]), tensor1_var(op.expand_dims(t0, 0, 1))
)
tensor1_case = Clause(
PatternConstructor(tensor1_var, [PatternVar(t1)]), tensor2_var(op.expand_dims(t1, 0, 1))
)
tensor2_case = Clause(
PatternConstructor(tensor2_var, [PatternVar(t2)]), tensor3_var(op.expand_dims(t2, 0, 1))
)
tensor3_case = Clause(
PatternConstructor(tensor3_var, [PatternVar(t3)]), tensor4_var(op.expand_dims(t3, 0, 1))
)
tensor4_case = Clause(
PatternConstructor(tensor4_var, [PatternVar(t4)]), tensor5_var(op.expand_dims(t4, 0, 1))
)
tensor5_case = Clause(
PatternConstructor(tensor5_var, [PatternVar(t5)]), tensor6_var(op.expand_dims(t5, 0, 1))
)
self.prelude.mod[expand_dims_var] = Function(
[x],
Match(
x,
[
tensor0_case,
tensor1_case,
tensor2_case,
tensor3_case,
tensor4_case,
tensor5_case,
],
False,
),
tensor_type_var(),
)
def define_tensor_concat(self):
"""Defines a function to concatenate two tensor_t on the first axis
tensor_concatenate(t) : tensor_t -> tensor_t -> tensor_t
"""
concat_name = self.get_name("tensor_concatenate")
concat_var = GlobalVar(concat_name)
tensor_type_var = self.tensor_type_var
x = Var("x", tensor_type_var())
y = Var("y", tensor_type_var())
tensor1_var = self.get_ctor("tensor1")
tensor2_var = self.get_ctor("tensor2")
tensor3_var = self.get_ctor("tensor3")
tensor4_var = self.get_ctor("tensor4")
t11 = Var("t11")
t12 = Var("t12")
t21 = Var("t21")
t22 = Var("t22")
t31 = Var("t31")
t32 = Var("t32")
t41 = Var("t41")
t42 = Var("t42")
tensor1_case = Clause(
PatternConstructor(tensor1_var, [PatternVar(t11)]),
Match(
y,
[
Clause(
PatternConstructor(tensor1_var, [PatternVar(t12)]),
tensor1_var(op.concatenate([t11, t12], axis=0)),
)
],
False,
),
)
tensor2_case = Clause(
PatternConstructor(tensor2_var, [PatternVar(t21)]),
Match(
y,
[
Clause(
PatternConstructor(tensor2_var, [PatternVar(t22)]),
tensor2_var(op.concatenate([t21, t22], axis=0)),
)
],
False,
),
)
tensor3_case = Clause(
PatternConstructor(tensor3_var, [PatternVar(t31)]),
Match(
y,
[
Clause(
PatternConstructor(tensor3_var, [PatternVar(t32)]),
tensor3_var(op.concatenate([t31, t32], axis=0)),
)
],
False,
),
)
tensor4_case = Clause(
PatternConstructor(tensor4_var, [PatternVar(t41)]),
Match(
y,
[
Clause(
PatternConstructor(tensor4_var, [PatternVar(t42)]),
tensor4_var(op.concatenate([t41, t42], axis=0)),
)
],
False,
),
)
# op.concatenate does not support tensor with rank higher than 4
self.prelude.mod[concat_var] = Function(
[x, y],
Match(x, [tensor1_case, tensor2_case, tensor3_case, tensor4_case], False),
tensor_type_var(),
)
def define_tensor_array(self):
"""Defines a function to create a tensor array with size n.
tensor_array(n) : Tensor[(), int32] -> list[tensor_t]
"""
tensor_array_constructor_name = self.get_name("tensor_array")
tensor_array_constructor_var = GlobalVar(tensor_array_constructor_name)
setattr(self.prelude, tensor_array_constructor_name, tensor_array_constructor_var)
tensor_nil_var = self.get_ctor("tensor_nil")
tensor_type_var = self.get_ctor("tensor_t")
n = Var("x", scalar_type("int32"))
body = If(
equal(n, const(0)),
self.nil(),
self.cons(tensor_nil_var(), tensor_array_constructor_var(subtract(n, const(1)))),
)
self.prelude.mod[tensor_array_constructor_var] = Function(
[n], body, self.list(tensor_type_var()), []
)
def define_tensor_array_read(self):
"""Defines a function to get the head of a list. Assume the list has at least one
element.
tensor_array_read(ta, n) : list[tensor_t] -> Tensor[(), int32] -> tensor_t
"""
read_name = self.get_name("tensor_array_read")
read_var = GlobalVar(read_name)
setattr(self.prelude, read_name, read_var)
tensor_type_var = self.tensor_type_var
tensor_array = Var("tensor_array", self.list(tensor_type_var()))
n = Var("x", scalar_type("int32"))
self.prelude.mod[read_var] = Function(