# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# Licensed 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.
#
"""
Remove no-ops from the graph.
This is a bit different from remove_unused_ops. That pass
is based on the graph structure - it removes ops tha are not
connected to the src_ops of any tensor. This pass, on the other
hand, removes things which are logically no-ops, like expands
with no expanded dims.
The reason it's not combined with removed_unused_ops is that
many of the passes in this file will want to call sanitize_sorted_graph,
but sanitize_sorted_graph calls remove_unused_ops.
Also, even if the passes in this file avoided sanitize_sorted_graph,
many other unrelated passes use sanitize_sorted_graph. We don't need to
call the passes in this file more than once.
"""
from typing import List
from aitemplate.compiler.base import IntImm, IntVar, JaggedIntVar, Operator, Tensor
from aitemplate.compiler.ops.tensor.expand import ExpandDimensionType
from aitemplate.compiler.transform import transform_utils
from aitemplate.utils import graph_utils, shape_utils
from aitemplate.utils.shape_utils import is_singleton_dimension
def _remove_id_ops(sorted_graph: List[Tensor]) -> List[Tensor]:
"""Remove identity ops."""
ops = graph_utils.get_sorted_ops(sorted_graph)
for op in ops:
if op._attrs["op"] != "identity":
continue
inputs = op._attrs["inputs"]
assert len(inputs) == 1, "identity must only have 1 input"
outputs = op._attrs["outputs"]
identity_output = outputs[0]
assert len(inputs) == 1, "identity must only have 1 output"
# skip a very special case where id takes an input and produces an output
if identity_output._attrs["is_output"] and inputs[0]._attrs["is_input"]:
continue
transform_utils.remove_single_tensor_op_from_sorted_graph(op)
return transform_utils.sanitize_sorted_graph(sorted_graph)
def _remove_no_op_concats(sorted_graph: List[Tensor]) -> List[Tensor]:
"""
Remove no-op concats from the graph. A no-op concat is where the output
tensor is exactly the same as the input tensor(s) and it isn't the model output.
This is the case when:
1. There is a single input tensor.
2. There is a single non-empty input tensor and the remaining input tensors
are empty.
x = Tensor(shape=[7])
empty1 = Tensor(shape=[0], value=[])
empty2 = Tensor(shape=[0], value=[])
y1 = ops.concatenate([x]) # Case 1
y2 = ops.concatenate([empty1]) # Case 1
y2 = ops.concatenate([empty1, x, empty2]) # Case 2
"""
def is_dim_gt_zero(dim):
if isinstance(dim, IntImm):
return dim.value() > 0
elif isinstance(dim, IntVar):
return dim.upper_bound() > 0
ops = graph_utils.get_sorted_ops(sorted_graph)
for op in ops:
if op._attrs["op"] != "concatenate":
continue
inputs = op._attrs["inputs"]
assert len(inputs) >= 1, "concat must have at least 1 input"
outputs = op._attrs["outputs"]
concat_output = outputs[0]
assert len(outputs) == 1, "concat must have a single output"
# Assumes non-empty tensors have non-zero dimensions.
# And empty tensors have dimensions of size 0.
is_input_non_empty = [
all(is_dim_gt_zero(dim) for dim in tensor.shape()) for tensor in inputs
]
n_non_empty = sum(is_input_non_empty)
if len(inputs) > 1 and n_non_empty > 1 or outputs[0]._attrs["is_output"]:
continue
idx = is_input_non_empty.index(True) if n_non_empty == 1 else 0
concat_input = inputs[idx]
for dst_op in concat_output.dst_ops():
transform_utils.replace_tensor_for_op(dst_op, concat_output, concat_input)
transform_utils.remove_tensor_from_sorted_graph(concat_output)
return transform_utils.sanitize_sorted_graph(sorted_graph)
def _remove_no_op_dynamic_slices(sorted_graph: List[Tensor]) -> List[Tensor]:
"""
Remove any no-op slices from the graph. A no-op slice is when the input tensor
and output tensor are exactly the same. This happens when the start indices
and end indices cover the entire dimension length.
x = Tensor([1, 2, 3])
y = x[:]
xx = Tensor([[1, 2, 3, 4], [5, 6, 7, 8]])
yy = xx[0:2, -4:4]
"""
ops = graph_utils.get_sorted_ops(sorted_graph)
for op in ops:
if op._attrs["op"] != "dynamic_slice":
continue
inputs = op._attrs["inputs"]
assert len(inputs) == 1, "dynamic_slice must only have 1 input"
outputs = op._attrs["outputs"]
assert len(inputs) == 1, "dynamic_slice must only have 1 output"
slice_input, slice_output = inputs[0], outputs[0]
if (
not shape_utils.is_same_shape(slice_input.shape(), slice_output.shape())
or slice_output._attrs["is_output"]
):
continue
for dst_op in slice_output.dst_ops():
transform_utils.replace_tensor_for_op(dst_op, slice_output, slice_input)
transform_utils.remove_tensor_from_sorted_graph(slice_output)
return transform_utils.sanitize_sorted_graph(sorted_graph)
def _remove_no_op_splits(sorted_graph: List[Tensor]) -> List[Tensor]:
"""
Remove any no-op split from the graph where the input tensor is non-jagged.
A no-op split is where the input tensor isn't divided into multiple parts.
This happens when the split_size_or_sections argument is:
1. an integer representing the length of the dimension indicated by dim
2. a singleton list containing the length of the dimension indicated by dim.
x = Tensor([1, 2, 3])
y1 = split(x, split_size_or_sections=3, dim=0) # Case 1
y2 = split(x, split_size_or_sections=[3], dim=0) # Case 2
xx = Tensor([[1, 2, 3, 4], [5, 6, 7, 8]])
yy1 = split(xx, split_size_or_sections=2, dim=0) # Case 1
yy2 = split(xx, split_size_or_sections=4, dim=1) # Case 1
yy3 = split(xx, split_size_or_sections=[2], dim=0) # Case 2
yy4 = split(xx, split_size_or_sections=[4], dim=1) # Case 2
"""
ops = graph_utils.get_sorted_ops(sorted_graph)
for op in ops:
if op._attrs["op"] != "split":
continue
inputs = op._attrs["inputs"]
assert len(inputs) == 1, "split must only have 1 input"
outputs = op._attrs["outputs"]
assert len(inputs) >= 1, "split must have at least 1 output"
split_dim = op._attrs["split_dim"]
split_input, split_output = inputs[0], outputs[0]
input_split_dim_len, output_split_dim_len = (
split_input._attrs["shape"][split_dim],
split_output._attrs["shape"][split_dim],
)
# No-op splits must have one output, and the input and output shapes
# must match along split_dim. We ignore no-op splits that are outputs.
if (
len(outputs) > 1
or input_split_dim_len != output_split_dim_len
or outputs[0]._attrs["is_output"]
):
continue
# Delete the split output in the graph.
for dst_op in list(split_output.dst_ops()):
transform_utils.replace_tensor_for_op(dst_op, split_output, split_input)
transform_utils.remove_tensor_from_sorted_graph(split_output)
return transform_utils.sanitize_sorted_graph(sorted_graph)
def _remove_no_op_expands(sorted_graph: List[Tensor]) -> List[Tensor]:
"""
Remove no-op expands from the graph. A no-op expand is one
that doesn't expand any singleton dimensions to values greater
than one.
x = Tensor([1, 2, 3])
y1 = ops.expand()(x, [-1, -1, -1]) # no-op
y2 = ops.expand()(x, [1, 2, -1]) # no-op
"""
ops = graph_utils.get_sorted_ops(sorted_graph)
for op in ops:
if op._attrs["op"] != "expand":
continue
outputs = op._attrs["outputs"]
assert len(outputs) == 1, "expand must only have 1 output"
expand_output = outputs[0]
if expand_output._attrs["is_output"]:
continue
inputs = op._attrs["inputs"]
assert len(inputs) >= 1, "expand must have at least 1 input"
expand_input = inputs[0]
assert len(op._attrs["dim_types"]) == len(
expand_output._attrs["shape"]
), "expand must have dim_type for every output dimension"
# If we just keep every dimension as-is, it is a no-op
if any(dt != ExpandDimensionType.KEEP_DIM for dt in op._attrs["dim_types"]):
continue
# This expand is a no-op, so we know that these shapes should
# be the same. However, the shape inference system may not be aware
# of that due to different IntVar names.
expand_input._attrs["shape"] = expand_output._attrs["shape"]
for dst in list(expand_output.dst_ops()):
transform_utils.replace_tensor_for_op(dst, expand_output, expand_input)
transform_utils.remove_tensor_from_sorted_graph(expand_output)
return transform_utils.sanitize_sorted_graph(sorted_graph)
def _fuse_expand_elementwise(sorted_graph: List[Tensor]) -> List[Tensor]:
"""
Eliminate expand ops that occur before elementwise when broadcasting
in elementwise can handle the unexpanded input.
Example:
x = Tensor([1, 2, 3])
y = Tensor([3, 2, 3])
z = ops.elementwise(FuncEnum.ADD)(ops.expand()(x, [3, 2, 3]), y)
The expand here is not required because elementwise broadcasting will just
do the right thing.
Note that this must occur before any pass that fuses elementwise into
other ops.
"""
def _is_compatible_with_broadcasting(
expand_output_dim: IntVar, elementwise_input_dim: IntVar
) -> bool:
return expand_output_dim == elementwise_input_dim or is_singleton_dimension(
expand_output_dim
)
def _replace_jagged_int_var(shape: List[IntVar]):
"""
If shape[0] is a JaggedIntVar, replace it with
the corresponding maximum dense shape.
"""
if shape and isinstance(shape[0], JaggedIntVar):
return shape[0].get_max_dense_shape() + shape[1:]
return shape
for op in graph_utils.get_sorted_ops(sorted_graph):
if op._attrs["op"] != "expand":
continue
outputs = op._attrs["outputs"]
assert len(outputs) == 1, "expand must only have 1 output"
expand_output = outputs[0]
if expand_output._attrs["is_output"]:
continue
expand_output_shape = _replace_jagged_int_var(expand_output._attrs["shape"])
def _can_fuse_with(dst_op: Operator) -> bool:
if dst_op._attrs["op"] != "elementwise":
return False
for elementwise_input in dst_op._attrs["inputs"]:
if elementwise_input is expand_output:
continue
elementwise_input_shape = _replace_jagged_int_var(
elementwise_input._attrs["shape"]
)
if not all(
_is_compatible_with_broadcasting(dim_a, dim_b)
for dim_a, dim_b in zip(
expand_output_shape,
elementwise_input_shape,
)
):
return False
return True
if not all(_can_fuse_with(dst) for dst in expand_output._attrs["dst_ops"]):
continue
inputs = op._attrs["inputs"]
assert len(inputs) >= 1, "expand must have at least 1 input"
expand_input = inputs[0]
for dst in list(expand_output.dst_ops()):
transform_utils.replace_tensor_for_op(dst, expand_output, expand_input)
transform_utils.remove_tensor_from_sorted_graph(expand_output)
[docs]def remove_no_ops(sorted_graph: List[Tensor]) -> List[Tensor]:
"""Remove no-ops from the graph.
Parameters
----------
sorted_graph : List[Tensor]
Input graph
Returns
-------
List[Tensor]
Graph after remove no-ops
"""
passes = [
_remove_id_ops,
_remove_no_op_concats,
_remove_no_op_dynamic_slices,
_remove_no_op_splits,
_remove_no_op_expands,
_fuse_expand_elementwise,
]
for f_pass in passes:
sorted_graph = f_pass(sorted_graph)
return sorted_graph