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# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
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# 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
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# 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
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import enum
from dataclasses import dataclass
from typing import Optional, Sequence, Tuple
from tripy import constraints, export, utils
from tripy.common.exception import raise_error
from tripy.frontend.trace.ops import utils as op_utils
from tripy.frontend.trace.ops.base import BaseTraceOp
import tripy.frontend.trace.ops.utils as op_utils
@dataclass(repr=False)
class Pooling(BaseTraceOp):
class Kind(enum.Enum):
def __init__(self, op):
self.op = op
MAX = "max"
AVG = "avg"
kind: Kind
kernel_dims: Sequence[int]
stride: Sequence[int]
padding: Sequence[Tuple[int]]
infer_rank = op_utils.InferRankPolicies.same_as_input()
def infer_dtypes(self):
self.outputs[0].dtype = self.inputs[0].dtype
def to_flat_ir(self, inputs, outputs):
from tripy.flat_ir.ops import ConstantOp, DivideOp, ReduceWindowOp
from tripy.flat_ir.tensor import FlatIRTensor
init_value = 0
init_const = FlatIRTensor.build(
shape=(),
rank=0,
dtype=outputs[0].dtype,
device=outputs[0].device,
reason_details=[
f"create the constant value tensor (containing {init_value}) for the initial value of a '{self.kind.op}' operation"
],
)
ConstantOp.build([], [init_const], data=init_value)
# extend parameters [spatial_dims,] -> [rank(input),]
extra_dims = inputs[0].rank - len(self.kernel_dims)
window_dims = [1] * extra_dims + list(self.kernel_dims)
window_strides = [1] * extra_dims + list(self.stride)
padding = [(0, 0)] * extra_dims + list(self.padding)
if self.kind.op == "max":
ReduceWindowOp.build(
[inputs[0], init_const],
outputs,
reduce_mode=self.kind.op,
window_dims=window_dims,
window_strides=window_strides,
padding=padding,
)
elif self.kind.op == "avg":
reduce_out = FlatIRTensor.build(
rank=outputs[0].rank,
dtype=outputs[0].dtype,
device=outputs[0].device,
reason_details=[f"create the output of reduce `{self.kind.op}` operation."],
)
ReduceWindowOp.build(
[inputs[0], init_const],
[reduce_out],
reduce_mode=self.kind.op,
window_dims=window_dims,
window_strides=window_strides,
padding=padding,
)
window_elements = 1
for dim in window_dims:
window_elements *= dim
# window_elements = compute_window_elements(self.kernel_dims, self.padding)
init_const = FlatIRTensor.build(
shape=(),
rank=0,
dtype=outputs[0].dtype,
device=outputs[0].device,
reason_details=[
f"create the constant value tensor (containing {window_elements}) for the divisor of average pool operation."
],
)
ConstantOp.build([], [init_const], data=window_elements)
with FlatIRTensor.context(
[f"expand the rank of constant tensor which is the divisor of average pool operation."]
):
init_const = op_utils.expand_rank_of_tensor(init_const, inputs[0].rank)
with FlatIRTensor.context([f"broadcast the inputs of division operation."]):
shape_of_input0 = op_utils.get_shape_of_tensor(reduce_out)
shape_of_input1 = op_utils.get_shape_of_tensor(init_const)
# Compute element-wise max of input shapes to get the desired output shape.
output_shape_tensor = op_utils.compute_shape_of_broadcast(
shape_of_input0,
shape_of_input1,
inputs[0].rank,
shape1_name=f"the shape of the first input {shape_of_input0}",
shape2_name=f"the shape of the second input {shape_of_input1}",
)
init_const = op_utils.insert_broadcast(
init_const,
out_rank=inputs[0].rank,
shape_of_target_tensor=output_shape_tensor,
tensor_details=f"left operand",
)
DivideOp.build([reduce_out, init_const], outputs)
[docs]
@export.public_api(document_under="operations/functions")
@constraints.dtypes(
constraints={"input": "T1", constraints.RETURN_VALUE: "T1"},
variables={"T1": ["float32", "float16", "int8"]},
)
def maxpool(
input: "tripy.Tensor",
kernel_dims: Sequence[int],
stride: Optional[Sequence[int]] = None,
padding: Optional[Sequence[Tuple[int]]] = None,
) -> "tripy.Tensor":
r"""
Applies a max pooling over the input tensor.
The output's non-spatial dimensions are the same as input. For each input spatial dimension
:math:`D_{i}`, the corresponding output dimension will be:
.. math::
D_{out_i} = \left\lfloor\frac{D_{i} + \text{padding_before[i]} + \text{padding_after[i]} -
\text{kernel_dims[i]}}{\text{stride[i]}} + 1\right\rfloor
Args:
input: The input tensor.
kernel_dims: The spatial shape of the pooling window. Only 2-D or 3-D ``kernel_dims`` are supported.
If the input has :class:`int8` datatype, ``kernel_dims`` can only be 2-D.
stride: A sequence of length :math:`M` indicating the stride of pooling across each spatial dimension,
where :math:`M` is the number of spatial dimensions, i.e. :math:`M = \text{rank(input)} - 2`.
Defaults to all 1.
padding: A sequence of pairs of integers of length :math:`M` indicating the zero padding
to apply to the input along each spatial dimension before and after the dimension respectively,
where :math:`M` is the number of spatial dimensions, i.e. :math:`M = \text{rank(input)} - 2`.
Defaults to all 0.
Returns:
The result tensor after the pooling operation.
.. code-block:: python
:linenos:
:caption: Example
input = tp.reshape(tp.arange(16, dtype=tp.float32), (1, 1, 4, 4))
output = tp.maxpool(input, kernel_dims=(2, 2))
pool_torch = torch.nn.MaxPool2d((2, 2), stride=1) # doc: omit
expected = pool_torch(torch.from_dlpack(input).to("cpu")) # doc: omit
assert torch.allclose(torch.from_dlpack(output).to("cpu"), expected)
"""
spatial_dims = len(kernel_dims)
if spatial_dims != 2 and spatial_dims != 3:
raise_error("Unsupported kernel_dims, must be 2D or 3D.", [f"Got kernel_dims={kernel_dims}"])
op_utils.check_conv_pooling_args(kernel_dims, stride, padding)
stride = utils.default(stride, [1] * spatial_dims)
padding = utils.default(padding, [(0, 0)] * spatial_dims)
return Pooling.build([input], Pooling.Kind.MAX, kernel_dims, stride, padding)
[docs]
@export.public_api(document_under="operations/functions")
@constraints.dtypes(
constraints={"input": "T1", constraints.RETURN_VALUE: "T1"},
variables={"T1": ["float32", "float16", "int8"]},
)
def avgpool(
input: "tripy.Tensor",
kernel_dims: Sequence[int],
stride: Optional[Sequence[int]] = None,
padding: Optional[Sequence[Tuple[int]]] = None,
) -> "tripy.Tensor":
r"""
Applies an average pooling over the input tensor.
The output's non-spatial dimensions are the same as input. For each input spatial dimension
:math:`D_{i}`, the corresponding output dimension will be:
.. math::
D_{out_i} = \left\lfloor\frac{D_{i} + \text{padding_before[i]} + \text{padding_after[i]} -
\text{kernel_dims[i]}}{\text{stride[i]}} + 1\right\rfloor
Args:
input: The input tensor.
kernel_dims: The spatial shape of the pooling window. Only 2-D or 3-D ``kernel_dims`` are supported.
If the input has :class:`int8` datatype, ``kernel_dims`` can only be 2-D.
stride: A sequence of length :math:`M` indicating the stride of pooling across each spatial dimension,
where :math:`M` is the number of spatial dimensions, i.e. :math:`M = \text{rank(input)} - 2`.
Defaults to all 1.
padding: A sequence of pairs of integers of length :math:`M` indicating the zero padding
to apply to the input along each spatial dimension before and after the dimension respectively,
where :math:`M` is the number of spatial dimensions, i.e. :math:`M = \text{rank(input)} - 2`.
Defaults to all 0.
Returns:
The result tensor after the pooling operation.
.. code-block:: python
:linenos:
:caption: Example
input = tp.reshape(tp.arange(16, dtype=tp.float32), (1, 1, 4, 4))
output = tp.avgpool(input, kernel_dims=(2, 2))
pool_torch = torch.nn.AvgPool2d((2, 2), stride=1) # doc: omit
expected = pool_torch(torch.from_dlpack(input).to("cpu")) # doc: omit
assert torch.allclose(torch.from_dlpack(output).to("cpu"), expected)
"""
spatial_dims = len(kernel_dims)
if spatial_dims != 2 and spatial_dims != 3:
raise_error("Unsupported kernel_dims, must be 2D or 3D.", [f"Got kernel_dims={kernel_dims}"])
op_utils.check_conv_pooling_args(kernel_dims, stride, padding)
stride = utils.default(stride, [1] * spatial_dims)
padding = utils.default(padding, [(0, 0)] * spatial_dims)
return Pooling.build([input], Pooling.Kind.AVG, kernel_dims, stride, padding)