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[IPU] add custom-op UTs 0/N #44328

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4 changes: 3 additions & 1 deletion python/paddle/fluid/tests/unittests/ipu/CMakeLists.txt
Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,6 @@ if(WITH_IPU)
RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}"
"test_*.py")
string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")

foreach(TEST_OP ${TEST_OPS})
py_test_modules(${TEST_OP} MODULES ${TEST_OP})
# set all UTs timeout to 200s
Expand All @@ -15,4 +14,7 @@ if(WITH_IPU)
set_tests_properties(test_elemetwise_x_op_ipu PROPERTIES TIMEOUT 300)
set_tests_properties(test_reduce_x_op_ipu PROPERTIES TIMEOUT 600)
set_tests_properties(test_save_load_ipu PROPERTIES TIMEOUT 600)

add_subdirectory(custom_ops)

endif()
12 changes: 12 additions & 0 deletions python/paddle/fluid/tests/unittests/ipu/custom_ops/CMakeLists.txt
Original file line number Diff line number Diff line change
@@ -0,0 +1,12 @@
if(WITH_IPU)
file(
GLOB CUSTOM_OP_TESTS
RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}"
"test_*.py")
string(REPLACE ".py" "" CUSTOM_OP_TESTS "${CUSTOM_OP_TESTS}")
foreach(CUSTOM_OP_TEST ${CUSTOM_OP_TESTS})
py_test(${CUSTOM_OP_TEST} SRCS ${CUSTOM_OP_TEST}.py)
endforeach()

add_subdirectory(deprecated)
endif()
Original file line number Diff line number Diff line change
@@ -0,0 +1,43 @@
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// 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.

#include "paddle/extension.h"

namespace {
std::vector<std::vector<int64_t>> InferShape(std::vector<int64_t> x_shape) {
return {x_shape};
}

std::vector<paddle::DataType> InferDtype(paddle::DataType x_dtype) {
return {x_dtype};
}

std::vector<paddle::Tensor> OpForward(const paddle::Tensor &x) { return {x}; }

std::vector<paddle::Tensor> OpBackward(const paddle::Tensor &x) { return {x}; }
} // namespace

// https://github.com/graphcore/popart/blob/sdk-release-2.5/willow/src/builder_impl.cpp#L1458
// only support one input
PD_BUILD_OP(checkpointoutput)
.Inputs({"X"})
.Outputs({"Out"})
.SetInferShapeFn(PD_INFER_SHAPE(InferShape))
.SetInferDtypeFn(PD_INFER_DTYPE(InferDtype))
.SetKernelFn(PD_KERNEL(OpForward));

PD_BUILD_GRAD_OP(checkpointoutput)
.Inputs({paddle::Grad("Out")})
.Outputs({paddle::Grad("X")})
.SetKernelFn(PD_KERNEL(OpBackward));
Original file line number Diff line number Diff line change
@@ -0,0 +1,42 @@
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// 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.

#include "paddle/extension.h"

namespace {
std::vector<std::vector<int64_t>> InferShape(std::vector<int64_t> x_shape) {
return {x_shape};
}

std::vector<paddle::DataType> InferDtype(paddle::DataType x_dtype) {
return {x_dtype};
}

std::vector<paddle::Tensor> OpForward(const paddle::Tensor &x) { return {x}; }

std::vector<paddle::Tensor> OpBackward(const paddle::Tensor &x) { return {x}; }
} // namespace

// https://github.com/graphcore/popart/blob/sdk-release-2.5/willow/src/builder.cpp#L502
PD_BUILD_OP(custom_detach)
.Inputs({"X"})
.Outputs({"Out"})
.SetInferShapeFn(PD_INFER_SHAPE(InferShape))
.SetInferDtypeFn(PD_INFER_DTYPE(InferDtype))
.SetKernelFn(PD_KERNEL(OpForward));

PD_BUILD_GRAD_OP(custom_detach)
.Inputs({paddle::Grad("Out")})
.Outputs({paddle::Grad("X")})
.SetKernelFn(PD_KERNEL(OpBackward));
Original file line number Diff line number Diff line change
@@ -0,0 +1,42 @@
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// 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.

#include "paddle/extension.h"

namespace {
std::vector<std::vector<int64_t>> InferShape(std::vector<int64_t> x_shape) {
return {x_shape};
}

std::vector<paddle::DataType> InferDtype(paddle::DataType x_dtype) {
return {x_dtype};
}

std::vector<paddle::Tensor> OpForward(const paddle::Tensor &x) { return {x}; }

std::vector<paddle::Tensor> OpBackward(const paddle::Tensor &x) { return {x}; }
} // namespace

// https://github.com/graphcore/popart/blob/sdk-release-2.5/willow/src/builder.gen.cpp#L620
PD_BUILD_OP(custom_identity)
.Inputs({"X"})
.Outputs({"Out"})
.SetInferShapeFn(PD_INFER_SHAPE(InferShape))
.SetInferDtypeFn(PD_INFER_DTYPE(InferDtype))
.SetKernelFn(PD_KERNEL(OpForward));

PD_BUILD_GRAD_OP(custom_identity)
.Inputs({paddle::Grad("Out")})
.Outputs({paddle::Grad("X")})
.SetKernelFn(PD_KERNEL(OpBackward));
62 changes: 62 additions & 0 deletions python/paddle/fluid/tests/unittests/ipu/custom_ops/custom_nll.cc
Original file line number Diff line number Diff line change
@@ -0,0 +1,62 @@
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// 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.

#include "paddle/extension.h"

namespace {
std::vector<std::vector<int64_t>> InferShape(
std::vector<int64_t> x_shape,
std::vector<int64_t> y_shape,
const std::string &reduction,
const int &ignoreIndex,
const bool &inputIsLogProbability) {
// reduction type: Sum, Mean, None
if (reduction == "None") {
return {y_shape};
} else {
return {{1}};
}
}

std::vector<paddle::DataType> InferDtype(paddle::DataType x_dtype,
paddle::DataType y_dtype) {
return {x_dtype};
}

std::vector<paddle::Tensor> OpForward(const paddle::Tensor &x,
const paddle::Tensor &y) {
return {x};
}

std::vector<paddle::Tensor> OpBackward(const paddle::Tensor &x) { return {x}; }
} // namespace

// https://github.com/graphcore/popart/blob/sdk-release-2.5/willow/src/builder.cpp#L775
// type of `reduction` is std::string
// `ignoreIndex` is optional, if no need, need to remove it manually(which is a
// new custom op in paddle)
PD_BUILD_OP(custom_nll)
.Inputs({"X", "Y"})
.Outputs({"Out"})
.Attrs({"reduction: std::string",
"ignoreIndex: int",
"inputIsLogProbability: bool"})
.SetInferShapeFn(PD_INFER_SHAPE(InferShape))
.SetInferDtypeFn(PD_INFER_DTYPE(InferDtype))
.SetKernelFn(PD_KERNEL(OpForward));

PD_BUILD_GRAD_OP(custom_nll)
.Inputs({paddle::Grad("Out")})
.Outputs({paddle::Grad("X")})
.SetKernelFn(PD_KERNEL(OpBackward));
Original file line number Diff line number Diff line change
@@ -0,0 +1,3 @@
if(WITH_IPU)
py_test(test_custom_nllloss_ipu SRCS test_custom_nllloss_ipu.py)
endif()
Original file line number Diff line number Diff line change
@@ -0,0 +1,53 @@
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// 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.

#include "paddle/extension.h"

std::vector<paddle::Tensor> Kernel_Function() { return {}; }
std::vector<paddle::Tensor> Kernel_Function_Grad() { return {}; }

// nllloss
std::vector<std::vector<int64_t>> InferShape_NllLoss(
std::vector<int64_t> x_shape,
std::vector<int64_t> y_shape,
const int& reduction,
const std::string& ignoreIndex,
const bool& inputIsLogProbability) {
// 0: sum, 1: mean, 2: none
if (reduction == 2) {
return {y_shape};
} else {
return {{1}};
}
}

std::vector<paddle::DataType> InferDtype_NllLoss(paddle::DataType x_dtype,
paddle::DataType y_dtype) {
return {x_dtype};
}

PD_BUILD_OP(custom_nll_loss)
.Inputs({"X", "Y"})
.Outputs({"Out"})
.Attrs({"reduction: int",
"ignoreIndex: std::string",
"inputIsLogProbability: bool"})
.SetKernelFn(PD_KERNEL(Kernel_Function))
.SetInferShapeFn(PD_INFER_SHAPE(InferShape_NllLoss))
.SetInferDtypeFn(PD_INFER_DTYPE(InferDtype_NllLoss));

PD_BUILD_GRAD_OP(custom_nll_loss)
.Inputs({paddle::Grad("Out")})
.Outputs({paddle::Grad("X")})
.SetKernelFn(PD_KERNEL(Kernel_Function_Grad));
Original file line number Diff line number Diff line change
@@ -0,0 +1,110 @@
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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.

import os
import unittest
import sys

import numpy as np
import paddle
import paddle.static
from paddle.utils.cpp_extension import load

sys.path.append(
os.path.dirname(os.path.dirname(os.path.dirname(
os.path.abspath(__file__)))))
from op_test_ipu import IPUOpTest


def load_custom_ops():
cur_dir = os.path.dirname(os.path.realpath(__file__))
custom_ops = load(name="custom_nll_loss",
sources=[f"{cur_dir}/custom_nllloss.cc"],
extra_cxx_cflags=['-DONNX_NAMESPACE=onnx'],
extra_ldflags=['-lpopfloat'])
return custom_ops


class TestBase(IPUOpTest):

def setUp(self):
self.load_custom_ops()
self.set_atol()
self.set_test_op()
self.set_training()
self.set_data_feed()
self.set_feed_attr()

@property
def fp16_enabled(self):
return False

def load_custom_ops(self):
self.custom_ops = load_custom_ops()

def set_data_feed(self):
x = np.random.rand(16, 20, 256).astype('float32')
label = np.random.uniform(0, 256, size=[16, 20]).astype('int32')
self.feed_fp32 = {
'x': x,
'label': label,
}

def set_test_op(self):
self.op = self.custom_ops.custom_nll_loss
self.op_attrs = {
"reduction": 0,
"ignoreindex": "0",
"inputislogprobability": False,
}

def set_feed_attr(self):
self.feed_shape = [x.shape for x in self.feed_fp32.values()]
self.feed_list = list(self.feed_fp32.keys())

@IPUOpTest.static_graph
def build_model(self):
x = paddle.static.data(name=self.feed_list[0],
shape=self.feed_shape[0],
dtype='float32')
label = paddle.static.data(name=self.feed_list[1],
shape=self.feed_shape[1],
dtype='int32')
out = self.op(x, label, **self.op_attrs)
out = paddle.mean(out)
self.fetch_list = [out.name]

def run_model(self, exec_mode):
self.run_op_test(exec_mode)

def test(self):
self.build_model()
# only test IPU_FP32
self.run_model(IPUOpTest.ExecutionMode.IPU_FP32)
print(self.output_dict)


class TestCase1(TestBase):

def set_test_op(self):
self.op = self.custom_ops.custom_nll_loss
self.op_attrs = {
"reduction": 0,
"ignoreindex": "None",
"inputislogprobability": False,
}


if __name__ == "__main__":
unittest.main()
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