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【NPU】Support npu op elementwise_div and elementwise_div_grad #31573
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08fdaa5
Support npu op elementwise_div and elementwise_div_grad
oyjxer 5265bb5
Merge branch 'ascendrc' of https://github.com/PaddlePaddle/Paddle int…
oyjxer a8686b5
Support npu op elementwise_div and elementwise_div_grad
oyjxer 826b668
Merge branch 'ascendrc' of https://github.com/PaddlePaddle/Paddle int…
oyjxer 149ea4b
Support npu op elementwise_div and elementwise_div_grad
oyjxer 365c7fb
Merge branch 'ascendrc' of https://github.com/PaddlePaddle/Paddle int…
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130 changes: 130 additions & 0 deletions
130
paddle/fluid/operators/elementwise/elementwise_div_op_npu.cc
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/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. | ||
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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 | ||
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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. */ | ||
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#include <memory> | ||
#include <string> | ||
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#include "paddle/fluid/operators/elementwise/elementwise_div_op.h" | ||
#include "paddle/fluid/operators/npu_op_runner.h" | ||
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namespace paddle { | ||
namespace operators { | ||
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using Tensor = framework::Tensor; | ||
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template <typename DeviceContext, typename T> | ||
class ElementwiseDivNPUKernel : public framework::OpKernel<T> { | ||
public: | ||
void Compute(const framework::ExecutionContext& ctx) const override { | ||
auto* x = ctx.Input<Tensor>("X"); | ||
auto* y = ctx.Input<Tensor>("Y"); | ||
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auto* out = ctx.Output<Tensor>("Out"); | ||
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auto place = ctx.GetPlace(); | ||
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out->mutable_data<T>(place); | ||
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auto stream = | ||
ctx.template device_context<paddle::platform::NPUDeviceContext>() | ||
.stream(); | ||
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auto runner = NpuOpRunner("Div", {*x, *y}, {*out}, {}); | ||
runner.Run(stream); | ||
} | ||
}; | ||
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template <typename DeviceContext, typename T> | ||
class ElementwiseDivGradNPUKernel : public framework::OpKernel<T> { | ||
public: | ||
void Compute(const framework::ExecutionContext& ctx) const override { | ||
auto* out = ctx.Input<Tensor>("Out"); | ||
auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out")); | ||
auto* x = ctx.Input<Tensor>("X"); | ||
auto* y = ctx.Input<Tensor>("Y"); | ||
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auto* dx = ctx.Output<Tensor>(framework::GradVarName("X")); | ||
auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y")); | ||
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auto place = ctx.GetPlace(); | ||
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dx->mutable_data<T>(place); | ||
dy->mutable_data<T>(place); | ||
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auto stream = | ||
ctx.template device_context<paddle::platform::NPUDeviceContext>() | ||
.stream(); | ||
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Tensor y_power(y->type()); | ||
y_power.mutable_data<T>(y->dims(), place); | ||
auto y_power_runner = NpuOpRunner("Power", {*y}, | ||
{y_power}, {{"power", static_cast<float>(-1)}}); | ||
y_power_runner.Run(stream); | ||
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Tensor tensor_zeros(x->type()); | ||
tensor_zeros.mutable_data<T>(x->dims(), place); | ||
auto tensor_zeros_runner = NpuOpRunner("ZerosLike", {*x}, | ||
{tensor_zeros}, {}); | ||
tensor_zeros_runner.Run(stream); | ||
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Tensor x_zero(paddle::framework::proto::VarType::BOOL); | ||
x_zero.mutable_data<bool>(x->dims(), place); | ||
auto x_zero_runner = NpuOpRunner("Equal", {*x, tensor_zeros}, {x_zero}, {}); | ||
x_zero_runner.Run(stream); | ||
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Tensor x_nozero(paddle::framework::proto::VarType::BOOL); | ||
x_nozero.mutable_data<bool>(x->dims(), place); | ||
auto x_nozero_runner = NpuOpRunner("LogicalNot", {x_zero}, {x_nozero}, {}); | ||
x_nozero_runner.Run(stream); | ||
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Tensor x_nozero_f(x->type()); | ||
x_nozero_f.mutable_data<T>(x->dims(), place); | ||
auto x_nozero_f_runner = NpuOpRunner("Cast", {x_nozero}, | ||
{x_nozero_f}, {{"dst_type", static_cast<int32_t>(0)}}); | ||
x_nozero_f_runner.Run(stream); | ||
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Tensor x_grad_w(x->type()); | ||
x_grad_w.mutable_data<T>(x->dims(), place); | ||
auto x_grad_w_runner = NpuOpRunner("Mul", {x_nozero_f, y_power}, | ||
{x_grad_w}, {}); | ||
x_grad_w_runner.Run(stream); | ||
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auto x_grad_runner = NpuOpRunner("Mul", {x_grad_w, *dout}, {*dx}, {}); | ||
x_grad_runner.Run(stream); | ||
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Tensor y_grad_w(x->type()); | ||
y_grad_w.mutable_data<T>(y->dims(), place); | ||
auto y_grad_w_runner = NpuOpRunner("Mul", {*out, y_power}, {y_grad_w}, {}); | ||
y_grad_w_runner.Run(stream); | ||
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auto y_grad_runner = NpuOpRunner("Mul", {y_grad_w, *dout}, {*dy}, {}); | ||
y_grad_runner.Run(stream); | ||
} | ||
}; | ||
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} // namespace operators | ||
} // namespace paddle | ||
namespace ops = paddle::operators; | ||
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REGISTER_OP_NPU_KERNEL( | ||
elementwise_div, | ||
ops::ElementwiseDivNPUKernel<paddle::platform::NPUDeviceContext, float>, | ||
ops::ElementwiseDivNPUKernel<paddle::platform::NPUDeviceContext, | ||
paddle::platform::float16>); | ||
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REGISTER_OP_NPU_KERNEL( | ||
elementwise_div_grad, | ||
ops::ElementwiseDivGradNPUKernel<paddle::platform::NPUDeviceContext, float>, | ||
ops::ElementwiseDivGradNPUKernel<paddle::platform::NPUDeviceContext, | ||
paddle::platform::float16>); |
170 changes: 170 additions & 0 deletions
170
python/paddle/fluid/tests/unittests/npu/test_elementwise_div_op_npu.py
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# Copyright (c) 2021 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. | ||
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from __future__ import print_function | ||
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import numpy as np | ||
import unittest | ||
import sys | ||
sys.path.append("..") | ||
from op_test import OpTest | ||
import paddle | ||
import paddle.fluid as fluid | ||
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paddle.enable_static() | ||
SEED = 2021 | ||
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@unittest.skipIf(not paddle.is_compiled_with_npu(), | ||
"core is not compiled with NPU") | ||
class TestElementwiseDiv(OpTest): | ||
def setUp(self): | ||
self.set_npu() | ||
self.op_type = "elementwise_div" | ||
self.place = paddle.NPUPlace(0) | ||
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self.init_dtype() | ||
np.random.seed(SEED) | ||
x = np.random.uniform(1, 2, [11, 17]).astype(self.dtype) | ||
y = np.random.uniform(1, 2, [11, 17]).astype(self.dtype) | ||
out = np.divide(x, y) | ||
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self.inputs = { | ||
'X': OpTest.np_dtype_to_fluid_dtype(x), | ||
'Y': OpTest.np_dtype_to_fluid_dtype(y) | ||
} | ||
self.attrs = {} | ||
self.outputs = {'Out': out} | ||
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def set_npu(self): | ||
self.__class__.use_npu = True | ||
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def init_dtype(self): | ||
self.dtype = np.float32 | ||
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def test_check_output(self): | ||
self.check_output_with_place(self.place, check_dygraph=False) | ||
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# TODO(ascendrc): Div grad test | ||
# def test_check_grad(self): | ||
# if self.dtype == np.float16: | ||
# return | ||
# self.check_grad(['X'], 'Out') | ||
# | ||
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@unittest.skipIf(not paddle.is_compiled_with_npu(), | ||
"core is not compiled with NPU") | ||
class TestElementwiseDivFp16(OpTest): | ||
def setUp(self): | ||
self.set_npu() | ||
self.op_type = "elementwise_div" | ||
self.place = paddle.NPUPlace(0) | ||
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self.init_dtype() | ||
np.random.seed(SEED) | ||
x = np.random.uniform(1, 2, [3, 4]).astype(self.dtype) | ||
y = np.random.uniform(1, 2, [3, 4]).astype(self.dtype) | ||
out = np.divide(x, y) | ||
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self.inputs = { | ||
'X': OpTest.np_dtype_to_fluid_dtype(x), | ||
'Y': OpTest.np_dtype_to_fluid_dtype(y) | ||
} | ||
self.attrs = {} | ||
self.outputs = {'Out': out} | ||
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def set_npu(self): | ||
self.__class__.use_npu = True | ||
self.__class__.no_need_check_grad = True | ||
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def init_dtype(self): | ||
self.dtype = np.float16 | ||
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def test_check_output(self): | ||
self.check_output_with_place(self.place, check_dygraph=False, atol=1e-5) | ||
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@unittest.skipIf(not paddle.is_compiled_with_npu(), | ||
"core is not compiled with NPU") | ||
class TestElementwiseDivNet(unittest.TestCase): | ||
def _test(self, run_npu=True): | ||
main_prog = paddle.static.Program() | ||
startup_prog = paddle.static.Program() | ||
main_prog.random_seed = SEED | ||
startup_prog.random_seed = SEED | ||
np.random.seed(SEED) | ||
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a_np = np.random.uniform(1, 2, [32, 32]).astype('float32') | ||
b_np = np.random.uniform(1, 2, [32, 32]).astype('float32') | ||
c_np = np.random.uniform(1, 2, [32, 32]).astype('float32') | ||
d_np = np.random.uniform(1, 2, [32, 32]).astype('float32') | ||
label_np = np.random.randint(2, size=(32, 1)).astype('int64') | ||
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with paddle.static.program_guard(main_prog, startup_prog): | ||
a = paddle.static.data(name="a", shape=[32, 32], dtype='float32') | ||
b = paddle.static.data(name="b", shape=[32, 32], dtype='float32') | ||
c = paddle.static.data(name="c", shape=[32, 32], dtype='float32') | ||
d = paddle.static.data(name="d", shape=[32, 32], dtype='float32') | ||
label = paddle.static.data( | ||
name="label", shape=[32, 1], dtype='int64') | ||
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e = paddle.multiply(a, b) | ||
f = paddle.multiply(c, d) | ||
g = paddle.divide(e, f) | ||
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fc_1 = fluid.layers.fc(input=g, size=128) | ||
prediction = fluid.layers.fc(input=fc_1, size=2, act='softmax') | ||
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cost = fluid.layers.cross_entropy(input=prediction, label=label) | ||
loss = fluid.layers.reduce_mean(cost) | ||
sgd = fluid.optimizer.SGD(learning_rate=0.01) | ||
sgd.minimize(loss) | ||
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if run_npu: | ||
place = paddle.NPUPlace(0) | ||
else: | ||
place = paddle.CPUPlace() | ||
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exe = paddle.static.Executor(place) | ||
exe.run(startup_prog) | ||
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print("Start run on {}".format(place)) | ||
for epoch in range(100): | ||
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pred_res, loss_res = exe.run(main_prog, | ||
feed={ | ||
"a": a_np, | ||
"b": b_np, | ||
"c": c_np, | ||
"d": d_np, | ||
"label": label_np | ||
}, | ||
fetch_list=[prediction, loss]) | ||
if epoch % 10 == 0: | ||
print("Epoch {} | Prediction[0]: {}, Loss: {}".format( | ||
epoch, pred_res[0], loss_res)) | ||
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return pred_res, loss_res | ||
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def test_npu(self): | ||
cpu_pred, cpu_loss = self._test(False) | ||
npu_pred, npu_loss = self._test(True) | ||
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self.assertTrue(np.allclose(npu_pred, cpu_pred)) | ||
self.assertTrue(np.allclose(npu_loss, cpu_loss)) | ||
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if __name__ == '__main__': | ||
unittest.main() |
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Please consider cases where the
dx==nullptr
ordy==nullptr
, and test them intest_elementwise_div_op_npu.py
. Please refer #31347And fix code of
ElementwiseMulGradNPUKernel
in #31571 for the same reason.There was a problem hiding this comment.
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fix