diff --git a/paddle/fluid/operators/where_op_npu.cc b/paddle/fluid/operators/where_op_npu.cc new file mode 100755 index 0000000000000000000000000000000000000000..6b7f5b1dd5be85532c770e1e37e7087e5a31c3e0 --- /dev/null +++ b/paddle/fluid/operators/where_op_npu.cc @@ -0,0 +1,96 @@ +// 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. + +#include "paddle/fluid/operators/where_op.h" +#include "paddle/fluid/operators/npu_op_runner.h" + +namespace paddle { +namespace operators { + +template +class WhereNPUKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto* condition = ctx.Input("Condition"); + auto* X = ctx.Input("X"); + auto* Y = ctx.Input("Y"); + auto* out = ctx.Output("Out"); + out->mutable_data(ctx.GetPlace()); + + const auto& runner = + NpuOpRunner("Select", {*condition, *X, *Y}, {*out}, {}); + + auto stream = + ctx.template device_context() + .stream(); + runner.Run(stream); + } +}; + +template +class WhereGradNPUKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto* condition = ctx.Input("Condition"); + auto* dout_t = ctx.Input(framework::GradVarName("Out")); + auto* dx_t = ctx.Output(framework::GradVarName("X")); + auto* dy_t = ctx.Output(framework::GradVarName("Y")); + + if (dx_t != nullptr) { + dx_t->mutable_data(ctx.GetPlace()); + } + if (dy_t != nullptr) { + dy_t->mutable_data(ctx.GetPlace()); + } + + auto stream = + ctx.template device_context() + .stream(); + + framework::Tensor tensor_zeros(dout_t->type()); + tensor_zeros.mutable_data(dout_t->dims(), ctx.GetPlace()); + const auto& runner = + NpuOpRunner("ZerosLike", {*dout_t}, {tensor_zeros}, {}); + runner.Run(stream); + + if (dx_t != nullptr) { + const auto& runner = NpuOpRunner( + "Select", {*condition, *dout_t, tensor_zeros}, {*dx_t}, {}); + runner.Run(stream); + } + if (dy_t != nullptr) { + const auto& runner = NpuOpRunner( + "Select", {*condition, tensor_zeros, *dout_t}, {*dy_t}, {}); + runner.Run(stream); + } + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; + +REGISTER_OP_NPU_KERNEL( + where, ops::WhereNPUKernel, + ops::WhereNPUKernel, + ops::WhereNPUKernel, + ops::WhereNPUKernel); + +REGISTER_OP_NPU_KERNEL( + where_grad, + ops::WhereGradNPUKernel, + ops::WhereGradNPUKernel, + ops::WhereGradNPUKernel, + ops::WhereGradNPUKernel); diff --git a/python/paddle/fluid/tests/unittests/npu/test_where_op_npu.py b/python/paddle/fluid/tests/unittests/npu/test_where_op_npu.py new file mode 100755 index 0000000000000000000000000000000000000000..cf877ff2872afa77931eea1194dbfeb281fce7f0 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/npu/test_where_op_npu.py @@ -0,0 +1,165 @@ +# 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. + +from __future__ import print_function, division + +import numpy as np +import unittest +import sys +sys.path.append("..") +from op_test import OpTest +import paddle +import paddle.fluid as fluid +from paddle.fluid import Program +from paddle.fluid.backward import append_backward + +paddle.enable_static() + + +class TestNPUWhereOp(OpTest): + def setUp(self): + self.op_type = "where" + self.set_npu() + self.init_config() + self.inputs = {'Condition': self.cond, 'X': self.x, 'Y': self.y} + self.outputs = {'Out': np.where(self.cond, self.x, self.y)} + + def init_config(self): + self.x = np.random.uniform(-3, 5, (100)).astype("float64") + self.y = np.random.uniform(-3, 5, (100)).astype("float64") + self.cond = np.zeros((100)).astype("bool") + + def set_npu(self): + self.__class__.use_npu = True + self.place = paddle.NPUPlace(0) + + def test_check_output(self): + self.check_output_with_place(self.place) + + def test_check_grad_normal(self): + self.check_grad_with_place(self.place, ['X', 'Y'], 'Out') + + +class TestNPUWhereOp2(TestNPUWhereOp): + def init_config(self): + self.x = np.random.uniform(-5, 5, (60, 2)).astype("float64") + self.y = np.random.uniform(-5, 5, (60, 2)).astype("float64") + self.cond = np.ones((60, 2)).astype("bool") + + +class TestNPUWhereOp3(TestNPUWhereOp): + def init_config(self): + self.x = np.random.uniform(-3, 5, (20, 2, 4)).astype("float64") + self.y = np.random.uniform(-3, 5, (20, 2, 4)).astype("float64") + self.cond = np.array(np.random.randint(2, size=(20, 2, 4)), dtype=bool) + + +class TestNPUWhereAPI(unittest.TestCase): + def setUp(self): + self.__class__.use_npu = True + self.place = paddle.NPUPlace(0) + self.init_data() + + def init_data(self): + self.shape = [10, 15] + self.cond = np.array(np.random.randint(2, size=self.shape), dtype=bool) + self.x = np.random.uniform(-2, 3, self.shape).astype(np.float32) + self.y = np.random.uniform(-2, 3, self.shape).astype(np.float32) + self.out = np.where(self.cond, self.x, self.y) + + def ref_x_backward(self, dout): + return np.where(self.cond == True, dout, 0) + + def ref_y_backward(self, dout): + return np.where(self.cond == False, dout, 0) + + def test_api(self): + for x_stop_gradient in [False, True]: + for y_stop_gradient in [False, True]: + train_prog = fluid.Program() + startup = fluid.Program() + with fluid.program_guard(train_prog, startup): + cond = fluid.data( + name='cond', shape=self.shape, dtype='bool') + x = fluid.data(name='x', shape=self.shape, dtype='float32') + y = fluid.data(name='y', shape=self.shape, dtype='float32') + + x.stop_gradient = x_stop_gradient + y.stop_gradient = y_stop_gradient + + result = paddle.where(cond, x, y) + append_backward(fluid.layers.mean(result)) + + exe = fluid.Executor(self.place) + exe.run(startup) + + fetch_list = [result, result.grad_name] + if x_stop_gradient is False: + fetch_list.append(x.grad_name) + if y_stop_gradient is False: + fetch_list.append(y.grad_name) + out = exe.run( + train_prog, + feed={'cond': self.cond, + 'x': self.x, + 'y': self.y}, + fetch_list=fetch_list) + assert np.array_equal(out[0], self.out) + + if x_stop_gradient is False: + assert np.array_equal(out[2], + self.ref_x_backward(out[1])) + if y.stop_gradient is False: + assert np.array_equal(out[3], + self.ref_y_backward(out[1])) + elif y.stop_gradient is False: + assert np.array_equal(out[2], + self.ref_y_backward(out[1])) + + def test_api_broadcast(self, use_cuda=False): + train_prog = fluid.Program() + startup = fluid.Program() + with fluid.program_guard(train_prog, startup): + x = fluid.layers.data(name='x', shape=[4, 1], dtype='float32') + y = fluid.layers.data(name='y', shape=[4, 2], dtype='float32') + x_i = np.array([[0.9383, 0.1983, 3.2, 1.2]]).astype("float32") + y_i = np.array([[1.0, 1.0, 1.0, 1.0], + [1.0, 1.0, 1.0, 1.0]]).astype("float32") + result = paddle.where(x > 1, x=x, y=y) + + exe = fluid.Executor(self.place) + exe.run(startup) + + out = exe.run(train_prog, + feed={'x': x_i, + 'y': y_i}, + fetch_list=[result]) + assert np.array_equal(out[0], np.where(x_i > 1, x_i, y_i)) + + +class TestWhereDygraphAPI(unittest.TestCase): + def test_api(self): + with fluid.dygraph.guard(paddle.NPUPlace(0)): + x_i = np.array([0.9383, 0.1983, 3.2, 1.2]).astype("float64") + y_i = np.array([1.0, 1.0, 1.0, 1.0]).astype("float64") + cond_i = np.array([False, False, True, True]).astype("bool") + x = fluid.dygraph.to_variable(x_i) + y = fluid.dygraph.to_variable(y_i) + cond = fluid.dygraph.to_variable(cond_i) + out = paddle.where(cond, x, y) + assert np.array_equal(out.numpy(), np.where(cond_i, x_i, y_i)) + + +if __name__ == '__main__': + unittest.main()