Support npu kernel for gather op (#31458)
* add gather npu op * code review done * update python new line * precommit * fix review * del commitrevert-31562-mean
xiayanming
5 years ago
committed by
GitHub
parent
15823bb0df
commit
e19195f795
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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");
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you may not use this file except in compliance with the License.
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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
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/fluid/operators/gather_op.h"
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#include <memory>
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#include <string>
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#include <vector>
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#include "paddle/fluid/framework/tensor_util.h"
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#include "paddle/fluid/operators/kron_op.h"
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#include "paddle/fluid/operators/npu_op_runner.h"
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namespace paddle {
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namespace operators {
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template <typename DeviceContext, typename T>
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class GatherOpNPUKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext &ctx) const override {
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auto *x = ctx.Input<Tensor>("X");
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auto *index = ctx.Input<Tensor>("Index");
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auto *out = ctx.Output<Tensor>("Out");
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out->mutable_data<T>(ctx.GetPlace());
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auto runner = NpuOpRunner("Gather", {*x, *index}, {*out},
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{{"validate_indices", true}});
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auto stream =
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ctx.template device_context<paddle::platform::NPUDeviceContext>()
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.stream();
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runner.Run(stream);
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}
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};
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template <typename DeviceContext, typename T>
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class GatherGradOpNPUKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext &ctx) const override {
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auto *index = ctx.Input<Tensor>("Index");
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auto *x = ctx.Input<Tensor>("X");
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auto *dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
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auto *dx = ctx.Output<Tensor>(framework::GradVarName("X"));
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// step1: Unsqueeze index
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const auto index_dims = index->dims();
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if (index_dims.size() == 1) {
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framework::Tensor tmp_index = UnsqueezeTo(*index, 2);
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index = &tmp_index;
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}
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auto stream =
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ctx.template device_context<paddle::platform::NPUDeviceContext>()
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.stream();
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// step2: ZerosLike x in device
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Tensor *tmp_zerox = const_cast<Tensor *>(x);
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Tensor zeroslike_xout(x->type());
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zeroslike_xout.Resize(x->dims());
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zeroslike_xout.mutable_data<T>(ctx.GetPlace());
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auto runner_zeroslike =
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NpuOpRunner("ZerosLike", {*x}, {zeroslike_xout}, {});
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runner_zeroslike.Run(stream);
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tmp_zerox = &zeroslike_xout;
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// step3: scatter(x_grad)
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dx->mutable_data<T>(ctx.GetPlace());
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auto runner_scatter = NpuOpRunner("TensorScatterUpdate",
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{*tmp_zerox, *index, *dout}, {*dx}, {});
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runner_scatter.Run(stream);
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_NPU_KERNEL(
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gather, ops::GatherOpNPUKernel<paddle::platform::NPUDeviceContext, float>,
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ops::GatherOpNPUKernel<paddle::platform::NPUDeviceContext,
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paddle::platform::float16>);
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REGISTER_OP_NPU_KERNEL(
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gather_grad,
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ops::GatherGradOpNPUKernel<paddle::platform::NPUDeviceContext, float>,
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ops::GatherGradOpNPUKernel<paddle::platform::NPUDeviceContext,
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paddle::platform::float16>);
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@ -0,0 +1,169 @@
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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");
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you may not use this file except in compliance with the License.
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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
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#ifndef _WIN32
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#include <unistd.h>
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#endif
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#include <string>
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#include <thread> // NOLINT
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#include <vector>
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#include "gtest/gtest.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/framework/operator.h"
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#include "paddle/fluid/framework/program_desc.h"
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#include "paddle/fluid/operators/gather_op.h"
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#include "paddle/fluid/operators/math/math_function.h"
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#include "paddle/fluid/string/printf.h"
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namespace f = paddle::framework;
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namespace p = paddle::platform;
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namespace m = paddle::operators::math;
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USE_OP(gather);
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USE_OP_DEVICE_KERNEL(gather, NPU);
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USE_OP(gather_grad);
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USE_OP_DEVICE_KERNEL(gather_grad, NPU);
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template <typename T>
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void Compare(f::Scope* scope, const p::DeviceContext& ctx,
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std::string op_type) {
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// init
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auto x = scope->Var("X");
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auto tensor_x = x->GetMutable<f::LoDTensor>();
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auto index = scope->Var("Index");
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auto tensor_index = index->GetMutable<f::LoDTensor>();
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std::vector<T> init_x;
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for (int64_t i = 1; i < 7; ++i) {
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// 1,2,3,4,5,6
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init_x.push_back(static_cast<T>(i));
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}
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// [[1, 2],[3, 4],[5, 6]]
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TensorFromVector(init_x, ctx, tensor_x);
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tensor_x->Resize(paddle::framework::make_ddim({3, 2}));
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std::vector<int> init_index = {1, 2};
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paddle::framework::TensorFromVector<int>(init_index, ctx, tensor_index);
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tensor_index->Resize(paddle::framework::make_ddim({2}));
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ctx.Wait();
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auto out = scope->Var("Out");
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auto tensor_out = out->GetMutable<f::LoDTensor>();
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// run
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f::AttributeMap attrs = {{"validate_indices", true}};
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auto op = f::OpRegistry::CreateOp(
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op_type, {{"X", {"X"}}, {"Index", {"Index"}}}, {{"Out", {"Out"}}}, attrs);
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auto place = ctx.GetPlace();
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op->Run(*scope, place);
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std::vector<T> out_vec;
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TensorToVector(*tensor_out, ctx, &out_vec);
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ctx.Wait();
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// ref:https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api/paddle/tensor/manipulation/gather_cn.html#gather
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for (int i = 0; i < static_cast<int>(out_vec.size()); ++i) {
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VLOG(3) << "out_vec[" << i << "] : " << out_vec[i];
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}
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uint32_t expected_size = 4;
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EXPECT_EQ((uint32_t)out_vec.size(), expected_size);
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// {3, 4, 5, 6}
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std::vector<T> expected_out_vec;
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for (int64_t i = 3; i < 7; ++i) {
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expected_out_vec.push_back(static_cast<T>(i));
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}
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for (uint32_t i = 0; i < out_vec.size(); i++) {
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EXPECT_EQ(out_vec[i], expected_out_vec[i]);
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}
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}
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template <typename T>
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void CompareGrad(f::Scope* scope, const p::DeviceContext& ctx,
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std::string op_type) {
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// init
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auto index = scope->Var("Index");
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auto tensor_index = index->GetMutable<f::LoDTensor>();
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auto x = scope->Var("X");
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auto tensor_x = x->GetMutable<f::LoDTensor>();
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auto dout = scope->Var("DOut");
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auto tensor_dout = dout->GetMutable<f::LoDTensor>();
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std::vector<int> init_index = {0, 1, 2, 0};
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paddle::framework::TensorFromVector<int>(init_index, ctx, tensor_index);
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tensor_index->Resize(paddle::framework::make_ddim({2, 2}));
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std::vector<T> init_x = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0};
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TensorFromVector(init_x, ctx, tensor_x);
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tensor_x->Resize(paddle::framework::make_ddim({3, 2}));
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std::vector<T> init_dout = {5.0, 10.0};
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TensorFromVector(init_dout, ctx, tensor_dout);
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tensor_dout->Resize(paddle::framework::make_ddim({2}));
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ctx.Wait();
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auto dx = scope->Var("DX");
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auto tensor_dx = dx->GetMutable<f::LoDTensor>();
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// run
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f::AttributeMap attrs;
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auto op = f::OpRegistry::CreateOp(
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op_type, {{"X", {"X"}}, {"Index", {"Index"}}, {"Out@GRAD", {"DOut"}}},
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{{"X@GRAD", {"DX"}}}, attrs);
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auto place = ctx.GetPlace();
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op->Run(*scope, place);
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std::vector<T> dx_vec;
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TensorToVector(*tensor_dx, ctx, &dx_vec);
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ctx.Wait();
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uint32_t expected_size = 3 * 2;
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EXPECT_EQ((uint32_t)dx_vec.size(), expected_size);
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std::vector<T> expected_dx_vec = {0.0, 5.0, 0.0, 0.0, 10.0, 0.0};
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for (uint32_t i = 0; i < dx_vec.size(); i++) {
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VLOG(3) << "dx_vec[i]=" << dx_vec[i];
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EXPECT_EQ(dx_vec[i], expected_dx_vec[i]);
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}
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}
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TEST(gather, NPU_fp32) {
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f::Scope scope;
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p::NPUDeviceContext ctx(p::NPUPlace(0));
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Compare<float>(&scope, ctx, "gather");
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}
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TEST(gather, NPU_fp16) {
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f::Scope scope;
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p::NPUDeviceContext ctx(p::NPUPlace(0));
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Compare<p::float16>(&scope, ctx, "gather");
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}
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TEST(gather_grad, NPU_fp32) {
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f::Scope scope;
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p::NPUDeviceContext ctx(p::NPUPlace(0));
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CompareGrad<float>(&scope, ctx, "gather_grad");
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}
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import numpy as np
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import unittest
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import sys
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sys.path.append("..")
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from op_test import OpTest, _set_use_system_allocator
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import paddle
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import paddle.fluid as fluid
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paddle.enable_static()
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@unittest.skipIf(not paddle.is_compiled_with_npu(),
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"core is not compiled with NPU")
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class TestGatherOp(OpTest):
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def setUp(self):
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self.set_npu()
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self.op_type = "gather"
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self.place = paddle.NPUPlace(0)
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self.init_dtype()
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self.init_input_output()
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self.inputs = {
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'X': OpTest.np_dtype_to_fluid_dtype(self.x),
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'Index': OpTest.np_dtype_to_fluid_dtype(self.index)
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}
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self.attrs = {'validate_indices': True}
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self.outputs = {'Out': self.out}
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def set_npu(self):
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self.__class__.use_npu = True
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def init_input_output(self):
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self.x = np.array([[1, 2], [3, 4], [5, 6]]).astype(self.dtype)
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self.index = np.array([1, 2]).astype(np.int)
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self.out = np.array([[3, 4], [5, 6]]).astype(self.dtype)
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def init_dtype(self):
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self.dtype = np.float32
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def test_check_output(self):
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self.check_output_with_place(self.place, check_dygraph=False)
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@unittest.skipIf(not paddle.is_compiled_with_npu(),
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"core is not compiled with NPU")
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class TestGatherAPI(unittest.TestCase):
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def test_name(self):
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data(name="x", shape=[3, 2], dtype="float32")
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index = paddle.static.data(name='index', shape=[1], dtype='int32')
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out = paddle.gather(x, index, name='gather')
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self.assertEqual(('gather' in out.name), True)
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def test_static(self):
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with paddle.static.program_guard(paddle.static.Program()):
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x_np = np.array([[1, 2], [3, 4], [5, 6]]).astype('float32')
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index_np = np.array([1, 2]).astype('int32')
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x = paddle.static.data(name="x", shape=[3, 2], dtype='float32')
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index = paddle.static.data(name="index", shape=[2], dtype='int32')
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z = paddle.gather(x, index)
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place = paddle.NPUPlace(0)
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exe = paddle.static.Executor(place)
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x_value, index_value, z_value = exe.run(
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feed={"x": x_np,
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"index": index_np}, fetch_list=[x, index, z])
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z_expected = np.array([[3, 4], [5, 6]])
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self.assertEqual(
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(x_value == x_np).all(),
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True,
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msg="x_value = {}, but expected {}".format(x_value, x_np))
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self.assertEqual(
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(index_value == index_np).all(),
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True,
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msg="index_value = {}, but expected {}".format(index_value,
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||||||
|
index_np))
|
||||||
|
self.assertEqual(
|
||||||
|
(z_value == z_expected).all(),
|
||||||
|
True,
|
||||||
|
msg="z_value = {}, but expected {}".format(z_value, z_expected))
|
||||||
|
|
||||||
|
def test_backward(self):
|
||||||
|
# TODO(ascendrc): Test backward after add grad npu op implemented.
|
||||||
|
pass
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
unittest.main()
|
||||||
Loading…
Reference in new issue