/* 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 #include "paddle/fluid/operators/activation_op.h" #include "paddle/fluid/operators/math/math_function.h" #include "paddle/fluid/platform/device/mlu/device_context.h" #include "paddle/fluid/platform/place.h" namespace fw = paddle::framework; namespace plat = paddle::platform; namespace math = paddle::operators::math; USE_OP(relu); USE_OP_DEVICE_KERNEL(relu, MLU); // relu template inline T relu(T x) { return x > 0 ? x : 0.; } template inline T relu_grad_dx(T x, T out, T dout) { return out > 0 ? dout : 0; } template void Compare(fw::Scope* scope, const plat::DeviceContext& ctx, std::string op_type) { // init auto x = scope->Var("X"); auto tensor_x = x->GetMutable(); const int num = 10; std::vector init_x; for (int64_t i = 0; i < num * num; ++i) { init_x.push_back(static_cast(i - 50)); } TensorFromVector(init_x, ctx, tensor_x); tensor_x->Resize({num, num}); auto place = ctx.GetPlace(); auto out = scope->Var("Out"); auto tensor_out = out->GetMutable(); fw::AttributeMap attrs; auto op = fw::OpRegistry::CreateOp(op_type, {{"X", {"X"}}}, {{"Out", {"Out"}}}, attrs); op->Run(*scope, place); ctx.Wait(); // eval time struct timeval start, end; gettimeofday(&start, NULL); for (int i = 0; i < 100; i++) { op->Run(*scope, place); } ctx.Wait(); gettimeofday(&end, NULL); int micros = (((end.tv_sec - start.tv_sec) * 1000000) + end.tv_usec) - (start.tv_usec); printf("used time: %d\n", micros / 100); // eval value std::vector out_vec; TensorToVector(*tensor_out, ctx, &out_vec); ctx.Wait(); for (uint32_t i = 0; i < out_vec.size(); i++) { EXPECT_FLOAT_EQ(out_vec[i], relu(init_x[i])); } } template void CompareGrad(fw::Scope* scope, const plat::DeviceContext& ctx, std::string op_type) { auto dout = scope->Var("DOut"); auto tensor_dout = dout->GetMutable(); auto out = scope->Var("Out"); auto tensor_out = out->GetMutable(); const int num = 10; std::vector init_dout; for (int64_t i = 0; i < num * num; ++i) { init_dout.push_back(static_cast(1.0)); } std::vector init_out; for (int64_t i = 0; i < num * num; ++i) { init_out.push_back(static_cast(i - 50)); } TensorFromVector(init_dout, ctx, tensor_dout); tensor_dout->Resize({num, num}); TensorFromVector(init_out, ctx, tensor_out); tensor_out->Resize({num, num}); auto dx = scope->Var("DX"); auto tensor_dx = dx->GetMutable(); // run auto place = ctx.GetPlace(); fw::AttributeMap attrs; auto op = fw::OpRegistry::CreateOp(op_type, {{"Out@GRAD", {"DOut"}}, {"Out", {"Out"}}}, {{"X@GRAD", {"DX"}}}, attrs); op->Run(*scope, place); ctx.Wait(); // eval time struct timeval start, end; gettimeofday(&start, NULL); for (int i = 0; i < 100; i++) { op->Run(*scope, place); } ctx.Wait(); gettimeofday(&end, NULL); int micros = (((end.tv_sec - start.tv_sec) * 1000000) + end.tv_usec) - (start.tv_usec); printf("used time: %d\n", micros / 100); // eval value std::vector dx_vec; TensorToVector(*tensor_dx, ctx, &dx_vec); ctx.Wait(); for (uint32_t i = 0; i < dx_vec.size(); i++) { EXPECT_FLOAT_EQ(dx_vec[i], relu_grad_dx(dx_vec[i], init_out[i], init_dout[i])); } } TEST(relu, MLU_fp32) { fw::Scope scope; auto* ctx = plat::DeviceContextPool::Instance().Get(plat::MLUPlace(0)); Compare(&scope, *ctx, "relu"); } TEST(relu_grad, MLU_fp32) { fw::Scope scope; auto* ctx = plat::DeviceContextPool::Instance().Get(plat::MLUPlace(0)); CompareGrad(&scope, *ctx, "relu_grad"); }