/* 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. */ #ifndef _WIN32 #include #endif #include #include // NOLINT #include #include "gtest/gtest.h" #include "paddle/fluid/framework/op_registry.h" #include "paddle/fluid/framework/operator.h" #include "paddle/fluid/framework/program_desc.h" #include "paddle/fluid/operators/dropout_op.h" #include "paddle/fluid/operators/math/math_function.h" #include "paddle/fluid/string/printf.h" namespace f = paddle::framework; namespace p = paddle::platform; namespace m = paddle::operators::math; USE_OP(gelu); USE_OP_DEVICE_KERNEL(gelu, NPU); template void Compare(f::Scope* scope, const p::DeviceContext& ctx) { // init auto x = scope->Var("X"); auto tensor_x = x->GetMutable(); std::vector init_x; for (int64_t i = 0; i < 10 * 10; ++i) { init_x.push_back(static_cast(1.0)); } TensorFromVector(init_x, ctx, tensor_x); tensor_x->Resize({10, 10}); auto out = scope->Var("Out"); auto tensor_out = out->GetMutable(); f::AttributeMap attrs; ctx.Wait(); // run auto place = ctx.GetPlace(); auto op = f::OpRegistry::CreateOp("gelu", {{"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); float expected = 0.841192; for (uint32_t i = 0; i < out_vec.size(); i++) { EXPECT_FLOAT_EQ(out_vec[i], static_cast(expected)); } } template void CompareGrad(f::Scope* scope, const p::DeviceContext& ctx) { auto dout = scope->Var("DOut"); auto tensor_dout = dout->GetMutable(); auto x = scope->Var("X"); auto tensor_x = x->GetMutable(); std::vector init_dout; for (int64_t i = 0; i < 10 * 10; ++i) { init_dout.push_back(static_cast(1.0)); } std::vector init_x; for (int64_t i = 0; i < 10 * 10; ++i) { init_x.push_back(static_cast(1.0)); } TensorFromVector(init_dout, ctx, tensor_dout); tensor_dout->Resize({10, 10}); TensorFromVector(init_x, ctx, tensor_x); tensor_x->Resize({10, 10}); auto dx = scope->Var("DX"); auto tensor_dx = dx->GetMutable(); f::AttributeMap attrs; ctx.Wait(); // run auto place = ctx.GetPlace(); auto op = f::OpRegistry::CreateOp("gelu_grad", {{"Out@GRAD", {"DOut"}}, {"X", {"X"}}}, {{"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); float expected = 1.082964; for (uint32_t i = 0; i < dx_vec.size(); i++) { EXPECT_FLOAT_EQ(dx_vec[i], static_cast(expected)); } } TEST(gelu, NPU_fp32) { f::Scope scope; p::NPUDeviceContext ctx(p::NPUPlace(0)); Compare(&scope, ctx); } TEST(gelu_grad, NPU) { f::Scope scope; p::NPUDeviceContext ctx(p::NPUPlace(0)); CompareGrad(&scope, ctx); }