From f74dff97ea625a557146fe8c11313e5242c7cd0a Mon Sep 17 00:00:00 2001 From: guosheng Date: Thu, 28 Dec 2017 17:36:16 +0800 Subject: [PATCH] Refine the activation type in the GRU operator related --- paddle/operators/gru_op.h | 19 +++++++---- paddle/operators/math/detail/gru_cpu_kernel.h | 34 +++++++++---------- paddle/operators/math/detail/gru_gpu_kernel.h | 10 +++--- paddle/operators/math/detail/gru_kernel.h | 17 +++++----- paddle/operators/math/gru_compute.cc | 12 +++---- paddle/operators/math/gru_compute.cu | 10 +++--- paddle/operators/math/gru_compute.h | 21 ++++++------ 7 files changed, 60 insertions(+), 63 deletions(-) diff --git a/paddle/operators/gru_op.h b/paddle/operators/gru_op.h index c6228864d7e..d7735212590 100644 --- a/paddle/operators/gru_op.h +++ b/paddle/operators/gru_op.h @@ -14,6 +14,7 @@ limitations under the License. */ #pragma once +#include "paddle/operators/math/detail/activation_functions.h" #include "paddle/operators/math/gru_compute.h" #include "paddle/operators/math/math_function.h" #include "paddle/operators/math/sequence2batch.h" @@ -70,7 +71,7 @@ class GRUKernel : public framework::OpKernel { } int frame_size = hidden_dims[1]; - math::hl_gru_value gru_value; + math::GRUMetaValue gru_value; gru_value.gate_weight = const_cast(weight_data); gru_value.state_weight = const_cast(weight_data + 2 * frame_size * frame_size); @@ -102,8 +103,10 @@ class GRUKernel : public framework::OpKernel { gru_value.reset_output_value = reset_hidden_prev_t.data(); math::GRUUnitFunctor::compute( dev_ctx, gru_value, frame_size, cur_batch_size, - math::ActiveType(context.Attr("activation")), - math::ActiveType(context.Attr("gate_activation"))); + math::detail::GetActivationType( + context.Attr("activation")), + math::detail::GetActivationType( + context.Attr("gate_activation"))); gru_value.prev_out_value = gru_value.output_value; } @@ -170,12 +173,12 @@ class GRUGradKernel : public framework::OpKernel { batch_hidden_grad.set_lod(batch_hidden->lod()); to_batch(dev_ctx, *hidden_grad, batch_hidden_grad, false, is_reverse); - math::hl_gru_value gru_value; + math::GRUMetaValue gru_value; gru_value.gate_weight = const_cast(weight_data); gru_value.state_weight = const_cast(weight_data + 2 * frame_size * frame_size); - math::hl_gru_grad gru_grad; + math::GRUMetaGrad gru_grad; if (weight_grad) { gru_grad.gate_weight_grad = weight_grad->mutable_data(context.GetPlace()); @@ -220,8 +223,10 @@ class GRUGradKernel : public framework::OpKernel { math::GRUUnitGradFunctor::compute( dev_ctx, gru_value, gru_grad, frame_size, cur_batch_size, - math::ActiveType(context.Attr("activation")), - math::ActiveType(context.Attr("gate_activation"))); + math::detail::GetActivationType( + context.Attr("activation")), + math::detail::GetActivationType( + context.Attr("gate_activation"))); } if (input_grad) { input_grad->mutable_data(context.GetPlace()); diff --git a/paddle/operators/math/detail/gru_cpu_kernel.h b/paddle/operators/math/detail/gru_cpu_kernel.h index 4c67dec9cbe..a61b232f427 100644 --- a/paddle/operators/math/detail/gru_cpu_kernel.h +++ b/paddle/operators/math/detail/gru_cpu_kernel.h @@ -28,7 +28,7 @@ template void hl_naive_gru_forward_reset_output(OpResetOutput op_reset_output, T *gate_value, T *reset_output_value, T *prev_output_value, int frame_size, - activation_mode_t active_gate) { + ActivationType active_gate) { T r_value_update_gate; T r_value_reset_gate; T r_value_reset_output; @@ -56,7 +56,7 @@ template void hl_naive_gru_forward_final_output(OpFinalOutput op_final_output, T *gate_value, T *prev_output_value, T *output_value, int frame_size, - activation_mode_t active_node) { + ActivationType active_node) { T r_value_update_gate; T r_value_frame_state; T r_prev_out = 0; @@ -83,7 +83,7 @@ template void hl_avx_gru_forward_reset_output(OpResetOutput op_reset_output, T *gate_value, T *reset_output_value, T *prev_output_value, int frame_size, - activation_mode_t active_gate) { + ActivationType active_gate) { #ifdef __AVX__ __m256 r_value_update_gate; __m256 r_value_reset_gate; @@ -113,7 +113,7 @@ template void hl_avx_gru_forward_final_output(OpFinalOutput op_final_output, T *gate_value, T *prev_output_value, T *output_value, int frame_size, - activation_mode_t active_node) { + ActivationType active_node) { #ifdef __AVX__ __m256 r_value_update_gate; __m256 r_value_frame_state; @@ -140,9 +140,8 @@ void hl_avx_gru_forward_final_output(OpFinalOutput op_final_output, template inline void forward_reset_output(OpResetOutput op_reset_output, - hl_gru_value value, int frame_size, - int batch_size, - activation_mode_t active_gate) { + GRUMetaValue value, int frame_size, + int batch_size, ActivationType active_gate) { for (int b = 0; b < batch_size; b++) { if (OpResetOutput::avx && !(frame_size & (8 - 1)) && (sizeof(T) == 4)) { hl_avx_gru_forward_reset_output( @@ -164,9 +163,8 @@ inline void forward_reset_output(OpResetOutput op_reset_output, template inline void forward_final_output(OpFinalOutput op_final_output, - hl_gru_value value, int frame_size, - int batch_size, - activation_mode_t active_node) { + GRUMetaValue value, int frame_size, + int batch_size, ActivationType active_node) { for (int b = 0; b < batch_size; b++) { if (OpFinalOutput::avx && !(frame_size & (8 - 1)) && (sizeof(T) == 4)) { hl_avx_gru_forward_final_output(op_final_output, value.gate_value, @@ -191,7 +189,7 @@ void hl_naive_gru_backward_state_grad(OpStateGrad op_state_grad, T *gate_value, T *gate_grad, T *prev_out_value, T *prev_out_grad, T *output_grad, int frame_size, - activation_mode_t active_node) { + ActivationType active_node) { T r_update_gate_value; T r_update_gate_grad; T r_frame_state_value; @@ -232,7 +230,7 @@ void hl_naive_gru_backward_reset_grad(OpResetGrad op_reset_grad, T *gate_value, T *gate_grad, T *prev_out_value, T *prev_out_grad, T *reset_output_grad, int frame_size, - activation_mode_t active_gate) { + ActivationType active_gate) { T r_update_gate_value; T r_update_gate_grad; T r_reset_gate_value; @@ -277,7 +275,7 @@ void hl_avx_gru_backward_state_grad(OpStateGrad op_state_grad, T *gate_value, T *gate_grad, T *prev_out_value, T *prev_out_grad, T *output_grad, int frame_size, - activation_mode_t active_node) { + ActivationType active_node) { #ifdef __AVX__ __m256 r_update_gate_value; __m256 r_update_gate_grad; @@ -320,7 +318,7 @@ void hl_avx_gru_backward_reset_grad(OpResetGrad op_reset_grad, T *gate_value, T *gate_grad, T *prev_out_value, T *prev_out_grad, T *reset_output_grad, int frame_size, - activation_mode_t active_gate) { + ActivationType active_gate) { #ifdef __AVX__ __m256 r_update_gate_value; __m256 r_update_gate_grad; @@ -364,9 +362,9 @@ void hl_avx_gru_backward_reset_grad(OpResetGrad op_reset_grad, T *gate_value, template inline void backward_state_grad(OpStateGrad op_state_grad, - hl_gru_value value, hl_gru_grad grad, + GRUMetaValue value, GRUMetaGrad grad, int frame_size, int batch_size, - activation_mode_t active_node) { + ActivationType active_node) { for (int b = 0; b < batch_size; b++) { if (OpStateGrad::avx && !(frame_size & (8 - 1)) && (sizeof(T) == 4)) { hl_avx_gru_backward_state_grad( @@ -393,9 +391,9 @@ inline void backward_state_grad(OpStateGrad op_state_grad, template inline void backward_reset_grad(OpResetGrad op_reset_grad, - hl_gru_value value, hl_gru_grad grad, + GRUMetaValue value, GRUMetaGrad grad, int frame_size, int batch_size, - activation_mode_t active_gate) { + ActivationType active_gate) { for (int b = 0; b < batch_size; b++) { if (OpResetGrad::avx && !(frame_size & (8 - 1)) && (sizeof(T) == 4)) { hl_avx_gru_backward_reset_grad( diff --git a/paddle/operators/math/detail/gru_gpu_kernel.h b/paddle/operators/math/detail/gru_gpu_kernel.h index d2edcb7f258..1783d460968 100644 --- a/paddle/operators/math/detail/gru_gpu_kernel.h +++ b/paddle/operators/math/detail/gru_gpu_kernel.h @@ -19,8 +19,6 @@ limitations under the License. */ #include "paddle/platform/cuda_helper.h" #include "paddle/platform/device_context.h" -#include - namespace paddle { namespace operators { namespace math { @@ -35,7 +33,7 @@ __global__ void KeGruForwardResetOutput(OpResetOutput op_reset_output, T *gate_value, T *reset_output_value, T *prev_output_value, int frame_size, int batch_size, - activation_mode_t active_gate) { + ActivationType active_gate) { const int frame_idx = blockIdx.x * blockDim.x + threadIdx.x; if (frame_idx >= frame_size) return; @@ -74,7 +72,7 @@ __global__ void KeGruForwardFinalOutput(OpFinalOutput op_final_output, T *gate_value, T *prev_output_value, T *output_value, int frame_size, int batch_size, - activation_mode_t active_node) { + ActivationType active_node) { const int frame_idx = blockIdx.x * blockDim.x + threadIdx.x; if (frame_idx >= frame_size) return; int batch_idx = 0; @@ -111,7 +109,7 @@ __global__ void KeGruBackwardStateGrad(OpStateGrad op_state_grad, T *gate_value, T *gate_grad, T *prev_out_value, T *prev_out_grad, T *output_grad, int frame_size, int batch_size, - activation_mode_t active_node) { + ActivationType active_node) { const int frame_idx = blockIdx.x * blockDim.x + threadIdx.x; if (frame_idx >= frame_size) return; int batch_idx = 0; @@ -159,7 +157,7 @@ __global__ void KeGruBackwardResetGrad(OpResetGrad op_reset_grad, T *gate_value, T *gate_grad, T *prev_out_value, T *prev_out_grad, T *reset_output_grad, int frame_size, int batch_size, - activation_mode_t active_gate) { + ActivationType active_gate) { const int frame_idx = blockIdx.x * blockDim.x + threadIdx.x; if (frame_idx >= frame_size) return; int batch_idx = 0; diff --git a/paddle/operators/math/detail/gru_kernel.h b/paddle/operators/math/detail/gru_kernel.h index acd84be01db..4d8245cb5d0 100644 --- a/paddle/operators/math/detail/gru_kernel.h +++ b/paddle/operators/math/detail/gru_kernel.h @@ -30,7 +30,7 @@ class gru_resetOutput { public: HOSTDEVICE void operator()(T &value_update_gate, T &value_reset_gate, T &prev_out, T &value_reset_output, - activation_mode_t act_gate) { + ActivationType act_gate) { value_update_gate = activation(value_update_gate, act_gate); value_reset_gate = activation(value_reset_gate, act_gate); value_reset_output = prev_out * value_reset_gate; @@ -43,7 +43,7 @@ class gru_resetOutput { HOSTDEVICE void operator()(__m256 &value_update_gate, __m256 &value_reset_gate, __m256 &prev_out, __m256 &value_reset_output, - activation_mode_t act_gate) { + ActivationType act_gate) { value_update_gate = activation(value_update_gate, act_gate); value_reset_gate = activation(value_reset_gate, act_gate); value_reset_output = _mm256_mul_ps(prev_out, value_reset_gate); @@ -57,7 +57,7 @@ class gru_finalOutput { public: HOSTDEVICE void operator()(T &value_update_gate, T &value_frame_state, T &prev_out, T &value_output, - activation_mode_t act_input) { + ActivationType act_input) { value_frame_state = activation(value_frame_state, act_input); value_output = prev_out - (value_update_gate * prev_out) + (value_update_gate * value_frame_state); @@ -69,8 +69,7 @@ class gru_finalOutput { static const bool avx = true; HOSTDEVICE void operator()(__m256 &value_update_gate, __m256 &value_frame_state, __m256 &prev_out, - __m256 &value_output, - activation_mode_t act_input) { + __m256 &value_output, ActivationType act_input) { value_frame_state = activation(value_frame_state, act_input); value_output = _mm256_add_ps( _mm256_sub_ps(prev_out, _mm256_mul_ps(value_update_gate, prev_out)), @@ -89,7 +88,7 @@ class gru_stateGrad { HOSTDEVICE void operator()(T &value_update_gate, T &grad_update_gate, T &value_frame_state, T &grad_frame_state, T &value_prev_out, T &grad_prev_out, - T &grad_output, activation_mode_t act_input) { + T &grad_output, ActivationType act_input) { grad_update_gate = (grad_output * value_frame_state); grad_update_gate -= (grad_output * value_prev_out); grad_prev_out -= (grad_output * value_update_gate); @@ -107,7 +106,7 @@ class gru_stateGrad { __m256 &value_frame_state, __m256 &grad_frame_state, __m256 &value_prev_out, __m256 &grad_prev_out, __m256 &grad_output, - activation_mode_t act_input) { + ActivationType act_input) { grad_update_gate = _mm256_mul_ps(grad_output, value_frame_state); grad_update_gate = _mm256_sub_ps( grad_update_gate, _mm256_mul_ps(grad_output, value_prev_out)); @@ -128,7 +127,7 @@ class gru_resetGrad { HOSTDEVICE void operator()(T &value_update_gate, T &grad_update_gate, T &value_reset_gate, T &grad_reset_gate, T &value_prev_out, T &grad_prev_out, - T &grad_reset_output, activation_mode_t act_gate) { + T &grad_reset_output, ActivationType act_gate) { grad_reset_gate = (grad_reset_output * value_prev_out); grad_prev_out += (grad_reset_output * value_reset_gate); grad_update_gate = @@ -144,7 +143,7 @@ class gru_resetGrad { __m256 &grad_update_gate, __m256 &value_reset_gate, __m256 &grad_reset_gate, __m256 &value_prev_out, __m256 &grad_prev_out, __m256 &grad_reset_output, - activation_mode_t act_gate) { + ActivationType act_gate) { grad_reset_gate = _mm256_mul_ps(grad_reset_output, value_prev_out); grad_prev_out = _mm256_add_ps( grad_prev_out, _mm256_mul_ps(grad_reset_output, value_reset_gate)); diff --git a/paddle/operators/math/gru_compute.cc b/paddle/operators/math/gru_compute.cc index d570c68cd45..101ab859624 100644 --- a/paddle/operators/math/gru_compute.cc +++ b/paddle/operators/math/gru_compute.cc @@ -21,9 +21,9 @@ namespace math { template struct GRUUnitFunctor { static void compute(const platform::CPUDeviceContext &context, - hl_gru_value value, int frame_size, int batch_size, - activation_mode_t active_node, - activation_mode_t active_gate) { + GRUMetaValue value, int frame_size, int batch_size, + const detail::ActivationType active_node, + const detail::ActivationType active_gate) { #ifndef __NVCC__ if (value.prev_out_value) { math::gemm( @@ -51,10 +51,10 @@ struct GRUUnitFunctor { template struct GRUUnitGradFunctor { static void compute(const platform::CPUDeviceContext &context, - hl_gru_value value, hl_gru_grad grad, + GRUMetaValue value, GRUMetaGrad grad, int frame_size, int batch_size, - activation_mode_t active_node, - activation_mode_t active_gate) { + const detail::ActivationType active_node, + const detail::ActivationType active_gate) { #ifndef __NVCC__ detail::backward_state_grad(detail::backward::gru_stateGrad(), value, grad, frame_size, batch_size, active_node); diff --git a/paddle/operators/math/gru_compute.cu b/paddle/operators/math/gru_compute.cu index dd518cd1e4b..aab3e2309bb 100644 --- a/paddle/operators/math/gru_compute.cu +++ b/paddle/operators/math/gru_compute.cu @@ -21,9 +21,8 @@ namespace math { template struct GRUUnitFunctor { static void compute(const platform::CUDADeviceContext &context, - hl_gru_value value, int frame_size, int batch_size, - activation_mode_t active_node, - activation_mode_t active_gate) { + GRUMetaValue value, int frame_size, int batch_size, + ActivationType active_node, ActivationType active_gate) { auto stream = context.stream(); dim3 threads; dim3 grid; @@ -88,10 +87,9 @@ struct GRUUnitFunctor { template struct GRUUnitGradFunctor { static void compute(const platform::CUDADeviceContext &context, - hl_gru_value value, hl_gru_grad grad, + GRUMetaValue value, GRUMetaGrad grad, int frame_size, int batch_size, - activation_mode_t active_node, - activation_mode_t active_gate) { + ActivationType active_node, ActivationType active_gate) { auto stream = context.stream(); dim3 threads; dim3 grid; diff --git a/paddle/operators/math/gru_compute.h b/paddle/operators/math/gru_compute.h index ca1343cb2c5..bf69147b506 100644 --- a/paddle/operators/math/gru_compute.h +++ b/paddle/operators/math/gru_compute.h @@ -11,7 +11,7 @@ limitations under the License. */ #pragma once -#include "paddle/operators/math/lstm_compute.h" +#include "paddle/operators/math/detail/activation_functions.h" #include "paddle/platform/device_context.h" #include "paddle/platform/enforce.h" @@ -19,9 +19,8 @@ namespace paddle { namespace operators { namespace math { -// TODO(guosheng): refine code style in gru_compute template -struct hl_gru_value { +struct GRUMetaValue { T *gate_weight; T *state_weight; T *gate_value; @@ -31,7 +30,7 @@ struct hl_gru_value { }; template -struct hl_gru_grad { +struct GRUMetaGrad { T *gate_weight_grad; T *state_weight_grad; T *gate_grad; @@ -42,18 +41,18 @@ struct hl_gru_grad { template struct GRUUnitFunctor { - static void compute(const DeviceContext &context, hl_gru_value value, + static void compute(const DeviceContext &context, GRUMetaValue value, int frame_size, int batch_size, - activation_mode_t active_node, - activation_mode_t active_gate); + const detail::ActivationType active_node, + const detail::ActivationType active_gate); }; template struct GRUUnitGradFunctor { - static void compute(const DeviceContext &context, hl_gru_value value, - hl_gru_grad grad, int frame_size, int batch_size, - activation_mode_t active_node, - activation_mode_t active_gate); + static void compute(const DeviceContext &context, GRUMetaValue value, + GRUMetaGrad grad, int frame_size, int batch_size, + const detail::ActivationType active_node, + const detail::ActivationType active_gate); }; } // namespace math -- GitLab