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[X86/ARM] add gru mode for rnn #7026
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011b6d9
[X86] Add GRU for RNN, complete elementwise op, move cast from arm to…
mjp9527 0723667
pre-commit
mjp9527 539d7c7
[ARM] add RNN-GRU OP; Optimize RNN-GRU OP
mjp9527 8c9aa2e
fix complie bug
mjp9527 b4e522f
fix elementwise left problem
mjp9527 a3ec029
merge develop
mjp9527 d9272a2
fix windows ci
mjp9527 0b143ec
change arm cast test to host cast test
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,248 @@ | ||
| // Copyright (c) 2019 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. | ||
|
|
||
| #pragma once | ||
|
|
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| #include "lite/backends/arm/math/sgemm.h" | ||
| #ifdef LITE_WITH_ARM | ||
| #include <arm_neon.h> | ||
| #endif | ||
|
|
||
| namespace paddle { | ||
| namespace lite { | ||
| namespace arm { | ||
| namespace math { | ||
|
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| template <typename T> | ||
| struct RNNGRUValue { | ||
| const T* gate_weight; | ||
| const T* state_weight; | ||
| const T* reset_bias; | ||
| T* gate_value; | ||
| T* reset_output_value; | ||
| T* output_value; | ||
| const T* prev_out_value; | ||
| }; | ||
|
|
||
| template <typename T> | ||
| void rnn_activation(const T* din, | ||
| T* dout, | ||
| int size, | ||
| lite_api::ActivationType act_type, | ||
| int threads) { | ||
| switch (act_type) { | ||
| case lite_api::ActivationType::kSigmoid: | ||
| act_sigmoid(din, dout, size, threads); | ||
| break; | ||
| case lite_api::ActivationType::kSigmoid_v2: | ||
| act_sigmoid(din, dout, size, threads); | ||
| break; | ||
| case lite_api::ActivationType::kTanh: | ||
| act_tanh(din, dout, size, threads); | ||
| break; | ||
| case lite_api::ActivationType::kTanh_v2: | ||
| act_tanh(din, dout, size, threads); | ||
| break; | ||
| case lite_api::ActivationType::kRelu: | ||
| act_relu(din, dout, size, threads); | ||
| break; | ||
| default: | ||
| LOG(FATAL) << "unsupport activation type:" << static_cast<int>(act_type); | ||
| break; | ||
| } | ||
| } | ||
|
|
||
| template <typename T> | ||
| void compute_kernel(RNNGRUValue<T> value, | ||
| int frame_size, | ||
| int batch_size, | ||
| lite_api::ActivationType active_node, | ||
| lite_api::ActivationType active_gate) { | ||
| auto value_reset_gate = value.gate_value; | ||
| auto value_update_gate = value.gate_value + frame_size; | ||
| auto value_reset_output = value.reset_output_value; | ||
| auto value_reset_bias = value.reset_bias; | ||
| auto cell_state_value = value.gate_value + 2 * frame_size; | ||
| auto value_output = value.output_value; | ||
| auto value_prev_out = value.prev_out_value; | ||
|
|
||
| for (int b = 0; b < batch_size; b++) { | ||
| rnn_activation(value_reset_gate, | ||
| value_reset_gate, | ||
| frame_size, | ||
| lite_api::ActivationType::kSigmoid_v2, | ||
| 1); | ||
| rnn_activation(value_update_gate, | ||
| value_update_gate, | ||
| frame_size, | ||
| lite_api::ActivationType::kSigmoid_v2, | ||
| 1); | ||
|
|
||
| for (int i = 0; i < frame_size; i++) { | ||
| value_reset_output[i] = | ||
| (value_reset_output[i] + value_reset_bias[i]) * value_reset_gate[i]; | ||
| cell_state_value[i] += value_reset_output[i]; | ||
| } | ||
|
|
||
| rnn_activation(cell_state_value, | ||
| cell_state_value, | ||
| frame_size, | ||
| lite_api::ActivationType::kTanh_v2, | ||
| 1); | ||
|
|
||
| if (value.prev_out_value) { | ||
| for (int i = 0; i < frame_size; i++) { | ||
| value_output[i] = (1.f - value_update_gate[i]) * cell_state_value[i] + | ||
| value_update_gate[i] * value_prev_out[i]; | ||
| } | ||
| } else { | ||
| for (int i = 0; i < frame_size; i++) { | ||
| value_output[i] = (1.f - value_update_gate[i]) * cell_state_value[i]; | ||
| } | ||
| } | ||
|
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| value_reset_gate += frame_size * 3; | ||
| value_update_gate += frame_size * 3; | ||
| value_reset_output += frame_size; | ||
| cell_state_value += frame_size * 3; | ||
| value_output += frame_size; | ||
| if (value.prev_out_value) { | ||
| value_prev_out += frame_size; | ||
| } | ||
| } | ||
| } | ||
|
|
||
| template <> | ||
| void compute_kernel<float>(RNNGRUValue<float> value, | ||
| int frame_size, | ||
| int batch_size, | ||
| lite_api::ActivationType active_node, | ||
| lite_api::ActivationType active_gate) { | ||
| auto value_reset_gate = value.gate_value; | ||
| auto value_update_gate = value.gate_value + frame_size; | ||
| auto value_reset_output = value.reset_output_value; | ||
| auto value_reset_bias = value.reset_bias; | ||
| auto cell_state_value = value.gate_value + 2 * frame_size; | ||
| auto value_output = value.output_value; | ||
| auto value_prev_out = value.prev_out_value; | ||
| int i = 0; | ||
| float32x4_t vec_one = vdupq_n_f32(1.f); | ||
|
|
||
| for (int b = 0; b < batch_size; b++) { | ||
| rnn_activation(value_reset_gate, | ||
| value_reset_gate, | ||
| frame_size, | ||
| lite_api::ActivationType::kSigmoid_v2, | ||
| 1); | ||
| rnn_activation(value_update_gate, | ||
| value_update_gate, | ||
| frame_size, | ||
| lite_api::ActivationType::kSigmoid_v2, | ||
| 1); | ||
|
|
||
| for (i = 0; i + 3 < frame_size; i += 4) { | ||
| float32x4_t vec_out = vld1q_f32(value_reset_output + i); | ||
| float32x4_t vec_reset = vld1q_f32(value_reset_gate + i); | ||
| float32x4_t vec_bias = vld1q_f32(value_reset_bias + i); | ||
| vec_out = vmulq_f32(vaddq_f32(vec_out, vec_bias), vec_reset); | ||
| vst1q_f32(value_reset_output + i, vec_out); | ||
| vst1q_f32(cell_state_value + i, | ||
| vaddq_f32(vec_out, vld1q_f32(cell_state_value + i))); | ||
| } | ||
| for (; i < frame_size; i++) { | ||
| value_reset_output[i] = | ||
| (value_reset_output[i] + value_reset_bias[i]) * value_reset_gate[i]; | ||
| cell_state_value[i] += value_reset_output[i]; | ||
| } | ||
|
|
||
| rnn_activation(cell_state_value, | ||
| cell_state_value, | ||
| frame_size, | ||
| lite_api::ActivationType::kTanh_v2, | ||
| 1); | ||
|
|
||
| if (value.prev_out_value) { | ||
| for (i = 0; i + 3 < frame_size; i += 4) { | ||
| float32x4_t vec_vug = vld1q_f32(value_update_gate + i); | ||
| float32x4_t vec_vpo = vld1q_f32(value_prev_out + i); | ||
| float32x4_t vec_csv = vld1q_f32(cell_state_value + i); | ||
| vec_vpo = vmulq_f32(vec_vug, vec_vpo); | ||
| float32x4_t vec_out = | ||
| vmlaq_f32(vec_vpo, vsubq_f32(vec_one, vec_vug), vec_csv); | ||
| vst1q_f32(value_output + i, vec_out); | ||
| } | ||
| for (; i < frame_size; i++) { | ||
| value_output[i] = (1.f - value_update_gate[i]) * cell_state_value[i] + | ||
| value_update_gate[i] * value_prev_out[i]; | ||
| } | ||
| } else { | ||
| for (i = 0; i + 3 < frame_size; i += 4) { | ||
| float32x4_t vec_vug = vld1q_f32(value_update_gate + i); | ||
| float32x4_t vec_csv = vld1q_f32(cell_state_value + i); | ||
| float32x4_t vec_out = vmulq_f32(vsubq_f32(vec_one, vec_vug), vec_csv); | ||
| vst1q_f32(value_output + i, vec_out); | ||
| } | ||
| for (; i < frame_size; i++) { | ||
| value_output[i] = (1.f - value_update_gate[i]) * cell_state_value[i]; | ||
| } | ||
| } | ||
|
|
||
| value_reset_gate += frame_size * 3; | ||
| value_update_gate += frame_size * 3; | ||
| value_reset_output += frame_size; | ||
| cell_state_value += frame_size * 3; | ||
| value_output += frame_size; | ||
| if (value.prev_out_value) { | ||
| value_prev_out += frame_size; | ||
| } | ||
| } | ||
| } | ||
|
|
||
| template <typename T> | ||
| struct RnnGruUnitFunctorV2 { | ||
| static void compute(ARMContext* ctx, | ||
| RNNGRUValue<T> value, | ||
| int frame_size, | ||
| int batch_size, | ||
| lite_api::ActivationType active_node, | ||
| lite_api::ActivationType active_gate) { | ||
| if (value.prev_out_value) { | ||
| operators::ActivationParam act_param; | ||
| act_param.has_active = false; | ||
| lite::arm::math::sgemm(false, | ||
| true, | ||
| batch_size, | ||
| frame_size, | ||
| frame_size, | ||
| 1.f, | ||
| value.prev_out_value, | ||
| frame_size, | ||
| value.state_weight, | ||
| frame_size, | ||
| 0.f, | ||
| value.reset_output_value, | ||
| frame_size, | ||
| nullptr, | ||
| false, | ||
| act_param, | ||
| ctx); | ||
| } | ||
| compute_kernel(value, frame_size, batch_size, active_node, active_gate); | ||
| } | ||
| }; | ||
|
|
||
| } // namespace math | ||
| } // namespace arm | ||
| } // namespace lite | ||
| } // namespace paddle | ||
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有时间新提Pr，将日期修改下：2019->2021