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paddle_pass_builder.cc
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paddle_pass_builder.cc
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// Copyright (c) 2018 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 "paddle/fluid/inference/api/paddle_pass_builder.h"
#ifdef PADDLE_WITH_CUDA
#include <cudnn.h>
#endif
#ifdef PADDLE_WITH_HIP
#include <miopen/miopen.h>
#endif
#include <glog/logging.h>
#include <algorithm>
#include <sstream>
namespace paddle {
void PaddlePassBuilder::AppendPass(const std::string &pass_type) {
passes_.push_back(pass_type);
}
void PaddlePassBuilder::TurnOnDebug() {
std::vector<std::string> passes;
auto it = std::begin(passes_);
while (it != std::end(passes_)) {
if (*it != "graph_viz_pass") {
it = passes_.insert(it + 1, "graph_viz_pass");
} else {
++it;
}
}
}
std::string PaddlePassBuilder::DebugString() {
std::stringstream ss;
ss << "Passes to apply:\n";
for (auto &pass : passes_) {
ss << " - " << pass << '\n';
}
return ss.str();
}
void PaddlePassBuilder::DeletePass(const std::string &pass_type) {
deleted_passes_.insert(pass_type);
auto it = std::begin(passes_);
while (it != std::end(passes_)) {
if (*it == pass_type) {
it = passes_.erase(it);
} else {
++it;
}
}
}
size_t PaddlePassBuilder::GetPassIndex(const std::string &pass_type) {
auto iter = std::find(std::begin(passes_), std::end(passes_), pass_type);
if (iter == std::end(passes_)) return -1;
return std::distance(std::begin(passes_), iter);
}
void PaddlePassBuilder::InsertPass(size_t idx, const std::string &pass_type) {
passes_.insert(std::begin(passes_) + idx, pass_type);
}
void PaddlePassBuilder::DeletePass(size_t idx) {
passes_.erase(std::begin(passes_) + idx);
}
void PaddlePassBuilder::AppendAnalysisPass(const std::string &pass) {
analysis_passes_.push_back(pass);
}
void PaddlePassBuilder::ClearPasses() { passes_.clear(); }
const std::vector<std::string> kTRTSubgraphPasses({
"adaptive_pool2d_convert_global_pass", //
"shuffle_channel_detect_pass", //
"quant_conv2d_dequant_fuse_pass", //
"delete_fill_constant_op_pass", //
"delete_quant_dequant_op_pass", //
"delete_quant_dequant_filter_op_pass", //
"delete_weight_dequant_linear_op_pass", //
"delete_quant_dequant_linear_op_pass", //
"identity_scale_op_clean_pass", //
"add_support_int8_pass", //
// "fc_fuse_pass", //
"simplify_with_basic_ops_pass", //
"trt_embedding_eltwise_layernorm_fuse_pass", //
"preln_embedding_eltwise_layernorm_fuse_pass", //
"delete_c_identity_op_pass", //
"trt_multihead_matmul_fuse_pass_v2", //
"trt_multihead_matmul_fuse_pass_v3", //
"vit_attention_fuse_pass", //
"trt_skip_layernorm_fuse_pass", //
"preln_skip_layernorm_fuse_pass", //
"preln_residual_bias_fuse_pass", //
"layernorm_shift_partition_fuse_pass", //
// "set_transformer_input_convert_pass", //
"conv_bn_fuse_pass", //
"unsqueeze2_eltwise_fuse_pass", //
"trt_squeeze2_matmul_fuse_pass", //
"trt_flatten2_matmul_fuse_pass", //
"trt_map_matmul_v2_to_mul_pass", //
"trt_map_matmul_v2_to_matmul_pass", //
"trt_map_matmul_to_mul_pass", //
"fc_fuse_pass", //
"conv_elementwise_add_fuse_pass", //
"remove_padding_recover_padding_pass", //
"delete_remove_padding_recover_padding_pass", //
// "yolo_box_fuse_pass", //
"dense_fc_to_sparse_pass", //
"dense_multihead_matmul_to_sparse_pass", //
"constant_folding_pass",
"tensorrt_subgraph_pass", //
"conv_bn_fuse_pass", //
#if CUDNN_VERSION >= 7100 // To run conv_fusion, the version of cudnn must be
// guaranteed at least v7
// cudnn8.0 has memory leak problem in conv + eltwise + act, so we
// disable the pass.
#if !(CUDNN_VERSION >= 8000 && CUDNN_VERSION < 8100)
"conv_elementwise_add_act_fuse_pass", //
"conv_elementwise_add2_act_fuse_pass", //
#endif
#endif
"transpose_flatten_concat_fuse_pass",
});
const std::vector<std::string> kDlnneSubgraphPasses({
"is_test_pass", //
"delete_dropout_op_pass", //
"simplify_with_basic_ops_pass", //
"conv_bn_fuse_pass", //
"depthwise_conv_bn_fuse_pass", //
"shuffle_channel_detect_pass", //
"dlnne_subgraph_pass", //
});
const std::vector<std::string> kLiteSubgraphPasses({
#ifdef PADDLE_WITH_LITE
"lite_subgraph_pass",
#endif
});
// TODO(inference): Most of the existing pass fusion operators do not
// support fp16/bf16 precision, temporarily use low precision pass to prevent
// running errors. After fusion operator supports low precision, delete this.
const std::vector<std::string> kGpuLowerPrecisionPasses{
"simplify_with_basic_ops_pass",
"conv_bn_fuse_pass",
"conv_eltwiseadd_bn_fuse_pass",
"conv_elementwise_add_act_fuse_pass",
"conv_elementwise_add2_act_fuse_pass",
"conv_elementwise_add_fuse_pass",
"multihead_matmul_fuse_pass_v2",
"fused_multi_transformer_encoder_pass",
"fused_multi_transformer_decoder_pass",
"fused_multi_transformer_encoder_fuse_qkv_pass",
"fused_multi_transformer_decoder_fuse_qkv_pass",
"multi_devices_fused_multi_transformer_encoder_fuse_qkv_pass",
"multi_devices_fused_multi_transformer_decoder_fuse_qkv_pass",
"gpu_cpu_map_matmul_v2_to_mul_pass",
"gpu_cpu_map_matmul_v2_to_matmul_pass",
"fc_fuse_pass",
"fc_elementwise_layernorm_fuse_pass",
"embedding_eltwise_layernorm_fuse_pass",
"runtime_context_cache_pass",
};
const std::vector<std::string> kTrtLowerPrecisionPasses{
"simplify_with_basic_ops_pass",
// "conv_bn_fuse_pass",
// "conv_eltwiseadd_bn_fuse_pass",
"trt_embedding_eltwise_layernorm_fuse_pass",
"trt_skip_layernorm_fuse_pass",
"trt_map_matmul_v2_to_mul_pass",
"trt_map_matmul_v2_to_matmul_pass",
"trt_map_matmul_to_mul_pass",
"fc_fuse_pass",
"tensorrt_subgraph_pass",
};
GpuPassStrategy::GpuPassStrategy() : PassStrategy({}) {
passes_.assign({
// "identity_scale_op_clean_pass", //
"is_test_pass", //
"simplify_with_basic_ops_pass", //
"conv_bn_fuse_pass", //
"conv_eltwiseadd_bn_fuse_pass", //
"embedding_eltwise_layernorm_fuse_pass", //
"multihead_matmul_fuse_pass_v2", //
"fused_multi_transformer_encoder_pass", //
"fused_multi_transformer_decoder_pass", //
"fused_multi_transformer_encoder_fuse_qkv_pass", //
"fused_multi_transformer_decoder_fuse_qkv_pass", //
"multi_devices_fused_multi_transformer_encoder_fuse_qkv_pass", //
"multi_devices_fused_multi_transformer_decoder_fuse_qkv_pass", //
"gpu_cpu_squeeze2_matmul_fuse_pass", //
"gpu_cpu_reshape2_matmul_fuse_pass", //
"gpu_cpu_flatten2_matmul_fuse_pass", //
"gpu_cpu_map_matmul_v2_to_mul_pass", //
"gpu_cpu_map_matmul_v2_to_matmul_pass", //
"matmul_scale_fuse_pass", //
"multihead_matmul_fuse_pass_v3", //
"gpu_cpu_map_matmul_to_mul_pass", //
"fc_fuse_pass", //
"fc_elementwise_layernorm_fuse_pass", //
#if CUDNN_VERSION >= 7100 // To run conv_fusion, the version of cudnn must be
// guaranteed at least v7
// cudnn8.0 has memory leak problem in conv + eltwise + act, so we
// disable the pass.
#if !(CUDNN_VERSION >= 8000 && CUDNN_VERSION < 8100)
"conv_elementwise_add_act_fuse_pass", //
"conv_elementwise_add2_act_fuse_pass", //
#endif
"conv_elementwise_add_fuse_pass", //
#endif //
"transpose_flatten_concat_fuse_pass", //
"constant_folding_pass",
// following pass should be located in the last, since it will
// work on all fused ops.
"runtime_context_cache_pass"
});
use_gpu_ = true;
}
void GpuPassStrategy::EnableCUDNN() {
if (!use_cudnn_) {
passes_.insert(passes_.begin(), "cudnn_placement_pass");
}
use_cudnn_ = true;
}
void GpuPassStrategy::EnableMKLDNN() {
LOG(ERROR) << "GPU not support MKLDNN yet";
}
void GpuPassStrategy::EnableMkldnnQuantizer() {
LOG(ERROR) << "GPU not support MKL-DNN quantization";
}
void GpuPassStrategy::EnableMkldnnBfloat16() {
LOG(ERROR) << "GPU not support MKL-DNN bfloat16";
}
void GpuPassStrategy::EnableMkldnnInt8() {
LOG(ERROR) << "GPU not support MKL-DNN int8";
}
CpuPassStrategy::CpuPassStrategy() : PassStrategy({}) {
// NOTE the large fusions should be located in the front, so that they will
// not be damaged by smaller ones.
passes_.assign({"simplify_with_basic_ops_pass", //
"layer_norm_fuse_pass",
"attention_lstm_fuse_pass", //
"seqconv_eltadd_relu_fuse_pass", //
// "seqpool_concat_fuse_pass", //
"seqpool_cvm_concat_fuse_pass", //
// "embedding_fc_lstm_fuse_pass", //
// TODO(wilber): fix correctness problem.
// "fc_lstm_fuse_pass", //
"mul_lstm_fuse_pass", //
"fc_gru_fuse_pass", //
"mul_gru_fuse_pass", //
"seq_concat_fc_fuse_pass", //
"gpu_cpu_squeeze2_matmul_fuse_pass", //
"gpu_cpu_reshape2_matmul_fuse_pass", //
"gpu_cpu_flatten2_matmul_fuse_pass", //
"matmul_v2_scale_fuse_pass", //
"gpu_cpu_map_matmul_v2_to_mul_pass", //
"gpu_cpu_map_matmul_v2_to_matmul_pass", //
"matmul_scale_fuse_pass", //
"gpu_cpu_map_matmul_to_mul_pass", //
"fc_fuse_pass", //
"repeated_fc_relu_fuse_pass", //
"squared_mat_sub_fuse_pass", //
"conv_bn_fuse_pass", //
"conv_eltwiseadd_bn_fuse_pass", //
"conv_transpose_bn_fuse_pass", //
"conv_transpose_eltwiseadd_bn_fuse_pass", //
"is_test_pass", //
"constant_folding_pass",
// following pass should be located in the last, since
// it will work on all fused ops.
"runtime_context_cache_pass"});
use_gpu_ = false;
}
void CpuPassStrategy::EnableCUDNN() { LOG(ERROR) << "CPU not support cuDNN"; }
void CpuPassStrategy::EnableMKLDNN() {
// TODO(Superjomn) Consider the way to mix CPU with GPU.
#ifdef PADDLE_WITH_MKLDNN
if (!use_mkldnn_) {
passes_.insert(passes_.begin(), "mkldnn_placement_pass");
for (auto &pass : std::vector<std::string>({
"depthwise_conv_mkldnn_pass", //
"conv_bn_fuse_pass", // Execute BN passes again to
"conv_eltwiseadd_bn_fuse_pass", // preserve correct pass order
"conv_affine_channel_mkldnn_fuse_pass", //
"conv_transpose_bn_fuse_pass", //
"conv_transpose_eltwiseadd_bn_fuse_pass", //
"conv_bias_mkldnn_fuse_pass", //
"conv_transpose_bias_mkldnn_fuse_pass",
// TODO(baoachun): Need to support 5-dimensional input.
// "conv3d_bias_mkldnn_fuse_pass", //
"conv_elementwise_add_mkldnn_fuse_pass",
"conv_activation_mkldnn_fuse_pass", //
"scale_matmul_fuse_pass", //
"reshape_transpose_matmul_mkldnn_fuse_pass", //
"matmul_transpose_reshape_mkldnn_fuse_pass", //
"matmul_elementwise_add_mkldnn_fuse_pass", //
"matmul_activation_mkldnn_fuse_pass", //
// Disabled due to topology-dependent speed-up
// "fc_mkldnn_pass",
// "fc_act_mkldnn_fuse_pass",
"fc_elementwise_add_mkldnn_fuse_pass", //
"batch_norm_act_fuse_pass", //
"softplus_activation_mkldnn_fuse_pass", //
"shuffle_channel_mkldnn_detect_pass", //
"elt_act_mkldnn_fuse_pass", //
"operator_scale_onednn_fuse_pass", //
// TODO(intel): Please fix the bug on windows.
// https://github.com/PaddlePaddle/Paddle/issues/29710
// "mkldnn_inplace_pass", // This pass should be activated after
// fuses. Disabled by default due to
// little gain and lots of problems
})) {
passes_.push_back(pass);
}
}
use_mkldnn_ = true;
#else
use_mkldnn_ = false;
#endif
}
void CpuPassStrategy::EnableMkldnnQuantizer() {
#ifdef PADDLE_WITH_MKLDNN
if (!use_mkldnn_quantizer_) {
passes_.push_back("cpu_quantize_placement_pass");
}
use_mkldnn_quantizer_ = true;
#else
use_mkldnn_quantizer_ = false;
#endif
}
void CpuPassStrategy::EnableMkldnnBfloat16() {
#ifdef PADDLE_WITH_MKLDNN
if (!use_mkldnn_bfloat16_) {
passes_.push_back("fc_mkldnn_pass");
passes_.push_back("fc_act_mkldnn_fuse_pass");
passes_.push_back("fc_elementwise_add_mkldnn_fuse_pass");
passes_.push_back("cpu_bfloat16_placement_pass");
passes_.push_back("cpu_bfloat16_pass");
passes_.push_back("cpu_quantize_squash_pass");
}
use_mkldnn_bfloat16_ = true;
#else
use_mkldnn_bfloat16_ = false;
#endif
}
void CpuPassStrategy::EnableMkldnnInt8() {
#ifdef PADDLE_WITH_MKLDNN
if (!use_mkldnn_int8_) {
passes_.clear();
passes_.push_back("quant_dequant_mkldnn_pass");
passes_.push_back("mkldnn_placement_pass");
passes_.push_back("simplify_with_basic_ops_pass");
passes_.push_back("constant_folding_pass");
passes_.push_back("layer_norm_fuse_pass");
passes_.push_back("attention_lstm_fuse_pass");
passes_.push_back("seqconv_eltadd_relu_fuse_pass");
passes_.push_back("fc_lstm_fuse_pass");
passes_.push_back("mul_lstm_fuse_pass");
passes_.push_back("fc_gru_fuse_pass");
passes_.push_back("mul_gru_fuse_pass");
passes_.push_back("multi_gru_fuse_pass");
passes_.push_back("multi_gru_seq_fuse_pass");
passes_.push_back("seq_concat_fc_fuse_pass");
passes_.push_back("gpu_cpu_squeeze2_matmul_fuse_pass");
passes_.push_back("gpu_cpu_reshape2_matmul_fuse_pass");
passes_.push_back("gpu_cpu_flatten2_matmul_fuse_pass");
passes_.push_back("matmul_v2_scale_fuse_pass");
passes_.push_back("squared_mat_sub_fuse_pass");
passes_.push_back("is_test_pass");
passes_.push_back("gpu_cpu_map_matmul_v2_to_mul_pass");
passes_.push_back("gpu_cpu_map_matmul_v2_to_matmul_pass");
passes_.push_back("matmul_scale_fuse_pass");
passes_.push_back("gpu_cpu_map_matmul_to_mul_pass");
passes_.push_back("repeated_fc_relu_fuse_pass");
passes_.push_back("depthwise_conv_mkldnn_pass");
passes_.push_back("conv_bn_fuse_pass");
passes_.push_back("conv_eltwiseadd_bn_fuse_pass");
passes_.push_back("conv_affine_channel_mkldnn_fuse_pass");
passes_.push_back("conv_transpose_bn_fuse_pass");
passes_.push_back("conv_transpose_eltwiseadd_bn_fuse_pass");
passes_.push_back("conv_bias_mkldnn_fuse_pass");
passes_.push_back("conv_transpose_bias_mkldnn_fuse_pass");
passes_.push_back("conv_elementwise_add_mkldnn_fuse_pass");
passes_.push_back("conv_activation_mkldnn_fuse_pass");
passes_.push_back("fc_fuse_pass");
passes_.push_back("repeated_fc_relu_fuse_pass");
passes_.push_back("fc_mkldnn_pass");
passes_.push_back("fc_act_mkldnn_fuse_pass");
passes_.push_back("matmul_transpose_reshape_mkldnn_fuse_pass");
passes_.push_back("batch_norm_act_fuse_pass");
passes_.push_back("softplus_activation_mkldnn_fuse_pass");
passes_.push_back("compute_propagate_scales_mkldnn_pass");
passes_.push_back("scale_matmul_fuse_pass");
passes_.push_back("reshape_transpose_matmul_mkldnn_fuse_pass");
passes_.push_back("matmul_elementwise_add_mkldnn_fuse_pass");
passes_.push_back("operator_scale_onednn_fuse_pass");
passes_.push_back("cpu_quantize_placement_pass");
passes_.push_back("cpu_quantize_pass");
passes_.push_back("cpu_quantize_squash_pass");
passes_.push_back("int8_scale_calculation_mkldnn_pass");
passes_.push_back("params_quantization_mkldnn_pass");
passes_.push_back("mkldnn_inplace_pass");
passes_.push_back("runtime_context_cache_pass");
}
use_mkldnn_int8_ = true;
#else
use_mkldnn_int8_ = false;
#endif
}
IpuPassStrategy::IpuPassStrategy() : PassStrategy({}) {
passes_.assign({"inference_process_pass"});
}
} // namespace paddle