Skip to content

P
Paddle

PaddlePaddle / Paddle
1 年多 前同步成功

通知 2302
Star 20931
Fork 5422
P
Paddle
未验证 提交 b50dbe0b 编写于 作者: W Wangzheee 提交者： GitHub
浏览文件
操作

[Paddle Inference] General optimization for no_varlen skiplayernorm (#49039) 

* General optimization for no_varlen embedding layernorm
上级 9df0ab32
隐藏空白更改
内联 并排
Showing with 369 addition and 991 deletion
paddle/fluid/framework/ir/trt_skip_layernorm_fuse_pass.cc
浏览文件 @ b50dbe0b
......@@ -169,8 +169,21 @@ void TrtSkipLayerNormFusePass::ApplyImpl(ir::Graph *graph) const {
// attrs
new_desc.SetAttr("epsilon", layer_norm->Op()->GetAttr("epsilon"));
new_desc.SetAttr("begin_norm_axis",
layer_norm->Op()->GetAttr("begin_norm_axis"));
if (new_desc.HasAttr("begin_norm_axis")) {
int32_t begin_norm_axis = PADDLE_GET_CONST(
int32_t, layer_norm->Op()->GetAttr("begin_norm_axis"));
int32_t input_rank =
static_cast<int32_t>(elementwise_out->Var()->GetShape().size());
if ((begin_norm_axis != -1) && (begin_norm_axis != input_rank - 1)) {
LOG(WARNING) << "skip_layernorm pass only support "
"layer_norm'begin_norm_axis == input_rank - 1.";
return;
}
new_desc.SetAttr("begin_norm_axis", begin_norm_axis);
}
int32_t hidden_size = layer_norm_scale->Var()->GetShape()[0];
new_desc.SetAttr("hidden_size", hidden_size);
auto fused_node = graph->CreateOpNode(&new_desc); // OpDesc will be copied.
......
paddle/fluid/inference/api/paddle_pass_builder.cc
浏览文件 @ b50dbe0b
......@@ -104,24 +104,27 @@ const std::vector<std::string> kTRTSubgraphPasses({
"multihead_matmul_roformer_fuse_pass", //
"constant_folding_pass", //
"vit_attention_fuse_pass", //
"trt_skip_layernorm_fuse_pass", //
"preln_skip_layernorm_fuse_pass", //
"layernorm_shift_partition_fuse_pass", //
"merge_layernorm_fuse_pass", //
"preln_residual_bias_fuse_pass", //
"preln_layernorm_x_fuse_pass", //
"reverse_roll_fuse_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", //
#if defined _WIN32 // Windows CI is TensorRT7.0. Remove this after upgrading.
#else
"trt_skip_layernorm_fuse_pass", //
"preln_skip_layernorm_fuse_pass", //
#endif
"layernorm_shift_partition_fuse_pass", //
"merge_layernorm_fuse_pass", //
"preln_residual_bias_fuse_pass", //
"preln_layernorm_x_fuse_pass", //
"reverse_roll_fuse_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", //
......
paddle/fluid/inference/tensorrt/convert/skip_layernorm.cc
浏览文件 @ b50dbe0b
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
/* Copyright (c) 2022 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.
......@@ -15,7 +15,6 @@ limitations under the License. */
#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
#include "paddle/fluid/inference/tensorrt/convert/utils.h"
#include "paddle/fluid/inference/tensorrt/engine.h"
#include "paddle/fluid/inference/tensorrt/plugin/skip_layernorm_op_plugin.h"
namespace paddle {
namespace inference {
......@@ -26,9 +25,20 @@ class SkipLayerNormOpConverter : public OpConverter {
void operator()(const framework::proto::OpDesc& op,
const framework::Scope& scope,
bool test_mode) override {
#if IS_TRT_VERSION_GE(6000)
VLOG(4) << "convert fused skip layernorm op to tensorrt layer";
PADDLE_ENFORCE_EQ(engine_->with_dynamic_shape(),
true,
platform::errors::InvalidArgument(
"Skip_layernorm must run the dynamic shape mode."));
framework::OpDesc op_desc(op, nullptr);
auto GetWeight =
[&](const std::string& arg_name) -> TensorRTEngine::Weight {
std::string var_name = op_desc.Input(arg_name).front();
auto* temp_var = scope.FindVar(var_name);
auto* temp_tensor = temp_var->GetMutable<phi::DenseTensor>();
auto weight = engine_->GetTrtWeight(var_name, *temp_tensor);
return weight;
};
// Declare inputs
auto* input1 = engine_->GetITensor(op_desc.Input("X")[0]);
auto* input2 = engine_->GetITensor(op_desc.Input("Y")[0]);
......@@ -36,173 +46,118 @@ class SkipLayerNormOpConverter : public OpConverter {
inputs.push_back(input1);
inputs.push_back(input2);
bool enable_int8 = op_desc.HasAttr("enable_int8");
bool enable_int8 = false;
if (op_desc.HasAttr("enable_int8")) {
enable_int8 = PADDLE_GET_CONST(bool, op_desc.GetAttr("enable_int8"));
}
auto bias_weight = GetWeight("Bias").get();
auto scale_weight = GetWeight("Scale").get();
nvinfer1::ILayer* layer = nullptr;
bool flag_varseqlen = engine_->use_varseqlen() &&
engine_->tensorrt_transformer_posid() != "" &&
engine_->tensorrt_transformer_maskid() != "";
if (flag_varseqlen) {
auto GetWeight =
[&](const std::string& arg_name) -> TensorRTEngine::Weight {
std::string var_name = op_desc.Input(arg_name).front();
auto* temp_var = scope.FindVar(var_name);
auto* temp_tensor = temp_var->GetMutable<phi::DenseTensor>();
auto weight = engine_->GetTrtWeight(var_name, *temp_tensor);
return weight;
};
auto bias_weight = GetWeight("Bias").get();
auto scale_weight = GetWeight("Scale").get();
if (engine_->with_interleaved()) {
VLOG(4)
<< "fused skip_layernorm op: use_varseqlen and with_interleaved";
if (!enable_int8) {
PADDLE_THROW(
platform::errors::Fatal("use with_interleaved must be int8."));
}
auto creator = GetPluginRegistry()->getPluginCreator(
"CustomSkipLayerNormPluginDynamic", "3");
PADDLE_ENFORCE_NE(
creator,
nullptr,
platform::errors::InvalidArgument(
"fail to get creator of CustomSkipLayerNormPluginDynamic"));
const std::vector<nvinfer1::PluginField> fields{
{"beta",
bias_weight.values,
GetPluginFieldType(bias_weight.type),
static_cast<int32_t>(bias_weight.count)},
{ "gamma",
scale_weight.values,
GetPluginFieldType(scale_weight.type),
static_cast<int32_t>(scale_weight.count) }};
nvinfer1::PluginFieldCollection* pluginPtr =
static_cast<nvinfer1::PluginFieldCollection*>(
malloc(sizeof(*pluginPtr) +
fields.size() * sizeof(nvinfer1::PluginField)));
pluginPtr->nbFields = static_cast<int>(fields.size());
pluginPtr->fields = fields.data();
auto pluginObj = creator->createPlugin(
"CustomSkipLayerNormPluginDynamic", pluginPtr);
auto plugin_layer = engine_->network()->addPluginV2(
inputs.data(), inputs.size(), *pluginObj);
PADDLE_ENFORCE_NE(
plugin_layer,
nullptr,
platform::errors::InvalidArgument(
"fail to add CustomSkipLayerNormPluginDynamic layer"));
layer = plugin_layer;
} else {
auto creator = GetPluginRegistry()->getPluginCreator(
"CustomSkipLayerNormPluginDynamic", "2");
PADDLE_ENFORCE_NE(
creator,
nullptr,
platform::errors::InvalidArgument(
"fail to get creator of CustomSkipLayerNormPluginDynamic"));
int type = static_cast<int>((engine_->WithFp16() == 1)
? nvinfer1::DataType::kHALF
: nvinfer1::DataType::kFLOAT);
int ld = input1->getDimensions().d[2]; // hidden dimension
PADDLE_ENFORCE_GT(ld,
0,
platform::errors::InvalidArgument(
"in CustomSkipLayerNormPluginDynamic hidden "
"dimension should > 0"));
if (enable_int8) {
type = static_cast<int>(nvinfer1::DataType::kHALF);
}
const std::vector<nvinfer1::PluginField> fields{
{"type_id", &type, nvinfer1::PluginFieldType::kINT32, 1},
{"ld", &ld, nvinfer1::PluginFieldType::kINT32, 1},
{"beta",
bias_weight.values,
GetPluginFieldType(bias_weight.type),
static_cast<int32_t>(bias_weight.count)},
{"gamma",
scale_weight.values,
GetPluginFieldType(scale_weight.type),
static_cast<int32_t>(scale_weight.count)},
};
nvinfer1::PluginFieldCollection* pluginPtr =
static_cast<nvinfer1::PluginFieldCollection*>(
malloc(sizeof(*pluginPtr) +
fields.size() *
sizeof(nvinfer1::PluginField))); // remember to free
pluginPtr->nbFields = static_cast<int>(fields.size());
pluginPtr->fields = fields.data();
auto pluginObj = creator->createPlugin(
"CustomSkipLayerNormPluginDynamic", pluginPtr);
auto plugin_layer = engine_->network()->addPluginV2(
inputs.data(), inputs.size(), *pluginObj);
PADDLE_ENFORCE_NE(
plugin_layer,
nullptr,
platform::errors::InvalidArgument(
"fail to add CustomSkipLayerNormPluginDynamic layer"));
layer = plugin_layer;
if (flag_varseqlen && engine_->with_interleaved()) {
VLOG(4) << "fused skip_layernorm op: use_varseqlen and with_interleaved";
if (!enable_int8) {
PADDLE_THROW(
platform::errors::Fatal("use with_interleaved must be int8."));
}
auto creator = GetPluginRegistry()->getPluginCreator(
"CustomSkipLayerNormPluginDynamic", "3");
PADDLE_ENFORCE_NE(
creator,
nullptr,
platform::errors::InvalidArgument(
"fail to get creator of CustomSkipLayerNormPluginDynamic"));
const std::vector<nvinfer1::PluginField> fields{
{"beta",
bias_weight.values,
GetPluginFieldType(bias_weight.type),
static_cast<int32_t>(bias_weight.count)},
{"gamma",
scale_weight.values,
GetPluginFieldType(scale_weight.type),
static_cast<int32_t>(scale_weight.count)}};
nvinfer1::PluginFieldCollection* pluginPtr =
static_cast<nvinfer1::PluginFieldCollection*>(
malloc(sizeof(nvinfer1::PluginFieldCollection) +
fields.size() * sizeof(nvinfer1::PluginField)));
pluginPtr->nbFields = static_cast<int32_t>(fields.size());
pluginPtr->fields = fields.data();
auto pluginObj =
creator->createPlugin("CustomSkipLayerNormPluginDynamic", pluginPtr);
free(pluginPtr);
auto plugin_layer = engine_->network()->addPluginV2(
inputs.data(), inputs.size(), *pluginObj);
PADDLE_ENFORCE_NE(
plugin_layer,
nullptr,
platform::errors::InvalidArgument(
"fail to add CustomSkipLayerNormPluginDynamic layer"));
layer = plugin_layer;
} else {
auto GetFp16Weight =
[&](const std::string& arg_name) -> TensorRTEngine::Weight {
std::string var_name = op_desc.Input(arg_name).front();
auto* temp_var = scope.FindVar(var_name);
auto* temp_tensor = temp_var->GetMutable<phi::DenseTensor>();
auto weight = engine_->GetFp16TrtWeight(var_name, *temp_tensor);
return weight;
};
auto GetFp32Weight =
[&](const std::string& arg_name) -> TensorRTEngine::Weight {
std::string var_name = op_desc.Input(arg_name).front();
auto* temp_var = scope.FindVar(var_name);
auto* temp_tensor = temp_var->GetMutable<phi::DenseTensor>();
auto weight = engine_->GetFp32TrtWeight(var_name, *temp_tensor);
return weight;
};
// bool with_fp16 = engine_->WithFp16() &&
// !engine_->disable_trt_plugin_fp16() &&
// (input1->getType() == nvinfer1::DataType::kHALF);
bool with_fp16 = false;
TensorRTEngine::Weight bias_weight, scale_weight;
if (with_fp16) {
bias_weight = GetFp16Weight("Bias");
scale_weight = GetFp16Weight("Scale");
} else {
bias_weight = GetFp32Weight("Bias");
scale_weight = GetFp32Weight("Scale");
auto creator = GetPluginRegistry()->getPluginCreator(
"CustomSkipLayerNormPluginDynamic", "2");
PADDLE_ENFORCE_NE(
creator,
nullptr,
platform::errors::InvalidArgument(
"fail to get creator of CustomSkipLayerNormPluginDynamic"));
int32_t type = static_cast<int32_t>((engine_->WithFp16() == 1)
? nvinfer1::DataType::kHALF
: nvinfer1::DataType::kFLOAT);
if (enable_int8) {
type = static_cast<int32_t>(nvinfer1::DataType::kHALF);
}
float eps = PADDLE_GET_CONST(float, op_desc.GetAttr("epsilon"));
plugin::SkipLayerNormPluginDynamic* plugin =
new plugin::SkipLayerNormPluginDynamic(
const_cast<void*>(
static_cast<const void*>(bias_weight.get().values)),
const_cast<void*>(
static_cast<const void*>(scale_weight.get().values)),
bias_weight.get().count,
scale_weight.get().count,
eps,
with_fp16);
layer = engine_->AddDynamicPlugin(inputs.data(), 2, plugin);
int32_t hidden_size =
PADDLE_GET_CONST(int32_t, op_desc.GetAttr("hidden_size"));
PADDLE_ENFORCE_GT(hidden_size,
0,
platform::errors::InvalidArgument(
"in CustomSkipLayerNormPluginDynamic hidden "
"dimension should > 0"));
const std::vector<nvinfer1::PluginField> fields{
{"type_id", &type, nvinfer1::PluginFieldType::kINT32, 1},
{"ld", &hidden_size, nvinfer1::PluginFieldType::kINT32, 1},
{"beta",
bias_weight.values,
GetPluginFieldType(bias_weight.type),
static_cast<int32_t>(bias_weight.count)},
{"gamma",
scale_weight.values,
GetPluginFieldType(scale_weight.type),
static_cast<int32_t>(scale_weight.count)},
};
nvinfer1::PluginFieldCollection* pluginPtr =
static_cast<nvinfer1::PluginFieldCollection*>(
malloc(sizeof(nvinfer1::PluginFieldCollection) +
fields.size() *
sizeof(nvinfer1::PluginField))); // remember to free
pluginPtr->nbFields = static_cast<int32_t>(fields.size());
pluginPtr->fields = fields.data();
auto pluginObj =
creator->createPlugin("CustomSkipLayerNormPluginDynamic", pluginPtr);
free(pluginPtr);
auto plugin_layer = engine_->network()->addPluginV2(
inputs.data(), inputs.size(), *pluginObj);
PADDLE_ENFORCE_NE(
plugin_layer,
nullptr,
platform::errors::InvalidArgument(
"fail to add CustomSkipLayerNormPluginDynamic layer"));
layer = plugin_layer;
}
auto output_name = op_desc.Output("Out")[0];
RreplenishLayerAndOutput(layer, "skip_layernorm", {output_name}, test_mode);
#else
PADDLE_THROW(platform::errors::Fatal(
"You are running the TRT Dynamic Shape mode, need to confirm that "
"your TRT version is no less than 6.0"));
#endif
}
};
......
paddle/fluid/inference/tensorrt/plugin/CMakeLists.txt
浏览文件 @ b50dbe0b
......@@ -12,7 +12,6 @@ list(
layer_norm_op_plugin.cu
instance_norm_op_plugin.cu
qkv_to_context_plugin.cu
skip_layernorm_op_plugin.cu
hard_swish_op_plugin.cu
stack_op_plugin.cu
anchor_generator_op_plugin.cu
......
paddle/fluid/inference/tensorrt/plugin/skip_layernorm_op_plugin.cu 已删除 100644 → 0
浏览文件 @ 9df0ab32
// 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 <cuda_runtime.h>
#include <stdio.h>
#include <cassert>
#include <cub/cub.cuh> // NOLINT
#include <vector>
#include "glog/logging.h"
#include "paddle/fluid/inference/tensorrt/plugin/skip_layernorm_op_plugin.h"
#include "paddle/fluid/operators/math/bert_encoder_functor.h"
namespace paddle {
namespace inference {
namespace tensorrt {
namespace plugin {
// Dynamic Plugin below.
#if IS_TRT_VERSION_GE(6000)
template <typename T>
void SkipLayerNormPluginDynamicImpl<T>::shareGPUData(
const SkipLayerNormPluginDynamicImplBase *anthor) {
auto *ptr = dynamic_cast<const SkipLayerNormPluginDynamicImpl<T> *>(anthor);
if (!ptr->is_initialized_) {
return;
}
scale_gpu_ = ptr->scale_gpu_;
bias_gpu_ = ptr->bias_gpu_;
}
template <typename T>
int SkipLayerNormPluginDynamicImpl<T>::initialize() {
if (is_initialized_) {
return 0;
}
if (bias_) {
cudaMalloc(&bias_gpu_, sizeof(T) * bias_size_);
cudaMemcpy(
bias_gpu_, bias_, bias_size_ * sizeof(T), cudaMemcpyHostToDevice);
}
if (scale_) {
cudaMalloc(&scale_gpu_, sizeof(T) * scale_size_);
cudaMemcpy(
scale_gpu_, scale_, scale_size_ * sizeof(T), cudaMemcpyHostToDevice);
}
is_initialized_ = true;
return 0;
}
template <typename T>
void SkipLayerNormPluginDynamicImpl<T>::terminate() {
if (bias_gpu_) {
cudaFree(bias_gpu_);
bias_gpu_ = nullptr;
}
if (scale_gpu_) {
cudaFree(scale_gpu_);
scale_gpu_ = nullptr;
}
}
int SkipLayerNormPluginDynamic::initialize() TRT_NOEXCEPT {
impl_->initialize();
return 0;
}
void SkipLayerNormPluginDynamic::terminate() TRT_NOEXCEPT {
impl_->terminate();
}
nvinfer1::DimsExprs SkipLayerNormPluginDynamic::getOutputDimensions(
int output_index,
const nvinfer1::DimsExprs *inputs,
int nb_inputs,
nvinfer1::IExprBuilder &expr_builder) TRT_NOEXCEPT {
return inputs[0];
}
bool SkipLayerNormPluginDynamic::supportsFormatCombination(
int pos,
const nvinfer1::PluginTensorDesc *in_out,
int nb_inputs,
int nb_outputs) TRT_NOEXCEPT {
PADDLE_ENFORCE_NOT_NULL(
in_out,
platform::errors::InvalidArgument(
"The input of swish plugin shoule not be nullptr."));
PADDLE_ENFORCE_EQ(nb_outputs,
1,
platform::errors::InvalidArgument(
"The SkipLayerNorm's output should be one"
"but it's (%d) outputs.",
nb_outputs));
PADDLE_ENFORCE_LT(
pos,
nb_inputs + nb_outputs,
platform::errors::InvalidArgument("The pos(%d) should be less than the "
"num(%d) of the input and the output.",
pos,
nb_inputs + nb_outputs));
const nvinfer1::PluginTensorDesc &desc = in_out[pos];
if (pos == 0) {
if (with_fp16_) {
#ifdef TRT_PLUGIN_FP16_AVALIABLE
return (desc.type == nvinfer1::DataType::kHALF) &&
(desc.format == nvinfer1::TensorFormat::kLINEAR);
#else
return (desc.type == nvinfer1::DataType::kFLOAT) &&
(desc.format == nvinfer1::TensorFormat::kLINEAR);
#endif
} else {
return (desc.type == nvinfer1::DataType::kFLOAT) &&
(desc.format == nvinfer1::TensorFormat::kLINEAR);
}
}
const nvinfer1::PluginTensorDesc &prev = in_out[pos - 1];
if (pos == 1) {
return desc.type == prev.type && desc.format == prev.format;
}
// output
return desc.type == prev.type && desc.format == prev.format;
}
nvinfer1::DataType SkipLayerNormPluginDynamic::getOutputDataType(
int index,
const nvinfer1::DataType *input_types,
int nb_inputs) const TRT_NOEXCEPT {
PADDLE_ENFORCE_EQ(index,
0,
platform::errors::InvalidArgument(
"The SkipLayerNorm Plugin only has one output, so the "
"index value should be 0, but get %d.",
index));
PADDLE_ENFORCE_EQ((input_types[0] == nvinfer1::DataType::kFLOAT ||
input_types[0] == nvinfer1::DataType::kHALF),
true,
platform::errors::InvalidArgument(
"The input type should be half or float"));
return input_types[0];
}
template <typename T>
int SkipLayerNormPluginDynamicImpl<T>::enqueue(
const nvinfer1::PluginTensorDesc *input_desc,
const nvinfer1::PluginTensorDesc *output_desc,
const void *const *inputs,
void *const *outputs,
void *workspace,
cudaStream_t stream) TRT_NOEXCEPT {
auto input_dims = input_desc[0].dims;
size_t num = ProductDim(input_dims);
int hidden = input_dims.d[2];
auto input_type = input_desc[0].type;
if (std::is_same<T, float>::value) {
PADDLE_ENFORCE_EQ(input_type == nvinfer1::DataType::kFLOAT,
true,
platform::errors::InvalidArgument(
"The SkipLayernorm Plugin only support fp32 input."));
} else if (std::is_same<T, half>::value) {
PADDLE_ENFORCE_EQ(input_type == nvinfer1::DataType::kHALF,
true,
platform::errors::InvalidArgument(
"The SkipLayernorm Plugin only support fp16 input."));
} else {
PADDLE_THROW(platform::errors::Fatal(
"Unsupport data type, the out type of SkipLayernorm should be "
"float or half."));
}
auto *output_d = reinterpret_cast<T *>(outputs[0]);
const T *input1 = reinterpret_cast<const T *>(inputs[0]);
const T *input2 = reinterpret_cast<const T *>(inputs[1]);
auto *output = reinterpret_cast<T *>(outputs[0]);
operators::math::SkipLayerNormFunctor<T> skip_layer_norm_func;
skip_layer_norm_func(
num, hidden, input1, input2, scale_gpu_, bias_gpu_, output, eps_, stream);
return cudaGetLastError() != cudaSuccess;
}
int SkipLayerNormPluginDynamic::enqueue(
const nvinfer1::PluginTensorDesc *input_desc,
const nvinfer1::PluginTensorDesc *output_desc,
const void *const *inputs,
void *const *outputs,
void *workspace,
cudaStream_t stream) TRT_NOEXCEPT {
impl_->enqueue(input_desc, output_desc, inputs, outputs, workspace, stream);
return cudaGetLastError() != cudaSuccess;
}
#endif
} // namespace plugin
} // namespace tensorrt
} // namespace inference
} // namespace paddle
paddle/fluid/inference/tensorrt/plugin/skip_layernorm_op_plugin.h 已删除 100644 → 0
浏览文件 @ 9df0ab32
// 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.
#pragma once
#include <algorithm>
#include <cstddef>
#include <string>
#include <vector>
#include "paddle/fluid/inference/tensorrt/engine.h"
#include "paddle/fluid/inference/tensorrt/plugin/trt_plugin.h"
#include "paddle/phi/common/data_type.h"
namespace paddle {
namespace inference {
namespace tensorrt {
namespace plugin {
#if IS_TRT_VERSION_GE(6000)
class SkipLayerNormPluginDynamicImplBase {
public:
SkipLayerNormPluginDynamicImplBase() {}
virtual ~SkipLayerNormPluginDynamicImplBase() {}
virtual int initialize() = 0;
virtual void terminate() = 0;
virtual int enqueue(const nvinfer1::PluginTensorDesc* inputDesc,
const nvinfer1::PluginTensorDesc* outputDesc,
const void* const* inputs,
void* const* outputs,
void* workspace,
cudaStream_t stream) = 0;
virtual void shareGPUData(
const SkipLayerNormPluginDynamicImplBase* anthor) = 0;
};
template <typename T>
class SkipLayerNormPluginDynamicImpl
: public SkipLayerNormPluginDynamicImplBase {
public:
explicit SkipLayerNormPluginDynamicImpl(
T* bias, T* scale, int bias_size, int scale_size, const float eps)
: bias_(bias),
scale_(scale),
bias_size_(bias_size),
scale_size_(scale_size),
eps_(eps) {}
~SkipLayerNormPluginDynamicImpl() {}
int initialize();
void terminate();
int enqueue(const nvinfer1::PluginTensorDesc* inputDesc,
const nvinfer1::PluginTensorDesc* outputDesc,
const void* const* inputs,
void* const* outputs,
void* workspace,
cudaStream_t stream) TRT_NOEXCEPT;
void shareGPUData(const SkipLayerNormPluginDynamicImplBase* anthor);
private:
T* bias_{nullptr};
T* scale_{nullptr};
// data on devices
T* bias_gpu_{nullptr};
T* scale_gpu_{nullptr};
int bias_size_;
int scale_size_;
float eps_;
bool is_initialized_{false};
};
class SkipLayerNormPluginDynamic : public DynamicPluginTensorRT {
public:
explicit SkipLayerNormPluginDynamic(void* bias,
void* scale,
int bias_size,
int scale_size,
float eps,
bool with_fp16)
: bias_(bias),
scale_(scale),
bias_size_(bias_size),
scale_size_(scale_size),
eps_(eps),
own_host_buff_(false) {
with_fp16_ = with_fp16;
if (with_fp16_) {
#ifdef TRT_PLUGIN_FP16_AVALIABLE
VLOG(1) << "TRT Plugin DataType selected. SkipLayerNorm-->fp16";
instantiateImpl<half>();
#else
PADDLE_THROW(platform::errors::Fatal(
"The Ernie(Bert) tensorRT plugin should be "
"complied with CUDA version >= 10.0 when running with fp16. "
"Please recomplie it or try to use fp32 by set "
"config.EnableTensorRtEngine(1 << 30, 1, 5, "
"AnalysisConfig::Precision::kFloat32, false, false) "));
#endif
} else {
VLOG(1) << "TRT Plugin DataType selected. SkipLayerNorm-->fp32";
instantiateImpl<float>();
}
}
SkipLayerNormPluginDynamic(void const* serial_data, size_t serial_length)
: own_host_buff_(true) {
// the first var is with_fp16, we will use it.
DeserializeValue(&serial_data, &serial_length, &with_fp16_);
DeserializeValue(&serial_data, &serial_length, &bias_size_);
DeserializeValue(&serial_data, &serial_length, &scale_size_);
DeserializeValue(&serial_data, &serial_length, &eps_);
if (with_fp16_) {
if (bias_size_) {
bias_ = new half[bias_size_];
memcpy(bias_, serial_data, sizeof(half) * bias_size_);
}
reinterpret_cast<char const*&>(serial_data) += bias_size_ * sizeof(half);
serial_length -= bias_size_ * sizeof(half);
if (scale_size_) {
scale_ = new half[scale_size_];
memcpy(scale_, serial_data, sizeof(half) * scale_size_);
}
reinterpret_cast<char const*&>(serial_data) += scale_size_ * sizeof(half);
serial_length -= scale_size_ * sizeof(half);
} else {
if (bias_size_) {
bias_ = new float[bias_size_];
memcpy(bias_, serial_data, sizeof(float) * bias_size_);
}
reinterpret_cast<char const*&>(serial_data) += bias_size_ * sizeof(float);
serial_length -= bias_size_ * sizeof(float);
if (scale_size_) {
scale_ = new float[scale_size_];
memcpy(scale_, serial_data, sizeof(float) * scale_size_);
}
reinterpret_cast<char const*&>(serial_data) +=
scale_size_ * sizeof(float);
serial_length -= scale_size_ * sizeof(float);
}
if (with_fp16_) {
#ifdef TRT_PLUGIN_FP16_AVALIABLE
instantiateImpl<half>();
#else
PADDLE_THROW(platform::errors::Fatal(
"The Ernie(Bert) tensorRT plugin should be "
"complied with CUDA version >= 10.0 when running with fp16. "
"Please recomplie it or try to use fp32 by set "
"config.EnableTensorRtEngine(1 << 30, 1, 5, "
"AnalysisConfig::Precision::kFloat32, false, false) "));
#endif
} else {
instantiateImpl<float>();
}
}
nvinfer1::IPluginV2DynamicExt* clone() const TRT_NOEXCEPT override {
auto ptr = new SkipLayerNormPluginDynamic(
bias_, scale_, bias_size_, scale_size_, eps_, with_fp16_);
ptr->shareGPUData(this);
return ptr;
}
const char* getPluginType() const TRT_NOEXCEPT override {
return "skip_layernorm_plugin";
}
int getNbOutputs() const TRT_NOEXCEPT override { return 1; }
int initialize() TRT_NOEXCEPT override;
void terminate() TRT_NOEXCEPT override;
size_t getSerializationSize() const TRT_NOEXCEPT override {
size_t sum_num = 0;
sum_num += SerializedSize(with_fp16_);
if (with_fp16_) {
sum_num += (bias_size_ + scale_size_) * sizeof(half);
} else {
sum_num += (bias_size_ + scale_size_) * sizeof(float);
}
sum_num += SerializedSize(bias_size_);
sum_num += SerializedSize(scale_size_);
sum_num += SerializedSize(eps_);
return sum_num;
}
void serialize(void* buffer) const TRT_NOEXCEPT override {
// the first var is for with_fp16, we will use it later;
SerializeValue(&buffer, with_fp16_);
SerializeValue(&buffer, bias_size_);
SerializeValue(&buffer, scale_size_);
SerializeValue(&buffer, eps_);
if (with_fp16_) {
for (int i = 0; i < bias_size_; ++i) {
SerializeValue(&buffer, reinterpret_cast<half*>(bias_)[i]);
}
for (int i = 0; i < scale_size_; ++i) {
SerializeValue(&buffer, reinterpret_cast<half*>(scale_)[i]);
}
} else {
for (int i = 0; i < bias_size_; ++i) {
SerializeValue(&buffer, reinterpret_cast<float*>(bias_)[i]);
}
for (int i = 0; i < scale_size_; ++i) {
SerializeValue(&buffer, reinterpret_cast<float*>(scale_)[i]);
}
}
}
nvinfer1::DimsExprs getOutputDimensions(int output_index,
const nvinfer1::DimsExprs* inputs,
int nb_inputs,
nvinfer1::IExprBuilder& expr_builder)
TRT_NOEXCEPT override;
bool supportsFormatCombination(int pos,
const nvinfer1::PluginTensorDesc* in_out,
int nb_inputs,
int nb_outputs) TRT_NOEXCEPT override;
void configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in,
int nb_inputs,
const nvinfer1::DynamicPluginTensorDesc* out,
int nb_outputs) TRT_NOEXCEPT override {}
size_t getWorkspaceSize(const nvinfer1::PluginTensorDesc* inputs,
int nb_inputs,
const nvinfer1::PluginTensorDesc* outputs,
int nb_outputs) const TRT_NOEXCEPT override {
return 0;
}
int enqueue(const nvinfer1::PluginTensorDesc* input_desc,
const nvinfer1::PluginTensorDesc* output_desc,
const void* const* inputs,
void* const* outputs,
void* workspace,
cudaStream_t stream) TRT_NOEXCEPT override;
nvinfer1::DataType getOutputDataType(int index,
const nvinfer1::DataType* input_types,
int nb_inputs) const
TRT_NOEXCEPT override;
void destroy() TRT_NOEXCEPT override {
if (own_host_buff_) {
if (with_fp16_) {
delete[] reinterpret_cast<half*>(bias_);
delete[] reinterpret_cast<half*>(scale_);
} else {
delete[] reinterpret_cast<float*>(bias_);
delete[] reinterpret_cast<float*>(scale_);
}
}
delete impl_;
delete this;
}
private:
void* bias_{nullptr};
void* scale_{nullptr};
int bias_size_;
int scale_size_;
float eps_;
bool own_host_buff_{false};
SkipLayerNormPluginDynamicImplBase* impl_{nullptr};
void shareGPUData(const SkipLayerNormPluginDynamic* anthor) {
impl_->shareGPUData(anthor->impl_);
}
template <typename U>
void instantiateImpl() {
impl_ = new SkipLayerNormPluginDynamicImpl<U>(reinterpret_cast<U*>(bias_),
reinterpret_cast<U*>(scale_),
bias_size_,
scale_size_,
eps_);
}
};
class SkipLayerNormPluginDynamicCreator : public nvinfer1::IPluginCreator {
public:
SkipLayerNormPluginDynamicCreator() {}
const char* getPluginName() const TRT_NOEXCEPT override {
return "skip_layernorm_plugin";
}
const char* getPluginVersion() const TRT_NOEXCEPT override { return "1"; }
const nvinfer1::PluginFieldCollection* getFieldNames() TRT_NOEXCEPT override {
return &field_collection_;
}
nvinfer1::IPluginV2* createPlugin(const char* name,
const nvinfer1::PluginFieldCollection* fc)
TRT_NOEXCEPT override {
return nullptr;
}
nvinfer1::IPluginV2* deserializePlugin(const char* name,
const void* serial_data,
size_t serial_length)
TRT_NOEXCEPT override {
return new SkipLayerNormPluginDynamic(serial_data, serial_length);
}
void setPluginNamespace(const char* lib_namespace) TRT_NOEXCEPT override {
plugin_namespace_ = lib_namespace;
}
const char* getPluginNamespace() const TRT_NOEXCEPT override {
return plugin_namespace_.c_str();
}
private:
std::string plugin_namespace_;
std::string plugin_name_;
nvinfer1::PluginFieldCollection field_collection_;
std::vector<nvinfer1::PluginField> plugin_attributes_;
};
REGISTER_TRT_PLUGIN_V2(SkipLayerNormPluginDynamicCreator);
#endif
} // namespace plugin
} // namespace tensorrt
} // namespace inference
} // namespace paddle
paddle/fluid/inference/tests/api/trt_dynamic_shape_ernie_test.cc
浏览文件 @ b50dbe0b
......@@ -145,7 +145,7 @@ void trt_ernie(bool with_fp16,
TEST(AnalysisPredictor, no_fp16) {
std::vector<float> result = {0.597841, 0.219972, 0.182187};
trt_ernie(false, result, 1e-5);
trt_ernie(false, result, 1e-4);
}
TEST(AnalysisPredictor, fp16) {
......@@ -158,7 +158,7 @@ TEST(AnalysisPredictor, fp16) {
TEST(AnalysisPredictor, no_fp16_bs2) {
std::vector<float> result = {
0.597841, 0.219972, 0.182187, 0.597841, 0.219972, 0.182187};
trt_ernie(false, result, 1e-5, 2);
trt_ernie(false, result, 1e-4, 2);
}
TEST(AnalysisPredictor, fp16_bs2) {
......
python/paddle/fluid/tests/unittests/ir/inference/inference_pass_test.py
浏览文件 @ b50dbe0b
......@@ -36,7 +36,7 @@ class InferencePassTest(unittest.TestCase):
self.enable_mkldnn = False
self.enable_mkldnn_bfloat16 = False
self.enable_trt = False
self.enable_tensorrt_varseqlen = True
self.enable_tensorrt_varseqlen = False
self.trt_parameters = None
self.dynamic_shape_params = None
self.enable_lite = False
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_skip_layernorm.py 已删除 100644 → 0
浏览文件 @ 9df0ab32
# 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.
import unittest
from functools import partial
from typing import Any, Dict, List
import numpy as np
from program_config import ProgramConfig, TensorConfig
from trt_layer_auto_scan_test import TrtLayerAutoScanTest
import paddle.inference as paddle_infer
class TrtConvertSkipLayernormTest(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
inputs = program_config.inputs
weights = program_config.weights
outputs = program_config.outputs
attrs = [
program_config.ops[i].attrs for i in range(len(program_config.ops))
]
# The input dimension should be less than or equal to the set axis.
if attrs[0]['begin_norm_axis'] >= 0:
if (
len(inputs['skip_layernorm_inputX_data'].shape)
<= attrs[0]['begin_norm_axis']
):
return False
# 2D input is not supported.
if self.dims == 2:
return False
return True
def sample_program_configs(self):
def generate_input1(attrs: List[Dict[str, Any]], batch):
if self.dims == 4:
return np.ones([batch, 6, 128, 768]).astype(np.float32)
elif self.dims == 3:
return np.ones([batch, 128, 768]).astype(np.float32)
elif self.dims == 2:
return np.ones([batch, 768]).astype(np.float32)
def generate_input2(attrs: List[Dict[str, Any]], batch):
if self.dims == 4:
return np.ones([batch, 6, 128, 768]).astype(np.float32)
elif self.dims == 3:
return np.ones([batch, 128, 768]).astype(np.float32)
elif self.dims == 2:
return np.ones([batch, 768]).astype(np.float32)
def generate_weight1(attrs: List[Dict[str, Any]]):
return np.random.random([768]).astype(np.float32)
def generate_weight2(attrs: List[Dict[str, Any]]):
return np.random.random([768]).astype(np.float32)
for dims in [2, 3, 4]:
for batch in [1, 2, 4]:
for epsilon in [1e-5]:
for begin_norm_axis in [0, 1, 2, -1]:
for enable_int8 in [False, True]:
self.dims = dims
dics = [
{
"epsilon": epsilon,
"begin_norm_axis": begin_norm_axis,
"enable_int8": enable_int8,
},
{},
]
ops_config = [
{
"op_type": "skip_layernorm",
"op_inputs": {
"X": ["skip_layernorm_inputX_data"],
"Y": ["skip_layernorm_inputY_data"],
"Bias": ["Bias"],
"Scale": ["Scale"],
},
"op_outputs": {
"Out": ["skip_layernorm_out"]
},
"op_attrs": dics[0],
}
]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={
"Bias": TensorConfig(
data_gen=partial(generate_weight1, dics)
),
"Scale": TensorConfig(
data_gen=partial(generate_weight2, dics)
),
},
inputs={
"skip_layernorm_inputX_data": TensorConfig(
data_gen=partial(
generate_input1, dics, batch
)
),
"skip_layernorm_inputY_data": TensorConfig(
data_gen=partial(
generate_input2, dics, batch
)
),
},
outputs=["skip_layernorm_out"],
)
yield program_config
def sample_predictor_configs(
self, program_config
) -> (paddle_infer.Config, List[int], float):
def generate_dynamic_shape(attrs):
if self.dims == 4:
self.dynamic_shape.min_input_shape = {
"skip_layernorm_inputX_data": [1, 6, 128, 768],
"skip_layernorm_inputY_data": [1, 6, 128, 768],
"Bias": [768],
"Scale": [768],
}
self.dynamic_shape.max_input_shape = {
"skip_layernorm_inputX_data": [4, 6, 768, 3072],
"skip_layernorm_inputY_data": [4, 6, 768, 3072],
"Bias": [3072],
"Scale": [3072],
}
self.dynamic_shape.opt_input_shape = {
"skip_layernorm_inputX_data": [2, 6, 128, 768],
"skip_layernorm_inputY_data": [2, 6, 128, 768],
"Bias": [768],
"Scale": [768],
}
elif self.dims == 3:
self.dynamic_shape.min_input_shape = {
"skip_layernorm_inputX_data": [1, 128, 768],
"skip_layernorm_inputY_data": [1, 128, 768],
"Bias": [768],
"Scale": [768],
}
self.dynamic_shape.max_input_shape = {
"skip_layernorm_inputX_data": [4, 768, 3072],
"skip_layernorm_inputY_data": [4, 768, 3072],
"Bias": [3072],
"Scale": [3072],
}
self.dynamic_shape.opt_input_shape = {
"skip_layernorm_inputX_data": [2, 128, 768],
"skip_layernorm_inputY_data": [2, 128, 768],
"Bias": [768],
"Scale": [768],
}
elif self.dims == 2:
self.dynamic_shape.min_input_shape = {
"skip_layernorm_inputX_data": [1, 768],
"skip_layernorm_inputY_data": [1, 768],
"Bias": [768],
"Scale": [768],
}
self.dynamic_shape.max_input_shape = {
"skip_layernorm_inputX_data": [4, 3072],
"skip_layernorm_inputY_data": [4, 3072],
"Bias": [3072],
"Scale": [3072],
}
self.dynamic_shape.opt_input_shape = {
"skip_layernorm_inputX_data": [2, 768],
"skip_layernorm_inputY_data": [2, 768],
"Bias": [768],
"Scale": [768],
}
def clear_dynamic_shape():
self.dynamic_shape.min_input_shape = {}
self.dynamic_shape.max_input_shape = {}
self.dynamic_shape.opt_input_shape = {}
def generate_trt_nodes_num(attrs, dynamic_shape):
if dynamic_shape:
return 1, 3
else:
return 0, 4
attrs = [
program_config.ops[i].attrs for i in range(len(program_config.ops))
]
# # for static_shape
# clear_dynamic_shape()
# self.trt_param.precision = paddle_infer.PrecisionType.Float32
# yield self.create_inference_config(), generate_trt_nodes_num(
# attrs, False), 1e-5
# self.trt_param.precision = paddle_infer.PrecisionType.Half
# yield self.create_inference_config(), generate_trt_nodes_num(
# attrs, False), (1e-3, 1e-3)
# for dynamic_shape
generate_dynamic_shape(attrs)
self.trt_param.precision = paddle_infer.PrecisionType.Float32
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, True
), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, True
), (1e-3, 1e-3)
def add_skip_trt_case(self):
pass
def test(self):
self.add_skip_trt_case()
self.run_test()
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/ir/inference/test_trt_skip_layernorm_fuse_pass.py 0 → 100644
浏览文件 @ b50dbe0b
# Copyright (c) 2022 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.
import os
import shutil
import unittest
import numpy as np
from inference_pass_test import InferencePassTest
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.core import AnalysisConfig, PassVersionChecker
class SkipLayernormFusePassTest0(InferencePassTest):
def setUp(self):
with fluid.program_guard(self.main_program, self.startup_program):
data1 = paddle.static.data(
name="data1", shape=[-1, 3, 128, 128], dtype="float32"
)
data2 = paddle.static.data(
name="data2", shape=[-1, 3, 128, 128], dtype="float32"
)
eltwise_out = self.append_eltwise(data1, data2)
out = paddle.nn.functional.layer_norm(
eltwise_out, eltwise_out.shape[1:]
)
self.feeds = {
"data1": np.random.random([1, 3, 128, 128]).astype("float32"),
"data2": np.random.random([1, 3, 128, 128]).astype("float32"),
}
self.enable_trt = True
self.trt_parameters = SkipLayernormFusePassTest0.TensorRTParam(
1 << 30, 32, 0, AnalysisConfig.Precision.Float32, True, False
)
self.dynamic_shape_params = (
SkipLayernormFusePassTest0.DynamicShapeParam(
{'data1': [1, 1, 1, 128], 'data2': [1, 1, 1, 128]},
{'data1': [1, 3, 128, 128], 'data2': [1, 3, 128, 128]},
{'data1': [1, 3, 128, 128], 'data2': [1, 3, 128, 128]},
False,
)
)
self.fetch_list = [out]
def append_eltwise(self, data1, data2):
return paddle.add(data1, data2)
def test_check_output(self):
if os.path.exists(self.path + "_opt_cache"):
shutil.rmtree(self.path + "_opt_cache")
if core.is_compiled_with_cuda():
use_gpu = True
self.check_output_with_option(use_gpu, atol=0.01, rtol=0.00001)
self.assertTrue(
PassVersionChecker.IsCompatible('tensorrt_subgraph_pass')
)
class SkipLayernormFusePassTest1(InferencePassTest):
def setUp(self):
with fluid.program_guard(self.main_program, self.startup_program):
data1 = paddle.static.data(
name="data1", shape=[-1, 256, 1536], dtype="float32"
)
data2 = paddle.static.data(
name="data2", shape=[-1, 256, 1536], dtype="float32"
)
eltwise_out = self.append_eltwise(data1, data2)
out = paddle.nn.functional.layer_norm(
eltwise_out, eltwise_out.shape[1:]
)
self.feeds = {
"data1": np.random.random([1, 256, 1536]).astype("float32"),
"data2": np.random.random([1, 256, 1536]).astype("float32"),
}
self.enable_trt = True
self.trt_parameters = SkipLayernormFusePassTest1.TensorRTParam(
1 << 30, 32, 0, AnalysisConfig.Precision.Float32, True, False
)
self.dynamic_shape_params = (
SkipLayernormFusePassTest1.DynamicShapeParam(
{'data1': [1, 1, 1], 'data2': [1, 1, 1]},
{'data1': [1, 384, 1536], 'data2': [1, 384, 1536]},
{'data1': [1, 384, 1536], 'data2': [1, 384, 1536]},
False,
)
)
self.fetch_list = [out]
def append_eltwise(self, data1, data2):
return paddle.add(data1, data2)
def test_check_output(self):
if os.path.exists(self.path + "_opt_cache"):
shutil.rmtree(self.path + "_opt_cache")
if core.is_compiled_with_cuda():
use_gpu = True
self.check_output_with_option(use_gpu, atol=0.01, rtol=0.00001)
self.assertTrue(
PassVersionChecker.IsCompatible('tensorrt_subgraph_pass')
)
class SkipLayernormFusePassTest2(InferencePassTest):
def setUp(self):
with fluid.program_guard(self.main_program, self.startup_program):
data1 = paddle.static.data(
name="data1", shape=[-1, 128, 64, 768], dtype="float32"
)
data2 = paddle.static.data(
name="data2", shape=[-1, 128, 64, 768], dtype="float32"
)
eltwise_out = self.append_eltwise(data1, data2)
out = paddle.nn.functional.layer_norm(
eltwise_out, eltwise_out.shape[1:]
)
self.feeds = {
"data1": np.random.random([1, 128, 64, 768]).astype("float32"),
"data2": np.random.random([1, 128, 64, 768]).astype("float32"),
}
self.enable_trt = True
self.trt_parameters = SkipLayernormFusePassTest2.TensorRTParam(
1 << 30, 32, 0, AnalysisConfig.Precision.Half, True, False
)
self.dynamic_shape_params = (
SkipLayernormFusePassTest2.DynamicShapeParam(
{'data1': [1, 1, 1, 1], 'data2': [1, 1, 1, 1]},
{'data1': [1, 128, 64, 768], 'data2': [1, 128, 64, 768]},
{'data1': [1, 128, 64, 768], 'data2': [1, 128, 64, 768]},
False,
)
)
self.fetch_list = [out]
def append_eltwise(self, data1, data2):
return paddle.add(data1, data2)
def test_check_output(self):
if os.path.exists(self.path + "_opt_cache"):
shutil.rmtree(self.path + "_opt_cache")
if core.is_compiled_with_cuda():
use_gpu = True
self.check_output_with_option(use_gpu, atol=0.1, rtol=0.00001)
self.assertTrue(
PassVersionChecker.IsCompatible('tensorrt_subgraph_pass')
)
class SkipLayernormFusePassTest3(InferencePassTest):
def setUp(self):
with fluid.program_guard(self.main_program, self.startup_program):
data1 = paddle.static.data(
name="data1", shape=[-1, 128, 128], dtype="float32"
)
data2 = paddle.static.data(
name="data2", shape=[-1, 128, 128], dtype="float32"
)
eltwise_out = self.append_eltwise(data1, data2)
out = paddle.nn.functional.layer_norm(
eltwise_out, eltwise_out.shape[1:]
)
self.feeds = {
"data1": np.random.random([1, 128, 128]).astype("float32"),
"data2": np.random.random([1, 128, 128]).astype("float32"),
}
self.enable_trt = True
self.trt_parameters = SkipLayernormFusePassTest3.TensorRTParam(
1 << 30, 32, 0, AnalysisConfig.Precision.Half, True, False
)
self.dynamic_shape_params = (
SkipLayernormFusePassTest3.DynamicShapeParam(
{'data1': [1, 1, 1], 'data2': [1, 1, 1]},
{'data1': [1, 128, 128], 'data2': [1, 128, 128]},
{'data1': [1, 128, 128], 'data2': [1, 128, 128]},
False,
)
)
self.fetch_list = [out]
def append_eltwise(self, data1, data2):
return paddle.add(data1, data2)
def test_check_output(self):
if os.path.exists(self.path + "_opt_cache"):
shutil.rmtree(self.path + "_opt_cache")
if core.is_compiled_with_cuda():
use_gpu = True
self.check_output_with_option(use_gpu, atol=0.1, rtol=0.00001)
self.assertTrue(
PassVersionChecker.IsCompatible('tensorrt_subgraph_pass')
)
if __name__ == "__main__":
unittest.main()
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或