diff --git a/paddle/fluid/inference/api/analysis_predictor.cc b/paddle/fluid/inference/api/analysis_predictor.cc index ba729fe0492e9cc93ab28e7297fb74c148bc2c15..baff7a6f57c525e4400768ce6e08c11e9cb0a703 100644 --- a/paddle/fluid/inference/api/analysis_predictor.cc +++ b/paddle/fluid/inference/api/analysis_predictor.cc @@ -1234,6 +1234,8 @@ USE_TRT_CONVERTER(roi_align); USE_TRT_CONVERTER(affine_channel); USE_TRT_CONVERTER(multiclass_nms); USE_TRT_CONVERTER(nearest_interp); +USE_TRT_CONVERTER(reduce_sum); +USE_TRT_CONVERTER(gather_nd); USE_TRT_CONVERTER(reshape); #endif diff --git a/paddle/fluid/inference/tensorrt/convert/CMakeLists.txt b/paddle/fluid/inference/tensorrt/convert/CMakeLists.txt index 99328e6076891392fad019635a6f02839b7b6b8a..c356ead850878fffb083c0096c57f7cabfa0da9d 100644 --- a/paddle/fluid/inference/tensorrt/convert/CMakeLists.txt +++ b/paddle/fluid/inference/tensorrt/convert/CMakeLists.txt @@ -12,6 +12,8 @@ nv_library(tensorrt_converter affine_channel_op.cc multiclass_nms_op.cc nearest_interp_op.cc + reduce_op.cc + gather_nd_op.cc reshape_op.cc DEPS tensorrt_engine tensorrt_plugin operator scope framework_proto op_registry) diff --git a/paddle/fluid/inference/tensorrt/convert/emb_eltwise_layernorm.cc b/paddle/fluid/inference/tensorrt/convert/emb_eltwise_layernorm.cc index 66a682db07b91195046d3d11031b8739b72b81c4..04c51202f022f65cf0c6b6a9671248acbc547a4a 100644 --- a/paddle/fluid/inference/tensorrt/convert/emb_eltwise_layernorm.cc +++ b/paddle/fluid/inference/tensorrt/convert/emb_eltwise_layernorm.cc @@ -40,10 +40,19 @@ class EmbEltwiseLayerNormOpConverter : public OpConverter { auto word_emb_name = op_desc.Input("WordEmbedding").front(); auto pos_emb_name = op_desc.Input("PosEmbedding").front(); auto sent_emb_name = op_desc.Input("SentEmbedding").front(); - std::vector id_names = {word_id_name, pos_id_name, - sent_id_name}; - std::vector emb_names = {word_emb_name, pos_emb_name, - sent_emb_name}; + + std::vector id_names; + std::vector emb_names; + + if (engine_->use_oss()) { + id_names = + std::vector{word_id_name, pos_id_name, sent_id_name}; + emb_names = + std::vector{word_emb_name, pos_emb_name, sent_emb_name}; + } else { + id_names = op_desc.Input("Ids"); + emb_names = op_desc.Input("Embs"); + } int input_num = id_names.size(); diff --git a/paddle/fluid/inference/tensorrt/convert/gather_nd_op.cc b/paddle/fluid/inference/tensorrt/convert/gather_nd_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..489fc987dfec2a13b4baccb06911c940b627d908 --- /dev/null +++ b/paddle/fluid/inference/tensorrt/convert/gather_nd_op.cc @@ -0,0 +1,58 @@ +/* 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. */ + +#include "paddle/fluid/inference/tensorrt/convert/op_converter.h" +#include "paddle/fluid/inference/tensorrt/plugin/gather_nd_op_plugin.h" + +namespace paddle { +namespace inference { +namespace tensorrt { + +class GatherNdOpConverter : public OpConverter { + public: + void operator()(const framework::proto::OpDesc& op, + const framework::Scope& scope, bool test_mode) override { + VLOG(4) << "convert a paddle gather_nd op to tensorrt gather_nd plugin"; + framework::OpDesc op_desc(op, nullptr); + + // Declare inputs + std::vector inputs; + auto* input = engine_->GetITensor(op_desc.Input("X")[0]); + auto* index = engine_->GetITensor(op_desc.Input("Index")[0]); + inputs.emplace_back(input); + inputs.emplace_back(index); + + nvinfer1::ILayer* layer = nullptr; + bool with_fp16 = engine_->WithFp16() && !engine_->disable_trt_plugin_fp16(); + plugin::GatherNdPluginDynamic* plugin = + new plugin::GatherNdPluginDynamic(with_fp16); + layer = engine_->AddDynamicPlugin(inputs.data(), inputs.size(), plugin); + + std::string layer_name = "gather_nd (Output: "; + auto output_name = op_desc.Output("Out")[0]; + layer->getOutput(0)->setName(output_name.c_str()); + engine_->SetITensor(output_name, layer->getOutput(0)); + layer_name += output_name; + if (test_mode) { + engine_->DeclareOutput(output_name); + } + layer->setName((layer_name + ")").c_str()); + } +}; + +} // namespace tensorrt +} // namespace inference +} // namespace paddle + +REGISTER_TRT_OP_CONVERTER(gather_nd, GatherNdOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/reduce_op.cc b/paddle/fluid/inference/tensorrt/convert/reduce_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..66d2680fe9969cf7857130f1aa6e6aef742ca805 --- /dev/null +++ b/paddle/fluid/inference/tensorrt/convert/reduce_op.cc @@ -0,0 +1,90 @@ +/* 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. */ + +#include +#include + +#include +#include +#include + +#include "paddle/fluid/inference/tensorrt/convert/op_converter.h" + +namespace paddle { +namespace framework { +class Scope; + +namespace proto { +class OpDesc; +} // namespace proto +} // namespace framework +} // namespace paddle + +namespace paddle { +namespace inference { +namespace tensorrt { + +class ReduceSumOpConverter : public OpConverter { + public: + void operator()(const framework::proto::OpDesc& op, + const framework::Scope& scope, bool test_mode) override { + VLOG(4) << "convert a paddle reduce_sum op to tensorrt reduce layer"; + framework::OpDesc op_desc(op, nullptr); + + auto* x = engine_->GetITensor(op_desc.Input("X").front()); + nvinfer1::Dims input_shape = x->getDimensions(); + int input_dims = input_shape.nbDims; + + bool keep_dim = BOOST_GET_CONST(bool, op_desc.GetAttr("keep_dim")); + std::vector dim = + BOOST_GET_CONST(std::vector, op_desc.GetAttr("dim")); + bool reduce_all = BOOST_GET_CONST(bool, op_desc.GetAttr("reduce_all")); + + // Now we only support dynamic_shape mode. + nvinfer1::IReduceLayer* layer = nullptr; + if (reduce_all) { + uint32_t reduce_dim = 0; + for (int i = 0; i < input_dims; ++i) { + reduce_dim |= 1 << i; + } + layer = TRT_ENGINE_ADD_LAYER(engine_, Reduce, *x, + nvinfer1::ReduceOperation::kSUM, reduce_dim, + keep_dim); + } else { + auto CvtToBitMask = [&](const std::vector& dims) -> uint32_t { + uint32_t res = 0; + for (auto x : dims) { + if (x < 0) { + res |= 1 << (x + input_dims); + } else { + res |= 1 << x; + } + } + return res; + }; + layer = TRT_ENGINE_ADD_LAYER(engine_, Reduce, *x, + nvinfer1::ReduceOperation::kSUM, + CvtToBitMask(dim), keep_dim); + } + + auto output_name = op_desc.Output("Out")[0]; + RreplenishLayerAndOutput(layer, "reduce_sum", {output_name}, test_mode); + } +}; + +} // namespace tensorrt +} // namespace inference +} // namespace paddle + +REGISTER_TRT_OP_CONVERTER(reduce_sum, ReduceSumOpConverter); diff --git a/paddle/fluid/inference/tensorrt/op_teller.cc b/paddle/fluid/inference/tensorrt/op_teller.cc index e7a48013b07f4399a325b90db25d8a17d13e12a0..07dc1a0684e8eac914e4e33aefc2fe72c1673aa8 100644 --- a/paddle/fluid/inference/tensorrt/op_teller.cc +++ b/paddle/fluid/inference/tensorrt/op_teller.cc @@ -13,6 +13,7 @@ // limitations under the License. #include "paddle/fluid/inference/tensorrt/op_teller.h" + #include "paddle/fluid/framework/block_desc.h" #include "paddle/fluid/framework/data_layout.h" @@ -122,11 +123,13 @@ struct SimpleOpTypeSetTeller : public Teller { "flatten2", "flatten", "gather", + "gather_nd", "yolo_box", "roi_align", "affine_channel", "nearest_interp", "anchor_generator", + "reduce_sum", }; }; @@ -324,6 +327,30 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8, if (!with_dynamic_shape || desc.Input("Axis").size() > 0) return false; } + if (op_type == "gather_nd") { + auto* block = desc.Block(); + auto x_var_name = desc.Input("X")[0]; + auto index_var_name = desc.Input("Index")[0]; + auto* x_var_desc = block->FindVar(x_var_name); + auto* index_var_desc = block->FindVar(index_var_name); + + // The index input must be int32 datatype. + if (index_var_desc->GetDataType() != + paddle::framework::proto::VarType_Type::VarType_Type_INT32) { + VLOG(3) << "gather_nd op Index input data type must be int32"; + return false; + } + + const auto index_shape = index_var_desc->GetShape(); + const auto x_shape = x_var_desc->GetShape(); + if (x_shape.size() != index_shape.size()) { + VLOG(3) << "gather_nd op Index input dims size [" << index_shape.size() + << " ] not equal to x dims size [" << x_shape.size() << "]"; + return false; + } + if (!with_dynamic_shape) return false; + } + if (op_type == "yolo_box") { if (with_dynamic_shape) return false; bool has_attrs = @@ -658,6 +685,20 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8, } } + if (op_type == "reduce_sum") { + if (!with_dynamic_shape) { + VLOG(3) << "the reduce_sum does not support static shape yet"; + return false; + } + + if (!(desc.HasAttr("keep_dim") && desc.HasAttr("dim") && + desc.HasAttr("reduce_all"))) { + VLOG(3) << "the reduce_sum does not have attr (keep_dim or dim or " + "reduce_all)"; + return false; + } + } + if (op_type == "reshape" || op_type == "reshape2") { if (!desc.HasAttr("shape") || with_dynamic_shape) { return false; diff --git a/paddle/fluid/inference/tensorrt/plugin/CMakeLists.txt b/paddle/fluid/inference/tensorrt/plugin/CMakeLists.txt index 1804e6c5571d3a15b0b9adc67dc535b46635caa8..26125d21ad7d1a8e9348ce547494289b3a7dd6ad 100644 --- a/paddle/fluid/inference/tensorrt/plugin/CMakeLists.txt +++ b/paddle/fluid/inference/tensorrt/plugin/CMakeLists.txt @@ -8,6 +8,7 @@ nv_library(tensorrt_plugin anchor_generator_op_plugin.cu yolo_box_op_plugin.cu roi_align_op_plugin.cu + gather_nd_op_plugin.cu DEPS enforce tensorrt_engine prelu tensor bert_encoder_functor) nv_test(test_split_plugin SRCS test_split_plugin.cc DEPS diff --git a/paddle/fluid/inference/tensorrt/plugin/gather_nd_op_plugin.cu b/paddle/fluid/inference/tensorrt/plugin/gather_nd_op_plugin.cu new file mode 100644 index 0000000000000000000000000000000000000000..5f4ac054c95b34f305d0213d773f5bcfe5e9e3e9 --- /dev/null +++ b/paddle/fluid/inference/tensorrt/plugin/gather_nd_op_plugin.cu @@ -0,0 +1,229 @@ +// 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. + +#include + +#include +#include +#include +#include +#include + +#include "NvInferRuntimeCommon.h" +#include "paddle/fluid/inference/tensorrt/plugin/gather_nd_op_plugin.h" +#include "paddle/fluid/inference/tensorrt/plugin/trt_plugin_factory.h" +#include "paddle/fluid/platform/place.h" + +namespace paddle { +namespace inference { +namespace tensorrt { +namespace plugin { + +#if IS_TRT_VERSION_GE(6000) + +template +__global__ void GatherNdCUDAKernel(const T* input, const int32_t* input_dims, + const IndexT* indices, T* output, + int32_t remain_size, int32_t slice_size, + int32_t end_size) { + CUDA_KERNEL_LOOP(i, remain_size * slice_size) { + int indices_i = i / slice_size; + int slice_i = i - indices_i * slice_size; // offset inside the slice + IndexT gather_i = 0; + int32_t temp = slice_size; + for (int32_t j = end_size - 1; j >= 0; --j) { + auto index_value = indices[indices_i * end_size + j]; + PADDLE_ENFORCE( + index_value >= 0 && index_value < input_dims[j], + "The index is out of bounds, " + "please check whether the dimensions of index and " + "input meet the requirements. It should " + "be less than [%d] and greater or equal to 0, but received [%d]", + input_dims[j], index_value); + gather_i += (index_value * temp); + temp *= input_dims[j]; + } + IndexT input_i = gather_i + slice_i; + *(output + i) = *(input + input_i); + } +} + +int GatherNdPluginDynamic::initialize() { return 0; } + +size_t GatherNdPluginDynamic::getSerializationSize() const { + return SerializedSize(with_fp16_); +} + +void GatherNdPluginDynamic::serialize(void* buffer) const { + SerializeValue(&buffer, with_fp16_); +} + +nvinfer1::DimsExprs GatherNdPluginDynamic::getOutputDimensions( + int output_index, const nvinfer1::DimsExprs* inputs, int nb_inputs, + nvinfer1::IExprBuilder& expr_builder) { + PADDLE_ENFORCE_EQ( + nb_inputs, 2, + platform::errors::InvalidArgument( + "The gather_nd plugin should have 2 input, but got %d.", nb_inputs)); + PADDLE_ENFORCE_EQ(output_index, 0, + platform::errors::InvalidArgument( + "When GetOutputDimensions in gather_nd " + "plugin, the output_index should be 0.")); + + nvinfer1::DimsExprs x_dims = inputs[0]; + nvinfer1::DimsExprs index_dims = inputs[1]; + + int32_t x_dims_size = x_dims.nbDims; + int32_t index_dims_size = index_dims.nbDims; + + // TODO(wilber): The result dims shoule be Index.shape[:-1] + + // X.shape[Index.shape[-1]:], but the trt DimsExprs is an expression we can't + // get the actual value. So we only support one scenario: input_dims.size == + // index_dims.size. + nvinfer1::DimsExprs ret(x_dims); + for (int i = 0; i < index_dims_size - 1; ++i) { + ret.d[i] = index_dims.d[i]; + } + + return ret; +} + +bool GatherNdPluginDynamic::supportsFormatCombination( + int pos, const nvinfer1::PluginTensorDesc* in_out, int nb_inputs, + int nb_outputs) { + PADDLE_ENFORCE_NOT_NULL( + in_out, platform::errors::InvalidArgument( + "The input of gather_nd plugin should not be nullptr.")); + + 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)); + (in_out && pos < (nb_inputs + nb_outputs)); + + const nvinfer1::PluginTensorDesc& in = in_out[pos]; + if (pos == 0) { + if (with_fp16_) { + return (in.type == nvinfer1::DataType::kFLOAT || + in.type == nvinfer1::DataType::kHALF) && + (in.format == nvinfer1::TensorFormat::kLINEAR); + } else { + return (in.type == nvinfer1::DataType::kFLOAT) && + (in.format == nvinfer1::TensorFormat::kLINEAR); + } + } else if (pos == 1) { + return in.type == nvinfer1::DataType::kINT32 && + in.format == nvinfer1::TensorFormat::kLINEAR; + } else if (pos == 2) { + return in.type == in_out[0].type && + in.format == nvinfer1::TensorFormat::kLINEAR; + } + + return true; +} + +nvinfer1::DataType GatherNdPluginDynamic::getOutputDataType( + int index, const nvinfer1::DataType* input_types, int nb_inputs) const { + return input_types[0]; +} + +int GatherNdPluginDynamic::enqueue( + const nvinfer1::PluginTensorDesc* input_desc, + const nvinfer1::PluginTensorDesc* output_desc, const void* const* inputs, + void* const* outputs, void* workspace, cudaStream_t stream) { + auto input_dims = input_desc[0].dims; + auto index_dims = input_desc[1].dims; + auto input_dims_size = input_dims.nbDims; + auto index_dims_size = index_dims.nbDims; + + std::vector input_shape, index_shape, out_shape; + for (int i = 0; i < input_dims.nbDims; i++) + input_shape.push_back(input_dims.d[i]); + for (int i = 0; i < index_dims.nbDims; i++) + index_shape.push_back(index_dims.d[i]); + // The out_shape is + // Index.shape[:-1] + X.shape[Index.shape[-1]:] + for (int i = 0; i < index_dims_size - 1; ++i) { + out_shape.emplace_back(index_shape[i]); + } + for (int i = index_shape[index_dims_size - 1]; i < input_dims_size; ++i) { + out_shape.emplace_back(input_shape[i]); + } + + // final dim + int end_size = index_shape[index_dims_size - 1]; + // remain dim + std::vector remain_ddim(index_shape.begin(), index_shape.end() - 1); + int remain_numel = std::accumulate(remain_ddim.begin(), remain_ddim.end(), 1, + std::multiplies()); + // slice size + int slice_size = 1; + for (int i = end_size; i < input_dims_size; ++i) { + slice_size *= input_shape[i]; + } + + auto input_type = input_desc[0].type; + if (input_type == nvinfer1::DataType::kFLOAT) { + VLOG(1) << "TRT Plugin DataType selected. gather_nd-->fp32"; + + const float* p_input = static_cast(inputs[0]); + const int32_t* p_index = static_cast(inputs[1]); + float* p_output = static_cast(outputs[0]); + + if (input_dims_data_ == nullptr) { + cudaMalloc(&input_dims_data_, input_shape.size() * sizeof(int)); + } + cudaMemcpyAsync(input_dims_data_, input_shape.data(), + sizeof(int) * input_shape.size(), cudaMemcpyHostToDevice, + stream); + + int block = 512; + int n = slice_size * remain_numel; + int grid = (n + block - 1) / block; + + GatherNdCUDAKernel<<>>( + p_input, input_dims_data_, p_index, p_output, remain_numel, slice_size, + end_size); + } else if (input_type == nvinfer1::DataType::kHALF) { + VLOG(1) << "TRT Plugin DataType selected. gather_nd-->fp16"; + + const half* p_input = static_cast(inputs[0]); + const int32_t* p_index = static_cast(inputs[1]); + half* p_output = static_cast(outputs[0]); + + if (input_dims_data_ == nullptr) { + cudaMalloc(&input_dims_data_, input_shape.size() * sizeof(int)); + } + cudaMemcpyAsync(input_dims_data_, input_shape.data(), + sizeof(int) * input_shape.size(), cudaMemcpyHostToDevice, + stream); + + int block = 512; + int n = slice_size * remain_numel; + int grid = (n + block - 1) / block; + + GatherNdCUDAKernel<<>>( + p_input, input_dims_data_, p_index, p_output, remain_numel, slice_size, + end_size); + } + + return cudaGetLastError() != cudaSuccess; +} +#endif + +} // namespace plugin +} // namespace tensorrt +} // namespace inference +} // namespace paddle diff --git a/paddle/fluid/inference/tensorrt/plugin/gather_nd_op_plugin.h b/paddle/fluid/inference/tensorrt/plugin/gather_nd_op_plugin.h new file mode 100644 index 0000000000000000000000000000000000000000..0a242238c81fb3b34888905a393bc992179712b2 --- /dev/null +++ b/paddle/fluid/inference/tensorrt/plugin/gather_nd_op_plugin.h @@ -0,0 +1,132 @@ +// 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. + +#pragma once + +#include +#include +#include +#include +#include "paddle/fluid/framework/tensor.h" +#include "paddle/fluid/inference/tensorrt/plugin/trt_plugin.h" + +namespace paddle { +namespace inference { +namespace tensorrt { +namespace plugin { + +#if IS_TRT_VERSION_GE(6000) +class GatherNdPluginDynamic : public DynamicPluginTensorRT { + public: + explicit GatherNdPluginDynamic(bool with_fp16) { with_fp16_ = with_fp16; } + + GatherNdPluginDynamic(void const* serial_data, size_t serial_length) { + DeserializeValue(&serial_data, &serial_length, &with_fp16_); + } + + nvinfer1::IPluginV2DynamicExt* clone() const override { + return new GatherNdPluginDynamic(with_fp16_); + } + + const char* getPluginType() const override { return "gather_nd_plugin"; } + int getNbOutputs() const override { return 1; } + int initialize() override; + + size_t getSerializationSize() const override; + void serialize(void* buffer) const override; + + nvinfer1::DimsExprs getOutputDimensions( + int outputIndex, const nvinfer1::DimsExprs* inputs, int nbInputs, + nvinfer1::IExprBuilder& exprBuilder) override; + + bool supportsFormatCombination(int pos, + const nvinfer1::PluginTensorDesc* inOut, + int nbInputs, int nbOutputs) override; + + void configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in, + int nbInputs, + const nvinfer1::DynamicPluginTensorDesc* out, + int nbOutputs) override {} + + size_t getWorkspaceSize(const nvinfer1::PluginTensorDesc* inputs, + int nbInputs, + const nvinfer1::PluginTensorDesc* outputs, + int nbOutputs) const override { + return 0; + } + + int enqueue(const nvinfer1::PluginTensorDesc* inputDesc, + const nvinfer1::PluginTensorDesc* outputDesc, + const void* const* inputs, void* const* outputs, void* workspace, + cudaStream_t stream) override; + nvinfer1::DataType getOutputDataType(int index, + const nvinfer1::DataType* inputTypes, + int nbInputs) const override; + + void destroy() override { + if (input_dims_data_) { + cudaFree(input_dims_data_); + } + delete this; + } + + private: + int32_t* input_dims_data_{nullptr}; +}; + +class GatherNdPluginDynamicCreator : public nvinfer1::IPluginCreator { + public: + GatherNdPluginDynamicCreator() {} + const char* getPluginName() const override { return "gather_nd_plugin"; } + + const char* getPluginVersion() const override { return "1"; } + + const nvinfer1::PluginFieldCollection* getFieldNames() override { + return &field_collection_; + } + + nvinfer1::IPluginV2* createPlugin( + const char* name, const nvinfer1::PluginFieldCollection* fc) override { + return nullptr; + } + + nvinfer1::IPluginV2* deserializePlugin(const char* name, + const void* serial_data, + size_t serial_length) override { + auto plugin = new GatherNdPluginDynamic(serial_data, serial_length); + return plugin; + } + + void setPluginNamespace(const char* lib_namespace) override { + plugin_namespace_ = lib_namespace; + } + + const char* getPluginNamespace() const override { + return plugin_namespace_.c_str(); + } + + private: + std::string plugin_namespace_; + std::string plugin_name_; + nvinfer1::PluginFieldCollection field_collection_{0, nullptr}; + std::vector plugin_attributes_; +}; + +REGISTER_TRT_PLUGIN_V2(GatherNdPluginDynamicCreator); +#endif + +} // namespace plugin +} // namespace tensorrt +} // namespace inference +} // namespace paddle diff --git a/paddle/fluid/operators/math/bert_encoder_functor.cu b/paddle/fluid/operators/math/bert_encoder_functor.cu index 512f9c62415e5d1b09a1b649e78c72ac2d9f2d88..4d7218cd89e04b5122ff4385abfb2c7305e40c0a 100644 --- a/paddle/fluid/operators/math/bert_encoder_functor.cu +++ b/paddle/fluid/operators/math/bert_encoder_functor.cu @@ -13,6 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. */ #include + #include "paddle/fluid/framework/tensor.h" #include "paddle/fluid/framework/tensor_util.h" #include "paddle/fluid/operators/math/bert_encoder_functor.h" @@ -311,6 +312,156 @@ __global__ void SoftmaxKernelWithEltadd2( #endif } +template +__global__ void SoftmaxKernelWithEltaddForLarge(T *qk_buf, const T *bias_qk, + const int batch_size, + const int head_num, + const int seq_len, + const unsigned mask) { + int qk_offset = blockIdx.x * seq_len; + assert(blockDim.x % 32 == 0); + + T stride_max = -1e20f; + for (int i = 0; i < seq_len; i += blockDim.x) { + stride_max = qk_buf[threadIdx.x + i + qk_offset] + + bias_qk[threadIdx.x + i + qk_offset] > + stride_max + ? qk_buf[threadIdx.x + i + qk_offset] + + bias_qk[threadIdx.x + i + qk_offset] + : stride_max; + } + T max_val = blockReduceMax(stride_max, mask); + + T stride_sum = 0.f; + for (int i = 0; i < seq_len; i += blockDim.x) { + stride_sum += __expf(qk_buf[threadIdx.x + i + qk_offset] + + bias_qk[threadIdx.x + i + qk_offset] - max_val); + } + T sum_val = blockReduceSum(stride_sum, mask); + + for (int i = 0; i < seq_len; i += blockDim.x) { + qk_buf[threadIdx.x + i + qk_offset] = + (T)(__expf(qk_buf[threadIdx.x + i + qk_offset] + + bias_qk[threadIdx.x + i + qk_offset] - max_val) / + sum_val); + } +} + +// HIP defined __HIP_NO_HALF_CONVERSIONS__ +#ifndef __HIPCC__ // @{ Half kernel: SoftmaxKernelWithEltadd +template <> +__global__ void SoftmaxKernelWithEltaddForLarge( + half *qk_buf, const half *bias_qk, const int batch_size, const int head_num, + const int seq_len, const unsigned mask) { +#if CUDA_ARCH_FP16_SUPPORTED(__CUDA_ARCH__) + int qk_offset = blockIdx.x * seq_len; + assert(blockDim.x % 32 == 0); + + float stride_max = -1e20f; + for (int i = 0; i < seq_len; i += blockDim.x) { + float tmp = static_cast(qk_buf[threadIdx.x + i + qk_offset] + + bias_qk[threadIdx.x + i + qk_offset]); + stride_max = tmp > stride_max ? tmp : stride_max; + } + float max_val = blockReduceMax(stride_max, mask); + + float stride_sum = 0.f; + for (int i = 0; i < seq_len; i += blockDim.x) { + float tmp = static_cast(qk_buf[threadIdx.x + i + qk_offset] + + bias_qk[threadIdx.x + i + qk_offset]); + stride_sum += __expf(tmp - max_val); + } + float sum_val = blockReduceSum(stride_sum, mask); + + for (int i = 0; i < seq_len; i += blockDim.x) { + float tmp = + __expf(static_cast(qk_buf[threadIdx.x + i + qk_offset] + + bias_qk[threadIdx.x + i + qk_offset]) - + max_val); + qk_buf[threadIdx.x + i + qk_offset] = (half)(tmp / sum_val); + } +#endif +} +#endif // @} End Half kernel: SoftmaxKernelWithEltadd + +template +__global__ void SoftmaxKernelWithEltaddForLarge2(T *qk_buf_, const T *bias_qk_, + const int batch_size, + const int head_num, + const int seq_len, + const unsigned mask) { + int qk_offset = blockIdx.x * seq_len; + assert(blockDim.x % 32 == 0); + + float2 stride_max = make_float2(-1e20f, -1e20f); + for (int i = 0; i < seq_len; i += blockDim.x) { + float2 cur = ToFloat2(qk_buf_[threadIdx.x + i + qk_offset] + + bias_qk_[threadIdx.x + i + qk_offset]); + stride_max.x = max(stride_max.x, cur.x); + stride_max.y = max(stride_max.y, cur.y); + } + float max_val = blockReduceMax(max(stride_max.x, stride_max.y), mask); + + float2 stride_sum = make_float2(0.f, 0.f); + for (int i = 0; i < seq_len; i += blockDim.x) { + float2 cur = ToFloat2(qk_buf_[threadIdx.x + i + qk_offset] + + bias_qk_[threadIdx.x + i + qk_offset]); + stride_sum.x += __expf(cur.x - max_val); + stride_sum.y += __expf(cur.y - max_val); + } + + float sum_val = + blockReduceSum(stride_sum.x + stride_sum.y, mask) + 1e-6f; + + for (int i = 0; i < seq_len; i += blockDim.x) { + float2 cur = ToFloat2(qk_buf_[threadIdx.x + i + qk_offset] + + bias_qk_[threadIdx.x + i + qk_offset]); + qk_buf_[threadIdx.x + i + qk_offset] = FloatsToPair( + __expf(cur.x - max_val) / sum_val, __expf(cur.y - max_val) / sum_val); + } +} + +template <> +__global__ void SoftmaxKernelWithEltaddForLarge2( + half2 *qk_buf_, const half2 *bias_qk_, const int batch_size, + const int head_num, const int seq_len, const unsigned mask) { +// operator "+" of half only suppotted after cuda version 10.0 +// HIP defined __HIP_NO_HALF_CONVERSIONS__ in hip.cmake +#if defined(PADDLE_WITH_CUDA) && \ + (CUDA_ARCH_FP16_SUPPORTED(__CUDA_ARCH__) && CUDA_VERSION >= 10000) + + int qk_offset = blockIdx.x * seq_len; + assert(blockDim.x % 32 == 0); + + float2 stride_max = make_float2(-1e20f, -1e20f); + for (int i = 0; i < seq_len; i += blockDim.x) { + float2 cur = ToFloat2(qk_buf_[threadIdx.x + i + qk_offset] + + bias_qk_[threadIdx.x + i + qk_offset]); + stride_max.x = max(stride_max.x, cur.x); + stride_max.y = max(stride_max.y, cur.y); + } + float max_val = blockReduceMax(max(stride_max.x, stride_max.y), mask); + + float2 stride_sum = make_float2(0.f, 0.f); + for (int i = 0; i < seq_len; i += blockDim.x) { + float2 cur = ToFloat2(qk_buf_[threadIdx.x + i + qk_offset] + + bias_qk_[threadIdx.x + i + qk_offset]); + stride_sum.x += __expf(cur.x - max_val); + stride_sum.y += __expf(cur.y - max_val); + } + + float sum_val = + blockReduceSum(stride_sum.x + stride_sum.y, mask) + 1e-6f; + + for (int i = 0; i < seq_len; i += blockDim.x) { + float2 cur = ToFloat2(qk_buf_[threadIdx.x + i + qk_offset] + + bias_qk_[threadIdx.x + i + qk_offset]); + qk_buf_[threadIdx.x + i + qk_offset] = FloatsToPair( + __expf(cur.x - max_val) / sum_val, __expf(cur.y - max_val) / sum_val); + } +#endif +} + template inline void MatMulWithHeadQK(const platform::CUDADeviceContext &context, int head_num, int seq_len, int size_per_head, @@ -332,31 +483,48 @@ inline void MatMulWithHeadQK(const platform::CUDADeviceContext &context, reinterpret_cast(qk_buf_), batch_size * head_num, seq_len * size_per_head, seq_len * size_per_head); - int grid = batch_size * head_num * seq_len; - int block = seq_len; - - // Align block to 32, also limit seq_len to max block size. - PADDLE_ENFORCE_LE(seq_len, 1024, platform::errors::InvalidArgument( - "seq_len should <= 1024, " - "but received seq_len is:%d", - seq_len)); - if (seq_len % 2 == 0) { - block = (seq_len <= 64) ? 32 : ((seq_len + 63) / 64) * 32; - if (std::is_same::value) { - SoftmaxKernelWithEltadd2<<>>( - reinterpret_cast(qk_buf_), - reinterpret_cast(bias_qk), batch_size, head_num, - seq_len / 2, FINAL_MASK); + if (seq_len <= 1024) { + int grid = batch_size * head_num * seq_len; + int block = seq_len; + + // Align block to 32, also limit seq_len to max block size. + if (seq_len % 2 == 0) { + block = (seq_len <= 64) ? 32 : ((seq_len + 63) / 64) * 32; + if (std::is_same::value) { + SoftmaxKernelWithEltadd2<<>>( + reinterpret_cast(qk_buf_), + reinterpret_cast(bias_qk), batch_size, head_num, + seq_len / 2, FINAL_MASK); + } else { + SoftmaxKernelWithEltadd2<__half2><<>>( + reinterpret_cast<__half2 *>(qk_buf_), + reinterpret_cast(bias_qk), batch_size, head_num, + seq_len / 2, FINAL_MASK); + } } else { - SoftmaxKernelWithEltadd2<__half2><<>>( - reinterpret_cast<__half2 *>(qk_buf_), - reinterpret_cast(bias_qk), batch_size, head_num, - seq_len / 2, FINAL_MASK); + block = (seq_len <= 32) ? 32 : ((seq_len + 31) / 32) * 32; + SoftmaxKernelWithEltadd<<>>( + qk_buf_, bias_qk, batch_size, head_num, seq_len, FINAL_MASK); } } else { - block = (seq_len <= 32) ? 32 : ((seq_len + 31) / 32) * 32; - SoftmaxKernelWithEltadd<<>>( - qk_buf_, bias_qk, batch_size, head_num, seq_len, FINAL_MASK); + int grid = batch_size * head_num * seq_len; + int block = 512; + if (seq_len % 2 == 0) { + if (std::is_same::value) { + SoftmaxKernelWithEltaddForLarge2<<>>( + reinterpret_cast(qk_buf_), + reinterpret_cast(bias_qk), batch_size, head_num, + seq_len / 2, FINAL_MASK); + } else { + SoftmaxKernelWithEltaddForLarge2<__half2><<>>( + reinterpret_cast<__half2 *>(qk_buf_), + reinterpret_cast(bias_qk), batch_size, head_num, + seq_len / 2, FINAL_MASK); + } + } else { + SoftmaxKernelWithEltaddForLarge<<>>( + qk_buf_, bias_qk, batch_size, head_num, seq_len, FINAL_MASK); + } } } diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_gather_nd_op.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_gather_nd_op.py new file mode 100644 index 0000000000000000000000000000000000000000..75f5328ac1c419015b379b08cc050f478a5b2112 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_gather_nd_op.py @@ -0,0 +1,93 @@ +# 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. + +from __future__ import print_function + +import unittest +import numpy as np +from inference_pass_test import InferencePassTest +import paddle.fluid as fluid +import paddle.fluid.core as core +from paddle.fluid.core import PassVersionChecker +from paddle.fluid.core import AnalysisConfig + + +class TRTGatherNdTest(InferencePassTest): + def setUp(self): + with fluid.program_guard(self.main_program, self.startup_program): + data = fluid.data(name="data", shape=[-1, 3, 4], dtype="float32") + index = fluid.data(name="index", shape=[-1, 2, 2], dtype="int32") + gather_nd = fluid.layers.gather_nd(data, index) + out = fluid.layers.batch_norm(gather_nd, is_test=True) + + self.feeds = { + "data": np.random.random([2, 3, 4]).astype("float32"), + "index": + np.array([[[0, 1], [1, 0]], [[1, 2], [0, 1]]]).astype("int32"), + } + self.enable_trt = True + self.trt_parameters = TRTGatherNdTest.TensorRTParam( + 1 << 30, 32, 1, AnalysisConfig.Precision.Float32, False, False) + self.fetch_list = [out] + self.dynamic_shape_params = TRTGatherNdTest.DynamicShapeParam({ + 'data': [1, 3, 4], + 'index': [1, 2, 2] + }, {'data': [3, 3, 4], + 'index': [3, 2, 2]}, {'data': [3, 3, 4], + 'index': [3, 2, 2]}, False) + + def test_check_output(self): + if core.is_compiled_with_cuda(): + use_gpu = True + self.check_output_with_option(use_gpu, flatten=True) + self.assertTrue( + PassVersionChecker.IsCompatible('tensorrt_subgraph_pass')) + + +class TRTGatherNdFp16Test(InferencePassTest): + def setUp(self): + with fluid.program_guard(self.main_program, self.startup_program): + data = fluid.data( + name="data", shape=[-1, 5120, 768], dtype="float32") + index = fluid.data(name="index", shape=[-1, 4096, 2], dtype="int32") + gather_nd = fluid.layers.gather_nd(data, index) + out = fluid.layers.batch_norm(gather_nd, is_test=True) + + index_data = np.zeros((1, 4096, 2), dtype='int32') + self.feeds = { + "data": np.random.random([1, 5120, 768]).astype("float32"), + "index": index_data, + } + self.enable_trt = True + self.trt_parameters = TRTGatherNdFp16Test.TensorRTParam( + 1 << 30, 32, 1, AnalysisConfig.Precision.Half, False, False) + self.fetch_list = [out] + self.dynamic_shape_params = TRTGatherNdFp16Test.DynamicShapeParam({ + 'data': [1, 5120, 768], + 'index': [1, 4096, 2] + }, {'data': [3, 5120, 768], + 'index': + [3, 4096, 2]}, {'data': [3, 5120, 768], + 'index': [3, 4096, 2]}, False) + + def test_check_output(self, atol=1e-3): + if core.is_compiled_with_cuda(): + use_gpu = True + self.check_output_with_option(use_gpu, flatten=True) + self.assertTrue( + PassVersionChecker.IsCompatible('tensorrt_subgraph_pass')) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_reduce_sum_op.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_reduce_sum_op.py new file mode 100644 index 0000000000000000000000000000000000000000..bb5e8e99b092690a2c88d75ba3a77de2dba2e720 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_reduce_sum_op.py @@ -0,0 +1,82 @@ +# 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. + +from __future__ import print_function + +import unittest +import numpy as np +from inference_pass_test import InferencePassTest +import paddle.fluid as fluid +import paddle.fluid.core as core +from paddle.fluid.core import PassVersionChecker +from paddle.fluid.core import AnalysisConfig + + +class TRTReduceSumTest(InferencePassTest): + def setUp(self): + with fluid.program_guard(self.main_program, self.startup_program): + data = fluid.data( + name="data", shape=[-1, 3, 10, 768], dtype="float32") + reduce_sum = fluid.layers.reduce_sum( + data, dim=[2, -1], keep_dim=True) + out = fluid.layers.batch_norm(reduce_sum, is_test=True) + + self.feeds = { + "data": np.random.random([3, 3, 10, 768]).astype("float32"), + } + self.enable_trt = True + self.trt_parameters = TRTReduceSumTest.TensorRTParam( + 1 << 30, 32, 1, AnalysisConfig.Precision.Float32, False, False) + self.fetch_list = [out] + self.dynamic_shape_params = TRTReduceSumTest.DynamicShapeParam({ + 'data': [1, 3, 8, 8] + }, {'data': [3, 3, 10, 768]}, {'data': [3, 3, 10, 768]}, False) + + def test_check_output(self): + if core.is_compiled_with_cuda(): + use_gpu = True + self.check_output_with_option(use_gpu, flatten=True) + self.assertTrue( + PassVersionChecker.IsCompatible('tensorrt_subgraph_pass')) + + +class TRTReduceSumAllTest(InferencePassTest): + def setUp(self): + with fluid.program_guard(self.main_program, self.startup_program): + data = fluid.data( + name="data", shape=[-1, 3, 10, 768], dtype="float32") + reduce_sum = fluid.layers.reduce_sum(data, keep_dim=True) + out = fluid.layers.batch_norm(reduce_sum, is_test=True) + + self.feeds = { + "data": np.random.random([3, 3, 10, 768]).astype("float32"), + } + self.enable_trt = True + self.trt_parameters = TRTReduceSumAllTest.TensorRTParam( + 1 << 30, 32, 1, AnalysisConfig.Precision.Float32, False, False) + self.fetch_list = [out] + self.dynamic_shape_params = TRTReduceSumAllTest.DynamicShapeParam({ + 'data': [1, 3, 8, 8] + }, {'data': [3, 3, 10, 768]}, {'data': [3, 3, 10, 768]}, False) + + def test_check_output(self): + if core.is_compiled_with_cuda(): + use_gpu = True + self.check_output_with_option(use_gpu, flatten=True) + self.assertTrue( + PassVersionChecker.IsCompatible('tensorrt_subgraph_pass')) + + +if __name__ == "__main__": + unittest.main()