!14491 mindspore lite gpu support int8
From: @yeyunpeng2020 Reviewed-by: @ddwsky,@HilbertDavid Signed-off-by: @ddwskypull/14491/MERGE
mindspore-ci-bot
4 years ago
committed by
Gitee
commit
adf4655aba
@ -1,7 +1,8 @@
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if(${SUPPORT_GPU} STREQUAL opencl)
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if(${SUPPORT_GPU} STREQUAL opencl)
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file(GLOB_RECURSE OPENCL_KERNEL_SRC
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file(GLOB_RECURSE OPENCL_KERNEL_SRC
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${CMAKE_CURRENT_SOURCE_DIR}/*.cc
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${CMAKE_CURRENT_SOURCE_DIR}/*.cc
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${CMAKE_CURRENT_SOURCE_DIR}/kernel/*.cc)
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${CMAKE_CURRENT_SOURCE_DIR}/kernel/*.cc
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${CMAKE_CURRENT_SOURCE_DIR}/kernel/int8/*.cc)
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add_library(opencl_kernel_mid OBJECT ${OPENCL_KERNEL_SRC})
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add_library(opencl_kernel_mid OBJECT ${OPENCL_KERNEL_SRC})
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add_dependencies(opencl_kernel_mid fbs_src)
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add_dependencies(opencl_kernel_mid fbs_src)
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endif()
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endif()
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@ -0,0 +1,19 @@
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__constant sampler_t smp_none = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_NONE | CLK_FILTER_NEAREST;
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__kernel void ElementAddInt8(__read_only image2d_t input_a, __read_only image2d_t input_b,
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__write_only image2d_t output, const int2 output_shape, float act_min, float act_max,
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const float4 scale, const char4 zero_point) {
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int X = get_global_id(0);
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int Y = get_global_id(1);
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if (X >= output_shape.x || Y >= output_shape.y) {
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return;
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}
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char4 a = convert_char4(read_imagei(input_a, smp_none, (int2)(X, Y)));
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char4 b = convert_char4(read_imagei(input_b, smp_none, (int2)(X, Y)));
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float4 real_a = convert_float4(a - zero_point.x) * scale.x;
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float4 real_b = convert_float4(b - zero_point.y) * scale.y;
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int4 result = convert_int4(round((real_a + real_b) / scale.z)) + zero_point.z;
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result = clamp(result, (FLT)(act_min), (FLT)(act_max));
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write_imagei(output, (int2)(X, Y), result);
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}
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@ -0,0 +1,234 @@
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/**
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* Copyright 2021 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "src/runtime/kernel/opencl/kernel/int8/arithmetic_int8.h"
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#include <vector>
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#include "nnacl/fp32/common_func_fp32.h"
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#include "schema/model_generated.h"
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#include "src/kernel_registry.h"
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#include "src/runtime/kernel/opencl/utils.h"
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#ifndef PROGRAM_WITH_IL
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#include "src/runtime/kernel/opencl/cl/int8/arithmetic.cl.inc"
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#endif
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using mindspore::kernel::KERNEL_ARCH::kGPU;
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using mindspore::lite::KernelRegistrar;
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using mindspore::lite::RET_ERROR;
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using mindspore::lite::RET_OK;
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using mindspore::lite::opencl::ImageSize;
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using mindspore::lite::opencl::MemType;
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using mindspore::schema::ActivationType_NO_ACTIVATION;
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using mindspore::schema::ActivationType_RELU;
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using mindspore::schema::ActivationType_RELU6;
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using mindspore::schema::EltwiseMode_MAXIMUM;
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using mindspore::schema::EltwiseMode_PROD;
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using mindspore::schema::EltwiseMode_SUM;
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using mindspore::schema::PrimitiveType_BiasAdd;
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using mindspore::schema::PrimitiveType_Eltwise;
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namespace mindspore::kernel {
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int ArithmeticInt8OpenCLKernel::CheckSpecs() {
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for (auto &tensor : in_tensors_) {
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if (tensor->data_type() != kNumberTypeInt8) {
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MS_LOG(ERROR) << "ArithmeticInt8OpenCLKernel only support int8 input";
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return RET_ERROR;
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}
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}
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for (auto &tensor : out_tensors_) {
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if (tensor->data_type() != kNumberTypeInt8) {
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MS_LOG(ERROR) << "ArithmeticInt8OpenCLKernel only support int8 output";
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return RET_ERROR;
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}
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}
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if (in_tensors_.size() != 2 || out_tensors_.size() != 1) {
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MS_LOG(ERROR) << "in size: " << in_tensors_.size() << ", out size: " << out_tensors_.size();
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return RET_ERROR;
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}
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auto *param = reinterpret_cast<const ArithmeticParameter *>(op_parameter_);
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if (!IsArithmetic(Type())) {
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MS_LOG(ERROR) << "UnSupported Operator: " << schema::EnumNamePrimitiveType(Type());
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return RET_ERROR;
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}
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if (Type() == schema::PrimitiveType_Eltwise) {
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auto mode = param->eltwise_mode_;
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if (mode != EltwiseMode_PROD && mode != EltwiseMode_SUM && mode != EltwiseMode_MAXIMUM) {
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MS_LOG(ERROR) << "Eltwise mode not support, mode:" << mode;
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return RET_ERROR;
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}
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}
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if (!(param->activation_type_ == ActivationType_NO_ACTIVATION || param->activation_type_ == ActivationType_RELU ||
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param->activation_type_ == ActivationType_RELU6)) {
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MS_LOG(ERROR) << "Unsupported activation type " << param->activation_type_;
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return RET_ERROR;
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}
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return RET_OK;
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}
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void ArithmeticInt8OpenCLKernel::SetGlobalLocal() {
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if (element_flag_) {
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global_size_ = {out_shape_.width, out_shape_.height};
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} else {
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global_size_ = {out_shape_.Slice, out_shape_.W, out_shape_.H * out_shape_.N};
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}
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AlignGlobalLocal(global_size_, {});
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}
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int ArithmeticInt8OpenCLKernel::InitWeights() {
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auto allocator = ocl_runtime_->GetAllocator();
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auto fp16_enable = ocl_runtime_->GetFp16Enable();
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for (int i = 0; i < 2; ++i) {
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const auto &in_tensor = in_tensors_.at(i);
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GpuTensorInfo in_shape = GpuTensorInfo(in_tensor);
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if (in_tensor->IsConst()) {
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std::vector<char> weight(in_shape.Image2DSize, 0);
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bool src_is_fp16 = in_tensor->data_type() == kNumberTypeFloat16;
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PackNHWCToNHWC4(in_tensor->data_c(), weight.data(), src_is_fp16, fp16_enable, in_shape);
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size_t dtype = fp16_enable ? CL_HALF_FLOAT : CL_FLOAT;
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ImageSize img_size{in_shape.width, in_shape.height, dtype};
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auto weight_ptr_ = allocator->Malloc(img_size, weight.data());
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weight_ptrs_.push_back(weight_ptr_);
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} else {
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weight_ptrs_.push_back(nullptr);
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}
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}
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return RET_OK;
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}
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void ArithmeticInt8OpenCLKernel::SetConstArgs() {
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int arg_idx = 3;
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if (!element_flag_) {
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cl_int4 in0_shape = {static_cast<int>(in0_shape_.N), static_cast<int>(in0_shape_.H), static_cast<int>(in0_shape_.W),
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static_cast<int>(in0_shape_.Slice)};
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cl_int4 in1_shape = {static_cast<int>(in1_shape_.N), static_cast<int>(in1_shape_.H), static_cast<int>(in1_shape_.W),
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static_cast<int>(in1_shape_.Slice)};
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cl_int4 out_shape = {static_cast<int>(out_shape_.N), static_cast<int>(out_shape_.H), static_cast<int>(out_shape_.W),
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static_cast<int>(out_shape_.Slice)};
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int broadcastC_flag = 0; // do not need broadcast in C4
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if (in0_shape_.C == 1 && in1_shape_.C != 1) {
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broadcastC_flag = 1; // BroadCast C4 in input0
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} else if (in0_shape_.C != 1 && in1_shape_.C == 1) {
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broadcastC_flag = 2; // BroadCast C4 in input1
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}
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ocl_runtime_->SetKernelArg(kernel_, arg_idx++, in0_shape);
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ocl_runtime_->SetKernelArg(kernel_, arg_idx++, in1_shape);
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ocl_runtime_->SetKernelArg(kernel_, arg_idx++, out_shape);
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ocl_runtime_->SetKernelArg(kernel_, arg_idx++, broadcastC_flag);
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} else {
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cl_int2 output_shape{static_cast<int>(global_range_[0]), static_cast<int>(global_range_[1])};
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ocl_runtime_->SetKernelArg(kernel_, arg_idx++, output_shape);
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}
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ocl_runtime_->SetKernelArg(kernel_, arg_idx++, activation_min_);
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ocl_runtime_->SetKernelArg(kernel_, arg_idx++, activation_max_);
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// set quantization parameter.
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auto input0_quant_param = in_tensors_[0]->quant_params().front();
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auto input1_quant_param = in_tensors_[1]->quant_params().front();
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auto output_quant_param = out_tensors_[0]->quant_params().front();
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cl_float4 scale = {static_cast<float>(input0_quant_param.scale), static_cast<float>(input1_quant_param.scale),
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static_cast<float>(output_quant_param.scale), 0.0};
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cl_char4 zero_point = {static_cast<int8_t>(input0_quant_param.zeroPoint),
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static_cast<int8_t>(input1_quant_param.zeroPoint),
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static_cast<int8_t>(output_quant_param.zeroPoint), 0};
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ocl_runtime_->SetKernelArg(kernel_, arg_idx++, scale); // scale
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ocl_runtime_->SetKernelArg(kernel_, arg_idx++, zero_point); // zero_point
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}
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int ArithmeticInt8OpenCLKernel::Prepare() {
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#ifdef PROGRAM_WITH_IL
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kernel_ = ocl_runtime_->GetKernelFromBinary(kernel_name_);
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#else
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in0_shape_ = GpuTensorInfo(in_tensors_[0]);
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in1_shape_ = GpuTensorInfo(in_tensors_[1]);
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out_shape_ = GpuTensorInfo(out_tensors_[0]);
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auto *param = reinterpret_cast<const ArithmeticParameter *>(op_parameter_);
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if (Type() == PrimitiveType_BiasAdd) {
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const_cast<ArithmeticParameter *>(param)->broadcasting_ = true;
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}
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element_flag_ = !param->broadcasting_;
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kernel_name_ = param->broadcasting_ ? "BroadcastNHWC4" : "Element";
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switch (Type()) {
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case PrimitiveType_MulFusion:
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kernel_name_ += "MulInt8";
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break;
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case PrimitiveType_AddFusion:
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kernel_name_ += "AddInt8";
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break;
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case PrimitiveType_SubFusion:
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kernel_name_ += "SubInt8";
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break;
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case PrimitiveType_DivFusion:
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kernel_name_ += "DivInt8";
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break;
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case PrimitiveType_Eltwise: {
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auto mode = param->eltwise_mode_;
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if (mode == EltwiseMode_PROD) {
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kernel_name_ += "MulInt8";
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} else if (mode == EltwiseMode_SUM) {
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kernel_name_ += "AddInt8";
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} else if (mode == EltwiseMode_MAXIMUM) {
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kernel_name_ += "MaximumInt8";
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}
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break;
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}
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default:
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kernel_name_ += schema::EnumNamePrimitiveType(Type());
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}
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if (param->activation_type_ == ActivationType_RELU) {
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activation_min_ = 0.f;
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} else if (param->activation_type_ == ActivationType_RELU6) {
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activation_min_ = 0.f;
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activation_max_ = 6.f;
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}
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std::string program_name = "Arithmetic";
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std::string source = arithmetic_source;
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ocl_runtime_->LoadSource(program_name, source);
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int error_code = ocl_runtime_->BuildKernel(kernel_, program_name, kernel_name_);
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#endif
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if (error_code != RET_OK) {
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return error_code;
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}
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SetGlobalLocal();
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// BiasAdd InitWeight will be called in opencl_subgraph prepare
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if (Type() != PrimitiveType_BiasAdd) {
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InitWeights();
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}
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SetConstArgs();
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MS_LOG(DEBUG) << kernel_name_ << " Init Done!";
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return RET_OK;
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}
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int ArithmeticInt8OpenCLKernel::Run() {
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MS_LOG(DEBUG) << this->name() << " Running!";
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auto input_0_ptr = weight_ptrs_[0] == nullptr ? in_tensors_[0]->data_c() : weight_ptrs_[0];
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auto input_1_ptr = weight_ptrs_[1] == nullptr ? in_tensors_[1]->data_c() : weight_ptrs_[1];
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int arg_idx = 0;
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ocl_runtime_->SetKernelArg(kernel_, arg_idx++, input_0_ptr);
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ocl_runtime_->SetKernelArg(kernel_, arg_idx++, input_1_ptr);
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ocl_runtime_->SetKernelArg(kernel_, arg_idx++, out_tensors_[0]->data_c());
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ocl_runtime_->RunKernel(kernel_, global_range_, local_range_, nullptr, &event_);
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return RET_OK;
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}
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REG_KERNEL(kGPU, kNumberTypeInt8, PrimitiveType_AddFusion, OpenCLKernelCreator<ArithmeticInt8OpenCLKernel>)
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} // namespace mindspore::kernel
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@ -0,0 +1,54 @@
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/**
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* Copyright 2021 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef MINDSPORE_LITE_SRC_RUNTIME_KERNEL_OPENCL_KERNEL_ARITHMETIC_H_
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#define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_OPENCL_KERNEL_ARITHMETIC_H_
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#include <vector>
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#include <set>
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#include <string>
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#include "src/runtime/kernel/arm/fp32/arithmetic_fp32.h"
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#include "src/runtime/kernel/opencl/opencl_kernel.h"
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namespace mindspore::kernel {
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extern std::set<schema::PrimitiveType> SupportedOpenCLArithmetics;
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class ArithmeticInt8OpenCLKernel : public OpenCLKernel {
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public:
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using OpenCLKernel::OpenCLKernel;
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~ArithmeticInt8OpenCLKernel() override = default;
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int Run() override;
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int Prepare() override;
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int CheckSpecs() override;
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int InitWeights() override;
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void SetConstArgs() override;
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void SetGlobalLocal() override;
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private:
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bool element_flag_{true};
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float activation_min_{-128};
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float activation_max_{127};
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GpuTensorInfo in0_shape_;
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GpuTensorInfo in1_shape_;
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GpuTensorInfo out_shape_;
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std::vector<void *> weight_ptrs_;
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std::string kernel_name_;
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};
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} // namespace mindspore::kernel
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#endif // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_OPENCL_KERNEL_ARITHMETIC_H_
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