forked from mindspore-Ecosystem/mindspore
!49852 Support inplace update v2 gpu kernel.
Merge pull request !49852 from ZPaC/inplace-update-v2
This commit is contained in:
Gitee
commit
4cfe8bb5c3
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@ -23,8 +23,6 @@ static std::unordered_map<std::string, int> op_type_map = {
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{"InplaceUpdate", INPLACE_OP_TYPE_UPDATE}, {"InplaceAdd", INPLACE_OP_TYPE_ADD}, {"InplaceSub", INPLACE_OP_TYPE_SUB}};
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bool InplaceOpGpuKernelMod::Init(const BaseOperatorPtr &base_operator, const std::vector<KernelTensorPtr> &inputs,
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const std::vector<KernelTensorPtr> &outputs) {
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// auto kernel_ptr_ = std::dynamic_pointer_cast<ops::InplaceUpdate>(base_operator);
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// kernel_name_ = kernel_ptr_->name();
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kernel_name_ = base_operator->name();
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auto iter = op_type_map.find(kernel_name_);
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if (iter == op_type_map.end()) {
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@ -0,0 +1,154 @@
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/**
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* Copyright 2023 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 "plugin/device/gpu/kernel/arrays/inplace_op_v2_gpu_kernel.h"
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#include <unordered_map>
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#include <string>
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namespace mindspore {
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namespace kernel {
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static std::unordered_map<std::string, int> op_type_map = {{"InplaceUpdateV2", INPLACE_OP_TYPE_UPDATE}};
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bool InplaceOpV2GpuKernelMod::Init(const BaseOperatorPtr &base_operator, const std::vector<KernelTensorPtr> &inputs,
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const std::vector<KernelTensorPtr> &outputs) {
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kernel_name_ = base_operator->name();
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auto iter = op_type_map.find(kernel_name_);
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if (iter == op_type_map.end()) {
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MS_LOG(ERROR) << "For InplaceOpV2 kernel, Can only support InplaceUpdateV2, but got " << kernel_name_;
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return false;
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}
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kernel_type_ = iter->second;
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if (inputs.empty() || outputs.empty()) {
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MS_LOG(ERROR) << "For '" << kernel_name_ << "' got empty inputs or outputs, which is invalid.";
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return false;
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}
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auto kernel_attr = GetKernelAttrFromTensors(inputs, outputs);
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auto [is_match, index] = MatchKernelAttr(kernel_attr, GetOpSupport());
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if (!is_match) {
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MS_LOG(ERROR) << "For '" << kernel_name_
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<< "', the kernel type should be in [float16, float32, float64, int32]"
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", but got: "
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<< kernel_attr;
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return false;
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}
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kernel_func_ = func_list_[index].second;
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unit_size_ = abstract::TypeIdSize(inputs[0]->GetDtype());
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return true;
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}
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int InplaceOpV2GpuKernelMod::Resize(const BaseOperatorPtr &base_operator, const std::vector<KernelTensorPtr> &inputs,
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const std::vector<KernelTensorPtr> &outputs,
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const std::map<uint32_t, tensor::TensorPtr> &) {
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for (const auto &input : inputs) {
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// If any input shape contains -1, means input shape is dynamic, so just return do nothing.
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auto input_shape = input->GetShapeVector();
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if (!IsValidShape(input_shape)) {
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return KRET_UNKNOWN_SHAPE;
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}
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}
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ResetResource();
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std::vector<int64_t> input_shape_x = std::vector<int64_t>(inputs.at(kIndex0)->GetDeviceShapeAdaptively().begin(),
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inputs.at(kIndex0)->GetDeviceShapeAdaptively().end());
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std::vector<int64_t> input_shape_indices = std::vector<int64_t>(
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inputs.at(kIndex1)->GetDeviceShapeAdaptively().begin(), inputs.at(kIndex1)->GetDeviceShapeAdaptively().end());
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std::vector<int64_t> input_shape_v = std::vector<int64_t>(inputs.at(kIndex2)->GetDeviceShapeAdaptively().begin(),
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inputs.at(kIndex2)->GetDeviceShapeAdaptively().end());
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band_size_ = 1;
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for (size_t i = 1; i < input_shape_x.size(); ++i) {
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band_size_ *= input_shape_x[i];
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}
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input_elements_x = std::accumulate(input_shape_x.begin(), input_shape_x.end(), 1, std::multiplies<int64_t>());
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input_elements_v = std::accumulate(input_shape_v.begin(), input_shape_v.end(), 1, std::multiplies<int64_t>());
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size_t input_size_x = input_elements_x * unit_size_;
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size_t indices_size = input_shape_indices.size() * sizeof(int32_t);
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size_t input_size_v = input_elements_v * unit_size_;
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input_size_list_.push_back(input_size_x);
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input_size_list_.push_back(IntToSize(indices_size));
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input_size_list_.push_back(input_size_v);
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output_size_list_.push_back(input_size_x);
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if (kernel_name_ == ops::kNameInplaceUpdateV2) {
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workspace_size_list_.push_back(indices_size);
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}
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return KRET_OK;
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}
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void InplaceOpV2GpuKernelMod::ResetResource() noexcept {
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band_size_ = 1;
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input_elements_x = 0;
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input_elements_v = 0;
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is_null_input_ = false;
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input_size_list_.clear();
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output_size_list_.clear();
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workspace_size_list_.clear();
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}
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template <typename T>
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bool InplaceOpV2GpuKernelMod::LaunchKernel(const std::vector<AddressPtr> &inputs,
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const std::vector<AddressPtr> &workspace,
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const std::vector<AddressPtr> &outputs) {
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T *input_x = GetDeviceAddress<T>(inputs, kIndex0);
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int32_t *input_indices = GetDeviceAddress<int32_t>(inputs, kIndex1);
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T *input_v = GetDeviceAddress<T>(inputs, kIndex2);
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T *output = GetDeviceAddress<T>(outputs, kIndex0);
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int32_t *indices_key_ptr = nullptr;
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if (kernel_name_ == ops::kNameInplaceUpdateV2) {
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indices_key_ptr = GetDeviceAddress<int32_t>(workspace, kIndex0);
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}
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auto cuda_stream = reinterpret_cast<cudaStream_t>(cuda_stream_);
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// Copy from 'x' into 'y'.
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CHECK_CUDA_RET_WITH_EXCEPT_NOTRACE(
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cudaMemcpyAsync(output, input_x, input_elements_x * unit_size_, cudaMemcpyDeviceToDevice, cuda_stream),
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"cudaMemcpyAsync output 'output' from 'input_x' failed.");
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CalInplaceOp(input_elements_v, input_v, output, input_indices, indices_key_ptr, band_size_, device_id_, kernel_type_,
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cuda_stream);
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return true;
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}
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std::vector<std::pair<KernelAttr, InplaceOpV2GpuKernelMod::InplaceOpFunc>> InplaceOpV2GpuKernelMod::func_list_ = {
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{KernelAttr()
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.AddInputAttr(kNumberTypeFloat16)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeFloat16)
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.AddOutputAttr(kNumberTypeFloat16),
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&InplaceOpV2GpuKernelMod::LaunchKernel<half>},
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{KernelAttr()
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.AddInputAttr(kNumberTypeFloat32)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeFloat32)
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.AddOutputAttr(kNumberTypeFloat32),
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&InplaceOpV2GpuKernelMod::LaunchKernel<float>},
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{KernelAttr()
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.AddInputAttr(kNumberTypeFloat64)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeFloat64)
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.AddOutputAttr(kNumberTypeFloat64),
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&InplaceOpV2GpuKernelMod::LaunchKernel<double>},
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{KernelAttr()
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeInt32)
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.AddInputAttr(kNumberTypeInt32)
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.AddOutputAttr(kNumberTypeInt32),
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&InplaceOpV2GpuKernelMod::LaunchKernel<int>}};
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std::vector<KernelAttr> InplaceOpV2GpuKernelMod::GetOpSupport() {
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std::vector<KernelAttr> support_list;
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(void)std::transform(func_list_.begin(), func_list_.end(), std::back_inserter(support_list),
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[](const std::pair<KernelAttr, InplaceOpFunc> &pair) { return pair.first; });
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return support_list;
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}
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MS_KERNEL_FACTORY_REG(NativeGpuKernelMod, InplaceUpdateV2, InplaceOpV2GpuKernelMod);
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} // namespace kernel
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} // namespace mindspore
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@ -0,0 +1,83 @@
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/**
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* Copyright 2023 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_CCSRC_BACKEND_KERNEL_COMPILER_GPU_ARRAYS_INPLACE_UPDATE_V2_GPU_KERNEL_H_
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#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_ARRAYS_INPLACE_UPDATE_V2_GPU_KERNEL_H_
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#include <vector>
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#include <string>
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#include <memory>
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#include <utility>
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#include <algorithm>
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#include <functional>
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#include <map>
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#include "mindspore/core/ops/inplace_update_v2.h"
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#include "abstract/utils.h"
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#include "plugin/factory/ms_factory.h"
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#include "plugin/device/gpu/kernel/gpu_kernel.h"
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#include "plugin/device/gpu/kernel/gpu_kernel_factory.h"
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#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/cuda_common.h"
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#include "plugin/device/gpu/kernel/cuda_impl/cuda_ops/inplace_op_impl.cuh"
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namespace mindspore {
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namespace kernel {
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class InplaceOpV2GpuKernelMod : public NativeGpuKernelMod {
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public:
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InplaceOpV2GpuKernelMod() { ResetResource(); }
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~InplaceOpV2GpuKernelMod() override = default;
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bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
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const std::vector<AddressPtr> &outputs, void *cuda_stream) override {
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if (is_null_input_) {
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return true;
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}
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cuda_stream_ = cuda_stream;
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return kernel_func_(this, inputs, workspace, outputs);
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}
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bool Init(const BaseOperatorPtr &base_operator, const std::vector<KernelTensorPtr> &inputs,
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const std::vector<KernelTensorPtr> &outputs) override;
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int Resize(const BaseOperatorPtr &base_operator, const std::vector<KernelTensorPtr> &inputs,
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const std::vector<KernelTensorPtr> &outputs, const std::map<uint32_t, tensor::TensorPtr> &) override;
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std::vector<KernelAttr> GetOpSupport() override;
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void ResetResource() noexcept;
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private:
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template <typename T>
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bool LaunchKernel(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
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const std::vector<AddressPtr> &outputs);
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using InplaceOpFunc =
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std::function<bool(InplaceOpV2GpuKernelMod *, const std::vector<kernel::AddressPtr> &,
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const std::vector<kernel::AddressPtr> &, const std::vector<kernel::AddressPtr> &)>;
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private:
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std::vector<int64_t> indices_;
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int kernel_type_{-1};
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size_t unit_size_{1};
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size_t input_elements_x;
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size_t input_elements_v;
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int64_t band_size_;
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InplaceOpFunc kernel_func_{};
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bool is_null_input_{false};
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void *cuda_stream_{nullptr};
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static std::vector<std::pair<KernelAttr, InplaceOpFunc>> func_list_;
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};
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} // namespace kernel
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} // namespace mindspore
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#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_GPU_ARRAYS_INPLACE_UPDATE_V2_GPU_KERNEL_H_
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@ -32,14 +32,14 @@ struct AddFunc {
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__device__ __forceinline__ void operator()(T *lhs, const T &rhs) { MsAtomicAdd(lhs, rhs); }
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};
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template <typename T>
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__global__ void InplaceUpdate(const size_t size, const T *input_v, T *output, const int64_t *indices,
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int64_t *indices_key, size_t indices_len, const int64_t band_size) {
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template <typename T, typename S>
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__global__ void InplaceUpdate(const size_t size, const T *input_v, T *output, const S *indices, S *indices_key,
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size_t indices_len, const int64_t band_size) {
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for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < size; pos += blockDim.x * gridDim.x) {
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size_t row = pos / band_size;
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if (row == indices_len || indices[row] != indices[row + 1]) {
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int x_row = indices[row];
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int v_row = indices_key[row];
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S x_row = indices[row];
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S v_row = indices_key[row];
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int offset = pos % band_size;
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int x_offset = x_row * band_size;
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output[x_offset + offset] = input_v[v_row * band_size + offset];
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@ -47,12 +47,12 @@ __global__ void InplaceUpdate(const size_t size, const T *input_v, T *output, co
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}
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return;
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}
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template <typename T, typename Func>
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__global__ void InplaceAddOrSub(const size_t size, const T *input_v, T *output, const int64_t *indices,
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template <typename T, typename S, typename Func>
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__global__ void InplaceAddOrSub(const size_t size, const T *input_v, T *output, const S *indices,
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const int64_t band_size) {
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for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < size; pos += blockDim.x * gridDim.x) {
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int v_row = pos / band_size;
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int x_row = indices[v_row];
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S x_row = indices[v_row];
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int offset = pos % band_size;
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int x_offset = x_row * band_size;
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Func()(&output[x_offset + offset], input_v[pos]);
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@ -60,9 +60,9 @@ __global__ void InplaceAddOrSub(const size_t size, const T *input_v, T *output,
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return;
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}
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template <typename T>
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void CalInplaceOp(const size_t size_v, const T *input_v, T *output, int64_t *indices, int64_t *indices_key,
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const int64_t band_size, const uint32_t &device_id, int op_type, cudaStream_t cuda_stream) {
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template <typename T, typename S>
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void CalInplaceOp(const size_t size_v, const T *input_v, T *output, S *indices, S *indices_key, const int64_t band_size,
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const uint32_t &device_id, int op_type, cudaStream_t cuda_stream) {
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int thread_num = 256 > size_v ? size_v : 256;
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if (op_type == INPLACE_OP_TYPE_UPDATE) {
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auto policy = thrust::cuda::par.on(cuda_stream);
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@ -75,10 +75,10 @@ void CalInplaceOp(const size_t size_v, const T *input_v, T *output, int64_t *ind
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InplaceUpdate<<<CUDA_BLOCKS_CAL(device_id, size_v, thread_num), thread_num, 0, cuda_stream>>>(
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size_v, input_v, output, indices, indices_key, indices_element, band_size);
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} else if (op_type == INPLACE_OP_TYPE_ADD) {
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InplaceAddOrSub<T, AddFunc<T>><<<CUDA_BLOCKS_CAL(device_id, size_v, thread_num), thread_num, 0, cuda_stream>>>(
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InplaceAddOrSub<T, S, AddFunc<T>><<<CUDA_BLOCKS_CAL(device_id, size_v, thread_num), thread_num, 0, cuda_stream>>>(
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size_v, input_v, output, indices, band_size);
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} else if (op_type == INPLACE_OP_TYPE_SUB) {
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InplaceAddOrSub<T, SubFunc<T>><<<CUDA_BLOCKS_CAL(device_id, size_v, thread_num), thread_num, 0, cuda_stream>>>(
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InplaceAddOrSub<T, S, SubFunc<T>><<<CUDA_BLOCKS_CAL(device_id, size_v, thread_num), thread_num, 0, cuda_stream>>>(
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size_v, input_v, output, indices, band_size);
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}
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return;
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@ -99,3 +99,19 @@ template CUDA_LIB_EXPORT void CalInplaceOp<double>(const size_t size_v, const do
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template CUDA_LIB_EXPORT void CalInplaceOp<int>(const size_t size_v, const int *input_v, int *output, int64_t *indices,
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int64_t *indices_key, const int64_t band_size,
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const uint32_t &device_id, int op_type, cudaStream_t cuda_stream);
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template CUDA_LIB_EXPORT void CalInplaceOp<half>(const size_t size_v, const half *input_v, half *output,
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int32_t *indices, int32_t *indices_key, const int64_t band_size,
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const uint32_t &device_id, int op_type, cudaStream_t cuda_stream);
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template CUDA_LIB_EXPORT void CalInplaceOp<float>(const size_t size_v, const float *input_v, float *output,
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int32_t *indices, int32_t *indices_key, const int64_t band_size,
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const uint32_t &device_id, int op_type, cudaStream_t cuda_stream);
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template CUDA_LIB_EXPORT void CalInplaceOp<double>(const size_t size_v, const double *input_v, double *output,
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int32_t *indices, int32_t *indices_key, const int64_t band_size,
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const uint32_t &device_id, int op_type, cudaStream_t cuda_stream);
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template CUDA_LIB_EXPORT void CalInplaceOp<int>(const size_t size_v, const int *input_v, int *output, int32_t *indices,
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int32_t *indices_key, const int64_t band_size,
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const uint32_t &device_id, int op_type, cudaStream_t cuda_stream);
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@ -25,9 +25,9 @@ enum BroadcastOpType {
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INPLACE_OP_TYPE_SUB = 2,
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};
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template <typename T>
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CUDA_LIB_EXPORT void CalInplaceOp(const size_t size_v, const T *input_v, T *output, int64_t *indices,
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int64_t *indices_key_ptr, const int64_t band_size, const uint32_t &device_id,
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int op_type, cudaStream_t cuda_stream);
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template <typename T, typename S>
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CUDA_LIB_EXPORT void CalInplaceOp(const size_t size_v, const T *input_v, T *output, S *indices, S *indices_key_ptr,
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const int64_t band_size, const uint32_t &device_id, int op_type,
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cudaStream_t cuda_stream);
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#endif // MINDSPORE_CCSRC_PLUGIN_DEVICE_GPU_KERNEL_CUDA_IMPL_CUDA_OPS_INPLACE_UPDATE_IMPL_CUH_
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|
|
|||
|
|
@ -67,8 +67,8 @@ from .inner_ops import (ScalarCast, Randperm, NoRepeatNGram, LambApplyOptimizerA
|
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FusedAdaFactorWithGlobalNorm)
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||||
from .linalg_ops import (Svd, Geqrf)
|
||||
from .math_ops import (Abs, ACos, Asin, Asinh, AddN, AccumulateNV2, AssignAdd, AssignSub, Atan2, BatchMatMul,
|
||||
BitwiseAnd, BitwiseOr, Ger,
|
||||
BitwiseXor, Inv, Invert, ApproximateEqual, InplaceAdd, InplaceSub, InplaceUpdate,
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||||
BitwiseAnd, BitwiseOr, Ger, BitwiseXor, Inv, Invert, ApproximateEqual,
|
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InplaceAdd, InplaceSub, InplaceUpdate, InplaceUpdateV2,
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ReduceMax, ReduceMin, ReduceMean, ReduceSum, ReduceAll, ReduceProd, CumProd, Cdist, ReduceAny,
|
||||
Cos, Cross, Div, DivNoNan, Equal, EqualCount, Exp, Expm1, Erf, Erfc, Floor, FloorDiv, FloorMod,
|
||||
Ceil, Acosh, Greater, GreaterEqual, Lerp, Less, LessEqual, Log, Log1p, LogicalAnd, Mod,
|
||||
|
|
@ -470,6 +470,7 @@ __all__ = [
|
|||
"DataFormatDimMap",
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"ApproximateEqual",
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||||
"InplaceUpdate",
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||||
"InplaceUpdateV2",
|
||||
"InTopK",
|
||||
"UniformCandidateSampler",
|
||||
"LogUniformCandidateSampler",
|
||||
|
|
|
|||
|
|
@ -1838,14 +1838,14 @@ class InplaceUpdateV2(Primitive):
|
|||
TypeError: If `indices` is a tuple and its element is not an int.
|
||||
|
||||
Supported Platforms:
|
||||
``Ascend``
|
||||
``Ascend` ``GPU```
|
||||
|
||||
Examples:
|
||||
>>> indices = (0, 1)
|
||||
>>> x = Tensor(np.array([[1, 2], [3, 4], [5, 6]]), mindspore.float32)
|
||||
>>> v = Tensor(np.array([[0.5, 1.0], [1.0, 1.5]]), mindspore.float32)
|
||||
>>> inplace_update = ops.InplaceUpdate(indices)
|
||||
>>> output = inplace_update(x, v)
|
||||
>>> inplace_update_v2 = ops.InplaceUpdateV2()
|
||||
>>> output = inplace_update_v2(x, indices, v)
|
||||
>>> print(output)
|
||||
[[0.5 1. ]
|
||||
[1. 1.5]
|
||||
|
|
|
|||
|
|
@ -0,0 +1,65 @@
|
|||
# Copyright 2023 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# 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.
|
||||
# ============================================================================
|
||||
|
||||
# This example should be run with multiple processes.
|
||||
|
||||
# Please refer to the Programming Guide > Distributed Training -> Distributed Parallel Usage Example
|
||||
|
||||
# on mindspore.cn and focus on the contents of these three parts: Configuring Distributed Environment
|
||||
|
||||
# Variables, Calling the Collective Communication Library, Running the Script.
|
||||
|
||||
import pytest
|
||||
|
||||
import mindspore
|
||||
from mindspore import context
|
||||
from mindspore import Tensor
|
||||
from mindspore.ops import operations as P
|
||||
from mindspore import nn
|
||||
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
||||
|
||||
|
||||
class NetInplaceUpdateV2(nn.Cell):
|
||||
def __init__(self, x, v):
|
||||
super(NetInplaceUpdateV2, self).__init__()
|
||||
self.x = x
|
||||
self.v = v
|
||||
self.inplace_update_v2 = P.InplaceUpdateV2()
|
||||
|
||||
def construct(self, indices):
|
||||
output = self.inplace_update_v2(self.x, indices, self.v)
|
||||
return output
|
||||
|
||||
|
||||
@pytest.mark.level1
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_inplace_update_fp16():
|
||||
"""
|
||||
Feature: ALL To ALL
|
||||
Description: test cases for InplaceUpdateV2
|
||||
Expectation: the result match to expect result
|
||||
"""
|
||||
x = Tensor([[1, 2], [3, 4], [5, 6]], mindspore.float16)
|
||||
v = Tensor([[0.5, 1.0], [1.0, 1.5]], mindspore.float16)
|
||||
inplace_update_v2 = NetInplaceUpdateV2(x, v)
|
||||
indices = Tensor(shape=[None], dtype=mindspore.int32)
|
||||
inplace_update_v2.set_inputs(indices)
|
||||
real_indices = Tensor([0, 1], dtype=mindspore.int32)
|
||||
|
||||
output = inplace_update_v2(real_indices)
|
||||
expect = Tensor([[0.5, 1.0], [1.0, 1.5], [5, 6]], mindspore.float16)
|
||||
assert (output.asnumpy() == expect).all()
|
||||
Loading…
Reference in New Issue