wehub-resource-sync
268 lines
7.8 KiB
C++
268 lines
7.8 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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#pragma once
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#include <algorithm>
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#include <functional>
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#include <iostream>
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#include <iterator>
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#include <memory>
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#include <numeric>
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#include <string>
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#include <vector>
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#include "paddle/phi/backends/gpu/rocm/miopen_helper.h"
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#include "paddle/phi/core/utils/data_type.h"
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namespace phi {
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namespace backends {
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namespace gpu {
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inline std::vector<int> TransformDimOrder(const std::vector<int>& dims) {
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std::vector<int> transformed_dims(dims.begin(), dims.end());
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int H, W, D, C;
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if (dims.size() == 4) {
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H = dims[1];
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W = dims[2];
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C = dims[3];
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transformed_dims[1] = C;
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transformed_dims[2] = H;
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transformed_dims[3] = W;
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} else {
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D = dims[1];
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H = dims[2];
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W = dims[3];
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C = dims[4];
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transformed_dims[1] = C;
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transformed_dims[2] = D;
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transformed_dims[3] = H;
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transformed_dims[4] = W;
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}
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return transformed_dims;
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}
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inline miopenDataType_t ToCudnnDataType(const DataType& t) {
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miopenDataType_t type = miopenFloat;
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switch (t) {
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case DataType::FLOAT16:
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type = miopenHalf;
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break;
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case DataType::FLOAT32:
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type = miopenFloat;
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break;
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case DataType::BFLOAT16:
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type = miopenBFloat16;
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break;
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default:
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break;
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}
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return type;
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}
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class ActivationDescriptor {
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public:
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using T = miopenActivationDescriptor;
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struct Deleter {
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void operator()(T* t) {
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if (t != nullptr) {
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PADDLE_ENFORCE_GPU_SUCCESS(
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phi::dynload::miopenDestroyActivationDescriptor(t));
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t = nullptr;
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}
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}
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};
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ActivationDescriptor() {
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T* raw_ptr;
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PADDLE_ENFORCE_GPU_SUCCESS(
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phi::dynload::miopenCreateActivationDescriptor(&raw_ptr));
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desc_.reset(raw_ptr);
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}
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template <typename T>
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void set(miopenActivationMode_t mode, const T& coef) {
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PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::miopenSetActivationDescriptor(
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desc_.get(), mode, static_cast<double>(coef), 0.0, 0.0));
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}
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T* desc() { return desc_.get(); }
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T* desc() const { return desc_.get(); }
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private:
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std::unique_ptr<T, Deleter> desc_;
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};
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class TensorDescriptor {
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public:
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using T = miopenTensorDescriptor;
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struct Deleter {
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void operator()(T* t) {
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if (t != nullptr) {
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PADDLE_ENFORCE_GPU_SUCCESS(
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phi::dynload::miopenDestroyTensorDescriptor(t));
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t = nullptr;
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}
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}
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};
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TensorDescriptor() {
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T* raw_ptr;
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PADDLE_ENFORCE_GPU_SUCCESS(
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phi::dynload::miopenCreateTensorDescriptor(&raw_ptr));
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desc_.reset(raw_ptr);
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}
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T* desc() { return desc_.get(); }
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T* desc() const { return desc_.get(); }
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void set(const DenseTensor& tensor, const int groups = 1) {
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auto dims = common::vectorize<int>(tensor.dims());
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std::vector<int> strides(dims.size());
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strides[dims.size() - 1] = 1;
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for (int i = dims.size() - 2; i >= 0; i--) {
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strides[i] = dims[i + 1] * strides[i + 1];
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}
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std::vector<int> dims_with_group(dims.begin(), dims.end());
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if (groups > 1) {
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dims_with_group[1] = dims_with_group[1] / groups;
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}
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PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::miopenSetTensorDescriptor(
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(miopenTensorDescriptor_t)(desc_.get()),
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ToCudnnDataType(tensor.dtype()),
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static_cast<int>(dims_with_group.size()),
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const_cast<int*>(dims_with_group.data()),
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const_cast<int*>(strides.data())));
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}
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void set(const DenseTensor& tensor, const miopenTensorFormat_t format) {
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const int groups = 1;
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PADDLE_ENFORCE_EQ(format,
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MIOPEN_TENSOR_NCHW,
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common::errors::InvalidArgument(
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"format should ONLY be NCHW in MIOPEN."));
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auto dims = common::vectorize<int>(tensor.dims());
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std::vector<int> strides(dims.size());
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strides[dims.size() - 1] = 1;
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for (int i = dims.size() - 2; i >= 0; i--) {
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strides[i] = dims[i + 1] * strides[i + 1];
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}
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std::vector<int> dims_with_group(dims.begin(), dims.end());
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if (groups > 1) {
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dims_with_group[1] = dims_with_group[1] / groups;
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}
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PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::miopenSetTensorDescriptor(
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(miopenTensorDescriptor_t)(desc_.get()),
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ToCudnnDataType(tensor.dtype()),
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static_cast<int>(dims_with_group.size()),
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const_cast<int*>(dims_with_group.data()),
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const_cast<int*>(strides.data())));
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}
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private:
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std::unique_ptr<T, Deleter> desc_;
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};
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class FilterDescriptor {
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public:
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using T = miopenTensorDescriptor;
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struct Deleter {
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void operator()(T* t) {
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if (t != nullptr) {
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PADDLE_ENFORCE_GPU_SUCCESS(
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phi::dynload::miopenDestroyTensorDescriptor(t));
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t = nullptr;
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}
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}
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};
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FilterDescriptor() {
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T* raw_ptr;
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PADDLE_ENFORCE_GPU_SUCCESS(
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phi::dynload::miopenCreateTensorDescriptor(&raw_ptr));
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desc_.reset(raw_ptr);
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}
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T* desc() { return desc_.get(); }
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T* desc() const { return desc_.get(); }
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void set(const DenseTensor& tensor,
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const miopenTensorFormat_t format,
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const int groups = 1) {
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PADDLE_ENFORCE_EQ(format,
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MIOPEN_TENSOR_NCHW,
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common::errors::InvalidArgument(
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"format should ONLY be NCHW in MIOPEN."));
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auto dims = common::vectorize<int>(tensor.dims());
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std::vector<int> strides(dims.size());
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strides[dims.size() - 1] = 1;
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for (int i = dims.size() - 2; i >= 0; i--) {
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strides[i] = dims[i + 1] * strides[i + 1];
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}
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std::vector<int> dims_with_group(dims.begin(), dims.end());
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if (groups > 1) {
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dims_with_group[1] = dims_with_group[1] / groups;
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}
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PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::miopenSetTensorDescriptor(
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(miopenTensorDescriptor_t)(desc_.get()),
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ToCudnnDataType(tensor.dtype()),
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static_cast<int>(dims_with_group.size()),
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const_cast<int*>(dims_with_group.data()),
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const_cast<int*>(strides.data())));
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}
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private:
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std::unique_ptr<T, Deleter> desc_;
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};
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class ConvolutionDescriptor {
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public:
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using T = miopenConvolutionDescriptor;
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struct Deleter {
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void operator()(T* t) {
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if (t != nullptr) {
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PADDLE_ENFORCE_GPU_SUCCESS(
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phi::dynload::miopenDestroyConvolutionDescriptor(t));
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t = nullptr;
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}
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}
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};
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ConvolutionDescriptor() {
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T* raw_ptr;
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PADDLE_ENFORCE_GPU_SUCCESS(
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phi::dynload::miopenCreateConvolutionDescriptor(&raw_ptr));
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desc_.reset(raw_ptr);
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}
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T* desc() { return desc_.get(); }
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T* desc() const { return desc_.get(); }
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void set(miopenDataType_t dtype,
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const std::vector<int>& pads,
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const std::vector<int>& strides,
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const std::vector<int>& dilations,
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bool allow_tf32,
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const int groups = 1) {
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PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::miopenInitConvolutionNdDescriptor(
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(miopenConvolutionDescriptor_t)desc_.get(),
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static_cast<int>(pads.size()),
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const_cast<int*>(pads.data()),
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const_cast<int*>(strides.data()),
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const_cast<int*>(dilations.data()),
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miopenConvolution));
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PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::miopenSetConvolutionGroupCount(
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(miopenConvolutionDescriptor_t)desc_.get(), groups));
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}
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private:
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std::unique_ptr<T, Deleter> desc_;
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};
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} // namespace gpu
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} // namespace backends
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} // namespace phi
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