wehub-resource-sync
185 lines
6.7 KiB
C++
185 lines
6.7 KiB
C++
/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include "paddle/phi/backends/onednn/onednn_reuse.h"
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#include "paddle/phi/core/kernel_registry.h"
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namespace phi {
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static DDim ValidateShape(const std::vector<int64_t>& shape,
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const DDim& in_dims) {
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const int64_t in_size = product(in_dims);
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auto in_dims_vec = vectorize(in_dims);
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bool all_positive = std::all_of(in_dims_vec.cbegin(),
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in_dims_vec.cend(),
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[](int64_t i) { return i > 0; });
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// only one dimension can be set to -1, whose size will be automatically
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// inferred
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const int64_t unk_dim_val = -1;
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const int64_t copy_dim_val = 0;
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std::vector<int64_t> output_shape(shape.size(), 0);
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int64_t capacity = 1;
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int unk_dim_idx = -1;
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for (size_t i = 0; i < shape.size(); ++i) {
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if (shape[i] == unk_dim_val) {
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PADDLE_ENFORCE_EQ(
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unk_dim_idx,
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-1,
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errors::InvalidArgument(
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"Only one dimension value of 'shape' in ReshapeOp can "
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"be -1. But received shape = [%s], shape[%d] is also -1.",
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make_ddim(shape),
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i));
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unk_dim_idx = static_cast<int>(i);
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} else if (shape[i] == copy_dim_val) {
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PADDLE_ENFORCE_LT(
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static_cast<int>(i),
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in_dims.size(),
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errors::InvalidArgument(
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"The index of 0 in `shape` must be less than "
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"the input tensor X's dimensions. "
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"But received shape = [%s], shape[%d] = 0, X's shape = [%s], "
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"X's dimensions = %d.",
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make_ddim(shape),
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i,
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in_dims,
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in_dims.size()));
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} else {
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PADDLE_ENFORCE_GT(
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shape[i],
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0,
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errors::InvalidArgument(
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"Each dimension value of 'shape' in ReshapeOp must not "
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"be negative except one unknown dimension. "
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"But received shape = [%s], shape[%d] = %d.",
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make_ddim(shape),
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i,
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shape[i]));
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}
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capacity *= (shape[i] ? shape[i] : in_dims[i]); // NOLINT
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output_shape[i] =
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(shape[i] ? static_cast<int64_t>(shape[i]) : in_dims[i]); // NOLINT
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}
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if (unk_dim_idx != -1) {
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if (all_positive) {
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// in_size < 0 and is un-determinate in compile time, skip the check,
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// for example, in_dims = [-1, 8, 1, 1], shape = [-1, 3, 8],
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// capacity = -24, in_size = -8, output_shape[0] = 0
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// the following check will fail.
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output_shape[unk_dim_idx] = -in_size / capacity;
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PADDLE_ENFORCE_EQ(
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output_shape[unk_dim_idx] * capacity,
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-in_size,
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errors::InvalidArgument(
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"The 'shape' attribute in ReshapeOp is invalid. "
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"The input tensor X'size must be divisible by known "
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"capacity of 'shape'. "
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"But received X's shape = [%s], X's size = %d, "
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"'shape' is [%s], known capacity of 'shape' is %d.",
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in_dims,
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in_size,
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make_ddim(shape),
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capacity));
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} else {
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output_shape[unk_dim_idx] = -1;
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}
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} else {
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if (all_positive) {
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PADDLE_ENFORCE_EQ(
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capacity,
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in_size,
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errors::InvalidArgument(
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"The 'shape' in ReshapeOp is invalid. "
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"The input tensor X'size must be equal to the capacity of "
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"'shape'. "
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"But received X's shape = [%s], X's size = %d, 'shape' is "
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"[%s], the capacity of 'shape' is %d.",
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in_dims,
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in_size,
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make_ddim(shape),
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capacity));
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}
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}
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return make_ddim(output_shape);
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}
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template <typename T, typename Context>
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void ExecuteReshape(const Context& dev_ctx,
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const DenseTensor& x,
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const IntArray& shape,
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const DDim& x_dims,
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DenseTensor* out) {
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auto out_dims = ValidateShape(shape.GetData(), x_dims);
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auto x_vec_dims = x.mem_desc().get_dims();
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funcs::ReorderOneDNNHandler reorder_handler(
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x_vec_dims,
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x.dtype(),
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funcs::ToOneDNNDataType(x.dtype()),
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dev_ctx.GetEngine());
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auto reorder_src_memory_p = reorder_handler.AcquireSrcMemory(
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x.mem_desc(), funcs::to_void_cast(x.data<T>()));
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out->Resize(x_dims); // to match x numel, format is changed later
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// reorder is done into a plain tag to allow usage with blocked formats
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auto reorder_dst_memory_p = reorder_handler.AcquireDstMemory(
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out, funcs::GetPlainOneDNNFormat(x_dims.size()), dev_ctx.GetPlace());
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auto reorder_p = reorder_handler.AcquireReorder(reorder_dst_memory_p,
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reorder_src_memory_p);
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auto& astream = OneDNNContext::tls().get_stream();
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reorder_p->execute(astream, *reorder_src_memory_p, *reorder_dst_memory_p);
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astream.wait();
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out->Resize(out_dims);
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const auto reshape_dims =
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out_dims.size() != 0 ? vectorize(out_dims) : std::vector<int64_t>{1};
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out->set_mem_desc(reorder_dst_memory_p->get_desc().reshape(reshape_dims));
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}
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template <typename T, typename Context>
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void ReshapeKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const IntArray& shape,
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DenseTensor* out) {
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if (x.numel() == 0) {
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dev_ctx.Alloc(out, x.dtype());
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}
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auto x_dims = x.dims();
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ExecuteReshape<T, Context>(dev_ctx, x, shape, x_dims, out);
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}
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template <typename T, typename Context>
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void ReshapeWithXShapeKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const IntArray& shape,
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DenseTensor* out,
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DenseTensor* xshape) {
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auto x_dims = slice_ddim(xshape->dims(), 1, xshape->dims().size());
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ExecuteReshape<T, Context>(dev_ctx, x, shape, x_dims, out);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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reshape, OneDNN, ONEDNN, phi::ReshapeKernel, float, phi::bfloat16) {}
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PD_REGISTER_KERNEL(reshape_with_xshape,
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OneDNN,
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ONEDNN,
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phi::ReshapeWithXShapeKernel,
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float,
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phi::bfloat16) {}
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