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[PHI] Migrate reshape kernel (PaddlePaddle#48749)
* reshape * typo * remove header
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| /* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. */ | ||
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| #include "paddle/phi/backends/onednn/onednn_reuse.h" | ||
| #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, | ||
| const DDim& in_dims) { | ||
| const int64_t in_size = product(in_dims); | ||
| auto in_dims_vec = vectorize(in_dims); | ||
| bool all_positive = std::all_of(in_dims_vec.cbegin(), | ||
| in_dims_vec.cend(), | ||
| [](int64_t i) { return i > 0; }); | ||
| // only one dimension can be set to -1, whose size will be automatically | ||
| // infered | ||
| const int64_t unk_dim_val = -1; | ||
| const int64_t copy_dim_val = 0; | ||
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| std::vector<int64_t> output_shape(shape.size(), 0); | ||
| int64_t capacity = 1; | ||
| int unk_dim_idx = -1; | ||
| for (size_t i = 0; i < shape.size(); ++i) { | ||
| if (shape[i] == unk_dim_val) { | ||
| PADDLE_ENFORCE_EQ( | ||
| unk_dim_idx, | ||
| -1, | ||
| errors::InvalidArgument( | ||
| "Only one dimension value of 'shape' in ReshapeOp can " | ||
| "be -1. But received shape = [%s], shape[%d] is also -1.", | ||
| make_ddim(shape), | ||
| i)); | ||
| unk_dim_idx = i; | ||
| } else if (shape[i] == copy_dim_val) { | ||
| PADDLE_ENFORCE_LT( | ||
| static_cast<int>(i), | ||
| in_dims.size(), | ||
| errors::InvalidArgument( | ||
| "The index of 0 in `shape` must be less than " | ||
| "the input tensor X's dimensions. " | ||
| "But received shape = [%s], shape[%d] = 0, X's shape = [%s], " | ||
| "X's dimensions = %d.", | ||
| make_ddim(shape), | ||
| i, | ||
| in_dims, | ||
| in_dims.size())); | ||
| } else { | ||
| PADDLE_ENFORCE_GT( | ||
| shape[i], | ||
| 0, | ||
| errors::InvalidArgument( | ||
| "Each dimension value of 'shape' in ReshapeOp must not " | ||
| "be negative except one unknown dimension. " | ||
| "But received shape = [%s], shape[%d] = %d.", | ||
| make_ddim(shape), | ||
| i, | ||
| shape[i])); | ||
| } | ||
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| capacity *= (shape[i] ? shape[i] : in_dims[i]); | ||
| output_shape[i] = (shape[i] ? static_cast<int64_t>(shape[i]) : in_dims[i]); | ||
| } | ||
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| if (unk_dim_idx != -1) { | ||
| if (all_positive) { | ||
| // in_size < 0 and is un-determinate in compile time, skip the check, | ||
| // for example, in_dims = [-1, 8, 1, 1], shape = [-1, 3, 8], | ||
| // capacity = -24, in_size = -8, output_shape[0] = 0 | ||
| // the following check will fail. | ||
| output_shape[unk_dim_idx] = -in_size / capacity; | ||
| PADDLE_ENFORCE_EQ( | ||
| output_shape[unk_dim_idx] * capacity, | ||
| -in_size, | ||
| errors::InvalidArgument( | ||
| "The 'shape' attribute in ReshapeOp is invalid. " | ||
| "The input tensor X'size must be divisible by known " | ||
| "capacity of 'shape'. " | ||
| "But received X's shape = [%s], X's size = %d, " | ||
| "'shape' is [%s], known capacity of 'shape' is %d.", | ||
| in_dims, | ||
| in_size, | ||
| make_ddim(shape), | ||
| capacity)); | ||
| } else { | ||
| output_shape[unk_dim_idx] = -1; | ||
| } | ||
| } else { | ||
| if (all_positive) { | ||
| PADDLE_ENFORCE_EQ( | ||
| capacity, | ||
| in_size, | ||
| errors::InvalidArgument( | ||
| "The 'shape' in ReshapeOp is invalid. " | ||
| "The input tensor X'size must be equal to the capacity of " | ||
| "'shape'. " | ||
| "But received X's shape = [%s], X's size = %d, 'shape' is " | ||
| "[%s], the capacity of 'shape' is %d.", | ||
| in_dims, | ||
| in_size, | ||
| make_ddim(shape), | ||
| capacity)); | ||
| } | ||
| } | ||
| return make_ddim(output_shape); | ||
| } | ||
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| template <typename T, typename Context> | ||
| void ExecuteReshape(const Context& dev_ctx, | ||
| const DenseTensor& x, | ||
| const IntArray& shape, | ||
| const DDim& x_dims, | ||
| DenseTensor* out) { | ||
| auto out_dims = ValidateShape(shape.GetData(), x_dims); | ||
| auto x_vec_dims = vectorize(x_dims); | ||
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| funcs::ReorderOneDNNHandler reorder_handler( | ||
| x_vec_dims, | ||
| x.dtype(), | ||
| funcs::ToOneDNNDataType(x.dtype()), | ||
| dev_ctx.GetEngine()); | ||
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| auto reorder_src_memory_p = reorder_handler.AcquireSrcMemory( | ||
| x.mem_desc(), funcs::to_void_cast(x.data<T>())); | ||
| out->Resize(x_dims); // to match x numel, format is changed later | ||
| // reorder is done into a plain tag to allow usage with blocked formats | ||
| auto reorder_dst_memory_p = reorder_handler.AcquireDstMemory( | ||
| out, funcs::GetPlainOneDNNFormat(x_dims.size()), dev_ctx.GetPlace()); | ||
| auto reorder_p = reorder_handler.AcquireReorder(reorder_dst_memory_p, | ||
| reorder_src_memory_p); | ||
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| auto& astream = OneDNNContext::tls().get_stream(); | ||
| 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); | ||
| out->set_mem_desc( | ||
| reorder_dst_memory_p->get_desc().reshape(vectorize(out_dims))); | ||
| } | ||
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| template <typename T, typename Context> | ||
| void ReshapeKernel(const Context& dev_ctx, | ||
| const DenseTensor& x, | ||
| const IntArray& shape, | ||
| DenseTensor* out) { | ||
| auto x_dims = x.dims(); | ||
| ExecuteReshape<T, Context>(dev_ctx, x, shape, x_dims, out); | ||
| } | ||
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| template <typename T, typename Context> | ||
| void ReshapeWithXShape(const Context& dev_ctx, | ||
| const DenseTensor& x, | ||
| const IntArray& shape, | ||
| DenseTensor* out, | ||
| DenseTensor* xshape) { | ||
| auto x_dims = slice_ddim(xshape->dims(), 1, xshape->dims().size()); | ||
| ExecuteReshape<T, Context>(dev_ctx, x, shape, x_dims, out); | ||
| } | ||
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| } // namespace phi | ||
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| PD_REGISTER_KERNEL( | ||
| reshape, OneDNN, ONEDNN, phi::ReshapeKernel, float, phi::dtype::bfloat16) {} | ||
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| PD_REGISTER_KERNEL(reshape_with_xshape, | ||
| OneDNN, | ||
| ONEDNN, | ||
| phi::ReshapeWithXShape, | ||
| float, | ||
| phi::dtype::bfloat16) {} |