wehub-resource-sync
137 lines
4.6 KiB
C++
137 lines
4.6 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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#include "paddle/phi/kernels/expand_kernel.h"
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#include "paddle/phi/backends/xpu/enforce_xpu.h"
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#include "paddle/phi/core/kernel_registry.h"
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namespace phi {
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template <typename T, typename Context>
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void ExpandKernel(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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using XPUType = typename XPUTypeTrait<T>::Type;
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auto in_dims = x.dims();
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auto numel = x.numel();
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auto expand_shape = shape.GetData();
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auto vec_in_dims = vectorize<int64_t>(in_dims);
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auto diff = expand_shape.size() - vec_in_dims.size();
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vec_in_dims.insert(vec_in_dims.begin(), diff, 1);
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auto final_expand_shape = vec_in_dims;
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bool has_zero_dim = false;
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for (size_t i = 0; i < vec_in_dims.size(); ++i) {
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if (i < diff) {
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PADDLE_ENFORCE_GE(
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expand_shape[i],
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0,
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common::errors::InvalidArgument(
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"The expanded size (%d) for non-existing dimensions must be "
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"positive for expand_v2 op.",
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expand_shape[i]));
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if (expand_shape[i] == 0) has_zero_dim = true;
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final_expand_shape[i] = expand_shape[i];
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} else if (expand_shape[i] == -1) {
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final_expand_shape[i] = vec_in_dims[i];
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} else if (expand_shape[i] == 0) {
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PADDLE_ENFORCE_EQ(
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vec_in_dims[i] == 1 || vec_in_dims[i] == expand_shape[i],
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true,
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common::errors::InvalidArgument(
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"The %d-th dimension of input tensor (%d) must match or be "
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"broadcastable to the corresponding dimension (%d) in shape.",
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i,
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vec_in_dims[i],
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expand_shape[i]));
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final_expand_shape[i] = 0;
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has_zero_dim = true;
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} else if (expand_shape[i] > 0) {
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PADDLE_ENFORCE_EQ(
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vec_in_dims[i] == 1 || vec_in_dims[i] == expand_shape[i],
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true,
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common::errors::InvalidArgument(
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"The %d-th dimension of input tensor (%d) must match or be "
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"broadcastable to the corresponding dimension (%d) in shape.",
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i,
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vec_in_dims[i],
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expand_shape[i]));
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final_expand_shape[i] = expand_shape[i];
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}
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}
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auto rank = x.dims().size();
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PADDLE_ENFORCE_GE(
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rank,
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0,
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common::errors::InvalidArgument(
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"The rank of the input 'X' for expand_v2_npu op must be positive, "
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"but the value received is %d.",
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rank));
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auto shape_size = final_expand_shape.size();
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PADDLE_ENFORCE_GE(
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shape_size,
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rank,
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common::errors::InvalidArgument(
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"The number (%d) of elements of 'shape' for expand_v2_npu op must "
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"be "
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"greater than or equal to the rank (%d) of the input 'X'.",
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shape_size,
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rank));
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DDim out_dims = make_ddim(final_expand_shape);
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out->Resize(out_dims);
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dev_ctx.template Alloc<T>(out);
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if (has_zero_dim || numel == 0) {
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return;
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}
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auto& x_shape = vec_in_dims;
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auto out_shape = vectorize<int64_t>(out_dims);
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if (shape_size == 0) {
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x_shape = {1};
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out_shape = {1};
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}
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int r = 0;
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if (std::is_same<T, bool>::value) {
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auto x_data = reinterpret_cast<const int8_t*>(x.data<T>());
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auto out_data = reinterpret_cast<int8_t*>(out->data<T>());
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r = xpu::broadcast<int8_t>(
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dev_ctx.x_context(), x_data, out_data, x_shape, out_shape);
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} else {
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auto x_data = reinterpret_cast<const XPUType*>(x.data<T>());
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auto out_data = reinterpret_cast<XPUType*>(out->data<T>());
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r = xpu::broadcast<XPUType>(
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dev_ctx.x_context(), x_data, out_data, x_shape, out_shape);
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}
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "broadcast");
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}
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} // namespace phi
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PD_REGISTER_KERNEL(expand,
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XPU,
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ALL_LAYOUT,
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phi::ExpandKernel,
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double,
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float,
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phi::float16,
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bool,
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uint8_t,
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int8_t,
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int16_t,
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int,
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int64_t,
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phi::bfloat16) {}
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