wehub-resource-sync
113 lines
3.9 KiB
C++
113 lines
3.9 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 <iostream>
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#include <random>
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/kernels/funcs/axis_utils.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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#include "paddle/phi/kernels/funcs/softmax.h"
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#include "paddle/phi/kernels/funcs/softmax_impl.h"
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namespace phi {
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template <typename Context, typename T, int64_t Rank>
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struct ArgMaxFunctor {
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void operator()(const Context& dev_ctx UNUSED,
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const DenseTensor& in,
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DenseTensor* index_tensor,
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const int64_t& axis) {
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auto in_eigen = EigenTensor<T, Rank>::From(in, in.dims());
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auto index_eigen = EigenTensor<int, Rank - 1>::From(*index_tensor);
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index_eigen = in_eigen.argmax(axis).template cast<int>();
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}
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};
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template <typename Context, typename T>
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struct GumbleNoiseGenerator;
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template <typename Context, typename T>
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struct OneHotGenerator;
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template <typename T, typename Context>
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void GumbelSoftmaxKernelHelper(const Context& dev_ctx,
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const DenseTensor& x,
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float temperature,
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bool hard,
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int axis,
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DenseTensor* out) {
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const int rank = x.dims().size();
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axis = funcs::CanonicalAxis(axis, rank);
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int64_t axis_dim = x.dims()[axis];
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PADDLE_ENFORCE_GT(temperature,
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0,
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common::errors::InvalidArgument(
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"The temperature must be greater than 0. But "
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"received temperature = %f",
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temperature));
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// allocate memory on device.
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dev_ctx.template Alloc<T>(out);
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if (out->numel() == 0) {
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return;
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}
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// For 0D Tensor
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if (rank == 0) {
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funcs::set_constant(dev_ctx, out, static_cast<T>(1.0));
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return;
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}
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// TODO(large-tensor): SoftmaxFunctor not support int64
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PADDLE_ENFORCE_LE_INT_MAX(axis_dim, "axis_dim");
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const int size_to_axis = funcs::SizeToAxis(axis, x.dims());
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const int size_from_axis = funcs::SizeFromAxis(axis, x.dims());
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DenseTensor x_noise_2d, out_2d(*out);
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x_noise_2d.Resize({size_to_axis, size_from_axis});
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out_2d.Resize({size_to_axis, size_from_axis});
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// generate gumbel noise and add it to X
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auto* x_noise_data = dev_ctx.template Alloc<T>(&x_noise_2d);
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GumbleNoiseGenerator<Context, T>::Transform(dev_ctx,
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x.data<T>(),
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x_noise_data,
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size_to_axis,
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size_from_axis,
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temperature);
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funcs::SoftmaxFunctor<Context, T>()(dev_ctx, axis_dim, &x_noise_2d, &out_2d);
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if (hard) {
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OneHotGenerator<Context, T>::Transform(dev_ctx, x, out, axis);
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}
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}
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template <typename T, typename Context>
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void GumbelSoftmaxKernel(const Context& dev_ctx,
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const DenseTensor& x,
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float temperature,
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bool hard,
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int axis,
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DenseTensor* out) {
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GumbelSoftmaxKernelHelper<T, Context>(
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dev_ctx, x, temperature, hard, axis, out);
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}
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} // namespace phi
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