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106 lines
3.8 KiB
106 lines
3.8 KiB
/* Copyright (c) 2020 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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#pragma once
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#include <string>
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#include <vector>
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/lod_tensor.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/framework/selected_rows.h"
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#include "paddle/fluid/framework/var_type_traits.h"
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#include "paddle/fluid/operators/math/blas.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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using LoDTensor = framework::LoDTensor;
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using SelectedRows = framework::SelectedRows;
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using DDim = framework::DDim;
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template <typename T>
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void dequant(const unsigned char *in, T *out, float min, float max,
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int emb_size, int pow_2_bits) {
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float scale = (max - min) / pow_2_bits;
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for (int i = 0; i < emb_size; ++i) {
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T x = scale * static_cast<int>(in[i]) + min;
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out[i] = x;
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}
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}
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constexpr int64_t kNoPadding = -1;
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template <typename T>
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class LookupTableDequantKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext &context) const override {
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auto *ids_t = context.Input<LoDTensor>("Ids"); // int tensor
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auto *output_t = context.Output<LoDTensor>("Out"); // float tensor
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auto *table_var = context.InputVar("W");
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auto id_name = context.InputNames("Ids").front();
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auto embedding_name = context.InputNames("W").front();
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auto out_name = context.OutputNames("Out").front();
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int64_t padding_idx = context.Attr<int64_t>("padding_idx");
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auto *ids = ids_t->data<int64_t>();
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int64_t ids_numel = ids_t->numel();
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PADDLE_ENFORCE_GE(
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table_var->Type(), framework::VarTypeTrait<LoDTensor>::kId,
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platform::errors::InvalidArgument("lookup table must be LodTensor"));
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auto *table_t = context.Input<LoDTensor>("W");
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int64_t row_number = table_t->dims()[0];
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int64_t quant_number = table_t->dims()[1];
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int64_t row_width = (quant_number - 2) * 4;
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auto *table = table_t->data<float>();
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auto *output = output_t->mutable_data<T>(context.GetPlace());
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int pow_2_bits = static_cast<int>(pow(2, 8));
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for (int64_t i = 0; i < ids_numel; ++i) {
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if (padding_idx != kNoPadding && ids[i] == padding_idx) {
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memset(output + i * row_width, 0, row_width * sizeof(T));
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} else {
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PADDLE_ENFORCE_LT(
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ids[i], row_number,
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platform::errors::InvalidArgument(
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"Variable value (input) of OP(fluid.layers.embedding) "
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"expected >= 0 and < %ld, but got %ld. Please check input "
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"value.",
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row_number, ids[i]));
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PADDLE_ENFORCE_GE(
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ids[i], 0,
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platform::errors::InvalidArgument(
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"Variable value (input) of OP(fluid.layers.embedding) "
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"expected >= 0 and < %ld, but got %ld. Please check input "
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"value.",
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row_number, ids[i]));
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float min = *(table + ids[i] * quant_number);
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float max = *(table + ids[i] * quant_number + 1);
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int offset = ids[i] * quant_number + 2;
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const unsigned char *tensor_buf =
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reinterpret_cast<const unsigned char *>(table + offset);
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dequant(tensor_buf, output + i * row_width, min, max, row_width,
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pow_2_bits);
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}
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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