wehub-resource-sync
116 lines
4.1 KiB
C++
116 lines
4.1 KiB
C++
/* Copyright (c) 2024 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/kernels/quantize_kernel.h"
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#include "paddle/phi/backends/onednn/onednn_reuse.h"
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#include "paddle/phi/core/compat/convert_utils.h"
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#include "paddle/phi/core/enforce.h"
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#include "paddle/phi/core/expect.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/core/utils/data_type.h"
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namespace phi {
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using dnnl::memory;
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template <typename T, typename Context>
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void QuantOpKernel(const Context& dev_ctx,
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const DenseTensor& input,
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bool is_negative_input,
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const float scale,
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const float shift,
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const std::string& output_format,
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bool bfloat16,
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DenseTensor* output) {
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const auto quantization_shift = static_cast<int32_t>(shift);
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const bool with_scale = scale != 1.0f;
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const bool with_shift = quantization_shift != 0.0f;
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PADDLE_ENFORCE_NE(scale,
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0.0f,
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common::errors::InvalidArgument(
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"Quantization scale must be different than 0.0f"));
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PADDLE_ENFORCE(
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quantization_shift <= 255 && quantization_shift >= 0,
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common::errors::InvalidArgument(
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"Quantization shift must be lower or equal to 255 and greater or "
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"equal to 0, but got %d",
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quantization_shift));
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auto x_tz = vectorize<int64_t>(input.dims());
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dnnl::primitive_attr attrs;
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static constexpr int32_t mask = 0;
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if (with_scale) {
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attrs.set_scales_mask(DNNL_ARG_SRC, mask);
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}
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if (with_shift) {
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attrs.set_zero_points_mask(DNNL_ARG_DST, mask);
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}
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auto x_type = funcs::ToOneDNNDataType(input.dtype());
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DataType out_dtype;
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if (bfloat16) {
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out_dtype = DataType::BFLOAT16;
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} else if (is_negative_input && !with_shift) {
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out_dtype = DataType::INT8;
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} else {
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out_dtype = DataType::UINT8;
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}
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auto out_type = funcs::ToOneDNNDataType(out_dtype);
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funcs::ReorderOneDNNHandler reorder_handler(
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x_tz, input.dtype(), x_type, out_dtype, out_type, dev_ctx.GetEngine());
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auto reorder_src_memory_p = reorder_handler.AcquireSrcMemory(
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input.mem_desc(), funcs::to_void_cast(input.data<T>()));
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auto reorder_dst_memory_p = reorder_handler.AcquireDstMemory(
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output, input.mem_desc(), dev_ctx.GetPlace());
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auto reorder_p = reorder_handler.AcquireReorder(
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reorder_dst_memory_p, reorder_src_memory_p, attrs);
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auto& astream = OneDNNContext::tls().get_stream();
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auto scales_md = dnnl::memory::desc(
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{1}, dnnl::memory::data_type::f32, dnnl::memory::format_tag::x);
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auto scales_mem = dnnl::memory(
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scales_md, dev_ctx.GetEngine(), funcs::to_void_cast<float>(&scale));
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auto zero_points_md = dnnl::memory::desc(
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{1}, dnnl::memory::data_type::s32, dnnl::memory::format_tag::x);
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auto zero_points_mem =
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dnnl::memory(zero_points_md,
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dev_ctx.GetEngine(),
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funcs::to_void_cast<int32_t>(&quantization_shift));
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std::unordered_map<int, dnnl::memory> reorder_args;
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reorder_args.insert({DNNL_ARG_SRC, *reorder_src_memory_p});
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reorder_args.insert({DNNL_ARG_DST, *reorder_dst_memory_p});
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if (with_scale) {
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reorder_args.insert({DNNL_ARG_ATTR_SCALES | DNNL_ARG_SRC, scales_mem});
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}
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if (with_shift) {
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reorder_args.insert(
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{DNNL_ARG_ATTR_ZERO_POINTS | DNNL_ARG_DST, zero_points_mem});
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}
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reorder_p->execute(astream, reorder_args);
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astream.wait();
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output->set_mem_desc(reorder_dst_memory_p->get_desc());
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}
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} // namespace phi
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PD_REGISTER_KERNEL(quantize, OneDNN, ONEDNN, phi::QuantOpKernel, float) {}
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