wehub-resource-sync
130 lines
4.1 KiB
C++
130 lines
4.1 KiB
C++
// Copyright (c) 2024 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/backends/onednn/onednn_reuse.h"
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#include "paddle/phi/core/kernel_registry.h"
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namespace phi {
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using dnnl::memory;
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namespace {
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inline uint8_t clip_to_uint8(float x) {
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return std::max(0L, std::min(255L, std::lround(x)));
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}
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} // namespace
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template <typename T, typename Context>
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void ReQuantOpKernel(const Context& dev_ctx,
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const DenseTensor& input,
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float scale_in,
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float scale_out,
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float shift_in,
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float shift_out,
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DenseTensor* out) {
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bool with_shift = shift_in != 0 || shift_out != 0;
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auto* output = out;
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PADDLE_ENFORCE_NE(
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scale_in,
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0.0f,
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common::errors::InvalidArgument("Scale of input cannot be 0.0"));
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PADDLE_ENFORCE_NE(
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scale_out,
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0.0f,
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common::errors::InvalidArgument("Scale of output cannot be 0.0"));
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if (shift_in != 0) {
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PADDLE_ENFORCE_EQ(
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input.dtype(),
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DataType::UINT8,
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common::errors::Unimplemented("Requantize does not support nonzero "
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"shift for signed input."));
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}
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auto src_tz = vectorize(input.dims());
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auto src_paddle_dt = input.dtype();
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auto dst_paddle_dt = with_shift ? DataType::UINT8 : src_paddle_dt;
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auto xstrides = input.mem_desc().get_strides();
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dnnl::primitive_attr attrs;
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int mask = 0;
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float reorder_scale = scale_in / scale_out;
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attrs.set_scales_mask(DNNL_ARG_DST, mask);
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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(scales_md,
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dev_ctx.GetEngine(),
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funcs::to_void_cast<float>(&reorder_scale));
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uint32_t reorder_shift =
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with_shift ? clip_to_uint8(shift_out - (1.0f / reorder_scale) * shift_in)
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: 0;
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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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funcs::ReorderOneDNNHandler reorder_handler(
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src_tz,
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src_paddle_dt,
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funcs::ToOneDNNDataType(src_paddle_dt),
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dst_paddle_dt,
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funcs::ToOneDNNDataType(dst_paddle_dt),
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dev_ctx.GetEngine());
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auto 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 dst_memory_p = reorder_handler.AcquireDstMemory(
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output, src_tz, xstrides, dev_ctx.GetPlace());
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auto reorder_p =
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reorder_handler.AcquireReorder(dst_memory_p, src_memory_p, attrs);
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auto& astream = OneDNNContext::tls().get_stream();
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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_out_mem =
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dnnl::memory(zero_points_md, dev_ctx.GetEngine(), &reorder_shift);
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std::unordered_map<int, dnnl::memory> reorder_args;
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reorder_args.insert({DNNL_ARG_SRC, *src_memory_p});
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reorder_args.insert({DNNL_ARG_DST, *dst_memory_p});
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reorder_args.insert({DNNL_ARG_ATTR_SCALES | DNNL_ARG_DST, scales_mem});
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// shift for DST
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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_out_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(dst_memory_p->get_desc());
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}
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} // namespace phi
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PD_REGISTER_KERNEL(requantize,
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OneDNN,
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ONEDNN,
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phi::ReQuantOpKernel,
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int8_t,
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uint8_t,
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phi::bfloat16) {}
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