wehub-resource-sync
110 lines
4.0 KiB
C++
110 lines
4.0 KiB
C++
// Copyright (c) 2023 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/dequantize_kernel.h"
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#include "paddle/phi/backends/onednn/onednn_context.h"
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#include "paddle/phi/backends/onednn/onednn_helper.h"
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#include "paddle/phi/backends/onednn/onednn_reuse.h"
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#include "paddle/phi/core/enforce.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 DeQuantKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const float quantization_scale,
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const float quantization_shift,
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DenseTensor* out) {
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PADDLE_ENFORCE(quantization_scale != 0.0f,
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common::errors::InvalidArgument(
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"Dequantization scale must be different than 0.0f"));
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const auto q_shift = static_cast<int32_t>(quantization_shift);
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PADDLE_ENFORCE_GE(q_shift,
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0,
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common::errors::InvalidArgument(
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"Dequantization shift must be greater or equal to 0"));
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PADDLE_ENFORCE_LE(q_shift,
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255,
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common::errors::InvalidArgument(
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"Dequantization shift must be lower or equal to 255"));
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const bool with_shift = q_shift != 0;
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auto x_tz = vectorize<int64_t>(x.dims());
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auto x_type = funcs::ToOneDNNDataType(x.dtype());
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auto out_type = funcs::ToOneDNNDataType(out->dtype());
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dnnl::primitive_attr attrs;
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static constexpr int32_t mask = 0; // same shift and scale for whole tensor
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attrs.set_scales_mask(DNNL_ARG_DST, mask);
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if (with_shift) {
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attrs.set_zero_points_mask(DNNL_ARG_SRC, mask);
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}
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funcs::ReorderOneDNNHandler reorder_handler(
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x_tz, x.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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x.mem_desc(), funcs::to_void_cast(x.data<T>()));
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auto reorder_dst_memory_p =
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reorder_handler.AcquireDstMemory(out, x.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 =
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dnnl::memory(scales_md,
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dev_ctx.GetEngine(),
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funcs::to_void_cast<float>(&quantization_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 = dnnl::memory(zero_points_md,
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dev_ctx.GetEngine(),
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funcs::to_void_cast<int32_t>(&q_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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reorder_args.insert({DNNL_ARG_ATTR_SCALES | DNNL_ARG_DST, scales_mem});
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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_SRC, 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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out->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(dequantize,
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OneDNN,
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ONEDNN,
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phi::DeQuantKernel,
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uint8_t,
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int8_t,
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phi::bfloat16) {
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kernel->OutputAt(0).SetDataType(phi::DataType::FLOAT32);
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}
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