// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. #include "paddle/phi/backends/xpu/enforce_xpu.h" #include "paddle/phi/core/kernel_registry.h" namespace phi { template void DeQuantizeKernelImpl(const Context& dev_ctx, const DenseTensor& x, float scale, DenseTensor* y) { using XPUInX = typename XPUTypeTrait::Type; using XPUOutY = typename XPUTypeTrait::Type; auto* y_data = dev_ctx.template Alloc(y); const auto* x_data = x.data(); int64_t len = x.numel(); int max_ptr_size = dev_ctx.x_context()->max_ptr_size(); xpu::ctx_guard RAII_GUARD(dev_ctx.x_context()); auto max_data = RAII_GUARD.alloc_l3_or_gm(max_ptr_size); int r = xpu::constant(dev_ctx.x_context(), max_data, max_ptr_size, scale); PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant"); r = xpu::dequantization( dev_ctx.x_context(), reinterpret_cast(x_data), reinterpret_cast(y_data), len, max_data); PADDLE_ENFORCE_XDNN_SUCCESS(r, "dequantization"); } template void DeQuantizeKernel(const Context& dev_ctx, const DenseTensor& x, DataType out_dtype, float scale, DenseTensor* y) { switch (out_dtype) { case DataType::FLOAT32: DeQuantizeKernelImpl(dev_ctx, x, scale, y); break; case DataType::FLOAT16: DeQuantizeKernelImpl(dev_ctx, x, scale, y); break; default: PADDLE_THROW(common::errors::Unavailable( "Not supported dequantize data type from %d -> %d ", x.dtype(), out_dtype)); } } } // namespace phi PD_REGISTER_KERNEL( dequantize_xpu, XPU, ALL_LAYOUT, phi::DeQuantizeKernel, int16_t, int8_t) {}