wehub-resource-sync
127 lines
4.9 KiB
Plaintext
127 lines
4.9 KiB
Plaintext
// Copyright (c) 2025 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/gpu/ap_variadic_kernel.h"
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#include "paddle/ap/include/axpr/data_type_util.h"
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#include "paddle/ap/include/kernel_dispatch/ap_variadic_kernel.h"
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#include "paddle/ap/include/paddle/phi/device_ctx.h"
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#include "paddle/common/enforce.h"
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#include "paddle/phi/backends/gpu/gpu_context.h"
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/core/kernel_registry.h"
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namespace phi {
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template <typename Context>
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void AllocateOutTensors(const Context& dev_ctx,
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const std::vector<DenseTensor*>& outs) {
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for (auto* out : outs) {
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auto out_dtype = ap::axpr::GetDataTypeFromPhiDataType(out->dtype());
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PADDLE_ENFORCE_EQ(out_dtype.HasOkValue(),
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true,
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common::errors::InvalidArgument(
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"GetDataTypeFromPhiDataType() failed !"));
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out_dtype.GetOkValue().Match(
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[&](const ap::axpr::CppDataType<ap::adt::Undefined>&) {
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PADDLE_THROW(common::errors::InvalidArgument(
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"allocate not support undefined !"));
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},
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[&](const ap::axpr::CppDataType<uint64_t>&) {
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PADDLE_THROW(common::errors::InvalidArgument(
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"allocate not support uint64_t !"));
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},
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[&](const ap::axpr::CppDataType<uint32_t>&) {
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PADDLE_THROW(common::errors::InvalidArgument(
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"allocate not support uint32_t !"));
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},
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[&](const ap::axpr::CppDataType<uint16_t>&) {
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PADDLE_THROW(common::errors::InvalidArgument(
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"allocate not support uint16_t !"));
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},
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[&](const auto& impl) {
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using tensor_type = typename std::decay_t<decltype(impl)>::type;
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dev_ctx.template Alloc<tensor_type>(out);
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});
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}
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return;
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}
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template <typename T, typename Context>
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void ApVariadicKernel(const Context& dev_ctx,
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const std::vector<const DenseTensor*>& xs,
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int num_outputs,
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const std::string& code_module_lambda,
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const std::string& infer_symbolic_lambda,
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const std::string& infer_meta_lambda,
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const std::string& kernel_dispatch_lambda,
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const std::string& kernel_dispatch_const_data_lambda,
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std::vector<DenseTensor*> outs) {
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PADDLE_ENFORCE_GT(
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xs.size(),
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0,
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common::errors::InvalidArgument(
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"At least 1 input is required. current number out uts: // %d",
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xs.size()));
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PADDLE_ENFORCE_GT(
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outs.size(),
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0,
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common::errors::InvalidArgument(
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"num_outputs must be greater than 1. current _outputs: // %d",
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outs.size()));
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AllocateOutTensors<Context>(dev_ctx, outs);
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std::shared_ptr<ap::kernel_dispatch::DeviceCtxImpl> impl =
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std::make_shared<ap::paddle::DeviceCtx<Context>>(&dev_ctx);
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ap::kernel_dispatch::DeviceCtx ap_device_ctx{impl};
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const auto& ret =
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ap::kernel_dispatch::ApVariadicKernel(ap_device_ctx,
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xs,
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num_outputs,
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code_module_lambda,
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infer_meta_lambda,
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kernel_dispatch_lambda,
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kernel_dispatch_const_data_lambda,
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outs);
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PADDLE_ENFORCE_EQ(
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ret.HasError(),
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false,
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common::errors::Fatal("ap_variadic failed. \nTraceback (most "
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"recent call last):\n%s\n%s: %s. ",
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ret.GetError().CallStackToString(),
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ret.GetError().class_name(),
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ret.GetError().msg()));
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}
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} // namespace phi
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#ifdef PADDLE_WITH_HIP
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PD_REGISTER_KERNEL(ap_variadic,
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GPU,
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ALL_LAYOUT,
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phi::ApVariadicKernel,
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float,
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double,
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phi::float16) {}
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#else
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PD_REGISTER_KERNEL(ap_variadic,
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GPU,
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ALL_LAYOUT,
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phi::ApVariadicKernel,
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float,
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double,
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phi::float16,
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phi::bfloat16) {}
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#endif
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