wehub-resource-sync
54 lines
1.8 KiB
C++
54 lines
1.8 KiB
C++
// Copyright (c) 2023 CINN 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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#pragma once
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#include <string>
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#include "paddle/cinn/ir/ir.h"
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#include "paddle/cinn/pass/pass.h"
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namespace cinn {
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namespace optim {
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class TransBufferWithDynamicShapePass : public FuncPass {
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public:
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TransBufferWithDynamicShapePass()
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: FuncPass("trans_buffer_with_dynamic_shape") {}
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LogicalResult Run(ir::LoweredFunc func) override;
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};
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/**
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* Transforms buffers' dynamic shapes to constant shapes and perform shared
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* memory usage checks.
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*
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* This pass is applicable in scenarios where tensor buffers have dynamic
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* shapes, especially in GPU computations. It's crucial for ensuring correct
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* memory allocation and preventing buffer overflows in shared memory usage on
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* GPUs.
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*
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* When applied, this pass will analyze tensor buffers and their shapes,
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* calculating the required memory size. For GPU local memory, it will attempt
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* to determine upper bounds for dynamic shapes. For GPU shared memory, it will
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* calculate the total shared memory usage and verify it against hardware
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* limits.
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*
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* Risks and limitations:
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* - Currently only checks shared memory usage against hardware limits for
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* NVIDIA GPUs and Hygon DCU.
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*/
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std::unique_ptr<FuncPass> CreateTransBufferWithDynamicShapePass();
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} // namespace optim
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} // namespace cinn
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