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8b11d81058
Re-landing https://github.com/pytorch/pytorch/pull/68111 ## Description Preview4 PR of this [RFC](https://github.com/pytorch/pytorch/issues/49444). On the basis of https://github.com/pytorch/pytorch/pull/50256, the below improvements are included: - The [preview4 release branch](https://github.com/oneapi-src/oneDNN/releases/tag/graph-v0.4.1) of the oneDNN Graph API is used - The fuser now works with the profiling graph executor. We have inserted type check nodes to guard the profiled tensor properties. ### User API: The optimization pass is disabled by default. Users could enable it by: ``` torch.jit.enable_onednn_fusion(True) ``` ### Performance: [pytorch/benchmark](https://github.com/pytorch/benchmark) tool is used to compare the performance: - SkyLake 8180 (1 socket of 28 cores):  - SkyLake 8180 (single thread):  \* By mapping hardswish to oneDNN Graph, it’s 8% faster than PyTorch JIT (NNC + OFI) \** We expect performance gain after mapping transpose, contiguous & view to oneDNN graph ops ### Directory structure of the integration code Fuser-related code are placed under: ``` torch/csrc/jit/codegen/onednn/ ``` Optimization pass registration is done in: ``` torch/csrc/jit/passes/onednn_graph_fuser.h ``` CMake for the integration code is: ``` caffe2/CMakeLists.txt ``` ## Limitations - In this PR, we have only supported the optimization on Linux platform. The support on Windows and MacOS will be enabled as the next step. - We have only optimized the inference use case. Pull Request resolved: https://github.com/pytorch/pytorch/pull/74596 Approved by: https://github.com/malfet
94 lines
2.7 KiB
C++
94 lines
2.7 KiB
C++
#pragma once
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#include <unordered_map>
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#include <oneapi/dnnl/dnnl_graph.hpp>
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#include <torch/csrc/jit/codegen/onednn/LlgaTensorImpl.h>
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#include <torch/csrc/jit/codegen/onednn/graph_helper.h>
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#include <torch/csrc/jit/ir/ir.h>
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#include <torch/csrc/jit/runtime/interpreter.h>
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namespace torch {
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namespace jit {
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namespace fuser {
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namespace onednn {
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using ArgSpec = LlgaTensorDesc;
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using ArgSpecs = std::vector<ArgSpec>;
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using RunArg = dnnl::graph::tensor;
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using RunArgs = std::vector<RunArg>;
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using TensorArgs = std::vector<at::Tensor>;
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class LlgaKernel {
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public:
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explicit LlgaKernel(const Node* fusionNode);
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void run(Stack& stack);
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void initialize(const TensorArgs& inputs);
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const std::string& debugName() const {
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return debugName_;
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}
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private:
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bool useOpaqueLayout(size_t offset) const;
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// PyTorch copy constants inside the subgraph instead of referencing them.
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// Constants inputs to the partition are no longer in the graph->inputs().
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// Need use the tid retrieved from the partition to find the missing
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// constant inputs.
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void initializeConstantInputs();
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ArgSpecs initializeInputSpecs(const TensorArgs& inputs);
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ArgSpecs initializeOutputSpecs() const;
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dnnl::graph::compiled_partition compile(
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const dnnl::graph::partition& partition);
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std::map<size_t, int64_t> initializeTensorIdToOccurence() const;
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std::tuple<RunArgs, RunArgs> prepareRunArgs(
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const TensorArgs& inputs,
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TensorArgs& outputs) const;
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static std::string genDebugName() {
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static size_t debugId = 0;
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return "LlgaPartition_" + std::to_string(debugId++);
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}
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static dnnl::graph::logical_tensor toLogicalTensor(const ArgSpec& s) {
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return s.logical_tensor();
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}
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at::Device device_ = at::kCPU;
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const Node* fusionNode_;
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std::shared_ptr<Graph> graph_;
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int64_t nGraphInputs_ = 0; // number of inputs to graph_ on the IR
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int64_t nOutputs_ = 0;
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std::map<size_t, Value*> tensorIdToValue_;
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std::vector<int64_t> runArgsIdx_;
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dnnl::graph::partition partition_;
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// nPartitionInputs_ is the actual number of inputs to partition_ of graph_
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// needed by the backend.
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// nPartitionInputs_ = nGraphInputs_ + constantInputs_.size() since Constant
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// inputs are copied to the inside of the subgraph
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int64_t nPartitionInputs_;
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dnnl::graph::compiled_partition compilation_;
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std::set<size_t> initializedInputIds_;
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std::vector<Value*> constantValues_;
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TensorArgs constantInputs_;
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ArgSpecs inputSpecs_;
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ArgSpecs outputSpecs_;
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std::vector<dnnl::graph::logical_tensor> constantLogicalTensors_;
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std::string debugName_;
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std::once_flag initialized_flag;
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bool is_initialized_ = false;
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};
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} // namespace onednn
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} // namespace fuser
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} // namespace jit
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} // namespace torch
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