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71ca600af9
Summary: Since caffe2 and torch have been consolidated, CAFFE2_API should be merged with TORCH_API. Addresses a TODO. Manually edited some references of the removed `CAFFE2_API`: * `CONTRIBUTING.md` * `caffe2/proto/CMakeLists.txt` * `cmake/ProtoBuf.cmake` * `c10/macros/Export.h` * `torch/csrc/WindowsTorchApiMacro.h` Pull Request resolved: https://github.com/pytorch/pytorch/pull/49496 Reviewed By: malfet, samestep Differential Revision: D25600726 Pulled By: janeyx99 fbshipit-source-id: 7e068d959e397ac183c097d7e9a9afeca5ddd782
94 lines
2.9 KiB
C++
94 lines
2.9 KiB
C++
#pragma once
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#include "caffe2/opt/backend_transformer_base.h"
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#include <unordered_set>
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namespace caffe2 {
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struct TvmTransformOptions final : public BackendTransformOptions {
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explicit TvmTransformOptions() : BackendTransformOptions() {}
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// Whether to enable profiling based jit
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bool profiling_based_jit{false};
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};
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class TORCH_API TvmTransformer final : public BackendTransformerBase {
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public:
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explicit TvmTransformer(const TvmTransformOptions& opts)
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: BackendTransformerBase(), opts_(opts) {}
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~TvmTransformer() override {}
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// Given workspace and predict net, cluster continuous parts that can be run
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// by TVM and create one TVMJit op for each clustered subgraph.
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// \param ws c2 workspace
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// \param pred_net c2 predict net
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// \param weight_names list of the names of the constant weights
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// \param shape_hints User provided shape info, usually for primary inputs so
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// that bound shape inference can have something to start
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// \param blocklisted_ops a set of ops that we don't want to lower to TVM in
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// terms of their net positions. This is very useful for debugging but for
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// normal runs it should be empty
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void transform(
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Workspace* ws,
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NetDef* pred_net,
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const std::vector<std::string>& weight_names,
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const ShapeInfoMap& shape_hints,
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const std::unordered_set<int>& blocklisted_ops) override;
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static const std::unordered_set<std::string>& getSupportedOps();
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static bool canConvertFullGraph(
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const caffe2::NetDef& net,
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const std::unordered_set<int>& blocklisted_ops);
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private:
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// Given TVM runnable subnets, contract them into one TVMJitOp
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NetDef buildTvmOp(
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const caffe2::NetDef& net,
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const std::unordered_set<std::string>& weights,
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const ShapeInfoMap& shape_hints);
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// Apply transform to cluster connected TVM runnable ops into one TVMJitOp
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NetDef applyTvmTransform(
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NetDef* pred_net,
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const std::unordered_set<std::string>& weights,
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const std::unordered_set<int>& blocklisted_ops,
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const ShapeInfoMap& shape_hints);
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// Options
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TvmTransformOptions opts_;
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// Track number of TVMJitOp we created
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int tvm_op_id_{0};
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// Model id
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std::string model_id_;
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};
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// Helper function to clean up a net and run tvm transform.
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TORCH_API void tvmTransform(
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NetDef* net,
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Workspace* ws,
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const std::vector<std::string>& input_names,
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const std::vector<std::string>& output_names,
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const std::vector<std::string>& weight_names,
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const ShapeInfoMap& shape_hints,
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const std::unordered_set<int>& blocklisted_ops,
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int32_t max_batch_size,
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int32_t max_seq_size,
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int32_t num_embeddings,
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int32_t embedding_size,
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int32_t tvm_min_ops,
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bool tvm_profiling_based_jit,
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bool debug);
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TORCH_API void cleanUpPredictNet(
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NetDef* net,
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const std::vector<std::string>& input_names,
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const std::vector<std::string>& output_names,
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const std::vector<std::string>& weight_names);
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} // namespace caffe2
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