Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46896
The idea of the memory model is quite similar to that of BlackBoxPredictor, however, it's more complicated in pt due to 1) tensor views that share storage with storage refcount bumps but with different TensorImpls, 2) tensors sharing the same TensorImpl and the same storage, but with no refcount bump of the StorageImpl, 3) data types such as TensorList and Tuples that have Tensors in them, 4) need to support non-out/out variant mix while we move the aten ops to out variants.
As a result, I have to make the following adjustments:
1) remove tensors in output Tuples from internal blob list;
2) for memory allocation/deallocation, get candidate Tensors from the outputs of ops with out variant, extract StorageImpls from the Tensors, dedup, and remove output tensor StorageImpls, and get the final list of blobs for memory planning;
3) during the clean_up_memory pass, clean up memory held by the StorageImpls as well as Tensors/Lists/Tuples in IValues that don't participate in memory planning to reduce overall memory usage
Risk:
PyTorch team is planning to deprecate the current resize_outout api, which we do rely on. This is a pretty big risk.
https://www.internalfb.com/intern/diffusion/FBS/browsefile/master/fbcode/caffe2/aten/src/ATen/native/Resize.cpp?commit=6457b329847607553d34e788a3a7092f41f38895&lines=9-23
Test Plan:
```
buck test //caffe2/test:static_runtime
buck test //caffe2/benchmarks/static_runtime:static_runtime_cpptest
buck test //caffe2/caffe2/fb/predictor:pytorch_predictor_test
```
Benchmarks:
```
MKL_NUM_THREADS=1 OMP_NUM_THREADS=1 numactl -m 0 -C 13 \
buck-out/opt/gen/caffe2/caffe2/fb/predictor/ptvsc2_predictor_bench \
--scripted_model=/home/hlu/ads/adindexer/adindexer_ctr_mobilefeed/pt/merge/traced_precomputation.pt \
--pt_inputs=/home/hlu/ads/adindexer/adindexer_ctr_mobilefeed/pt/merge/container_precomputation_bs1.pt \
--iters=1000 --warmup_iters=10000 --num_threads=1 --pt_enable_static_runtime=true \
--pt_cleanup_activations=true --pt_enable_out_variant=false
```
|pt_cleanup_activations |pt_enable_out_variant |old ms/iter |new ms/iter |
|--- |--- |--- |--- |
|0 |0 |0.31873 |0.30228 |
|0 |1 |0.30018 |0.29184 |
|1 |0 |0.35246 |0.31895 |
|1 |1 |0.35742 |0.30417 |
Reviewed By: bwasti, raziel
Differential Revision: D24471854
fbshipit-source-id: 4ac37dca7d2a0c362120a7f02fd3995460c9a55c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46219
- Refactor StaticRuntime and group common data structures, the jit graph, and the script module into a separate struct `InferenceModule`:
```
struct InferenceModule {
explicit InferenceModule(const torch::jit::Module& m);
explicit InferenceModule(std::shared_ptr<torch::jit::Graph> g);
torch::jit::Module module;
std::shared_ptr<torch::jit::Graph> graph;
std::unique_ptr<c10::FunctionSchema> schema;
std::unordered_map<Value*, size_t> value_to_reg;
std::vector<size_t> input_regs; // inputs to the graph
std::vector<size_t> output_regs; // outputs of the graph
std::vector<size_t> internals;
};
```
which is stored in the PyTorchPredictor, as well as the static runtime, and shared across threads. Then this is what's left inside the Static Runtime:
```
mutable std::vector<IValue> reg_;
// The nodes we need to run
std::vector<ProcessedNode> nodes_;
```
`reg_` holds all the weights and activations, which is different across threads during running. `nodes_` holds the op nodes and input/output registers, and is the same across threads for now. We could potentially put other stateful data structures in it, so I kept it inside the static runtime. It could be easily moved into the `InferenceModule` if we decide not to anything else into `ProcessedNode`.
- Added StaticRuntimeOptions so we can toggle certain optimizations on/off, for testing and benchmarking. `cleanup_activations` is an example.
- Integration with PyTorchPredictor. Added a lockfree stack in the PyTorchPredictor to hold all the static runtime instances. Benchmark shows that the `push` and `pop` combo takes about 80 ns, which is quite acceptable.
This diff focuses on threading model only. Benchmarks will be separate.
Reviewed By: bwasti
Differential Revision: D24237078
fbshipit-source-id: fd0d6347f02b4526ac17dec1f731db48424bade1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46308
This PR adds a hand optimized version of DeepAndWide model with the goal
of estimating overheads of static runtime. While static runtime is
currently much faster than the existing JIT interpreter, it would be
useful to understand how close we are to an absolutely 0-overhead
system. Currently, this "ideal" implementation is 2x faster than the
static runtime on batchsize=1.
Full benchmark results:
```
Running build/bin/static_runtime_bench
Run on (24 X 2394.71 MHz CPU s)
CPU Caches:
L1 Data 32K (x24)
L1 Instruction 32K (x24)
L2 Unified 4096K (x24)
L3 Unified 16384K (x24)
------------------------------------------------------------------------------
Benchmark Time CPU Iterations
------------------------------------------------------------------------------
BM_deep_wide_base/1 59518 ns 59500 ns 10909
BM_deep_wide_base/8 74635 ns 74632 ns 9317
BM_deep_wide_base/20 82186 ns 82147 ns 9119
BM_deep_wide_fast/1 13851 ns 13851 ns 49825 << new
BM_deep_wide_fast/8 22497 ns 22497 ns 32089 << new
BM_deep_wide_fast/20 23868 ns 23841 ns 31184 << new
BM_deep_wide_jit_graph_executor/1 62786 ns 62786 ns 10835
BM_deep_wide_jit_graph_executor/8 76730 ns 76718 ns 7529
BM_deep_wide_jit_graph_executor/20 78886 ns 78883 ns 8769
BM_deep_wide_jit_profiling_executor/1 69504 ns 69490 ns 10309
BM_deep_wide_jit_profiling_executor/8 75718 ns 75715 ns 9199
BM_deep_wide_jit_profiling_executor/20 75364 ns 75364 ns 9010
BM_deep_wide_static/1 40324 ns 40318 ns 17232
BM_deep_wide_static/8 50327 ns 50319 ns 13335
BM_deep_wide_static/20 53075 ns 53071 ns 12855
BM_deep_wide_static_threaded/threads:8 6258 ns 49873 ns 14008
```
PS: The implementation could probably be optimized even more.
Differential Revision: D24300702
Test Plan: Imported from OSS
Reviewed By: dzhulgakov
Pulled By: ZolotukhinM
fbshipit-source-id: 7870bdef127c39d11bcaa4f03a60eb80a46be58e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45892
Previously we were using hashtable (`std::unordered_map` in OSS, `folly::F14FastMap` in fb) for workspace, a container for all the IValues in the graph. Hashtable based lookups can be expensive. This diff replaces the hashtable with `std::vector` and extra bookkeepings are introduced to keep track of the indices of graph inputs/outputs in `StaticRuntime` and op inputs/outputs in `ProcessedNode`.
Reviewed By: dzhulgakov
Differential Revision: D24098763
fbshipit-source-id: 337f835ee144985029b5fa2ab98f9bcc5e3606b6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45639
`StaticRuntime::run_individual` is to mimic the caffe2 operator benchmark `SimpleNet::TEST_Benchmark`, so we can accurate information on the operator breakdown. We found that the PyTorch AutogradProfiler adds a lot of overhead to small models, such as the adindexer precomputation_merge net, 100% for batch_size 1, 33% for batch_size 20. This implementation adds very little overhead, as shown in the test plan.
Test Plan: Test results are fb internal only.
Reviewed By: yinghai, dzhulgakov
Differential Revision: D24012088
fbshipit-source-id: f32eb420aace93e2de421a15e4209fce6a3d90f0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43647
Nothing fancy, just a basic implementation of the graph executor without using stack machine.
Reviewed By: bwasti
Differential Revision: D23208413
fbshipit-source-id: e483bb6ad7ba8591bbe1767e669654d82f42c356
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43182
We should avoid using `deepcopy` on the module because it involves copying the weights.
Comparing the implementation of `c10::ivalue::Object::copy()` vs `c10::ivalue::Object::deepcopy()`, the only difference is `deepcopy` copies the attributes (slots) while `copy` does not.
Reviewed By: bwasti
Differential Revision: D23171770
fbshipit-source-id: 3cd711c6a2a19ea31d1ac1ab2703a0248b5a4ef3
Summary:
This PR whitelists and simplifies graphs to help with development later on. Key to note in this PR is the use of both a pattern substitution and the registration of custom operators. This will likely be one of the main optimization types done in this folder.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43024
Reviewed By: hlu1
Differential Revision: D23114262
Pulled By: bwasti
fbshipit-source-id: e25aa3564dcc8a2b48cfd1561b3ee2a4780ae462
Summary:
The premise of this approach is that a small subset of neural networks are well represented by a data flow graph. The README contains more information.
The name is subject to change, but I thought it was a cute reference to fire.
suo let me know if you'd prefer this in a different spot. Since it lowers a JIT'd module directly I assumed the JIT folder would be appropriate. There is no exposed Python interface yet (but is mocked up in `test_accelerant.py`)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42753
Reviewed By: zou3519
Differential Revision: D23043771
Pulled By: bwasti
fbshipit-source-id: 5353731e3aae31c08b5b49820815da98113eb551
