Measuring peak memory on the first run can capture cases where compiled artifacts leak into runtime, but it also introduces a lot of noise from cudnn/triton autotuning which generally uses as much memory as it can. Setting this flag as a default will need some discussion, so I will only add it to unblock compiled backward benchmarking (where all autotuning memory use is exposed)
```
e.g. resnet50
# without --warm-peak-memory
memory: eager: 1.95 GB, dynamo: 6.68 GB, ratio: 0.29
# with --warm-peak-memory
memory: eager: 1.96 GB, dynamo: 2.06 GB, ratio: 0.95
```
This issue may also affect large models. Here's an example case of cudnn_convolution_backward autotuning allocating 30GB to tune a model otherwise using 5GB memory:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124326
Approved by: https://github.com/jansel
ghstack dependencies: #119411
Summary:
Replacing `torch._export.aot_compile` callsites with
```
ep = torch.export._trace._export(.., predispatch=True) # Traces the given program into predispatch IR
so_path = torch._inductor.aot_compile_ep(ep, ...) # Takes an exported program and compiles it into a .so
```
This allows us to explicitly split up the export step from AOTInductor. We can later modify tests to do `export + serialize + deserialize + inductor` to mimic internal production use cases better.
Test Plan: CI
Differential Revision: D54808612
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122225
Approved by: https://github.com/SherlockNoMad, https://github.com/khabinov
After this, the sam_fast benchmark can now be run in the pytorch repo:
```
SEGMENT_ANYTHING_FAST_USE_FLASH_4=0 benchmarks/dynamo/torchbench.py --inference --amp --performance --backend=inductor --explain --only sam_fast
```
sam_fast is designed for inference only, with cuda and amp on. The code adds these restrictions to the benchmark.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121420
Approved by: https://github.com/oulgen, https://github.com/msaroufim
As reported in https://github.com/pytorch/pytorch/issues/119434, `detectron2_fcos_r_50_fpn` failed with dynamic shape testing, we propose to skip the dynamic batch size testing of this model in this PR.
* Error msg is
```
File "/home/jiayisun/pytorch/benchmarks/dynamo/common.py", line 3877, in run
assert marked, f"nothing in example_inputs had a dim with {batch_size}"
AssertionError: nothing in example_inputs had a dim with 4
```
* Root Cause is
Benchmark code will only annotate the inputs' dim as dynamic when its size equals to batch size c617e7b407/benchmarks/dynamo/common.py (L3867-L3871). If it fails to find any dim equals to batch size, above error throws.
However, the inputs of `detectron2_fcos_r_50_fpn` are as follows:
```
([{'file_name': '/home/jiayisun/benchmark/torchbenchmark/data/.data/coco2017-minimal/coco/val2017/000000001268.jpg', 'height': 427, 'width': 640, 'image_id': 1268, 'image': tensor([[[147., 124., 82., ..., 3., 4., 5.],
[125., 104., 65., ..., 3., 3., 4.],
[ 87., 68., 34., ..., 2., 2., 2.],
...,
[ 47., 45., 41., ..., 45., 45., 45.],
[ 46., 44., 40., ..., 44., 45., 46.],
[ 46., 44., 40., ..., 43., 45., 46.]],
[[154., 129., 84., ..., 3., 4., 5.],
[133., 110., 69., ..., 3., 3., 4.],
[ 95., 76., 43., ..., 2., 2., 2.],
...,
[ 44., 42., 38., ..., 34., 37., 39.],
[ 43., 41., 37., ..., 35., 39., 41.],
[ 43., 41., 37., ..., 35., 40., 43.]],
[[171., 140., 85., ..., 3., 4., 5.],
[147., 120., 71., ..., 3., 3., 4.],
[103., 83., 47., ..., 2., 2., 2.],
...,
[ 46., 44., 40., ..., 16., 20., 22.],
[ 45., 43., 39., ..., 17., 22., 26.],
[ 45., 43., 39., ..., 18., 24., 28.]]])}, ... ],)
```
None of the inputs' dim will equal to input batch size, so I think we may need to skip the dynamic batch size testing for this model.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120697
Approved by: https://github.com/jgong5, https://github.com/leslie-fang-intel, https://github.com/desertfire
Fix https://github.com/pytorch/pytorch/issues/120545 . The reason why these models fail accuracy test with freezing is due to the conv-batchnorm fusion. Conv-batchnorm fusion causes relative big numerical churn.
For the failed TIMM models, raising the tolerance to `8 * 1e-2` can make the test pass.
For the failed TB models, the numerical difference is too large. Having a discussion with @eellison , we decided to skip them with freezing for now.
One the other hand, we probably should dig more why the conv-bn fusion cause such large numerical difference.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121054
Approved by: https://github.com/eellison
As reported in https://github.com/pytorch/pytorch/issues/119434, `pyhpc_isoneutral_mixing`, `pyhpc_equation_of_state` and `pyhpc_turbulent_kinetic_energy` failed with dynamic shape testing, we propose to skip the dynamic batch size testing of these 3 models in this PR.
* Error msg is
```
File "/localdisk/leslie/torch_inductor_community/pytorch/benchmarks/dynamo/common.py", line 3879, in run
assert marked, f"nothing in example_inputs had a dim with {batch_size}"
AssertionError: nothing in example_inputs had a dim with 1048576
```
* Root Cause is
* Benchmark code will only annotate the inputs' dim as dynamic when its size equals to batch size c617e7b407/benchmarks/dynamo/common.py (L3867-L3871). If it fails to find any dim equals to batch size, above error throws.
* However, for these 3 models, none of the inputs' dim will equal to input batch size since the [relationship of dim sizes](26b85eadde/torchbenchmark/models/pyhpc_equation_of_state/__init__.py (L12-L16))
```
shape = (
math.ceil(2 * size ** (1/3)),
math.ceil(2 * size ** (1/3)),
math.ceil(0.25 * size ** (1/3)),
)
```
* Another thing is `pyhpc_isoneutral_mixing`, `pyhpc_equation_of_state` can pass the dynamic batch size accuracy testing, because the batch size has been set to 4 in accuracy testing (c617e7b407/benchmarks/dynamo/common.py (L3456)) and `math.ceil(2 * size ** (1/3))` happens equaling to 4.
* Since the dim sizes of input has above relationship, running the these models in dynamic shape, we may need to annotate `dim[0](s0) = dim[2](s1) * 8`, per the discussion in https://github.com/pytorch/pytorch/issues/117477#issuecomment-1897108756 @avikchaudhuri, looks like we are not expressible for this case. So, I think we may need to skip the dynamic batch size testing for these 3 models.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120599
Approved by: https://github.com/jgong5, https://github.com/desertfire
Prior to onnx export, the model is deepcopied to avoid modifications that may affect later performance profiling. However this increases the memory requirement on the device.
This PR modifies the script to deepcopy and export the model on another device when possible.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118710
Approved by: https://github.com/thiagocrepaldi
- Removes an outdated assert that prevents perf tests from running DDP, we now have single node --multiprocess and perf tests are already wrapping the model using `deepcopy_and_maybe_ddp`
- Append rank name to traces to avoid all ranks trying to create the same file
- Renames `deepcopy_and_maybe_ddp` to `deepcopy_and_maybe_parallelize` to include FSDP
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113332
Approved by: https://github.com/H-Huang, https://github.com/wconstab
Adds `--compile-autograd` flag to benchmark suite to run accuracy and performance tests. Also adds autograd_captures and autograd_compiles to dynamo stats
e.g. accuracy_inductor.csv
```
dev,name,batch_size,accuracy,calls_captured,unique_graphs,graph_breaks,unique_graph_breaks,autograd_captures,autograd_compiles
cuda,BERT_pytorch,4,pass,2655,2,8,7,1,1
cuda,Background_Matting,4,pass_due_to_skip,0,0,0,0,0,0
cuda,DALLE2_pytorch,0,eager_fail_to_run,0,0,0,0,0,0
cuda,LearningToPaint,4,pass,639,2,8,7,1,1
...
```
e.g. speedup_inductor.csv
```
dev,name,batch_size,speedup,abs_latency,compilation_latency,compression_ratio,eager_peak_mem,dynamo_peak_mem,calls_captured,unique_graphs,graph_breaks,unique_graph_breaks,autograd_captures,autograd_compiles
cuda,hf_T5,8,1.214311,136.236793,88.350570,0.751322,18.754706,24.962275,3298,2,8,8,1,1
cuda,hf_T5,8,1.226645,135.431856,52.461461,1.040973,18.754706,18.016508,795,1,7,7,0,0
...
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117196
Approved by: https://github.com/jansel
Sometimes, the first statement that sets this variable in the try block fails due to out of memory issues and the finally block tries to delete this variable, but it was not written to in the first place.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116260
Approved by: https://github.com/lezcano
Summary: Right now when load_model fails (either because of loading error or validation eager run failure), the result won't be logged in generated csv files. Let's log them in csv so that they are monitored by the expected results checking.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114784
Approved by: https://github.com/malfet
Might be some upstream updates, the previous hack starts to not pick up model names, updating to use the other more appropriate variable.
Also fix a bug with an unused argument that was supposed to be removed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115108
Approved by: https://github.com/thiagocrepaldi
Previously both `optimize_ctx` call and `experiment` call will do export and session creation, ending up doubling the resource cost. This PR makes `experiment` call re-use the onnx model created by `optimize_ctx`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114907
Approved by: https://github.com/thiagocrepaldi
ghstack dependencies: #110178
