On Intel Max 1550, models like Super_SloMo can actually pass accuracy test after set deterministic, because we do not use atomic in upsampling bilinear backward in some cases when running on XPU. Furthermore, I guess the only reason not to set deterministic on these models is just avoiding errors. We should use warn_only = True.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149028
Approved by: https://github.com/guangyey, https://github.com/desertfire
Co-authored-by: Yu, Guangye <106960996+guangyey@users.noreply.github.com>
As `cuBLAS` workspaces are already per-stream, there shouldn't be kernel execution overlap with `cuBLASLt` kernels.
This PR reuses `cuBLAS` workspaces for `cuBLASLt` for the following benefits:
+ caching (`cuBLAS` workspaces were already cached, so now we get that for `cuBLASLt`)
+ "free" workspace size bump for `cuBLASLt` `cuBLASLt` workspace sizes were previously smaller than those for `cuBLAS` by default which potentially hurts performance, and we encountered difficulty in increasing the size due to downstream OOMs , see also #120925
+ fixes behavior broken behavior with the memtracker; https://github.com/pytorch/pytorch/pull/139442 attempted to handle peaky allocation behavior that broke memtracker equivalence tests but it didn't seem to fully work, here the cached/reused `cuBLAS` workspace seems to fix it
+ one environment variable to rule them all: `CUBLAS_WORKSPACE_CONFIG` applies directly to `cuBLASLt` without a confusing `CUBLASLT_WORKSPACE_SIZE` that users would also need to consider
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145130
Approved by: https://github.com/ngimel
Changes in this PR:
1. Add `is_structseq` and `is_structseq_class` functions to determine a object or a class is PyStructSequence.
2. Add a generic class `structseq` which can be used as the registration key for PyStructSequence types like `namedtuple` for Named Tuple types.
3. Change `is_namedtuple` to accept subclasses of namedtuple to be namedtuple. Before this PR, only namedtuple class directly created by `collections.namedtuple` or `typing.NamedTuple` were namedtuple classes while their subclasses were not. This PR makes `is_namedtuple` return true for subclasses of namedtuple class.
Resolves#75982. New tests are included in this PR.
- #75982
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113257
Approved by: https://github.com/zou3519
Summary:
The AOTI lowering for model 699109736 and other new models worked before D70075331, but failed after with error "RuntimeError: CUDA error: CUBLAS_STATUS_EXECUTION_FAILED when calling cublasLtMatmul with transpose_mat1 1 transpose_mat2 0 m 4096 n 10 k 7936 mat1_ld 7936 mat2_ld 7936 result_ld 4096 abcType 2 computeType 68 scaleType 0"
So we revert D70075331 as a workaround now.
Test Plan: The model could be lowered and published successfully. e.g. 702869739_16
Differential Revision: D70823254
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148824
Approved by: https://github.com/eqy
Changes in this PR:
1. Add `is_structseq` and `is_structseq_class` functions to determine a object or a class is PyStructSequence.
2. Add a generic class `structseq` which can be used as the registration key for PyStructSequence types like `namedtuple` for Named Tuple types.
3. Change `is_namedtuple` to accept subclasses of namedtuple to be namedtuple. Before this PR, only namedtuple class directly created by `collections.namedtuple` or `typing.NamedTuple` were namedtuple classes while their subclasses were not. This PR makes `is_namedtuple` return true for subclasses of namedtuple class.
Resolves#75982. New tests are included in this PR.
- #75982
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113257
Approved by: https://github.com/zou3519
tts_angular with cudagraph is flaky. Its speedup varies from .05 to 1.01. This PR disables cudagraph for tts_angular to avoid the noise. Since tts_angular shows ~1x speedup while other torchbench models show ~2x speedup, skipping tts_angular would wrongly bump the cudagraph speedup. So this PR only disables cudagraph for tts_angular instead of skipping tts_angular.
[Dashboard ](https://github.com/pytorch/pytorch/actions/runs/13597394087)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148221
Approved by: https://github.com/eellison
Reference: https://docs.astral.sh/ruff/formatter/black/#assert-statements
> Unlike Black, Ruff prefers breaking the message over breaking the assertion, similar to how both Ruff and Black prefer breaking the assignment value over breaking the assignment target:
>
> ```python
> # Input
> assert (
> len(policy_types) >= priority + num_duplicates
> ), f"This tests needs at least {priority+num_duplicates} many types."
>
>
> # Black
> assert (
> len(policy_types) >= priority + num_duplicates
> ), f"This tests needs at least {priority+num_duplicates} many types."
>
> # Ruff
> assert len(policy_types) >= priority + num_duplicates, (
> f"This tests needs at least {priority + num_duplicates} many types."
> )
> ```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144546
Approved by: https://github.com/malfet
As `cuBLAS` workspaces are already per-stream, there shouldn't be kernel execution overlap with `cuBLASLt` kernels.
This PR reuses `cuBLAS` workspaces for `cuBLASLt` for the following benefits:
+ caching (`cuBLAS` workspaces were already cached, so now we get that for `cuBLASLt`)
+ "free" workspace size bump for `cuBLASLt` `cuBLASLt` workspace sizes were previously smaller than those for `cuBLAS` by default which potentially hurts performance, and we encountered difficulty in increasing the size due to downstream OOMs , see also #120925
+ fixes behavior broken behavior with the memtracker; https://github.com/pytorch/pytorch/pull/139442 attempted to handle peaky allocation behavior that broke memtracker equivalence tests but it didn't seem to fully work, here the cached/reused `cuBLAS` workspace seems to fix it
+ one environment variable to rule them all: `CUBLAS_WORKSPACE_CONFIG` applies directly to `cuBLASLt` without a confusing `CUBLASLT_WORKSPACE_SIZE` that users would also need to consider
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145130
Approved by: https://github.com/ngimel
As `cuBLAS` workspaces are already per-stream, there shouldn't be kernel execution overlap with `cuBLASLt` kernels.
This PR reuses `cuBLAS` workspaces for `cuBLASLt` for the following benefits:
+ caching (`cuBLAS` workspaces were already cached, so now we get that for `cuBLASLt`)
+ "free" workspace size bump for `cuBLASLt` `cuBLASLt` workspace sizes were previously smaller than those for `cuBLAS` by default which potentially hurts performance, and we encountered difficulty in increasing the size due to downstream OOMs , see also #120925
+ fixes behavior broken behavior with the memtracker; https://github.com/pytorch/pytorch/pull/139442 attempted to handle peaky allocation behavior that broke memtracker equivalence tests but it didn't seem to fully work, here the cached/reused `cuBLAS` workspace seems to fix it
+ one environment variable to rule them all: `CUBLAS_WORKSPACE_CONFIG` applies directly to `cuBLASLt` without a confusing `CUBLASLT_WORKSPACE_SIZE` that users would also need to consider
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145130
Approved by: https://github.com/ngimel
Issue:
https://github.com/pytorch/pytorch/issues/144888
Torchbench of timm lcnet_050 model fails on accuracy in case of `--frezing` `--inference` `--bfloat16`
`res_error==0.12`
If to turn off convolution inductor constant folding - `res_error==0.016`
`float16 error ~ 0.00669`
`float16 without conv folding ~ 0.0018`
convolution folding results in increase of error almost at one order of magnitude.
I think we should revisit and try to do something to improve the accuracy for conv folding.
E.g. For example doing conv folding at compilation time with float64?
At the moment I am adding counters to identify if convolution folding happened, and in case of bfloat16 and conv_folding - increase multiplier to the max level (10) to pass accuracy test.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145623
Approved by: https://github.com/eellison
Fixes https://github.com/pytorch/pytorch/issues/144772
The eager warmup runs causes the model to change state so that later when we export it, the model is different than when we export it directly out of box. For some reason exporting the model with the changed state causes issues but exporting the inital model is ok. This is the reason why the accuracy checks pass but the performance check fails when exporting.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145858
Approved by: https://github.com/desertfire
This is one of a series of PRs to update us to PEP585 (changing Dict -> dict, List -> list, etc). Most of the PRs were completely automated with RUFF as follows:
Since RUFF UP006 is considered an "unsafe" fix first we need to enable unsafe fixes:
```
--- a/tools/linter/adapters/ruff_linter.py
+++ b/tools/linter/adapters/ruff_linter.py
@@ -313,6 +313,7 @@
"ruff",
"check",
"--fix-only",
+ "--unsafe-fixes",
"--exit-zero",
*([f"--config={config}"] if config else []),
"--stdin-filename",
```
Then we need to tell RUFF to allow UP006 (as a final PR once all of these have landed this will be made permanent):
```
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -40,7 +40,7 @@
[tool.ruff]
-target-version = "py38"
+target-version = "py39"
line-length = 88
src = ["caffe2", "torch", "torchgen", "functorch", "test"]
@@ -87,7 +87,6 @@
"SIM116", # Disable Use a dictionary instead of consecutive `if` statements
"SIM117",
"SIM118",
- "UP006", # keep-runtime-typing
"UP007", # keep-runtime-typing
]
select = [
```
Finally running `lintrunner -a --take RUFF` will fix up the deprecated uses.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145101
Approved by: https://github.com/bobrenjc93
I discovered this issue when trying to search for the accuracy results on the database and couldn't find any. It turns out that the results is there on the JSON file, for example `"metric": {"name": "accuracy", "benchmark_values": ["pass_due_to_skip"]}`, but inserting them into the database fails because benchmark values is a list of strings here while the expectation is that it's a list of numbers.
ClickHouse doesn't support mix types atm. It has a Variant type https://clickhouse.com/docs/en/sql-reference/data-types/variant, but this isn't recommended by CH team themselves. So, the remaining option is to store this in the `extra_info` field. This field is a dictionary, so it can goes there.
### Testing
https://github.com/pytorch/pytorch/actions/runs/12421747715
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143611
Approved by: https://github.com/kit1980
Summary:
To facilitate PSS-2 upgrade, this uses `ndt.NDArray` instead of `nd.ndarray` in type annotations. In Numpy-1.19 (PSS-1) it's an alias to `nd.ndarray` -- a noop.
In Numpy-1.24, `ndt.NDArray` a proper generic type, and without this change uses of `nd.ndarray` generate this Pyre type error:
```counterexample
Invalid type parameters [24]: Generic type `np.ndarray` expects 2 type parameters.
```
Test Plan: Sandcastle plus visual inspection
Differential Revision: D62977370
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136288
Approved by: https://github.com/kit1980
Running Torchbench llama with dynamic size failed with
```
File "/localdisk/leslie/torch_inductor_community/pytorch/torch/fx/experimental/symbolic_shapes.py", line 4182, in produce_guards
raise ConstraintViolationError(
torch.fx.experimental.symbolic_shapes.ConstraintViolationError: Constraints violated (L['inputs'][0].size()[0])! For more information, run with TORCH_LOGS="+dynamic".
- Not all values of RelaxedUnspecConstraint(L['inputs'][0].size()[0]) are valid because L['inputs'][0].size()[0] was inferred to be a constant (32).
```
Skip this model for marking dynamic dim.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135960
Approved by: https://github.com/ezyang
Previously we were accomodating `torch._dynamo.mark_dynamic()` for export's dynamic shapes. Here we clean things up and ignore it, requiring users to specify an export input for `dynamic_shapes`.
Note: there's 4 decorators relevant to export, `mark_dynamic, maybe_mark_dynamic, mark_static, mark_unbacked`. User calls that involve export have only been `mark_dynamic()`, and we use `maybe_mark_dynamic` under the hood for `Dim.AUTO`, but we could start using others. One reason I decided to not warn and just silently ignore is these decorators cause the tensors to carry dynamic info, and it'll be hard to tell whether the markers are from export or user calls when re-exporting with the same inputs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135536
Approved by: https://github.com/avikchaudhuri
Summary:
By default, performance tests (speedup experiments) will run the baseline and test backend alternately.
However, this does not work for the torchao backend, which will change the model in-place, therefore the baseline run will also run with torchao backend since the model has already been quantized.
Add a new experiment "latency_experiment" to run performance tests non-alternately (first run baseline for a few iterations, then run the test backend).
other changes:
need to add torch.compiler.cudagraph_mark_step_begin() to avoid the
slowdown from # Unable to hit fast path of CUDAGraphs because of pending, uninvoked backwards
also updated the torchao APIs to the current versions
X-link: https://github.com/pytorch/benchmark/pull/2394
Test Plan:
python run_benchmark.py torchao --only AlbertForMaskedLM --quantization noquant --performance --inference --bfloat16 --inductor-compile-mode max-autotune python run_benchmark.py torchao --only BartForCausalLM --quantization noquant --performance --inference --bfloat16 --inductor-compile-mode max-autotune python run_benchmark.py torchao --only timm_efficientnet --quantization noquant --performance --inference --bfloat16 --inductor-compile-mode max-autotune
(should all be ~1.0
0.997x
1.006x
0.994x
Reviewed By: xuzhao9
Differential Revision: D60252821
Pulled By: HDCharles
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131935
Approved by: https://github.com/xuzhao9
Summary:
By default, performance tests (speedup experiments) will run the baseline and test backend alternately.
However, this does not work for the torchao backend, which will change the model in-place, therefore the baseline run will also run with torchao backend since the model has already been quantized.
Add a new experiment "latency_experiment" to run performance tests non-alternately (first run baseline for a few iterations, then run the test backend).
Test Plan:
```
buck2 run mode/opt //pytorch/benchmark:pt2 -- --only AlbertForMaskedLM --quantization noquant --performance --inference --bfloat16
```
```
buck2 run mode/opt //pytorch/benchmark:pt2 -- --only AlbertForMaskedLM --quantization autoquant --performance --inference --bfloat16 --inductor-compile-mode max-autotune
```
Differential Revision: D59332736
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130136
Approved by: https://github.com/jerryzh168
This PR batch the fix for a few accuracy failures issues during training by raising tolerance. I do that only for models that I think it fails not due to real issue.
## sebotnet33ts_256
The accuracy test for this model start to fail around June 05 [link](https://hud.pytorch.org/benchmark/timm_models/inductor_with_cudagraphs?dashboard=torchinductor&startTime=Sun%2C%2002%20Jun%202024%2007%3A19%3A38%20GMT&stopTime=Tue%2C%2002%20Jul%202024%2007%3A19%3A38%20GMT&granularity=day&mode=training&dtype=amp&lBranch=main&lCommit=04a0d856207d83c2031e4b9cb6825ba3e0092850&rBranch=main&rCommit=e62925930f6a62f6aeeb1fe1a661a9bd3352b53d&model=sebotnet33ts_256).
I can not repro locally, but from the log from the dashboard:
```
RMSE (res-fp64): 0.09441, (ref-fp64): 0.02971 and shape=torch.Size([1536]). res.dtype: torch.float32, multiplier: 3.000000, tol: 0.040000
```
raising the tolerance should fix it.
## DebertaForQuestionAnswering
This model fails accuracy test on the dashboard only in max-autotune mode. I can not repro locally by command:
```
TORCHINDUCTOR_MAX_AUTOTUNE=1 time python benchmarks/dynamo/huggingface.py --accuracy --no-translation-validation --training --amp --backend inductor --device cuda --only DebertaForQuestionAnswering
```
From error message on the dashboard:
```
RMSE (res-fp64): 0.01803, (ref-fp64): 0.00537 and shape=torch.Size([2]). res.dtype: torch.float32, multiplier: 3.000000, tol: 0.010000
```
0.02 tolerance should suppress this error.
## gluon_inception_v3
This model fail on the dashboard in max-autotune mode. I can not repro locally by command
```
TORCHINDUCTOR_MAX_AUTOTUNE=1 time python benchmarks/dynamo/timm_models.py --accuracy --training --amp --backend inductor --disable-cudagraphs --device cuda --only gluon_inception_v3
```
From error message on the dashboard
```
RMSE (res-fp64): 0.02798, (ref-fp64): 0.00730 and shape=torch.Size([384]). res.dtype: torch.float32, multiplier: 3.000000, tol: 0.010000
Accuracy failed for key name Mixed_7c.branch3x3dbl_3a.bn.running_var
```
raising tolerance should suppress this error.
# mobilenetv3_large_100
Fail in MA model. I can not repro locally by command
```
TORCHINDUCTOR_MAX_AUTOTUNE=1 time python benchmarks/dynamo/timm_models.py --accuracy --training --amp --backend inductor --disable-cudagraphs --device cuda --only
```
The error message on the dashboard is
```
RMSE (res-fp64): 0.29754, (ref-fp64): 0.05205 and shape=torch.Size([]). res.dtype: torch.float32, multiplier: 3.000000, tol: 0.040000
```
The tensor is so small that the noise can be high. I use larger multiplier for smaller tensor in torch._dynamo.utils.same.
# yolov3
Fail on dashboard with error
```
Error on the dashboard: RMSE (res-fp64): 0.01278, (ref-fp64): 0.00246 and shape=torch.Size([256]). res.dtype: torch.float32, multiplier: 3.000000, tol: 0.001000
```
Fix it by using a larger multiplier for smaller tensors and raising the tolereance.
# timm_efficientdet
Fail on the dashboard with error
```
E0623 18:37:43.638000 139924418725056 torch/_dynamo/utils.py:1468] RMSE (res-fp64): 0.00096, (ref-fp64): 0.00009 and shape=torch.Size([2]). res.dtype: torch.float32, multiplier: 3.000000, tol: 0.001000
```
But I can not repro locally with command
```
time python benchmarks/dynamo/torchbench.py --backend inductor --amp --performance --only timm_efficientdet --training
```
Raise the tolerance should fix.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129941
Approved by: https://github.com/jansel
ghstack dependencies: #129996
This PR batch the fix for a few accuracy failures issues during training by raising tolerance. I do that only for models that I think it fails not due to real issue.
## sebotnet33ts_256
The accuracy test for this model start to fail around June 05 [link](https://hud.pytorch.org/benchmark/timm_models/inductor_with_cudagraphs?dashboard=torchinductor&startTime=Sun%2C%2002%20Jun%202024%2007%3A19%3A38%20GMT&stopTime=Tue%2C%2002%20Jul%202024%2007%3A19%3A38%20GMT&granularity=day&mode=training&dtype=amp&lBranch=main&lCommit=04a0d856207d83c2031e4b9cb6825ba3e0092850&rBranch=main&rCommit=e62925930f6a62f6aeeb1fe1a661a9bd3352b53d&model=sebotnet33ts_256).
I can not repro locally, but from the log from the dashboard:
```
RMSE (res-fp64): 0.09441, (ref-fp64): 0.02971 and shape=torch.Size([1536]). res.dtype: torch.float32, multiplier: 3.000000, tol: 0.040000
```
raising the tolerance should fix it.
## DebertaForQuestionAnswering
This model fails accuracy test on the dashboard only in max-autotune mode. I can not repro locally by command:
```
TORCHINDUCTOR_MAX_AUTOTUNE=1 time python benchmarks/dynamo/huggingface.py --accuracy --no-translation-validation --training --amp --backend inductor --device cuda --only DebertaForQuestionAnswering
```
From error message on the dashboard:
```
RMSE (res-fp64): 0.01803, (ref-fp64): 0.00537 and shape=torch.Size([2]). res.dtype: torch.float32, multiplier: 3.000000, tol: 0.010000
```
0.02 tolerance should suppress this error.
## gluon_inception_v3
This model fail on the dashboard in max-autotune mode. I can not repro locally by command
```
TORCHINDUCTOR_MAX_AUTOTUNE=1 time python benchmarks/dynamo/timm_models.py --accuracy --training --amp --backend inductor --disable-cudagraphs --device cuda --only gluon_inception_v3
```
From error message on the dashboard
```
RMSE (res-fp64): 0.02798, (ref-fp64): 0.00730 and shape=torch.Size([384]). res.dtype: torch.float32, multiplier: 3.000000, tol: 0.010000
Accuracy failed for key name Mixed_7c.branch3x3dbl_3a.bn.running_var
```
raising tolerance should suppress this error.
# mobilenetv3_large_100
Fail in MA model. I can not repro locally by command
```
TORCHINDUCTOR_MAX_AUTOTUNE=1 time python benchmarks/dynamo/timm_models.py --accuracy --training --amp --backend inductor --disable-cudagraphs --device cuda --only
```
The error message on the dashboard is
```
RMSE (res-fp64): 0.29754, (ref-fp64): 0.05205 and shape=torch.Size([]). res.dtype: torch.float32, multiplier: 3.000000, tol: 0.040000
```
The tensor is so small that the noise can be high. I use larger multiplier for smaller tensor in torch._dynamo.utils.same.
# yolov3
Fail on dashboard with error
```
Error on the dashboard: RMSE (res-fp64): 0.01278, (ref-fp64): 0.00246 and shape=torch.Size([256]). res.dtype: torch.float32, multiplier: 3.000000, tol: 0.001000
```
Fix it by using a larger multiplier for smaller tensors and raising the tolereance.
# timm_efficientdet
Fail on the dashboard with error
```
E0623 18:37:43.638000 139924418725056 torch/_dynamo/utils.py:1468] RMSE (res-fp64): 0.00096, (ref-fp64): 0.00009 and shape=torch.Size([2]). res.dtype: torch.float32, multiplier: 3.000000, tol: 0.001000
```
But I can not repro locally with command
```
time python benchmarks/dynamo/torchbench.py --backend inductor --amp --performance --only timm_efficientdet --training
```
Raise the tolerance should fix.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129941
Approved by: https://github.com/jansel
ghstack dependencies: #129996
**Performance mode Issue**: When dynamo benchmarks performance warm-up failed, the result will be not written into csv file. But the accuracy will be written as `fail_to_run` even when dynamo pass failed. So the accuracy model number is not aligned with performance model number for each of their csv files.
- **Fix**: The warm-up failed models will be recorded into csv file shown as following:
**Accuracy mode issue**: `detectron2_fasterrcnn_r` models failed on accuracy mode, but was tested successfully on performance mode. The accuracy failure is same as PR ee557d8f61.
```
Dynamic Shape:
Traceback (most recent call last):
File "benchmarks/dynamo/torchbench.py", line 449, in <module>
torchbench_main()
File "benchmarks/dynamo/torchbench.py", line 445, in torchbench_main
main(TorchBenchmarkRunner(), original_dir)
File "/workspace/pytorch/benchmarks/dynamo/common.py", line 3650, in main
process_entry(0, runner, original_dir, args)
File "/workspace/pytorch/benchmarks/dynamo/common.py", line 3582, in process_entry
return run(runner, args, original_dir)
File "/workspace/pytorch/benchmarks/dynamo/common.py", line 4163, 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
```
- **Fix**: same as PR ee557d8f61, the batch_size will be skipped to set as 4 when testing dynamic shapes.
Dynamic shapes passrate improved from 89% -> **95%**
| Comp Item | Compiler | suite | before | After fix |
|-----------|----------|------------|------------|------------|
| Pass Rate | Inductor | torchbench | 89%, 73/82 | 95%, 79/83 |
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126764
Approved by: https://github.com/jansel
As reported in https://github.com/pytorch/pytorch/issues/119434, `hf_T5_generate` 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/torch/_dynamo/guards.py", line 705, in SHAPE_ENV
guards = output_graph.shape_env.produce_guards(
File "/home/jiayisun/pytorch/torch/fx/experimental/symbolic_shapes.py", line 3253, in produce_guards
raise ConstraintViolationError(
torch.fx.experimental.symbolic_shapes.ConstraintViolationError: Constraints violated (L['inputs_tensor'].size()[0])! For more information, run with TORCH_LOGS="+dynamic".
- Not all values of RelaxedUnspecConstraint(L['inputs_tensor'].size()[0]) are valid because L['inputs_tensor'].size()[0] was inferred to be a constant (4).
```
* Root Cause is
This error happens while creating guard for this [model script line](https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/modeling_t5.py#L561): `scores += position_bias_masked`
I run it with TORCH_LOGS="+dynamic" and got the key line : `I0305 00:21:00.849974 140376923287424 torch/fx/experimental/symbolic_shapes.py:3963] [6/0_1] eval Eq(s0, 4) [guard added] at miniconda3/envs/pt2/lib/python3.9/site-packages/transformers/models/t5/modeling_t5.py:561 in forward (_refs/__init__.py:403 in _broadcast_shapes)`
The reason for this error is that the batch dimension of `inputs_tensor` in the dynamic batch size test is marked as dynamic shape `s0`, so the batch dimension of `scores` generated by a series of operations with `inputs_tensor` is also `s0`. However, because the function of creating `attention_mask` is not in Dynamo but in python. The batch dimension of `attention_mask` is the real shape `4`, and the batch dimension of `position_bias_masked` generated by a series of operations with `attention_mask` is also the real shape `4`, not the dynamic shape `s0`. The current line of `scores += position_bias_masked` requires creating a guard and check whether the batch dimension of `scores` is always equal to the batch dimension of `position_bias_masked`, Eq(s0, 4), the error happens.
So the root cause of this error is that the function of creating `attention_mask` not in Dynamo but in python. The reason why the function of `attention_mask` not in Dynamo is that Dynamo has a graph break on this function (happened in the [model script line](https://github.com/huggingface/transformers/blob/main/src/transformers/generation/utils.py#L476): `is_pad_token_in_inputs = (pad_token_id is not None) and (pad_token_id in inputs)`) due to the following error:
`torch._dynamo.exc.Unsupported: Tensor.item`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121129
Approved by: https://github.com/leslie-fang-intel, https://github.com/ezyang
Fixes
> ERROR: expected to be in states [<TrainingState.FORWARD_BACKWARD: 2>] but current state is TrainingState.IDLE
Error that would occur when composing pt2 fsdp and cudagraphs. Cudagraphs caches output tensor impls in the fast path, so we were inadvertently accumulating multiple hooks on what should have been fresh allocations.
from code comment:
```
# this output represents a fresh allocated tensor.
# We return the same TensorImpl from run to run to avoid overhead.
# autograd.Function will reset the Autograd meta of output tensors
# as part of aot_autograd, but _backward_hooks are stored on tensors separately,
# so we need to manually reset hooks.
``
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126914
Approved by: https://github.com/awgu, https://github.com/xmfan
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127126
Approved by: https://github.com/kit1980
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127126
Approved by: https://github.com/kit1980
ghstack dependencies: #127122, #127123, #127124, #127125
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127122
Approved by: https://github.com/kit1980
# Motivation
## for `torch.amp.GradScaler`,
- `torch.cpu.amp.GradScaler(args...)` is completely equivalent to `torch. amp.GradScaler("cpu", args...)`.
- `torch.cuda.amp.GradScaler(args...)` is completely equivalent to `torch.amp.GradScaler("cuda", args...)`.
So, we intend to depreate them and **strongly recommend** developer to use `torch.amp.GradScaler`.
## for `custom_fwd` and `custom_bwd`,
this is a good solution to make the custom function run with or without effect even in an autocast-enabled region and can be shared by other backends, like CPU and XPU.
So we generalize it to be device-agnostic and put them int `torch/amp/autocast_mode.py` and re-expose to `torch.amp.custom_fwd` and `torch.amp.custom_bwd`. Meanwhile, we deprecate `torch.cuda.amp.custom_fwd` and `torch.cuda.amp.custom_bwd`.
# Additional Context
Add UT to cover the deprecated warning.
No need for more UTs to cover the functionality of `torch.amp.custom_f/bwd`, the existing UTs that previously covered the functionality of `torch.cuda.amp.custom_f/bwd` can cover them.
To facilitate the review, we separate these code changes to two PRs. The first PR cover `torch.amp.GradScaler`. The follow-up covers `custom_fwd` and `custom_bwd`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126527
Approved by: https://github.com/jgong5, https://github.com/gujinghui, https://github.com/janeyx99, https://github.com/EikanWang
Summary: It seems that most (all?) of our utilities for examining benchmark output expect single-line entries per benchmark. The way the --warm-start-latency flag is currently implemented, it means that we'll see two entries for every benchmark run (one for the warm-up run and one for the actual run). This PR adds a --disable-output flag that we can use for the first run to suppress populating the csv. This way, the existing utilities like `benchmarks/dynamo/check_accuracy.py` will function without any changes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125953
Approved by: https://github.com/desertfire
ghstack dependencies: #125917
Summary: This change introduces a new flagg to perform a "warm start" test from the benchmark harness. The idea is to test a model twice: first with a fresh inductor cache (i.e., a "cold start"), and then a second run in a fresh process with the cache available (i.e. a "warm start"). We can later add this mode to CI runs to collect compile times for warm start.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125353
Approved by: https://github.com/eellison, https://github.com/desertfire
# Motivation
As discussed in [#124479](https://github.com/pytorch/pytorch/pull/124479), `torch.amp.autocast` can NOT be completely equivalent to `torch.cuda.amp.autocast` and `torch.cpu.amp.autocast` since `torch.amp.autocast` has NOT the default `dtype` for CPU (`torch.bfloat16` by default) and CUDA (`torch.float16` by default) respectively. We would like `torch.amp.autocast` to be more generic to help the developer/customer write the device-agnostic code. Because there are not enough reasons to add device-specific autocast `torch.xxx.amp.autocast` for each device backend.
# Solution
When `None` is passed to `dtype`, we should use `torch.get_autocast_dtype` to get the related dtype for each backend. Meanwhile, `torch.get_autocast_dtype` is necessary to be supported in JIT path for BC.
# Additional Context
With this PR, `torch.amp.autocast(device_type='cuda')` is equivalent to `torch.cuda.amp.autocast`.
Add two new UTs to cover this change in eager and jit path respectively.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125103
Approved by: https://github.com/albanD, https://github.com/jgong5, https://github.com/gujinghui
A common complaint when working with data-dependent code in PyTorch is that it's hard to tell how far you are from the finish line: every time a GuardOnDataDependentSymNode error is hit, you have to somehow fix or workaround it to see the next one.
This PR adds a new mode `torch._functorch.config.fake_tensor_propagate_real_tensors` which modifies fake tensors to also propagate real tensors. This means that when we try to guard on a data-dependent SymNode, we can actually produce a real result. We also produce a warning which you should consult to figure out what the crux points are.
I ran this on vision_maskrcnn. In the baseline (without this mode), the model has 27 graph breaks, resulting in 40 graphs. With this mode on, the model has only 11 graph breaks, resulting in 15 graphs (the remaining graph breaks are due to missing functionality for item() on float tensor and some other Dynamo missing features.) You get a list of things that would have errored like this:
```
WARNING:torch.fx.experimental.symbolic_shapes:propagate_real_tensors evaluate_expr(Max(1, u1) < 2) -> True
WARNING:torch.fx.experimental.symbolic_shapes:propagate_real_tensors evaluate_expr(Eq(Max(1, u1), 1)) -> True
WARNING:torch.fx.experimental.symbolic_shapes:propagate_real_tensors evaluate_expr(Eq(Max(1, u1), 1)) -> True
WARNING:torch.fx.experimental.symbolic_shapes:propagate_real_tensors evaluate_expr(Ne(Max(1, u1), 1)) -> False
WARNING:torch.fx.experimental.symbolic_shapes:propagate_real_tensors evaluate_expr(Max(1, u0) < 2) -> True
WARNING:torch.fx.experimental.symbolic_shapes:propagate_real_tensors evaluate_expr(Eq(Max(1, u0), 1)) -> True
WARNING:torch.fx.experimental.symbolic_shapes:propagate_real_tensors evaluate_expr(Eq(Max(1, u0), 1)) -> True
WARNING:torch.fx.experimental.symbolic_shapes:propagate_real_tensors evaluate_expr(Ne(Max(1, u0), 1)) -> False
WARNING:torch.fx.experimental.symbolic_shapes:propagate_real_tensors evaluate_expr(Max(1, u1) < 2) -> True
WARNING:torch.fx.experimental.symbolic_shapes:propagate_real_tensors evaluate_expr(Eq(Max(1, u1), 1)) -> True
WARNING:torch.fx.experimental.symbolic_shapes:propagate_real_tensors evaluate_expr(Eq(Max(1, u1), 1)) -> True
WARNING:torch.fx.experimental.symbolic_shapes:propagate_real_tensors evaluate_expr(Ne(Max(1, u1), 1)) -> False
WARNING:torch.fx.experimental.symbolic_shapes:propagate_real_tensors evaluate_expr(Max(1, u0) < 2) -> True
WARNING:torch.fx.experimental.symbolic_shapes:propagate_real_tensors evaluate_expr(Eq(Max(1, u0), 1)) -> True
WARNING:torch.fx.experimental.symbolic_shapes:propagate_real_tensors evaluate_expr(Eq(Max(1, u0), 1)) -> True
WARNING:torch.fx.experimental.symbolic_shapes:propagate_real_tensors evaluate_expr(Ne(Max(1, u0), 1)) -> False
WARNING:torch.fx.experimental.symbolic_shapes:propagate_real_tensors evaluate_expr(Max(1, u1) < 2) -> False
WARNING:torch.fx.experimental.symbolic_shapes:propagate_real_tensors evaluate_expr(Eq(Max(1, u1), 1)) -> False
WARNING:torch.fx.experimental.symbolic_shapes:propagate_real_tensors evaluate_expr(Ne(Max(1, u1), 1)) -> True
WARNING:torch.fx.experimental.symbolic_shapes:propagate_real_tensors evaluate_expr(Max(1, u0) < 2) -> False
WARNING:torch.fx.experimental.symbolic_shapes:propagate_real_tensors evaluate_expr(Eq(Max(1, u0), 1)) -> False
```
Potential later follow ups:
* Improve the warning messages (in particular, should provide user frames)
* GC real tensors when they are no longer needed by tracing. Right now, this will use A LOT of memory, equal to as if your GC was broken and every intermediate tensor was kept live
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125115
Approved by: https://github.com/IvanKobzarev
Biggest movement is 4% HF inference, 9% TIMM inference. Note, this is max-autotune mode so we are more tolerant of compilation increases. We could improve compilation time by limiting:
```
# Take how many of the top triton kernels to benchmark epilogue
max_epilogue_benchmarked_choices = 3
```
There is a hf_Whisper failure which you can repro on main without this stack with `TORCHINDUCTOR_MAX_AUTOTUNE_GEMM_BACKENDS=TRITON TORCHINDUCTOR_MAX_AUTOTUNE=1 python benchmarks/dynamo/torchbench.py --backend inductor --amp --accuracy --training --only hf_Whisper`. When you turn off epilogue fusion, it fixes the accuracy. I bisected the failure to an epilogue, however when you compare the results of that epilogue with the corresponding separate kernels the results of the output are equivalent.
Inference:
<img width="1686" alt="image" src="https://github.com/pytorch/pytorch/assets/11477974/0b240080-cd33-4c08-89d3-583103b1fb0c">
Training:
<img width="1329" alt="Screenshot 2024-04-16 at 6 16 30 PM" src="https://github.com/pytorch/pytorch/assets/11477974/db0afcc9-7288-4c27-84ce-4fc1a5690788">
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124031
Approved by: https://github.com/Chillee, https://github.com/shunting314
ghstack dependencies: #124030, #122642, #123229, #122825
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
