When minifying, the after-aot minifier ignores non-floating values by
default but does check them when running the the initial graph dump step.
This means we may capture a graph that doesn't fail the tester and doesn't have
any meaningful divergence.
For example, the derivative of `elu(x)` depends on `x > 0` so this value is
saved for backwards and so becomes a graph output. However, the difference
between `FLT_MIN` and `0` in `x` is now enough to trigger an accuracy failure.
I fix this by adding a config variable and environment variable to ignore these
non floating point values.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123006
Approved by: https://github.com/ezyang
ghstack dependencies: #123005
Currently when there is a print/warning in the graph, dynamo graph breaks causing export to fail. However export would like to just skip over these print/warning calls: https://github.com/pytorch/pytorch/issues/113792.
Additionally there's a torch.compile feature request to "reorder prints" so that instead of graph breaking when hitting prints/logging, we can skip over these prints to create larger compiled graphs, and then print the results out after those compiled graphs: https://github.com/pytorch/pytorch/issues/93739. This PR also adds the `reorderable_logging_functions` config for users to register logging functions to be reordered (like `print` or a custom logging function). Printout of the bytecode after reordering the prints looks like the following: P914736600
There are some limitations to the printing right now:
* You can only register logging functions, not methods
* Inputs to the logging functions can only be tensors, constants, and format strings
* Inputs to the logging functions which will later be mutated in-place will not be printed correctly
TODO: Add the following tests
* print function with argument of nested data structure;
* print function with argument of nested data structure being updated inside of compile region (this would test if we handle side effect correctly);
* custom defined logging functions with nn.Module or nn.Module attribute arguments;
* custom defined logging functions with submodule input/output as arguments (we need to handle the mapping and fused-out value);
* custom defined logging functions with tensor argument and mutation inside of the function (TBD: this may increase memory usage);
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116106
Approved by: https://github.com/yanboliang
Currently when there is a print/warning in the graph, dynamo graph breaks causing export to fail. However export would like to just skip over these print/warning calls: https://github.com/pytorch/pytorch/issues/113792.
Additionally there's a torch.compile feature request to "reorder prints" so that instead of graph breaking when hitting prints/logging, we can skip over these prints to create larger compiled graphs, and then print the results out after those compiled graphs: https://github.com/pytorch/pytorch/issues/93739. This PR also adds the `reorderable_logging_functions` config for users to register logging functions to be reordered (like `print` or a custom logging function). Printout of the bytecode after reordering the prints looks like the following: P914736600
There are some limitations to the printing right now:
* You can only register logging functions, not methods
* Inputs to the logging functions can only be tensors, constants, and format strings
* Inputs to the logging functions which will later be mutated in-place will not be printed correctly
TODO: Add the following tests
* print function with argument of nested data structure;
* print function with argument of nested data structure being updated inside of compile region (this would test if we handle side effect correctly);
* custom defined logging functions with nn.Module or nn.Module attribute arguments;
* custom defined logging functions with submodule input/output as arguments (we need to handle the mapping and fused-out value);
* custom defined logging functions with tensor argument and mutation inside of the function (TBD: this may increase memory usage);
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116106
Approved by: https://github.com/yanboliang
Tacotron2 causes massive loop unrolling resulting in very large graphs (26k nodes) which was causing inductor (and tracing itself) to choke.
The unrolling size is controlled by the environment variable TORCHDYNAMO_MAX_LOOP_UNROLL_NODES which defaults to the arbitrary value 5000.
This updates the tacotron2 timings as follows:
eager timing: 3m:23s -> 35s
aot_eager timing: 4m:12s -> 39s
inductor timing: 22m:24s ->1m
For reference the big loop in tacotron2 was this one (model.py[405]):
```
while len(mel_outputs) < decoder_inputs.size(0) - 1:
decoder_input = decoder_inputs[len(mel_outputs)]
mel_output, gate_output, attention_weights = self.decode(decoder_input)
mel_outputs += [mel_output.squeeze(1)]
gate_outputs += [gate_output.squeeze(1)]
alignments += [attention_weights]
```
which gets unrolled and inlined adding about 36 nodes to the graph per iteration.
Fixes#98467
Relates to #102839 which hopefully will result in a better fix.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120023
Approved by: https://github.com/yanboliang
**Summary**
The reducer of `DistributedDataParallel` is implemented with C++ and it is not easy to trace the allreduce launched in the reducer. This PR modifies `DistributedDataParallel` to launch one allreduce per gradient when `compiled_autograd` is enabled. The changes allow us to use `compiled_autograd` to trace the allreduce and later be optimized (fused) in the Inductor.
**Key Logic**
1. If `ddp_python_hook` is True, we assume `compiled_autograd` is used. `DistributedDataParallel` registers `compiled_accum_grad_hook` for all parameters.
2. In the first forward() call, if `DistributedDataParallel` is not compiled, all `compiled_accum_grad_hook` are deregistered. If `DistributedDataParallel` is compiled, all `compiled_accum_grad_hook` will be compiled by `compiled_autograd`.
3. `compiled_accum_grad_hook` launches an allreduce to reduce the gradient of the parameter.
**Bucketing**
The compiled backward is slow because there is no bucketing for the allreduces. We rely on Inductor to bucket the allreduces.
The bucketing is done in a separate PR.
Differential Revision: [D49428482](https://our.internmc.facebook.com/intern/diff/D49428482/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110662
Approved by: https://github.com/wconstab
I feel it's easier to open a new PR rather than iterating on the previous PR (https://github.com/pytorch/pytorch/pull/105257 ) since this is more like a rewrite.
In this PR, instead of changing GraphModule directly which can easily causes BC issue, I create a LazyGraphModule class as Zachary & Jason suggested in comments from the previous PR.
The difference between LazyGraphModule and GraphModule is mainly about how re-compile for the graph module happens. In GraphModule the recompilation happens 'eagerly': constructing a GraphModule will cause the recompilation. While in LazyGraphModule, we just mark the module as needing recompilation. The real recompilation only happens when absolutely required (e.g. call forward method, access the code property etc.). In a lot of cases in torch.compile, the real recompilation eventually is not triggered at all. This can save a few seconds of compilation time.
By default, GraphModule rather than LazyGraphModule is used. `use_lazy_graph_module(True)` context manager can be used to pick LazyGraphModule instead. This has been applied to the torch.compile stack.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117911
Approved by: https://github.com/jansel
Due to not all tests in the Dynamo shard actually running in CI, we've
started to bitrot on this implementation. Since our plan is to trace
into the functorch implementations instead of construct a HOP
(which is what capture_func_transforms=True does), let's turn off this
config by default.
Test Plan:
- Tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115267
Approved by: https://github.com/voznesenskym, https://github.com/guilhermeleobas
`install_config_module` makes a regular module into a ConfigModule with
extra methods defined on it. mypy thinks those extra methods (or module
functions) are undefined since it cannot analyze something so
dynamic. As a workaround, I've created a fake module that defines these
extra functions, which I import into the config modules during type
checking.
As part of this change, I've also added more types to config_utils.py
and enabled typechecking for torch/_dynamo/config.py.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112130
Approved by: https://github.com/jansel
Attempt number 2 at https://github.com/pytorch/pytorch/issues/108950.
Improves debugging for guard failures/recompilations by:
- only running guard fail reason generation during recompilation, instead of when a guard fails during dynamo cache lookup (so generating guard failure reasons is not on the critical path)
- ~~always reporting all guard failures~~ Reports the first-failing guard failure for each cache entry.
We don't expect a performance hit since the guard fail reasons are only generated at recompile time rather than runtime. Perf benchmark to check this (https://hud.pytorch.org/benchmark/torchbench/inductor_with_cudagraphs?startTime=Fri,%2027%20Oct%202023%2017:42:43%20GMT&stopTime=Fri,%2003%20Nov%202023%2017:42:43%20GMT&granularity=hour&mode=training&dtype=amp&lBranch=gh/williamwen42/62/head&lCommit=f4724f5ffc6d17ceae513a42fc18627be7b85482&rBranch=main&rCommit=29f3d392bf230072e3bffae37b078e770cae1956). We may also need to verify this on benchmarks where guard fails are common.
Sample script:
```python
import torch
def generate_data(b):
return (
torch.randn(b, 3, 32, 32).to(torch.float32).cuda(),
torch.randint(1000, (b,)).cuda(),
)
from torchvision.models import resnet18
def init_model():
return resnet18().to(torch.float32).cuda()
model = init_model()
model_opt = torch.compile(model, dynamic=False)
for b in range(16, 32):
data = generate_data(b)
model_opt(data[0])
```
Sample logs:
```bash
(/data/users/williamwen/py310-env) [williamwen@devgpu020.odn1 /data/users/williamwen/pytorch (wwen/log-all-guards)]$ python playground5.py
/data/users/williamwen/pytorch/torch/_inductor/compile_fx.py:141: UserWarning: TensorFloat32 tensor cores for float32 matrix multiplication available but not enabled. Consider setting `torch.set_float32_matmul_precision('high')` for better performance.
warnings.warn(
[2023-11-06 14:50:47,605] torch._dynamo.convert_frame: [WARNING] torch._dynamo hit config.cache_size_limit (8)
[2023-11-06 14:50:47,605] torch._dynamo.convert_frame: [WARNING] function: 'forward' (/data/users/williamwen/torchvision/torchvision/models/resnet.py:284)
[2023-11-06 14:50:47,605] torch._dynamo.convert_frame: [WARNING] last reason: tensor 'L['x']' size mismatch at index 0. expected 16, actual 24
[2023-11-06 14:50:47,605] torch._dynamo.convert_frame: [WARNING] To log all recompilation reasons, use TORCH_LOGS="recompiles".
[2023-11-06 14:50:47,605] torch._dynamo.convert_frame: [WARNING] To diagnose recompilation issues, see https://pytorch.org/docs/master/compile/troubleshooting.html.
(/data/users/williamwen/py310-env) [williamwen@devgpu020.odn1 /data/users/williamwen/pytorch (wwen/log-all-guards)]$ TORCH_LOGS="recompiles" python playground5.py
/data/users/williamwen/pytorch/torch/_inductor/compile_fx.py:141: UserWarning: TensorFloat32 tensor cores for float32 matrix multiplication available but not enabled. Consider setting `torch.set_float32_matmul_precision('high')` for better performance.
warnings.warn(
[2023-11-06 14:53:31,591] torch._dynamo.guards.__recompiles: [DEBUG] Recompiling function forward in /data/users/williamwen/torchvision/torchvision/models/resnet.py:284
[2023-11-06 14:53:31,591] torch._dynamo.guards.__recompiles: [DEBUG] triggered by the following guard failure(s):
[2023-11-06 14:53:31,591] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 16, actual 17
[2023-11-06 14:53:41,333] torch._dynamo.guards.__recompiles: [DEBUG] Recompiling function forward in /data/users/williamwen/torchvision/torchvision/models/resnet.py:284
[2023-11-06 14:53:41,333] torch._dynamo.guards.__recompiles: [DEBUG] triggered by the following guard failure(s):
[2023-11-06 14:53:41,333] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 17, actual 18
[2023-11-06 14:53:41,333] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 16, actual 18
[2023-11-06 14:53:50,463] torch._dynamo.guards.__recompiles: [DEBUG] Recompiling function forward in /data/users/williamwen/torchvision/torchvision/models/resnet.py:284
[2023-11-06 14:53:50,463] torch._dynamo.guards.__recompiles: [DEBUG] triggered by the following guard failure(s):
[2023-11-06 14:53:50,463] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 18, actual 19
[2023-11-06 14:53:50,463] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 17, actual 19
[2023-11-06 14:53:50,463] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 16, actual 19
[2023-11-06 14:53:59,848] torch._dynamo.guards.__recompiles: [DEBUG] Recompiling function forward in /data/users/williamwen/torchvision/torchvision/models/resnet.py:284
[2023-11-06 14:53:59,848] torch._dynamo.guards.__recompiles: [DEBUG] triggered by the following guard failure(s):
[2023-11-06 14:53:59,848] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 19, actual 20
[2023-11-06 14:53:59,848] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 18, actual 20
[2023-11-06 14:53:59,848] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 17, actual 20
[2023-11-06 14:53:59,848] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 16, actual 20
[2023-11-06 14:54:08,549] torch._dynamo.guards.__recompiles: [DEBUG] Recompiling function forward in /data/users/williamwen/torchvision/torchvision/models/resnet.py:284
[2023-11-06 14:54:08,549] torch._dynamo.guards.__recompiles: [DEBUG] triggered by the following guard failure(s):
[2023-11-06 14:54:08,549] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 20, actual 21
[2023-11-06 14:54:08,549] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 19, actual 21
[2023-11-06 14:54:08,549] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 18, actual 21
[2023-11-06 14:54:08,549] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 17, actual 21
[2023-11-06 14:54:08,549] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 16, actual 21
[2023-11-06 14:54:17,795] torch._dynamo.guards.__recompiles: [DEBUG] Recompiling function forward in /data/users/williamwen/torchvision/torchvision/models/resnet.py:284
[2023-11-06 14:54:17,795] torch._dynamo.guards.__recompiles: [DEBUG] triggered by the following guard failure(s):
[2023-11-06 14:54:17,795] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 21, actual 22
[2023-11-06 14:54:17,795] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 20, actual 22
[2023-11-06 14:54:17,795] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 19, actual 22
[2023-11-06 14:54:17,795] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 18, actual 22
[2023-11-06 14:54:17,795] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 17, actual 22
[2023-11-06 14:54:17,795] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 16, actual 22
[2023-11-06 14:54:27,430] torch._dynamo.guards.__recompiles: [DEBUG] Recompiling function forward in /data/users/williamwen/torchvision/torchvision/models/resnet.py:284
[2023-11-06 14:54:27,430] torch._dynamo.guards.__recompiles: [DEBUG] triggered by the following guard failure(s):
[2023-11-06 14:54:27,430] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 22, actual 23
[2023-11-06 14:54:27,430] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 21, actual 23
[2023-11-06 14:54:27,430] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 20, actual 23
[2023-11-06 14:54:27,430] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 19, actual 23
[2023-11-06 14:54:27,430] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 18, actual 23
[2023-11-06 14:54:27,430] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 17, actual 23
[2023-11-06 14:54:27,430] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 16, actual 23
[2023-11-06 14:54:36,744] torch._dynamo.guards.__recompiles: [DEBUG] Recompiling function forward in /data/users/williamwen/torchvision/torchvision/models/resnet.py:284
[2023-11-06 14:54:36,744] torch._dynamo.guards.__recompiles: [DEBUG] triggered by the following guard failure(s):
[2023-11-06 14:54:36,744] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 23, actual 24
[2023-11-06 14:54:36,744] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 22, actual 24
[2023-11-06 14:54:36,744] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 21, actual 24
[2023-11-06 14:54:36,744] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 20, actual 24
[2023-11-06 14:54:36,744] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 19, actual 24
[2023-11-06 14:54:36,744] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 18, actual 24
[2023-11-06 14:54:36,744] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 17, actual 24
[2023-11-06 14:54:36,744] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 16, actual 24
[2023-11-06 14:54:36,744] torch._dynamo.convert_frame: [WARNING] torch._dynamo hit config.cache_size_limit (8)
[2023-11-06 14:54:36,744] torch._dynamo.convert_frame: [WARNING] function: 'forward' (/data/users/williamwen/torchvision/torchvision/models/resnet.py:284)
[2023-11-06 14:54:36,744] torch._dynamo.convert_frame: [WARNING] last reason: tensor 'L['x']' size mismatch at index 0. expected 16, actual 24
[2023-11-06 14:54:36,744] torch._dynamo.convert_frame: [WARNING] To log all recompilation reasons, use TORCH_LOGS="recompiles".
[2023-11-06 14:54:36,744] torch._dynamo.convert_frame: [WARNING] To diagnose recompilation issues, see https://pytorch.org/docs/master/compile/troubleshooting.html.
[2023-11-06 14:54:45,922] torch._dynamo.guards.__recompiles: [DEBUG] Recompiling function _forward_impl in /data/users/williamwen/torchvision/torchvision/models/resnet.py:266
[2023-11-06 14:54:45,922] torch._dynamo.guards.__recompiles: [DEBUG] triggered by the following guard failure(s):
[2023-11-06 14:54:45,922] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 24, actual 25
[2023-11-06 14:54:54,691] torch._dynamo.guards.__recompiles: [DEBUG] Recompiling function _forward_impl in /data/users/williamwen/torchvision/torchvision/models/resnet.py:266
[2023-11-06 14:54:54,691] torch._dynamo.guards.__recompiles: [DEBUG] triggered by the following guard failure(s):
[2023-11-06 14:54:54,691] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 25, actual 26
[2023-11-06 14:54:54,691] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 24, actual 26
[2023-11-06 14:55:03,591] torch._dynamo.guards.__recompiles: [DEBUG] Recompiling function _forward_impl in /data/users/williamwen/torchvision/torchvision/models/resnet.py:266
[2023-11-06 14:55:03,591] torch._dynamo.guards.__recompiles: [DEBUG] triggered by the following guard failure(s):
[2023-11-06 14:55:03,591] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 26, actual 27
[2023-11-06 14:55:03,591] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 25, actual 27
[2023-11-06 14:55:03,591] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 24, actual 27
[2023-11-06 14:55:12,384] torch._dynamo.guards.__recompiles: [DEBUG] Recompiling function _forward_impl in /data/users/williamwen/torchvision/torchvision/models/resnet.py:266
[2023-11-06 14:55:12,384] torch._dynamo.guards.__recompiles: [DEBUG] triggered by the following guard failure(s):
[2023-11-06 14:55:12,384] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 27, actual 28
[2023-11-06 14:55:12,384] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 26, actual 28
[2023-11-06 14:55:12,384] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 25, actual 28
[2023-11-06 14:55:12,384] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 24, actual 28
[2023-11-06 14:55:21,442] torch._dynamo.guards.__recompiles: [DEBUG] Recompiling function _forward_impl in /data/users/williamwen/torchvision/torchvision/models/resnet.py:266
[2023-11-06 14:55:21,442] torch._dynamo.guards.__recompiles: [DEBUG] triggered by the following guard failure(s):
[2023-11-06 14:55:21,442] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 28, actual 29
[2023-11-06 14:55:21,442] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 27, actual 29
[2023-11-06 14:55:21,442] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 26, actual 29
[2023-11-06 14:55:21,442] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 25, actual 29
[2023-11-06 14:55:21,442] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 24, actual 29
[2023-11-06 14:55:30,315] torch._dynamo.guards.__recompiles: [DEBUG] Recompiling function _forward_impl in /data/users/williamwen/torchvision/torchvision/models/resnet.py:266
[2023-11-06 14:55:30,315] torch._dynamo.guards.__recompiles: [DEBUG] triggered by the following guard failure(s):
[2023-11-06 14:55:30,315] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 29, actual 30
[2023-11-06 14:55:30,315] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 28, actual 30
[2023-11-06 14:55:30,315] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 27, actual 30
[2023-11-06 14:55:30,315] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 26, actual 30
[2023-11-06 14:55:30,315] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 25, actual 30
[2023-11-06 14:55:30,315] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 24, actual 30
[2023-11-06 14:55:39,839] torch._dynamo.guards.__recompiles: [DEBUG] Recompiling function _forward_impl in /data/users/williamwen/torchvision/torchvision/models/resnet.py:266
[2023-11-06 14:55:39,839] torch._dynamo.guards.__recompiles: [DEBUG] triggered by the following guard failure(s):
[2023-11-06 14:55:39,839] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 30, actual 31
[2023-11-06 14:55:39,839] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 29, actual 31
[2023-11-06 14:55:39,839] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 28, actual 31
[2023-11-06 14:55:39,839] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 27, actual 31
[2023-11-06 14:55:39,839] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 26, actual 31
[2023-11-06 14:55:39,839] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 25, actual 31
[2023-11-06 14:55:39,839] torch._dynamo.guards.__recompiles: [DEBUG] - tensor 'L['x']' size mismatch at index 0. expected 24, actual 31
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110325
Approved by: https://github.com/ezyang, https://github.com/jon-chuang
This PR:
- adds the pt2 compliant tag. This tag specifies that the operator works
with the PT2 compilation APIs. A custom op author should test their
ops with opcheck if they choose to add this tag.
- adds a config for Dynamo to allow only pt2 compliant ops into the
graph and graph break on all other OpOverload/OpOverloadPacket.
Bikeshedding help wanted on the name of the tag. It should be easily
grep-able so we can set up rules for it.
Test Plan:
- new tests
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111933
Approved by: https://github.com/ezyang
ghstack dependencies: #111912, #111915, #111948
