This PR adds `@comptime`, a decorator that causes a given function to be executed at compile time when Dynamo is symbolically evaluating their program. To query the Dynamo state, we offer a public ComptimeContext API which provides a limited set of APIs for querying Dynamo's internal state. We intend for users to use this API and plan to keep it stable. Here are some things you can do with it:
* You want to breakpoint Dynamo compilation when it starts processing a particular line of user code: give comptime a function that calls breakpoint
* You want to manually induce a graph break for testing purposes; give comptime a function that calls unimplemented
* You want to perform a debug print, but you don't want to induce a graph break; give comptime a function that prints.
* You can print what the symbolic locals at a given point in time are.
* You can print out the partial graph the Dynamo had traced at this point.
* (My original motivating use case.) You want to add some facts to the shape env, so that a guard evaluation on an unbacked SymInt doesn't error with data-dependent. Even if you don't know what the final user API for this should be, with comptime you can hack out something quick and dirty. (This is not in this PR, as it depends on some other in flight PRs.)
Check out the tests to see examples of comptime in action.
In short, comptime is a very powerful debugging tool that lets you drop into Dynamo from user code, without having to manually jerry-rig pdb inside Dynamo to trigger after N calls.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90983
Approved by: https://github.com/jansel
Tracing `torch.backends.cudnn.is_acceptable(Tensor) -> bool:` fails with:
```
...
File "/scratch/dberard/dynamo38/pytorch/torch/_dynamo/variables/functions.py", line 196, in call_function
return super(UserFunctionVariable, self).call_function(tx, args, kwargs)
File "/scratch/dberard/dynamo38/pytorch/torch/_dynamo/variables/functions.py", line 67, in call_function
return tx.inline_user_function_return(
File "/scratch/dberard/dynamo38/pytorch/torch/_dynamo/symbolic_convert.py", line 426, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/scratch/dberard/dynamo38/pytorch/torch/_dynamo/symbolic_convert.py", line 1698, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/scratch/dberard/dynamo38/pytorch/torch/_dynamo/symbolic_convert.py", line 1752, in inline_call_
tracer.run()
File "/scratch/dberard/dynamo38/pytorch/torch/_dynamo/symbolic_convert.py", line 485, in run
and self.step()
File "/scratch/dberard/dynamo38/pytorch/torch/_dynamo/symbolic_convert.py", line 455, in step
getattr(self, inst.opname)(inst)
File "/scratch/dberard/dynamo38/pytorch/torch/_dynamo/symbolic_convert.py", line 281, in wrapper
return inner_fn(self, inst)
File "/scratch/dberard/dynamo38/pytorch/torch/_dynamo/symbolic_convert.py", line 912, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/scratch/dberard/dynamo38/pytorch/torch/_dynamo/symbolic_convert.py", line 389, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/scratch/dberard/dynamo38/pytorch/torch/_dynamo/variables/torch.py", line 431, in call_function
tensor_variable = wrap_fx_proxy(
File "/scratch/dberard/dynamo38/pytorch/torch/_dynamo/variables/builder.py", line 662, in wrap_fx_proxy
return wrap_fx_proxy_cls(
File "/scratch/dberard/dynamo38/pytorch/torch/_dynamo/variables/builder.py", line 820, in wrap_fx_proxy_cls
raise AssertionError(
AssertionError: torch.* op returned non-Tensor bool call_function <function is_acceptable at 0x7f00deefb790>
```
So instead, evaluate `is_acceptable()` and convert the result to a constant. The result of `is_acceptable(tensor) -> bool` depends on:
* dtype/device of the input tensor (this should already be guarded)
* properties of the build & whether cudnn is available
* some global state that gets initialized during the first call to `torch.backends.cudnn._init()` (this is NOT guarded in this PR)
Note: this fixes tts_angular with FSDP. This was an issue with FSDP because FSDP modules are interpreted as UnspecializedNNModules, and UnspecializedNNModules try to inline calls. In comparison, NNModules (e.g. when the tts_angular model is not wrapped in FSDP) do not inline calls and instead evaluate subsequent calls. In subsequent calls, cudnn.is_acceptable would be skipped by eval_frame.py:catch_errors because it is not in an allowlist.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90323
Approved by: https://github.com/jansel
This PR introduces a new function we can pass to torch._dynamo.optimize - guard_failure_fn. Usage is in the PR, and the one stacked on top of it, but the gist of it is that it emits failed guard reason strings alongside code. This is useful for tests and debugging, as it gives far finer grained assertions and control than the compile counter alone.
This is a resubmit of https://github.com/pytorch/pytorch/pull/90129
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90371
Approved by: https://github.com/ezyang
The current cond implementation is silently incorrect when
there are outstanding side effects, since the locally tracked
side effects are lost when the recursive export call is made.
At least we raise an assert now.
I'm working on a refactor of cond which should be able to sidestep
this problem. Maybe.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: [D41746973](https://our.internmc.facebook.com/intern/diff/D41746973)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90208
Approved by: https://github.com/voznesenskym
This is a group of bug fixes for [7k github models](https://github.com/pytorch/torchdynamo/issues/1884), it would fix 30+ model tests.
* Support ```tensor.type()```.
* Support ```tensor.get_device()```.
* Support ```torch.nn.functional._Reduction.get_enum```.
* Support ```torch._utils._get_device_index()```.
* Fallback ```tensor.data_ptr()```.
* ```FakeTensor``` always returns 0
* For no fake tensor propagation, we ```clone``` the input tensor, which makes no sense to track the original ```data_ptr```. And I don't think this is a very popular API.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89486
Approved by: https://github.com/jansel
Fix bugs in [7k github models](https://github.com/pytorch/torchdynamo/issues/1884).
* Legacy code still use ```tensor.data```, I think we can use ```tensor.detach``` to rewrite, not sure if there is anything I didn't anticipate.
* Support ```tensor.layout```.
The root cause of these issues are: dynamo wraps unimplemented ```tensor.x``` call into ```GetAttrVariable(TensorVariable, x)```, but this op was not inserted into FX graph. Hence, during the fake tensor propagation, it throws ```KeyError: 'example_value` ```.
For these two popular attributes, Dynamo should support them anyway. However, if dynamo should support ___all___ ```tensor.x``` call and not fallback to ```GetAttrVariable```, I think it's debatable.
If I turn off fake tensor propagation, it works well even not including this fix. So I'm curious if we should improve the fake propagation to cover similar cases. cc @mlazos @soumith @voznesenskym @penguinwu @anijain2305 @EikanWang @jgong5 @Guobing-Chen @chunyuan-w @XiaobingSuper @zhuhaozhe @blzheng @Xia-Weiwen @wenzhe-nrv @jiayisunx @desertfire @jansel @eellison
```
Traceback (most recent call last):
File "/scratch/ybliang/work/repos/pytorch/torch/_dynamo/convert_frame.py", line 404, in _compile
out_code = transform_code_object(code, transform)
File "/scratch/ybliang/work/repos/pytorch/torch/_dynamo/bytecode_transformation.py", line 341, in transform_code_object
transformations(instructions, code_options)
File "/scratch/ybliang/work/repos/pytorch/torch/_dynamo/convert_frame.py", line 392, in transform
tracer.run()
File "/scratch/ybliang/work/repos/pytorch/torch/_dynamo/symbolic_convert.py", line 1523, in run
super().run()
File "/scratch/ybliang/work/repos/pytorch/torch/_dynamo/symbolic_convert.py", line 389, in run
and self.step()
File "/scratch/ybliang/work/repos/pytorch/torch/_dynamo/symbolic_convert.py", line 359, in step
getattr(self, inst.opname)(inst)
File "/scratch/ybliang/work/repos/pytorch/torch/_dynamo/symbolic_convert.py", line 193, in wrapper
return inner_fn(self, inst)
File "/scratch/ybliang/work/repos/pytorch/torch/_dynamo/symbolic_convert.py", line 865, in CALL_FUNCTION_KW
self.call_function(fn, args, kwargs)
File "/scratch/ybliang/work/repos/pytorch/torch/_dynamo/symbolic_convert.py", line 301, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/scratch/ybliang/work/repos/pytorch/torch/_dynamo/variables/torch.py", line 407, in call_function
tensor_variable = wrap_fx_proxy(
File "/scratch/ybliang/work/repos/pytorch/torch/_dynamo/variables/builder.py", line 636, in wrap_fx_proxy
return wrap_fx_proxy_cls(
File "/scratch/ybliang/work/repos/pytorch/torch/_dynamo/variables/builder.py", line 676, in wrap_fx_proxy_cls
example_value = get_fake_value(proxy.node, tx)
File "/scratch/ybliang/work/repos/pytorch/torch/_dynamo/utils.py", line 1024, in get_fake_value
args, kwargs = torch.fx.node.map_arg((node.args, node.kwargs), visit)
File "/scratch/ybliang/work/repos/pytorch/torch/fx/node.py", line 613, in map_arg
return map_aggregate(a, lambda x: fn(x) if isinstance(x, Node) else x)
File "/scratch/ybliang/work/repos/pytorch/torch/fx/node.py", line 621, in map_aggregate
t = tuple(map_aggregate(elem, fn) for elem in a)
File "/scratch/ybliang/work/repos/pytorch/torch/fx/node.py", line 621, in <genexpr>
t = tuple(map_aggregate(elem, fn) for elem in a)
File "/scratch/ybliang/work/repos/pytorch/torch/fx/node.py", line 627, in map_aggregate
return immutable_dict((k, map_aggregate(v, fn)) for k, v in a.items())
File "/scratch/ybliang/work/repos/pytorch/torch/fx/node.py", line 627, in <genexpr>
return immutable_dict((k, map_aggregate(v, fn)) for k, v in a.items())
File "/scratch/ybliang/work/repos/pytorch/torch/fx/node.py", line 631, in map_aggregate
return fn(a)
File "/scratch/ybliang/work/repos/pytorch/torch/fx/node.py", line 613, in <lambda>
return map_aggregate(a, lambda x: fn(x) if isinstance(x, Node) else x)
File "/scratch/ybliang/work/repos/pytorch/torch/_dynamo/utils.py", line 1022, in visit
return n.meta["example_value"]
KeyError: 'example_value\n\nfrom user code:\n File "./generated/test_BayesWatch_pytorch_prunes.py", line 108, in forward\n return torch.zeros([x.size()[0], self.channels, x.size()[2] // self.spatial, x.size()[3] // self.spatial], dtype=x.dtype, layout=x.layout, device=x.device)\n\nSet torch._dynamo.config.verbose=True for more information\n\n\nYou can suppress this exception and fall back to eager by setting:\n torch._dynamo.config.suppress_errors = True\n'
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89257
Approved by: https://github.com/jansel
Fixes error from 7k github models: https://github.com/jansel/pytorch-jit-paritybench/blob/master/generated/test_arashwan_matrixnet.py
Error:
```
AssertionError: torch.* op returned non-Tensor bool call_function <function is_tensor at 0x7fca94d0faf0>
from user code:
File "/scratch/ybliang/work/repos/pytorch-jit-paritybench/generated/test_arashwan_matrixnet.py", line 749, in scatter
return scatter_map(inputs)
File "/scratch/ybliang/work/repos/pytorch-jit-paritybench/generated/test_arashwan_matrixnet.py", line 741, in scatter_map
assert not torch.is_tensor(obj), 'Tensors not supported in scatter.'
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88704
Approved by: https://github.com/jansel
I noticed that a lot of bugs are being suppressed by torchdynamo's default
error suppression, and worse yet, there's no way to unsuppress them. After
discussion with voz and soumith, we decided that we will unify error suppression
into a single option (suppress_errors) and default suppression to False.
If your model used to work and no longer works, try TORCHDYNAMO_SUPPRESS_ERRORS=1
to bring back the old suppression behavior.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
cc @jansel @lezcano @fdrocha @mlazos @soumith @voznesenskym @yanboliang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87440
Approved by: https://github.com/voznesenskym, https://github.com/albanD
