This makes it so that `GraphModule.recompile()` will also recompile any submodules that are also graph modules, which allows us to pass all existing regional inductor tests without skipping.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/166002
Approved by: https://github.com/oulgen
ghstack dependencies: #165996
Summary:
I am really skeptical about inductor sizevars creating an empty shape env when not provided with one
i think we should fail there if the graph has dynamic shapes and no shape env is provided.
however i wonder if there are actually use cases that depends on the shape env not being there?
Reasoning APIs depends on facts in the shape env. and assumes some stuff exists for specific symbols.
Test Plan:
Fix the bug reported in creating simple e2e unit test is not trivial
https://www.internalfb.com/diff/D82337184
Rollback Plan:
Differential Revision: D82412384
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162927
Approved by: https://github.com/ezyang, https://github.com/eellison, https://github.com/jansel
This PR is quite large in that it covers most of rough edges in the new strict export flow:
1. Handle nn_module_stack correctly now that we are tracing wrapper module
2. module_call_spec needs to get queried from source directly because we are not running the bytecode anymore.
3. Correct input and output handling.
@diff-train-skip-merge
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162183
Approved by: https://github.com/zhxchen17
This PR is quite large in that it covers most of rough edges in the new strict export flow:
1. Handle nn_module_stack correctly now that we are tracing wrapper module
2. module_call_spec needs to get queried from source directly because we are not running the bytecode anymore.
3. Correct input and output handling.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162183
Approved by: https://github.com/zhxchen17
ghstack dependencies: #162167
This PR removes the integration point torch.fx had with torch::deploy (and another minor change).
Note: This PR has some broken mypy errors, but I believe those should have been in the code base beforehand, and should be fixed in a separate PR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158291
Approved by: https://github.com/albanD
ghstack dependencies: #158290
----
- First, we add a new expanded_def to FX, which will expand the
definitions of variables into multiple lines, one per variable
definition. This makes extremely long args/return lists much
more readable.
- Next, we extend this mechanism to also print out descriptors on
placeholders and return values, as comments, if available. This
is how we will test descriptors.
- We update tlparse for AOTAutograd to use this format.
- We update expect tests to use this format and update their formats,
so you can inspect what it can look at. There may be other tests
I should update, open to suggestions.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158708
Approved by: https://github.com/wconstab
ghstack dependencies: #158624
This PR removes the integration point torch.fx had with torch::deploy (and another minor change).
Note: This PR has some broken mypy errors, but I believe those should have been in the code base beforehand, and should be fixed in a separate PR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158291
Approved by: https://github.com/albanD
ghstack dependencies: #158288, #158290
This PR removes the integration point torch.fx had with torch::deploy (and another minor change).
Note: This PR has some broken mypy errors, but I believe those should have been in the code base beforehand, and should be fixed in a separate PR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158291
Approved by: https://github.com/albanD
ghstack dependencies: #158288, #158290
Summary:
urrently the node.meta["stack_trace"] is not preserved when we torch package/load GraphModule, which means the original stack trace is lost. When we re-trace the packaged graph module, we just get a stack trace like fx-generated._0......
Adding the node.meta["stack_trace"] to torch packaged graph module
Test Plan:
```
buck2 run @//mode/dev-nosan fbcode//caffe2/test:package -- -r TestPackageFX
```
Rollback Plan:
Differential Revision: D76379692
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155638
Approved by: https://github.com/angelayi
Teach the graph printer how to allow overriding printing SymTypes (`SymInt`, `SymFloat`, `SymBool`) and then use that to reuse the fast SymNode printing from `torch._inductor.utils.sympy_str()` to make computing the cache key faster.
On my computer the repro from #151823 goes from 480s -> 80s (still terrible... but better).
Fixes#151823
Pull Request resolved: https://github.com/pytorch/pytorch/pull/151928
Approved by: https://github.com/laithsakka
Previously, in non-strict path, we always error when trying to inplace update a constant tensor because those constant tensors are not actually wrapped by functional tensors. This is correct behaviour in torch.compile, because dynamo makes all constant tensors into buffers and AOTDispatcher just lifts them and wraps them in functional tensors. However, in non-strict, there is no such step that registers constants as buffers so AOTDispatcher panics when it sees these dangling constant tensors when functioanalizing.
Due to recent change in the IR, this is no longer an issue in non-strict path because we don't call AOTDispatcher at training IR level, but now it is a problem for both strict and non-strict when we lower to inference. (lowering to inference is very similar to non-strict tracing) As a result, we have at least one external (https://github.com/pytorch/pytorch/issues/141336) and internal issues reported due to this difference.
To fix this, there are two ways:
1. Make functionalization be aware of constant tensors and map them to functional tensors on the fly. This makes functionalization invariant uglier and could potentially open up a gate for more nasty bugs.
2. Special handle this in export. This seems more aligned with what dynamo does today so i think we should do it this way. I think the current state could benefit from more refactors to make the run_deocmpositions to be more similar to strict export (because both of them now handle this constant registerinig logic) but it is bit complicated to do it now because strict export version of this logic is also not complete because it doesn't take into account of export graph renaming pass etc). I will follow up with more refactors after this PR (T213466691) to unblock users faster.
For future reference:
Why are we not doing "turning constants into non-persistent buffers and never de-register"? The reason is because in some internal models, they rely on module.to to reliably work to move params/buffers to correct device. As a result, buffers are moved while constants are not. In composibility meeting, we agreed that export won't do device agnostic tracing going forward (it will provide a way to specify FakeTensor in CPU that can be configured to be run on GPU), so after that is done, we can always turn constants into non-persistent buffers which will simplify export's constant handling.
Differential Revision: [D68610739](https://our.internmc.facebook.com/intern/diff/D68610739)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145593
Approved by: https://github.com/avikchaudhuri
Summary:
- use GraphTransformObserver + replace_node hooks to track node sources when they are replaced
- add pre_grad_graph tracking to tlparse
- add the node provenance information to post_grad_graph tlparse. This is for the frontend to create a mapping between pre_grad and post_grad graph. See an example frontend (this is just a prototype) here: https://drive.google.com/file/d/1cMHH_0y4FJUSS9tATwGQvA72O0Lth8eh/view?usp=sharing
- change "action" of NodeSource from a single action to a list of actions.
- It's BC-Breaking because we removed `GraphTransformObserver`'s class methods `on_node_erase` and `on_node_erase` .
https://docs.google.com/document/d/1dGh9myqNhywmbfP0Quzx_f04bghDFlj8cawj8MopiO8/edit?tab=t.0
The front-end code that takes in the tlparse result is in https://github.com/yushangdi/compiler_explorer.
ghstack-source-id: 260390519
Test Plan:
```
buck2 run mode/dev-nosan fbcode//caffe2/test:fx -- -r test_graph_transform_observer
buck run mode/dev-nosan fbcode//caffe2/test:fx -- -r node_source
buck run mode/dev-nosan fbcode//caffe2/test:fx -- -r graph_provenance
```
Front-end example screenshots on a real model, 93% coverage rate between pre_grad_graph and post_grad_graph
{F1973584210}{F1973584209}
```
buck2 build --show-output mode/opt -c=python.package_style=inplace -c fbcode.enable_gpu_sections=true -c fbcode.platform=platform010 -c fbcode.split-dwarf=true -c fbcode.nvcc_arch=a100,h100 caffe2/torch/fb/model_transform/experimental/benchmark:mts_gpu_benchmark
MODEL_ENTITY_ID=644688112
SNAPSHOT_ID=32
MODULE=merge
TORCH_COMPILE_DEBUG=1 CUDA_VISIBLE_DEVICES=7 TORCH_LOGS="+inductor,+schedule,output_code,graph_code" TORCHINDUCTOR_MAX_AUTOTUNE=1 TORCHINDUCTOR_UNIQUE_KERNEL_NAMES=1 ../buck-out/v2/gen/fbcode/ec86b05dd59e84db/caffe2/torch/fb/model_transform/experimental/benchmark/__mts_gpu_benchmark__/mts_gpu_benchmark.par --local-model /home/bahuang/models/${MODEL_ENTITY_ID}/${SNAPSHOT_ID}/gpu_lowering/input.predictor.disagg.gpu.merge --lower-backend AOT_INDUCTOR_EP --gpu-trace --aot-inductor-config="{'max_autotune':
True}"
buck2 run mode/dev-nosan fbcode//caffe2/test/inductor:auto_functionalize
```
Differential Revision: D65006709
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144277
Approved by: https://github.com/desertfire
Summary: Change fx graph module's _replace_hook from a single hook, to a list of hooks. This is to prepare to registering more hooks for inductor provenance tracking, where we might need to register multiple hooks for node replacement.
Test Plan:
```
buck run mode/dev-nosan caffe2/test:fx -- -r test_hooks_for_node_update
buck run mode/dev-nosan caffe2/test:test_export -- -r test_replace_hook
```
Differential Revision: D66726724
Pull Request resolved: https://github.com/pytorch/pytorch/pull/142006
Approved by: https://github.com/zhxchen17
When we populate unlifted graph module, we actually only "unlift" constant tensor inputs which is problematic because export de-duplicates aliasing constants. As a result, we only register one constant instead of two constants. This PR fixes that by querying ep.constants table instead of ep.graph_signature.lifted_tensor_constants.
Differential Revision: [D63743111](https://our.internmc.facebook.com/intern/diff/D63743111)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/137162
Approved by: https://github.com/pianpwk
Taking inspiration from `GraphModule.print_readable` (aka I copied its [code](17b45e905a/torch/fx/graph_module.py (L824))), I added a `print_readable` to the unflattened module, because it's kind of nontrivial to print the contents of this module.
Example print from `python test/export/test_unflatten.py -k test_unflatten_nested`
```
class UnflattenedModule(torch.nn.Module):
def forward(self, x: "f32[2, 3]"):
# No stacktrace found for following nodes
rootparam: "f32[2, 3]" = self.rootparam
# File: /data/users/angelayi/pytorch2/test/export/test_unflatten.py:99 in forward, code: x = x * self.rootparam
mul: "f32[2, 3]" = torch.ops.aten.mul.Tensor(x, rootparam); x = rootparam = None
# No stacktrace found for following nodes
foo: "f32[2, 3]" = self.foo(mul); mul = None
bar: "f32[2, 3]" = self.bar(foo); foo = None
return (bar,)
class foo(torch.nn.Module):
def forward(self, mul: "f32[2, 3]"):
# No stacktrace found for following nodes
child1param: "f32[2, 3]" = self.child1param
nested: "f32[2, 3]" = self.nested(mul); mul = None
# File: /data/users/angelayi/pytorch2/test/export/test_unflatten.py:79 in forward, code: return x + self.child1param
add: "f32[2, 3]" = torch.ops.aten.add.Tensor(nested, child1param); nested = child1param = None
return add
class nested(torch.nn.Module):
def forward(self, mul: "f32[2, 3]"):
# File: /data/users/angelayi/pytorch2/test/export/test_unflatten.py:67 in forward, code: return x / x
div: "f32[2, 3]" = torch.ops.aten.div.Tensor(mul, mul); mul = None
return div
class bar(torch.nn.Module):
def forward(self, add: "f32[2, 3]"):
# No stacktrace found for following nodes
child2buffer: "f32[2, 3]" = self.child2buffer
# File: /data/users/angelayi/pytorch2/test/export/test_unflatten.py:87 in forward, code: return x - self.child2buffer
sub: "f32[2, 3]" = torch.ops.aten.sub.Tensor(add, child2buffer); add = child2buffer = None
return sub
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128617
Approved by: https://github.com/zhxchen17, https://github.com/pianpwk
Taking inspiration from `GraphModule.print_readable` (aka I copied its [code](17b45e905a/torch/fx/graph_module.py (L824))), I added a `print_readable` to the unflattened module, because it's kind of nontrivial to print the contents of this module.
Example print from `python test/export/test_unflatten.py -k test_unflatten_nested`
```
class UnflattenedModule(torch.nn.Module):
def forward(self, x: "f32[2, 3]"):
# No stacktrace found for following nodes
rootparam: "f32[2, 3]" = self.rootparam
# File: /data/users/angelayi/pytorch2/test/export/test_unflatten.py:99 in forward, code: x = x * self.rootparam
mul: "f32[2, 3]" = torch.ops.aten.mul.Tensor(x, rootparam); x = rootparam = None
# No stacktrace found for following nodes
foo: "f32[2, 3]" = self.foo(mul); mul = None
bar: "f32[2, 3]" = self.bar(foo); foo = None
return (bar,)
class foo(torch.nn.Module):
def forward(self, mul: "f32[2, 3]"):
# No stacktrace found for following nodes
child1param: "f32[2, 3]" = self.child1param
nested: "f32[2, 3]" = self.nested(mul); mul = None
# File: /data/users/angelayi/pytorch2/test/export/test_unflatten.py:79 in forward, code: return x + self.child1param
add: "f32[2, 3]" = torch.ops.aten.add.Tensor(nested, child1param); nested = child1param = None
return add
class nested(torch.nn.Module):
def forward(self, mul: "f32[2, 3]"):
# File: /data/users/angelayi/pytorch2/test/export/test_unflatten.py:67 in forward, code: return x / x
div: "f32[2, 3]" = torch.ops.aten.div.Tensor(mul, mul); mul = None
return div
class bar(torch.nn.Module):
def forward(self, add: "f32[2, 3]"):
# No stacktrace found for following nodes
child2buffer: "f32[2, 3]" = self.child2buffer
# File: /data/users/angelayi/pytorch2/test/export/test_unflatten.py:87 in forward, code: return x - self.child2buffer
sub: "f32[2, 3]" = torch.ops.aten.sub.Tensor(add, child2buffer); add = child2buffer = None
return sub
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128617
Approved by: https://github.com/zhxchen17, https://github.com/pianpwk
Taking inspiration from `GraphModule.print_readable` (aka I copied its [code](17b45e905a/torch/fx/graph_module.py (L824))), I added a `print_readable` to the unflattened module, because it's kind of nontrivial to print the contents of this module.
Example print from `python test/export/test_unflatten.py -k test_unflatten_nested`
```
class UnflattenedModule(torch.nn.Module):
def forward(self, x: "f32[2, 3]"):
# No stacktrace found for following nodes
rootparam: "f32[2, 3]" = self.rootparam
# File: /data/users/angelayi/pytorch2/test/export/test_unflatten.py:99 in forward, code: x = x * self.rootparam
mul: "f32[2, 3]" = torch.ops.aten.mul.Tensor(x, rootparam); x = rootparam = None
# No stacktrace found for following nodes
foo: "f32[2, 3]" = self.foo(mul); mul = None
bar: "f32[2, 3]" = self.bar(foo); foo = None
return (bar,)
class foo(torch.nn.Module):
def forward(self, mul: "f32[2, 3]"):
# No stacktrace found for following nodes
child1param: "f32[2, 3]" = self.child1param
nested: "f32[2, 3]" = self.nested(mul); mul = None
# File: /data/users/angelayi/pytorch2/test/export/test_unflatten.py:79 in forward, code: return x + self.child1param
add: "f32[2, 3]" = torch.ops.aten.add.Tensor(nested, child1param); nested = child1param = None
return add
class nested(torch.nn.Module):
def forward(self, mul: "f32[2, 3]"):
# File: /data/users/angelayi/pytorch2/test/export/test_unflatten.py:67 in forward, code: return x / x
div: "f32[2, 3]" = torch.ops.aten.div.Tensor(mul, mul); mul = None
return div
class bar(torch.nn.Module):
def forward(self, add: "f32[2, 3]"):
# No stacktrace found for following nodes
child2buffer: "f32[2, 3]" = self.child2buffer
# File: /data/users/angelayi/pytorch2/test/export/test_unflatten.py:87 in forward, code: return x - self.child2buffer
sub: "f32[2, 3]" = torch.ops.aten.sub.Tensor(add, child2buffer); add = child2buffer = None
return sub
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128617
Approved by: https://github.com/zhxchen17, https://github.com/pianpwk
Fixes [internal error](https://fb.workplace.com/groups/1075192433118967/permalink/1416709435633930/).
The issue is that the asserting nodes added in the `insert_deferred_runtime_assertion` pass do not contain metadata that the ExportedProgram requires the graph to have. One solution to fix this is to retrace the entire module, or another solution is to manually add back this metadata.
This diff implements the latter solution (manually add back the metadata) through hooking into fx.graph's `create_node` function, and adding export-specific metadata for every node that is created. The reason I did this is so that the `insert_deferred_runtime_assertion` does not have to know about what metadata export wants.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125414
Approved by: https://github.com/zhxchen17, https://github.com/BoyuanFeng
