Some sympy Functions aren't supported by sympy_interp(); we can't turn them into FX nodes, so currently the runtime asserts CSE pass avoids CSE'ing on any expression containing a sympy Function. https://github.com/pytorch/pytorch/pull/132325 started tracking unsupported functions, so we switch the check to that to be more precise. We also check for and skip unsupported functions when adding asserts - previously we only did the check for CSE, and not adding new expressions.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132457
Approved by: https://github.com/avikchaudhuri
Summary: Finishing up the mechanism to "register" certain types of operators to a registry so that the serializer can handle them correctly. This is expected to be firstly used by executorch.
Test Plan: buck run mode/opt caffe2/test:test_export -- -r test_export_with_extension_op_serialization
Differential Revision: D59825148
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130851
Approved by: https://github.com/angelayi
Summary: This diff updates the ExportedProgram class in PyTorch to allow for multiple verifiers to be attached to it. This is done by adding a new field to the ExportedProgram schema called "verifiers" which is a list of strings representing the names of the verifiers to be attached to the program. The verifiers are loaded using the "load_verifier" function which is defined in the "torch._export.serde.serialize" module. The "exported_program.dialect" field is also deprecated in favor of the "verifiers" field.
Test Plan: CI
Differential Revision: D59408546
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130364
Approved by: https://github.com/angelayi, https://github.com/ydwu4
At a high level, the idea behind this PR is:
* Make it clearer what the promotion and int/float rules for various Sympy operations are. Operators that previously were polymorphic over int/float are now split into separate operators for clarity. We never do mixed int/float addition/multiplication etc in sympy, instead, we always promote to the appropriate operator. (However, equality is currently not done correctly.)
* Enforce strict typing on ValueRanges: if you have a ValueRange for a float, the lower and upper MUST be floats, and so forth for integers.
The story begins in **torch/utils/_sympy/functions.py**. Here, I make some changes to how we represent certain operations in sympy expressions:
* FloorDiv now only supports integer inputs; to do float floor division, do a truediv and then a trunc. Additionally, we remove the divide out addition by gcd optimization, because sympy gcd is over fields and is willing to generate rationals (but rationals are bad for ValueRange strict typing).
* ModularIndexing, LShift, RShift now assert they are given integer inputs.
* Mod only supports integer inputs; eventually we will support FloatMod (left for later work, when we build out Sympy support for floating operations). Unfortunately, I couldn't assert integer inputs here, because of a bad interaction with sympy's inequality solver that is used by the offline solver
* TrueDiv is split into FloatTrueDiv and IntTrueDiv. This allows for us to eventually generate accurate code for Python semantics IntTrueDiv, which is written in a special way to preserve precision when the inputs are >= 2**53 beyond what first coercing the integer to floats and then doing true division.
* Trunc is split to TruncToFloat and TruncToInt.
* Round is updated to return a float, not an int, making it consistent with the round op handler in Inductor. To get Python-style conversion to int, we call TruncToInt on the result.
* RoundDecimal updated to consistently only ever return a float
* Add ToFloat for explicit coercion to float (required so we can enforce strict ValueRanges typing)
In **torch/__init__.py**, we modify SymInt and SymFloat to appropriately call into new bindings that route to these refined sympy operations. Also, we modify `torch.sym_min` and `torch.sym_max` to have promotion semantics (if one argument is a float, the return result is always a float), making them inconsistent with builtins.min/max, but possible to do type analysis without runtime information.
We also need to introduce some new op handlers in **torch/_inductor/ops_handler.py**:
* `to_int` for truncation to int64, directly corresponding to TruncToInt; this can be implemented by trunc and dtype, but with a dedicated handler it is more convenient for roundtripping in Sympy
* `int_truediv` for Python-style integer true division, which has higher precision than casting to floats and then running `truediv`
These changes have consequences. First, we need to make some administrative changes:
* Actually wire up these Sympy functions from SymInt/SymFloat in **torch/fx/experimental/sym_node.py**, including the new promotion rules (promote2)
* Add support for new Sympy functions in **torch/utils/_sympy/interp.py**, **torch/utils/_sympy/reference.py**
* In particular, in torch.utils._sympy.reference, we have a strong preference to NOT do nontrivial compute, instead, everything in ops handler should map to a singular sympy function
* TODO: I chose to roundtrip mod back to our Mod function, but I think I'm going to have to deal with the C/Python inconsistency this to fix tests here
* Add printer support for the Sympy functions in **torch/_inductor/codegen/common.py**, **torch/_inductor/codegen/cpp_utils.py**, **torch/_inductor/codegen/triton.py**. `int_truediv` and mixed precision equality is currently not implemented soundly, so we will lose precision in codegen for large values. TODO: The additions here are not exhaustive yet
* Update ValueRanges logic to use new sympy functions in **torch/utils/_sympy/value_ranges.py**. In general, we prefer to use the new Sympy function rather than try to roll things by hand, which is what was done previously for many VR analysis functions.
In **torch/fx/experimental/symbolic_shapes.py** we need to make some symbolic reasoning adjustments:
* Avoid generation of rational subexpressions by removing simplification of `x // y` into `floor(x / y)`. This simplification then triggers an addition simplification rule `(x + y) / c --> x / c + y / c` which is bad because x / c is a rational number now
* `_assert_bound_is_rational` is no more, we no longer generate rational bounds
* Don't intersect non-int value ranges with the `int_range`
* Support more sympy Functions for guard SYMPY_INTERP
* Assert the type of value range is consistent with the variable type
The new asserts uncovered necessary bug fixes:
* **torch/_inductor/codegen/cpp.py**, **torch/_inductor/select_algorithm.py**, **torch/_inductor/sizevars.py** - Ensure Wild/Symbol manually allocated in Inductor is marked `is_integer` so it's accepted to build expressions
* **torch/_inductor/utils.py** - make sure you actually pass in sympy.Expr to these functions
* **torch/_inductor/ir.py** - make_contiguous_strides_for takes int/SymInt, not sympy.Expr!
* **torch/export/dynamic_shapes.py** - don't use infinity to represent int ranges, instead use sys.maxsize - 1
Because of the removal of some symbolic reasoning that produced rationals, some of our symbolic reasoning has gotten worse and we are unable to simplify some guards. Check the TODO at **test/test_proxy_tensor.py**
**Reland notes.** This requires this internal fbcode diff https://www.internalfb.com/phabricator/paste/view/P1403322587 but I cannot prepare the diff codev due to https://fb.workplace.com/groups/osssupport/posts/26343544518600814/
It also requires this Executorch PR https://github.com/pytorch/executorch/pull/3911 but the ET PR can be landed prior to this landing.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126905
Approved by: https://github.com/xadupre, https://github.com/lezcano
This PR adds a registration function and a global registry for GraphModuleSerializer. After this PR, custom serialization methods can be done through registration instead of subclassing for ease of maintenance.
## Changes
- Add a test case where it injects custom op to test serialization.
- Add custom op handler
- Change allowed op for verifier
Co-authored-by: Zhengxu Chen <zhxchen17@outlook.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126550
Approved by: https://github.com/zhxchen17
This PR adds a new metadata, `torch_fn` which is meant to replace `source_fn_stack` as `source_fn_stack` is not entirely well defined between strict/nonstrict. Previous discussion [here](https://docs.google.com/document/d/1sPmmsmh6rZFWH03QBOe49MaXrQkP8SxoG8AOMb-pFk4/edit#heading=h.anmx9qknhvm).
`torch_fn` represents the torch function that a particular aten operator came from. For example, `torch.nn.Linear` goes down to the `torch.nn.functional.linear` at the `__torch_function__` layer, and then `aten.t/aten.addmm` in the `__torch_dispatch__` layer. So the nodes `aten.t/aten.addmm` will now have the `torch_fn` metadata containing the `torch.nn.functional.linear`.
The `torch_fn` metadata is a tuple of 2 strings: a unique identifier for each torch function call, and the actual torch function `f"{fn.__class__}.{fn.__name__}"`. The purpose of the first value is to distinguish between 2 consecutive calls to the same function. For example, if we had 2 calls to `torch.nn.Linear`, the nodes and corresponding metadata would look something like:
```
aten.t - ("linear_1", "builtin_function_or_method.linear"),
aten.addmm - ("linear_1", "builtin_function_or_method.linear"),
aten.t - ("linear_2", "builtin_function_or_method.linear"),
aten.addmm - ("linear_2", "builtin_function_or_method.linear"),
```
Higher order ops -- currently we can get the torch_fn metadata for nodes within the HOO's subgraph, but after retracing, this becomes the `(cond, higher_order_op.cond)` :( This is because `fx_traceback.set_current_meta` points to the cond node in the toplevel graph, rather than the original node in the subgraph. I think this is because `fx.Interpreter` does not go into the cond subgraphs. (will discuss with Yidi more ab this)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122693
Approved by: https://github.com/tugsbayasgalan
We would like to improve consistency for nn_module_stack metadata in torch.export.
This PR ensures that all tests in test/export/test_export.py has the following constraints:
- Remove nn_module_stack for all placeholder & output nodes, for all modules and submodules
- Ensure nn_module_stack is present for all other node types for the top-level module (there is still an issue with torch.cond submodules having empty fields)
- Add these checks to _export() in _trace.py (we would add this in the Verifier, but downstream apps construct ExportedPrograms separate from _export(), and metadata may not be maintained there)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120661
Approved by: https://github.com/avikchaudhuri
Summary:
X-link: https://github.com/pytorch/executorch/pull/1817
Basic support for non-persistent buffers, which are buffers that do not show up in the state dict.
One weird twist is that most of our other systems (FX, aot_export, dynamo) have completely buggy handling of non-persistent buffers. I tried to go on a wild goose chase to fix them all, but it got to be too much. So I introduced some sad rewrite passes in `_export` make the final state dict correctly align with the original module's state dict.
This exposed some bugs/ambiguous handling of parameters/buffers in existing test code. For example, `TestSaveLoad.test_save_buffer` traced over a module that was not in the root module hierarchy and caused some weird behavior. I think we should error explicitly on use cases like this: https://github.com/pytorch/pytorch/issues/118410. For now I just rewrote the tests or skipped them.
As a side effect, this diff tightened up quite a few sloppy behaviors around state dict handling:
- Tensor attributes were getting promoted to be buffers—bad!
- Tracing through a module not in the children of the root module would add its parameters/buffers to the state dict—bad!
This behavior is unlikely to show up in user code since the model would be totally broken, but did show up in a bunch of tests.
#buildmore
Test Plan:
unit tests
sandcastle
Differential Revision: D53340041
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118969
Approved by: https://github.com/guangy10, https://github.com/huydhn, https://github.com/titaiwangms
Summary:
X-link: https://github.com/pytorch/executorch/pull/1769
Basic support for non-persistent buffers, which are buffers that do not show up in the state dict.
One weird twist is that most of our other systems (FX, aot_export, dynamo) have completely buggy handling of non-persistent buffers. I tried to go on a wild goose chase to fix them all, but it got to be too much. So I introduced some sad rewrite passes in `_export` make the final state dict correctly align with the original module's state dict.
This exposed some bugs/ambiguous handling of parameters/buffers in existing test code. For example, `TestSaveLoad.test_save_buffer` traced over a module that was not in the root module hierarchy and caused some weird behavior. I think we should error explicitly on use cases like this: https://github.com/pytorch/pytorch/issues/118410. For now I just rewrote the tests or skipped them.
Test Plan: added a unit test
Differential Revision: D53253905
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118722
Approved by: https://github.com/SherlockNoMad, https://github.com/angelayi
Summary:
We used to skip verifier when the signature object is not the "correct" one (usually from some deprecated frontend). This was very useful when we wanted to pay a small cost to enable verifier path to be called everywhere for torch export.
Now I believe no tests are relying on this behavior so we should remove this weird branch.
Test Plan: CI
Differential Revision: D53024506
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118139
Approved by: https://github.com/suo
Added support for constant outputs. We will just embed the constant directly into the output, like `return (x, 1)`.
Also adds support for None input/outputs. For None inputs we address it the same way we do to constants, which is that a placeholder with no users will be inserted into the graph, and the None will be embedded into whatever operator is using the None. For None outputs, we will also address the same way we do constants, which is that we embed it into the output, like `return (x, None)`.
Differential Revision: D52881070
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117894
Approved by: https://github.com/zhxchen17
Summary:
We intend to preserve autograd ops for predispatch export. Therefore, we
need to exempt the autograd ops in some places, e.g. verifier and
proxy_tensor.py.
Test Plan:
python test/export/test_export.py -k test_predispatch_export_with_autograd_op
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116527
Approved by: https://github.com/tugsbayasgalan
ghstack dependencies: #116339
Summary:
Currently we have a very ugly specialization on edge dialect in verifier like the following:
```
# TODO Remove this branch.
if ep.dialect == "EDGE": # !!! Don't change this allowlist. !!!
pass
else:
raise e
```
In this diff we do some additional work to make signature checking also work in exir. We decouple the transformation stack in torch export and exir so that different layers of the stack can evolve in their own fashion and the team can divide and conquer them seperately.
Test Plan: CI
Differential Revision: D52499225
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116705
Approved by: https://github.com/tugsbayasgalan
Summary:
Based on discussions with Sherlock + Zhengxu in D51118067, updated the internal thrift schema to match the OSS schema.
Verifier failures:
* Test contains a None as input, resulting in no meta["val"]
* Test contains torch.autograd.grad_mode.set_grad_enabled as an op, which also results in no meta["val"]
* torch.autograd.grad_mode.set_grad_enabled is also not a valid op
* Test adds a "parameter" to the state dict but the parameter is not an nn.Parameter, causing an assertion failure
So to bypass these failures I did the following hacks(?):
* Before creating the exported program in deserialization, populate nodes w/o meta["val"] with meta["val"] = None
* Add torch.autograd.grad_mode.set_grad_enabled to the skip opset
* Duplicated ExportGraphSignature into aot_export.py so that the graph signature checks will be skipped
Configerator changes in D51343615
Test Plan: CI
Reviewed By: zhxchen17
Differential Revision: D51342921
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113810
Approved by: https://github.com/zhxchen17
Summary: yolo fixing issues. See Test plan
Test Plan:
buck2 run 'fbcode//mode/dev' fbcode//executorch/examples/portable/test:test_export -- -r test_mv3_export_to_executorch
[Need acl to repro this but the error message looks straight forward]
buck2 test 'fbcode//mode/dev-nosan' fbcode//pye/model_inventory/nlu_stella_cap:nlu_stella_cap_test -- --exact 'pye/model_inventory/nlu_stella_cap:nlu_stella_cap_test - test_export_to_backend_dynamic_quantized (pye.model_inventory.nlu_stella_cap.NluStellaCapTest.NluStellaCapTest)'
Differential Revision: D51128480
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113296
Approved by: https://github.com/tugsbayasgalan
Summary: Turn on verifier check for exportec program ctor. Note that this effectively detect a large surface of spec violations, so we also spend some time fixing them one by one in this diff.
Test Plan: CI
Differential Revision: D51014944
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113075
Approved by: https://github.com/angelayi
A resubmit of https://github.com/pytorch/pytorch/pull/108447. Copy over the descriptions:
This is a follow-up of the discussion in https://github.com/pytorch/pytorch/pull/108356, where we want to repalce source_fn with source_fn_stack
Before this PR, for the following example:
```python
backend = EagerAndRecordGraphs()
@torch.compile(backend=backend, fullgraph=True)
def cond_f(pred, pred2, x, y):
def true_fn(pred2, x, y):
return x + y
def false_fn(pred2, x, y):
def true_fn2(x, y):
return x.sin() - y.cos()
def false_fn2(x, y):
return x.cos() - y.sin()
return control_flow.cond(pred2, true_fn2, false_fn2, (x, y))
return control_flow.cond(pred, true_fn, false_fn, (pred2, x, y))
```
The graph captured is shown below:
```python
class GraphModule(torch.nn.Module):
def forward(self, L_pred_ : torch.Tensor, L_pred2_ : torch.Tensor, L_x_ : torch.Tensor, L_y_ : torch.Tensor):
l_pred_ = L_pred_
l_pred2_ = L_pred2_
l_x_ = L_x_
l_y_ = L_y_
cond_true_1 = self.cond_true_1
cond_false_1 = self.cond_false_1
cond = torch.ops.higher_order.cond(l_pred_, cond_true_1, cond_false_1, [l_pred2_, l_x_, l_y_]); l_pred_ = cond_true_1 = cond_false_1 = l_pred2_ = l_x_ = l_y_ = None
return (cond,)
class GraphModule(torch.nn.Module):
def forward(self, l_pred2_, l_x_, l_y_):
add = l_x_ + l_y_; l_x_ = l_y_ = None
return add
class GraphModule(torch.nn.Module):
def forward(self, l_pred2_, l_x_, l_y_):
cond_true_0 = self.cond_true_0
cond_false_0 = self.cond_false_0
cond = torch.ops.higher_order.cond(l_pred2_, cond_true_0, cond_false_0, [l_x_, l_y_]); l_pred2_ = cond_true_0 = cond_false_0 = l_x_ = l_y_ = None
return cond
class GraphModule(torch.nn.Module):
def forward(self, l_x_, l_y_):
sin = l_x_.sin(); l_x_ = None
cos = l_y_.cos(); l_y_ = None
sub = sin - cos; sin = cos = None
return sub
class GraphModule(torch.nn.Module):
def forward(self, l_x_, l_y_):
cos = l_x_.cos(); l_x_ = None
sin = l_y_.sin(); l_y_ = None
sub = cos - sin; cos = sin = None
return sub
```
the source_fn for inner cond, sin, cos will be a (name, target) tuple:
```
('cond', <torch._ops.HigherOrderOperator object at xxx>)
('sin', 'sin')
('cos', 'cos')
('sub'. <built-in function sub>)
```
After this pr, the source_fn_stack will be a list of (name, target) tuple. The bottom of stack is the end of the list.
```
[('cond', <torch._ops.HigherOrderOperator object at xxx>), ('cond', <torch._ops.HigherOrderOperator object at xxx>)],
[('cond', <torch._ops.HigherOrderOperator object at xxx>), ('cond', <torch._ops.HigherOrderOperator object at xxx>), ('sin', 'sin')],
[('cond', <torch._ops.HigherOrderOperator object at xxx>), ('cond', <torch._ops.HigherOrderOperator object at xxx>), ('cos', 'cos')]
[('cond', <torch._ops.HigherOrderOperator object at xxx>), ('cond', <torch._ops.HigherOrderOperator object at xxx>), ('sub', <built-in function sub>)]
```
Test Plan:
See added tests in test_higher_order_ops.py and modify existing test.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108595
Approved by: https://github.com/angelayi, https://github.com/zou3519
Summary: Forward fix t53725825. New map implementation breaks multiple internal tests. forward fix it for some of them. To unblock others, mark unfixed ones are expectedFailure first.
Test Plan: Test with CI.
Reviewed By: angelayi
Differential Revision: D46084287
Pull Request resolved: https://github.com/pytorch/pytorch/pull/102009
Approved by: https://github.com/angelayi
