Summary:
resnet152 spent about 15 minutes writing warning messages in _unlift
during `to_executorch` because they're all written to unbuffered stderr
by the `warnings` module.
These warnings are almost always about get_attr nodes referencing a
non-existent name:
```lang=py
warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
'not reference an nn.Module, nn.Parameter, or buffer, which is '
'what \'get_attr\' Nodes typically target'
)
```
I'm not aware of a way to configure the warnings module to write this out
at most once, so I'm just going to disable the lint for now.
Test Plan:
Re-ran resnet152 with Executorch and the XNNPackBackend, it is much faster now
Differential Revision: D62156090
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135069
Approved by: https://github.com/yushangdi
#### Description
Transform quantized operation properly. Add de/quantization before and after the quantized operation.
#### Test Plan
`pytest test/export/test_converter.py -s -k test_ts2ep_convert_quantized_model`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133026
Approved by: https://github.com/angelayi
#### Description
Transform quantized operation properly. Add de/quantization before and after the quantized operation.
#### Test Plan
`pytest test/export/test_converter.py -s -k test_ts2ep_convert_quantized_model`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131915
Approved by: https://github.com/angelayi
python_code(verbose=True) (or print_readable()) generates a string with the code representing the fx graph, with extra annotations indicating the size or stride of the tensor. Currently, it'll only shows sizes/strides for FakeTensors provided in metadata. For subclass tensors like NestedTensor, the outer class (provided in the node metadata) will be a non-FakeTensor and the inner tensors will be fake. This PR expands the conditional to show sizes/strides for all tensors, not just FakeTensors.
Testing: I ran this test script (below), ran it with `TORCH_LOGS=+dynamo` and found in the logs the graph shown below - we see that the input nested tensor has sizes and strides associated with it. Also, I stacked a diff on top of this one that forces the readable graph to be generated whenever PT2 is in use in tests, which should hopefully find any issues; https://github.com/pytorch/pytorch/pull/132195 shows no significant failures except for preexisting failures.
test script:
```python
import torch
def fn(x):
return x.cos()
nt = torch.nested.nested_tensor_from_jagged(
torch.randn(10, 10),
torch.tensor([0, 1, 3, 6, 10]),
)
torch.compile(fn)(nt)
```
logs excerpt:
```
[0/0] [__graph_code] TRACED GRAPH
[0/0] [__graph_code] ===== __compiled_fn_1 =====
[0/0] [__graph_code] /data/users/dberard/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.M
[0/0] [__graph_code] def forward(self, L_x_: "f32[4, zf1, 10][10*zf1, 10, 1]cpu", zf1: "Sym(zf1)"):
[0/0] [__graph_code] l_x_ = L_x_
[0/0] [__graph_code]
[0/0] [__graph_code] # File: /data/users/dberard/scripts/nt_print_graph.py:4 in fn, code: return x.c
[0/0] [__graph_code] cos: "f32[4, zf1, 10][10*zf1, 10, 1]cpu" = l_x_.cos(); l_x_ = None
[0/0] [__graph_code] return (cos,)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132192
Approved by: https://github.com/Chillee
Fixes the failure in `test/export/test_export_training_ir_to_run_decomp.py ` caused by dead code elimination removing node with side effects.
For background, in export, we may want to export higher-level IRs that are not functional, so we need to check for side effects more carefully.
A call_function node is impure if it has at least one mutable argument.
Fixed the tests below:
test_to_module_with_mutated_buffer_multiple_update_sub_later
test_export_input_mutation_static_shape
test_buffer_util
Another attempt modifying the original DCE pass is made in PR #130395, but it breaks some other tests, so here we add a flag and use it for export only.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130552
Approved by: 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
- sets it as a fake stack trace as we don't have a generic comment feature
- when verbose is disabled, still adds a contextmanager and flag checks. the alternative is to use MACROS, but that wouldn't be usable with TORCH_LOGS
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124954
Approved by: https://github.com/jansel
Summary: `has_triton` causes some import time cycles. Lets use `has_triton_package` which is enough.
Test Plan:
```
buck2 test 'fbcode//mode/opt' fbcode//fblearner/flow/projects/model_processing/pytorch_model_export_utils/logical_transformations/tests:filter_inference_feature_metadata_test -- --exact 'fblearner/flow/projects/model_processing/pytorch_model_export_utils/logical_transformations/tests:filter_inference_feature_metadata_test - test_collect_features_from_graph_module_nodes (fblearner.flow.projects.model_processing.pytorch_model_export_utils.logical_transformations.tests.filter_inference_feature_metadata_test.FilterInferenceFromFeatureMetadataTest)'
```
now passes
Differential Revision: D55001430
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122059
Approved by: https://github.com/aakhundov
Summary: Unless we register triton to be a special import, FX graph import mechanism imports it as `from fx-generated._0 import triton as triton` which is obviously broken.
Test Plan:
I could not figure out how to write a test for this but
```
buck2 run 'fbcode//mode/dev-nosan' fbcode//tgif/lib/tests/gpu_tests:lowering_pass_test -- -r test_default_ait_lowering_multi_hardwares
```
now passes
Differential Revision: D54990782
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122041
Approved by: https://github.com/aakhundov
Putting this PR as an RFC since I have resorted to some horrible hacks in order to make this work.
```
(Pdb) p triton.language.float32
triton.language.fp32
(Pdb) p str(triton.language.float32)
'fp32'
(Pdb) p repr(triton.language.float32)
'triton.language.fp32'
```
This means that we need to "rewrite" them for fx graph and inductor execution.
This PR allows Mamba2 to work with `torch.compile`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121690
Approved by: https://github.com/Skylion007
This pull request aims to complete most of the support for vectorizing int32 and int64 data types except for indirect indexing and masks. The basic data type support for uint32 and uint64 is also added but without vectorization. More vectorized conversion functions are added between integer and float. In order to support int64 vectors, a new VectorizedN class to handle vectors of arbitrary length. Below are the details:
1. Complete most of the int32 and int64 vectorization support including load, store, reduction, constant and conversion. The indirect indexing and masks will be addressed in follow-up PRs, after which, the legality checking logic in `CppVecKernelChecker` can be further simplified.
2. Util functions for conversion between integer and float vectors (in cpp_prefix.h and ATen vec). Ideally, we'd better move them from cpp_prefix.h to ATen vec to simplify cpp_prefix.h, will be addressed in follow-up PRs.
3. Introduced a new template class VectorizedN, designed to handle vectors of arbitrary length by encapsulating multiple Vectorized<T> instances. This class supports most of the operations of `Vectorized<T>`. It makes the support of int64 vectorization simpler. I will also apply it to bf16/fp16/int8 in the follow-up PRs for better efficiency. For example, bf16 currently only uses half of the vector lanes. With `VectorizedN`, we can use full of the lanes and map bf16 vector to `VectorizedN<float,2>` on conversion.
4. Basic data type support is added for uint32 and uint64 (in graph.py). Vectorization support will be added later but not of high priority due to fewer usages.
Next steps:
- [ ] Refactor the vector mask handling to support data types other than float. Currently vector masks are implemented with float vectors.
- [ ] Fully utilize vector lanes for bfloat16/float16/int8.
- [ ] Support indirect indexing with vectorized index via scalarization.
- [ ] Clean up `CppVecKernelChecker`.
- [ ] Simplify `cpp_prefix.h` including refactoring vector conversion logic.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119001
Approved by: https://github.com/peterbell10, https://github.com/jansel
When working with internal flows, it can sometimes be ambiguous what
version of the code they are working with. In this case, having the
function name available in the stack trace can help identify what you
are looking at.
Example now looks like:
```
[DEBUG] # File: /data/users/ezyang/a/pytorch/a.py:5 in f, code: return x + x
```
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117459
Approved by: https://github.com/Skylion007
Updates flake8 to v6.1.0 and fixes a few lints using sed and some ruff tooling.
- Replace `assert(0)` with `raise AssertionError()`
- Remove extraneous parenthesis i.e.
- `assert(a == b)` -> `assert a == b`
- `if(x > y or y < z):`->`if x > y or y < z:`
- And `return('...')` -> `return '...'`
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116591
Approved by: https://github.com/albanD, https://github.com/malfet
This PR fixes two cases when fx generated code is invalid in python (syntax error):
1. multiple type annotation in one line: `var1: annotation1, var2: annotation2 = function_call()`
2. invalid type annotation for scalars like `var1: f32[] = function_call()`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113345
Approved by: https://github.com/ezyang
