Commit Graph

28 Commits

Author SHA1 Message Date
Yuanyuan Chen
8de85896e0 Enable ruff rule E721 (#165162)
`E721` checks for object type comparisons using == and other comparison operators. This is useful because it is recommended to use `is` for type comparisons.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165162
Approved by: https://github.com/Skylion007
2025-10-13 01:48:55 +00:00
PyTorch MergeBot
816fb7f48d Revert "Enable ruff rule E721 (#165162)"
This reverts commit 9e7c19f72b.

Reverted https://github.com/pytorch/pytorch/pull/165162 on behalf of https://github.com/pytorch-auto-revert due to Reverted automatically by pytorch's autorevert, to avoid this behaviour add the tag autorevert: disable ([comment](https://github.com/pytorch/pytorch/pull/165162#issuecomment-3393328271))
2025-10-11 13:25:40 +00:00
Yuanyuan Chen
9e7c19f72b Enable ruff rule E721 (#165162)
`E721` checks for object type comparisons using == and other comparison operators. This is useful because it is recommended to use `is` for type comparisons.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165162
Approved by: https://github.com/Skylion007
2025-10-11 06:43:53 +00:00
Jiang, Yanbing
f2f25a5444 Upgrade submodule oneDNN to v3.7.1 (#148293)
This PR is to upgrade submodule oneDNN to v3.7.1.

## Improvements

- Improved performance of convolution and matmul primitives on Intel Xeon processors with Intel AMX instruction set support (formerly Sapphire Rapids and Granite Rapids).
- Improved performance of int8 and fp32 forward convolution primitive on processors with Intel AVX2 instruction set support.
- Improved performance of fp8 matmul primitives with bf16 and fp16 bias data type on Intel Xeon processors with Intel AMX instruction set support (formerly Sapphire Rapids and Granite Rapids).
- Introduced initial optimizations for Intel GPUs based on Xe3 architecture.
- Added bfloat16 support for SDPA, implemented fp16 and bf16 gemm kernel in SDPA.
- Fixed f16 matmul accuracy, the issue of SDPA cannot dispatched to ukernel, bf16/fp16/fp32 conv performance, INT8 Kernel trigger page fault, deconvolution precision issue on complex128 and fp64 and gemm correctness issue in float16 issues.
- Improved bf16 matmul performance with fp32 destination with Arm Compute Library (ACL).
- Improved bf16 to fp32 reorder performance.
- Improved bf16 reorder performance.
- Improved bf16 convolution with ACL.

Fixes https://github.com/pytorch/pytorch/issues/136348.

## Validation results on CPU

1. NLP models accuracy/inference/training
![image](https://github.com/user-attachments/assets/859279b8-1631-4268-b226-7de9ac5870d8)

![image](https://github.com/user-attachments/assets/30ec7151-41ca-482a-9d2d-0c4850e75bab)

2. Torchbench cpu userbenchmark inference & training

![image](https://github.com/user-attachments/assets/71c9807c-caf9-4385-9990-d2ab637031cd)

3. Inductor quantization

![image](https://github.com/user-attachments/assets/3d2a3bd3-82fa-4566-8050-7ea5d6b61675)

4. Dynamo benchmarks
![image](https://github.com/user-attachments/assets/554ecce3-c85c-4a0e-88f1-2e73983c5dcd)
![image](https://github.com/user-attachments/assets/148c88f8-4367-4428-bb54-ce8a4deefd1b)
![image](https://github.com/user-attachments/assets/f2e744f4-d710-4699-acf4-1f130ecfadf1)
![image](https://github.com/user-attachments/assets/97128b80-4d0e-495a-aeda-dde3e70c96fd)
![image](https://github.com/user-attachments/assets/a9afce37-684c-45c0-b938-6dd7e0383805)
![image](https://github.com/user-attachments/assets/b8714236-9681-4fbe-8d98-be93deedab88)
![image](https://github.com/user-attachments/assets/4423061f-d133-45ba-98bd-d2f739e50431)
![image](https://github.com/user-attachments/assets/7955da10-3d23-493e-99fa-658f7f40035b)

## Validation results on XPU
Accuracy is same as baseline. Performance is shown below.
![image](https://github.com/user-attachments/assets/7645304d-5b1d-43f9-b840-9f846ed380a0)

## Validation results on ARM
![image](https://github.com/user-attachments/assets/080f7c02-0238-436f-ad20-5a9e3f6aafbb)
![image](https://github.com/user-attachments/assets/443742aa-ca61-41de-ae80-5d4c65cd0c87)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/148293
Approved by: https://github.com/mingfeima, https://github.com/atalman
2025-03-04 13:56:45 +00:00
cyy
b7832f0339 Enable ASAN in CUDA tests (#147812)
Fixes #ISSUE_NUMBER

Pull Request resolved: https://github.com/pytorch/pytorch/pull/147812
Approved by: https://github.com/janeyx99
2025-03-04 02:50:39 +00:00
PyTorch MergeBot
e72b4c61bf Revert "Upgrade submodule oneDNN to v3.7 (#147498)"
This reverts commit 576ed1e400.

Reverted https://github.com/pytorch/pytorch/pull/147498 on behalf of https://github.com/wdvr due to failing some tests on trunk - see below ([comment](https://github.com/pytorch/pytorch/pull/147498#issuecomment-2679867286))
2025-02-24 22:57:39 +00:00
Jiang, Yanbing
576ed1e400 Upgrade submodule oneDNN to v3.7 (#147498)
This PR is to upgrade submodule oneDNN to v3.7.

## Improvements

- Improved performance of convolution and matmul primitives on Intel Xeon processors with Intel AMX instruction set support (formerly Sapphire Rapids and Granite Rapids).
- Improved performance of int8 and fp32 forward convolution primitive on processors with Intel AVX2 instruction set support.
- Improved performance of fp8 matmul primitives with bf16 and fp16 bias data type on Intel Xeon processors with Intel AMX instruction set support (formerly Sapphire Rapids and Granite Rapids).
- Introduced initial optimizations for Intel GPUs based on Xe3 architecture.
- Added bfloat16 support for SDPA, implemented fp16 and bf16 gemm kernel in SDPA.
- Fixed f16 matmul accuracy, the issue of SDPA cannot dispatched to ukernel, bf16/fp16/fp32 conv performance, INT8 Kernel trigger page fault, deconvolution precision issue on complex128 and fp64 and gemm correctness issue in float16 issues.
- Improved bf16 matmul performance with fp32 destination with Arm Compute Library (ACL).
- Improved bf16 to fp32 reorder performance.
- Improved bf16 reorder performance.
- Improved bf16 convolution with ACL.

Fixes https://github.com/pytorch/pytorch/issues/136348.

## Validation results on CPU

1. NLP models accuracy/inference/training
![image](https://github.com/user-attachments/assets/859279b8-1631-4268-b226-7de9ac5870d8)

![image](https://github.com/user-attachments/assets/30ec7151-41ca-482a-9d2d-0c4850e75bab)

2. Torchbench cpu userbenchmark inference & training

![image](https://github.com/user-attachments/assets/71c9807c-caf9-4385-9990-d2ab637031cd)

3. Inductor quantization

![image](https://github.com/user-attachments/assets/3d2a3bd3-82fa-4566-8050-7ea5d6b61675)

4. Dynamo benchmarks
![image](https://github.com/user-attachments/assets/554ecce3-c85c-4a0e-88f1-2e73983c5dcd)
![image](https://github.com/user-attachments/assets/148c88f8-4367-4428-bb54-ce8a4deefd1b)
![image](https://github.com/user-attachments/assets/f2e744f4-d710-4699-acf4-1f130ecfadf1)
![image](https://github.com/user-attachments/assets/97128b80-4d0e-495a-aeda-dde3e70c96fd)
![image](https://github.com/user-attachments/assets/a9afce37-684c-45c0-b938-6dd7e0383805)
![image](https://github.com/user-attachments/assets/b8714236-9681-4fbe-8d98-be93deedab88)
![image](https://github.com/user-attachments/assets/4423061f-d133-45ba-98bd-d2f739e50431)
![image](https://github.com/user-attachments/assets/7955da10-3d23-493e-99fa-658f7f40035b)

## Validation results on XPU
Accuracy is same as baseline. Performance is shown below.
![image](https://github.com/user-attachments/assets/7645304d-5b1d-43f9-b840-9f846ed380a0)

## Validation results on ARM
![image](https://github.com/user-attachments/assets/080f7c02-0238-436f-ad20-5a9e3f6aafbb)
![image](https://github.com/user-attachments/assets/443742aa-ca61-41de-ae80-5d4c65cd0c87)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/147498
Approved by: https://github.com/fadara01, https://github.com/mingfeima, https://github.com/atalman
2025-02-24 14:32:51 +00:00
Sun, Jiayi
23e2f8ab3a [Inductor] add flag for linear binary folding and turn it off by default (#142108)
Fix https://github.com/pytorch/pytorch/issues/141755.

Summary:
linear binary folding results in a timm_model(levit_128) accuracy regression, this PR adds flag `enable_linear_binary_folding` for linear binary folding and turn it off by default.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/142108
Approved by: https://github.com/jgong5, https://github.com/leslie-fang-intel, https://github.com/jansel
2024-12-06 07:12:29 +00:00
Sun, Jiayi
93e3c91679 [inductor] support linear+binary foldinig for freezing path (#138807)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/138807
Approved by: https://github.com/jgong5, https://github.com/jansel

Co-authored-by: Jiong Gong <jiong.gong@intel.com>
2024-11-20 05:34:09 +00:00
Aaron Orenstein
7f98781f84 Fix autodeps from D62049222 that pyfmt broke (#136455)
Summary: `arc lint` changed the formatting which then caused autodeps to be confused.

Test Plan:
this passes:
```
arc lint --skip AUTODEPS
fbpython fbcode/tools/build/buck/linters/lint_autoformat.py --linter=autodeps --default-exec-timeout=1800 -- fbcode/caffe2/test/inductor/test_memory_planning.py
```

Differential Revision: D63277059

Pull Request resolved: https://github.com/pytorch/pytorch/pull/136455
Approved by: https://github.com/bobrenjc93, https://github.com/oulgen
2024-09-24 05:06:12 +00:00
Aaron Orenstein
8c356ce3da Fix lint errors in fbcode (#135614)
Summary: Fixed a bunch of fbcode imports that happened to work but confused autodeps.  After this autodeps still suggests "improvements" to TARGETS (which breaks our builds) but at least it can find all the imports.

Test Plan:
```
fbpython fbcode/tools/build/buck/linters/lint_autoformat.py --linter=autodeps --default-exec-timeout=1800 -- fbcode/caffe2/TARGETS fbcode/caffe2/test/TARGETS
```
Before:
```
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "test_export" (from caffe2/test/export/testing.py:229) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See https://fbur$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "testing" (from caffe2/test/export/test_export.py:87) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See https://fburl$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "test_export" (from caffe2/test/export/test_serdes.py:9) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See https://fb$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "testing" (from caffe2/test/export/test_serdes.py:10) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See https://fburl$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "testing" (from caffe2/test/export/test_retraceability.py:7) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See https:$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "test_export" (from caffe2/test/export/test_retraceability.py:6) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See ht$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "testing" (from caffe2/test/export/test_export_nonstrict.py:7) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See http$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "test_export" (from caffe2/test/export/test_export_nonstrict.py:6) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See $
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "test_export" (from caffe2/test/export/test_export_training_ir_to_run_decomp.py:8) when processing rule "test_export". Please make sure it's listed in the srcs parameter of an$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "testing" (from caffe2/test/export/test_export_training_ir_to_run_decomp.py:10) when processing rule "test_export". Please make sure it's listed in the srcs parameter of anoth$
ERROR while processing caffe2/test/TARGETS: Found "//python/typeshed_internal:typeshed_internal_library" owner for "cv2" but it is protected by visibility rules: [] (from caffe2/test/test_bundled_images.py:7) when processing rule "test_bundled_$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "caffe2.test.profiler_test_cpp_thread_lib" (from caffe2/test/profiler/test_cpp_thread.py:29) when processing rule "profiler_test_cpp_thread". Please make sure it's listed in t$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "torch._utils_internal.get_file_path_2" (from caffe2/test/test_custom_ops.py:23) when processing rule "custom_ops". Please make sure it's listed in the srcs parameter of anoth$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "torch._utils_internal.get_file_path_2" (from caffe2/test/test_public_bindings.py:13) when processing rule "public_bindings". Please make sure it's listed in the srcs paramete$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "torch._C._profiler.symbolize_tracebacks" (from caffe2/test/test_cuda.py:3348) when processing rule "test_cuda". Please make sure it's listed in the srcs parameter of another $
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "torch._C._profiler.gather_traceback" (from caffe2/test/test_cuda.py:3348) when processing rule "test_cuda". Please make sure it's listed in the srcs parameter of another rule$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for include <torch/csrc/autograd/profiler_kineto.h> (from caffe2/test/profiler/test_cpp_thread.cpp:2) when processing profiler_test_cpp_thread_lib.  Some things to try:
```

Differential Revision: D62049222

Pull Request resolved: https://github.com/pytorch/pytorch/pull/135614
Approved by: https://github.com/oulgen, https://github.com/laithsakka
2024-09-13 02:04:34 +00:00
Xu Han
900c5083ed [inductor] calibration inductor windows uts (9/N) (#134425)
enable Windows inductor UTs of `test/inductor/test_binary_folding.py`

Failed UT depends on https://github.com/pytorch/pytorch/pull/134427
Need to rebase after https://github.com/pytorch/pytorch/pull/134427 merged.
```cmd
2024-08-25T23:32:23.0905727Z Traceback (most recent call last):
2024-08-25T23:32:23.0906516Z   File "C:\actions-runner\_work\pytorch\pytorch\test\inductor\test_binary_folding.py", line 18, in <module>
2024-08-25T23:32:23.0908200Z     from inductor.test_inductor_freezing import TestCase
2024-08-25T23:32:23.0909883Z   File "C:\actions-runner\_work\pytorch\pytorch\test\inductor\test_inductor_freezing.py", line 39, in <module>
2024-08-25T23:32:23.0911128Z     raise unittest.SkipTest("requires sympy/functorch/filelock")
2024-08-25T23:32:23.0911801Z unittest.case.SkipTest: requires sympy/functorch/filelock
2024-08-25T23:32:23.0912370Z Got exit code 1
2024-08-25T23:32:23.0913155Z No stepcurrent file found. Either pytest didn't get to run (e.g. import error) or file got deleted (contact dev infra)
```

Local test pass:
<img width="1898" alt="image" src="https://github.com/user-attachments/assets/4a6e3f66-4bbc-4aab-8f0d-2e2318046e53">

Pull Request resolved: https://github.com/pytorch/pytorch/pull/134425
Approved by: https://github.com/ezyang, https://github.com/jansel
2024-08-26 20:57:41 +00:00
Xuehai Pan
134bc4fc34 [BE][Easy][12/19] enforce style for empty lines in import segments in test/i*/ (#129763)
See https://github.com/pytorch/pytorch/pull/129751#issue-2380881501. Most changes are auto-generated by linter.

You can review these PRs via:

```bash
git diff --ignore-all-space --ignore-blank-lines HEAD~1
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/129763
Approved by: https://github.com/jansel
2024-07-18 07:49:19 +00:00
PyTorch MergeBot
b732b52f1e Revert "[BE][Easy][12/19] enforce style for empty lines in import segments in test/i*/ (#129763)"
This reverts commit aecc746fcc.

Reverted https://github.com/pytorch/pytorch/pull/129763 on behalf of https://github.com/XuehaiPan due to need reland after rerunning lintrunner on main ([comment](https://github.com/pytorch/pytorch/pull/129763#issuecomment-2235736732))
2024-07-18 06:39:58 +00:00
Xuehai Pan
aecc746fcc [BE][Easy][12/19] enforce style for empty lines in import segments in test/i*/ (#129763)
See https://github.com/pytorch/pytorch/pull/129751#issue-2380881501. Most changes are auto-generated by linter.

You can review these PRs via:

```bash
git diff --ignore-all-space --ignore-blank-lines HEAD~1
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/129763
Approved by: https://github.com/jansel
2024-07-18 05:13:41 +00:00
xinan.lin
cc518ebd38 [Inductor Intel GPU backend Upstream] Reuse inductor test for Intel GPU (PART 2) (#124147)
Reuse Inductor test case for Intel GPU.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/124147
Approved by: https://github.com/EikanWang, https://github.com/jansel
2024-06-16 08:07:05 +00:00
Michael Lazos
402b289f3b Properly register parameter for binary folding test (#128356)
This PR properly registers the tensor used in the module compute as a parameter. This bug was hidden previously because all tensors on the nn modules would be considered constant by dynamo, with inlining NN modules, this is no longer the case.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/128356
Approved by: https://github.com/anijain2305
ghstack dependencies: #128355
2024-06-11 06:48:26 +00:00
Sam Larsen
4cd503c1f3 Enable FX graph cache for a batch of inductor tests (#121696)
Summary: Get more FX graph cache coverage by enabling it for these unit tests

Pull Request resolved: https://github.com/pytorch/pytorch/pull/121696
Approved by: https://github.com/eellison
2024-03-14 03:39:59 +00:00
PyTorch MergeBot
6bffde99b0 Revert "[inductor] Move things into torch/testing/_internal/inductor_utils.py (#113275)"
This reverts commit 66d09f8217.

Reverted https://github.com/pytorch/pytorch/pull/113275 on behalf of https://github.com/huydhn due to Sorry for reverting your stack, but it is failing to list test internally with buck2 ([comment](https://github.com/pytorch/pytorch/pull/113275#issuecomment-1811666004))
2023-11-15 01:44:26 +00:00
PyTorch MergeBot
1e60174891 Revert "[dynamo] Add run_inductor_tests entrypoint (#113278)"
This reverts commit b00311ce9e.

Reverted https://github.com/pytorch/pytorch/pull/113278 on behalf of https://github.com/huydhn due to Sorry for reverting your stack, but it is failing to list test internally with buck2 ([comment](https://github.com/pytorch/pytorch/pull/113278#issuecomment-1811646325))
2023-11-15 01:19:48 +00:00
Jason Ansel
b00311ce9e [dynamo] Add run_inductor_tests entrypoint (#113278)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113278
Approved by: https://github.com/yanboliang
2023-11-11 08:54:43 +00:00
Jason Ansel
66d09f8217 [inductor] Move things into torch/testing/_internal/inductor_utils.py (#113275)
This PR is just moving things around, so code shared by multiple tests files is in torch/testing/_internal/inductor_utils.py.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/113275
Approved by: https://github.com/yanboliang
ghstack dependencies: #113242
2023-11-11 03:17:35 +00:00
Peter Bell
c4fe817a69 [inductor] Fix test_dist on pre-sm80 and add skipCUDAIf decorator (#113384)
`test_dist` uses bfloat16 which isn't well supported by triton on pre-sm80
hardware, so split the test in two and add a skip. This also adds a
`skipCUDAIf` decorator which only skips on CUDA devices so the test still runs
on CPU.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/113384
Approved by: https://github.com/lezcano
2023-11-10 19:45:33 +00:00
PyTorch MergeBot
68bf0f1e7d Revert "[inductor] Move things into torch/testing/_internal/inductor_utils.py (#113275)"
This reverts commit c967dc526a.

Reverted https://github.com/pytorch/pytorch/pull/113275 on behalf of https://github.com/PaliC due to the diff this is stacked on top of appears to be causing inductor failures internally ([comment](https://github.com/pytorch/pytorch/pull/113275#issuecomment-1805131017))
2023-11-10 05:40:55 +00:00
Jason Ansel
c967dc526a [inductor] Move things into torch/testing/_internal/inductor_utils.py (#113275)
This PR is just moving things around, so code shared by multiple tests files is in torch/testing/_internal/inductor_utils.py.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/113275
Approved by: https://github.com/yanboliang
2023-11-10 00:11:09 +00:00
Kaichao You
b30ee35a6f [Inductor][FX]Support efficient conv bn eval (#108757)
This PR adds an `efficient_conv_bn_eval_graph_transform` pass to the inductor. It tries to identify consecutive conv + bn **computation** with bn in eval mode, and changes it to a more efficient implementation. It does not modify parameters, which makes it **support training** without any pain. If no such patterns are identified, it does nothing. Therefore, it is backward compatible.

It has great benefit in terms of memory footprint:

For resnet50 with input batchsize 64, image size 224, forward + backward training:

| Technique                   | Memory Footprint (GB)      | Remarks                                   |
|-------------------------------|----------------------------|-------------------------------------------|
| Eager Mode  | 5.18          |                                           |
| torch.compile                 | 5.46         | Strangely, not saving memory              |
| torch.compile with this PR                       | 2.88          | **Saves about 50% memory! **         |

The script to measure the memory footprint:

```python
from torchvision.models.resnet import resnet50
import torch

net = resnet50().eval().cuda()

input = torch.randn(64, 3, 224, 224).cuda()

opt_net = torch.compile(net) # Use torch.compile
# opt_net = net # Eager mode

current_memory = torch.cuda.memory_allocated()
torch.cuda.reset_peak_memory_stats()

for i in range(10):
    opt_net.zero_grad()
    output = opt_net(input)
    output.sum().backward()
    del output

peak_memory = torch.cuda.max_memory_allocated()
additional_peak_memory = peak_memory - current_memory
print(f"Additional peak memory used: {additional_peak_memory / (1024 ** 3)} GB")
```

More results can be found in the corresponding paper: (this method is called Tune Mode in the tables).

<img width="709" alt="image" src="https://github.com/pytorch/pytorch/assets/23236638/db4815b0-d93e-4726-b1d5-e6651f256484">

<img width="653" alt="image" src="https://github.com/pytorch/pytorch/assets/23236638/22e5e1ab-6129-4c3d-a875-3c7343293b2e">

Note: the difference between this PR and https://github.com/pytorch/pytorch/pull/106372 is that, https://github.com/pytorch/pytorch/pull/106372 tries to fix and change the implementation of `torch.fx.experimental.optimization.fuse`, which causes compatibility issues; this PR only introduces a new graph transform passes, and does not break the previous code.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/108757
Approved by: https://github.com/jansel
2023-09-20 08:10:02 +00:00
XiaobingSuper
9c1802f8e3 inductor: using binary folding path to do conv+bn folding (#105650)
This path will use binary folding to do conv+bn folding to avoid using ```make_fx``` which meets tracing errors in some model dynamic shape path.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/105650
Approved by: https://github.com/eellison
2023-07-26 07:37:47 +00:00
XiaobingSuper
837363c72f inductor: support conv+binary foldinig for freezing path (#105048)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105048
Approved by: https://github.com/jgong5, https://github.com/eellison
2023-07-26 01:50:30 +00:00