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5419 Commits

Author SHA1 Message Date
Xuehai Pan
4dce5b71a0 [build] modernize build-frontend: python setup.py develop/install -> [uv ]pip install --no-build-isolation [-e ]. (#156027) 
Modernize the development installation:

```bash
# python setup.py develop
python -m pip install --no-build-isolation -e .

# python setup.py install
python -m pip install --no-build-isolation .
```

Now, the `python setup.py develop` is a wrapper around `python -m pip install -e .` since `setuptools>=80.0`:

- pypa/setuptools#4955

`python setup.py install` is deprecated and will emit a warning during run. The warning will become an error on October 31, 2025.

- 9c4d383631/setuptools/command/install.py (L58-L67)

> ```python
> SetuptoolsDeprecationWarning.emit(
>     "setup.py install is deprecated.",
>     """
>     Please avoid running ``setup.py`` directly.
>     Instead, use pypa/build, pypa/installer or other
>     standards-based tools.
>     """,
>     see_url="https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html",
>     due_date=(2025, 10, 31),
> )
> ```

- pypa/setuptools#3849

Additional Resource:

- [Why you shouldn't invoke setup.py directly](https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/156027
Approved by: https://github.com/ezyang
 2025-07-09 11:24:27 +00:00
Xuehai Pan
4cc8b60d1b [BE][1/16] fix typos in torch/ (#156311) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156311
Approved by: https://github.com/albanD
 2025-07-09 11:02:22 +00:00
Xuehai Pan
924fc52e18 [BE] add a linter to check consistency for cmake minimum version in requirements (#156961) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156961
Approved by: https://github.com/ezyang, https://github.com/malfet
 2025-07-09 10:44:17 +00:00
Xuehai Pan
84b77ec128 [BE] add a minimal linter to check pyproject.toml consistency (#156017) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156017
Approved by: https://github.com/ezyang
 2025-07-08 08:17:36 +00:00
dependabot[bot]
bbb930aba2 Bump urllib3 from 2.2.2 to 2.5.0 in /tools/build/bazel (#156390) 
Bumps [urllib3](https://github.com/urllib3/urllib3) from 2.2.2 to 2.5.0.
- [Release notes](https://github.com/urllib3/urllib3/releases)
- [Changelog](https://github.com/urllib3/urllib3/blob/main/CHANGES.rst)
- [Commits](https://github.com/urllib3/urllib3/compare/2.2.2...2.5.0)

---
updated-dependencies:
- dependency-name: urllib3
  dependency-version: 2.5.0
  dependency-type: indirect
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
 2025-07-07 17:13:21 -07:00
Ke Wen
99c1a6bdd9 [SymmMem] Find NVSHMEM from system installation (#157513) 
Previously we only search for NVSHMEM from pip install location.
This PR adds search in system locations deemed default by CMake.
Related: #157453 untars NVSHMEM into `/usr/local` on our CI machines.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/157513
Approved by: https://github.com/atalman, https://github.com/Skylion007
 2025-07-04 03:34:44 +00:00
Xuehai Pan
3fd84a8592 [BE][PYFMT] migrate PYFMT for torch/[a-c]*/ to ruff format (#144554) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144554
Approved by: https://github.com/soulitzer
 2025-07-03 18:56:07 +00:00
Laith Sakka
7cfd054075 [attempt 2] Compute contiguity symbolically to avoid dde, and introduce c++ sym_is_contiguous (#157472) 
Summary:
When we compute contiguity for a tensor with dynamic shapes we first:
1) Try to compute it without guarding.
2) If all shapes hinted, compute it with potentially adding guards.
3) if any input is not hinted, compute it symbolically.

sym_is_contiguous return a SymBool that is then either evaluated or guard_or_false can be called
on it to avoid data dependent errors.

ex:
 bool is_contiguous = input.sym_is_contiguous().guard_or_false(__FILE__, __LINE__);
is_contiguous_or_false is a helper function that does that.

In this PR I only handle default contiguity, will follow up with changes for other formats like  channel_last .
We use this patter in this PR for several locations to avoid DDEs.

Test Plan:
contbuild & OSS CI,

Rollback Plan:

Reviewed By: malfet

Differential Revision: D77639021

Pull Request resolved: https://github.com/pytorch/pytorch/pull/157472
Approved by: https://github.com/aorenste
 2025-07-02 23:12:29 +00:00
Xuehai Pan
11c07c848c [BE][14/16] fix typos in torch/ (torch/fx/) (#156604) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156604
Approved by: https://github.com/jingsh
ghstack dependencies: #156318, #156320, #156602
 2025-07-02 22:55:29 +00:00
Xuehai Pan
db259bd6b8 [BE][12/16] fix typos in torch/ (#156602) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156602
Approved by: https://github.com/justinchuby, https://github.com/albanD
ghstack dependencies: #156318, #156320
 2025-07-02 22:55:29 +00:00
Xuehai Pan
d5cdc36943 [BE][10/16] fix typos in torch/ (torch/csrc/jit/) (#156320) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156320
Approved by: https://github.com/albanD
ghstack dependencies: #156318
 2025-07-02 22:55:29 +00:00
Xuehai Pan
541584d22e [BE][8/16] fix typos in torch/ (torch/csrc/jit/) (#156318) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156318
Approved by: https://github.com/albanD
 2025-07-02 22:55:29 +00:00
Xuehai Pan
0e9d8032a3 [build] remove cmake cache and reconfigure again if it is invalid (#156958) 
See also:

- astral-sh/uv#14269

Pull Request resolved: https://github.com/pytorch/pytorch/pull/156958
Approved by: https://github.com/Skylion007
ghstack dependencies: #156742
 2025-07-02 18:46:32 +00:00
PyTorch MergeBot
c6a27bae36 Revert "[do not revert] Compute contiguity symbolically to avoid dde, and introduce c++ sym_is_contiguous (#155590)" 
This reverts commit d0a9629435aaceb5acbf31aad70f2109cb8a3ea2.

Reverted https://github.com/pytorch/pytorch/pull/155590 on behalf of https://github.com/laithsakka due to was asked by to land this internally  ([comment](https://github.com/pytorch/pytorch/pull/155590#issuecomment-3025796794))
 2025-07-01 22:58:14 +00:00
Laith Sakka
d0a9629435 [do not revert] Compute contiguity symbolically to avoid dde, and introduce c++ sym_is_contiguous (#155590) 
When we compute contiguity for a tensor with dynamic shapes we first:
1) Try to compute it without guarding.
2) If all shapes hinted, compute it with potentially adding guards.
3) if any input is not hinted, compute it symbolically.

sym_is_contiguous return a SymBool that is then either evaluated or guard_or_false can be called
on it to avoid data dependent errors.

ex:
 bool is_contiguous = input.sym_is_contiguous().guard_or_false(__FILE__, __LINE__);
is_contiguous_or_false is a helper function that does that.

In this PR I only handle default contiguity, will follow up with changes for other formats like  channel_last .
We use this patter in this PR for several locations to avoid DDEs.
Differential Revision: [D77183032](https://our.internmc.facebook.com/intern/diff/D77183032)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/155590
Approved by: https://github.com/ezyang
 2025-07-01 21:39:38 +00:00
PyTorch MergeBot
6401d1d53d Revert "Fused RMSNorm implementation (#153666)" 
This reverts commit e1aee86646aa6d1b9cb9d34351e43936401c5efc.

Reverted https://github.com/pytorch/pytorch/pull/153666 on behalf of https://github.com/davidberard98 due to causing build failures on main branch [GH job link](https://github.com/pytorch/pytorch/actions/runs/16007148842/job/45156382001) [HUD commit link](e1aee86646) ([comment](https://github.com/pytorch/pytorch/pull/153666#issuecomment-3025146176))
 2025-07-01 18:46:45 +00:00
AaronWang04
e1aee86646 Fused RMSNorm implementation (#153666) 
Relevant #72643

Benchmarked versus unfused torch implementation and torch.compile implementation. Around 9x speedup vs unfused implementation on cuda and slightly faster vs inductor compile on 5090.

```py
import torch
import torch.nn as nn

class RMSNorm(nn.Module):
    def __init__(self, dim, eps=1e-5):
        super().__init__()
        self.eps = eps
        self.scale = nn.Parameter(torch.ones(dim))

    def forward(self, x):
        norm_x = x.norm(2, dim=-1, keepdim=True)
        rms_x = norm_x * torch.rsqrt(torch.tensor(x.shape[-1], dtype=x.dtype))
        x_normed = x / (rms_x + self.eps)
        return self.scale * x_normed

def benchmark_rmsnorm_cuda(input_shape, normalized_dim, num_iterations=100, warmup_iterations=10, dtype=torch.float16):
    rms_norm_layer = torch.nn.RMSNorm(normalized_dim, device='cuda', dtype=dtype)
    input_data = torch.randn(input_shape, device='cuda', dtype=dtype)

    for _ in range(warmup_iterations):
        _ = rms_norm_layer(input_data)
    torch.cuda.synchronize()

    start_event = torch.cuda.Event(enable_timing=True)
    end_event = torch.cuda.Event(enable_timing=True)
    start_event.record()
    for _ in range(num_iterations):
        _ = rms_norm_layer(input_data)

    end_event.record()
    torch.cuda.synchronize()
    elapsed_time_ms = start_event.elapsed_time(end_event)
    avg_time_ms = elapsed_time_ms / num_iterations

    print(f"--- RMSNorm CUDA Benchmark ---")
    print(f"Input Shape: {input_shape}")
    print(f"Normalized Dimension: {normalized_dim}")
    print(f"Benchmark Iterations: {num_iterations}")
    print(f"--- Fused Implementation ---")
    print(f"Average Time per Iteration: {avg_time_ms:.4f} ms")
    print(f"Total Time for {num_iterations} Iterations: {elapsed_time_ms:.3f} ms")

    compiled_rms_norm = torch.compile(RMSNorm(dim=normalized_dim)).cuda()
    for _ in range(warmup_iterations):
        _ = compiled_rms_norm(input_data)
    torch.cuda.synchronize()

    start_event = torch.cuda.Event(enable_timing=True)
    end_event = torch.cuda.Event(enable_timing=True)
    start_event.record()
    for _ in range(num_iterations):
        _ = compiled_rms_norm(input_data)
    end_event.record()
    torch.cuda.synchronize()
    elapsed_time_ms = start_event.elapsed_time(end_event)
    avg_time_ms = elapsed_time_ms / num_iterations

    print(f"--- TorchCompile Implementation ---")
    print(f"Average Time per Iteration: {avg_time_ms:.4f} ms")
    print(f"Total Time for {num_iterations} Iterations: {elapsed_time_ms:.3f} ms")

    print("-" * 50)

if __name__ == '__main__':
    parameter_sets = [
        {'batch_size': 16, 'sequence_length': 256, 'hidden_features': 512, 'dtype': torch.float16},
        {'batch_size': 32, 'sequence_length': 512, 'hidden_features': 768, 'dtype': torch.float16},
        {'batch_size': 64, 'sequence_length': 1024, 'hidden_features': 1024, 'dtype': torch.float16},
        {'batch_size': 32, 'sequence_length': 512, 'hidden_features': 768, 'dtype': torch.float32},
        {'batch_size': 8, 'sequence_length': 2048, 'hidden_features': 2048, 'dtype': torch.float16},
    ]

    num_benchmark_iterations = 200
    num_warmup_iterations = 20

    for params in parameter_sets:
        batch_size = params['batch_size']
        sequence_length = params['sequence_length']
        hidden_features = params['hidden_features']
        data_type = params.get('dtype', torch.float16)

        shape = (batch_size, sequence_length, hidden_features)
        norm_dim_to_normalize = hidden_features

        print(f"Benchmarking with: BS={batch_size}, SeqLen={sequence_length}, Hidden={hidden_features}, DType={data_type}")
        benchmark_rmsnorm_cuda(input_shape=shape,
                               normalized_dim=norm_dim_to_normalize,
                               num_iterations=num_benchmark_iterations,
                               warmup_iterations=num_warmup_iterations,
                               dtype=data_type)
```

Here are the triton compile tests ran on a 5090 (comparing this branch vs main)
```py
import torch
import torch.nn as nn
from torch._inductor.utils import run_and_get_code, run_fw_bw_and_get_code

torch.manual_seed(0)

device = torch.device("cuda")

for batch in range(0, 9):
    for i in range(9, 16):
        normalized_shape_arg = (2**batch, 2**i)
        input_tensor = torch.randn(2**batch, 2**i, device=device, requires_grad=True)
        weight_tensor = torch.randn(2**batch, 2**i,device=device, requires_grad=True)

        model = torch.nn.functional.rms_norm
        compiled_model = torch.compile(model)
        loss = torch.randn_like(input_tensor)

        num_iter = 5
        for j in range(num_iter):
            output = compiled_model(input_tensor, normalized_shape_arg, weight_tensor)
            output.backward(loss)

        start_event = torch.cuda.Event(enable_timing=True)
        end_event = torch.cuda.Event(enable_timing=True)
        start_event.record()
        num_iter = 10
        for j in range(num_iter):
            output = compiled_model(input_tensor, normalized_shape_arg, weight_tensor)
            output.backward(loss)

        end_event.record()
        torch.cuda.synchronize()

        elapsed_time_ms = start_event.elapsed_time(end_event)
        avg_time_ms = round(elapsed_time_ms / num_iter, 5)
        print(2**batch, 2**i, avg_time_ms)
```
main
```
32 512 0.1812
32 1024 0.19021
32 2048 0.18871
32 4096 0.17019
32 8192 0.21944
32 16384 0.38871
32 32768 0.83282
64 512 0.14705
64 1024 0.13987
64 2048 0.14111
64 4096 0.21699
64 8192 0.43141
64 16384 0.90652
64 32768 2.18573
128 512 0.19361
128 1024 0.1963
128 2048 0.20122
128 4096 0.38888
128 8192 0.93795
128 16384 2.23437
128 32768 5.50079
256 512 0.16722
256 1024 0.22856
256 2048 0.39421
256 4096 0.96621
256 8192 2.48746
256 16384 5.53571
256 32768 11.97932
```
current branch
```
32 512 0.16328
32 1024 0.18104
32 2048 0.15508
32 4096 0.14356
32 8192 0.20111
32 16384 0.45974
32 32768 0.94799
64 512 0.16874
64 1024 0.18701
64 2048 0.16107
64 4096 0.20152
64 8192 0.46568
64 16384 0.96599
64 32768 2.21661
128 512 0.14982
128 1024 0.15565
128 2048 0.22241
128 4096 0.46128
128 8192 0.88883
128 16384 2.3097
128 32768 5.84448
256 512 0.14346
256 1024 0.2007
256 2048 0.45927
256 4096 0.87876
256 8192 2.10571
256 16384 5.73948
256 32768 12.98581
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153666
Approved by: https://github.com/ngimel
 2025-07-01 18:22:24 +00:00
Xuehai Pan
b146e1a264 [BE] remove duplicates in generated torch._VF.__all__ (#157365) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/157365
Approved by: https://github.com/Skylion007
 2025-07-01 15:43:20 +00:00
PyTorch MergeBot
1586521461 Revert "Compute contiguity symbolically to avoid dde, and introduce c++ sym_is_contiguous (#155590)" 
This reverts commit 2c76f31221e117b217b8a6a96a5405f626d2218a.

Reverted https://github.com/pytorch/pytorch/pull/155590 on behalf of https://github.com/jeanschmidt due to Breaking 1000s of internal builds, it cant be properly landed internally, there are no options except revert and codev. ([comment](https://github.com/pytorch/pytorch/pull/155590#issuecomment-3023503929))
 2025-07-01 11:23:00 +00:00
Xuehai Pan
754699610b [BE] always use uv pip if possible in pip_init.py for lintrunner init (#157199) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/157199
Approved by: https://github.com/ezyang
 2025-07-01 06:07:29 +00:00
Catherine Lee
f40efde2a4 [CI] Add prebuild command option, set prebuild command option for CI to build flash attention (#156236) 
Build flash attention separately in build using 2 jobs since it OOMs on more, then the rest of the job uses 6
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156236
Approved by: https://github.com/malfet
 2025-07-01 02:53:22 +00:00
PyTorch MergeBot
d5e6f42094 Revert "Use std::string_view in torchgen (#157050)" 
This reverts commit 064288cbab94c9931ca2296a2b9723e864f9050a.

Reverted https://github.com/pytorch/pytorch/pull/157050 on behalf of https://github.com/jeanschmidt due to Seems to have broken internal builds, more details on D77449943. @ezyang may I count on your help to get those changes merged? ([comment](https://github.com/pytorch/pytorch/pull/157050#issuecomment-3020222668))
 2025-06-30 18:08:54 +00:00
Xuehai Pan
f8293116f5 [BE][13/16] fix typos in torch/ (torch/ao/) (#156603) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156603
Approved by: https://github.com/msaroufim
 2025-06-29 04:34:04 +00:00
Xuehai Pan
90b973a2e2 [BE] parse CMake version from cmake -E capabilities instead of cmake --version (#157073) 
`cmake -E capabilities` produces a JSON format that is more machine-friendly.

```console
$ cmake --version
cmake version 4.0.3

CMake suite maintained and supported by Kitware (kitware.com/cmake).
$ cmake -E capabilities | jq '.version.string'
"4.0.3"
$ cmake -E capabilities | jq
{
  "debugger": true,
  "fileApi": {
    "requests": [
      {
        "kind": "codemodel",
        "version": [
          {
            "major": 2,
            "minor": 8
          }
        ]
      },
      {
        "kind": "configureLog",
        "version": [
          {
            "major": 1,
            "minor": 0
          }
        ]
      },
      {
        "kind": "cache",
        "version": [
          {
            "major": 2,
            "minor": 0
          }
        ]
      },
      {
        "kind": "cmakeFiles",
        "version": [
          {
            "major": 1,
            "minor": 1
          }
        ]
      },
      {
        "kind": "toolchains",
        "version": [
          {
            "major": 1,
            "minor": 0
          }
        ]
      }
    ]
  },
  "generators": [
    {
      "extraGenerators": [],
      "name": "Watcom WMake",
      "platformSupport": false,
      "toolsetSupport": false
    },
    {
      "extraGenerators": [
        "Kate"
      ],
      "name": "Ninja Multi-Config",
      "platformSupport": false,
      "toolsetSupport": false
    },
    {
      "extraGenerators": [
        "CodeBlocks",
        "CodeLite",
        "Eclipse CDT4",
        "Kate",
        "Sublime Text 2"
      ],
      "name": "Ninja",
      "platformSupport": false,
      "toolsetSupport": false
    },
    {
      "extraGenerators": [],
      "name": "Xcode",
      "platformSupport": false,
      "toolsetSupport": true
    },
    {
      "extraGenerators": [
        "CodeBlocks",
        "CodeLite",
        "Eclipse CDT4",
        "Kate",
        "Sublime Text 2"
      ],
      "name": "Unix Makefiles",
      "platformSupport": false,
      "toolsetSupport": false
    }
  ],
  "serverMode": false,
  "tls": true,
  "version": {
    "isDirty": false,
    "major": 4,
    "minor": 0,
    "patch": 3,
    "string": "4.0.3",
    "suffix": ""
  }
}
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/157073
Approved by: https://github.com/Skylion007
 2025-06-28 23:20:10 +00:00
PyTorch MergeBot
2eb744c08d Revert "[BE] parse CMake version from cmake -E capabilities instead of cmake --version (#157073)" 
This reverts commit 0c58bdd8fb5f269aef100af8e2c43cfcf5f1f9dd.

Reverted https://github.com/pytorch/pytorch/pull/157073 on behalf of https://github.com/XuehaiPan due to break libtorch build on Windows ([comment](https://github.com/pytorch/pytorch/pull/157073#issuecomment-3015273679))
 2025-06-28 13:40:19 +00:00
Xuehai Pan
0c58bdd8fb [BE] parse CMake version from cmake -E capabilities instead of cmake --version (#157073) 
`cmake -E capabilities` produces a JSON format that is more machine-friendly.

```console
$ cmake --version
cmake version 4.0.3

CMake suite maintained and supported by Kitware (kitware.com/cmake).
$ cmake -E capabilities | jq '.version.string'
"4.0.3"
$ cmake -E capabilities | jq
{
  "debugger": true,
  "fileApi": {
    "requests": [
      {
        "kind": "codemodel",
        "version": [
          {
            "major": 2,
            "minor": 8
          }
        ]
      },
      {
        "kind": "configureLog",
        "version": [
          {
            "major": 1,
            "minor": 0
          }
        ]
      },
      {
        "kind": "cache",
        "version": [
          {
            "major": 2,
            "minor": 0
          }
        ]
      },
      {
        "kind": "cmakeFiles",
        "version": [
          {
            "major": 1,
            "minor": 1
          }
        ]
      },
      {
        "kind": "toolchains",
        "version": [
          {
            "major": 1,
            "minor": 0
          }
        ]
      }
    ]
  },
  "generators": [
    {
      "extraGenerators": [],
      "name": "Watcom WMake",
      "platformSupport": false,
      "toolsetSupport": false
    },
    {
      "extraGenerators": [
        "Kate"
      ],
      "name": "Ninja Multi-Config",
      "platformSupport": false,
      "toolsetSupport": false
    },
    {
      "extraGenerators": [
        "CodeBlocks",
        "CodeLite",
        "Eclipse CDT4",
        "Kate",
        "Sublime Text 2"
      ],
      "name": "Ninja",
      "platformSupport": false,
      "toolsetSupport": false
    },
    {
      "extraGenerators": [],
      "name": "Xcode",
      "platformSupport": false,
      "toolsetSupport": true
    },
    {
      "extraGenerators": [
        "CodeBlocks",
        "CodeLite",
        "Eclipse CDT4",
        "Kate",
        "Sublime Text 2"
      ],
      "name": "Unix Makefiles",
      "platformSupport": false,
      "toolsetSupport": false
    }
  ],
  "serverMode": false,
  "tls": true,
  "version": {
    "isDirty": false,
    "major": 4,
    "minor": 0,
    "patch": 3,
    "string": "4.0.3",
    "suffix": ""
  }
}
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/157073
Approved by: https://github.com/Skylion007
 2025-06-28 13:35:30 +00:00
cyy
064288cbab Use std::string_view in torchgen (#157050) 
Let the generated code use std::sv

Pull Request resolved: https://github.com/pytorch/pytorch/pull/157050
Approved by: https://github.com/ezyang
 2025-06-27 06:36:10 +00:00
Laith Sakka
2c76f31221 Compute contiguity symbolically to avoid dde, and introduce c++ sym_is_contiguous (#155590) 
When we compute contiguity for a tensor with dynamic shapes we first:
1) Try to compute it without guarding.
2) If all shapes hinted, compute it with potentially adding guards.
3) if any input is not hinted, compute it symbolically.

sym_is_contiguous return a SymBool that is then either evaluated or guard_or_false can be called
on it to avoid data dependent errors.

ex:
 bool is_contiguous = input.sym_is_contiguous().guard_or_false(__FILE__, __LINE__);
is_contiguous_or_false is a helper function that does that.

In this PR I only handle default contiguity, will follow up with changes for other formats like  channel_last .
We use this patter in this PR for several locations to avoid DDEs.
Differential Revision: [D77183032](https://our.internmc.facebook.com/intern/diff/D77183032)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/155590
Approved by: https://github.com/ezyang
 2025-06-27 04:59:52 +00:00
IvanKobzarev
2f94f69b7c [aotd] Support mutations of the same input in fw and bw (#155354) 
Original issue: https://github.com/pytorch/pytorch/issues/154820

The issue happens when there is a mutation for the same input in forward AND in backward.

AOTD emited copy_ after joint_function tracing. This made this fx-node to correspond to the side effects of both mutations (in forward and in backward).
After that partitioner can put it either in forward or in backward.

The fix:

1/ Introduce joint_function.handle that allows to set "post_forward" callback, to be able to check inputs state after forward

We do not want to apply the mutation after joint, if we already applied it in forward. For that we need "mutation_counter" and memorize the version of mutation that we applied for  forward mutation.

2/ Exposing mutation_counter to python

We want to keep invariant that copy_ exist only in the end of joint graph.

3/ We memorize mutation_counter and state of the inputs after forward, using the handle post_forward.
Emit post_forward mutations after joint graph fully traced.

add for post_forward mutations "must_be_in_forward" tag (similar to existing "must_be_in_backward") to keep them in forward.

4/ Ban recompute of the source of mutation. Recompute can apply the same op (e.g. add) in forward and backward.
For this set MUST_SAVE for the source of mutation in forward.

proxy_tensor changes:

By default proxy tensor updates tensor_tracker. In this case applied mutations will be chained.
But we want that this copy_ will be independent and applied just to primals.
For this introducing a contextmanager to be able to disable update of tensor_tracker for adding forward mutations.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/155354
Approved by: https://github.com/bdhirsh
 2025-06-26 14:05:54 +00:00
Xuehai Pan
162ca185ff [BE][PYFMT] migrate PYFMT for torch/_[a-h]*/ to ruff format (#144551) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144551
Approved by: https://github.com/ezyang
ghstack dependencies: #148186
 2025-06-25 06:16:06 +00:00
cyy
41910d7a94 Move use of c10::string_view to std::string_view (#152509) 
Eliminate use of c10::string_view in OSS.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152509
Approved by: https://github.com/ezyang
 2025-06-25 01:57:49 +00:00
Sidharth
a00a697c17 [dynamo] updated version of detecting any differences between PRs unimplemented_v2() callsites and graph_break_registry json file (#156237) 
This PR runs an automatic check as part of dynamo_wrapped to make sure that all unimplemented_v2() callsites are mapped to the JSON file. It also fixes the issue of the CI not able to expand the hints, which was the root cause of the previous workflow failure. If not, the dev gets a message giving them instructions on how to update the JSON file. I also updated a dynamic gb_type to static and updated its test_error_message to include the GBID link for the graph break (before the link would not be produced).

Testing:
I ran the file with the argument to ensure all cases were covered, and also tested the test in CI.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/156237
Approved by: https://github.com/williamwen42
 2025-06-24 18:12:23 +00:00
Xuehai Pan
f5e6e52f25 [BE][PYFMT] migrate PYFMT for test/inductor/ to ruff format (#148186) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148186
Approved by: https://github.com/jansel
 2025-06-24 11:12:11 +00:00
Xuehai Pan
6d5c789ad5 [BE][PYFMT] migrate PYFMT for test/[a-h]*/ to ruff format (#144555) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144555
Approved by: https://github.com/ezyang
ghstack dependencies: #144551, #144554
 2025-06-24 04:53:54 +00:00
PyTorch MergeBot
e600e044a7 Revert "[aotd] Support mutations of the same input in fw and bw (#155354)" 
This reverts commit 3f920f3d8f5bd15d2222758f21f9a5d36e4dad1f.

Reverted https://github.com/pytorch/pytorch/pull/155354 on behalf of https://github.com/malfet due to Not sure why CI was green, but it breaks tons of tests, see 930b575389/1 ([comment](https://github.com/pytorch/pytorch/pull/155354#issuecomment-2998780884))
 2025-06-24 04:42:14 +00:00
IvanKobzarev
3f920f3d8f [aotd] Support mutations of the same input in fw and bw (#155354) 
Original issue: https://github.com/pytorch/pytorch/issues/154820

The issue happens when there is a mutation for the same input in forward AND in backward.

AOTD emited copy_ after joint_function tracing. This made this fx-node to correspond to the side effects of both mutations (in forward and in backward).
After that partitioner can put it either in forward or in backward.

The fix:

1/ Introduce joint_function.handle that allows to set "post_forward" callback, to be able to check inputs state after forward

We do not want to apply the mutation after joint, if we already applied it in forward. For that we need "mutation_counter" and memorize the version of mutation that we applied for  forward mutation.

2/ Exposing mutation_counter to python

We want to keep invariant that copy_ exist only in the end of joint graph.

3/ We memorize mutation_counter and state of the inputs after forward, using the handle post_forward.
Emit post_forward mutations after joint graph fully traced.

add for post_forward mutations "must_be_in_forward" tag (similar to existing "must_be_in_backward") to keep them in forward.

4/ Ban recompute of the source of mutation. Recompute can apply the same op (e.g. add) in forward and backward.
For this set MUST_SAVE for the source of mutation in forward.

proxy_tensor changes:

By default proxy tensor updates tensor_tracker. In this case applied mutations will be chained.
But we want that this copy_ will be independent and applied just to primals.
For this introducing a contextmanager to be able to disable update of tensor_tracker for adding forward mutations.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/155354
Approved by: https://github.com/bdhirsh
 2025-06-23 22:25:45 +00:00
Tom Ritchford
98a34e8d4b Move code out of individual token linters (#152256) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152256
Approved by: https://github.com/Skylion007
 2025-06-23 18:16:33 +00:00
Xuehai Pan
d55dc00f84 [BE][11/16] fix typos in torch/ (torch/csrc/distributed/) (#156321) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156321
Approved by: https://github.com/jingsh
ghstack dependencies: #156313, #156314, #156315, #156316, #156317, #156319
 2025-06-23 02:57:50 +00:00
Xuehai Pan
ced90016c1 [BE][7/16] fix typos in torch/ (torch/csrc/) (#156317) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156317
Approved by: https://github.com/albanD
ghstack dependencies: #156313, #156314, #156315, #156316
 2025-06-23 02:57:41 +00:00
Xuehai Pan
4ccc0381de [BE][5/16] fix typos in torch/ (torch/distributed/) (#156315) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156315
Approved by: https://github.com/Skylion007, https://github.com/albanD
ghstack dependencies: #156313, #156314
 2025-06-23 02:57:28 +00:00
Xuehai Pan
6ff6630375 [BE][3/16] fix typos in torch/ (torch/_inductor/) (#156313) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156313
Approved by: https://github.com/jingsh
 2025-06-23 02:57:12 +00:00
PyTorch MergeBot
f1331f3f1b Revert "[BE][3/16] fix typos in torch/ (torch/_inductor/) (#156313)" 
This reverts commit 3627270bdf17b0fb6f528ca1cb87d6f2ec32680a.

Reverted https://github.com/pytorch/pytorch/pull/156313 on behalf of https://github.com/atalman due to export/test_torchbind.py::TestCompileTorchbind::test_compile_error_on_input_aliasing_contents_backend_aot_eager [GH job link](https://github.com/pytorch/pytorch/actions/runs/15804799771/job/44548489912) [HUD commit link](c95f7fa874) ([comment](https://github.com/pytorch/pytorch/pull/156313#issuecomment-2994171213))
 2025-06-22 12:31:57 +00:00
PyTorch MergeBot
145d4cdc11 Revert "[BE][5/16] fix typos in torch/ (torch/distributed/) (#156315)" 
This reverts commit c2f0292bd5b4b3206f5b295e96f81cd6c178eb18.

Reverted https://github.com/pytorch/pytorch/pull/156315 on behalf of https://github.com/atalman due to export/test_torchbind.py::TestCompileTorchbind::test_compile_error_on_input_aliasing_contents_backend_aot_eager [GH job link](https://github.com/pytorch/pytorch/actions/runs/15804799771/job/44548489912) [HUD commit link](c95f7fa874) ([comment](https://github.com/pytorch/pytorch/pull/156313#issuecomment-2994171213))
 2025-06-22 12:31:57 +00:00
PyTorch MergeBot
035a68d25a Revert "[BE][7/16] fix typos in torch/ (torch/csrc/) (#156317)" 
This reverts commit ee72815f1180fe2d8bcdb23493999256169ac2fa.

Reverted https://github.com/pytorch/pytorch/pull/156317 on behalf of https://github.com/atalman due to export/test_torchbind.py::TestCompileTorchbind::test_compile_error_on_input_aliasing_contents_backend_aot_eager [GH job link](https://github.com/pytorch/pytorch/actions/runs/15804799771/job/44548489912) [HUD commit link](c95f7fa874) ([comment](https://github.com/pytorch/pytorch/pull/156313#issuecomment-2994171213))
 2025-06-22 12:31:56 +00:00
PyTorch MergeBot
4b55871e06 Revert "[BE][11/16] fix typos in torch/ (torch/csrc/distributed/) (#156321)" 
This reverts commit c95f7fa874a3116f1067f9092456ee7281003614.

Reverted https://github.com/pytorch/pytorch/pull/156321 on behalf of https://github.com/atalman due to export/test_torchbind.py::TestCompileTorchbind::test_compile_error_on_input_aliasing_contents_backend_aot_eager [GH job link](https://github.com/pytorch/pytorch/actions/runs/15804799771/job/44548489912) [HUD commit link](c95f7fa874) ([comment](https://github.com/pytorch/pytorch/pull/156321#issuecomment-2994163667))
 2025-06-22 12:27:36 +00:00
Sidharth
aeaf6b59e2 [dynamo] Weblink generation when unimplemented_v2() is called (#156033) 
This PR includes the GBID weblink whenever a user encounters a graph break. I also had to include the JSON file in setup.py, so it can be part of the files that are packaged in during CI. It also fixes the issue of the hardcoded error messages stripping away one of the '/' in 'https'.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/156033
Approved by: https://github.com/williamwen42
 2025-06-22 11:39:31 +00:00
Xuehai Pan
c95f7fa874 [BE][11/16] fix typos in torch/ (torch/csrc/distributed/) (#156321) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156321
Approved by: https://github.com/jingsh
ghstack dependencies: #156313, #156314, #156315, #156316, #156317, #156319
 2025-06-22 08:43:49 +00:00
Xuehai Pan
ee72815f11 [BE][7/16] fix typos in torch/ (torch/csrc/) (#156317) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156317
Approved by: https://github.com/albanD
ghstack dependencies: #156313, #156314, #156315, #156316
 2025-06-22 08:43:41 +00:00
Xuehai Pan
c2f0292bd5 [BE][5/16] fix typos in torch/ (torch/distributed/) (#156315) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156315
Approved by: https://github.com/Skylion007, https://github.com/albanD
ghstack dependencies: #156313, #156314
 2025-06-22 08:43:26 +00:00
Xuehai Pan
3627270bdf [BE][3/16] fix typos in torch/ (torch/_inductor/) (#156313) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156313
Approved by: https://github.com/jingsh
 2025-06-22 08:43:09 +00:00