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debug on KJT issue #2058

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Summary:

context

  • In the IR export workflow, the module takes KJTs as input and produces an ExportedProgram of the module
  • KJT actually has a variable length for the values and weights
  • This dynamic nature of KJT needs to be explicitly passed to torch.export

changes

  • add a util function to mark the input KJT's dynamic shape
  • add in the test of how to correctly specify the dynamics shapes for the input KJT

results

  • input KJTs with different value lengths
(Pdb) feature1.values()
tensor([0, 1, 2, 3, 2, 3])
(Pdb) feature2.values()
tensor([0, 1, 2, 3, 2, 3, 4])
  • exported_program can take those input KJTs
(Pdb) ep.module()(feature1)
[tensor([[-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16],
        [-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16]]), tensor([[-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16, -1.4368e-15,
         -1.4368e-15, -1.4368e-15, -1.4368e-15],
        [-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16, -1.4368e-15,
         -1.4368e-15, -1.4368e-15, -1.4368e-15]])]
(Pdb) ep.module()(feature2)
[tensor([[-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16],
        [-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16]]), tensor([[-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16, -1.4368e-15,
         -1.4368e-15, -1.4368e-15, -1.4368e-15],
        [-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16, -1.4368e-15,
         -1.4368e-15, -1.4368e-15, -1.4368e-15]])]
  • deserialized module can take those input KJTs
(Pdb) deserialized_model(feature1)
[tensor([[ 0.2630,  0.1473, -0.3691,  0.2261],
        [ 0.0000,  0.0000,  0.0000,  0.0000]],
       grad_fn=<SplitWithSizesBackward0>), tensor([[ 0.2198, -0.1648, -0.0121,  0.1998, -0.0384, -0.2458, -0.6844,  0.8741],
        [ 0.1313,  0.2968, -0.2979, -0.2150, -0.2593,  0.6758,  1.0010,  0.9052]],
       grad_fn=<SplitWithSizesBackward0>)]
(Pdb) deserialized_model(feature2)
[tensor([[ 0.2630,  0.1473, -0.3691,  0.2261],
        [ 0.0000,  0.0000,  0.0000,  0.0000]],
       grad_fn=<SplitWithSizesBackward0>), tensor([[ 0.2198, -0.1648, -0.0121,  0.1998, -0.0384, -0.2458, -0.6844,  0.8741],
        [ 0.1313,  0.2968, -0.2979, -0.2150, -0.9359,  0.1123,  0.5834, -0.1357]],
       grad_fn=<SplitWithSizesBackward0>)]

Differential Revision: D57824907

@facebook-github-bot facebook-github-bot added the CLA Signed This label is managed by the Facebook bot. Authors need to sign the CLA before a PR can be reviewed. label May 29, 2024
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This pull request was exported from Phabricator. Differential Revision: D57824907

TroyGarden added a commit to TroyGarden/torchrec that referenced this pull request May 29, 2024
@TroyGarden @facebook-github-bot
debug on KJT issue (pytorch#2058)
f5bbdb1 
Summary:

# context
* In the IR export workflow, the module takes KJTs as input and produces an `ExportedProgram` of the module
* KJT actually has a variable length for the values and weights
* This dynamic nature of KJT needs to be explicitly passed to torch.export

# changes
* add a util function to mark the input KJT's dynamic shape
* add in the test of how to correctly specify the dynamics shapes for the input KJT

# results
* input KJTs with different value lengths
```
(Pdb) feature1.values()
tensor([0, 1, 2, 3, 2, 3])
(Pdb) feature2.values()
tensor([0, 1, 2, 3, 2, 3, 4])
```
* exported_program can take those input KJTs
```
(Pdb) ep.module()(feature1)
[tensor([[-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16],
        [-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16]]), tensor([[-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16, -1.4368e-15,
         -1.4368e-15, -1.4368e-15, -1.4368e-15],
        [-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16, -1.4368e-15,
         -1.4368e-15, -1.4368e-15, -1.4368e-15]])]
(Pdb) ep.module()(feature2)
[tensor([[-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16],
        [-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16]]), tensor([[-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16, -1.4368e-15,
         -1.4368e-15, -1.4368e-15, -1.4368e-15],
        [-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16, -1.4368e-15,
         -1.4368e-15, -1.4368e-15, -1.4368e-15]])]
```
* deserialized module can take those input KJTs
```
(Pdb) deserialized_model(feature1)
[tensor([[ 0.2630,  0.1473, -0.3691,  0.2261],
        [ 0.0000,  0.0000,  0.0000,  0.0000]],
       grad_fn=<SplitWithSizesBackward0>), tensor([[ 0.2198, -0.1648, -0.0121,  0.1998, -0.0384, -0.2458, -0.6844,  0.8741],
        [ 0.1313,  0.2968, -0.2979, -0.2150, -0.2593,  0.6758,  1.0010,  0.9052]],
       grad_fn=<SplitWithSizesBackward0>)]
(Pdb) deserialized_model(feature2)
[tensor([[ 0.2630,  0.1473, -0.3691,  0.2261],
        [ 0.0000,  0.0000,  0.0000,  0.0000]],
       grad_fn=<SplitWithSizesBackward0>), tensor([[ 0.2198, -0.1648, -0.0121,  0.1998, -0.0384, -0.2458, -0.6844,  0.8741],
        [ 0.1313,  0.2968, -0.2979, -0.2150, -0.9359,  0.1123,  0.5834, -0.1357]],
       grad_fn=<SplitWithSizesBackward0>)]
```

Differential Revision: D57824907
@facebook-github-bot
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This pull request was exported from Phabricator. Differential Revision: D57824907

TroyGarden added a commit to TroyGarden/torchrec that referenced this pull request May 29, 2024
@TroyGarden @facebook-github-bot
util function for marking input KJT dynamic (pytorch#2058)
6acc122 
Summary:

# context
* In the IR export workflow, the module takes KJTs as input and produces an `ExportedProgram` of the module
* KJT actually has a variable length for the values and weights
* This dynamic nature of KJT needs to be explicitly passed to torch.export

# changes
* add a util function to mark the input KJT's dynamic shape
* add in the test of how to correctly specify the dynamics shapes for the input KJT

# results
* input KJTs with different value lengths
```
(Pdb) feature1.values()
tensor([0, 1, 2, 3, 2, 3])
(Pdb) feature2.values()
tensor([0, 1, 2, 3, 2, 3, 4])
```
* exported_program can take those input KJTs
```
(Pdb) ep.module()(feature1)
[tensor([[-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16],
        [-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16]]), tensor([[-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16, -1.4368e-15,
         -1.4368e-15, -1.4368e-15, -1.4368e-15],
        [-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16, -1.4368e-15,
         -1.4368e-15, -1.4368e-15, -1.4368e-15]])]
(Pdb) ep.module()(feature2)
[tensor([[-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16],
        [-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16]]), tensor([[-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16, -1.4368e-15,
         -1.4368e-15, -1.4368e-15, -1.4368e-15],
        [-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16, -1.4368e-15,
         -1.4368e-15, -1.4368e-15, -1.4368e-15]])]
```
* deserialized module can take those input KJTs
```
(Pdb) deserialized_model(feature1)
[tensor([[ 0.2630,  0.1473, -0.3691,  0.2261],
        [ 0.0000,  0.0000,  0.0000,  0.0000]],
       grad_fn=<SplitWithSizesBackward0>), tensor([[ 0.2198, -0.1648, -0.0121,  0.1998, -0.0384, -0.2458, -0.6844,  0.8741],
        [ 0.1313,  0.2968, -0.2979, -0.2150, -0.2593,  0.6758,  1.0010,  0.9052]],
       grad_fn=<SplitWithSizesBackward0>)]
(Pdb) deserialized_model(feature2)
[tensor([[ 0.2630,  0.1473, -0.3691,  0.2261],
        [ 0.0000,  0.0000,  0.0000,  0.0000]],
       grad_fn=<SplitWithSizesBackward0>), tensor([[ 0.2198, -0.1648, -0.0121,  0.1998, -0.0384, -0.2458, -0.6844,  0.8741],
        [ 0.1313,  0.2968, -0.2979, -0.2150, -0.9359,  0.1123,  0.5834, -0.1357]],
       grad_fn=<SplitWithSizesBackward0>)]
```

Differential Revision: D57824907
@facebook-github-bot
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This pull request was exported from Phabricator. Differential Revision: D57824907

TroyGarden added a commit to TroyGarden/torchrec that referenced this pull request May 30, 2024
@TroyGarden @facebook-github-bot
util function for marking input KJT dynamic (pytorch#2058)
cb5d996 
Summary:

# context
* In the IR export workflow, the module takes KJTs as input and produces an `ExportedProgram` of the module
* KJT actually has a variable length for the values and weights
* This dynamic nature of KJT needs to be explicitly passed to torch.export

# changes
* add a util function to mark the input KJT's dynamic shape
* add in the test of how to correctly specify the dynamics shapes for the input KJT

# results
* input KJTs with different value lengths
```
(Pdb) feature1.values()
tensor([0, 1, 2, 3, 2, 3])
(Pdb) feature2.values()
tensor([0, 1, 2, 3, 2, 3, 4])
```
* exported_program can take those input KJTs
```
(Pdb) ep.module()(feature1)
[tensor([[-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16],
        [-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16]]), tensor([[-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16, -1.4368e-15,
         -1.4368e-15, -1.4368e-15, -1.4368e-15],
        [-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16, -1.4368e-15,
         -1.4368e-15, -1.4368e-15, -1.4368e-15]])]
(Pdb) ep.module()(feature2)
[tensor([[-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16],
        [-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16]]), tensor([[-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16, -1.4368e-15,
         -1.4368e-15, -1.4368e-15, -1.4368e-15],
        [-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16, -1.4368e-15,
         -1.4368e-15, -1.4368e-15, -1.4368e-15]])]
```
* deserialized module can take those input KJTs
```
(Pdb) deserialized_model(feature1)
[tensor([[ 0.2630,  0.1473, -0.3691,  0.2261],
        [ 0.0000,  0.0000,  0.0000,  0.0000]],
       grad_fn=<SplitWithSizesBackward0>), tensor([[ 0.2198, -0.1648, -0.0121,  0.1998, -0.0384, -0.2458, -0.6844,  0.8741],
        [ 0.1313,  0.2968, -0.2979, -0.2150, -0.2593,  0.6758,  1.0010,  0.9052]],
       grad_fn=<SplitWithSizesBackward0>)]
(Pdb) deserialized_model(feature2)
[tensor([[ 0.2630,  0.1473, -0.3691,  0.2261],
        [ 0.0000,  0.0000,  0.0000,  0.0000]],
       grad_fn=<SplitWithSizesBackward0>), tensor([[ 0.2198, -0.1648, -0.0121,  0.1998, -0.0384, -0.2458, -0.6844,  0.8741],
        [ 0.1313,  0.2968, -0.2979, -0.2150, -0.9359,  0.1123,  0.5834, -0.1357]],
       grad_fn=<SplitWithSizesBackward0>)]
```

Differential Revision: D57824907
@facebook-github-bot
Copy link
Contributor

This pull request was exported from Phabricator. Differential Revision: D57824907

TroyGarden added a commit to TroyGarden/torchrec that referenced this pull request May 30, 2024
@TroyGarden @facebook-github-bot
util function for marking input KJT dynamic (pytorch#2058)
00229a4 
Summary:

# context
* In the IR export workflow, the module takes KJTs as input and produces an `ExportedProgram` of the module
* KJT actually has a variable length for the values and weights
* This dynamic nature of KJT needs to be explicitly passed to torch.export

# changes
* add a util function to mark the input KJT's dynamic shape
* add in the test of how to correctly specify the dynamics shapes for the input KJT

# results
* input KJTs with different value lengths
```
(Pdb) feature1.values()
tensor([0, 1, 2, 3, 2, 3])
(Pdb) feature2.values()
tensor([0, 1, 2, 3, 2, 3, 4])
```
* exported_program can take those input KJTs
```
(Pdb) ep.module()(feature1)
[tensor([[-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16],
        [-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16]]), tensor([[-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16, -1.4368e-15,
         -1.4368e-15, -1.4368e-15, -1.4368e-15],
        [-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16, -1.4368e-15,
         -1.4368e-15, -1.4368e-15, -1.4368e-15]])]
(Pdb) ep.module()(feature2)
[tensor([[-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16],
        [-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16]]), tensor([[-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16, -1.4368e-15,
         -1.4368e-15, -1.4368e-15, -1.4368e-15],
        [-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16, -1.4368e-15,
         -1.4368e-15, -1.4368e-15, -1.4368e-15]])]
```
* deserialized module can take those input KJTs
```
(Pdb) deserialized_model(feature1)
[tensor([[ 0.2630,  0.1473, -0.3691,  0.2261],
        [ 0.0000,  0.0000,  0.0000,  0.0000]],
       grad_fn=<SplitWithSizesBackward0>), tensor([[ 0.2198, -0.1648, -0.0121,  0.1998, -0.0384, -0.2458, -0.6844,  0.8741],
        [ 0.1313,  0.2968, -0.2979, -0.2150, -0.2593,  0.6758,  1.0010,  0.9052]],
       grad_fn=<SplitWithSizesBackward0>)]
(Pdb) deserialized_model(feature2)
[tensor([[ 0.2630,  0.1473, -0.3691,  0.2261],
        [ 0.0000,  0.0000,  0.0000,  0.0000]],
       grad_fn=<SplitWithSizesBackward0>), tensor([[ 0.2198, -0.1648, -0.0121,  0.1998, -0.0384, -0.2458, -0.6844,  0.8741],
        [ 0.1313,  0.2968, -0.2979, -0.2150, -0.9359,  0.1123,  0.5834, -0.1357]],
       grad_fn=<SplitWithSizesBackward0>)]
```

Differential Revision: D57824907
@TroyGarden @facebook-github-bot
util function for marking input KJT dynamic (pytorch#2058)
2535136 
Summary:

# context
* In the IR export workflow, the module takes KJTs as input and produces an `ExportedProgram` of the module
* KJT actually has a variable length for the values and weights
* This dynamic nature of KJT needs to be explicitly passed to torch.export

# changes
* add a util function to mark the input KJT's dynamic shape
* add in the test of how to correctly specify the dynamics shapes for the input KJT

# results
* input KJTs with different value lengths
```
(Pdb) feature1.values()
tensor([0, 1, 2, 3, 2, 3])
(Pdb) feature2.values()
tensor([0, 1, 2, 3, 2, 3, 4])
```
* exported_program can take those input KJTs
```
(Pdb) ep.module()(feature1)
[tensor([[-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16],
        [-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16]]), tensor([[-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16, -1.4368e-15,
         -1.4368e-15, -1.4368e-15, -1.4368e-15],
        [-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16, -1.4368e-15,
         -1.4368e-15, -1.4368e-15, -1.4368e-15]])]
(Pdb) ep.module()(feature2)
[tensor([[-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16],
        [-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16]]), tensor([[-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16, -1.4368e-15,
         -1.4368e-15, -1.4368e-15, -1.4368e-15],
        [-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16, -1.4368e-15,
         -1.4368e-15, -1.4368e-15, -1.4368e-15]])]
```
* deserialized module can take those input KJTs
```
(Pdb) deserialized_model(feature1)
[tensor([[ 0.2630,  0.1473, -0.3691,  0.2261],
        [ 0.0000,  0.0000,  0.0000,  0.0000]],
       grad_fn=<SplitWithSizesBackward0>), tensor([[ 0.2198, -0.1648, -0.0121,  0.1998, -0.0384, -0.2458, -0.6844,  0.8741],
        [ 0.1313,  0.2968, -0.2979, -0.2150, -0.2593,  0.6758,  1.0010,  0.9052]],
       grad_fn=<SplitWithSizesBackward0>)]
(Pdb) deserialized_model(feature2)
[tensor([[ 0.2630,  0.1473, -0.3691,  0.2261],
        [ 0.0000,  0.0000,  0.0000,  0.0000]],
       grad_fn=<SplitWithSizesBackward0>), tensor([[ 0.2198, -0.1648, -0.0121,  0.1998, -0.0384, -0.2458, -0.6844,  0.8741],
        [ 0.1313,  0.2968, -0.2979, -0.2150, -0.9359,  0.1123,  0.5834, -0.1357]],
       grad_fn=<SplitWithSizesBackward0>)]
```

Reviewed By: PaulZhang12

Differential Revision: D57824907
@facebook-github-bot
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This pull request was exported from Phabricator. Differential Revision: D57824907

PaulZhang12 pushed a commit that referenced this pull request Jun 5, 2024
@TroyGarden @PaulZhang12
util function for marking input KJT dynamic (#2058)
a57f1c6 
Summary:
Pull Request resolved: #2058

# context
* In the IR export workflow, the module takes KJTs as input and produces an `ExportedProgram` of the module
* KJT actually has a variable length for the values and weights
* This dynamic nature of KJT needs to be explicitly passed to torch.export

# changes
* add a util function to mark the input KJT's dynamic shape
* add in the test of how to correctly specify the dynamics shapes for the input KJT

# results
* input KJTs with different value lengths
```
(Pdb) feature1.values()
tensor([0, 1, 2, 3, 2, 3])
(Pdb) feature2.values()
tensor([0, 1, 2, 3, 2, 3, 4])
```
* exported_program can take those input KJTs
```
(Pdb) ep.module()(feature1)
[tensor([[-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16],
        [-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16]]), tensor([[-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16, -1.4368e-15,
         -1.4368e-15, -1.4368e-15, -1.4368e-15],
        [-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16, -1.4368e-15,
         -1.4368e-15, -1.4368e-15, -1.4368e-15]])]
(Pdb) ep.module()(feature2)
[tensor([[-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16],
        [-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16]]), tensor([[-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16, -1.4368e-15,
         -1.4368e-15, -1.4368e-15, -1.4368e-15],
        [-2.8735e-16, -2.8735e-16, -2.8735e-16, -2.8735e-16, -1.4368e-15,
         -1.4368e-15, -1.4368e-15, -1.4368e-15]])]
```
* deserialized module can take those input KJTs
```
(Pdb) deserialized_model(feature1)
[tensor([[ 0.2630,  0.1473, -0.3691,  0.2261],
        [ 0.0000,  0.0000,  0.0000,  0.0000]],
       grad_fn=<SplitWithSizesBackward0>), tensor([[ 0.2198, -0.1648, -0.0121,  0.1998, -0.0384, -0.2458, -0.6844,  0.8741],
        [ 0.1313,  0.2968, -0.2979, -0.2150, -0.2593,  0.6758,  1.0010,  0.9052]],
       grad_fn=<SplitWithSizesBackward0>)]
(Pdb) deserialized_model(feature2)
[tensor([[ 0.2630,  0.1473, -0.3691,  0.2261],
        [ 0.0000,  0.0000,  0.0000,  0.0000]],
       grad_fn=<SplitWithSizesBackward0>), tensor([[ 0.2198, -0.1648, -0.0121,  0.1998, -0.0384, -0.2458, -0.6844,  0.8741],
        [ 0.1313,  0.2968, -0.2979, -0.2150, -0.9359,  0.1123,  0.5834, -0.1357]],
       grad_fn=<SplitWithSizesBackward0>)]
```

Reviewed By: PaulZhang12

Differential Revision: D57824907

fbshipit-source-id: 615f602314e6517dba37e83eea5066de5950dc42
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