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use total_L is None for dense_to_jagged #2599

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use total_L is None for dense_to_jagged #2599

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ColinPeppler
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@ColinPeppler ColinPeppler commented May 16, 2024 •
edited

Summary:

Could not guard on data-dependent expression Ne(u0, 0)

RuntimeError: Failed running call_function fbgemm.dense_to_jagged(*(FakeTensor(..., device='cuda:0', size=(s1, 2540, 512), dtype=torch.bfloat16), [FakeTensor(..., device='cuda:0', size=(s1 + 1,), dtype=torch.int64)]), **{'total_L': None}):
Could not guard on data-dependent expression Ne(u0, 0) (unhinted: Ne(u0, 0)).  (Size-like symbols: u0)
ATTENTION: guard_size_oblivious would fix the error, evaluating expression to True.
Maybe you need to add guard_size_oblivious to framework code, see doc below for more guidance.
Potential framework code culprit (scroll up for full backtrace):
  File "/mnt/xarfuse/uid-119376/a7f0c177-seed-nspid4026531836_cgpid3083025-ns-4026531841/fbgemm_gpu/sparse_ops.py", line 467, in dense_to_jagged_forward
    if not total_L:


User Stack (most recent call last):
  (snipped, see stack below for prefix)
  File "<eval_with_key>.82", line 424, in forward
    dense_to_jagged = torch.ops.fbgemm.dense_to_jagged(matmul_22, [asynchronous_complete_cumsum_9], total_L = None);  matmul_22 = None

using total_L is None instead will avoid this guard.

Differential Revision: D57465638

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This pull request was exported from Phabricator. Differential Revision: D57465638

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ColinPeppler added a commit to ColinPeppler/FBGEMM that referenced this pull request May 16, 2024
@ColinPeppler @facebook-github-bot
use total_L is None for dense_to_jagged (pytorch#2599)
93fc517 
Summary:

# Could not guard on data-dependent expression Ne(u0, 0)

```
RuntimeError: Failed running call_function fbgemm.dense_to_jagged(*(FakeTensor(..., device='cuda:0', size=(s1, 2540, 512), dtype=torch.bfloat16), [FakeTensor(..., device='cuda:0', size=(s1 + 1,), dtype=torch.int64)]), **{'total_L': None}):
Could not guard on data-dependent expression Ne(u0, 0) (unhinted: Ne(u0, 0)).  (Size-like symbols: u0)
ATTENTION: guard_size_oblivious would fix the error, evaluating expression to True.
Maybe you need to add guard_size_oblivious to framework code, see doc below for more guidance.
Potential framework code culprit (scroll up for full backtrace):
  File "/mnt/xarfuse/uid-119376/a7f0c177-seed-nspid4026531836_cgpid3083025-ns-4026531841/fbgemm_gpu/sparse_ops.py", line 467, in dense_to_jagged_forward
    if not total_L:


User Stack (most recent call last):
  (snipped, see stack below for prefix)
  File "<eval_with_key>.82", line 424, in forward
    dense_to_jagged = torch.ops.fbgemm.dense_to_jagged(matmul_22, [asynchronous_complete_cumsum_9], total_L = None);  matmul_22 = None
```

Differential Revision: D57465638
@facebook-github-bot
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This pull request was exported from Phabricator. Differential Revision: D57465638

ColinPeppler added a commit to ColinPeppler/FBGEMM that referenced this pull request May 16, 2024
@ColinPeppler @facebook-github-bot
use total_L is None for dense_to_jagged (pytorch#2599)
2711c98 
Summary:

# Could not guard on data-dependent expression Ne(u0, 0)

```
RuntimeError: Failed running call_function fbgemm.dense_to_jagged(*(FakeTensor(..., device='cuda:0', size=(s1, 2540, 512), dtype=torch.bfloat16), [FakeTensor(..., device='cuda:0', size=(s1 + 1,), dtype=torch.int64)]), **{'total_L': None}):
Could not guard on data-dependent expression Ne(u0, 0) (unhinted: Ne(u0, 0)).  (Size-like symbols: u0)
ATTENTION: guard_size_oblivious would fix the error, evaluating expression to True.
Maybe you need to add guard_size_oblivious to framework code, see doc below for more guidance.
Potential framework code culprit (scroll up for full backtrace):
  File "/mnt/xarfuse/uid-119376/a7f0c177-seed-nspid4026531836_cgpid3083025-ns-4026531841/fbgemm_gpu/sparse_ops.py", line 467, in dense_to_jagged_forward
    if not total_L:


User Stack (most recent call last):
  (snipped, see stack below for prefix)
  File "<eval_with_key>.82", line 424, in forward
    dense_to_jagged = torch.ops.fbgemm.dense_to_jagged(matmul_22, [asynchronous_complete_cumsum_9], total_L = None);  matmul_22 = None
```

Differential Revision: D57465638
@facebook-github-bot
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This pull request was exported from Phabricator. Differential Revision: D57465638

ColinPeppler added a commit to ColinPeppler/FBGEMM that referenced this pull request May 28, 2024
@ColinPeppler @facebook-github-bot
use total_L is None for dense_to_jagged (pytorch#2599)
9c742f1 
Summary:

# Could not guard on data-dependent expression Ne(u0, 0)

```
RuntimeError: Failed running call_function fbgemm.dense_to_jagged(*(FakeTensor(..., device='cuda:0', size=(s1, 2540, 512), dtype=torch.bfloat16), [FakeTensor(..., device='cuda:0', size=(s1 + 1,), dtype=torch.int64)]), **{'total_L': None}):
Could not guard on data-dependent expression Ne(u0, 0) (unhinted: Ne(u0, 0)).  (Size-like symbols: u0)
ATTENTION: guard_size_oblivious would fix the error, evaluating expression to True.
Maybe you need to add guard_size_oblivious to framework code, see doc below for more guidance.
Potential framework code culprit (scroll up for full backtrace):
  File "/mnt/xarfuse/uid-119376/a7f0c177-seed-nspid4026531836_cgpid3083025-ns-4026531841/fbgemm_gpu/sparse_ops.py", line 467, in dense_to_jagged_forward
    if not total_L:


User Stack (most recent call last):
  (snipped, see stack below for prefix)
  File "<eval_with_key>.82", line 424, in forward
    dense_to_jagged = torch.ops.fbgemm.dense_to_jagged(matmul_22, [asynchronous_complete_cumsum_9], total_L = None);  matmul_22 = None
```

Reviewed By: frank-wei

Differential Revision: D57465638
@facebook-github-bot
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This pull request was exported from Phabricator. Differential Revision: D57465638

ColinPeppler added a commit to ColinPeppler/FBGEMM that referenced this pull request May 28, 2024
@ColinPeppler @facebook-github-bot
use total_L is None for dense_to_jagged (pytorch#2599)
b433d97 
Summary:

# Could not guard on data-dependent expression Ne(u0, 0)

```
RuntimeError: Failed running call_function fbgemm.dense_to_jagged(*(FakeTensor(..., device='cuda:0', size=(s1, 2540, 512), dtype=torch.bfloat16), [FakeTensor(..., device='cuda:0', size=(s1 + 1,), dtype=torch.int64)]), **{'total_L': None}):
Could not guard on data-dependent expression Ne(u0, 0) (unhinted: Ne(u0, 0)).  (Size-like symbols: u0)
ATTENTION: guard_size_oblivious would fix the error, evaluating expression to True.
Maybe you need to add guard_size_oblivious to framework code, see doc below for more guidance.
Potential framework code culprit (scroll up for full backtrace):
  File "/mnt/xarfuse/uid-119376/a7f0c177-seed-nspid4026531836_cgpid3083025-ns-4026531841/fbgemm_gpu/sparse_ops.py", line 467, in dense_to_jagged_forward
    if not total_L:


User Stack (most recent call last):
  (snipped, see stack below for prefix)
  File "<eval_with_key>.82", line 424, in forward
    dense_to_jagged = torch.ops.fbgemm.dense_to_jagged(matmul_22, [asynchronous_complete_cumsum_9], total_L = None);  matmul_22 = None
```

Reviewed By: frank-wei

Differential Revision: D57465638
@facebook-github-bot
Copy link
Contributor

This pull request was exported from Phabricator. Differential Revision: D57465638

ColinPeppler added a commit to ColinPeppler/FBGEMM that referenced this pull request May 28, 2024
@ColinPeppler @facebook-github-bot
use total_L is None for dense_to_jagged (pytorch#2599)
c9c1bd6 
Summary:

# Could not guard on data-dependent expression Ne(u0, 0)

```
RuntimeError: Failed running call_function fbgemm.dense_to_jagged(*(FakeTensor(..., device='cuda:0', size=(s1, 2540, 512), dtype=torch.bfloat16), [FakeTensor(..., device='cuda:0', size=(s1 + 1,), dtype=torch.int64)]), **{'total_L': None}):
Could not guard on data-dependent expression Ne(u0, 0) (unhinted: Ne(u0, 0)).  (Size-like symbols: u0)
ATTENTION: guard_size_oblivious would fix the error, evaluating expression to True.
Maybe you need to add guard_size_oblivious to framework code, see doc below for more guidance.
Potential framework code culprit (scroll up for full backtrace):
  File "/mnt/xarfuse/uid-119376/a7f0c177-seed-nspid4026531836_cgpid3083025-ns-4026531841/fbgemm_gpu/sparse_ops.py", line 467, in dense_to_jagged_forward
    if not total_L:


User Stack (most recent call last):
  (snipped, see stack below for prefix)
  File "<eval_with_key>.82", line 424, in forward
    dense_to_jagged = torch.ops.fbgemm.dense_to_jagged(matmul_22, [asynchronous_complete_cumsum_9], total_L = None);  matmul_22 = None
```

Reviewed By: frank-wei

Differential Revision: D57465638
@facebook-github-bot
Copy link
Contributor

This pull request was exported from Phabricator. Differential Revision: D57465638

@ColinPeppler @facebook-github-bot
use total_L is None for dense_to_jagged + fix linter errors (pytorch#…
f1aa0ad 
…2599)

Summary:

# Could not guard on data-dependent expression Ne(u0, 0)

```
RuntimeError: Failed running call_function fbgemm.dense_to_jagged(*(FakeTensor(..., device='cuda:0', size=(s1, 2540, 512), dtype=torch.bfloat16), [FakeTensor(..., device='cuda:0', size=(s1 + 1,), dtype=torch.int64)]), **{'total_L': None}):
Could not guard on data-dependent expression Ne(u0, 0) (unhinted: Ne(u0, 0)).  (Size-like symbols: u0)
ATTENTION: guard_size_oblivious would fix the error, evaluating expression to True.
Maybe you need to add guard_size_oblivious to framework code, see doc below for more guidance.
Potential framework code culprit (scroll up for full backtrace):
  File "/mnt/xarfuse/uid-119376/a7f0c177-seed-nspid4026531836_cgpid3083025-ns-4026531841/fbgemm_gpu/sparse_ops.py", line 467, in dense_to_jagged_forward
    if not total_L:


User Stack (most recent call last):
  (snipped, see stack below for prefix)
  File "<eval_with_key>.82", line 424, in forward
    dense_to_jagged = torch.ops.fbgemm.dense_to_jagged(matmul_22, [asynchronous_complete_cumsum_9], total_L = None);  matmul_22 = None
```

Reviewed By: frank-wei

Differential Revision: D57465638
@facebook-github-bot
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This pull request was exported from Phabricator. Differential Revision: D57465638

frank-wei
frank-wei approved these changes May 28, 2024
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LGTM!

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This pull request has been merged in b633904.

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@frank-wei frank-wei frank-wei approved these changes

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