bug fix: ensure 4d input in _scaled_dot_product_attention_math_mps #146623
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/146623
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit 71b52ac with merge base 8a4dd76 ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
Skylion007
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Feb 6, 2025
Co-authored-by: Aaron Gokaslan <aaronGokaslan@gmail.com>
Skylion007
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Feb 6, 2025
| auto final_out = (sq ? out.squeeze(0) : out); | ||
| auto final_attn = (sq ? attn.squeeze(0) : attn); | ||
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| return {final_out, final_attn}; |
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| return {final_out, final_attn}; | |
| return {std::move(final_out), stdd::move(final_a 8000 ttn}); |
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@hellopahe thank you for the PR, can you please sign the CLA? |
malfet
reviewed
Feb 6, 2025
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And last but not least, this PR could benefit from a test, so that it would not regress again |
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I wonder if for 2.6.1 milestone one can land a smaller fix to just fall back to Math implementation if ndim is 3 (cc: @manuelcandales ) |
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
…tential dangling reference
malfet
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Feb 12, 2025
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@pytorchbot merge -f "Lint + MPS are green" |
Merge startedYour change will be merged immediately since you used the force (-f) flag, bypassing any CI checks (ETA: 1-5 minutes). Please use Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
Raymo111
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Feb 20, 2025
…146623) This pr addresses the issue in the MPS backend for `_scaled_dot_product_attention_math_mps` where a 3d input like (num_heads, seq_len, query_dim) cannot be automatically treated as (1, num_heads, seq_len, query_dim), which can be inferred on cpu or cuda, which can be circumvented by adding a util function to ensure a 4d shape. The issue was found in hiyouga/LLaMA-Factory#6835, in [transformers qwen2_vl](https://github.com/huggingface/transformers/blob/1590c664306766f32ba68c50e67f14d61b16925d/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py#L373C14-L373C93), 3d q/k/v were passed into sdpa function, which lead to an error. Considering consistency, since this pattern might pop up elsewhere in the transformers codebase, I think it makes more sense to maintain the same intuition across all platforms. --- reproduce code: ``` import torch import torch.nn.functional as F head_num, seq_len, embed_dim = 16, 16, 80 bsz = 1 q = torch.randn(head_num, seq_len, embed_dim) k = torch.randn(head_num, seq_len, embed_dim) v = torch.randn(head_num, seq_len, embed_dim) attention_mask = torch.ones(1, seq_len, seq_len) oo_cpu = F.scaled_dot_product_attention( q.to("cpu"), k.to("cpu"), v.to("cpu"), attention_mask.to("cpu"), dropout_p=0.0 ) if torch.backends.mps.is_available(): oo_mps = F.scaled_dot_product_attention( q.to("mps"), k.to("mps"), v.to("mps"), attention_mask.to("mps"), dropout_p=0.0 ) assert torch.allclose(oo_cpu, oo_mps.to("cpu"), atol=1e-5) ``` error outputs: ``` Traceback (most recent call last): File "/opt/homebrew/Caskroom/miniconda/base/envs/torch-dev/lib/python3.10/site-packages/IPython/core/interactiveshell.py", line 3577, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-2-5169b8d2c5dd>", line 21, in <module> oo_mps = F.scaled_dot_product_attention( IndexError: Dimension out of range (expected to be in range of [-3, 2], but got 3) ``` hardware and envs: ``` torch 2.6.0 apple m3 max ``` --- Pull Request resolved: #146623 Approved by: https://github.com/malfet Co-authored-by: Aaron Gokaslan <aaronGokaslan@gmail.com> Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
Ryo-not-rio
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that referenced
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Feb 24, 2025
…ytorch#146623) This pr addresses the issue in the MPS backend for `_scaled_dot_product_attention_math_mps` where a 3d input like (num_heads, seq_len, query_dim) cannot be automatically treated as (1, num_heads, seq_len, query_dim), which can be inferred on cpu or cuda, which can be circumvented by adding a util function to ensure a 4d shape. The issue was found in hiyouga/LLaMA-Factory#6835, in [transformers qwen2_vl](https://github.com/huggingface/transformers/blob/1590c664306766f32ba68c50e67f14d61b16925d/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py#L373C14-L373C93), 3d q/k/v were passed into sdpa function, which lead to an error. Considering consistency, since this pattern might pop up elsewhere in the transformers codebase, I think it makes more sense to maintain the same intuition across all platforms. --- reproduce code: ``` import torch import torch.nn.functional as F head_num, seq_len, embed_dim = 16, 16, 80 bsz = 1 q = torch.randn(head_num, seq_len, embed_dim) k = torch.randn(head_num, seq_len, embed_dim) v = torch.randn(head_num, seq_len, embed_dim) attention_mask = torch.ones(1, seq_len, seq_len) oo_cpu = F.scaled_dot_product_attention( q.to("cpu"), k.to("cpu"), v.to("cpu"), attention_mask.to("cpu"), dropout_p=0.0 ) if torch.backends.mps.is_available(): oo_mps = F.scaled_dot_product_attention( q.to("mps"), k.to("mps"), v.to("mps"), attention_mask.to("mps"), dropout_p=0.0 ) assert torch.allclose(oo_cpu, oo_mps.to("cpu"), atol=1e-5) ``` error outputs: ``` Traceback (most recent call last): File "/opt/homebrew/Caskroom/miniconda/base/envs/torch-dev/lib/python3.10/site-packages/IPython/core/interactiveshell.py", line 3577, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-2-5169b8d2c5dd>", line 21, in <module> oo_mps = F.scaled_dot_product_attention( IndexError: Dimension out of range (expected to be in range of [-3, 2], but got 3) ``` hardware and envs: ``` torch 2.6.0 apple m3 max ``` --- Pull Request resolved: pytorch#146623 Approved by: https://github.com/malfet Co-authored-by: Aaron Gokaslan <aaronGokaslan@gmail.com> Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
majing921201
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that referenced
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Mar 4, 2025
…ytorch#146623) This pr addresses the issue in the MPS backend for `_ 8000 scaled_dot_product_attention_math_mps` where a 3d input like (num_heads, seq_len, query_dim) cannot be automatically treated as (1, num_heads, seq_len, query_dim), which can be inferred on cpu or cuda, which can be circumvented by adding a util function to ensure a 4d shape. The issue was found in hiyouga/LLaMA-Factory#6835, in [transformers qwen2_vl](https://github.com/huggingface/transformers/blob/1590c664306766f32ba68c50e67f14d61b16925d/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py#L373C14-L373C93), 3d q/k/v were passed into sdpa function, which lead to an error. Considering consistency, since this pattern might pop up elsewhere in the transformers codebase, I think it makes more sense to maintain the same intuition across all platforms. --- reproduce code: ``` import torch import torch.nn.functional as F head_num, seq_len, embed_dim = 16, 16, 80 bsz = 1 q = torch.randn(head_num, seq_len, embed_dim) k = torch.randn(head_num, seq_len, embed_dim) v = torch.randn(head_num, seq_len, embed_dim) attention_mask = torch.ones(1, seq_len, seq_len) oo_cpu = F.scaled_dot_product_attention( q.to("cpu"), k.to("cpu"), v.to("cpu"), attention_mask.to("cpu"), dropout_p=0.0 ) if torch.backends.mps.is_available(): oo_mps = F.scaled_dot_product_attention( q.to("mps"), k.to("mps"), v.to("mps"), attention_mask.to("mps"), dropout_p=0.0 ) assert torch.allclose(oo_cpu, oo_mps.to("cpu"), atol=1e-5) ``` error outputs: ``` Traceback (most recent call last): File "/opt/homebrew/Caskroom/miniconda/base/envs/torch-dev/lib/python3.10/site-packages/IPython/core/interactiveshell.py", line 3577, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-2-5169b8d2c5dd>", line 21, in <module> oo_mps = F.scaled_dot_product_attention( IndexError: Dimension out of range (expected to be in range of [-3, 2], but got 3) ``` hardware and envs: ``` torch 2.6.0 apple m3 max ``` --- Pull Request resolved: pytorch#146623 Approved by: https://github.com/malfet Co-authored-by: Aaron Gokaslan <aaronGokaslan@gmail.com> Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
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This pr addresses the issue in the MPS backend for
_scaled_dot_product_attention_math_mpswhere a 3d input like (num_heads, seq_len, query_dim) cannot be automatically treated as (1, num_heads, seq_len, query_dim), which can be inferred on cpu or cuda, which can be circumvented by adding a util function to ensure a 4d shape.The issue was found in hiyouga/LLaMA-Factory#6835, in transformers qwen2_vl, 3d q/k/v were passed into sdpa function, which lead to an error.
Considering consistency, since this pattern might pop up elsewhere in the transformers codebase, I think it makes more sense to maintain the same intuition across all platforms.
reproduce code:
error outputs:
hardware and envs: