[Inductor][CPP] Fix issue in CPP GEMM Template Prune Tensor #141798
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/141798
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…141798) **Summary** When addressing [issue pytorch#134998](pytorch#134998), we will verify if any node in the current graph shares the same storage as the node we intend to prune. In the implementation, we assumed that when creating the `GraphLowering` in post-grad phase, there would be no `submodules`, and all `get_attr` nodes would correspond to a `torch.Tensor`. However, this assumption proves incorrect when enabling `FlexAttention`. In this scenario, `submodules` are present as `get_attr` node in post-grad phase. For example: ``` V1128 23:23:47.071000 1965794 torch/_inductor/compile_fx.py:875] [0/1] [__post_grad_graphs] class sdpa_score30(torch.nn.Module): V1128 23:23:47.071000 1965794 torch/_inductor/compile_fx.py:875] [0/1] [__post_grad_graphs] def forward(self, arg0_1: "bf16[][]cpu", arg1_1: "i32[][]cpu", arg2_1: "i32[][]cpu", arg3_1: "i32[][]cpu", arg4_1: "i32[][]cpu"): V1128 23:23:47.071000 1965794 torch/_inductor/compile_fx.py:875] [0/1] [__post_grad_graphs] return arg0_1 V1128 23:23:45.482000 1965794 torch/_inductor/freezing.py:118] [0/1] sdpa_score30 = self.sdpa_score30 V1128 23:23:45.482000 1965794 torch/_inductor/freezing.py:118] [0/1] sdpa_mask30 = self.sdpa_mask30 V1128 23:23:45.482000 1965794 torch/_inductor/freezing.py:118] [0/1] flex_attention_30 = torch.ops.higher_order.flex_attention(add_276, index_put_60, index_put_61, sdpa_score30, (_frozen_param293, _frozen_param295, _frozen_param296, _frozen_param297, _frozen_param298, _frozen_param299, _frozen_param300, _frozen_param301, 64, 64, sdpa_mask30), 0.08838834764831843, {'SKIP_MASK_SCORE': True, 'PRESCALE_QK': False, 'ROWS_GUARANTEED_SAFE': False, 'BLOCKS_ARE_CONTIGUOUS': False, 'OUTPUT_LOGSUMEXP': False}, (), (_frozen_param294,)); add_276 = sdpa_score30 = sdpa_mask30 = None V1128 23:23:45.482000 1965794 torch/_inductor/freezing.py:118] [0/1] getitem_60: "bf16[1, 32, 1, 128]" = flex_attention_30[0]; flex_attention_30 = None ``` We added an extra check in the implementation to ensure only comparing the `get_attr` node with `torch.Tensor`. It is difficult to reproduce this issue using pure high-order operators. Adding a unit test after pytorch#141453 lands would be more straightforward. Pull Request resolved: pytorch#141798 Approved by: https://github.com/jgong5
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Stack from ghstack (oldest at bottom):
Summary
When addressing issue #134998, we will verify if any node in the current graph shares the same storage as the node we intend to prune. In the implementation, we assumed that when creating the
GraphLoweringin post-grad phase, there would be nosubmodules, and allget_attrnodes would correspond to atorch.Tensor. However, this assumption proves incorrect when enablingFlexAttention. In this scenario,submodulesare present asget_attrnode in post-grad phase. For example:We added an extra check in the implementation to ensure only comparing the
get_attrnode withtorch.Tensor. It is difficult to reproduce this issue using pure high-order operators. Adding a unit test after #141453 lands would be more straightforward.cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @chauhang @aakhundov