inductor: align baddbmm behavior with eager mode for beta=0 and input has nan value #96087
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… has nan value [ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/96087
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XiaobingSuper
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Mar 6, 2023
… has nan value ghstack-source-id: c73c188145d52452fad395ef0a3a3df1979d30e7 Pull Request resolved: #96087
…0 and input has nan value" For ```torch.baddbmm(input, mat1,mat2, beta=0)```, if ```beta``` is zero, the multiplication of value ```input*beta``` will be ignored for the eager mode(always gets zero number, see https://pytorch.org/docs/stable/generated/torch.baddbmm.html?highlight=torch+baddbmm#torch.baddbmm), but the inductor is not, the inductor will get a different value if the input has a ```nan``` of ```inf``` value: ``` def fn_test(input, mat1, mat2): return torch.baddbmm(input, mat1, mat2, beta=0.0) opt_fn = torch._dynamo.optimize("inductor")(fn_test) a, b, c = [torch.rand((3,2,2)) for _ in range(3)] real_out = fn_test(a, b, c) a[:] = torch.nan compiled_out = opt_fn(a, b,c) print(compiled_out) print(real_out) ``` before this PR, the output will be like this: ``` tensor([[[0.4272, 0.6037], [0.4279, 0.4219]], [[0.0838, 0.4873], [0.1210, 0.5516]], [[ nan, nan], [ nan, nan]]]) tensor([[[0.4272, 0.6037], [0.4279, 0.4219]], [[0.0838, 0.4873], [0.1210, 0.5516]], [[0.4985, 0.1072], [0.0857, 0.0186]]]) ``` cc soumith voznesenskym penguinwu anijain2305 EikanWang jgong5 Guobing-Chen zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx peterbell10 desertfire [ghstack-poisoned]
XiaobingSuper
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Mar 6, 2023
… has nan value ghstack-source-id: 5e8a805ed833853964814ed463be83a9edc346e2 Pull Request resolved: #96087
…0 and input has nan value" For ```torch.baddbmm(input, mat1,mat2, beta=0)```, if ```beta``` is zero, the multiplication of value ```input*beta``` will be ignored for the eager mode(always gets zero number, see https://pytorch.org/docs/stable/generated/torch.baddbmm.html?highlight=torch+baddbmm#torch.baddbmm), but the inductor is not, the inductor will get a different value if the input has a ```nan``` of ```inf``` value: ``` def fn_test(input, mat1, mat2): return torch.baddbmm(input, mat1, mat2, beta=0.0) opt_fn = torch._dynamo.optimize("inductor")(fn_test) a, b, c = [torch.rand((3,2,2)) for _ in range(3)] real_out = fn_test(a, b, c) a[:] = torch.nan compiled_out = opt_fn(a, b,c) print(compiled_out) print(real_out) ``` before this PR, the output will be like this: ``` tensor([[[0.4272, 0.6037], [0.4279, 0.4219]], [[0.0838, 0.4873], [0.1210, 0.5516]], [[ nan, nan], [ nan, nan]]]) tensor([[[0.4272, 0.6037], [0.4279, 0.4219]], [[0.0838, 0.4873], [0.1210, 0.5516]], [[0.4985, 0.1072], [0.0857, 0.0186]]]) ``` cc soumith voznesenskym penguinwu anijain2305 EikanWang jgong5 Guobing-Chen zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx peterbell10 desertfire [ghstack-poisoned]
XiaobingSuper
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Mar 6, 2023
… has nan value ghstack-source-id: 9a13a4b605e97cdad29de8ce91e676c32aca9085 Pull Request resolved: #96087
jansel
approved these changes
Mar 6, 2023
ngimel
approved these changes
Mar 6, 2023
…0 and input has nan value" For ```torch.baddbmm(input, mat1,mat2, beta=0)```, if ```beta``` is zero, the multiplication of value ```input*beta``` will be ignored for the eager mode(always gets zero number, see https://pytorch.org/docs/stable/generated/torch.baddbmm.html?highlight=torch+baddbmm#torch.baddbmm), but the inductor is not, the inductor will get a different value if the input has a ```nan``` of ```inf``` value: ``` def fn_test(input, mat1, mat2): return torch.baddbmm(input, mat1, mat2, beta=0.0) opt_fn = torch._dynamo.optimize("inductor")(fn_test) a, b, c = [torch.rand((3,2,2)) for _ in range(3)] real_out = fn_test(a, b, c) a[:] = torch.nan compiled_out = opt_fn(a, b,c) print(compiled_out) print(real_out) ``` before this PR, the output will be like this: ``` tensor([[[0.4272, 0.6037], [0.4279, 0.4219]], [[0.0838, 0.4873], [0.1210, 0.5516]], [[ nan, nan], [ nan, nan]]]) tensor([[[0.4272, 0.6037], [0.4279, 0.4219]], [[0.0838, 0.4873], [0.1210, 0.5516]], [[0.4985, 0.1072], [0.0857, 0.0186]]]) ``` cc soumith voznesenskym penguinwu anijain2305 EikanWang jgong5 Guobing-Chen zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx peterbell10 desertfire [ghstack-poisoned]
XiaobingSuper
added a commit
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this pull request
Mar 7, 2023
… has nan value ghstack-source-id: 218831d7bdb0ee079e8a3a022c76c9a1c6949c7d Pull Request resolved: #96087
jgong5
approved these changes
Mar 7, 2023
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ydwu4
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Mar 10, 2023
… has nan value (pytorch#96087) For ```torch.baddbmm(input, mat1,mat2, beta=0)```, if ```beta``` is zero, the multiplication of value ```input*beta``` will be ignored for the eager mode(always gets zero number, see https://pytorch.org/docs/stable/generated/torch.baddbmm.html?highlight=torch+baddbmm#torch.baddbmm), but the inductor is not, the inductor will get a different value if the input has a ```nan``` of ```inf``` value: ``` def fn_test(input, mat1, mat2): return torch.baddbmm(input, mat1, mat2, beta=0.0) opt_fn = torch._dynamo.optimize("inductor")(fn_test) a, b, c = [torch.rand((3,2,2)) for _ in range(3)] real_out = fn_test(a, b, c) a[:] = torch.nan compiled_out = opt_fn(a, b,c) print(compiled_out) print(real_out) ``` before this PR, the output will be like this: ``` tensor([[[0.4272, 0.6037], [0.4279, 0.4219]], [[0.0838, 0.4873], [0.1210, 0.5516]], [[ nan, nan], [ nan, nan]]]) tensor([[[0.4272, 0.6037], [0.4279, 0.4219]], [[0.0838, 0.4873], [0.1210, 0.5516]], [[0.4985, 0.1072], [0.0857, 0.0186]]]) ``` Pull Request resolved: pytorch#96087 Approved by: https://github.com/jansel, https://github.com/ngimel, https://github.com/jgong5
cyyever
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to cyyever/pytorch_private
that referenced
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Mar 12, 2023
… has nan value (#96087) For ```torch.baddbmm(input, mat1,mat2, beta=0)```, if ```beta``` is zero, the multiplication of value ```input*beta``` will be ignored for the eager mode(always gets zero number, see https://pytorch.org/docs/stable/generated/torch.baddbmm.html?highlight=torch+baddbmm#torch.baddbmm), but the inductor is not, the inductor will get a different value if the input has a ```nan``` of ```inf``` value: ``` def fn_test(input, mat1, mat2): return torch.baddbmm(input, mat1, mat2, beta=0.0) opt_fn = torch._dynamo.optimize("inductor")(fn_test) a, b, c = [torch.rand((3,2,2)) for _ in range(3)] real_out = fn_test(a, b, c) a[:] = torch.nan compiled_out = opt_fn(a, b,c) print(compiled_out) print(real_out) ``` before this PR, the output will be like this: ``` tensor([[[0.4272, 0.6037], [0.4279, 0.4219]], [[0.0838, 0.4873], [0.1210, 0.5516]], [[ nan, nan], [ nan, nan]]]) tensor([[[0.4272, 0.6037], [0.4279, 0.4219]], [[0.0838, 0.4873], [0.1210, 0.5516]], [[0.4985, 0.1072], [0.0857, 0.0186]]]) ``` Pull Request resolved: pytorch/pytorch#96087 Approved by: https://github.com/jansel, https://github.com/ngimel, https://github.com/jgong5
cyyever
pushed a commit
to cyyever/pytorch_private
that referenced
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Mar 12, 2023
… has nan value (#96087) For ```torch.baddbmm(input, mat1,mat2, beta=0)```, if ```beta``` is zero, the multiplication of value ```input*beta``` will be ignored for the eager mode(always gets zero number, see https://pytorch.org/docs/stable/generated/torch.baddbmm.html?highlight=torch+baddbmm#torch.baddbmm), but the inductor is not, the inductor will get a different value if the input has a ```nan``` of ```inf``` value: ``` def fn_test(input, mat1, mat2): return torch.baddbmm(input, mat1, mat2, beta=0.0) opt_fn = torch._dynamo.optimize("inductor")(fn_test) a, b, c = [torch.rand((3,2,2)) for _ in range(3)] real_out = fn_test(a, b, c) a[:] = torch.nan compiled_out = opt_fn(a, b,c) print(compiled_out) print(real_out) ``` before this PR, the output will be like this: ``` tensor([[[0.4272, 0.6037], [0.4279, 0.4219]], [[0.0838, 0.4873], [0.1210, 0.5516]], [[ nan, nan], [ nan, nan]]]) tensor([[[0.4272, 0.6037], [0.4279, 0.4219]], [[0.0838, 0.4873], [0.1210, 0.5516]], [[0.4985, 0.1072], [0.0857, 0.0186]]]) ``` Pull Request resolved: pytorch/pytorch#96087 Approved by: https://github.com/jansel, https://github.com/ngimel, https://github.com/jgong5
ydwu4
added a commit
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Mar 13, 2023
… has nan value (pytorch#96087) For ```torch.baddbmm(input, mat1,mat2, beta=0)```, if ```beta``` is zero, the multiplication of value ```input*beta``` will be ignored for the eager mode(always gets zero number, see https://pytorch.org/docs/stable/generated/torch.baddbmm.html?highlight=torch+baddbmm#torch.baddbmm), but the inductor is not, the inductor will get a different value if the input has a ```nan``` of ```inf``` value: ``` def fn_test(input, mat1, mat2): return torch.baddbmm(input, mat1, mat2, beta=0.0) opt_fn = torch._dynamo.optimize("inductor")(fn_test) a, b, c = [torch.rand((3,2,2)) for _ in range(3)] real_out = fn_test(a, b, c) a[:] = torch.nan compiled_out = opt_fn(a, b,c) print(compiled_out) print(real_out) ``` before this PR, the output will be like this: ``` tensor([[[0.4272, 0.6037], [0.4279, 0.4219]], [[0.0838, 0.4873], [0.1210, 0.5516]], [[ nan, nan], [ nan, nan]]]) tensor([[[0.4272, 0.6037], [0.4279, 0.4219]], [[0.0838, 0.4873], [0.1210, 0.5516]], [[0.4985, 0.1072], [0.0857, 0.0186]]]) ``` Pull Request resolved: pytorch#96087 Approved by: https://github.com/jansel, https://github.com/ngimel, https://github.com/jgong5
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Stack from ghstack (oldest at bottom):
For
torch.baddbmm(input, mat1,mat2, beta=0), ifbetais zero, the multiplication of valueinput*betawill be ignored for the eager mode(always gets zero number, see https://pytorch.org/docs/stable/generated/torch.baddbmm.html?highlight=torch+baddbmm#torch.baddbmm), but the inductor is not, the inductor will get a different value if the input has ananofinfvalue:before this PR, the output will be like this:
cc @soumith @voznesenskym @penguinwu @anijain2305 @EikanWang @jgong5 @Guobing-Chen @zhuhaozhe @blzheng @Xia-Weiwen @wenzhe-nrv @jiayisunx @peterbell10 @desertfire