Make it possible to trace resnet with unbacked batch size#95222
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ezyang wants to merge 5 commits intogh/ezyang/1838/basefrom
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Make it possible to trace resnet with unbacked batch size#95222ezyang wants to merge 5 commits intogh/ezyang/1838/basefrom
ezyang wants to merge 5 commits intogh/ezyang/1838/basefrom
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Signed-off-by: Edward Z. Yang <ezyang@meta.com> [ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/95222
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This was referenced Feb 21, 2023
Signed-off-by: Edward Z. Yang <ezyangmeta.com> [ghstack-poisoned]
Signed-off-by: Edward Z. Yang <ezyangmeta.com> [ghstack-poisoned]
Signed-off-by: Edward Z. Yang <ezyangmeta.com> [ghstack-poisoned]
bdhirsh
reviewed
Feb 21, 2023
| AT_ERROR("reshape is not implemented for sparse tensors"); | ||
| } | ||
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| if (self.is_contiguous() && !self.is_mkldnn()) { |
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Are we eventually going to have to handle short-circuiting for channels last here too?
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I don't plan on adding it unless it shows up as a thing.
Signed-off-by: Edward Z. Yang <ezyangmeta.com> [ghstack-poisoned]
ezyang
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Feb 23, 2023
This takes the strategy described in https://docs.google.com/document/d/1lFRYAJo5nrfxRhwIzGnfi2pbLpU6T4ytSRSuLJ5qebI/edit# It is essentially #95222 but squashed and with changes that are unnecessary given that we assume nonzero returns > 1. What's in the PR: * nonzero now supports meta propagation. When `capture_dynamic_output_shape_ops`, it will return a tensor with an unbacked SymInt representing the size in question. * The unbacked SymInt is UNSOUNDLY assumed to be not equal to 0/1. We will still error if you guard otherwise. * PrimTorch pointwise operators are updated to use empty_permuted, to avoid guarding on unbacked SymInt from empty_strided (tested in `test_dynamic_pointwise_scalar`) * Convolution is updated to skip backend selection if batch is unbacked, to avoid guarding on unbacked SymInt (tested in `test_unbacked_batch_resnet`) * I kept the helper utilities like `definitely_true` for working with possibly unbacked SymInts. They're not used right now but maybe someone will find them useful. * Added `constrain_unify` to let you specify two unbacked SymInts must have the same value Signed-off-by: Edward Z. Yang <ezyangmeta.com> cc soumith voznesenskym yanboliang penguinwu anijain2305 EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx desertfire [ghstack-poisoned]
ezyang
added a commit
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Feb 23, 2023
This takes the strategy described in https://docs.google.com/document/d/1lFRYAJo5nrfxRhwIzGnfi2pbLpU6T4ytSRSuLJ5qebI/edit# It is essentially #95222 but squashed and with changes that are unnecessary given that we assume nonzero returns > 1. What's in the PR: * nonzero now supports meta propagation. When `capture_dynamic_output_shape_ops`, it will return a tensor with an unbacked SymInt representing the size in question. * The unbacked SymInt is UNSOUNDLY assumed to be not equal to 0/1. We will still error if you guard otherwise. * PrimTorch pointwise operators are updated to use empty_permuted, to avoid guarding on unbacked SymInt from empty_strided (tested in `test_dynamic_pointwise_scalar`) * Convolution is updated to skip backend selection if batch is unbacked, to avoid guarding on unbacked SymInt (tested in `test_unbacked_batch_resnet`) * I kept the helper utilities like `definitely_true` for working with possibly unbacked SymInts. They're not used right now but maybe someone will find them useful. * Added `constrain_unify` to let you specify two unbacked SymInts must have the same value Signed-off-by: Edward Z. Yang <ezyangmeta.com> cc soumith voznesenskym yanboliang penguinwu anijain2305 EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx desertfire [ghstack-poisoned]
pytorchmergebot
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Feb 24, 2023
This takes the strategy described in https://docs.google.com/document/d/1lFRYAJo5nrfxRhwIzGnfi2pbLpU6T4ytSRSuLJ5qebI/edit# It is essentially #95222 but squashed and with changes that are unnecessary given that we assume nonzero returns > 1. What's in the PR: * nonzero now supports meta propagation. When `capture_dynamic_output_shape_ops`, it will return a tensor with an unbacked SymInt representing the size in question. * The unbacked SymInt is UNSOUNDLY assumed to be not equal to 0/1. We will still error if you guard otherwise. * PrimTorch pointwise operators are updated to use empty_permuted, to avoid guarding on unbacked SymInt from empty_strided (tested in `test_dynamic_pointwise_scalar`) * Convolution is updated to skip backend selection if batch is unbacked, to avoid guarding on unbacked SymInt (tested in `test_unbacked_batch_resnet`) * I kept the helper utilities like `definitely_true` for working with possibly unbacked SymInts. They're not used right now but maybe someone will find them useful. * Added `constrain_unify` to let you specify two unbacked SymInts must have the same value Signed-off-by: Edward Z. Yang <ezyang@meta.com> Pull Request resolved: #95387 Approved by: https://github.com/voznesenskym
cyyever
pushed a commit
to cyyever/pytorch_private
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Feb 25, 2023
This takes the strategy described in https://docs.google.com/document/d/1lFRYAJo5nrfxRhwIzGnfi2pbLpU6T4ytSRSuLJ5qebI/edit# It is essentially pytorch/pytorch#95222 but squashed and with changes that are unnecessary given that we assume nonzero returns > 1. What's in the PR: * nonzero now supports meta propagation. When `capture_dynamic_output_shape_ops`, it will return a tensor with an unbacked SymInt representing the size in question. * The unbacked SymInt is UNSOUNDLY assumed to be not equal to 0/1. We will still error if you guard otherwise. * PrimTorch pointwise operators are updated to use empty_permuted, to avoid guarding on unbacked SymInt from empty_strided (tested in `test_dynamic_pointwise_scalar`) * Convolution is updated to skip backend selection if batch is unbacked, to avoid guarding on unbacked SymInt (tested in `test_unbacked_batch_resnet`) * I kept the helper utilities like `definitely_true` for working with possibly unbacked SymInts. They're not used right now but maybe someone will find them useful. * Added `constrain_unify` to let you specify two unbacked SymInts must have the same value Signed-off-by: Edward Z. Yang <ezyang@meta.com> Pull Request resolved: pytorch/pytorch#95387 Approved by: https://github.com/voznesenskym
cyyever
pushed a commit
to cyyever/pytorch_private
that referenced
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Feb 25, 2023
This takes the strategy described in https://docs.google.com/document/d/1lFRYAJo5nrfxRhwIzGnfi2pbLpU6T4ytSRSuLJ5qebI/edit# It is essentially pytorch/pytorch#95222 but squashed and with changes that are unnecessary given that we assume nonzero returns > 1. What's in the PR: * nonzero now supports meta propagation. When `capture_dynamic_output_shape_ops`, it will return a tensor with an unbacked SymInt representing the size in question. * The unbacked SymInt is UNSOUNDLY assumed to be not equal to 0/1. We will still error if you guard otherwise. * PrimTorch pointwise operators are updated to use empty_permuted, to avoid guarding on unbacked SymInt from empty_strided (tested in `test_dynamic_pointwise_scalar`) * Convolution is updated to skip backend selection if batch is unbacked, to avoid guarding on unbacked SymInt (tested in `test_unbacked_batch_resnet`) * I kept the helper utilities like `definitely_true` for working with possibly unbacked SymInts. They're not used right now but maybe someone will find them useful. * Added `constrain_unify` to let you specify two unbacked SymInts must have the same value Signed-off-by: Edward Z. Yang <ezyang@meta.com> Pull Request resolved: pytorch/pytorch#95387 Approved by: https://github.com/voznesenskym
cyyever
pushed a commit
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that referenced
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Mar 5, 2023
This takes the strategy described in https://docs.google.com/document/d/1lFRYAJo5nrfxRhwIzGnfi2pbLpU6T4ytSRSuLJ5qebI/edit# It is essentially pytorch/pytorch#95222 but squashed and with changes that are unnecessary given that we assume nonzero returns > 1. What's in the PR: * nonzero now supports meta propagation. When `capture_dynamic_output_shape_ops`, it will return a tensor with an unbacked SymInt representing the size in question. * The unbacked SymInt is UNSOUNDLY assumed to be not equal to 0/1. We will still error if you guard otherwise. * PrimTorch pointwise operators are updated to use empty_permuted, to avoid guarding on unbacked SymInt from empty_strided (tested in `test_dynamic_pointwise_scalar`) * Convolution is updated to skip backend selection if batch is unbacked, to avoid guarding on unbacked SymInt (tested in `test_unbacked_batch_resnet`) * I kept the helper utilities like `definitely_true` for working with possibly unbacked SymInts. They're not used right now but maybe someone will find them useful. * Added `constrain_unify` to let you specify two unbacked SymInts must have the same value Signed-off-by: Edward Z. Yang <ezyang@meta.com> Pull Request resolved: pytorch/pytorch#95387 Approved by: https://github.com/voznesenskym
jhavukainen
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Mar 15, 2024
…95387) This takes the strategy described in https://docs.google.com/document/d/1lFRYAJo5nrfxRhwIzGnfi2pbLpU6T4ytSRSuLJ5qebI/edit# It is essentially pytorch#95222 but squashed and with changes that are unnecessary given that we assume nonzero returns > 1. What's in the PR: * nonzero now supports meta propagation. When `capture_dynamic_output_shape_ops`, it will return a tensor with an unbacked SymInt representing the size in question. * The unbacked SymInt is UNSOUNDLY assumed to be not equal to 0/1. We will still error if you guard otherwise. * PrimTorch pointwise operators are updated to use empty_permuted, to avoid guarding on unbacked SymInt from empty_strided (tested in `test_dynamic_pointwise_scalar`) * Convolution is updated to skip backend selection if batch is unbacked, to avoid guarding on unbacked SymInt (tested in `test_unbacked_batch_resnet`) * I kept the helper utilities like `definitely_true` for working with possibly unbacked SymInts. They're not used right now but maybe someone will find them useful. * Added `constrain_unify` to let you specify two unbacked SymInts must have the same value Signed-off-by: Edward Z. Yang <ezyang@meta.com> Pull Request resolved: pytorch#95387 Approved by: https://github.com/voznesenskym
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Signed-off-by: Edward Z. Yang ezyang@meta.com