Add memory format support to ones_like operator#27270
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VitalyFedyunin wants to merge 16 commits intogh/VitalyFedyunin/7/basefrom
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Add memory format support to ones_like operator#27270VitalyFedyunin wants to merge 16 commits intogh/VitalyFedyunin/7/basefrom
ones_like operator#27270VitalyFedyunin wants to merge 16 commits intogh/VitalyFedyunin/7/basefrom
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Adds memory_format keyword argument (positional for cpp). 'Preserve' behavior now follows next rules: 1) If tensor is non-overlapping and dense - output tensor will have the same strides as input tensor. 2) If not (1) and tensor is stored in the channels last format, output tensor going to have channels last format. 3) Output tensor is going to be contiguous in all other cases. --- Dense tensor is the tensor that store values in a contiguous block of memory. Non-overlapping tensor is the tensor in which elements occupy individual non-repetitive memory. [ghstack-poisoned]
This was referenced Oct 3, 2019
VitalyFedyunin
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Oct 3, 2019
Adds memory_format keyword argument (positional for cpp). 'Preserve' behavior now follows next rules: 1) If tensor is non-overlapping and dense - output tensor will have the same strides as input tensor. 2) If not (1) and tensor is stored in the channels last format, output tensor going to have channels last format. 3) Output tensor is going to be contiguous in all other cases. --- Dense tensor is the tensor that store values in a contiguous block of memory. Non-overlapping tensor is the tensor in which elements occupy individual non-repetitive memory. ghstack-source-id: 7956b5a Pull Request resolved: #27270
Adds memory_format keyword argument (positional for cpp). 'Preserve' behavior now follows next rules: 1) If tensor is non-overlapping and dense - output tensor will have the same strides as input tensor. 2) If not (1) and tensor is stored in the channels last format, output tensor going to have channels last format. 3) Output tensor is going to be contiguous in all other cases. --- Dense tensor is the tensor that store values in a contiguous block of memory. Non-overlapping tensor is the tensor in which elements occupy individual non-repetitive memory. [ghstack-poisoned]
VitalyFedyunin
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Oct 3, 2019
Adds memory_format keyword argument (positional for cpp). 'Preserve' behavior now follows next rules: 1) If tensor is non-overlapping and dense - output tensor will have the same strides as input tensor. 2) If not (1) and tensor is stored in the channels last format, output tensor going to have channels last format. 3) Output tensor is going to be contiguous in all other cases. --- Dense tensor is the tensor that store values in a contiguous block of memory. Non-overlapping tensor is the tensor in which elements occupy individual non-repetitive memory. ghstack-source-id: b2e3fdb Pull Request resolved: #27270
This was referenced Oct 8, 2019
Adds memory_format keyword argument (positional for cpp). 'Preserve' behavior now follows next rules: 1) If tensor is non-overlapping and dense - output tensor will have the same strides as input tensor. 2) If not (1) and tensor is stored in the channels last format, output tensor going to have channels last format. 3) Output tensor is going to be contiguous in all other cases. --- Dense tensor is the tensor that store values in a contiguous block of memory. Non-overlapping tensor is the tensor in which elements occupy individual non-repetitive memory. [ghstack-poisoned]
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Adds memory_format keyword argument (positional for cpp). 'Preserve' behavior now follows next rules: 1) If tensor is non-overlapping and dense - output tensor will have the same strides as input tensor. 2) If not (1) and tensor is stored in the channels last format, output tensor going to have channels last format. 3) Output tensor is going to be contiguous in all other cases. --- Dense tensor is the tensor that store values in a contiguous block of memory. Non-overlapping tensor is the tensor in which elements occupy individual non-repetitive memory. [ghstack-poisoned]
This was referenced Oct 14, 2019
…tor" Adds memory_format keyword argument (positional for cpp). 'Preserve' behavior now follows next rules: 1) If tensor is non-overlapping and dense - output tensor will have the same strides as input tensor. 2) If not (1) and tensor is stored in the channels last format, output tensor going to have channels last format. 3) Output tensor is going to be contiguous in all other cases. --- Dense tensor is the tensor that store values in a contiguous block of memory. Non-overlapping tensor is the tensor in which elements occupy individual non-repetitive memory. [ghstack-poisoned]
…perator" Adds memory_format keyword argument (positional for cpp). 'Preserve' behavior now follows next rules: 1) If tensor is non-overlapping and dense - output tensor will have the same strides as input tensor. 2) If not (1) and tensor is stored in the channels last format, output tensor going to have channels last format. 3) Output tensor is going to be contiguous in all other cases. --- Dense tensor is the tensor that store values in a contiguous block of memory. Non-overlapping tensor is the tensor in which elements occupy individual non-repetitive memory. [ghstack-poisoned]
…operator" Adds memory_format keyword argument (positional for cpp). 'Preserve' behavior now follows next rules: 1) If tensor is non-overlapping and dense - output tensor will have the same strides as input tensor. 2) If not (1) and tensor is stored in the channels last format, output tensor going to have channels last format. 3) Output tensor is going to be contiguous in all other cases. --- Dense tensor is the tensor that store values in a contiguous block of memory. Non-overlapping tensor is the tensor in which elements occupy individual non-repetitive memory. [ghstack-poisoned]
This was referenced Oct 16, 2019
…ike` operator" Adds memory_format keyword argument (positional for cpp). 'Preserve' behavior now follows next rules: 1) If tensor is non-overlapping and dense - output tensor will have the same strides as input tensor. 2) If not (1) and tensor is stored in the channels last format, output tensor going to have channels last format. 3) Output tensor is going to be contiguous in all other cases. --- Dense tensor is the tensor that store values in a contiguous block of memory. Non-overlapping tensor is the tensor in which elements occupy individual non-repetitive memory. [ghstack-poisoned]
…tor" Adds memory_format keyword argument (positional for cpp). 'Preserve' behavior now follows next rules: 1) If tensor is non-overlapping and dense - output tensor will have the same strides as input tensor. 2) If not (1) and tensor is stored in the channels last format, output tensor going to have channels last format. 3) Output tensor is going to be contiguous in all other cases. --- Dense tensor is the tensor that store values in a contiguous block of memory. Non-overlapping tensor is the tensor in which elements occupy individual non-repetitive memory. [ghstack-poisoned]
ezyang
reviewed
Oct 18, 2019
| return torch.randn((10, 3, 32, 32), device=device, dtype=torch.float32).contiguous(memory_format=torch.channels_last) | ||
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| def transformation_fn(tensor, **kwargs): | ||
| return torch.ones_like(tensor, **kwargs) |
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I feel like there should be a way to write these tests with less boilerplate
ezyang
approved these changes
Oct 18, 2019
This was referenced Oct 18, 2019
…tor" Adds memory_format keyword argument (positional for cpp). 'Preserve' behavior now follows next rules: 1) If tensor is non-overlapping and dense - output tensor will have the same strides as input tensor. 2) If not (1) and tensor is stored in the channels last format, output tensor going to have channels last format. 3) Output tensor is going to be contiguous in all other cases. --- Dense tensor is the tensor that store values in a contiguous block of memory. Non-overlapping tensor is the tensor in which elements occupy individual non-repetitive memory. Differential Revision: [D17980312](https://our.internmc.facebook.com/intern/diff/D17980312) [ghstack-poisoned]
…erator" Adds memory_format keyword argument (positional for cpp). 'Preserve' behavior now follows next rules: 1) If tensor is non-overlapping and dense - output tensor will have the same strides as input tensor. 2) If not (1) and tensor is stored in the channels last format, output tensor going to have channels last format. 3) Output tensor is going to be contiguous in all other cases. --- Dense tensor is the tensor that store values in a contiguous block of memory. Non-overlapping tensor is the tensor in which elements occupy individual non-repetitive memory. Differential Revision: [D17980312](https://our.internmc.facebook.com/intern/diff/D17980312) [ghstack-poisoned]
Adds memory_format keyword argument (positional for cpp). 'Preserve' behavior now follows next rules: 1) If tensor is non-overlapping and dense - output tensor will have the same strides as input tensor. 2) If not (1) and tensor is stored in the channels last format, output tensor going to have channels last format. 3) Output tensor is going to be contiguous in all other cases. --- Dense tensor is the tensor that store values in a contiguous block of memory. Non-overlapping tensor is the tensor in which elements occupy individual non-repetitive memory. Differential Revision: [D17980312](https://our.internmc.facebook.com/intern/diff/D17980312) [ghstack-poisoned]
Adds memory_format keyword argument (positional for cpp). 'Preserve' behavior now follows next rules: 1) If tensor is non-overlapping and dense - output tensor will have the same strides as input tensor. 2) If not (1) and tensor is stored in the channels last format, output tensor going to have channels last format. 3) Output tensor is going to be contiguous in all other cases. --- Dense tensor is the tensor that store values in a contiguous block of memory. Non-overlapping tensor is the tensor in which elements occupy individual non-repetitive memory. Differential Revision: [D17980312](https://our.internmc.facebook.com/intern/diff/D17980312) [ghstack-poisoned]
…to `ones_like` operator" Adds memory_format keyword argument (positional for cpp). 'Preserve' behavior now follows next rules: 1) If tensor is non-overlapping and dense - output tensor will have the same strides as input tensor. 2) If not (1) and tensor is stored in the channels last format, output tensor going to have channels last format. 3) Output tensor is going to be contiguous in all other cases. --- Dense tensor is the tensor that store values in a contiguous block of memory. Non-overlapping tensor is the tensor in which elements occupy individual non-repetitive memory. Differential Revision: [D17980312](https://our.internmc.facebook.com/intern/diff/D17980312) [ghstack-poisoned]
…e` operator" Adds memory_format keyword argument (positional for cpp). 'Preserve' behavior now follows next rules: 1) If tensor is non-overlapping and dense - output tensor will have the same strides as input tensor. 2) If not (1) and tensor is stored in the channels last format, output tensor going to have channels last format. 3) Output tensor is going to be contiguous in all other cases. --- Dense tensor is the tensor that store values in a contiguous block of memory. Non-overlapping tensor is the tensor in which elements occupy individual non-repetitive memory. Differential Revision: [D17980312](https://our.internmc.facebook.com/intern/diff/D17980312) [ghstack-poisoned]
Closed
zdevito
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Oct 25, 2019
Summary: Pull Request resolved: pytorch/pytorch#27270 Adds memory_format keyword argument (positional for cpp). 'Preserve' behavior now follows next rules: 1) If tensor is non-overlapping and dense - output tensor will have the same strides as input tensor. 2) If not (1) and tensor is stored in the channels last format, output tensor going to have channels last format. 3) Output tensor is going to be contiguous in all other cases. --- Dense tensor is the tensor that store values in a contiguous block of memory. Non-overlapping tensor is the tensor in which elements occupy individual non-repetitive memory. Test Plan: Imported from OSS Differential Revision: D17980312 Pulled By: VitalyFedyunin fbshipit-source-id: 5da9530f6b239306dbb66d1dfeefe88237f13bbd
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@VitalyFedyunin merged this pull request in 2c339a2. |
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@VitalyFedyunin merged this pull request in 2c339a2. |
facebook-github-bot
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Oct 31, 2019
…8839) Summary: Reapply reverted and fix files `gen_variable_type.py` `test_jit.py` #27891 Cleanup testing of _like operators #27890 Add memory format support to randn_like operator #27889 Add memory format support to randint_like operator #27562 Add memory format support to zeros_like operator #27561 Add memory format support to rand_like operator #27270 Add memory format support to ones_like operator #27262 Add memory format support to full_like operator Pull Request resolved: #28839 Test Plan: Imported from GitHub, without a `Test Plan:` line. buck test mode/dev //language_technology/neural_mt/os/pytorch_translate/test:test_onnx -- 'test_forced_decoder_export_vocab_reduction \(language_technology\.neural_mt\.os\.pytorch_translate\.test\.test_onnx\.TestONNX\)' Differential Revision: D18203397 Pulled By: VitalyFedyunin fbshipit-source-id: eea41cbd4c232cf5a54172b1e1b16b173798f298
zdevito
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Oct 31, 2019
…8839) Summary: Reapply reverted and fix files `gen_variable_type.py` `test_jit.py` pytorch/pytorch#27891 Cleanup testing of _like operators pytorch/pytorch#27890 Add memory format support to randn_like operator pytorch/pytorch#27889 Add memory format support to randint_like operator pytorch/pytorch#27562 Add memory format support to zeros_like operator pytorch/pytorch#27561 Add memory format support to rand_like operator pytorch/pytorch#27270 Add memory format support to ones_like operator pytorch/pytorch#27262 Add memory format support to full_like operator Pull Request resolved: pytorch/pytorch#28839 Test Plan: Imported from GitHub, without a `Test Plan:` line. buck test mode/dev //language_technology/neural_mt/os/pytorch_translate/test:test_onnx -- 'test_forced_decoder_export_vocab_reduction \(language_technology\.neural_mt\.os\.pytorch_translate\.test\.test_onnx\.TestONNX\)' Differential Revision: D18203397 Pulled By: VitalyFedyunin fbshipit-source-id: eea41cbd4c232cf5a54172b1e1b16b173798f298
xxtEchjovs44
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Jan 29, 2020
Adds memory_format keyword argument (positional for cpp). 'Preserve' behavior now follows next rules: 1) If tensor is non-overlapping and dense - output tensor will have the same strides as input tensor. 2) If not (1) and tensor is stored in the channels last format, output tensor going to have channels last format. 3) Output tensor is going to be contiguous in all other cases. --- Dense tensor is the tensor that store values in a contiguous block of memory. Non-overlapping tensor is the tensor in which elements occupy individual non-repetitive memory. ghstack-source-id: 7c04ab3 Pull Request resolved: pytorch/pytorch#27270
thiagocrepaldi
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Feb 4, 2020
Summary: Pull Request resolved: pytorch#27270 Adds memory_format keyword argument (positional for cpp). 'Preserve' behavior now follows next rules: 1) If tensor is non-overlapping and dense - output tensor will have the same strides as input tensor. 2) If not (1) and tensor is stored in the channels last format, output tensor going to have channels last format. 3) Output tensor is going to be contiguous in all other cases. --- Dense tensor is the tensor that store values in a contiguous block of memory. Non-overlapping tensor is the tensor in which elements occupy individual non-repetitive memory. Test Plan: Imported from OSS Differential Revision: D17980312 Pulled By: VitalyFedyunin fbshipit-source-id: 5da9530f6b239306dbb66d1dfeefe88237f13bbd
karansachdev-1012
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Feb 17, 2026
Adds memory_format keyword argument (positional for cpp). 'Preserve' behavior now follows next rules: 1) If tensor is non-overlapping and dense - output tensor will have the same strides as input tensor. 2) If not (1) and tensor is stored in the channels last format, output tensor going to have channels last format. 3) Output tensor is going to be contiguous in all other cases. --- Dense tensor is the tensor that store values in a contiguous block of memory. Non-overlapping tensor is the tensor in which elements occupy individual non-repetitive memory. ghstack-source-id: e3254eb Pull Request resolved: pytorch/pytorch#27270
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Stack from ghstack:
resize_op. #28292 Add memory format support to theresize_op.operator==of TensorOptions as confusing one #28076 Killoperator==of TensorOptions as confusing oneresize_as_operator #27979 Add memory format support toresize_as_operatorrandn_likeoperator #27890 Add memory format support torandn_likeoperatorrandint_likeoperator #27889 Add memory format support torandint_likeoperatorzeros_likeoperator #27562 Add memory format support tozeros_likeoperatorrand_likeoperator #27561 Add memory format support torand_likeoperatorones_likeoperator #27270 Add memory format support toones_likeoperatorfull_likeoperator #27262 Add memory format support tofull_likeoperatorempty_likeoperator #27244 Add memory format support toempty_likeoperatorAdds memory_format keyword argument (positional for cpp).
'Preserve' behavior now follows next rules:
Dense tensor is the tensor that store values in a contiguous block of memory.
Non-overlapping tensor is the tensor in which elements occupy individual non-repetitive memory.
Differential Revision: D17980312