Add memory format support to randn_like operator#27890
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VitalyFedyunin wants to merge 15 commits intogh/VitalyFedyunin/12/basefrom
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Add memory format support to randn_like operator#27890VitalyFedyunin wants to merge 15 commits intogh/VitalyFedyunin/12/basefrom
randn_like operator#27890VitalyFedyunin wants to merge 15 commits intogh/VitalyFedyunin/12/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 14, 2019
…ator" 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]
… 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
…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. [ghstack-poisoned]
…ator" 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]
…ator" 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: [D17980314](https://our.internmc.facebook.com/intern/diff/D17980314) [ghstack-poisoned]
ezyang
approved these changes
Oct 18, 2019
This was referenced Oct 18, 2019
…ator" 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: [D17980314](https://our.internmc.facebook.com/intern/diff/D17980314) [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. Differential Revision: [D17980314](https://our.internmc.facebook.com/intern/diff/D17980314) [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: [D17980314](https://our.internmc.facebook.com/intern/diff/D17980314) [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: [D17980314](https://our.internmc.facebook.com/intern/diff/D17980314) [ghstack-poisoned]
…to `randn_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: [D17980314](https://our.internmc.facebook.com/intern/diff/D17980314) [ghstack-poisoned]
…"Add memory format support to `randn_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: [D17980314](https://our.internmc.facebook.com/intern/diff/D17980314) [ghstack-poisoned]
…ke` 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: [D17980314](https://our.internmc.facebook.com/intern/diff/D17980314) [ghstack-poisoned]
Closed
…pport to `randn_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: [D17980314](https://our.internmc.facebook.com/intern/diff/D17980314) [ghstack-poisoned]
zdevito
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Oct 25, 2019
Summary: Pull Request resolved: pytorch/pytorch#27890 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: D17980314 Pulled By: VitalyFedyunin fbshipit-source-id: a2cf3b1b2df1a4956da971fd47ce69487b2c09e9
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@VitalyFedyunin merged this pull request in 69b0e06. |
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
thiagocrepaldi
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Feb 4, 2020
Summary: Pull Request resolved: pytorch#27890 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: D17980314 Pulled By: VitalyFedyunin fbshipit-source-id: a2cf3b1b2df1a4956da971fd47ce69487b2c09e9
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: 9eeb637 Pull Request resolved: pytorch/pytorch#27890
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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: D17980314