[s2s] round runtime in run_eval #6798
Merged
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
stas00
pushed a commit
to stas00/transformers
that referenced
this pull request
Aug 30, 2020
sgugger
added a commit
that referenced
this pull request
Aug 31, 2020
* Only access loss tensor every logging_steps * tensor.item() was being called every step. This must not be done for XLA:TPU tensors as it's terrible for performance causing TPU<>CPU communication at each step. On RoBERTa MLM for example, it reduces step time by 30%, should be larger for smaller step time models/tasks. * Train batch size was not correct in case a user uses the `per_gpu_train_batch_size` flag * Avg reduce loss accross eval shards * Fix style (#6803) * t5 model should make decoder_attention_mask (#6800) * [s2s] Test hub configs in self-scheduled CI (#6809) * [s2s] round runtime in run_eval (#6798) * Pegasus finetune script: add --adafactor (#6811) * [bart] rename self-attention -> attention (#6708) * [tests] fix typos in inputs (#6818) * Fixed open in colab link (#6825) * Add model card for singbert lite. Update widget for singbert and singbert-large. (#6827) * BR_BERTo model card (#6793) * clearly indicate shuffle=False (#6312) * Clarify shuffle * clarify shuffle Co-authored-by: Kevin Canwen Xu <canwenxu@126.com> * [s2s README] Add more dataset download instructions (#6737) * Style * Patch logging issue * Set default logging level to `WARNING` instead of `INFO` * TF Flaubert w/ pre-norm (#6841) * Dataset and DataCollator for BERT Next Sentence Prediction (NSP) task (#6644) * add datacollator and dataset for next sentence prediction task * bug fix (numbers of special tokens & truncate sequences) * bug fix (+ dict inputs support for data collator) * add padding for nsp data collator; renamed cached files to avoid conflict. * add test for nsp data collator * Style Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr> * Fix in Adafactor docstrings (#6845) * Fix resuming training for Windows (#6847) * Only access loss tensor every logging_steps * tensor.item() was being called every step. This must not be done for XLA:TPU tensors as it's terrible for performance causing TPU<>CPU communication at each step. On RoBERTa MLM for example, it reduces step time by 30%, should be larger for smaller step time models/tasks. * Train batch size was not correct in case a user uses the `per_gpu_train_batch_size` flag * Avg reduce loss accross eval shards * comments Co-authored-by: Sam Shleifer <sshleifer@gmail.com> Co-authored-by: Stas Bekman <stas00@users.noreply.github.com> Co-authored-by: Thomas Ashish Cherian <6967017+PandaWhoCodes@users.noreply.github.com> Co-authored-by: Zane Lim <zyuanlim@gmail.com> Co-authored-by: Rodolfo De Nadai <rdenadai@gmail.com> Co-authored-by: xujiaze13 <37360975+xujiaze13@users.noreply.github.com> Co-authored-by: Kevin Canwen Xu <canwenxu@126.com> Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr> Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Huang Lianzhe <hlz@pku.edu.cn> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
stas00
added a commit
to stas00/transformers
that referenced
this pull request
Sep 1, 2020
* Only access loss tensor every logging_steps * tensor.item() was being called every step. This must not be done for XLA:TPU tensors as it's terrible for performance causing TPU<>CPU communication at each step. On RoBERTa MLM for example, it reduces step time by 30%, should be larger for smaller step time models/tasks. * Train batch size was not correct in case a user uses the `per_gpu_train_batch_size` flag * Avg reduce loss accross eval shards * Fix style (huggingface#6803) * t5 model should make decoder_attention_mask (huggingface#6800) * [s2s] Test hub configs in self-scheduled CI (huggingface#6809) * [s2s] round runtime in run_eval (huggingface#6798) * Pegasus finetune script: add --adafactor (huggingface#6811) * [bart] rename self-attention -> attention (huggingface#6708) * [tests] fix typos in inputs (huggingface#6818) * Fixed open in colab link (huggingface#6825) * Add model card for singbert lite. Update widget for singbert and singbert-large. (huggingface#6827) * BR_BERTo model card (huggingface#6793) * clearly indicate shuffle=False (huggingface#6312) * Clarify shuffle * clarify shuffle Co-authored-by: Kevin Canwen Xu <canwenxu@126.com> * [s2s README] Add more dataset download instructions (huggingface#6737) * Style * Patch logging issue * Set default logging level to `WARNING` instead of `INFO` * TF Flaubert w/ pre-norm (huggingface#6841) * Dataset and DataCollator for BERT Next Sentence Prediction (NSP) task (huggingface#6644) * add datacollator and dataset for next sentence prediction task * bug fix (numbers of special tokens & truncate sequences) * bug fix (+ dict inputs support for data collator) * add padding for nsp data collator; renamed cached files to avoid conflict. * add test for nsp data collator * Style Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr> * Fix in Adafactor docstrings (huggingface#6845) * Fix resuming training for Windows (huggingface#6847) * Only access loss tensor every logging_steps * tensor.item() was being called every step. This must not be done for XLA:TPU tensors as it's terrible for performance causing TPU<>CPU communication at each step. On RoBERTa MLM for example, it reduces step time by 30%, should be larger for smaller step time models/tasks. * Train batch size was not correct in case a user uses the `per_gpu_train_batch_size` flag * Avg reduce loss accross eval shards * comments Co-authored-by: Sam Shleifer <sshleifer@gmail.com> Co-authored-by: Stas Bekman <stas00@users.noreply.github.com> Co-authored-by: Thomas Ashish Cherian <6967017+PandaWhoCodes@users.noreply.github.com> Co-authored-by: Zane Lim <zyuanlim@gmail.com> Co-authored-by: Rodolfo De Nadai <rdenadai@gmail.com> Co-authored-by: xujiaze13 <37360975+xujiaze13@users.noreply.github.com> Co-authored-by: Kevin Canwen Xu <canwenxu@126.com> Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr> Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Huang Lianzhe <hlz@pku.edu.cn> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Zigur
pushed a commit
to Zigur/transformers
that referenced
this pull request
Oct 26, 2020
Zigur
pushed a commit
to Zigur/transformers
that referenced
this pull request
Oct 26, 2020
* Only access loss tensor every logging_steps * tensor.item() was being called every step. This must not be done for XLA:TPU tensors as it's terrible for performance causing TPU<>CPU communication at each step. On RoBERTa MLM for example, it reduces step time by 30%, should be larger for smaller step time models/tasks. * Train batch size was not correct in case a user uses the `per_gpu_train_batch_size` flag * Avg reduce loss accross eval shards * Fix style (huggingface#6803) * t5 model should make decoder_attention_mask (huggingface#6800) * [s2s] Test hub configs in self-scheduled CI (huggingface#6809) * [s2s] round runtime in run_eval (huggingface#6798) * Pegasus finetune script: add --adafactor (huggingface#6811) * [bart] rename self-attention -> attention (huggingface#6708) * [tests] fix typos in inputs (huggingface#6818) * Fixed open in colab link (huggingface#6825) * Add model card for singbert lite. Update widget for singbert and singbert-large. (huggingface#6827) * BR_BERTo model card (huggingface#6793) * clearly indicate shuffle=False (huggingface#6312) * Clarify shuffle * clarify shuffle Co-authored-by: Kevin Canwen Xu <canwenxu@126.com> * [s2s README] Add more dataset download instructions (huggingface#6737) * Style * Patch logging issue * Set default logging level to `WARNING` instead of `INFO` * TF Flaubert w/ pre-norm (huggingface#6841) * Dataset and DataCollator for BERT Next Sentence Prediction (NSP) task (huggingface#6644) * add datacollator and dataset for next sentence prediction task * bug fix (numbers of special tokens & truncate sequences) * bug fix (+ dict inputs support for data collator) * add padding for nsp data collator; renamed cached files to avoid conflict. * add test for nsp data collator * Style Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr> * Fix in Adafactor docstrings (huggingface#6845) * Fix resuming training for Windows (huggingface#6847) * Only access loss tensor every logging_steps * tensor.item() was being called every step. This must not be done for XLA:TPU tensors as it's terrible for performance causing TPU<>CPU communication at each step. On RoBERTa MLM for example, it reduces step time by 30%, should be larger for smaller step time models/tasks. * Train batch size was not correct in case a user uses the `per_gpu_train_batch_size` flag * Avg reduce loss accross eval shards * comments Co-authored-by: Sam Shleifer <sshleifer@gmail.com> Co-authored-by: Stas Bekman <stas00@users.noreply.github.com> Co-authored-by: Thomas Ashish Cherian <6967017+PandaWhoCodes@users.noreply.github.com> Co-authored-by: Zane Lim <zyuanlim@gmail.com> Co-authored-by: Rodolfo De Nadai <rdenadai@gmail.com> Co-authored-by: xujiaze13 <37360975+xujiaze13@users.noreply.github.com> Co-authored-by: Kevin Canwen Xu <canwenxu@126.com> Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr> Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Huang Lianzhe <hlz@pku.edu.cn> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
fabiocapsouza
pushed a commit
to fabiocapsouza/transformers
that referenced
this pull request
Nov 15, 2020
fabiocapsouza
pushed a commit
to fabiocapsouza/transformers
that referenced
this pull request
Nov 15, 2020
* Only access loss tensor every logging_steps * tensor.item() was being called every step. This must not be done for XLA:TPU tensors as it's terrible for performance causing TPU<>CPU communication at each step. On RoBERTa MLM for example, it reduces step time by 30%, should be larger for smaller step time models/tasks. * Train batch size was not correct in case a user uses the `per_gpu_train_batch_size` flag * Avg reduce loss accross eval shards * Fix style (huggingface#6803) * t5 model should make decoder_attention_mask (huggingface#6800) * [s2s] Test hub configs in self-scheduled CI (huggingface#6809) * [s2s] round runtime in run_eval (huggingface#6798) * Pegasus finetune script: add --adafactor (huggingface#6811) * [bart] rename self-attention -> attention (huggingface#6708) * [tests] fix typos in inputs (huggingface#6818) * Fixed open in colab link (huggingface#6825) * Add model card for singbert lite. Update widget for singbert and singbert-large. (huggingface#6827) * BR_BERTo model card (huggingface#6793) * clearly indicate shuffle=False (huggingface#6312) * Clarify shuffle * clarify shuffle Co-authored-by: Kevin Canwen Xu <canwenxu@126.com> * [s2s README] Add more dataset download instructions (huggingface#6737) * Style * Patch logging issue * Set default logging level to `WARNING` instead of `INFO` * TF Flaubert w/ pre-norm (huggingface#6841) * Dataset and DataCollator for BERT Next Sentence Prediction (NSP) task (huggingface#6644) * add datacollator and dataset for next sentence prediction task * bug fix (numbers of special tokens & truncate sequences) * bug fix (+ dict inputs support for data collator) * add padding for nsp data collator; renamed cached files to avoid conflict. * add test for nsp data collator * Style Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr> * Fix in Adafactor docstrings (huggingface#6845) * Fix resuming training for Windows (huggingface#6847) * Only access loss tensor every logging_steps * tensor.item() was being called every step. This must not be done for XLA:TPU tensors as it's terrible for performance causing TPU<>CPU communication at each step. On RoBERTa MLM for example, it reduces step time by 30%, should be larger for smaller step time models/tasks. * Train batch size was not correct in case a user uses the `per_gpu_train_batch_size` flag * Avg reduce loss accross eval shards * comments Co-authored-by: Sam Shleifer <sshleifer@gmail.com> Co-authored-by: Stas Bekman <stas00@users.noreply.github.com> Co-authored-by: Thomas Ashish Cherian <6967017+PandaWhoCodes@users.noreply.github.com> Co-authored-by: Zane Lim <zyuanlim@gmail.com> Co-authored-by: Rodolfo De Nadai <rdenadai@gmail.com> Co-authored-by: xujiaze13 <37360975+xujiaze13@users.noreply.github.com> Co-authored-by: Kevin Canwen Xu <canwenxu@126.com> Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr> Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Huang Lianzhe <hlz@pku.edu.cn> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
fabiocapsouza
added a commit
to fabiocapsouza/transformers
that referenced
this pull request
Nov 15, 2020
This reverts commit 2c37208.
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Fixes #{issue number}