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DanielPFlorian

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Transformers-Github-Issues

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[ "Ok, I know it was my fault. I didn't add the argument `--use-external-format` (gpt2-xl is more than 2GB)\r\nActually I had to open the convert_graph_to_onnx.py file and read each argument's description\r\nThanks again, I'm closing the issue now." ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6919?src=pr&el=h1) Report\n> Merging [#6919](https://codecov.io/gh/huggingface/transformers/pull/6919?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/dfa10a41ba3fd9c5289bebd3baeff8792b1b2281?el=desc) will **decrease** coverage by `0.20%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6919?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6919 +/- ##\n==========================================\n- Coverage 80.02% 79.82% -0.21% \n==========================================\n Files 157 157 \n Lines 28586 28586 \n==========================================\n- Hits 22876 22818 -58 \n- Misses 5710 5768 +58 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6919?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/configuration\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/6919/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX29wZW5haS5weQ==) | `34.28% <0.00%> (-62.86%)` | :arrow_down: |\n| [src/transformers/tokenization\\_mbart.py](https://codecov.io/gh/huggingface/transformers/pull/6919/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fbWJhcnQucHk=) | `36.50% <0.00%> (-60.32%)` | :arrow_down: |\n| [src/transformers/tokenization\\_albert.py](https://codecov.io/gh/huggingface/transformers/pull/6919/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYWxiZXJ0LnB5) | `28.84% <0.00%> (-58.66%)` | :arrow_down: |\n| [src/transformers/modeling\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/6919/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19vcGVuYWkucHk=) | `23.87% <0.00%> (-57.10%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/6919/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9kaXN0aWxiZXJ0LnB5) | `64.47% <0.00%> (-34.36%)` | :arrow_down: |\n| [src/transformers/configuration\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/6919/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX2JhcnQucHk=) | `90.00% <0.00%> (-4.00%)` | :arrow_down: |\n| [src/transformers/generation\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6919/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3V0aWxzLnB5) | `96.66% <0.00%> (-0.28%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6919/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `87.29% <0.00%> (+0.32%)` | :arrow_up: |\n| [src/transformers/modeling\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/6919/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iYXJ0LnB5) | `95.40% <0.00%> (+0.34%)` | :arrow_up: |\n| [src/transformers/tokenization\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6919/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHMucHk=) | `89.84% <0.00%> (+0.39%)` | :arrow_up: |\n| ... and [9 more](https://codecov.io/gh/huggingface/transformers/pull/6919/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6919?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6919?src=pr&el=footer). Last update [dfa10a4...252c784](https://codecov.io/gh/huggingface/transformers/pull/6919?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "The `AlbertTokenizer` in `transformers` is a SentencePiece based tokenizer, so it cannot load `vocab.txt`. You could try loading it in `BertTokenizer`, as it seems to be a wordpiece tokenizer vocabulary.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "@mfuntowicz - Since T5 relies on google's sentencepiece tokenizer for now, can we do anything against it before our own sentencepiece tokenizer is implemented? ", "Verified that this is a problem with the original T5 sentencepience tokenizer. Opened an issue with the Google's T5 repository. https://github.com/google-research/text-to-text-transfer-transformer/issues/390", "Closing this issue , quoting from T5 github issue\r\n> > { is OOV because we intentionally removed any pages with { or } from C4 to avoid pre-training on anything other than natural language. So, it gets encoded to ??. SentencePiece has a byte fallback feature but it was not available when we trained our sentencepiece model." ]

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[ "I can confirm it was previously checking the model weights and re-downloading if the weights had been changed. Investigating.", "This is due to the CDN caching files, with a 24 hour delay. After 24 hours it should download your file, but if you want it now you can use the `use_cdn` flag and set it to `False`. You can see the documentation for this [here](https://github.com/huggingface/transformers/blob/master/src/transformers/file_utils.py#L573-L585).", "Thank you for the hint, @LysandreJik. So `from_pretrained(mname, use_cdn=False)`\r\n\r\nBut that might be tricky for end users who won't know that the code base has changed yet the model weights they get are out sync.\r\n\r\nIs there a way to signal CDN to invalidate the cache for some files? It could then be done from the upload util.\r\n\r\n\r\n\r\n", "FWIW, I wrote a one liner to force cache update for the 4 models I'm working at the moment.\r\n```\r\nPYTHONPATH=\"src\" python -c 'from transformers import AutoModel; [AutoModel.from_pretrained(\"stas/fsmt-wmt19-\"+p, use_cdn=False) for p in [\"en-ru\",\"ru-en\",\"en-de\",\"de-en\"]]'\r\n```\r\nI now have that in my script, so I don't need to think about it.", "@LysandreJik, unfortunately this doesn't solve the issue\r\n\r\n`AutoModel.from_pretrained(mname, use_cdn=False)`\r\n\r\nIndeed forces a download of the recently updated model - but then if this flag is no longer used in the application - it still downloads the CDN cached version and ends up using the wrong version.\r\n\r\nSo, basically, this results in 2 copies (different hashes) sitting in the cache dir. \r\n\r\nAnd normal usage w/o using `use_cdn=False` looks up the old version and not the new one. (so things like `run_eval.py` still use the old one)\r\n\r\nThanks.\r\n", "can you run `AutoModel.from_pretrained(mname, use_cdn=False)` in a debugger and check whether the downloaded url is a `https://cdn.huggingface.co` or a `https://s3.amazonaws.com/models.huggingface.co` url?", "I can do that, but I already checked that it downloads the updated model w/ `use_cdn=False`. But then if you run it again w/o `use_cdn=False` it ignores the new download and uses the old model again (if I delete the cached version, it redownloads the old cached version w/o `use_cdn=False` ).", "Oh yeah ok, I see. Can you `run_eval.py` on a local folder path then?", "> Can you `run_eval.py` on a local folder path then?\r\n\r\nYes. Except others can't as they don't have my local copy.\r\n\r\ne.g. @sshleifer wants to eval my PR https://github.com/huggingface/transformers/pull/6940, but now has to wait till tomorrow for CDN to expire (or hack around it).\r\n\r\nLast night I uploaded an experimental model, which proved to be invalid, thought I re-downloaded it OK as it was working OK and made a PR, except I was testing against the non-current cached version, which was a good one.", "Can we please re-open this ticket? It hasn't been resolved", "Can we add a `--no_cdn` boolean flag to `run_eval.py` that would then call `AutoModelForSeq2SeqLM.from_pretrained(use_cdn=False)`?\r\n\r\nIn our dev workflow we mostly don't use the cdn while the files are still in-flux. Cloudfront invalidation comes with its own set of issues so it's better to view cdn as a means to distribute permanent files. (for this reason we don't serve config.json files from Cloudfront)", "> Can we add a `--no_cdn` boolean flag to `run_eval.py` that would then call `AutoModelForSeq2SeqLM.from_pretrained(use_cdn=False)`?\r\n\r\nIt could be done. I have a feeling then there will be others.\r\n\r\nPerhaps an alternative solution would be to introduce an env var, that would transparently override cdn cache in any situation w/o needing to change every script? `TRANSFORMERS_USE_CDN=False`?\r\n\r\n> In our dev workflow we mostly don't use the cdn while the files are still in-flux. Cloudfront invalidation comes with its own set of issues so it's better to view cdn as a means to distribute permanent files. (for this reason we don't serve config.json files from Cloudfront)\r\n\r\nUnderstood!\r\n\r\nHow do you let others onto testing the model files? Putting them on dropbox or something and sharing the link?\r\n", "No, just S3 links!", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "https://github.com/huggingface/transformers/pull/8324 should resolve this." ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6915?src=pr&el=h1) Report\n> Merging [#6915](https://codecov.io/gh/huggingface/transformers/pull/6915?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/4ebb52afdb4dc4bcd599e7cb503763e5d4afc962?el=desc) will **increase** coverage by `2.01%`.\n> The diff coverage is `100.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6915?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6915 +/- ##\n==========================================\n+ Coverage 77.81% 79.83% +2.01% \n==========================================\n Files 157 157 \n Lines 28853 28853 \n==========================================\n+ Hits 22452 23034 +582 \n+ Misses 6401 5819 -582 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6915?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/6915/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9kaXN0aWxiZXJ0LnB5) | `98.82% <100.00%> (ø)` | |\n| [src/transformers/modeling\\_tf\\_electra.py](https://codecov.io/gh/huggingface/transformers/pull/6915/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9lbGVjdHJhLnB5) | `25.13% <0.00%> (-73.83%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/6915/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9vcGVuYWkucHk=) | `22.58% <0.00%> (-72.26%)` | :arrow_down: |\n| [src/transformers/tokenization\\_marian.py](https://codecov.io/gh/huggingface/transformers/pull/6915/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fbWFyaWFuLnB5) | `32.20% <0.00%> (-66.95%)` | :arrow_down: |\n| [src/transformers/tokenization\\_pegasus.py](https://codecov.io/gh/huggingface/transformers/pull/6915/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fcGVnYXN1cy5weQ==) | `46.03% <0.00%> (-49.21%)` | :arrow_down: |\n| [src/transformers/modeling\\_marian.py](https://codecov.io/gh/huggingface/transformers/pull/6915/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19tYXJpYW4ucHk=) | `60.00% <0.00%> (-30.00%)` | :arrow_down: |\n| [src/transformers/modeling\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/6915/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190NS5weQ==) | `76.70% <0.00%> (-6.07%)` | :arrow_down: |\n| [src/transformers/activations.py](https://codecov.io/gh/huggingface/transformers/pull/6915/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9hY3RpdmF0aW9ucy5weQ==) | `85.00% <0.00%> (-5.00%)` | :arrow_down: |\n| [src/transformers/tokenization\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/6915/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYXV0by5weQ==) | `95.55% <0.00%> (-2.23%)` | :arrow_down: |\n| [src/transformers/modeling\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/6915/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19vcGVuYWkucHk=) | `80.96% <0.00%> (-1.30%)` | :arrow_down: |\n| ... and [23 more](https://codecov.io/gh/huggingface/transformers/pull/6915/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6915?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6915?src=pr&el=footer). Last update [4ebb52a...481baa3](https://codecov.io/gh/huggingface/transformers/pull/6915?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "I run a test with this change on my ubuntu 18.04 with a 2080Ti GPU, tensorflow-gpu 2.2.0:\r\n```\r\nfrom tensorflow.keras.layers import Input, Embedding, Bidirectional, GRU, Dense\r\nfrom tensorflow.keras.models import Model\r\nfrom transformers import TFDistilBertModel\r\nfrom tensorflow.keras.mixed_precision import experimental as mixed_precision\r\npolicy = mixed_precision.Policy('mixed_float16')\r\nmixed_precision.set_policy(policy)\r\n\r\nbert = TFDistilBertModel.from_pretrained('distilbert-base-uncased')\r\ninputs = Input(shape=(None,), dtype='int32')\r\nbert_out = bert(inputs)[0]\r\noutput = Dense(9, activation='softmax', dtype='float32')(bert_out)\r\nmodel = Model(inputs, output)\r\nmodel.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\r\nmodel.summary()\r\nx = [[5, 2, 3] * 3] * 100\r\ny = [[1, 2, 3] * 3] * 100\r\nmodel.fit(x=x, y=y, epochs=20, batch_size=16)\r\n```\r\nAnd get error info:\r\n```\r\nTraceback (most recent call last):\r\n File \"test.py\", line 8, in <module>\r\n bert = TFDistilBertModel.from_pretrained('distilbert-base-uncased')\r\n File \"/home/xingya/transformers/src/transformers/modeling_tf_utils.py\", line 602, in from_pretrained\r\n model(model.dummy_inputs, training=False) # build the network with dummy inputs\r\n File \"/home/xingya/.conda/envs/transformers/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py\", line 968, in __call__\r\n outputs = self.call(cast_inputs, *args, **kwargs)\r\n File \"/home/xingya/transformers/src/transformers/modeling_tf_distilbert.py\", line 615, in call\r\n outputs = self.distilbert(inputs, **kwargs)\r\n File \"/home/xingya/.conda/envs/transformers/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py\", line 968, in __call__\r\n outputs = self.call(cast_inputs, *args, **kwargs)\r\n File \"/home/xingya/transformers/src/transformers/modeling_tf_distilbert.py\", line 508, in call\r\n tfmr_output = self.transformer(\r\n File \"/home/xingya/.conda/envs/transformers/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py\", line 968, in __call__\r\n outputs = self.call(cast_inputs, *args, **kwargs)\r\n File \"/home/xingya/transformers/src/transformers/modeling_tf_distilbert.py\", line 401, in call\r\n layer_outputs = layer_module(hidden_state, attn_mask, head_mask[i], output_attentions, training=training)\r\n File \"/home/xingya/.conda/envs/transformers/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py\", line 968, in __call__\r\n outputs = self.call(cast_inputs, *args, **kwargs)\r\n File \"/home/xingya/transformers/src/transformers/modeling_tf_distilbert.py\", line 355, in call\r\n ffn_output = self.ffn(sa_output, training=training) # (bs, seq_length, dim)\r\n File \"/home/xingya/.conda/envs/transformers/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py\", line 968, in __call__\r\n outputs = self.call(cast_inputs, *args, **kwargs)\r\n File \"/home/xingya/transformers/src/transformers/modeling_tf_distilbert.py\", line 304, in call\r\n x = self.activation(x)\r\n File \"/home/xingya/.conda/envs/transformers/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py\", line 968, in __call__\r\n outputs = self.call(cast_inputs, *args, **kwargs)\r\n File \"/home/xingya/.conda/envs/transformers/lib/python3.8/site-packages/tensorflow/python/keras/layers/core.py\", line 420, in call\r\n return self.activation(inputs)\r\n File \"/home/xingya/transformers/src/transformers/modeling_tf_distilbert.py\", line 79, in gelu\r\n cdf = 0.5 * (1.0 + tf.math.erf(x / tf.math.sqrt(2.0)))\r\n File \"/home/xingya/.conda/envs/transformers/lib/python3.8/site-packages/tensorflow/python/ops/math_ops.py\", line 984, in binary_op_wrapper\r\n return func(x, y, name=name)\r\n File \"/home/xingya/.conda/envs/transformers/lib/python3.8/site-packages/tensorflow/python/ops/math_ops.py\", line 1081, in _truediv_python3\r\n raise TypeError(\"x and y must have the same dtype, got %r != %r\" %\r\nTypeError: x and y must have the same dtype, got tf.float16 != tf.float32\r\n```\r\nI made a modification to L299:\r\n`self.activation = (\r\n tf.keras.layers.Activation(gelu, dtype='float32') if config.activation == \"gelu\" else tf.keras.activations.relu\r\n )`\r\nAnd then the model began to train, however the loss don't decrease and the accuracy is always 0:\r\n```\r\n7/7 [==============================] - 0s 28ms/step - loss: 2.1972 - accuracy: 0.0000e+00\r\nEpoch 2/20\r\n7/7 [==============================] - 0s 29ms/step - loss: 2.1972 - accuracy: 0.0000e+00\r\nEpoch 3/20\r\n7/7 [==============================] - 0s 30ms/step - loss: 2.1972 - accuracy: 0.0000e+00\r\nEpoch 4/20\r\n7/7 [==============================] - 0s 31ms/step - loss: 2.1972 - accuracy: 0.0000e+00\r\n```\r\n\r\nI have trid this code in float32 precision, and it works. \r\n```\r\nEpoch 1/20\r\n7/7 [==============================] - 0s 31ms/step - loss: 2.5418 - accuracy: 0.2800\r\nEpoch 2/20\r\n7/7 [==============================] - 0s 33ms/step - loss: 1.2452 - accuracy: 0.3356\r\nEpoch 3/20\r\n7/7 [==============================] - 0s 31ms/step - loss: 1.1438 - accuracy: 0.3267\r\nEpoch 4/20\r\n7/7 [==============================] - 0s 33ms/step - loss: 1.1219 - accuracy: 0.3400\r\n```", "@xuxingya , the accuracy not improved during training is due to a line \r\n\r\n > scores = scores - 1e30 * (1.0 - mask)\r\n\r\nwhile `1e30` with `half precision` will cause `nan` values. I am still trying to figure out a way to deal with it.", "@xuxingya Would you mind to run the test on your side again, please? I tested it with your example, and it is fine now.", "@chiapas Yes, I run the test and now it's fine." ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6914?src=pr&el=h1) Report\n> Merging [#6914](https://codecov.io/gh/huggingface/transformers/pull/6914?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/4ebb52afdb4dc4bcd599e7cb503763e5d4afc962?el=desc) will **increase** coverage by `1.21%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6914?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6914 +/- ##\n==========================================\n+ Coverage 77.81% 79.03% +1.21% \n==========================================\n Files 157 157 \n Lines 28853 28853 \n==========================================\n+ Hits 22452 22804 +352 \n+ Misses 6401 6049 -352 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6914?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_mobilebert.py](https://codecov.io/gh/huggingface/transformers/pull/6914/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9tb2JpbGViZXJ0LnB5) | `24.55% <0.00%> (-72.36%)` | :arrow_down: |\n| [src/transformers/tokenization\\_pegasus.py](https://codecov.io/gh/huggingface/transformers/pull/6914/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fcGVnYXN1cy5weQ==) | `30.15% <0.00%> (-65.08%)` | :arrow_down: |\n| [src/transformers/modeling\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/6914/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19yb2JlcnRhLnB5) | `77.73% <0.00%> (-19.35%)` | :arrow_down: |\n| [src/transformers/data/data\\_collator.py](https://codecov.io/gh/huggingface/transformers/pull/6914/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL2RhdGFfY29sbGF0b3IucHk=) | `92.30% <0.00%> (-0.41%)` | :arrow_down: |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6914/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `88.05% <0.00%> (+0.18%)` | :arrow_up: |\n| [src/transformers/tokenization\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6914/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHMucHk=) | `89.84% <0.00%> (+0.39%)` | :arrow_up: |\n| [src/transformers/generation\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6914/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3V0aWxzLnB5) | `97.21% <0.00%> (+0.83%)` | :arrow_up: |\n| [src/transformers/configuration\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6914/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3V0aWxzLnB5) | `96.64% <0.00%> (+1.34%)` | :arrow_up: |\n| [src/transformers/modeling\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/6914/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19kaXN0aWxiZXJ0LnB5) | `97.84% <0.00%> (+1.61%)` | :arrow_up: |\n| [src/transformers/modelcard.py](https://codecov.io/gh/huggingface/transformers/pull/6914/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGNhcmQucHk=) | `85.18% <0.00%> (+2.46%)` | :arrow_up: |\n| ... and [12 more](https://codecov.io/gh/huggingface/transformers/pull/6914/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6914?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6914?src=pr&el=footer). Last update [4ebb52a...408286d](https://codecov.io/gh/huggingface/transformers/pull/6914?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "Hi! Yes, this isn't an issue, this is the intended behavior. It's the standard behavior with Sphinx/ReadTheDocs. You can see a similar example with the [PyTorch docs](https://pytorch.org/docs/stable/tensors.html)." ]

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[ "Hi, are you sure your issue comes from the tokenizer? If you encode your text using `encode_plus` and `batch_encode_plus`, do you see a difference in the tokens generated?", "I only use encode_plus and batch_encode_plus and call model inference. I do not think the model inference is the problem as you see in the function calls. so I think it is coming from encode_plus and batch_encode_plus. Regarding your question, I see that that batch_encode_plus add ones at the end of the list \" 1, 1, 1, 1, 1, 1]\". and I thought this is this difference may be a reason for the problem.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6911?src=pr&el=h1) Report\n> Merging [#6911](https://codecov.io/gh/huggingface/transformers/pull/6911?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/4ebb52afdb4dc4bcd599e7cb503763e5d4afc962?el=desc) will **increase** coverage by `2.25%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6911?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6911 +/- ##\n==========================================\n+ Coverage 77.81% 80.06% +2.25% \n==========================================\n Files 157 157 \n Lines 28853 28853 \n==========================================\n+ Hits 22452 23102 +650 \n+ Misses 6401 5751 -650 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6911?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_mobilebert.py](https://codecov.io/gh/huggingface/transformers/pull/6911/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9tb2JpbGViZXJ0LnB5) | `24.55% <0.00%> (-72.36%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_flaubert.py](https://codecov.io/gh/huggingface/transformers/pull/6911/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9mbGF1YmVydC5weQ==) | `24.53% <0.00%> (-63.81%)` | :arrow_down: |\n| [src/transformers/modeling\\_marian.py](https://codecov.io/gh/huggingface/transformers/pull/6911/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19tYXJpYW4ucHk=) | `60.00% <0.00%> (-30.00%)` | :arrow_down: |\n| [src/transformers/modeling\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/6911/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190NS5weQ==) | `76.70% <0.00%> (-6.07%)` | :arrow_down: |\n| [src/transformers/activations.py](https://codecov.io/gh/huggingface/transformers/pull/6911/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9hY3RpdmF0aW9ucy5weQ==) | `85.00% <0.00%> (-5.00%)` | :arrow_down: |\n| [src/transformers/modeling\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/6911/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iYXJ0LnB5) | `93.90% <0.00%> (-0.51%)` | :arrow_down: |\n| [src/transformers/data/data\\_collator.py](https://codecov.io/gh/huggingface/transformers/pull/6911/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL2RhdGFfY29sbGF0b3IucHk=) | `92.30% <0.00%> (-0.41%)` | :arrow_down: |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6911/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `87.50% <0.00%> (-0.37%)` | :arrow_down: |\n| [src/transformers/tokenization\\_utils\\_base.py](https://codecov.io/gh/huggingface/transformers/pull/6911/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHNfYmFzZS5weQ==) | `93.76% <0.00%> (-0.14%)` | :arrow_down: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6911/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `82.66% <0.00%> (+0.25%)` | :arrow_up: |\n| ... and [18 more](https://codecov.io/gh/huggingface/transformers/pull/6911/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6911?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6911?src=pr&el=footer). Last update [4ebb52a...87055d8](https://codecov.io/gh/huggingface/transformers/pull/6911?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "I personally wouldn't like having a pre-commit hook change all my commits without me being able to see the end result.\r\nOn my setup, I have a pre-push hook that aborts a push if make quality fails. I think if we had an install script, we could handle both options?", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "Hi! bring back this because I think in suggest pre-commit instead of `make ...`\r\n\r\nWith the pre-commit, we can see the results/modifications, like by example:\r\n\r\n`git add .`\r\n`git commit -m \"any\"` **this will run the pre-commit**\r\n- if everything it's ok at the pre-commit pipeline, the commit will be created\r\n- else if he modifies something (like black or style hook) he will not create the commit and change the files\r\n - when this occurs, we can see with git diff what the pre-commit change, or can just use the `--show-diff-on-failure` flag when running pre-commit.\r\n\r\nI think that doesn't need everybody use pre-commit, can use both option (the actual format with running manually `make ...` and also using pre-commit) – but maybe don't make sense because will duplicate things? \r\n\r\nA little setup for pre-commit, i have tested here:\r\n\r\nadd `.pre-commit-config.yaml` - \r\n```yml\r\nrepos:\r\n- repo: https://github.com/psf/black\r\n rev: 22.1.0\r\n hooks:\r\n - id: black\r\n- repo: https://github.com/pycqa/isort\r\n rev: 5.10.1\r\n hooks:\r\n - id: isort\r\n name: isort (python)\r\n- repo: https://github.com/PyCQA/flake8\r\n rev: 4.0.1\r\n hooks:\r\n - id: flake8\r\n- repo: local\r\n hooks:\r\n - id: autogenerate_code\r\n name: autogenerate_code\r\n entry: python setup.py deps_table_update\r\n language: python\r\n types: [python]\r\n pass_filenames: false\r\n - id: extra_style_checks\r\n name: extra_style_checks\r\n entry: make extra_style_checks\r\n language: system\r\n```\r\nNote:\r\n - The hooks _autogenerate_code_ and _extra_style_checks_, can be call using the make command or running the python.\r\n\r\nInstall pre-commit:\r\n`pre-commit install`\r\n\r\nModify src/transformers/activations.py:\r\n```diff\r\n@@ -31,7 +31,8 @@ class NewGELUActivation(nn.Module):\r\n \"\"\"\r\n def forward(self, input: Tensor) -> Tensor:\r\n- return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.044715 * torch.pow(input, 3.0))))\r\n+ return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 /\r\n+ math.pi) * (input + 0.044715 * torch.pow(input, 3.0))))\r\n```\r\n```console\r\n$ git add -u\r\n$ git commit -m \"test pre-commit pipeline\"\r\n\r\nblack....................................................................Failed\r\n- hook id: black\r\n- files were modified by this hook\r\n\r\nreformatted src/transformers/activations.py\r\n\r\nAll done! ✨ 🍰 ✨\r\n1 file reformatted.\r\n\r\nisort (python)...........................................................Passed\r\nflake8...................................................................Passed\r\nautogenerate_code........................................................Passed\r\nextra_style_checks.......................................................Passed\r\n\r\n$ git status\r\nOn branch master\r\nYour branch is up to date with 'origin/master'.\r\n\r\nChanges to be committed:\r\n (use \"git restore --staged <file>...\" to unstage)\r\n modified: src/transformers/activations.py\r\n\r\nChanges not staged for commit:\r\n (use \"git add <file>...\" to update what will be committed)\r\n (use \"git restore <file>...\" to discard changes in working directory)\r\n modified: src/transformers/activations.py\r\n\r\n$ git diff\r\n--- a/src/transformers/activations.py\r\n+++ b/src/transformers/activations.py\r\n@@ -31,8 +31,7 @@ class NewGELUActivation(nn.Module):\r\n \"\"\"\r\n \r\n def forward(self, input: Tensor) -> Tensor:\r\n- return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 /\r\n- math.pi) * (input + 0.044715 * torch.pow(input, 3.0))))\r\n+ return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.044715 * torch.pow(input, 3.0))))\r\n```\r\n\r\n\r\nto show git diff automatically after the pre-commit can add:\r\n```yml\r\n- repo: local\r\n hooks:\r\n - id: git-diff\r\n name: git diff\r\n entry: git diff --exit-code\r\n language: system\r\n pass_filenames: false\r\n always_run: true\r\n```\r\n", "Even though I originally created this thread 1.5 years later I now agree with @sgugger, that I don't want format changes done while pushing - I need to see what has been changed since sometimes the autoformatter messes things up badly and I need to rewrite things to make the end result readable.\r\n\r\nIf this can be done as an option and not a requirement then I'm not against it, but there needs to be a way to validate/reformat files before git is involved.\r\n\r\nBTW, `precommit` can be run manually as well and not via git, which doesn't require `pre-commit install`:\r\n\r\n```\r\npre-commit run --all-files\r\n```\r\n\r\nAnd we have 2 ways to reformat files: `fixup` (fast - only modified files) - `style` (slow)", "yes use pre-commit don't make sense if does not want to always run the pipeline...\r\n\r\nAbout the `fixup` and `style`, i think can be done equal... by default pre-commit will run just in modified files (files at the commit) and if wants to run for all files can do as you shows.\r\nFor me, by default, i think makes sense always just run at modified files. And if the autoformatter messes things we can see, and if we prefer not to use some hook (like the autoformatter that have messed up something), by example run again with `SKIP=black ...`\r\n\r\nAnd the pre-commit tool will not let the commit be created if something fails, if the dev wants “force” the failed hook will need to add the `SKIP=hook ...` before the commit command", "(i personally agree with @sgugger that local hooks are best left as user-level tooling)" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6908?src=pr&el=h1) Report\n> Merging [#6908](https://codecov.io/gh/huggingface/transformers/pull/6908?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/0f360d3d1c606d6d79cdf1efa53c3d719249573d?el=desc) will **increase** coverage by `0.71%`.\n> The diff coverage is `87.71%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6908?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6908 +/- ##\n==========================================\n+ Coverage 80.23% 80.95% +0.71% \n==========================================\n Files 161 164 +3 \n Lines 30119 30925 +806 \n==========================================\n+ Hits 24167 25035 +868 \n+ Misses 5952 5890 -62 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6908?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/commands/convert.py](https://codecov.io/gh/huggingface/transformers/pull/6908/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb21tYW5kcy9jb252ZXJ0LnB5) | `26.98% <20.00%> (-0.61%)` | :arrow_down: |\n| [src/transformers/modeling\\_funnel.py](https://codecov.io/gh/huggingface/transformers/pull/6908/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19mdW5uZWwucHk=) | `86.76% <86.76%> (ø)` | |\n| [src/transformers/tokenization\\_funnel.py](https://codecov.io/gh/huggingface/transformers/pull/6908/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fZnVubmVsLnB5) | `97.67% <97.67%> (ø)` | |\n| [src/transformers/\\_\\_init\\_\\_.py](https://codecov.io/gh/huggingface/transformers/pull/6908/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9fX2luaXRfXy5weQ==) | `99.31% <100.00%> (+0.01%)` | :arrow_up: |\n| [src/transformers/configuration\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/6908/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX2F1dG8ucHk=) | `93.47% <100.00%> (+0.14%)` | :arrow_up: |\n| [src/transformers/configuration\\_funnel.py](https://codecov.io/gh/huggingface/transformers/pull/6908/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX2Z1bm5lbC5weQ==) | `100.00% <100.00%> (ø)` | |\n| [src/transformers/modeling\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/6908/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19hdXRvLnB5) | `78.97% <100.00%> (+0.12%)` | :arrow_up: |\n| [src/transformers/tokenization\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/6908/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYXV0by5weQ==) | `97.87% <100.00%> (+2.22%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_electra.py](https://codecov.io/gh/huggingface/transformers/pull/6908/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9lbGVjdHJhLnB5) | `25.13% <0.00%> (-73.83%)` | :arrow_down: |\n| [src/transformers/modeling\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/6908/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19vcGVuYWkucHk=) | `72.25% <0.00%> (-10.00%)` | :arrow_down: |\n| ... and [15 more](https://codecov.io/gh/huggingface/transformers/pull/6908/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6908?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6908?src=pr&el=footer). Last update [0f360d3...8c684cc](https://codecov.io/gh/huggingface/transformers/pull/6908?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Awesome! The model seems quite complex so I didn't really understand all the functionality. \r\nA couple of things from my side:\r\n\r\n1) IMO, it's super useful to have hard coded integration tests in the test file which makes the model a lot easier to maintain (every change can quickly be checked by making sure the model stays mathematically equivalent).\r\n\r\n2) I guess a couple of comments and assert statements would be nice to make the code a bit easier to understand\r\n\r\n3) Personally, I don't like single letter variables. Search replace commands don't work on such variables and it is very difficult to understand what they mean. ", "Thanks for all the comments. I think I replied/addressed all of them except the fast small integration tests, which are going to take a bit more work (starting on this now). Let me know if I missed anything since there are a lot of comments!", "All checkpoints uploaded so I updated the incomplete lists. Also added mention of the model in all indexes, the model summary and the big table of pretrained models (sorry about the diff on that file, Funnel Transformer is one character too long and required to add an extra space on every line).\r\n\r\nShould be good to merge at the beginning of next week!", "@sgugger although you've named the models \"`funnel-base`\", \"`funnel-medium`\" so on so forth, the paper talks about all this in a different format, could a docstring be added saying `funnel-base` is `B4-4-4H768` and same for the rest. If someone wants to replicate the papers' results that would be great.\r\n\r\nedit: my bad, its there in the comments next to the model name, but still would be better in a docstring too. Sorry!\r\n" ]

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[ "Results for 1):\r\n\r\n```\r\n1 / 1\r\n\r\n==================== INFERENCE - SPEED - RESULT ====================\r\n--------------------------------------------------------------------------------\r\n Model Name Batch Size Seq Length Time in s \r\n--------------------------------------------------------------------------------\r\nType: multiple - Script: True 500 128 2.575 \r\nType: multiple - Script: True 500 512 3.898 \r\nType: multiple - Script: True 2500 128 13.173 \r\nType: multiple - Script: True 2500 512 18.263 \r\n--------------------------------------------------------------------------------\r\n1 / 1\r\n\r\n==================== INFERENCE - SPEED - RESULT ====================\r\n--------------------------------------------------------------------------------\r\n Model Name Batch Size Seq Length Time in s \r\n--------------------------------------------------------------------------------\r\nType: multiple - Script: False 500 128 3.733 \r\nType: multiple - Script: False 500 512 3.857 \r\nType: multiple - Script: False 2500 128 19.101 \r\nType: multiple - Script: False 2500 512 19.356 \r\n--------------------------------------------------------------------------------\r\n```\r\n\r\nFor the smaller sequence length 128 we can see a significant speed-up (~30%) - for the longer sequence length 512, the speed-up is much smaller (and only for the bigger list of inputs).", "Results for 2)\r\n\r\n\r\n```\r\n==================== INFERENCE - SPEED - RESULT ====================\r\n--------------------------------------------------------------------------------\r\n Model Name Batch Size Seq Length Time in s \r\n--------------------------------------------------------------------------------\r\n Type: batched - Script: True 512 128 0.819 \r\n Type: batched - Script: True 512 512 3.769 \r\n Type: batched - Script: True 4096 128 6.705 \r\n Type: batched - Script: True 4096 512 26.549 \r\n--------------------------------------------------------------------------------\r\n1 / 1\r\n\r\n==================== INFERENCE - SPEED - RESULT ====================\r\n--------------------------------------------------------------------------------\r\n Model Name Batch Size Seq Length Time in s \r\n--------------------------------------------------------------------------------\r\nType: batched - Script: False 512 128 0.837 \r\nType: batched - Script: False 512 512 3.88 \r\nType: batched - Script: False 4096 128 6.75 \r\nType: batched - Script: False 4096 512 27.162 \r\n--------------------------------------------------------------------------------\r\n```\r\n\r\nHere no clear speed gains can be seen. ", "I'm not sure I understand all the interactions in the benchmarking framework, but I think in line 9 (non-script model) we should be returning torch.jit.trace(model, sample_input), not the untraced model. And the sample input would have be max_length for it to work. That's were most of the gain comes from.\r\nThen the comparison is between using torch.jit.trace() and torch.jit.script(). Or maybe I'm missing some code that does that elsewhere? \r\n\r\n", "Okey, yeah that makes sense! I changed the benchmarking script accordingly and have the following results now: \r\n\r\n1)\r\n```\r\n==================== INFERENCE - SPEED - RESULT ====================\r\n--------------------------------------------------------------------------------\r\n Model Name Batch Size Seq Length Time in s \r\n--------------------------------------------------------------------------------\r\nType: multiple - Script: True 500 128 1.793 \r\nType: multiple - Script: True 500 512 3.628 \r\nType: multiple - Script: True 2500 128 8.774 \r\nType: multiple - Script: True 2500 512 19.471 \r\n--------------------------------------------------------------------------------\r\n1 / 1\r\n\r\n==================== INFERENCE - SPEED - RESULT ====================\r\n--------------------------------------------------------------------------------\r\n Model Name Batch Size Seq Length Time in s \r\n--------------------------------------------------------------------------------\r\nType: multiple - Trace: True 500 128 1.83 \r\nType: multiple - Trace: True 500 512 3.783 \r\nType: multiple - Trace: True 2500 128 9.083 \r\nType: multiple - Trace: True 2500 512 20.569 \r\n--------------------------------------------------------------------------------\r\n```\r\n\r\nand \r\n\r\n2) \r\n```\r\n==================== INFERENCE - SPEED - RESULT ====================\r\n--------------------------------------------------------------------------------\r\n Model Name Batch Size Seq Length Time in s \r\n--------------------------------------------------------------------------------\r\n Type: batched - Script: True 512 128 1.043 \r\n Type: batched - Script: True 512 512 4.913 \r\n Type: batched - Script: True 4096 128 8.499 \r\n Type: batched - Script: True 4096 512 34.187 \r\n--------------------------------------------------------------------------------\r\n1 / 1\r\n\r\n==================== INFERENCE - SPEED - RESULT ====================\r\n--------------------------------------------------------------------------------\r\n Model Name Batch Size Seq Length Time in s \r\n--------------------------------------------------------------------------------\r\nType: batched - Trace: True 512 128 1.046 \r\nType: batched - Trace: True 512 512 4.916 \r\nType: batched - Trace: True 4096 128 8.042 \r\nType: batched - Trace: True 4096 512 30.874 \r\n--------------------------------------------------------------------------------\r\n```\r\n\r\n=> So my understanding is now that `torch.trace(...)` is much more efficient for dynamic input shapes than not using torch.jit at all, but I also don't see how `torch.script(...)` is better than `torch.trace(...)`. If our models are compatible with `torch.trace(...)`, why do we need to have a model that is compatible with `torch.script(...)`? It is definitely more convenient to just call `torch.trace(model)` without having to provide any `input_ids`, but I'm not 100% sure whether it's worth a huge refactoring. \r\n\r\nalso cc @sgugger @LysandreJik ", "We saw different behavior in our experiments a few months ago. Will try to reproduce and update here.", "> We saw different behavior in our experiments a few months ago. Will try to reproduce and update here.\r\n\r\nWas `torch.script()` much faster than `torch.trace()` in your experiments?", "In our experiments, using trace(model, example_input) would result in a model that would only accept a sequence of the same length as example_sequence, whereas script(model) had no such restriction. This is the case mentioned in your documentation here: https://huggingface.co/transformers/torchscript.html#dummy-inputs-and-standard-lengths\r\n\r\nWhat that meant in practice is that you needed to trace with an example sequence of length = max_length, and then pad every example of length < max_length with zeros. Since the speed of the model is basically linear in the sequence length, for a set of inputs with varying sequence lengths we got a speed up of avg_len/max_length by using script() instead of trace().\r\n\r\nUpon further investigation, it looks like when we ran these experiments, several months ago, we were using Torch 1.2. It looks like in Torch 1.3 the fixed-length problem is no longer an issue for your BERT models (we still encounter it with other models architectures we build). So there's no longer a big speed gain from script() vs trace().\r\n\r\nThere are still some good reasons for preferring script() to trace() - scripting is guaranteed to capture the model codepath logic, whereas tracing might miss a logic branch if the example input doesn't flow through it. Also, currently tracing your models produces several warnings like the one below. But I'm not sure if those on their own are enough of a motivation to make major changes in your code base.\r\n```\r\nTracerWarning: Converting a tensor to a Python index might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\r\n```", "> In our experiments, using trace(model, example_input) would result in a model that would only accept a sequence of the same length as example_sequence, whereas script(model) had no such restriction. This is the case mentioned in your documentation here: https://huggingface.co/transformers/torchscript.html#dummy-inputs-and-standard-lengths\r\n> \r\n> What that meant in practice is that you needed to trace with an example sequence of length = max_length, and then pad every example of length < max_length with zeros. Since the speed of the model is basically linear in the sequence length, for a set of inputs with varying sequence lengths we got a speed up of avg_len/max_length by using script() instead of trace().\r\n> \r\n> Upon further investigation, it looks like when we ran these experiments, several months ago, we were using Torch 1.2. It looks like in Torch 1.3 the fixed-length problem is no longer an issue for your BERT models (we still encounter it with other models architectures we build). So there's no longer a big speed gain from script() vs trace().\r\n> \r\n> There are still some good reasons for preferring script() to trace() - scripting is guaranteed to capture the model codepath logic, whereas tracing might miss a logic branch if the example input doesn't flow through it. Also, currently tracing your models produces several warnings like the one below. But I'm not sure if those on their own are enough of a motivation to make major changes in your code base.\r\n> \r\n> ```\r\n> TracerWarning: Converting a tensor to a Python index might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\r\n> ```\r\n\r\n@sgugger - what are your thoughts on this? ", "I think adding the scriptable layers seems cleaner to make sure everything works right with scripting/tracing. Not the approach in this PR but the other linked in a comment (@sbrody18 I don't know if you saw my PR to rebase on master for this branch). It ends up with most changes being helpful to read the code (type annotations and asserts) and a few extra classes for the scriptable layers but not much added code.", "@sgugger I agree - I think the extra benefit of the type and None-checking is really helpful to prevent bugs and makes the code better.\r\nI saw your PR late Friday and didn't have time to look into it. Will try to do so by end of day.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6905?src=pr&el=h1) Report\n> Merging [#6905](https://codecov.io/gh/huggingface/transformers/pull/6905?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/8f2723caf0f1bf7e1f639d28d004f81c96d19bbc?el=desc) will **decrease** coverage by `0.12%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6905?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6905 +/- ##\n==========================================\n- Coverage 79.81% 79.69% -0.13% \n==========================================\n Files 157 157 \n Lines 28853 28853 \n==========================================\n- Hits 23029 22994 -35 \n- Misses 5824 5859 +35 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6905?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_electra.py](https://codecov.io/gh/huggingface/transformers/pull/6905/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9lbGVjdHJhLnB5) | `25.13% <0.00%> (-73.83%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/6905/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9vcGVuYWkucHk=) | `22.58% <0.00%> (-72.26%)` | :arrow_down: |\n| [src/transformers/tokenization\\_marian.py](https://codecov.io/gh/huggingface/transformers/pull/6905/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fbWFyaWFuLnB5) | `32.20% <0.00%> (-66.95%)` | :arrow_down: |\n| [src/transformers/tokenization\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/6905/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYmFydC5weQ==) | `42.10% <0.00%> (-57.90%)` | :arrow_down: |\n| [src/transformers/modeling\\_marian.py](https://codecov.io/gh/huggingface/transformers/pull/6905/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19tYXJpYW4ucHk=) | `60.00% <0.00%> (-30.00%)` | :arrow_down: |\n| [src/transformers/tokenization\\_xlm\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/6905/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25feGxtX3JvYmVydGEucHk=) | `84.52% <0.00%> (-10.72%)` | :arrow_down: |\n| [src/transformers/tokenization\\_gpt2.py](https://codecov.io/gh/huggingface/transformers/pull/6905/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fZ3B0Mi5weQ==) | `87.50% <0.00%> (-9.73%)` | :arrow_down: |\n| [src/transformers/modeling\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/6905/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190NS5weQ==) | `76.70% <0.00%> (-6.07%)` | :arrow_down: |\n| [src/transformers/activations.py](https://codecov.io/gh/huggingface/transformers/pull/6905/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9hY3RpdmF0aW9ucy5weQ==) | `85.00% <0.00%> (-5.00%)` | :arrow_down: |\n| [src/transformers/tokenization\\_utils\\_base.py](https://codecov.io/gh/huggingface/transformers/pull/6905/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHNfYmFzZS5weQ==) | `89.97% <0.00%> (-4.07%)` | :arrow_down: |\n| ... and [14 more](https://codecov.io/gh/huggingface/transformers/pull/6905/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6905?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6905?src=pr&el=footer). Last update [8f2723c...0037bd4](https://codecov.io/gh/huggingface/transformers/pull/6905?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Thx for fixing this!" ]

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[ "Didn't realize that `postprocess_next_token_scores` mutates its argument." ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6903?src=pr&el=h1) Report\n> Merging [#6903](https://codecov.io/gh/huggingface/transformers/pull/6903?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/485da7222f7f9ca9854db1a6df027b00d348d017?el=desc) will **increase** coverage by `0.29%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6903?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6903 +/- ##\n==========================================\n+ Coverage 79.30% 79.59% +0.29% \n==========================================\n Files 157 157 \n Lines 28853 28853 \n==========================================\n+ Hits 22882 22966 +84 \n+ Misses 5971 5887 -84 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6903?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/configuration\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/6903/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX2F1dG8ucHk=) | `93.18% <ø> (ø)` | |\n| [src/transformers/configuration\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6903/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3V0aWxzLnB5) | `96.64% <ø> (+0.67%)` | :arrow_up: |\n| [src/transformers/modelcard.py](https://codecov.io/gh/huggingface/transformers/pull/6903/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGNhcmQucHk=) | `85.18% <ø> (ø)` | |\n| [src/transformers/modeling\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/6903/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19hdXRvLnB5) | `78.73% <ø> (ø)` | |\n| [src/transformers/modeling\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/6903/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19kaXN0aWxiZXJ0LnB5) | `97.84% <ø> (ø)` | |\n| [src/transformers/modeling\\_encoder\\_decoder.py](https://codecov.io/gh/huggingface/transformers/pull/6903/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19lbmNvZGVyX2RlY29kZXIucHk=) | `92.00% <ø> (ø)` | |\n| [src/transformers/modeling\\_tf\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/6903/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9hdXRvLnB5) | `66.86% <ø> (ø)` | |\n| [src/transformers/modeling\\_tf\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/6903/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9kaXN0aWxiZXJ0LnB5) | `64.47% <ø> (-34.36%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6903/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `87.29% <ø> (+0.32%)` | :arrow_up: |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6903/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `87.50% <ø> (ø)` | |\n| ... and [23 more](https://codecov.io/gh/huggingface/transformers/pull/6903/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6903?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6903?src=pr&el=footer). Last update [485da72...e8fd79c](https://codecov.io/gh/huggingface/transformers/pull/6903?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "Thanks for reporting! The PR mentioned above should fix all of those." ]

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[ "I don't see anything blocking with this. Wdyt @sgugger @julien-c ?", "We can give a warning but then the rest of the method will fail. Are you thinking of aborting the save entirely for models that are not `PretrainedModel`s? Also, why are you not inheriting from `PretrainedModel` in your example? Is there something limiting?\r\n\r\nNote that Trainer is not supposed to be a generic training loop, but we can surely make it a bit more flexible.", "Yes, `Trainer` is not a general loop, but it works for custom models as I've tried. Majority of its parts are generalized. `PreTrainedModel` also inherits from `nn.Module`, so users can do that, although its quite common for users to inherit from `nn.Module` directly. I'm not sure how the method will fail ? We can just add a warning instead of raising a `ValueError`. The reason why I'm saying is that users would want to do more than just what `transformers` provide out of the box (for instance justing using `AutoModel` and not `SequenceClassification` models (I'm seeing a growing interest in using such models). I think `nlp` is heading towards that direction (making everything general). This works fine for all cases, I guess:\r\n```\r\nfrom types import MethodType\r\n\r\ndef _save(self, output_dir: Optional[str] = None):\r\n output_dir = output_dir if output_dir is not None else self.args.output_dir\r\n os.makedirs(output_dir, exist_ok=True)\r\n logger.info(\"Saving model checkpoint to %s\", output_dir)\r\n\r\n torch.save(\r\n {\"model_state_dict\": self.model.state_dict()},\r\n os.path.join(output_dir, \"pytorch_model.bin\"),\r\n )\r\n\r\n # Good practice: save your training arguments together with the trained model\r\n torch.save(self.args, os.path.join(output_dir, \"training_args.bin\"))\r\n\r\ntrainer._save = MethodType(_save, trainer)\r\n```\r\nWhere do you think the approach may not work ? After providing the warning, its upto users if they further want to make changes by overriding this method (they would know that `transformers` is not responsible anymore since its not a `PreTrainedModel`. Current method completely breaks the training due to `ValueError`.\r\nThis is optional, I felt that it would be useful to have. I'll open a PR if you approve.", "`save_pretrained` does more than the method you mention, but we could refactor the code inside to work with all models probably. I don't see any place it uses specific stuff from `PretrainedModel`. The thing we don't want is to add and maintain too generic code, but if it's easy enough I see no objection.\r\n\r\nYou didn't tell me why subclassing `PreTrainedModel` did not work however ;-) That is what I would expect a user building a custom model using transformers to do .", "The `PreTrainedModel` is a generic class amongst all models in `transformers`, all classes pertaining to it comply in terms of the methods it provides and can use functionalities such as `init_weights`, `prune_heads`. They might not work for custom models. For instance, some methods require `.config.` attribute which custom models may not directly have. I guess one can define their custom model to be exactly what `PreTrainedModel` requires them to be (haven't looked into that), but that would be asking users to read through what `PreTrainedModel` expects or maybe specifying in docs. It's totally up to you what you expect the users to do in case they use custom models.", "After some internal discussion with @julien-c we will lower the requirement from `PreTrainedModel` to some lower abstractclass/protocol so the user knows exactly what they have to implement for their model to work seamlessly with `Trainer`. I will work on this end of this week beginning of next. ", "Sounds good. I'll look forward to that part then." ]

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[ "DistilBERT can support sentence pair-like inputs but does not make use of token type IDs. It detects sentence pairs according to the special tokens. cc @VictorSanh ", "@Yusifu Did you find a solution for this problem? I'm also doing sentence-pair classification (NLI) with Distilbert.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Hey @wulaoshi - I don't fully understand your question. Could you maybe post such a higher level question on the forum at `discuss.huggingface.co` ? :-) ", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6898?src=pr&el=h1) Report\n> Merging [#6898](https://codecov.io/gh/huggingface/transformers/pull/6898?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/d822ab636b6a14ed50f7bca0797c1de42c19de61?el=desc) will **increase** coverage by `1.00%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6898?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6898 +/- ##\n==========================================\n+ Coverage 79.61% 80.62% +1.00% \n==========================================\n Files 157 157 \n Lines 28826 28826 \n==========================================\n+ Hits 22951 23241 +290 \n+ Misses 5875 5585 -290 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6898?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_flaubert.py](https://codecov.io/gh/huggingface/transformers/pull/6898/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9mbGF1YmVydC5weQ==) | `24.53% <0.00%> (-63.81%)` | :arrow_down: |\n| [src/transformers/tokenization\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/6898/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYmFydC5weQ==) | `42.10% <0.00%> (-57.90%)` | :arrow_down: |\n| [src/transformers/modeling\\_marian.py](https://codecov.io/gh/huggingface/transformers/pull/6898/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19tYXJpYW4ucHk=) | `60.00% <0.00%> (-30.00%)` | :arrow_down: |\n| [src/transformers/activations.py](https://codecov.io/gh/huggingface/transformers/pull/6898/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9hY3RpdmF0aW9ucy5weQ==) | `85.00% <0.00%> (-5.00%)` | :arrow_down: |\n| [src/transformers/tokenization\\_utils\\_base.py](https://codecov.io/gh/huggingface/transformers/pull/6898/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHNfYmFzZS5weQ==) | `90.10% <0.00%> (-3.93%)` | :arrow_down: |\n| [src/transformers/tokenization\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/6898/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYXV0by5weQ==) | `95.55% <0.00%> (-2.23%)` | :arrow_down: |\n| [src/transformers/configuration\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6898/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3V0aWxzLnB5) | `95.97% <0.00%> (-0.68%)` | :arrow_down: |\n| [src/transformers/modeling\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/6898/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iYXJ0LnB5) | `93.90% <0.00%> (-0.34%)` | :arrow_down: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6898/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `82.41% <0.00%> (-0.26%)` | :arrow_down: |\n| [src/transformers/generation\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6898/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3V0aWxzLnB5) | `97.21% <0.00%> (+0.27%)` | :arrow_up: |\n| ... and [7 more](https://codecov.io/gh/huggingface/transformers/pull/6898/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6898?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6898?src=pr&el=footer). Last update [d822ab6...6b67e49](https://codecov.io/gh/huggingface/transformers/pull/6898?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6897?src=pr&el=h1) Report\n> Merging [#6897](https://codecov.io/gh/huggingface/transformers/pull/6897?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/d822ab636b6a14ed50f7bca0797c1de42c19de61?el=desc) will **increase** coverage by `0.77%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6897?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6897 +/- ##\n==========================================\n+ Coverage 79.61% 80.39% +0.77% \n==========================================\n Files 157 157 \n Lines 28826 28826 \n==========================================\n+ Hits 22951 23174 +223 \n+ Misses 5875 5652 -223 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6897?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/6897/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iZXJ0LnB5) | `88.42% <ø> (ø)` | |\n| [src/transformers/tokenization\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/6897/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYmFydC5weQ==) | `42.10% <0.00%> (-57.90%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/6897/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9yb2JlcnRhLnB5) | `45.41% <0.00%> (-47.81%)` | :arrow_down: |\n| [src/transformers/modeling\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/6897/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19yb2JlcnRhLnB5) | `57.29% <0.00%> (-39.79%)` | :arrow_down: |\n| [src/transformers/tokenization\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/6897/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fcm9iZXJ0YS5weQ==) | `76.71% <0.00%> (-21.92%)` | :arrow_down: |\n| [src/transformers/tokenization\\_utils\\_base.py](https://codecov.io/gh/huggingface/transformers/pull/6897/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHNfYmFzZS5weQ==) | `86.85% <0.00%> (-7.19%)` | :arrow_down: |\n| [src/transformers/tokenization\\_utils\\_fast.py](https://codecov.io/gh/huggingface/transformers/pull/6897/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHNfZmFzdC5weQ==) | `92.14% <0.00%> (-2.15%)` | :arrow_down: |\n| [src/transformers/tokenization\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/6897/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fb3BlbmFpLnB5) | `82.57% <0.00%> (-1.52%)` | :arrow_down: |\n| [src/transformers/tokenization\\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/6897/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYmVydC5weQ==) | `91.96% <0.00%> (-0.45%)` | :arrow_down: |\n| [src/transformers/data/data\\_collator.py](https://codecov.io/gh/huggingface/transformers/pull/6897/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL2RhdGFfY29sbGF0b3IucHk=) | `92.30% <0.00%> (-0.41%)` | :arrow_down: |\n| ... and [13 more](https://codecov.io/gh/huggingface/transformers/pull/6897/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6897?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6897?src=pr&el=footer). Last update [d822ab6...b6c59a1](https://codecov.io/gh/huggingface/transformers/pull/6897?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "You can try the following changes:\r\n\r\n```python\r\nfrom transformers import BertPreTrainedModel, RobertaModel, ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST, RobertaConfig\r\n\r\nclass RobertaForMD(BertPreTrainedModel): # Metaphor Detection, modified from BertForTokenClassification\r\n config_class = RobertaConfig\r\n pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST\r\n base_model_prefix = \"roberta\"\r\n \r\n def __init__(self, config):\r\n super().__init__(config)\r\n self.num_labels = config.num_labels\r\n```", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

[ { "id": 1838412367, "node_id": "MDU6TGFiZWwxODM4NDEyMzY3", "url": "https://api.github.com/repos/huggingface/transformers/labels/model%20card", "name": "model card", "color": "92d5f4", "default": false, "description": "Related to pretrained model cards" } ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6893?src=pr&el=h1) Report\n> Merging [#6893](https://codecov.io/gh/huggingface/transformers/pull/6893?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/d822ab636b6a14ed50f7bca0797c1de42c19de61?el=desc) will **increase** coverage by `0.46%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6893?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6893 +/- ##\n==========================================\n+ Coverage 79.61% 80.08% +0.46% \n==========================================\n Files 157 157 \n Lines 28826 28826 \n==========================================\n+ Hits 22951 23086 +135 \n+ Misses 5875 5740 -135 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6893?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [...c/transformers/modeling\\_tf\\_transfo\\_xl\\_utilities.py](https://codecov.io/gh/huggingface/transformers/pull/6893/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl90cmFuc2ZvX3hsX3V0aWxpdGllcy5weQ==) | `10.00% <0.00%> (-76.00%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_transfo\\_xl.py](https://codecov.io/gh/huggingface/transformers/pull/6893/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl90cmFuc2ZvX3hsLnB5) | `19.85% <0.00%> (-68.29%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_flaubert.py](https://codecov.io/gh/huggingface/transformers/pull/6893/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9mbGF1YmVydC5weQ==) | `24.53% <0.00%> (-63.81%)` | :arrow_down: |\n| [src/transformers/modeling\\_marian.py](https://codecov.io/gh/huggingface/transformers/pull/6893/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19tYXJpYW4ucHk=) | `60.00% <0.00%> (-30.00%)` | :arrow_down: |\n| [src/transformers/modeling\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/6893/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19yb2JlcnRhLnB5) | `77.73% <0.00%> (-19.35%)` | :arrow_down: |\n| [src/transformers/tokenization\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/6893/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fcm9iZXJ0YS5weQ==) | `87.67% <0.00%> (-10.96%)` | :arrow_down: |\n| [src/transformers/tokenization\\_utils\\_base.py](https://codecov.io/gh/huggingface/transformers/pull/6893/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHNfYmFzZS5weQ==) | `86.44% <0.00%> (-7.59%)` | :arrow_down: |\n| [src/transformers/activations.py](https://codecov.io/gh/huggingface/transformers/pull/6893/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9hY3RpdmF0aW9ucy5weQ==) | `85.00% <0.00%> (-5.00%)` | :arrow_down: |\n| [src/transformers/tokenization\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/6893/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fb3BlbmFpLnB5) | `82.57% <0.00%> (-1.52%)` | :arrow_down: |\n| [src/transformers/tokenization\\_utils\\_fast.py](https://codecov.io/gh/huggingface/transformers/pull/6893/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHNfZmFzdC5weQ==) | `92.85% <0.00%> (-1.43%)` | :arrow_down: |\n| ... and [17 more](https://codecov.io/gh/huggingface/transformers/pull/6893/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6893?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6893?src=pr&el=footer). Last update [d822ab6...3979cda](https://codecov.io/gh/huggingface/transformers/pull/6893?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "👍 " ]

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[ "Those requirements are all in [here](https://github.com/huggingface/transformers/blob/master/examples/requirements.txt). Are you sure you ran `pip install -r ./examples/requirements.txt` as mentioned in the [README of all examples](https://github.com/huggingface/transformers/tree/master/examples#important-note)?\r\n\r\nThey are not, and won't be requirements of the main library since they are only used for some specific tasks.", "Huh, probably just a local issue then. Thanks!" ]

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[ "I don't see the transformers code that creates this bug. In 3.1.0, `DistilBertConfig` definitely has a 'return_dict' `attribute`. I tried to use your code to investigate the error, but it fails on the line `flair_sent = flair.models.TextClassifier.load('en-sentiment')` for me.\r\n\r\nHappy to investigate a code sample that uses transformers and creates the bug, but this looks like a problem to report on the fair GitHub. ", "> I don't see the transformers code that creates this bug. In 3.1.0, `DistilBertConfig` definitely has a 'return_dict' `attribute`. I tried to use your code to investigate the error, but it fails on the line `flair_sent = flair.models.TextClassifier.load('en-sentiment')` for me.\r\n> \r\n> Happy to investigate a code sample that uses transformers and creates the bug, but this looks like a problem to report on the fair GitHub.\r\n\r\nI already did, just in case I wanted to report the bug in here. Thank you anyway!", "Don't hesitate to reopen if it ends up being on our side, with a small repro using only transformers ideally.", "It ended being on flair side. Here I'll attached the link for future references [/flairNLP/flair/issues/1841](https://github.com/flairNLP/flair/issues/1841)" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6890?src=pr&el=h1) Report\n> Merging [#6890](https://codecov.io/gh/huggingface/transformers/pull/6890?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/3726754a6c646adcf9cb2135ab7f72dffe074473?el=desc) will **decrease** coverage by `0.49%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6890?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6890 +/- ##\n==========================================\n- Coverage 80.05% 79.56% -0.50% \n==========================================\n Files 157 157 \n Lines 28822 28822 \n==========================================\n- Hits 23074 22932 -142 \n- Misses 5748 5890 +142 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6890?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6890/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `82.66% <ø> (ø)` | |\n| [src/transformers/modeling\\_tf\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/6890/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9vcGVuYWkucHk=) | `22.58% <0.00%> (-72.26%)` | :arrow_down: |\n| [src/transformers/configuration\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/6890/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX29wZW5haS5weQ==) | `34.28% <0.00%> (-62.86%)` | :arrow_down: |\n| [src/transformers/tokenization\\_albert.py](https://codecov.io/gh/huggingface/transformers/pull/6890/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYWxiZXJ0LnB5) | `28.84% <0.00%> (-58.66%)` | :arrow_down: |\n| [src/transformers/modeling\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/6890/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19vcGVuYWkucHk=) | `23.87% <0.00%> (-48.39%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/6890/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9kaXN0aWxiZXJ0LnB5) | `64.47% <0.00%> (-34.36%)` | :arrow_down: |\n| [src/transformers/tokenization\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/6890/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fcm9iZXJ0YS5weQ==) | `87.67% <0.00%> (-10.96%)` | :arrow_down: |\n| [src/transformers/tokenization\\_utils\\_base.py](https://codecov.io/gh/huggingface/transformers/pull/6890/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHNfYmFzZS5weQ==) | `86.85% <0.00%> (-7.05%)` | :arrow_down: |\n| [src/transformers/tokenization\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/6890/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fb3BlbmFpLnB5) | `82.57% <0.00%> (-1.52%)` | :arrow_down: |\n| [src/transformers/tokenization\\_utils\\_fast.py](https://codecov.io/gh/huggingface/transformers/pull/6890/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHNfZmFzdC5weQ==) | `92.85% <0.00%> (-1.43%)` | :arrow_down: |\n| ... and [7 more](https://codecov.io/gh/huggingface/transformers/pull/6890/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6890?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6890?src=pr&el=footer). Last update [3726754...eb044f1](https://codecov.io/gh/huggingface/transformers/pull/6890?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6889?src=pr&el=h1) Report\n> Merging [#6889](https://codecov.io/gh/huggingface/transformers/pull/6889?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/311992630cfd6c776bc2672d94dcd81624ad023b?el=desc) will **increase** coverage by `0.64%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6889?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6889 +/- ##\n==========================================\n+ Coverage 79.06% 79.71% +0.64% \n==========================================\n Files 157 157 \n Lines 28823 28823 \n==========================================\n+ Hits 22789 22976 +187 \n+ Misses 6034 5847 -187 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6889?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_electra.py](https://codecov.io/gh/huggingface/transformers/pull/6889/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9lbGVjdHJhLnB5) | `25.13% <0.00%> (-73.83%)` | :arrow_down: |\n| [src/transformers/tokenization\\_marian.py](https://codecov.io/gh/huggingface/transformers/pull/6889/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fbWFyaWFuLnB5) | `32.20% <0.00%> (-66.95%)` | :arrow_down: |\n| [src/transformers/tokenization\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/6889/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdDUucHk=) | `59.43% <0.00%> (-35.85%)` | :arrow_down: |\n| [src/transformers/modeling\\_marian.py](https://codecov.io/gh/huggingface/transformers/pull/6889/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19tYXJpYW4ucHk=) | `60.00% <0.00%> (-30.00%)` | :arrow_down: |\n| [src/transformers/data/data\\_collator.py](https://codecov.io/gh/huggingface/transformers/pull/6889/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL2RhdGFfY29sbGF0b3IucHk=) | `87.04% <0.00%> (-5.27%)` | :arrow_down: |\n| [src/transformers/activations.py](https://codecov.io/gh/huggingface/transformers/pull/6889/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9hY3RpdmF0aW9ucy5weQ==) | `85.00% <0.00%> (-5.00%)` | :arrow_down: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6889/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `83.95% <0.00%> (-1.51%)` | :arrow_down: |\n| [src/transformers/modeling\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/6889/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iYXJ0LnB5) | `93.90% <0.00%> (-0.51%)` | :arrow_down: |\n| [src/transformers/tokenization\\_utils\\_base.py](https://codecov.io/gh/huggingface/transformers/pull/6889/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHNfYmFzZS5weQ==) | `93.63% <0.00%> (-0.41%)` | :arrow_down: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6889/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `82.41% <0.00%> (-0.26%)` | :arrow_down: |\n| ... and [9 more](https://codecov.io/gh/huggingface/transformers/pull/6889/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6889?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6889?src=pr&el=footer). Last update [3119926...e119e42](https://codecov.io/gh/huggingface/transformers/pull/6889?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6888?src=pr&el=h1) Report\n> Merging [#6888](https://codecov.io/gh/huggingface/transformers/pull/6888?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/311992630cfd6c776bc2672d94dcd81624ad023b?el=desc) will **decrease** coverage by `0.84%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6888?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6888 +/- ##\n==========================================\n- Coverage 79.06% 78.22% -0.85% \n==========================================\n Files 157 157 \n Lines 28823 28823 \n==========================================\n- Hits 22789 22546 -243 \n- Misses 6034 6277 +243 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6888?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_albert.py](https://codecov.io/gh/huggingface/transformers/pull/6888/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9hbGJlcnQucHk=) | `21.47% <0.00%> (-69.44%)` | :arrow_down: |\n| [src/transformers/pipelines.py](https://codecov.io/gh/huggingface/transformers/pull/6888/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9waXBlbGluZXMucHk=) | `25.63% <0.00%> (-54.32%)` | :arrow_down: |\n| [src/transformers/tokenization\\_pegasus.py](https://codecov.io/gh/huggingface/transformers/pull/6888/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fcGVnYXN1cy5weQ==) | `46.03% <0.00%> (-49.21%)` | :arrow_down: |\n| [src/transformers/optimization.py](https://codecov.io/gh/huggingface/transformers/pull/6888/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9vcHRpbWl6YXRpb24ucHk=) | `34.28% <0.00%> (-48.00%)` | :arrow_down: |\n| [src/transformers/optimization\\_tf.py](https://codecov.io/gh/huggingface/transformers/pull/6888/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9vcHRpbWl6YXRpb25fdGYucHk=) | `33.33% <0.00%> (-24.33%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/6888/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9hdXRvLnB5) | `48.79% <0.00%> (-18.08%)` | :arrow_down: |\n| [src/transformers/data/processors/squad.py](https://codecov.io/gh/huggingface/transformers/pull/6888/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL3Byb2Nlc3NvcnMvc3F1YWQucHk=) | `13.76% <0.00%> (-14.38%)` | :arrow_down: |\n| [src/transformers/modeling\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/6888/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19hdXRvLnB5) | `64.36% <0.00%> (-14.37%)` | :arrow_down: |\n| [src/transformers/modeling\\_mobilebert.py](https://codecov.io/gh/huggingface/transformers/pull/6888/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19tb2JpbGViZXJ0LnB5) | `79.21% <0.00%> (-10.25%)` | :arrow_down: |\n| [src/transformers/data/data\\_collator.py](https://codecov.io/gh/huggingface/transformers/pull/6888/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL2RhdGFfY29sbGF0b3IucHk=) | `87.04% <0.00%> (-5.27%)` | :arrow_down: |\n| ... and [21 more](https://codecov.io/gh/huggingface/transformers/pull/6888/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6888?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6888?src=pr&el=footer). Last update [3119926...157d717](https://codecov.io/gh/huggingface/transformers/pull/6888?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "Thanks @mrm8488 , cc @dccuchile" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6886?src=pr&el=h1) Report\n> Merging [#6886](https://codecov.io/gh/huggingface/transformers/pull/6886?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/311992630cfd6c776bc2672d94dcd81624ad023b?el=desc) will **increase** coverage by `1.04%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6886?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6886 +/- ##\n==========================================\n+ Coverage 79.06% 80.10% +1.04% \n==========================================\n Files 157 157 \n Lines 28823 28823 \n==========================================\n+ Hits 22789 23089 +300 \n+ Misses 6034 5734 -300 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6886?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_electra.py](https://codecov.io/gh/huggingface/transformers/pull/6886/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9lbGVjdHJhLnB5) | `25.13% <0.00%> (-73.83%)` | :arrow_down: |\n| [src/transformers/tokenization\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/6886/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYmFydC5weQ==) | `42.10% <0.00%> (-57.90%)` | :arrow_down: |\n| [src/transformers/modeling\\_marian.py](https://codecov.io/gh/huggingface/transformers/pull/6886/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19tYXJpYW4ucHk=) | `60.00% <0.00%> (-30.00%)` | :arrow_down: |\n| [src/transformers/activations.py](https://codecov.io/gh/huggingface/transformers/pull/6886/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9hY3RpdmF0aW9ucy5weQ==) | `85.00% <0.00%> (-5.00%)` | :arrow_down: |\n| [src/transformers/modeling\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/6886/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iYXJ0LnB5) | `93.90% <0.00%> (-0.51%)` | :arrow_down: |\n| [src/transformers/tokenization\\_utils\\_base.py](https://codecov.io/gh/huggingface/transformers/pull/6886/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHNfYmFzZS5weQ==) | `93.76% <0.00%> (-0.28%)` | :arrow_down: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6886/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `82.41% <0.00%> (-0.26%)` | :arrow_down: |\n| [src/transformers/modeling\\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/6886/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iZXJ0LnB5) | `88.42% <0.00%> (+0.32%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6886/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl91dGlscy5weQ==) | `86.97% <0.00%> (+0.65%)` | :arrow_up: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6886/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `86.21% <0.00%> (+0.75%)` | :arrow_up: |\n| ... and [6 more](https://codecov.io/gh/huggingface/transformers/pull/6886/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6886?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6886?src=pr&el=footer). Last update [3119926...0e167b5](https://codecov.io/gh/huggingface/transformers/pull/6886?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "@sshleifer want to update the inference API so that the correct pipeline shows up at https://huggingface.co/akhooli/mbart-large-cc25-en-ar ? (cc @mfuntowicz)", "Seems fixed?\r\nhttps://huggingface.co/akhooli/mbart-large-cc25-en-ar\r\n\r\n", "> \r\n> \r\n> Seems fixed?\r\n> https://huggingface.co/akhooli/mbart-large-cc25-en-ar\r\n> \r\nSure, just after the model card was merged. Not sure if it was due to the 'translation' tag in the card or some other magic done by your team.", "Just uploaded https://huggingface.co/akhooli/mbart-large-cc25-ar-en and it seems inference type is not recognized automatically. It defaults to fill-mask (model card submitted).", "model card merged." ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6884?src=pr&el=h1) Report\n> Merging [#6884](https://codecov.io/gh/huggingface/transformers/pull/6884?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/3726754a6c646adcf9cb2135ab7f72dffe074473?el=desc) will **decrease** coverage by `0.22%`.\n> The diff coverage is `100.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6884?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6884 +/- ##\n==========================================\n- Coverage 80.05% 79.83% -0.23% \n==========================================\n Files 157 157 \n Lines 28822 28823 +1 \n==========================================\n- Hits 23074 23010 -64 \n- Misses 5748 5813 +65 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6884?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_electra.py](https://codecov.io/gh/huggingface/transformers/pull/6884/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19lbGVjdHJhLnB5) | `82.18% <100.00%> (+0.05%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_electra.py](https://codecov.io/gh/huggingface/transformers/pull/6884/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9lbGVjdHJhLnB5) | `25.13% <0.00%> (-73.83%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/6884/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9vcGVuYWkucHk=) | `22.58% <0.00%> (-72.26%)` | :arrow_down: |\n| [src/transformers/tokenization\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/6884/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYmFydC5weQ==) | `42.10% <0.00%> (-57.90%)` | :arrow_down: |\n| [src/transformers/modeling\\_marian.py](https://codecov.io/gh/huggingface/transformers/pull/6884/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19tYXJpYW4ucHk=) | `60.00% <0.00%> (-30.00%)` | :arrow_down: |\n| [src/transformers/tokenization\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/6884/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fcm9iZXJ0YS5weQ==) | `76.71% <0.00%> (-21.92%)` | :arrow_down: |\n| [src/transformers/tokenization\\_utils\\_base.py](https://codecov.io/gh/huggingface/transformers/pull/6884/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHNfYmFzZS5weQ==) | `86.58% <0.00%> (-7.32%)` | :arrow_down: |\n| [src/transformers/activations.py](https://codecov.io/gh/huggingface/transformers/pull/6884/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9hY3RpdmF0aW9ucy5weQ==) | `85.00% <0.00%> (-5.00%)` | :arrow_down: |\n| [src/transformers/tokenization\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/6884/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYXV0by5weQ==) | `95.55% <0.00%> (-2.23%)` | :arrow_down: |\n| [src/transformers/tokenization\\_utils\\_fast.py](https://codecov.io/gh/huggingface/transformers/pull/6884/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHNfZmFzdC5weQ==) | `92.14% <0.00%> (-2.15%)` | :arrow_down: |\n| ... and [16 more](https://codecov.io/gh/huggingface/transformers/pull/6884/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6884?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6884?src=pr&el=footer). Last update [3726754...8f97406](https://codecov.io/gh/huggingface/transformers/pull/6884?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "I understand it makes the code slightly cleaner; in terms of speed it is most likely negligible (compared to the embedding lookup, for example).\r\n\r\nBut not sure what to do now as all the pretrained models (that used a lot of compute to pretrain) don't work anymore in the new update.", "Hey @Laksh1997 - note that this line does not break anything. You can neglect warnings about `position_ids` since those are created at instantiation. Will open a PR to fix the warning", "@patrickvonplaten seems to break it for me:\r\n\r\n```\r\n\r\n16:43:52\r\nTraceback (most recent call last):\r\n\r\n16:43:52\r\nFile \"/opt/conda/envs/py36/bin/transformervae\", line 33, in <module>\r\n\r\n16:43:52\r\nsys.exit(load_entry_point('exs-transformervae', 'console_scripts', 'transformervae')())\r\n\r\n16:43:52\r\nFile \"/opt/conda/envs/py36/lib/python3.6/site-packages/click/core.py\", line 829, in __call__\r\n\r\n16:43:52\r\nreturn self.main(*args, **kwargs)\r\n\r\n16:43:52\r\nFile \"/opt/conda/envs/py36/lib/python3.6/site-packages/click/core.py\", line 782, in main\r\n\r\n16:43:52\r\nrv = self.invoke(ctx)\r\n\r\n16:43:52\r\nFile \"/opt/conda/envs/py36/lib/python3.6/site-packages/click/core.py\", line 1259, in invoke\r\n\r\n16:43:52\r\nreturn _process_result(sub_ctx.command.invoke(sub_ctx))\r\n\r\n16:43:52\r\nFile \"/opt/conda/envs/py36/lib/python3.6/site-packages/click/core.py\", line 1066, in invoke\r\n\r\n16:43:52\r\nreturn ctx.invoke(self.callback, **ctx.params)\r\n\r\n16:43:52\r\nFile \"/opt/conda/envs/py36/lib/python3.6/site-packages/click/core.py\", line 610, in invoke\r\n\r\n16:43:52\r\nreturn callback(*args, **kwargs)\r\n\r\n16:43:52\r\nFile \"/app/transformervae/cli.py\", line 355, in train\r\n\r\n16:43:52\r\nmodel = model_cls(hparams, pretrained_model=pretrained_model_path_or_config)\r\n\r\n16:43:52\r\nFile \"/app/transformervae/models/regression.py\", line 35, in __init__\r\n\r\n16:43:52\r\npretrained_model,\r\n\r\n16:43:52\r\nFile \"/app/transformervae/models/finetuning_model.py\", line 37, in __init__\r\n\r\n16:43:52\r\nself.encoder, self.tokenizer = self.load_pretrained_encoder(pretrained_model)\r\n\r\n16:43:52\r\nFile \"/app/transformervae/models/finetuning_model.py\", line 89, in load_pretrained_encoder\r\n\r\n16:43:52\r\npl_model = AutoModel.load(pretrained_model)\r\n\r\n16:43:52\r\nFile \"/app/transformervae/models/automodel.py\", line 98, in load\r\n\r\n16:43:52\r\nreturn model_cls.load(path)\r\n\r\n16:43:52\r\nFile \"/app/transformervae/models/base.py\", line 229, in load\r\n\r\n16:43:52\r\nreturn cls.load_from_checkpoint(filepath)\r\n\r\n16:43:52\r\nFile \"/opt/conda/envs/py36/lib/python3.6/site-packages/pytorch_lightning/core/saving.py\", line 169, in load_from_checkpoint\r\n\r\n16:43:52\r\nmodel = cls._load_model_state(checkpoint, *args, **kwargs)\r\n\r\n16:43:52\r\nFile \"/opt/conda/envs/py36/lib/python3.6/site-packages/pytorch_lightning/core/saving.py\", line 207, in _load_model_state\r\n\r\n16:43:52\r\nmodel.load_state_dict(checkpoint['state_dict'])\r\n\r\n16:43:52\r\nFile \"/opt/conda/envs/py36/lib/python3.6/site-packages/torch/nn/modules/module.py\", line 1045, in load_state_dict\r\n\r\n16:43:52\r\nself.__class__.__name__, \"\\n\\t\".join(error_msgs)))\r\n\r\n16:43:52\r\nRuntimeError: Error(s) in loading state_dict for ElectraLanguageModel:\r\n\r\n16:43:52\r\nMissing key(s) in state_dict: \"generator_model.electra.embeddings.position_ids\", \"discriminator_model.electra.embeddings.position_ids\".\r\n```", "Note, `generator_model.electra` is `ElectraModel`, which uses `BertEmbeddings`.", "Can you send me a code snippet so that I can reproduce your error? \r\n", "It's a big library. But I can try to recreate in a Colab. One sec.", "@patrickvonplaten Colab: https://colab.research.google.com/drive/167CwTImG5T-4c9xeIVEkH9Xrracbn30h?usp=sharing\r\n\r\nLet me know if you can access?", "It also breaks to me. The attribute embedding.position_ids can't be loaded if the model artifact is trained with v3.0.2. So it will raise an KeyError", "Hey @Laksh1997, I can't access the notebook - could you make it public for everybody to see? :-) ", "@patrickvonplaten apologies. Here is the script:\r\n\r\n```python\r\n!pip install transformers==3.0.2\r\n\r\nfrom transformers import ElectraModel, ElectraConfig\r\nimport torch\r\nimport transformers\r\n\r\nprint(transformers.__version__)\r\n\r\nmodel = ElectraModel(ElectraConfig())\r\nstate_dict = model.state_dict()\r\ntorch.save(state_dict, 'checkpoint.pt')\r\n```\r\n\r\n```python\r\n!pip install transformers==3.1.0\r\n\r\nfrom transformers import ElectraModel, ElectraConfig\r\nimport torch\r\nimport transformers\r\n\r\nprint(transformers.__version__)\r\n\r\nmodel = ElectraModel(ElectraConfig())\r\nstate_dict = torch.load('checkpoint.pt')\r\nmodel.load_state_dict(state_dict)\r\n\r\n```", "I encountered the same issue. Old checkpoints (3.0.2) can not be loaded in (3.1.0) due to KeyError.", "@Barcavin @easonnie As a temporary fix, I've just reverted back to 3.0.2. @patrickvonplaten I am hoping something can be done !", "Hi, while we work on patching this issue, you can still use version v3.1.0 by using the `from_pretrained` method. Taking @Laksh1997's example, you would do:\r\n\r\n1. Save the checkpoint in `saved_model_location/pytorch_model.bin`\r\n\r\n```py\r\nfrom transformers import ElectraModel, ElectraConfig\r\nimport torch\r\nimport transformers\r\n\r\nprint(transformers.__version__)\r\n\r\nmodel = ElectraModel(ElectraConfig())\r\nstate_dict = model.state_dict()\r\ntorch.save(state_dict, 'saved_model_location/pytorch_model.bin')\r\n```\r\n\r\n2. Load it using the method `.from_pretrained`\r\n\r\n```py\r\nfrom transformers import ElectraModel, ElectraConfig\r\nimport transformers\r\n\r\nprint(transformers.__version__)\r\n\r\nmodel = ElectraModel.from_pretrained(\"saved_model_location\", config=ElectraConfig())\r\n``` ", "You can also use the `load_state_dict` method with the `strict` option set to `False`:\r\n\r\n```py\r\nmodel.load_state_dict(state_dict, strict=False)\r\n```", "The reason this additional buffer is here now is due to this [PR](https://github.com/huggingface/transformers/pull/5773#issue-449530988). \r\n\r\nIs there a reason why you would use the `load_state_dict` instead of `from_pretrained`, as `from_pretrained` exists in part to prevent such issues from happening?", "Hi @LysandreJik \r\n\r\nThanks for the proposed solution. \r\n\r\nIn my case, I am using Pytorch Lightning which has its own saving and loading infrastructure. Thus the `from_pretrained` method can't exactly be used.\r\n\r\nThe `strict` flag is a good patch for now.\r\n\r\nI think, in general, when building on top of the library, for complex projects one cannot rely on `from_pretrained`, especially if using other ecosystems.", "Using the `strict` flag can enable a number of errors to go undetected, so I would refrain from using it. I think the best solution is to use version 3.0.2 for already trained models until the fix comes out.", "Any update on this @LysandreJik @patrickvonplaten ?", "As the `torch.load` method in `strict` mode does not allow unexpected/missing keys, this is an issue that won't be resolved. Three options are available here:\r\n- Use the recommended `from_pretrained` method, which exists specifically to work around this kind of issues\r\n- Use the `torch.load` method with the `strict` flag set to `False`\r\n- Pin to version v3.0.2 if none of these can be applied.\r\n\r\nMinor changes in model infrastructure can unfortunately happen as we try to optimize for efficiency, which will lead to this kind of issues. We're internally working on having our models on the hub be versionable, which should solve most of these problems. It's at least a couple of months away, however.", "@LysandreJik That is unfortunate that the library will probably have to be pinned, as the first two options are unviable for reasons described in this thread. Especially because pretraining large models is computationally quite expensive (100s of GPU hours)...", "You can also use the work-around explained [here](https://github.com/huggingface/transformers/issues/6882#issuecomment-685509938) if you want to convert your weights to the updated architecture.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "Just wanted to add that there is another non-trivial reason why `from_pretrained` might not be useful in all cases: fine-tuning. If I fine-tune BERT's weights on a specific dataset, most likely I will have to use `load_state_dict` afterwards to use the new weights, rather than the original weights that `from_pretrained` would load.", "@LysandreJik @Laksh1997 Setting the [persistent flag ](https://pytorch.org/docs/master/generated/torch.jit.ScriptModule.html#torch.jit.ScriptModule.register_buffer)to False when registering the buffer will avoid adding it to the state_dict and can address the BC issue. ", "Hello there, \r\n\r\nI encountered the same problem. I was using transformers version 4.7.0; but the checkpoint was trained with transformer 3.0.2. I just did `pip uninstall transformers`, and then `pip install transformers==3.0.2` for running the training. Presumably, you can try: `model.load_state_dict(state_dict, strict=False)` as well. However, I don't feel comfortable with the latter solution since I think that might affect the model performance in abstraction –since `position_ids` **might** be used by the model, and putting some random values when it's not present in pre-trained checkpoint might ruin the performance. So safer way is to down-grade the transformers, in my opinion. \r\n\r\nHope this helps you out!", "Can someone confirm if the `position_ids` are used by the model and by not loading it correctly would it affect the performance of the model in transfer learning or continuing to train or inference? Thank you", "I think it's safe to use `model.load_state_dict(state_dict, strict=False)` if the only missing information is the `position_ids` buffer. This tensor is indeed used by the model, but it's just a constant tensor containing a list of integers from 0 to the maximum number of position embeddings. The tensor is first created in the constructor of the `BertEmbeddings` class, in this line:\r\n\r\nhttps://github.com/huggingface/transformers/blob/fcf83011dffce3f2e8aad906f07c1ec14668f877/src/transformers/models/bert/modeling_bert.py#L182\r\n\r\nAs such, it's not really part of the optimizable parameters of the model. This means that it doesn't matter if `position_ids` is not available when calling `load_state_dict`, because the line above will create it anyway in the constructor with the required values.", "> I think it's safe to use `model.load_state_dict(state_dict, strict=False)` if the only missing information is the `position_ids` buffer. This tensor is indeed used by the model, but it's just a constant tensor containing a list of integers from 0 to the maximum number of position embeddings. The tensor is first created in the constructor of the `BertEmbeddings` class, in this line:\r\n> \r\n> https://github.com/huggingface/transformers/blob/fcf83011dffce3f2e8aad906f07c1ec14668f877/src/transformers/models/bert/modeling_bert.py#L182\r\n> \r\n> As such, it's not really part of the optimizable parameters of the model. This means that it doesn't matter if `position_ids` is not available when calling `load_state_dict`, because the line above will create it anyway in the constructor with the required values.\r\n\r\nThank you very much @dfdazac for your detailed reply. " ]

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[ "It was just the naming of \"layer_norm\" instead of \"LayerNorm\" I changed the script and now it works.", "@blueberry-cake which script was that naming in? ", "@blueberry-cake could you tell me the details of how you solve this problem? I have this problem, too，I do not understand the word \"It was just the naming of \"layer_norm\" instead of \"LayerNorm\" I changed the script and now it works.\" Thanks for your help in advance！", "Hi, I encountered the same problem. I spent quite a while googling online but didn't get a solution. Could you please let me know if you get the solution? @blueberry-cake @roxannemiller @ankunw ", "> Hi, I encountered the same problem. I spent quite a while googling online but didn't get a solution. Could you please let me know if you get the solution? @blueberry-cake @roxannemiller @ankunw\r\n\r\nmaybe you could use he latest transformer have a try", "No it still doesn't work. Sign :(", "So I solved this problem with other people's help. Basically, I need to change the key name in my tf1 checkpoints. Here is the code. For further details, please see: https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/guide/migrate/migrating_checkpoints.ipynb?hl=id#scrollTo=NPQsXQveuQiC\r\n\r\n```\r\nimport re\r\ndef change_name(checkpoint_path, output_prefix):\r\n ckpt = tf.train.Checkpoint(vars={name: variable}) \r\n ckpt.restore(converted_ckpt_path)\r\n \"\"\"\r\n Args:\r\n checkpoint_path: Path to the TF1 checkpoint.\r\n output_prefix: Path prefix to the converted checkpoint.\r\n\r\n Returns:\r\n Path to the converted checkpoint.\r\n \"\"\"\r\n vars = {}\r\n reader = tf.train.load_checkpoint(checkpoint_path)\r\n dtypes = reader.get_variable_to_dtype_map()\r\n\r\n for key in dtypes.keys():\r\n new_key = key\r\n if key=='bert/embeddings/layer_normalization/beta' or key=='bert/embeddings/layer_normalization/gamma':\r\n new_key=key.replace('layer_normalization','LayerNorm')\r\n elif re.search('layer_normalization_+\\d+',key):\r\n new_key = re.sub('layer_normalization_+\\d+','LayerNorm',key)\r\n elif re.search('layer_normalization',key):\r\n new_key = re.sub('layer_normalization','LayerNorm',key)\r\n print(new_key)\r\n vars[new_key] = tf.Variable(reader.get_tensor(key))\r\n \r\n return tf1.train.Saver(var_list=vars).save(sess=None, save_path=output_prefix)", "> So I solved this problem with other people's help. Basically, I need to change the key name in my tf1 checkpoints. Here is the code. For further details, please see: https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/guide/migrate/migrating_checkpoints.ipynb?hl=id#scrollTo=NPQsXQveuQiC\r\n> \r\n> ```\r\n> import re\r\n> def change_name(checkpoint_path, output_prefix):\r\n> ckpt = tf.train.Checkpoint(vars={name: variable}) \r\n> ckpt.restore(converted_ckpt_path)\r\n> \"\"\"\r\n> Args:\r\n> checkpoint_path: Path to the TF1 checkpoint.\r\n> output_prefix: Path prefix to the converted checkpoint.\r\n> \r\n> Returns:\r\n> Path to the converted checkpoint.\r\n> \"\"\"\r\n> vars = {}\r\n> reader = tf.train.load_checkpoint(checkpoint_path)\r\n> dtypes = reader.get_variable_to_dtype_map()\r\n> \r\n> for key in dtypes.keys():\r\n> new_key = key\r\n> if key=='bert/embeddings/layer_normalization/beta' or key=='bert/embeddings/layer_normalization/gamma':\r\n> new_key=key.replace('layer_normalization','LayerNorm')\r\n> elif re.search('layer_normalization_+\\d+',key):\r\n> new_key = re.sub('layer_normalization_+\\d+','LayerNorm',key)\r\n> elif re.search('layer_normalization',key):\r\n> new_key = re.sub('layer_normalization','LayerNorm',key)\r\n> print(new_key)\r\n> vars[new_key] = tf.Variable(reader.get_tensor(key))\r\n> \r\n> return tf1.train.Saver(var_list=vars).save(sess=None, save_path=output_prefix)\r\n> ```\r\n\r\nDear friend, is there a complete integration of your code in \"convert_bert_original_tf_checkpoint_to_pytorch.py\"? I don't know how to adjust it using your code." ]

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[ "I have added a model_init function in the PyTorch Trainer to support hp-search. Is it possible to use this instead of changing the `args`? This would make a very big difference between the PT Trainer and TF Trainer.", "OK, I will check this. Thanks.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "Hello did you have any success fixing this? Can I help? I'm on a tight art student collegiate budget and tpu speed would be awesome if not necessary. I've spent like 20-30 hours on fixing the tpu issue myself and no luck. Any help getting run_clm.py on a tpu so I can quickly iterate would be awesome. But generally I'd love to move mainly to tpu but I'm not sure its there yet. New to open source really want to learn as much as possible. Can I help?", "@arccoxx This PR should be closed because we have identified two different issues:\r\n\r\n1. The first one don't come from Transformers but from TensorFlow. To make it short, TPU don't handle `tf.data.Dataset.from_generator`, Google is currently working on it and we have to wait they release the fix once they have one.\r\n2. Currently you cannot train a LM from scratch with any TF model. We are currently working on this, and it will be possible in our next release.\r\n\r\nSo for your project the best solution would be to use the PyTorch version that works on TPU and you can train from scratch any LM model.", "> @arccoxx This PR should be closed because we have identified two different issues:\r\n> \r\n> 1. The first one don't come from Transformers but from TensorFlow. To make it short, TPU don't handle `tf.data.Dataset.from_generator`, Google is currently working on it and we have to wait they release the fix once they have one.\r\n> 2. Currently you cannot train a LM from scratch with any TF model. We are currently working on this, and it will be possible in our next release.\r\n> \r\n> So for your project the best solution would be to use the PyTorch version that works on TPU and you can train from scratch any LM model.\r\n\r\nI was not able to get any pytorch version to run on xla. Is there any reference notebook that could be linked? I tried finetuning in native pytorch, running (pytorch) tuner, run_language_modeling with multiple transformers library versions 2.1.0-2.9.1, and run_clm with 3.4.0 all with no luck. Ive also tried building a pytorch lightning module and no luck. As the speedup would be that helpful (provided it can handle gpt2 medium) it would be awesome to figure out reduce these compatibility issues. My hope is to then use the tpu in a more complicated model that will use this fine tuned model. Any help would be super appreciated. Thank you!" ]

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[ "Thanks for the quick reviews @LysandreJik and @sgugger! And yep updating black version did it (I think) thanks." ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6878?src=pr&el=h1) Report\n> Merging [#6878](https://codecov.io/gh/huggingface/transformers/pull/6878?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/3726754a6c646adcf9cb2135ab7f72dffe074473?el=desc) will **decrease** coverage by `3.21%`.\n> The diff coverage is `100.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6878?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6878 +/- ##\n==========================================\n- Coverage 80.05% 76.84% -3.22% \n==========================================\n Files 157 157 \n Lines 28822 28825 +3 \n==========================================\n- Hits 23074 22150 -924 \n- Misses 5748 6675 +927 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6878?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/\\_\\_init\\_\\_.py](https://codecov.io/gh/huggingface/transformers/pull/6878/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9fX2luaXRfXy5weQ==) | `99.28% <ø> (ø)` | |\n| [src/transformers/modeling\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/6878/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19hdXRvLnB5) | `64.36% <ø> (-14.37%)` | :arrow_down: |\n| [src/transformers/modeling\\_xlm\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/6878/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ194bG1fcm9iZXJ0YS5weQ==) | `100.00% <100.00%> (ø)` | |\n| [...c/transformers/modeling\\_tf\\_transfo\\_xl\\_utilities.py](https://codecov.io/gh/huggingface/transformers/pull/6878/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl90cmFuc2ZvX3hsX3V0aWxpdGllcy5weQ==) | `10.00% <0.00%> (-76.00%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_transfo\\_xl.py](https://codecov.io/gh/huggingface/transformers/pull/6878/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl90cmFuc2ZvX3hsLnB5) | `19.85% <0.00%> (-68.29%)` | :arrow_down: |\n| [src/transformers/modeling\\_transfo\\_xl.py](https://codecov.io/gh/huggingface/transformers/pull/6878/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190cmFuc2ZvX3hsLnB5) | `16.25% <0.00%> (-63.52%)` | :arrow_down: |\n| [src/transformers/configuration\\_transfo\\_xl.py](https://codecov.io/gh/huggingface/transformers/pull/6878/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3RyYW5zZm9feGwucHk=) | `27.27% <0.00%> (-61.82%)` | :arrow_down: |\n| [src/transformers/modeling\\_transfo\\_xl\\_utilities.py](https://codecov.io/gh/huggingface/transformers/pull/6878/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190cmFuc2ZvX3hsX3V0aWxpdGllcy5weQ==) | `6.71% <0.00%> (-59.71%)` | :arrow_down: |\n| [src/transformers/pipelines.py](https://codecov.io/gh/huggingface/transformers/pull/6878/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9waXBlbGluZXMucHk=) | `25.63% <0.00%> (-54.32%)` | :arrow_down: |\n| [src/transformers/optimization\\_tf.py](https://codecov.io/gh/huggingface/transformers/pull/6878/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9vcHRpbWl6YXRpb25fdGYucHk=) | `33.33% <0.00%> (-24.33%)` | :arrow_down: |\n| ... and [24 more](https://codecov.io/gh/huggingface/transformers/pull/6878/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6878?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6878?src=pr&el=footer). Last update [3726754...06cc500](https://codecov.io/gh/huggingface/transformers/pull/6878?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "cc @laibamehnaz ", "> cc @laibamehnaz\r\n\r\nThank you :)" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6877?src=pr&el=h1) Report\n> Merging [#6877](https://codecov.io/gh/huggingface/transformers/pull/6877?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/a32d85f0d405be53117b96075eef2875d2185892?el=desc) will **increase** coverage by `0.16%`.\n> The diff coverage is `100.00%`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6877?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6877 +/- ##\n==========================================\n+ Coverage 80.48% 80.65% +0.16% \n==========================================\n Files 157 157 \n Lines 28794 28796 +2 \n==========================================\n+ Hits 23175 23224 +49 \n+ Misses 5619 5572 -47 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6877?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_bert.py](https://codecov.io/gh/huggingface/transformers/pull/6877/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9iZXJ0LnB5) | `98.38% <100.00%> (+<0.01%)` | :arrow_up: |\n| [src/transformers/modeling\\_tf\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/6877/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9vcGVuYWkucHk=) | `22.58% <0.00%> (-72.26%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_flaubert.py](https://codecov.io/gh/huggingface/transformers/pull/6877/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9mbGF1YmVydC5weQ==) | `24.53% <0.00%> (-63.81%)` | :arrow_down: |\n| [src/transformers/modeling\\_marian.py](https://codecov.io/gh/huggingface/transformers/pull/6877/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19tYXJpYW4ucHk=) | `60.00% <0.00%> (-30.00%)` | :arrow_down: |\n| [src/transformers/modeling\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/6877/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190NS5weQ==) | `77.63% <0.00%> (-6.21%)` | :arrow_down: |\n| [src/transformers/data/data\\_collator.py](https://codecov.io/gh/huggingface/transformers/pull/6877/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL2RhdGFfY29sbGF0b3IucHk=) | `86.63% <0.00%> (-5.27%)` | :arrow_down: |\n| [src/transformers/activations.py](https://codecov.io/gh/huggingface/transformers/pull/6877/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9hY3RpdmF0aW9ucy5weQ==) | `85.00% <0.00%> (-5.00%)` | :arrow_down: |\n| [src/transformers/tokenization\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/6877/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYXV0by5weQ==) | `95.55% <0.00%> (-2.23%)` | :arrow_down: |\n| [src/transformers/configuration\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6877/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3V0aWxzLnB5) | `95.97% <0.00%> (-0.68%)` | :arrow_down: |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6877/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `87.50% <0.00%> (-0.56%)` | :arrow_down: |\n| ... and [11 more](https://codecov.io/gh/huggingface/transformers/pull/6877/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6877?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6877?src=pr&el=footer). Last update [a32d85f...ddbccd8](https://codecov.io/gh/huggingface/transformers/pull/6877?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "As a comparision. When running this line on current `master`:\r\n```\r\nTF_CPP_MIN_LOG_LEVEL=3 python examples/benchmarking/run_benchmark_tf.py --models bert-base-uncased --no_memory --batch_sizes 1 --sequence_lengths 128 256 512\r\n```\r\n\r\none gets the following results: \r\n\r\n```\r\n==================== INFERENCE - SPEED - RESULT ====================\r\n--------------------------------------------------------------------------------\r\n Model Name Batch Size Seq Length Time in s \r\n--------------------------------------------------------------------------------\r\n bert-base-uncased 1 128 0.006 \r\n bert-base-uncased 1 256 0.009 \r\n bert-base-uncased 1 512 0.017 \r\n--------------------------------------------------------------------------------\r\n\r\n==================== ENVIRONMENT INFORMATION ====================\r\n- transformers_version: 3.0.2\r\n- framework: TensorFlow\r\n- eager_mode: False\r\n- use_xla: False\r\n- framework_version: 2.3.0\r\n- python_version: 3.6.10\r\n- system: Linux\r\n- cpu: x86_64\r\n- architecture: 64bit\r\n- date: 2020-09-01\r\n- time: 11:23:30.836691\r\n- fp16: False\r\n- use_multiprocessing: True\r\n- only_pretrain_model: False\r\n- cpu_ram_mb: 32088\r\n- use_gpu: True\r\n- num_gpus: 1\r\n- gpu: TITAN RTX\r\n- gpu_ram_mb: 24217\r\n- gpu_power_watts: 280.0\r\n- gpu_performance_state: 2\r\n- use_tpu: False\r\n```\r\n\r\nfor a TITAN RTX GPU.\r\n\r\nWhen running the above line on this branch, one gets the following results:\r\n\r\n```\r\n==================== INFERENCE - SPEED - RESULT ====================\r\n--------------------------------------------------------------------------------\r\n Model Name Batch Size Seq Length Time in s \r\n--------------------------------------------------------------------------------\r\n bert-base-uncased 1 128 0.006 \r\n bert-base-uncased 1 256 0.008 \r\n bert-base-uncased 1 512 0.016 \r\n--------------------------------------------------------------------------------\r\n\r\n==================== ENVIRONMENT INFORMATION ====================\r\n- transformers_version: 3.0.2\r\n- framework: TensorFlow\r\n- eager_mode: False\r\n- use_xla: False\r\n- framework_version: 2.3.0\r\n- python_version: 3.6.10\r\n- system: Linux\r\n- cpu: x86_64\r\n- architecture: 64bit\r\n- date: 2020-09-01\r\n- time: 11:28:12.021389\r\n- fp16: False\r\n- use_multiprocessing: True\r\n- only_pretrain_model: False\r\n- cpu_ram_mb: 32088\r\n- use_gpu: True\r\n- num_gpus: 1\r\n- gpu: TITAN RTX\r\n- gpu_ram_mb: 24217\r\n- gpu_power_watts: 280.0\r\n- gpu_performance_state: 2\r\n- use_tpu: False\r\n```\r\n\r\nSo, I cannot see a real difference here :-/ @jlei2", "I will see whether the benchmark results are better on a GPU-V100 GPU. \r\n@jlei2 - could you post the code you used to benchmark HF Bert vs. Google Bert ? This would help a lot for reproducability.", "oops didn't see it was [WIP]", "@patrickvonplaten can you update your code with the version given by @jlei2 [here](https://github.com/jlei2/transformers/pull/2) when you have time please. Thanks a lot!" ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n", "This issue has been stale for 1 month." ]

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[ { "id": 1314768611, "node_id": "MDU6TGFiZWwxMzE0NzY4NjEx", "url": "https://api.github.com/repos/huggingface/transformers/labels/wontfix", "name": "wontfix", "color": "ffffff", "default": true, "description": null } ]

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[ "What costs the most is the gradient computation, storing few predictions is ok general. I can run a sequence classification training with a batch of 32 of 128 sequence length and an acummulation of 3 with a 8GB GPU.\r\n\r\nDid you encounter during your experiments a memory issue? If yes, let me know and I will look at it.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Indeed this seems very problematic. Let's look into it cc @sgugger ", "Some hints - The main process takes 3.5x more RAM than the other processes individually.", "Do you have a commit id that gives the first graph, so we can look into the diff?", "I think I'm having a similar issue. I'm using `n1-highmem-16 (16 vCPUs, 104 GB memory)` with `v3-8` TPU for pre-training a RoBERTa model on 24GB text data.\r\n\r\nI was able to load the dataset using `nlp` (https://github.com/huggingface/nlp/issues/532), but it eats up all the available memory during training.\r\n\r\n<img width=\"860\" alt=\"Screen Shot 2020-09-01 at 9 19 17 PM\" src=\"https://user-images.githubusercontent.com/20531705/91850804-213cb700-ec99-11ea-853a-2e8a433bfbff.png\">\r\n\r\n(master branch on Aug 25 installed with `pip install git+https://github.com/huggingface/transformers`. Not sure how to check a commit id...)\r\n ", "Same question. I was wondering are there any strategies implemented to save memory?\r\nSomething like lazyDataloader?", "@sgugger I retried a run with the commit id 86c07e634f3624cdf3f9e4e81ca53b808c4b22c6 (20 Aug) and it seems to not have this memory blowup that we see on the current master \r\n\r\n", "@shizhediao Because the default behavior of Huggingface TPU Trainer is to load features into memory 8 times into all the processes separately, it quickly eats up vast amounts of system memory.\r\nThere are two options to save memory-\r\n1. Write a lazy loading Dataset whose `__getitem__` function quickly loads features from disk when provided with the key. This could save the most memory. Even though I haven't tested this I suspect the disk random lookup and IO in the critical path of the training loop could become a bottleneck.\r\n2. Cache the features in memory only once and share them among all the processes. I did this by using an in-memory key value server Redis by dumping all the pickled features to redis server and writing the `__getitem__` function where it loads the key from the redis server when requested. I saw empirically that this made by training about 20% faster on my workload than loading all the features 8 times into memory (probably due to cache thrashing). I used unix sockets to make the lookups even faster.", "Thanks for your reply!\r\nWould you like to share your code or are there any open-sourced code I can refer to?\r\nThanks!", "Sure, this is in the `__init__` function of my Dataset function. As compared to Huggingface TextDataset, this particular way sped up training by 20% for me while using around 1/7 memory and generating features faster (due to less tail-latency in multiprocessing and not writing and reading features from disk)\r\n```\r\n file_paths_copy = copy.deepcopy(file_paths)\r\n file_paths_copy = sorted(file_paths_copy) #multiprocess env, we want all processes to have the files in the same order\r\n self.redis = redis.Redis(unix_socket_path=\"/tmp/redis.sock\")\r\n self.pipe = self.redis.pipeline()\r\n file_lineno_map = {}\r\n line_counter = 0\r\n for file in file_paths_copy:\r\n num_lines = count_lines(file)\r\n\r\n file_lineno_map[file] = line_counter\r\n line_counter += num_lines\r\n # This is so that lines in each file gets assigned a unique line number in a multi-process env\r\n self.num_examples = line_counter\r\n for index, file_path in enumerate(file_paths_copy): # Can be multiple files\r\n if index % xm.xrt_world_size() == xm.get_ordinal():\r\n # If this process is assigned to process the following file, so we can use 8 cpu cores to load data parallely\r\n\r\n logger.info(\"Creating features from dataset file at %s\", file_path)\r\n with open(file_path, encoding=\"utf-8\") as f:\r\n for line_num, line in enumerate(f.read().splitlines()): # Text to Text file where each file is an example and source and target is separated by a tab symbol\r\n if (len(line) > 0 and not line.isspace()):\r\n if line.find('\\t') == -1:\r\n logger.warning(\r\n f\"Encountered a line without tab separator in file {file_path} line {line_num+1}\"\r\n )\r\n continue\r\n input, output = line.split('\\t')\r\n features = self.text_pair_to_features(input, output)\r\n\r\n key = line_num + file_lineno_map[\r\n file_path] if not self.val else \"val-\" + str(\r\n line_num + file_lineno_map[file_path]) # The name of the redis key\r\n\r\n self.pipe.set(key, pickle.dumps(features))\r\n if line_num % self.num_operations_pipelined == 1:\r\n self.pipe.execute() # So that we only dump to redis as a batch, can speed up writing\r\n self.pipe.execute()\r\n if is_torch_tpu_available():\r\n xm.rendezvous(tag=\"featuresGenerated\") # So that the multi-process environment all wait for each other before doing anything else\r\n```\r\nWith the `__getitem__` function being\r\n```\r\n def __getitem__(self, i) -> Dict[str, torch.Tensor]:\r\n if self.val:\r\n key = f\"val-{i}\"\r\n else:\r\n key = i\r\n example = pickle.loads(self.redis.get(key))\r\n return {\"input_ids\": example[0], \"attention_masks\": example[1], \"labels\": example[2]}\r\n```", "Thanks so much!", "Cool dataset!\r\n\r\n`Seq2SeqDataset` is also lazy, but no redis. I wonder the speed difference: https://github.com/huggingface/transformers/blob/master/examples/seq2seq/utils.py#L159\r\n\r\n@patil-suraj is this going to be an issue for `Seq2SeqTrainer`? We can't read all examples into memory for MT.", "@sshleifer Not sure. I have yet to experiment with `Seq2SeqTrainer` on TPU so can't say much. But I have managed to successfully train t5-base and on TPU using `Trainer` with lazy dataset.", "@sshleifer @patil-suraj I studied the linecache way of doing things and the reasons for not going with linecache for me were\r\n- Our data files are on mounted network disks so first byte access time would be too large.\r\n- Data sharded in multiple files leading to linecache being less effective as compared to just one file.\r\n- I also suspect how much would linecache help because we are not reading lines sequentially where caching would have helped but rather reading random lines where reading a whole block of text from disk would still mean that on average we only use only one line from the block.\r\n- I am also generally wary of involving disks in the critical path of the training loop as disks are very slow. Given that TPU requires higher input feed rate and evidence that Huggingface Trainer only uses a single CPU worker rather than many which could have helped with CPU generating features from disk in parallel while the TPU was working. See https://github.com/huggingface/transformers/issues/6316 . I believe if multiple workers were allowed in DataLoader then loading features from disk would be a valid solution.", "@misrasaurabh1 We just merged a simple fix that was obviously leaking memory for training (non-detached tensors) and that came from a recent change, so it might very well be the source of your leaks. Could you confirm whether or not current master has the leak or not? If so, using the same fix in the evaluation loop should also fix the eval memory leak we currently have.", "Yes, with the latest master the memory leak during training is not there anymore! Memory usage seems to be constant during training.\r\n\r\n\r\n\r\nAlthough if the same `.detach()` method would fix the evaluation memory leak, that would be huge! I could go down from a 32-CPU 208GB machine I am using right now to something like 16-CPU 64GB machine resulting in big monetary savings over time.", "Will look at the evaluation leak a bit more. From a first read, it looks like everything is properly detached, so it seems like this leak has another cause.\r\n\r\nThanks a lot for checking!", "\r\n\r\n> @shizhediao Because the default behavior of Huggingface TPU Trainer is to load features into memory 8 times into all the processes separately, it quickly eats up vast amounts of system memory.\r\n> There are two options to save memory-\r\n> \r\n> 1. Write a lazy loading Dataset whose `__getitem__` function quickly loads features from disk when provided with the key. This could save the most memory. Even though I haven't tested this I suspect the disk random lookup and IO in the critical path of the training loop could become a bottleneck.\r\n> 2. Cache the features in memory only once and share them among all the processes. I did this by using an in-memory key value server Redis by dumping all the pickled features to redis server and writing the `__getitem__` function where it loads the key from the redis server when requested. I saw empirically that this made by training about 20% faster on my workload than loading all the features 8 times into memory (probably due to cache thrashing). I used unix sockets to make the lookups even faster.\r\n\r\nRecently I had the same issue and such behavior is on GPU as well. One good solution is to use memory-mapped dataset, which is in spirit similar to Option 1 here. I used the awesome [huggingface/datasets](https://github.com/huggingface/datasets) library which provides memory-mapped dataset class automatically through Apache Arrow and it is fairly easy to use. I reduced my RAM usage from 90G to 6G and it won't grow with the dataset size.", "Is there any update on this? Is the memory leak during evaluation fixed?", "@sgugger Is the memory leak during evaluation fixed by https://github.com/huggingface/transformers/pull/7767 ?", "I don't know, as I have not had time to investigate the leak during evaluation on TPUs yet.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

[ { "id": 1314768611, "node_id": "MDU6TGFiZWwxMzE0NzY4NjEx", "url": "https://api.github.com/repos/huggingface/transformers/labels/wontfix", "name": "wontfix", "color": "ffffff", "default": true, "description": null } ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "Hi, we have this [test](https://github.com/huggingface/transformers/blob/master/tests/test_configuration_auto.py#L45) to prevent exactly this. In what situation did you face an issue?", "@LysandreJik There is no problem for pretrained models of huggingface transformers, because the config class of them are inherited from \"PretrainedConfig\". However, for users who want to add new models, their self-defined config class may be inherited from config class of some existing pretrained models. For example, I am trying to add a new model based on \"BART\" and my NewBartConig is inherited from \"BartConig\". My new tokenizer will not be used because a “NewBartConig” object is an instance of \"BartConig\" and bart tokenizer will be used incorrectly.", "Yes, but we have similar issues with models, for example the `RobertaModel` inherits from `BertModel`. The test I mentioned above checks that (the example here is for configurations but we have the same test for models and tokenizers).\r\n\r\nCurrently the way to make sure your tokenizer is used and not the one on which it's depending is to put your tokenizer above the one it's inheriting from in the mapping. The for loop will then see this one first and use this one instead of the next one.", "@LysandreJik Changing the order of items in TOKENIZER_MAPPING can solve the problem indeed. But get the rid of mapping order is more user-friendly, right? Close the pull request if you don't think the pr is necessary. Thanks for the review" ]

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[ "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "I run the same demo program on another server. The program can work properly. " ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6867?src=pr&el=h1) Report\n> Merging [#6867](https://codecov.io/gh/huggingface/transformers/pull/6867?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/59a6a32a61a87f9a1cccb57c3b4df725384d34ae?el=desc) will **decrease** coverage by `1.75%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6867?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6867 +/- ##\n==========================================\n- Coverage 79.91% 78.16% -1.76% \n==========================================\n Files 157 157 \n Lines 28795 28795 \n==========================================\n- Hits 23012 22508 -504 \n- Misses 5783 6287 +504 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6867?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_mobilebert.py](https://codecov.io/gh/huggingface/transformers/pull/6867/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9tb2JpbGViZXJ0LnB5) | `24.55% <0.00%> (-72.36%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_albert.py](https://codecov.io/gh/huggingface/transformers/pull/6867/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9hbGJlcnQucHk=) | `21.47% <0.00%> (-69.44%)` | :arrow_down: |\n| [src/transformers/trainer.py](https://codecov.io/gh/huggingface/transformers/pull/6867/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmVyLnB5) | `13.54% <0.00%> (-41.13%)` | :arrow_down: |\n| [src/transformers/tokenization\\_mbart.py](https://codecov.io/gh/huggingface/transformers/pull/6867/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fbWJhcnQucHk=) | `57.14% <0.00%> (-39.69%)` | :arrow_down: |\n| [src/transformers/training\\_args.py](https://codecov.io/gh/huggingface/transformers/pull/6867/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmluZ19hcmdzLnB5) | `66.66% <0.00%> (-25.00%)` | :arrow_down: |\n| [src/transformers/modeling\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/6867/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19yb2JlcnRhLnB5) | `77.37% <0.00%> (-19.71%)` | :arrow_down: |\n| [src/transformers/trainer\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6867/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90cmFpbmVyX3V0aWxzLnB5) | `59.57% <0.00%> (-19.15%)` | :arrow_down: |\n| [src/transformers/data/datasets/glue.py](https://codecov.io/gh/huggingface/transformers/pull/6867/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL2RhdGFzZXRzL2dsdWUucHk=) | `83.58% <0.00%> (-2.99%)` | :arrow_down: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6867/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `82.41% <0.00%> (-0.26%)` | :arrow_down: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6867/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `86.46% <0.00%> (+0.25%)` | :arrow_up: |\n| ... and [16 more](https://codecov.io/gh/huggingface/transformers/pull/6867/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6867?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6867?src=pr&el=footer). Last update [59a6a32...e59e26a](https://codecov.io/gh/huggingface/transformers/pull/6867?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6866?src=pr&el=h1) Report\n> Merging [#6866](https://codecov.io/gh/huggingface/transformers/pull/6866?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/59a6a32a61a87f9a1cccb57c3b4df725384d34ae?el=desc) will **decrease** coverage by `0.24%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6866?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6866 +/- ##\n==========================================\n- Coverage 79.91% 79.67% -0.25% \n==========================================\n Files 157 157 \n Lines 28795 28795 \n==========================================\n- Hits 23012 22942 -70 \n- Misses 5783 5853 +70 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6866?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/tokenization\\_transfo\\_xl.py](https://codecov.io/gh/huggingface/transformers/pull/6866/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdHJhbnNmb194bC5weQ==) | `20.53% <0.00%> (-21.21%)` | :arrow_down: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6866/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `83.95% <0.00%> (-2.26%)` | :arrow_down: |\n| [src/transformers/file\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6866/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9maWxlX3V0aWxzLnB5) | `82.41% <0.00%> (-0.26%)` | :arrow_down: |\n| [src/transformers/data/data\\_collator.py](https://codecov.io/gh/huggingface/transformers/pull/6866/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9kYXRhL2RhdGFfY29sbGF0b3IucHk=) | `92.30% <0.00%> (+0.40%)` | :arrow_up: |\n| [src/transformers/tokenization\\_reformer.py](https://codecov.io/gh/huggingface/transformers/pull/6866/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fcmVmb3JtZXIucHk=) | `95.00% <0.00%> (+13.33%)` | :arrow_up: |\n| [src/transformers/tokenization\\_xlnet.py](https://codecov.io/gh/huggingface/transformers/pull/6866/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25feGxuZXQucHk=) | `90.09% <0.00%> (+23.42%)` | :arrow_up: |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6866?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6866?src=pr&el=footer). Last update [59a6a32...b4f1cad](https://codecov.io/gh/huggingface/transformers/pull/6866?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n", "The next step is to look for other places those class attributes are defined and remove them:\r\n\r\n\r\n```bash\r\n$ git grep test_pruning | grep tf\r\ntests/test_modeling_tf_distilbert.py: test_pruning = True\r\ntests/test_modeling_tf_longformer.py: test_pruning = False # pruning is not supported\r\ntests/test_modeling_tf_transfo_xl.py: test_pruning = False\r\ntests/test_modeling_tf_xlnet.py: test_pruning = False\r\n```\r\n```bash\r\n$ git grep test_torchscript | grep tf\r\ntests/test_modeling_tf_common.py: test_torchscript = True\r\ntests/test_modeling_tf_distilbert.py: test_torchscript = True\r\ntests/test_modeling_tf_longformer.py: test_torchscript = False\r\ntests/test_modeling_tf_transfo_xl.py: test_torchscript = False\r\n```\r\n", "Thanks for the support.\r\nI also found `test_head_masking` which was unused. So deleted it too. Let me know if you didn't want that to happen.\r\n\r\n```bash\r\n$ git grep -e \"test_head\" | grep tf\r\ntests/test_modeling_tf_distilbert.py: test_head_masking = True\r\ntests/test_modeling_tf_longformer.py: test_headmasking = False # head masking is not supported\r\n```\r\n\r\nThanks\r\n\r\nPS: suggestion for any other issue, which I can pick up would be great. I am looking under label `help wanted`, etc" ]

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[ "There are no pre-trained reformer weights yet -> so that's a no sadly", "Following this issue for updates.", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

[ { "id": 1314768611, "node_id": "MDU6TGFiZWwxMzE0NzY4NjEx", "url": "https://api.github.com/repos/huggingface/transformers/labels/wontfix", "name": "wontfix", "color": "ffffff", "default": true, "description": null } ]

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[ "The model architecture is simple:\r\n\r\n", "Sorry for the late reply. This is because you did not respect the signature of `TFBertMainLayer` in order to properly use it you can do:\r\n\r\n```python\r\nimport tensorflow as tf\r\nfrom transformers import TFBertForSequenceClassification\r\n\r\na = tf.constant([[1,2,3,4,5]])\r\nb = tf.constant([[1,1,1,1,1]])\r\ninp = {\"input_ids\": a, \"attention_mask\": b}\r\nmodel = TFBertForSequenceClassification.from_pretrained(\"bert-base-cased\")\r\nmodel._saved_model_inputs_spec = None\r\nmodel._set_save_spec(inp)\r\ntf.saved_model.save(model, \"/tmp\")\r\nmodel = tf.keras.models.load_model(\"/tmp\")\r\n```", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

[ { "id": 1314768611, "node_id": "MDU6TGFiZWwxMzE0NzY4NjEx", "url": "https://api.github.com/repos/huggingface/transformers/labels/wontfix", "name": "wontfix", "color": "ffffff", "default": true, "description": null } ]

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[ "I think that's reasonable, the point of `skip_special_tokens` isn't to skip unknown tokens. cf @mfuntowicz @thomwolf @n1t0 ", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6862?src=pr&el=h1) Report\n> Merging [#6862](https://codecov.io/gh/huggingface/transformers/pull/6862?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/367235ee52537ff7cada5e1c5c41cdd78731f092?el=desc) will **increase** coverage by `3.77%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6862?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6862 +/- ##\n==========================================\n+ Coverage 76.27% 80.04% +3.77% \n==========================================\n Files 157 157 \n Lines 28795 28794 -1 \n==========================================\n+ Hits 21963 23049 +1086 \n+ Misses 6832 5745 -1087 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6862?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/configuration\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6862/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9jb25maWd1cmF0aW9uX3V0aWxzLnB5) | `95.97% <ø> (-0.70%)` | :arrow_down: |\n| [src/transformers/tokenization\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/6862/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fcm9iZXJ0YS5weQ==) | `87.67% <0.00%> (-10.96%)` | :arrow_down: |\n| [src/transformers/tokenization\\_utils\\_base.py](https://codecov.io/gh/huggingface/transformers/pull/6862/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHNfYmFzZS5weQ==) | `86.58% <0.00%> (-7.19%)` | :arrow_down: |\n| [src/transformers/modeling\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/6862/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190NS5weQ==) | `77.63% <0.00%> (-6.21%)` | :arrow_down: |\n| [src/transformers/tokenization\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/6862/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYXV0by5weQ==) | `95.55% <0.00%> (-2.23%)` | :arrow_down: |\n| [src/transformers/tokenization\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/6862/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fb3BlbmFpLnB5) | `82.57% <0.00%> (-1.52%)` | :arrow_down: |\n| [src/transformers/generation\\_tf\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6862/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `83.95% <0.00%> (-1.51%)` | :arrow_down: |\n| [src/transformers/tokenization\\_utils\\_fast.py](https://codecov.io/gh/huggingface/transformers/pull/6862/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHNfZmFzdC5weQ==) | `92.85% <0.00%> (-1.43%)` | :arrow_down: |\n| [src/transformers/modeling\\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/6862/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ191dGlscy5weQ==) | `87.50% <0.00%> (-0.56%)` | :arrow_down: |\n| ... and [12 more](https://codecov.io/gh/huggingface/transformers/pull/6862/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6862?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6862?src=pr&el=footer). Last update [367235e...b1b2b17](https://codecov.io/gh/huggingface/transformers/pull/6862?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6861?src=pr&el=h1) Report\n> Merging [#6861](https://codecov.io/gh/huggingface/transformers/pull/6861?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/431ab19d7a467905018b165bc29b2a1130c1b188?el=desc) will **increase** coverage by `3.38%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6861?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6861 +/- ##\n==========================================\n+ Coverage 76.81% 80.20% +3.38% \n==========================================\n Files 157 157 \n Lines 28795 28795 \n==========================================\n+ Hits 22118 23094 +976 \n+ Misses 6677 5701 -976 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6861?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [src/transformers/modeling\\_tf\\_electra.py](https://codecov.io/gh/huggingface/transformers/pull/6861/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9lbGVjdHJhLnB5) | `25.13% <0.00%> (-73.83%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/6861/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9vcGVuYWkucHk=) | `22.58% <0.00%> (-72.26%)` | :arrow_down: |\n| [src/transformers/tokenization\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/6861/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYmFydC5weQ==) | `42.10% <0.00%> (-57.90%)` | :arrow_down: |\n| [src/transformers/modeling\\_marian.py](https://codecov.io/gh/huggingface/transformers/pull/6861/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19tYXJpYW4ucHk=) | `60.00% <0.00%> (-30.00%)` | :arrow_down: |\n| [src/transformers/modeling\\_t5.py](https://codecov.io/gh/huggingface/transformers/pull/6861/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190NS5weQ==) | `77.63% <0.00%> (-6.02%)` | :arrow_down: |\n| [src/transformers/activations.py](https://codecov.io/gh/huggingface/transformers/pull/6861/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9hY3RpdmF0aW9ucy5weQ==) | `85.00% <0.00%> (-5.00%)` | :arrow_down: |\n| [src/transformers/tokenization\\_dpr.py](https://codecov.io/gh/huggingface/transformers/pull/6861/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fZHByLnB5) | `53.15% <0.00%> (-4.51%)` | :arrow_down: |\n| [src/transformers/tokenization\\_auto.py](https://codecov.io/gh/huggingface/transformers/pull/6861/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYXV0by5weQ==) | `95.55% <0.00%> (-2.23%)` | :arrow_down: |\n| [src/transformers/modeling\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/6861/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19vcGVuYWkucHk=) | `80.96% <0.00%> (-1.30%)` | :arrow_down: |\n| [src/transformers/modeling\\_bart.py](https://codecov.io/gh/huggingface/transformers/pull/6861/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19iYXJ0LnB5) | `95.08% <0.00%> (-0.51%)` | :arrow_down: |\n| ... and [25 more](https://codecov.io/gh/huggingface/transformers/pull/6861/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6861?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6861?src=pr&el=footer). Last update [431ab19...39d237c](https://codecov.io/gh/huggingface/transformers/pull/6861?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "Note that there are multiple frameworks that provide generic training loops. The goal of `Trainer` (I'm assuming you're talking about it since there is no `train.py` file) is not to replace them or compete with them but to provide an easy way to train and finetune Transformers models. Those models don't take nested inputs, so Trainer does not support this. Those models are expected to return the loss as the first item of their output, so Trainer expects it too.\r\n\r\nMaking Trainer more easily customizable by providing better hooks for subclassing (your use case could be done by overriding the two private methods you mention for instance) is something we are working on, but we won't have a base Trainer that is too generic, it will remain customized to the models the library provides.", "Thank you for your consideration and comments! ", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]

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[ "# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6859?src=pr&el=h1) Report\n> Merging [#6859](https://codecov.io/gh/huggingface/transformers/pull/6859?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/bbdba0a76d70ff347884cbe62e0f13de903d84c7?el=desc) will **increase** coverage by `2.94%`.\n> The diff coverage is `n/a`.\n\n[](https://codecov.io/gh/huggingface/transformers/pull/6859?src=pr&el=tree)\n\n```diff\n@@ Coverage Diff @@\n## master #6859 +/- ##\n==========================================\n+ Coverage 77.22% 80.17% +2.94% \n==========================================\n Files 157 157 \n Lines 28793 28793 \n==========================================\n+ Hits 22235 23084 +849 \n+ Misses 6558 5709 -849 \n```\n\n\n| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/6859?src=pr&el=tree) | Coverage Δ | |\n|---|---|---|\n| [...c/transformers/modeling\\_tf\\_transfo\\_xl\\_utilities.py](https://codecov.io/gh/huggingface/transformers/pull/6859/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl90cmFuc2ZvX3hsX3V0aWxpdGllcy5weQ==) | `10.00% <0.00%> (-76.00%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_transfo\\_xl.py](https://codecov.io/gh/huggingface/transformers/pull/6859/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl90cmFuc2ZvX3hsLnB5) | `19.85% <0.00%> (-68.29%)` | :arrow_down: |\n| [src/transformers/modeling\\_tf\\_flaubert.py](https://codecov.io/gh/huggingface/transformers/pull/6859/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9mbGF1YmVydC5weQ==) | `24.53% <0.00%> (-63.81%)` | :arrow_down: |\n| [src/transformers/modeling\\_marian.py](https://codecov.io/gh/huggingface/transformers/pull/6859/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19tYXJpYW4ucHk=) | `60.00% <0.00%> (-30.00%)` | :arrow_down: |\n| [src/transformers/modeling\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/6859/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ19yb2JlcnRhLnB5) | `77.37% <0.00%> (-19.71%)` | :arrow_down: |\n| [src/transformers/tokenization\\_roberta.py](https://codecov.io/gh/huggingface/transformers/pull/6859/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fcm9iZXJ0YS5weQ==) | `87.67% <0.00%> (-10.96%)` | :arrow_down: |\n| [src/transformers/tokenization\\_utils\\_base.py](https://codecov.io/gh/huggingface/transformers/pull/6859/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHNfYmFzZS5weQ==) | `86.44% <0.00%> (-7.59%)` | :arrow_down: |\n| [src/transformers/activations.py](https://codecov.io/gh/huggingface/transformers/pull/6859/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9hY3RpdmF0aW9ucy5weQ==) | `85.00% <0.00%> (-5.00%)` | :arrow_down: |\n| [src/transformers/tokenization\\_openai.py](https://codecov.io/gh/huggingface/transformers/pull/6859/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fb3BlbmFpLnB5) | `82.57% <0.00%> (-1.52%)` | :arrow_down: |\n| [src/transformers/tokenization\\_utils\\_fast.py](https://codecov.io/gh/huggingface/transformers/pull/6859/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fdXRpbHNfZmFzdC5weQ==) | `92.85% <0.00%> (-1.43%)` | :arrow_down: |\n| ... and [17 more](https://codecov.io/gh/huggingface/transformers/pull/6859/diff?src=pr&el=tree-more) | |\n\n------\n\n[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/6859?src=pr&el=continue).\n> **Legend** - [Click here to learn more](https://docs.codecov.io/docs/codecov-delta)\n> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`\n> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/6859?src=pr&el=footer). Last update [bbdba0a...b8eb2a3](https://codecov.io/gh/huggingface/transformers/pull/6859?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).\n" ]

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[ "Indeed! Do you want to open a PR to fix this?", "@LysandreJik \r\n\r\nI can do that. However @patrickvonplaten has already self-assigned for this. How do you think, @patrickvonplaten?", "Hey @chiapas, it would be great if you can open a PR for it :-) ", "Hi @patrickvonplaten , OK, that would be my first contribution to transformers :)" ]

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[ "Cool!" ]

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[ "Hey @andifunke, \r\n\r\nThanks a lot for your issue! Could you link the different implementation of `torch.multinomial` between PT v1.5.1 and PT v1.6.0 ? \r\nI understand your argument, but I think setting `replacement=True` is logically false...", "Hi @patrickvonplaten ,\r\n\r\nthanks for your reply!\r\n\r\n> Could you link the different implementation of torch.multinomial between PT v1.5.1 and PT v1.6.0 ?\r\n\r\nSure. The PR for the implementation is here: https://github.com/pytorch/pytorch/pull/39742 and the merge commit here: https://github.com/pytorch/pytorch/commit/97dfdaaad89c2082c90aebfa9180293847cffd60\r\n\r\n> I understand your argument, but I think setting replacement=True is logically false...\r\n\r\nI agree, it feels a bit hacky, but let me give you an example, why I think this workaround is justified:\r\n\r\nThe following code will behave differently in PT1.5.1 vs 1.6:\r\n\r\n```python\r\nimport torch\r\n\r\ntorch.manual_seed(0)\r\nt = torch.rand(10, 10)\r\n\r\ntorch.manual_seed(0)\r\na = torch.multinomial(t, num_samples=1, replacement=False)\r\n\r\ntorch.manual_seed(0)\r\nb = torch.multinomial(t, num_samples=1, replacement=True)\r\n\r\ntorch.__version__, a, b, all(a == b)\r\n```\r\n\r\nPytorch 1.5.1:\r\n\r\n```\r\nOut[1]: \r\n('1.5.1',\r\n tensor([[9],\r\n [7],\r\n [3],\r\n [9],\r\n [7],\r\n [6],\r\n [1],\r\n [3],\r\n [5],\r\n [1]]),\r\n tensor([[9],\r\n [7],\r\n [3],\r\n [9],\r\n [7],\r\n [6],\r\n [1],\r\n [3],\r\n [5],\r\n [1]]),\r\n True)\r\n```\r\n\r\nPytorch 1.6:\r\n\r\n```\r\n('1.6.0',\r\n tensor([[7],\r\n [7],\r\n [6],\r\n [1],\r\n [6],\r\n [1],\r\n [9],\r\n [5],\r\n [1],\r\n [2]]),\r\n tensor([[9],\r\n [7],\r\n [3],\r\n [9],\r\n [7],\r\n [6],\r\n [1],\r\n [3],\r\n [5],\r\n [1]]),\r\n False)\r\n```\r\n\r\nThis of course breaks reproducibility between versions when generating text.", "Oh, and here is another option, if `replacement=True` feels irritating:\r\n\r\nYou could use `torch.distributions.categorical.Categorical` instead, which uses the same sampling approach.\r\n\r\nexample:\r\n```python\r\nimport torch\r\n\r\ntorch.manual_seed(0)\r\nt = torch.rand(10, 10)\r\n\r\ntorch.manual_seed(0)\r\na = torch.distributions.categorical.Categorical(t).sample()\r\n\r\ntorch.manual_seed(0)\r\nb = torch.multinomial(t, num_samples=1, replacement=True)\r\n\r\ntorch.__version__, a, b, all(a == b.reshape(10))\r\n```\r\n\r\n```\r\nOut[1]:\r\n('1.6.0',\r\n tensor([9, 7, 3, 9, 7, 6, 1, 3, 5, 1]),\r\n tensor([[9],\r\n [7],\r\n [3],\r\n [9],\r\n [7],\r\n [6],\r\n [1],\r\n [3],\r\n [5],\r\n [1]]),\r\n True)\r\n```\r\n", "Hey @andifunke, \r\n\r\nThanks for your detailed comments - this is great! So it seems like the change was made to speed up the `torch.multinomial(do_replacement=False)` function. This is not really of interest to us though as it will never be the bottleneck in the `.generate()` function. \r\n\r\nI agree with you that we want to keep backward compatibility here. I think the best option in this case in to use `torch.distributions.categorical.Categorical(t).sample()` in this case.\r\n\r\nWill open a PR about it :-) ", "Great, thanks!", "Actually, I just noticed that `torch.distributions.categorical.Categorical(...)` uses `torch.multinomial` under the hood with `do_replacement=True` - so that this is not a better option. \r\n\r\nI'm not 100% sure how to proceed here now. @LysandreJik, @sgugger - what is your opinion on that? \r\n\r\nThe problem is the following: Because of a change in PyTorch's `torch.multinomial` function for 1.6, our generation method with `do_sample=True` yields different results when setting `torch.manual_seed(0)` between torch > 1.6 and 1.6.\r\n\r\nAs @andifunke pointed out, a simple fix would be to set `do_replacement=True`, which is logically not correct IMO, but it does not make a difference for sampling with `num_beams = 1`. For sampling with `num_beams > 1`.\r\n\r\nDo you guys think we should go for the simple fix of `do_replacement=True` to keep backward compatibility when using `torch.manual_seed(0)` ? \r\nIt seems like backwards compatibility for `num_beams > 1` is broken either way since it would be false to set `do_replacement=True` there. ", "Can we copy the old implementation somewhere and just use that or is it hidden in C/CUDA?", "Did we also reach out to the PyTorch team and make sure they are aware of this BC?", "Looks like this is hidden in C/CUDA: https://github.com/pytorch/pytorch/pull/39742/files .\r\nNot sure whether the PyTorch is aware of it...", "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.\n" ]