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Unverified Commit 8894b817 authored by Yih-Dar's avatar Yih-Dar Committed by GitHub
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Use real tokenizers if tiny version(s) creation has issue(s) (#22428) 



Fix some tiny model creation issues
Co-authored-by: default avatarydshieh <ydshieh@users.noreply.github.com>
parent 9b494a15
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Showing with 333 additions and 136 deletions
.github/workflows/update_tiny_models.yml
View file @ 8894b817
name: Self-hosted runner (push)
name: Update Tiny Models
on:
push:
......
tests/models/bigbird_pegasus/test_modeling_bigbird_pegasus.py
View file @ 8894b817
......@@ -268,6 +268,15 @@ class BigBirdPegasusModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineT
# Also torchscript is not an important feature to have in the beginning.
test_torchscript = False
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "QAPipelineTests" and not tokenizer_name.endswith("Fast"):
return True
return False
# overwrite from GenerationTesterMixin to solve problem
# with conflicting random seeds
def _get_input_ids_and_config(self):
......
tests/models/xlm_roberta_xl/test_modeling_xlm_roberta_xl.py
View file @ 8894b817
......@@ -387,6 +387,15 @@ class XLMRobertaXLModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTes
else {}
)
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "QAPipelineTests" and not tokenizer_name.endswith("Fast"):
return True
return False
def setUp(self):
self.model_tester = XLMRobertaXLModelTester(self)
self.config_tester = ConfigTester(self, config_class=XLMRobertaXLConfig, hidden_size=37)
......
tests/models/xmod/test_modeling_xmod.py
View file @ 8894b817
......@@ -384,6 +384,15 @@ class XmodModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
else {}
)
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "QAPipelineTests" and not tokenizer_name.endswith("Fast"):
return True
return False
def setUp(self):
self.model_tester = XmodModelTester(self)
self.config_tester = ConfigTester(self, config_class=XmodConfig, hidden_size=37)
......
tests/utils/tiny_model_summary.json
View file @ 8894b817
......@@ -21,6 +21,7 @@
},
"AlbertForMaskedLM": {
"tokenizer_classes": [
"AlbertTokenizer",
"AlbertTokenizerFast"
],
"processor_classes": [],
......@@ -28,10 +29,11 @@
"AlbertForMaskedLM",
"TFAlbertForMaskedLM"
],
"sha": "75ab12f94d4a1edd9610636547c5fb515e240e2b"
"sha": "d29de71ac29e1019c3a7762f7357f750730cb037"
},
"AlbertForMultipleChoice": {
"tokenizer_classes": [
"AlbertTokenizer",
"AlbertTokenizerFast"
],
"processor_classes": [],
......@@ -39,10 +41,11 @@
"AlbertForMultipleChoice",
"TFAlbertForMultipleChoice"
],
"sha": "ba1531e4373cccce03195928b3ba2f6825311980"
"sha": "242aecce6a589a2964c0f695621fa22a83751579"
},
"AlbertForPreTraining": {
"tokenizer_classes": [
"AlbertTokenizer",
"AlbertTokenizerFast"
],
"processor_classes": [],
......@@ -50,10 +53,11 @@
"AlbertForPreTraining",
"TFAlbertForPreTraining"
],
"sha": "6022449842a83d9cea298c4fbaf1e1e1c0db3568"
"sha": "41330be4b271687f4d88ddc96346c12aa11de983"
},
"AlbertForQuestionAnswering": {
"tokenizer_classes": [
"AlbertTokenizer",
"AlbertTokenizerFast"
],
"processor_classes": [],
......@@ -61,10 +65,11 @@
"AlbertForQuestionAnswering",
"TFAlbertForQuestionAnswering"
],
"sha": "1b6584d6a267dae8ff20b9f89e2b424a7972fb45"
"sha": "040b81c15f437f4722349dc5b41fccd17ebd7fdc"
},
"AlbertForSequenceClassification": {
"tokenizer_classes": [
"AlbertTokenizer",
"AlbertTokenizerFast"
],
"processor_classes": [],
......@@ -72,10 +77,11 @@
"AlbertForSequenceClassification",
"TFAlbertForSequenceClassification"
],
"sha": "1e709531344ee0e4a34777c79507a07b69130958"
"sha": "39c1a0e2c1c2623106d3211d751e9b32f23a91a0"
},
"AlbertForTokenClassification": {
"tokenizer_classes": [
"AlbertTokenizer",
"AlbertTokenizerFast"
],
"processor_classes": [],
......@@ -83,10 +89,11 @@
"AlbertForTokenClassification",
"TFAlbertForTokenClassification"
],
"sha": "f6c0d721d6d9f0751ab975148932948d5853fcc8"
"sha": "359c3f4a311a4053a6f6d6a880db5f82c8e3ff1f"
},
"AlbertModel": {
"tokenizer_classes": [
"AlbertTokenizer",
"AlbertTokenizerFast"
],
"processor_classes": [],
......@@ -94,7 +101,7 @@
"AlbertModel",
"TFAlbertModel"
],
"sha": "62974edf8b7246a943f6ecc8a3f7bfca052351ff"
"sha": "34a63314686b64aaeb595ddb95006f1ff2ffda17"
},
"AlignModel": {
"tokenizer_classes": [
......@@ -318,133 +325,146 @@
},
"BigBirdForCausalLM": {
"tokenizer_classes": [
"BigBirdTokenizer",
"BigBirdTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"BigBirdForCausalLM"
],
"sha": "04578a05f11c0006e4e5deaf38b48889a8dc8f4f"
"sha": "5c7a487af5248d9c01b45d5481b7d7bb9b36e1b5"
},
"BigBirdForMaskedLM": {
"tokenizer_classes": [
"BigBirdTokenizer",
"BigBirdTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"BigBirdForMaskedLM"
],
"sha": "52b8e93488b5d8235711543d4671ea08ea3f9560"
"sha": "476ef8225c0f69270b577706ad4f1dda13e4dde5"
},
"BigBirdForMultipleChoice": {
"tokenizer_classes": [
"BigBirdTokenizer",
"BigBirdTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"BigBirdForMultipleChoice"
],
"sha": "56459f9bcde6a36870e4d743295f6ce69ba5fc7b"
"sha": "cf93eaa1019987112c171a407745bc183a20513a"
},
"BigBirdForPreTraining": {
"tokenizer_classes": [
"BigBirdTokenizer",
"BigBirdTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"BigBirdForPreTraining"
],
"sha": "49f55d7252dd9151722b330fa02073c6d809c55e"
"sha": "5fb9efa13334431e7c186a9fa314b89c4a1eee72"
},
"BigBirdForQuestionAnswering": {
"tokenizer_classes": [
"BigBirdTokenizer",
"BigBirdTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"BigBirdForQuestionAnswering"
],
"sha": "a5b3c8567610d4dde63c282d6fb6fd2ec04cbb39"
"sha": "f82f88bd71fba819a8ffb0692915d3529e705417"
},
"BigBirdForSequenceClassification": {
"tokenizer_classes": [
"BigBirdTokenizer",
"BigBirdTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"BigBirdForSequenceClassification"
],
"sha": "2b29389f623fa7af3ffb08c51e9bcbda270ae9ee"
"sha": "ea398090858f9af93b54fc9a8d65cfed78ac27ff"
},
"BigBirdForTokenClassification": {
"tokenizer_classes": [
"BigBirdTokenizer",
"BigBirdTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"BigBirdForTokenClassification"
],
"sha": "beda63d67d07b133e603f51aea6b84cae29b9ea7"
"sha": "2cdea118999fa58ba9fb0162d99e2ffa146c3df1"
},
"BigBirdModel": {
"tokenizer_classes": [
"BigBirdTokenizer",
"BigBirdTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"BigBirdModel"
],
"sha": "814adde7ccd69821684a0c6124401f0c180d700c"
"sha": "9c55989f31df156194e6997606fb14d9897e0300"
},
"BigBirdPegasusForCausalLM": {
"tokenizer_classes": [
"PegasusTokenizer",
"PegasusTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"BigBirdPegasusForCausalLM"
],
"sha": "e1a5b87220073127f718fec558cbc86795b6ed61"
"sha": "49bc8816c666dee32e27cd8e00136b604eb85243"
},
"BigBirdPegasusForConditionalGeneration": {
"tokenizer_classes": [
"PegasusTokenizer",
"PegasusTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"BigBirdPegasusForConditionalGeneration"
],
"sha": "40fad528589229426174241a641034f1f971a2b7"
"sha": "e791aa6d1af5a76ca0926d95b1f28bd2d8adf376"
},
"BigBirdPegasusForQuestionAnswering": {
"tokenizer_classes": [
"PegasusTokenizer",
"PegasusTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"BigBirdPegasusForQuestionAnswering"
],
"sha": "18d836e06a02bd1dc36af9a5eeaf3d326b1d368a"
"sha": "7650e076713ca707a37062adc8c9c1cd60dad7c7"
},
"BigBirdPegasusForSequenceClassification": {
"tokenizer_classes": [
"PegasusTokenizer",
"PegasusTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"BigBirdPegasusForSequenceClassification"
],
"sha": "a451bdb3f36fb76af06a51274d19fd88729443e6"
"sha": "02500e8ebd9c53528750013fb963fbdc2be34034"
},
"BigBirdPegasusModel": {
"tokenizer_classes": [
"PegasusTokenizer",
"PegasusTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"BigBirdPegasusModel"
],
"sha": "3ece62a543ced3755e5ae239bcc3a4a5a6b75dd4"
"sha": "b07c5304dfba673cf8b9cf5cd1aa45fbfea1c2f3"
},
"BioGptForCausalLM": {
"tokenizer_classes": [
......@@ -1240,6 +1260,7 @@
},
"DebertaV2ForMaskedLM": {
"tokenizer_classes": [
"DebertaV2Tokenizer",
"DebertaV2TokenizerFast"
],
"processor_classes": [],
......@@ -1247,20 +1268,22 @@
"DebertaV2ForMaskedLM",
"TFDebertaV2ForMaskedLM"
],
"sha": "9089b6afa8f66fd16503fca2b7c54b50c2195123"
"sha": "a053dedc2cdf32918a84277cb0c05186604496a5"
},
"DebertaV2ForMultipleChoice": {
"tokenizer_classes": [
"DebertaV2Tokenizer",
"DebertaV2TokenizerFast"
],
"processor_classes": [],
"model_classes": [
"DebertaV2ForMultipleChoice"
],
"sha": "7acf4b7415b2869e225a5ed82e68f1c0374e9668"
"sha": "07e39f520ce239b39ef8cb24cd7874d06c791063"
},
"DebertaV2ForQuestionAnswering": {
"tokenizer_classes": [
"DebertaV2Tokenizer",
"DebertaV2TokenizerFast"
],
"processor_classes": [],
......@@ -1268,10 +1291,11 @@
"DebertaV2ForQuestionAnswering",
"TFDebertaV2ForQuestionAnswering"
],
"sha": "17ef18fefddc0ec61a972eea06af430059ae3759"
"sha": "9cecb3a7fc6b95099122283644ea1f8ced287d1b"
},
"DebertaV2ForSequenceClassification": {
"tokenizer_classes": [
"DebertaV2Tokenizer",
"DebertaV2TokenizerFast"
],
"processor_classes": [],
......@@ -1279,10 +1303,11 @@
"DebertaV2ForSequenceClassification",
"TFDebertaV2ForSequenceClassification"
],
"sha": "1ef484d43eb15ac6b1f8be393c3d59bea2267dc9"
"sha": "df9ea1f5c0f2ccd139b21cfb3963a5a5ebfb5b81"
},
"DebertaV2ForTokenClassification": {
"tokenizer_classes": [
"DebertaV2Tokenizer",
"DebertaV2TokenizerFast"
],
"processor_classes": [],
......@@ -1290,10 +1315,11 @@
"DebertaV2ForTokenClassification",
"TFDebertaV2ForTokenClassification"
],
"sha": "5c4e629b5b03957a546f7f76c31f6887f99fc17c"
"sha": "51fe01989df38a540ac1abca5ee71a51365defd5"
},
"DebertaV2Model": {
"tokenizer_classes": [
"DebertaV2Tokenizer",
"DebertaV2TokenizerFast"
],
"processor_classes": [],
......@@ -1301,7 +1327,7 @@
"DebertaV2Model",
"TFDebertaV2Model"
],
"sha": "a1945cc2bb1ef207f8fdeb4ea146711ade1db77a"
"sha": "211df4bd1a4a9b66c97af3f9231a5d2af8de7b9f"
},
"DeformableDetrForObjectDetection": {
"tokenizer_classes": [],
......@@ -1881,83 +1907,91 @@
},
"FNetForMaskedLM": {
"tokenizer_classes": [
"FNetTokenizer",
"FNetTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"FNetForMaskedLM"
],
"sha": "235602782fb4b4ff0291c999cc174b4a257f1e7f"
"sha": "91eaae1eac894af5d96c0221ec9bcef7f1af41c8"
},
"FNetForMultipleChoice": {
"tokenizer_classes": [
"FNetTokenizer",
"FNetTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"FNetForMultipleChoice"
],
"sha": "d84b9ee07323895465a29f234e9109b66fd623cf"
"sha": "c15d98d5f7a6f3ef3099b1257949bee208d5466e"
},
"FNetForNextSentencePrediction": {
"tokenizer_classes": [
"FNetTokenizer",
"FNetTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"FNetForNextSentencePrediction"
],
"sha": "9b11a763f599a95c3dff8e4255cd952a04101a65"
"sha": "c59440b44d07d61fc45a90ded7fc11d6f25b143d"
},
"FNetForPreTraining": {
"tokenizer_classes": [
"FNetTokenizer",
"FNetTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"FNetForPreTraining"
],
"sha": "ed6ec245f3a8f7b53c7b09b020cfae1f8c4aaf7d"
"sha": "c05f55ccfb2f2533babd3c6e99de7749bc8081da"
},
"FNetForQuestionAnswering": {
"tokenizer_classes": [
"FNetTokenizer",
"FNetTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"FNetForQuestionAnswering"
],
"sha": "c77c577acae60cd268b0eebdbffcbd75f8e31141"
"sha": "47788e49dd435653fa2aa4b3ccae3572a870758e"
},
"FNetForSequenceClassification": {
"tokenizer_classes": [
"FNetTokenizer",
"FNetTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"FNetForSequenceClassification"
],
"sha": "6e6dbab691e5ec18e04b98514f1656dc3a842192"
"sha": "a3049b896ea6c5a32c364989c3afe604ee58b9fc"
},
"FNetForTokenClassification": {
"tokenizer_classes": [
"FNetTokenizer",
"FNetTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"FNetForTokenClassification"
],
"sha": "b27e341994ef7913dcdd72326d3475c9668d07d5"
"sha": "3bcdafca57d544bb81e2f7eead1e512c168582fc"
},
"FNetModel": {
"tokenizer_classes": [
"FNetTokenizer",
"FNetTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"FNetModel"
],
"sha": "38041395aa488da543dec9ed86318d9ec4d4839e"
"sha": "48fa66de37df126504db3b658806135eb877f505"
},
"FSMTForConditionalGeneration": {
"tokenizer_classes": [
......@@ -2828,23 +2862,25 @@
},
"LongT5ForConditionalGeneration": {
"tokenizer_classes": [
"T5Tokenizer",
"T5TokenizerFast"
],
"processor_classes": [],
"model_classes": [
"LongT5ForConditionalGeneration"
],
"sha": "3c83684439875f33fc0f79d164e7b446245cbc74"
"sha": "c685cbbe706ad5c9a28689631765726a1874dcc7"
},
"LongT5Model": {
"tokenizer_classes": [
"T5Tokenizer",
"T5TokenizerFast"
],
"processor_classes": [],
"model_classes": [
"LongT5Model"
],
"sha": "3b825c077d41103476d88bd93a6a9af52194b99e"
"sha": "6b468e55e2490565e6155690201086ac00c72062"
},
"LongformerForMaskedLM": {
"tokenizer_classes": [
......@@ -3042,7 +3078,7 @@
"model_classes": [
"MBartForCausalLM"
],
"sha": "e0f8bdbcc8afd38ef9a9d78ad96d3e93ddd19d71"
"sha": "a45044f8056328d20a764356eca3d0746a7a195e"
},
"MBartForConditionalGeneration": {
"tokenizer_classes": [
......@@ -3054,7 +3090,7 @@
"MBartForConditionalGeneration",
"TFMBartForConditionalGeneration"
],
"sha": "9d2a48747c3671ef7a9b0c202d79c677b6d6f272"
"sha": "171e918962d6c0ee56c6b070858e19e16c8dd09f"
},
"MBartForQuestionAnswering": {
"tokenizer_classes": [
......@@ -3065,7 +3101,7 @@
"model_classes": [
"MBartForQuestionAnswering"
],
"sha": "0ad08bf0c1c39d358b92b38f75cbe04609167c3f"
"sha": "1ee08565d24777335595e0d2940e454abdcff731"
},
"MBartForSequenceClassification": {
"tokenizer_classes": [
......@@ -3076,7 +3112,7 @@
"model_classes": [
"MBartForSequenceClassification"
],
"sha": "05ed9ea84c02f4a5347f5c235d38f9df891cb811"
"sha": "53e9c88ecfa2475d27afe099ffa7a8bcdb7ef7e4"
},
"MBartModel": {
"tokenizer_classes": [
......@@ -3088,7 +3124,7 @@
"MBartModel",
"TFMBartModel"
],
"sha": "21e9aa6eff4ae280e299ac33c94f1a317300571b"
"sha": "2d492b34d69dd63b411990d5c8bb692fd637e91c"
},
"MCTCTForCTC": {
"tokenizer_classes": [],
......@@ -3767,63 +3803,69 @@
},
"NystromformerForMaskedLM": {
"tokenizer_classes": [
"AlbertTokenizer",
"AlbertTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"NystromformerForMaskedLM"
],
"sha": "a21609ab92ea2ea438a4c78f9c2faf0594681fb2"
"sha": "37036847783f1e65e81ecd43803270a1ecb276f3"
},
"NystromformerForMultipleChoice": {
"tokenizer_classes": [
"AlbertTokenizer",
"AlbertTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"NystromformerForMultipleChoice"
],
"sha": "417bcc3c7636f1b0a5cbd7371ddce566c302e52d"
"sha": "42a077d5ab6830e20560466eaccc525eff10c3ae"
},
"NystromformerForQuestionAnswering": {
"tokenizer_classes": [
"AlbertTokenizer",
"AlbertTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"NystromformerForQuestionAnswering"
],
"sha": "206c4a09179f8eda76dab66a0b8d5bd61e031a79"
"sha": "1cfaf79051731824db4f09989f093f87f4fceec5"
},
"NystromformerForSequenceClassification": {
"tokenizer_classes": [
"AlbertTokenizer",
"AlbertTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"NystromformerForSequenceClassification"
],
"sha": "0e0f7cc4d7cf9a759ac5d1a2ebbcc6026b4c7df3"
"sha": "d75231203066df41e9b6b25dbee9ad40e8515c18"
},
"NystromformerForTokenClassification": {
"tokenizer_classes": [
"AlbertTokenizer",
"AlbertTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"NystromformerForTokenClassification"
],
"sha": "3b140a528dd8d259f4edf229430edc8f7633473e"
"sha": "5a499dc96e106bf41fc9166f2ad06527ec7ca14e"
},
"NystromformerModel": {
"tokenizer_classes": [
"AlbertTokenizer",
"AlbertTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"NystromformerModel"
],
"sha": "761c4db14685ba334ec012a5075847b3439da7d1"
"sha": "2b6adb37ec473b15d71e2eb459acea08df6940ce"
},
"OPTForCausalLM": {
"tokenizer_classes": [
......@@ -4001,16 +4043,18 @@
},
"PegasusForCausalLM": {
"tokenizer_classes": [
"PegasusTokenizer",
"PegasusTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"PegasusForCausalLM"
],
"sha": "bedf3a4a4188e44309417b70243f210c68b0a6f8"
"sha": "6e685a698302a3ba33e5379d3a37eb0bc1ae2f70"
},
"PegasusForConditionalGeneration": {
"tokenizer_classes": [
"PegasusTokenizer",
"PegasusTokenizerFast"
],
"processor_classes": [],
......@@ -4018,10 +4062,11 @@
"PegasusForConditionalGeneration",
"TFPegasusForConditionalGeneration"
],
"sha": "cacda18e0c679447ca1f38895fc08650df13d571"
"sha": "15e58ee2ebc14b6e80ef2891259057ee5f049be2"
},
"PegasusModel": {
"tokenizer_classes": [
"PegasusTokenizer",
"PegasusTokenizerFast"
],
"processor_classes": [],
......@@ -4029,27 +4074,29 @@
"PegasusModel",
"TFPegasusModel"
],
"sha": "a3e9e2ccb99adb9ac068f7ab74946bbdbfed52ec"
"sha": "fa36b24523db411ef77903453346b8be81ef73fe"
},
"PegasusXForConditionalGeneration": {
"tokenizer_classes": [
"PegasusTokenizer",
"PegasusTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"PegasusXForConditionalGeneration"
],
"sha": "7edff1791c2c20a6983202836a5b9c732f64631e"
"sha": "7588a8120f26a36c1687c14bdf1e9f9656891c1a"
},
"PegasusXModel": {
"tokenizer_classes": [
"PegasusTokenizer",
"PegasusTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"PegasusXModel"
],
"sha": "921751ff0cf59e7cfe909fa6803b5d5ceb0d3059"
"sha": "a0bdff627416ac3c39c22d081f5d88d8b8fd99cc"
},
"PerceiverForImageClassificationConvProcessing": {
"tokenizer_classes": [
......@@ -4250,6 +4297,7 @@
},
"RemBertForCausalLM": {
"tokenizer_classes": [
"RemBertTokenizer",
"RemBertTokenizerFast"
],
"processor_classes": [],
......@@ -4257,10 +4305,11 @@
"RemBertForCausalLM",
"TFRemBertForCausalLM"
],
"sha": "0b85a3f867b04078e2b3070dbb7033475946415f"
"sha": "8d9ae3d74a0e0a8958b4ee8c9dca3632abf52ef9"
},
"RemBertForMaskedLM": {
"tokenizer_classes": [
"RemBertTokenizer",
"RemBertTokenizerFast"
],
"processor_classes": [],
......@@ -4268,10 +4317,11 @@
"RemBertForMaskedLM",
"TFRemBertForMaskedLM"
],
"sha": "706f273263519d12cb35736b3b03a96908f752bf"
"sha": "b7c27d01e1cc3bef9ddd6a78627d700b3bffd759"
},
"RemBertForMultipleChoice": {
"tokenizer_classes": [
"RemBertTokenizer",
"RemBertTokenizerFast"
],
"processor_classes": [],
......@@ -4279,10 +4329,11 @@
"RemBertForMultipleChoice",
"TFRemBertForMultipleChoice"
],
"sha": "e03cd3b6eddb3ad9045e4d6d1053812a393a9417"
"sha": "2fe192677b9740cf24dd559339d46925e8ac23d4"
},
"RemBertForQuestionAnswering": {
"tokenizer_classes": [
"RemBertTokenizer",
"RemBertTokenizerFast"
],
"processor_classes": [],
......@@ -4290,10 +4341,11 @@
"RemBertForQuestionAnswering",
"TFRemBertForQuestionAnswering"
],
"sha": "882925e63c946dfb9679cd80a4c4909fbf1e4f2c"
"sha": "22b8ba44681b96292a1cf7f6df4ba6bb7937ec6e"
},
"RemBertForSequenceClassification": {
"tokenizer_classes": [
"RemBertTokenizer",
"RemBertTokenizerFast"
],
"processor_classes": [],
......@@ -4301,10 +4353,11 @@
"RemBertForSequenceClassification",
"TFRemBertForSequenceClassification"
],
"sha": "d315ecf3be15d52df6d7a137702f896265f112b1"
"sha": "20f3e89341ea15266d2685a8798142fba03c3f98"
},
"RemBertForTokenClassification": {
"tokenizer_classes": [
"RemBertTokenizer",
"RemBertTokenizerFast"
],
"processor_classes": [],
......@@ -4312,10 +4365,11 @@
"RemBertForTokenClassification",
"TFRemBertForTokenClassification"
],
"sha": "622e82ab7284c36acb1260a171623b650f0a8f52"
"sha": "15712ff753708da3cf0550e76e73a5d0bba7784e"
},
"RemBertModel": {
"tokenizer_classes": [
"RemBertTokenizer",
"RemBertTokenizerFast"
],
"processor_classes": [],
......@@ -4323,7 +4377,7 @@
"RemBertModel",
"TFRemBertModel"
],
"sha": "7dc1b9ee6493a52c377a4ceda0e6a24780c33281"
"sha": "59cc6d099b1ded0aaead8684457415b129f79e86"
},
"ResNetBackbone": {
"tokenizer_classes": [],
......@@ -5038,26 +5092,29 @@
},
"SwitchTransformersForConditionalGeneration": {
"tokenizer_classes": [
"T5Tokenizer",
"T5TokenizerFast"
],
"processor_classes": [],
"model_classes": [
"SwitchTransformersForConditionalGeneration"
],
"sha": "332cf96ed681e6cadb5b0788d1925224b62f9cf4"
"sha": "c8fcd2bb735894c78db7f1e5b51afc78aced7adb"
},
"SwitchTransformersModel": {
"tokenizer_classes": [
"T5Tokenizer",
"T5TokenizerFast"
],
"processor_classes": [],
"model_classes": [
"SwitchTransformersModel"
],
"sha": "a367b372751ab0e647724c25fc3698f5d46bce20"
"sha": "275bbf6d389bfd0540b9f824c609c6b22a577328"
},
"T5ForConditionalGeneration": {
"tokenizer_classes": [
"T5Tokenizer",
"T5TokenizerFast"
],
"processor_classes": [],
......@@ -5065,10 +5122,11 @@
"T5ForConditionalGeneration",
"TFT5ForConditionalGeneration"
],
"sha": "718845103ac90ab0c0594b6a7149ca10e5680f7e"
"sha": "593fd6072a4e265f5cc73b1973cd8af76b261f29"
},
"T5Model": {
"tokenizer_classes": [
"T5Tokenizer",
"T5TokenizerFast"
],
"processor_classes": [],
......@@ -5076,7 +5134,7 @@
"T5Model",
"TFT5Model"
],
"sha": "e47412452747fd8a9921371fa8f7cc92c9be973f"
"sha": "eb3d20dda0ba77c1de618d78116a1a0c784c515c"
},
"TableTransformerForObjectDetection": {
"tokenizer_classes": [],
......@@ -5776,6 +5834,7 @@
},
"XGLMForCausalLM": {
"tokenizer_classes": [
"XGLMTokenizer",
"XGLMTokenizerFast"
],
"processor_classes": [],
......@@ -5783,10 +5842,11 @@
"TFXGLMForCausalLM",
"XGLMForCausalLM"
],
"sha": "95dc5c571119b6bf7d7c58e98897be4c724ca4ee"
"sha": "d5381ce297c249d559937c6bb6316cf1fdad2613"
},
"XGLMModel": {
"tokenizer_classes": [
"XGLMTokenizer",
"XGLMTokenizerFast"
],
"processor_classes": [],
......@@ -5794,7 +5854,7 @@
"TFXGLMModel",
"XGLMModel"
],
"sha": "fcc8ad648ae1561cb4b35a1fd31c68b58296ef00"
"sha": "2b5cef167822cfaa558d259af1722e2f785cd3d5"
},
"XLMForMultipleChoice": {
"tokenizer_classes": [
......@@ -5853,73 +5913,80 @@
},
"XLMRobertaXLForCausalLM": {
"tokenizer_classes": [
"XLMRobertaTokenizer",
"XLMRobertaTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"XLMRobertaXLForCausalLM"
],
"sha": "54d7d901a31fcea581772a62a574f88ea994d931"
"sha": "fc05408e5b33a31638476ef337719dfbb7615ef3"
},
"XLMRobertaXLForMaskedLM": {
"tokenizer_classes": [
"XLMRobertaTokenizer",
"XLMRobertaTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"XLMRobertaXLForMaskedLM"
],
"sha": "b9b8090515146aa703fc30da03f3c0995ad2ac58"
"sha": "e96f198eede757e5ae2c87632fdcfb341073ef6e"
},
"XLMRobertaXLForMultipleChoice": {
"tokenizer_classes": [
"XLMRobertaTokenizer",
"XLMRobertaTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"XLMRobertaXLForMultipleChoice"
],
"sha": "c6e7ef0a2d640d14f5b0333ffcd32fb2fb83312d"
"sha": "52732625f1bfbbb7cb4ba1cf0963de596d81822d"
},
"XLMRobertaXLForQuestionAnswering": {
"tokenizer_classes": [
"XLMRobertaTokenizer",
"XLMRobertaTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"XLMRobertaXLForQuestionAnswering"
],
"sha": "4e433426b85bf8c1cea9261e91bd52d78d7bf397"
"sha": "da388fdd2d28e0757eb0c2b2c612a8ff03af2223"
},
"XLMRobertaXLForSequenceClassification": {
"tokenizer_classes": [
"XLMRobertaTokenizer",
"XLMRobertaTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"XLMRobertaXLForSequenceClassification"
],
"sha": "0b8361e84d0d75c0c7540e8dfcc339d37f683800"
"sha": "980721187633bcf21ac0b8edbed933527f4611df"
},
"XLMRobertaXLForTokenClassification": {
"tokenizer_classes": [
"XLMRobertaTokenizer",
"XLMRobertaTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"XLMRobertaXLForTokenClassification"
],
"sha": "70c4a472222415f8611c25ac7eea48944a6fa0aa"
"sha": "37a97280faf6fef0bd946d3934d77a1b60fbf473"
},
"XLMRobertaXLModel": {
"tokenizer_classes": [
"XLMRobertaTokenizer",
"XLMRobertaTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"XLMRobertaXLModel"
],
"sha": "df4e5bb09f2fa1e5582dc9424882e544c8b28aed"
"sha": "8fbeb39a984912e47f5d24a31be61639031a0fc3"
},
"XLMWithLMHeadModel": {
"tokenizer_classes": [
......@@ -5942,7 +6009,7 @@
"TFXLNetForMultipleChoice",
"XLNetForMultipleChoice"
],
"sha": "874306eaf380012b985180563bf140d604272f40"
"sha": "8bb7e28d0cd1e93154d3232baf5e9c79acaf9f1a"
},
"XLNetForQuestionAnsweringSimple": {
"tokenizer_classes": [
......@@ -5954,7 +6021,7 @@
"TFXLNetForQuestionAnsweringSimple",
"XLNetForQuestionAnsweringSimple"
],
"sha": "8c6ac70e1946da872e5f93853247728d672a1dee"
"sha": "fabd06a45d947f3d46f1b8dce2186cf3b27776dc"
},
"XLNetForSequenceClassification": {
"tokenizer_classes": [
......@@ -5966,7 +6033,7 @@
"TFXLNetForSequenceClassification",
"XLNetForSequenceClassification"
],
"sha": "3bba8894ba1d8e34cd807fd5a0f858449abf65b4"
"sha": "e3c194f24537ebf2c474ade60becb9397696edec"
},
"XLNetForTokenClassification": {
"tokenizer_classes": [
......@@ -5978,7 +6045,7 @@
"TFXLNetForTokenClassification",
"XLNetForTokenClassification"
],
"sha": "70598399e95e6df689fe5459ef9f016e55cc05ee"
"sha": "16aa15029aa667046d504c4a88ceddfdd5b5fb40"
},
"XLNetLMHeadModel": {
"tokenizer_classes": [
......@@ -5990,7 +6057,7 @@
"TFXLNetLMHeadModel",
"XLNetLMHeadModel"
],
"sha": "fef32495d187c73201ba4e2854559bcc68e41e22"
"sha": "c9a98cc982a16ca162832a8cbea25116479bb938"
},
"XLNetModel": {
"tokenizer_classes": [
......@@ -6002,77 +6069,84 @@
"TFXLNetModel",
"XLNetModel"
],
"sha": "bebc65e9a3da5c0007713a61f6719293d361baa3"
"sha": "1d6e231942135faf32b8d9a97773d8f6c85ca561"
},
"XmodForCausalLM": {
"tokenizer_classes": [
"XLMRobertaTokenizer",
"XLMRobertaTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"XmodForCausalLM"
],
"sha": "daf792dff8fef2f3d99eb5ee63b206032b3f69d7"
"sha": "c6b746071f2f067099a8fb4f57ce3c27a7e4b67d"
},
"XmodForMaskedLM": {
"tokenizer_classes": [
"XLMRobertaTokenizer",
"XLMRobertaTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"XmodForMaskedLM"
],
"sha": "b7c020898ef638ed4c2f420431b4efc0075c0e32"
"sha": "e1085818f4ed3c6073b2038635e5f3061208923d"
},
"XmodForMultipleChoice": {
"tokenizer_classes": [
"XLMRobertaTokenizer",
"XLMRobertaTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"XmodForMultipleChoice"
],
"sha": "1fce9673e9f557d3204d504ebb86e285b20937f8"
"sha": "c63042cdf196be3fed846421b345d439b2483f69"
},
"XmodForQuestionAnswering": {
"tokenizer_classes": [
"XLMRobertaTokenizer",
"XLMRobertaTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"XmodForQuestionAnswering"
],
"sha": "fc9ebdfeb481281375c25d585569404cc48b02da"
"sha": "75acd3071fae9978c82618cd0f090c87aabc1f23"
},
"XmodForSequenceClassification": {
"tokenizer_classes": [
"XLMRobertaTokenizer",
"XLMRobertaTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"XmodForSequenceClassification"
],
"sha": "bba89c4e18b6a29a1865462a75cb8bde12e7cc0c"
"sha": "523a16570be048618913ac17ccd00d343bcb5e99"
},
"XmodForTokenClassification": {
"tokenizer_classes": [
"XLMRobertaTokenizer",
"XLMRobertaTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"XmodForTokenClassification"
],
"sha": "efe3dad234d5a60f1165887d8b4bb1627e271508"
"sha": "a0f0a02732b4579670dad11a69ae244ebd777b49"
},
"XmodModel": {
"tokenizer_classes": [
"XLMRobertaTokenizer",
"XLMRobertaTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"XmodModel"
],
"sha": "62f3ca59e02a9637b12ce81566f1996c16fad872"
"sha": "bc286de0035450e7dcd6bcce78098a967b9c2b6c"
},
"YolosForObjectDetection": {
"tokenizer_classes": [],
......@@ -6102,7 +6176,7 @@
"model_classes": [
"YosoForMaskedLM"
],
"sha": "4ff7ab217e6d05d40ae8afd37df1a0bf5c6ab25f"
"sha": "cb291bedcbec199ea195f086e3ebea6fab026bba"
},
"YosoForMultipleChoice": {
"tokenizer_classes": [
......@@ -6112,7 +6186,7 @@
"model_classes": [
"YosoForMultipleChoice"
],
"sha": "c42bd94e4563cdfeb28e07aef80e801188706d9d"
"sha": "cf2d3a3f0628bc9d0da68ea8de26b12016453fee"
},
"YosoForQuestionAnswering": {
"tokenizer_classes": [
......@@ -6122,7 +6196,7 @@
"model_classes": [
"YosoForQuestionAnswering"
],
"sha": "284f170b902c65ee257a2e8f255fa672cb4700c5"
"sha": "e8c3091f674588adfa3371b3de0427a9b39dd03f"
},
"YosoForSequenceClassification": {
"tokenizer_classes": [
......@@ -6132,7 +6206,7 @@
"model_classes": [
"YosoForSequenceClassification"
],
"sha": "2e0a37a4dd12cee00eb15e4ecd0de4231a638966"
"sha": "88132cbaa1a9a87f65b6f9813c388011377f18cf"
},
"YosoForTokenClassification": {
"tokenizer_classes": [
......@@ -6142,7 +6216,7 @@
"model_classes": [
"YosoForTokenClassification"
],
"sha": "50dd3fe46f28f0efaacf8aa311b09f67b10edb74"
"sha": "fd2219856608d3dba70dc7b1a06af629903dec31"
},
"YosoModel": {
"tokenizer_classes": [
......@@ -6152,6 +6226,6 @@
"model_classes": [
"YosoModel"
],
"sha": "6b42cca4d4f9204a3aa1197664663656719095cb"
"sha": "e144d9f1fe39c21eda1177702640e126892605ce"
}
}
\ No newline at end of file
utils/create_dummy_models.py
View file @ 8894b817
......@@ -405,7 +405,11 @@ def get_tiny_config(config_class, model_class=None, **model_tester_kwargs):
for _tester_classes in models_to_model_testers.values():
tester_classes.extend(_tester_classes)
if len(tester_classes) > 0:
model_tester_class = sorted(tester_classes, key=lambda x: x.__name__)[0]
# sort with the length of the class names first, then the alphabetical order
# This is to avoid `T5EncoderOnlyModelTest` is used instead of `T5ModelTest`, which has
# `is_encoder_decoder=False` and causes some pipeline tests failing (also failures in `Optimum` CI).
# TODO: More fine grained control of the desired tester class.
model_tester_class = sorted(tester_classes, key=lambda x: (len(x.__name__), x.__name__))[0]
except ModuleNotFoundError:
error = f"Tiny config not created for {model_type} - cannot find the testing module from the model name."
raise ValueError(error)
......@@ -484,21 +488,67 @@ def convert_processors(processors, tiny_config, output_folder, result):
This method should not fail: we catch the errors and put them in `result["warnings"]` with descriptive messages.
"""
def _sanity_check(fast_tokenizer, slow_tokenizer, keep_fast_tokenizer=False):
"""Set tokenizer(s) to `None` if the fast/slow tokenizers have different values for `vocab_size` or `length`.
If `keep_fast_tokenizer=True`, the fast tokenizer will be kept.
"""
# sanity check 1: fast and slow tokenizers should be compatible (vocab_size)
if fast_tokenizer is not None and slow_tokenizer is not None:
if fast_tokenizer.vocab_size != slow_tokenizer.vocab_size:
warning_messagae = (
"The fast/slow tokenizers "
f"({fast_tokenizer.__class__.__name__}/{slow_tokenizer.__class__.__name__}) have different "
"vocabulary size: "
f"fast_tokenizer.vocab_size = {fast_tokenizer.vocab_size} and "
f"slow_tokenizer.vocab_size = {slow_tokenizer.vocab_size}."
)
result["warnings"].append(warning_messagae)
if not keep_fast_tokenizer:
fast_tokenizer = None
slow_tokenizer = None
# sanity check 2: fast and slow tokenizers should be compatible (length)
if fast_tokenizer is not None and slow_tokenizer is not None:
if len(fast_tokenizer) != len(slow_tokenizer):
warning_messagae = (
f"The fast/slow tokenizers () have different length: "
f"len(fast_tokenizer) = {len(fast_tokenizer)} and "
f"len(slow_tokenizer) = {len(slow_tokenizer)}."
)
result["warnings"].append(warning_messagae)
if not keep_fast_tokenizer:
fast_tokenizer = None
slow_tokenizer = None
return fast_tokenizer, slow_tokenizer
tokenizers = []
feature_extractors = []
for processor in processors:
if isinstance(processor, PreTrainedTokenizerBase):
if processor.__class__.__name__ not in {x.__class__.__name__ for x in tokenizers}:
tokenizers.append(processor)
elif isinstance(processor, BaseImageProcessor):
if processor.__class__.__name__ not in {x.__class__.__name__ for x in feature_extractors}:
feature_extractors.append(processor)
elif isinstance(processor, FeatureExtractionMixin):
if processor.__class__.__name__ not in {x.__class__.__name__ for x in feature_extractors}:
feature_extractors.append(processor)
elif isinstance(processor, ProcessorMixin):
# Currently, we only have these 2 possibilities
if hasattr(processor, "tokenizer"):
if processor.tokenizer.__class__.__name__ not in {x.__class__.__name__ for x in tokenizers}:
tokenizers.append(processor.tokenizer)
# Currently, we only have these 2 possibilities
if hasattr(processor, "image_processor"):
if processor.image_processor.__class__.__name__ not in {
x.__class__.__name__ for x in feature_extractors
}:
feature_extractors.append(processor.image_processor)
elif hasattr(processor, "feature_extractor"):
if processor.feature_extractor.__class__.__name__ not in {
x.__class__.__name__ for x in feature_extractors
}:
feature_extractors.append(processor.feature_extractor)
# check the built processors have the unique type
......@@ -511,15 +561,29 @@ def convert_processors(processors, tiny_config, output_folder, result):
fast_tokenizer = None
slow_tokenizer = None
for tokenizer in tokenizers:
if isinstance(tokenizer, PreTrainedTokenizerFast):
if fast_tokenizer is None:
fast_tokenizer = tokenizer
else:
slow_tokenizer = tokenizer
# If the (original) fast/slow tokenizers don't correspond, keep only the fast tokenizer.
# This doesn't necessarily imply the fast/slow tokenizers in a single Hub repo. has issues.
# It's more of an issue in `build_processor` which tries to get a checkpoint with as much effort as possible.
# For `YosoModel` (which uses `AlbertTokenizer(Fast)`), its real (Hub) checkpoint doesn't contain valid files to
# load the slower tokenizer (`AlbertTokenizer`), and it ends up finding the (canonical) checkpoint of `AlbertModel`,
# which has different vocabulary.
# TODO: Try to improve `build_processor`'s definition and/or usage to avoid the above situation in the first place.
fast_tokenizer, slow_tokenizer = _sanity_check(fast_tokenizer, slow_tokenizer, keep_fast_tokenizer=True)
original_fast_tokenizer, original_slow_tokenizer = fast_tokenizer, slow_tokenizer
if fast_tokenizer:
try:
# Wav2Vec2ForCTC , ByT5Tokenizer etc. all are already small enough and have no fast version that can
# be retrained
if fast_tokenizer.vocab_size > TARGET_VOCAB_SIZE:
fast_tokenizer = convert_tokenizer(tokenizer)
fast_tokenizer = convert_tokenizer(fast_tokenizer)
except Exception:
result["warnings"].append(
(
......@@ -527,14 +591,25 @@ def convert_processors(processors, tiny_config, output_folder, result):
traceback.format_exc(),
)
)
continue
elif slow_tokenizer is None:
slow_tokenizer = tokenizer
# Make sure the fast tokenizer can be saved
# If `fast_tokenizer` exists, `slow_tokenizer` should correspond to it.
if fast_tokenizer:
# Make sure the fast tokenizer can be saved
try:
fast_tokenizer.save_pretrained(output_folder)
# We don't save it to `output_folder` at this moment - only at the end of this function.
with tempfile.TemporaryDirectory() as tmpdir:
fast_tokenizer.save_pretrained(tmpdir)
try:
slow_tokenizer = AutoTokenizer.from_pretrained(tmpdir, use_fast=False)
except Exception:
result["warnings"].append(
(
f"Failed to load the slow tokenizer saved from {fast_tokenizer.__class__.__name__}.",
traceback.format_exc(),
)
)
# Let's just keep the fast version
slow_tokenizer = None
except Exception:
result["warnings"].append(
(
......@@ -544,24 +619,43 @@ def convert_processors(processors, tiny_config, output_folder, result):
)
fast_tokenizer = None
# Make sure the slow tokenizer (if any) corresponds to the fast version (as it might be converted above)
# If the (possibly converted) fast/slow tokenizers don't correspond, set them to `None`, and use the original
# tokenizers.
fast_tokenizer, slow_tokenizer = _sanity_check(fast_tokenizer, slow_tokenizer, keep_fast_tokenizer=False)
# If there is any conversion failed, we keep the original tokenizers.
if (original_fast_tokenizer is not None and fast_tokenizer is None) or (
original_slow_tokenizer is not None and slow_tokenizer is None
):
warning_messagae = (
"There are some issues when converting the fast/slow tokenizers. The original tokenizers from the Hub "
" will be used instead."
)
result["warnings"].append(warning_messagae)
# Let's use the original version at the end (`original_fast_tokenizer` and `original_slow_tokenizer`)
fast_tokenizer = original_fast_tokenizer
slow_tokenizer = original_slow_tokenizer
# Make sure the fast tokenizer can be saved
if fast_tokenizer:
# We don't save it to `output_folder` at this moment - only at the end of this function.
with tempfile.TemporaryDirectory() as tmpdir:
try:
slow_tokenizer = AutoTokenizer.from_pretrained(output_folder, use_fast=False)
fast_tokenizer.save_pretrained(tmpdir)
except Exception:
result["warnings"].append(
(
f"Failed to load the slow tokenizer saved from {fast_tokenizer.__class__.__name__}.",
f"Failed to save the fast tokenizer for {fast_tokenizer.__class__.__name__}.",
traceback.format_exc(),
)
)
# Let's just keep the fast version
slow_tokenizer = None
# If the fast version can't be created and saved, let's use the slow version
if not fast_tokenizer and slow_tokenizer:
fast_tokenizer = None
# Make sure the slow tokenizer can be saved
if slow_tokenizer:
# We don't save it to `output_folder` at this moment - only at the end of this function.
with tempfile.TemporaryDirectory() as tmpdir:
try:
slow_tokenizer.save_pretrained(output_folder)
slow_tokenizer.save_pretrained(tmpdir)
except Exception:
result["warnings"].append(
(
......@@ -883,7 +977,9 @@ def get_config_overrides(config_class, processors):
return config_overrides
# Get some properties of the (already converted) tokenizer (smaller vocab size, special token ids, etc.)
vocab_size = tokenizer.vocab_size
# We use `len(tokenizer)` instead of `tokenizer.vocab_size` to avoid potential issues for tokenizers with non-empty
# `added_tokens_encoder`. One example is the `DebertaV2Tokenizer` where the mask token is the extra token.
vocab_size = len(tokenizer)
config_overrides["vocab_size"] = vocab_size
# Used to create a new model tester with `tokenizer.vocab_size` in order to get the (updated) special token ids.
......
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