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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: on:
push: push:
......
tests/models/bigbird_pegasus/test_modeling_bigbird_pegasus.py
View file @ 8894b817
...@@ -268,6 +268,15 @@ class BigBirdPegasusModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineT ...@@ -268,6 +268,15 @@ class BigBirdPegasusModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineT
# Also torchscript is not an important feature to have in the beginning. # Also torchscript is not an important feature to have in the beginning.
test_torchscript = False 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 # overwrite from GenerationTesterMixin to solve problem
# with conflicting random seeds # with conflicting random seeds
def _get_input_ids_and_config(self): 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 ...@@ -387,6 +387,15 @@ class XLMRobertaXLModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTes
else {} 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): def setUp(self):
self.model_tester = XLMRobertaXLModelTester(self) self.model_tester = XLMRobertaXLModelTester(self)
self.config_tester = ConfigTester(self, config_class=XLMRobertaXLConfig, hidden_size=37) 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 ...@@ -384,6 +384,15 @@ class XmodModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
else {} 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): def setUp(self):
self.model_tester = XmodModelTester(self) self.model_tester = XmodModelTester(self)
self.config_tester = ConfigTester(self, config_class=XmodConfig, hidden_size=37) self.config_tester = ConfigTester(self, config_class=XmodConfig, hidden_size=37)
......
tests/utils/tiny_model_summary.json
View file @ 8894b817
...@@ -21,6 +21,7 @@ ...@@ -21,6 +21,7 @@
}, },
"AlbertForMaskedLM": { "AlbertForMaskedLM": {
"tokenizer_classes": [ "tokenizer_classes": [
"AlbertTokenizer",
"AlbertTokenizerFast" "AlbertTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
...@@ -28,10 +29,11 @@ ...@@ -28,10 +29,11 @@
"AlbertForMaskedLM", "AlbertForMaskedLM",
"TFAlbertForMaskedLM" "TFAlbertForMaskedLM"
], ],
"sha": "75ab12f94d4a1edd9610636547c5fb515e240e2b" "sha": "d29de71ac29e1019c3a7762f7357f750730cb037"
}, },
"AlbertForMultipleChoice": { "AlbertForMultipleChoice": {
"tokenizer_classes": [ "tokenizer_classes": [
"AlbertTokenizer",
"AlbertTokenizerFast" "AlbertTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
...@@ -39,10 +41,11 @@ ...@@ -39,10 +41,11 @@
"AlbertForMultipleChoice", "AlbertForMultipleChoice",
"TFAlbertForMultipleChoice" "TFAlbertForMultipleChoice"
], ],
"sha": "ba1531e4373cccce03195928b3ba2f6825311980" "sha": "242aecce6a589a2964c0f695621fa22a83751579"
}, },
"AlbertForPreTraining": { "AlbertForPreTraining": {
"tokenizer_classes": [ "tokenizer_classes": [
"AlbertTokenizer",
"AlbertTokenizerFast" "AlbertTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
...@@ -50,10 +53,11 @@ ...@@ -50,10 +53,11 @@
"AlbertForPreTraining", "AlbertForPreTraining",
"TFAlbertForPreTraining" "TFAlbertForPreTraining"
], ],
"sha": "6022449842a83d9cea298c4fbaf1e1e1c0db3568" "sha": "41330be4b271687f4d88ddc96346c12aa11de983"
}, },
"AlbertForQuestionAnswering": { "AlbertForQuestionAnswering": {
"tokenizer_classes": [ "tokenizer_classes": [
"AlbertTokenizer",
"AlbertTokenizerFast" "AlbertTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
...@@ -61,10 +65,11 @@ ...@@ -61,10 +65,11 @@
"AlbertForQuestionAnswering", "AlbertForQuestionAnswering",
"TFAlbertForQuestionAnswering" "TFAlbertForQuestionAnswering"
], ],
"sha": "1b6584d6a267dae8ff20b9f89e2b424a7972fb45" "sha": "040b81c15f437f4722349dc5b41fccd17ebd7fdc"
}, },
"AlbertForSequenceClassification": { "AlbertForSequenceClassification": {
"tokenizer_classes": [ "tokenizer_classes": [
"AlbertTokenizer",
"AlbertTokenizerFast" "AlbertTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
...@@ -72,10 +77,11 @@ ...@@ -72,10 +77,11 @@
"AlbertForSequenceClassification", "AlbertForSequenceClassification",
"TFAlbertForSequenceClassification" "TFAlbertForSequenceClassification"
], ],
"sha": "1e709531344ee0e4a34777c79507a07b69130958" "sha": "39c1a0e2c1c2623106d3211d751e9b32f23a91a0"
}, },
"AlbertForTokenClassification": { "AlbertForTokenClassification": {
"tokenizer_classes": [ "tokenizer_classes": [
"AlbertTokenizer",
"AlbertTokenizerFast" "AlbertTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
...@@ -83,10 +89,11 @@ ...@@ -83,10 +89,11 @@
"AlbertForTokenClassification", "AlbertForTokenClassification",
"TFAlbertForTokenClassification" "TFAlbertForTokenClassification"
], ],
"sha": "f6c0d721d6d9f0751ab975148932948d5853fcc8" "sha": "359c3f4a311a4053a6f6d6a880db5f82c8e3ff1f"
}, },
"AlbertModel": { "AlbertModel": {
"tokenizer_classes": [ "tokenizer_classes": [
"AlbertTokenizer",
"AlbertTokenizerFast" "AlbertTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
...@@ -94,7 +101,7 @@ ...@@ -94,7 +101,7 @@
"AlbertModel", "AlbertModel",
"TFAlbertModel" "TFAlbertModel"
], ],
"sha": "62974edf8b7246a943f6ecc8a3f7bfca052351ff" "sha": "34a63314686b64aaeb595ddb95006f1ff2ffda17"
}, },
"AlignModel": { "AlignModel": {
"tokenizer_classes": [ "tokenizer_classes": [
...@@ -318,133 +325,146 @@ ...@@ -318,133 +325,146 @@
}, },
"BigBirdForCausalLM": { "BigBirdForCausalLM": {
"tokenizer_classes": [ "tokenizer_classes": [
"BigBirdTokenizer",
"BigBirdTokenizerFast" "BigBirdTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
"model_classes": [ "model_classes": [
"BigBirdForCausalLM" "BigBirdForCausalLM"
], ],
"sha": "04578a05f11c0006e4e5deaf38b48889a8dc8f4f" "sha": "5c7a487af5248d9c01b45d5481b7d7bb9b36e1b5"
}, },
"BigBirdForMaskedLM": { "BigBirdForMaskedLM": {
"tokenizer_classes": [ "tokenizer_classes": [
"BigBirdTokenizer",
"BigBirdTokenizerFast" "BigBirdTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
"model_classes": [ "model_classes": [
"BigBirdForMaskedLM" "BigBirdForMaskedLM"
], ],
"sha": "52b8e93488b5d8235711543d4671ea08ea3f9560" "sha": "476ef8225c0f69270b577706ad4f1dda13e4dde5"
}, },
"BigBirdForMultipleChoice": { "BigBirdForMultipleChoice": {
"tokenizer_classes": [ "tokenizer_classes": [
"BigBirdTokenizer",
"BigBirdTokenizerFast" "BigBirdTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
"model_classes": [ "model_classes": [
"BigBirdForMultipleChoice" "BigBirdForMultipleChoice"
], ],
"sha": "56459f9bcde6a36870e4d743295f6ce69ba5fc7b" "sha": "cf93eaa1019987112c171a407745bc183a20513a"
}, },
"BigBirdForPreTraining": { "BigBirdForPreTraining": {
"tokenizer_classes": [ "tokenizer_classes": [
"BigBirdTokenizer",
"BigBirdTokenizerFast" "BigBirdTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
"model_classes": [ "model_classes": [
"BigBirdForPreTraining" "BigBirdForPreTraining"
], ],
"sha": "49f55d7252dd9151722b330fa02073c6d809c55e" "sha": "5fb9efa13334431e7c186a9fa314b89c4a1eee72"
}, },
"BigBirdForQuestionAnswering": { "BigBirdForQuestionAnswering": {
"tokenizer_classes": [ "tokenizer_classes": [
"BigBirdTokenizer",
"BigBirdTokenizerFast" "BigBirdTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
"model_classes": [ "model_classes": [
"BigBirdForQuestionAnswering" "BigBirdForQuestionAnswering"
], ],
"sha": "a5b3c8567610d4dde63c282d6fb6fd2ec04cbb39" "sha": "f82f88bd71fba819a8ffb0692915d3529e705417"
}, },
"BigBirdForSequenceClassification": { "BigBirdForSequenceClassification": {
"tokenizer_classes": [ "tokenizer_classes": [
"BigBirdTokenizer",
"BigBirdTokenizerFast" "BigBirdTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
"model_classes": [ "model_classes": [
"BigBirdForSequenceClassification" "BigBirdForSequenceClassification"
], ],
"sha": "2b29389f623fa7af3ffb08c51e9bcbda270ae9ee" "sha": "ea398090858f9af93b54fc9a8d65cfed78ac27ff"
}, },
"BigBirdForTokenClassification": { "BigBirdForTokenClassification": {
"tokenizer_classes": [ "tokenizer_classes": [
"BigBirdTokenizer",
"BigBirdTokenizerFast" "BigBirdTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
"model_classes": [ "model_classes": [
"BigBirdForTokenClassification" "BigBirdForTokenClassification"
], ],
"sha": "beda63d67d07b133e603f51aea6b84cae29b9ea7" "sha": "2cdea118999fa58ba9fb0162d99e2ffa146c3df1"
}, },
"BigBirdModel": { "BigBirdModel": {
"tokenizer_classes": [ "tokenizer_classes": [
"BigBirdTokenizer",
"BigBirdTokenizerFast" "BigBirdTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
"model_classes": [ "model_classes": [
"BigBirdModel" "BigBirdModel"
], ],
"sha": "814adde7ccd69821684a0c6124401f0c180d700c" "sha": "9c55989f31df156194e6997606fb14d9897e0300"
}, },
"BigBirdPegasusForCausalLM": { "BigBirdPegasusForCausalLM": {
"tokenizer_classes": [ "tokenizer_classes": [
"PegasusTokenizer",
"PegasusTokenizerFast" "PegasusTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
"model_classes": [ "model_classes": [
"BigBirdPegasusForCausalLM" "BigBirdPegasusForCausalLM"
], ],
"sha": "e1a5b87220073127f718fec558cbc86795b6ed61" "sha": "49bc8816c666dee32e27cd8e00136b604eb85243"
}, },
"BigBirdPegasusForConditionalGeneration": { "BigBirdPegasusForConditionalGeneration": {
"tokenizer_classes": [ "tokenizer_classes": [
"PegasusTokenizer",
"PegasusTokenizerFast" "PegasusTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
"model_classes": [ "model_classes": [
"BigBirdPegasusForConditionalGeneration" "BigBirdPegasusForConditionalGeneration"
], ],
"sha": "40fad528589229426174241a641034f1f971a2b7" "sha": "e791aa6d1af5a76ca0926d95b1f28bd2d8adf376"
}, },
"BigBirdPegasusForQuestionAnswering": { "BigBirdPegasusForQuestionAnswering": {
"tokenizer_classes": [ "tokenizer_classes": [
"PegasusTokenizer",
"PegasusTokenizerFast" "PegasusTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
"model_classes": [ "model_classes": [
"BigBirdPegasusForQuestionAnswering" "BigBirdPegasusForQuestionAnswering"
], ],
"sha": "18d836e06a02bd1dc36af9a5eeaf3d326b1d368a" "sha": "7650e076713ca707a37062adc8c9c1cd60dad7c7"
}, },
"BigBirdPegasusForSequenceClassification": { "BigBirdPegasusForSequenceClassification": {
"tokenizer_classes": [ "tokenizer_classes": [
"PegasusTokenizer",
"PegasusTokenizerFast" "PegasusTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
"model_classes": [ "model_classes": [
"BigBirdPegasusForSequenceClassification" "BigBirdPegasusForSequenceClassification"
], ],
"sha": "a451bdb3f36fb76af06a51274d19fd88729443e6" "sha": "02500e8ebd9c53528750013fb963fbdc2be34034"
}, },
"BigBirdPegasusModel": { "BigBirdPegasusModel": {
"tokenizer_classes": [ "tokenizer_classes": [
"PegasusTokenizer",
"PegasusTokenizerFast" "PegasusTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
"model_classes": [ "model_classes": [
"BigBirdPegasusModel" "BigBirdPegasusModel"
], ],
"sha": "3ece62a543ced3755e5ae239bcc3a4a5a6b75dd4" "sha": "b07c5304dfba673cf8b9cf5cd1aa45fbfea1c2f3"
}, },
"BioGptForCausalLM": { "BioGptForCausalLM": {
"tokenizer_classes": [ "tokenizer_classes": [
...@@ -1240,6 +1260,7 @@ ...@@ -1240,6 +1260,7 @@
}, },
"DebertaV2ForMaskedLM": { "DebertaV2ForMaskedLM": {
"tokenizer_classes": [ "tokenizer_classes": [
"DebertaV2Tokenizer",
"DebertaV2TokenizerFast" "DebertaV2TokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
...@@ -1247,20 +1268,22 @@ ...@@ -1247,20 +1268,22 @@
"DebertaV2ForMaskedLM", "DebertaV2ForMaskedLM",
"TFDebertaV2ForMaskedLM" "TFDebertaV2ForMaskedLM"
], ],
"sha": "9089b6afa8f66fd16503fca2b7c54b50c2195123" "sha": "a053dedc2cdf32918a84277cb0c05186604496a5"
}, },
"DebertaV2ForMultipleChoice": { "DebertaV2ForMultipleChoice": {
"tokenizer_classes": [ "tokenizer_classes": [
"DebertaV2Tokenizer",
"DebertaV2TokenizerFast" "DebertaV2TokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
"model_classes": [ "model_classes": [
"DebertaV2ForMultipleChoice" "DebertaV2ForMultipleChoice"
], ],
"sha": "7acf4b7415b2869e225a5ed82e68f1c0374e9668" "sha": "07e39f520ce239b39ef8cb24cd7874d06c791063"
}, },
"DebertaV2ForQuestionAnswering": { "DebertaV2ForQuestionAnswering": {
"tokenizer_classes": [ "tokenizer_classes": [
"DebertaV2Tokenizer",
"DebertaV2TokenizerFast" "DebertaV2TokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
...@@ -1268,10 +1291,11 @@ ...@@ -1268,10 +1291,11 @@
"DebertaV2ForQuestionAnswering", "DebertaV2ForQuestionAnswering",
"TFDebertaV2ForQuestionAnswering" "TFDebertaV2ForQuestionAnswering"
], ],
"sha": "17ef18fefddc0ec61a972eea06af430059ae3759" "sha": "9cecb3a7fc6b95099122283644ea1f8ced287d1b"
}, },
"DebertaV2ForSequenceClassification": { "DebertaV2ForSequenceClassification": {
"tokenizer_classes": [ "tokenizer_classes": [
"DebertaV2Tokenizer",
"DebertaV2TokenizerFast" "DebertaV2TokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
...@@ -1279,10 +1303,11 @@ ...@@ -1279,10 +1303,11 @@
"DebertaV2ForSequenceClassification", "DebertaV2ForSequenceClassification",
"TFDebertaV2ForSequenceClassification" "TFDebertaV2ForSequenceClassification"
], ],
"sha": "1ef484d43eb15ac6b1f8be393c3d59bea2267dc9" "sha": "df9ea1f5c0f2ccd139b21cfb3963a5a5ebfb5b81"
}, },
"DebertaV2ForTokenClassification": { "DebertaV2ForTokenClassification": {
"tokenizer_classes": [ "tokenizer_classes": [
"DebertaV2Tokenizer",
"DebertaV2TokenizerFast" "DebertaV2TokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
...@@ -1290,10 +1315,11 @@ ...@@ -1290,10 +1315,11 @@
"DebertaV2ForTokenClassification", "DebertaV2ForTokenClassification",
"TFDebertaV2ForTokenClassification" "TFDebertaV2ForTokenClassification"
], ],
"sha": "5c4e629b5b03957a546f7f76c31f6887f99fc17c" "sha": "51fe01989df38a540ac1abca5ee71a51365defd5"
}, },
"DebertaV2Model": { "DebertaV2Model": {
"tokenizer_classes": [ "tokenizer_classes": [
"DebertaV2Tokenizer",
"DebertaV2TokenizerFast" "DebertaV2TokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
...@@ -1301,7 +1327,7 @@ ...@@ -1301,7 +1327,7 @@
"DebertaV2Model", "DebertaV2Model",
"TFDebertaV2Model" "TFDebertaV2Model"
], ],
"sha": "a1945cc2bb1ef207f8fdeb4ea146711ade1db77a" "sha": "211df4bd1a4a9b66c97af3f9231a5d2af8de7b9f"
}, },
"DeformableDetrForObjectDetection": { "DeformableDetrForObjectDetection": {
"tokenizer_classes": [], "tokenizer_classes": [],
...@@ -1881,83 +1907,91 @@ ...@@ -1881,83 +1907,91 @@
}, },
"FNetForMaskedLM": { "FNetForMaskedLM": {
"tokenizer_classes": [ "tokenizer_classes": [
"FNetTokenizer",
"FNetTokenizerFast" "FNetTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
"model_classes": [ "model_classes": [
"FNetForMaskedLM" "FNetForMaskedLM"
], ],
"sha": "235602782fb4b4ff0291c999cc174b4a257f1e7f" "sha": "91eaae1eac894af5d96c0221ec9bcef7f1af41c8"
}, },
"FNetForMultipleChoice": { "FNetForMultipleChoice": {
"tokenizer_classes": [ "tokenizer_classes": [
"FNetTokenizer",
"FNetTokenizerFast" "FNetTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
"model_classes": [ "model_classes": [
"FNetForMultipleChoice" "FNetForMultipleChoice"
], ],
"sha": "d84b9ee07323895465a29f234e9109b66fd623cf" "sha": "c15d98d5f7a6f3ef3099b1257949bee208d5466e"
}, },
"FNetForNextSentencePrediction": { "FNetForNextSentencePrediction": {
"tokenizer_classes": [ "tokenizer_classes": [
"FNetTokenizer",
"FNetTokenizerFast" "FNetTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
"model_classes": [ "model_classes": [
"FNetForNextSentencePrediction" "FNetForNextSentencePrediction"
], ],
"sha": "9b11a763f599a95c3dff8e4255cd952a04101a65" "sha": "c59440b44d07d61fc45a90ded7fc11d6f25b143d"
}, },
"FNetForPreTraining": { "FNetForPreTraining": {
"tokenizer_classes": [ "tokenizer_classes": [
"FNetTokenizer",
"FNetTokenizerFast" "FNetTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
"model_classes": [ "model_classes": [
"FNetForPreTraining" "FNetForPreTraining"
], ],
"sha": "ed6ec245f3a8f7b53c7b09b020cfae1f8c4aaf7d" "sha": "c05f55ccfb2f2533babd3c6e99de7749bc8081da"
}, },
"FNetForQuestionAnswering": { "FNetForQuestionAnswering": {
"tokenizer_classes": [ "tokenizer_classes": [
"FNetTokenizer",
"FNetTokenizerFast" "FNetTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
"model_classes": [ "model_classes": [
"FNetForQuestionAnswering" "FNetForQuestionAnswering"
], ],
"sha": "c77c577acae60cd268b0eebdbffcbd75f8e31141" "sha": "47788e49dd435653fa2aa4b3ccae3572a870758e"
}, },
"FNetForSequenceClassification": { "FNetForSequenceClassification": {
"tokenizer_classes": [ "tokenizer_classes": [
"FNetTokenizer",
"FNetTokenizerFast" "FNetTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
"model_classes": [ "model_classes": [
"FNetForSequenceClassification" "FNetForSequenceClassification"
], ],
"sha": "6e6dbab691e5ec18e04b98514f1656dc3a842192" "sha": "a3049b896ea6c5a32c364989c3afe604ee58b9fc"
}, },
"FNetForTokenClassification": { "FNetForTokenClassification": {
"tokenizer_classes": [ "tokenizer_classes": [
"FNetTokenizer",
"FNetTokenizerFast" "FNetTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
"model_classes": [ "model_classes": [
"FNetForTokenClassification" "FNetForTokenClassification"
], ],
"sha": "b27e341994ef7913dcdd72326d3475c9668d07d5" "sha": "3bcdafca57d544bb81e2f7eead1e512c168582fc"
}, },
"FNetModel": { "FNetModel": {
"tokenizer_classes": [ "tokenizer_classes": [
"FNetTokenizer",
"FNetTokenizerFast" "FNetTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
"model_classes": [ "model_classes": [
"FNetModel" "FNetModel"
], ],
"sha": "38041395aa488da543dec9ed86318d9ec4d4839e" "sha": "48fa66de37df126504db3b658806135eb877f505"
}, },
"FSMTForConditionalGeneration": { "FSMTForConditionalGeneration": {
"tokenizer_classes": [ "tokenizer_classes": [
...@@ -2828,23 +2862,25 @@ ...@@ -2828,23 +2862,25 @@
}, },
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"XLNetForSequenceClassification" "XLNetForSequenceClassification"
], ],
"sha": "3bba8894ba1d8e34cd807fd5a0f858449abf65b4" "sha": "e3c194f24537ebf2c474ade60becb9397696edec"
}, },
"XLNetForTokenClassification": { "XLNetForTokenClassification": {
"tokenizer_classes": [ "tokenizer_classes": [
...@@ -5978,7 +6045,7 @@ ...@@ -5978,7 +6045,7 @@
"TFXLNetForTokenClassification", "TFXLNetForTokenClassification",
"XLNetForTokenClassification" "XLNetForTokenClassification"
], ],
"sha": "70598399e95e6df689fe5459ef9f016e55cc05ee" "sha": "16aa15029aa667046d504c4a88ceddfdd5b5fb40"
}, },
"XLNetLMHeadModel": { "XLNetLMHeadModel": {
"tokenizer_classes": [ "tokenizer_classes": [
...@@ -5990,7 +6057,7 @@ ...@@ -5990,7 +6057,7 @@
"TFXLNetLMHeadModel", "TFXLNetLMHeadModel",
"XLNetLMHeadModel" "XLNetLMHeadModel"
], ],
"sha": "fef32495d187c73201ba4e2854559bcc68e41e22" "sha": "c9a98cc982a16ca162832a8cbea25116479bb938"
}, },
"XLNetModel": { "XLNetModel": {
"tokenizer_classes": [ "tokenizer_classes": [
...@@ -6002,77 +6069,84 @@ ...@@ -6002,77 +6069,84 @@
"TFXLNetModel", "TFXLNetModel",
"XLNetModel" "XLNetModel"
], ],
"sha": "bebc65e9a3da5c0007713a61f6719293d361baa3" "sha": "1d6e231942135faf32b8d9a97773d8f6c85ca561"
}, },
"XmodForCausalLM": { "XmodForCausalLM": {
"tokenizer_classes": [ "tokenizer_classes": [
"XLMRobertaTokenizer",
"XLMRobertaTokenizerFast" "XLMRobertaTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
"model_classes": [ "model_classes": [
"XmodForCausalLM" "XmodForCausalLM"
], ],
"sha": "daf792dff8fef2f3d99eb5ee63b206032b3f69d7" "sha": "c6b746071f2f067099a8fb4f57ce3c27a7e4b67d"
}, },
"XmodForMaskedLM": { "XmodForMaskedLM": {
"tokenizer_classes": [ "tokenizer_classes": [
"XLMRobertaTokenizer",
"XLMRobertaTokenizerFast" "XLMRobertaTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
"model_classes": [ "model_classes": [
"XmodForMaskedLM" "XmodForMaskedLM"
], ],
"sha": "b7c020898ef638ed4c2f420431b4efc0075c0e32" "sha": "e1085818f4ed3c6073b2038635e5f3061208923d"
}, },
"XmodForMultipleChoice": { "XmodForMultipleChoice": {
"tokenizer_classes": [ "tokenizer_classes": [
"XLMRobertaTokenizer",
"XLMRobertaTokenizerFast" "XLMRobertaTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
"model_classes": [ "model_classes": [
"XmodForMultipleChoice" "XmodForMultipleChoice"
], ],
"sha": "1fce9673e9f557d3204d504ebb86e285b20937f8" "sha": "c63042cdf196be3fed846421b345d439b2483f69"
}, },
"XmodForQuestionAnswering": { "XmodForQuestionAnswering": {
"tokenizer_classes": [ "tokenizer_classes": [
"XLMRobertaTokenizer",
"XLMRobertaTokenizerFast" "XLMRobertaTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
"model_classes": [ "model_classes": [
"XmodForQuestionAnswering" "XmodForQuestionAnswering"
], ],
"sha": "fc9ebdfeb481281375c25d585569404cc48b02da" "sha": "75acd3071fae9978c82618cd0f090c87aabc1f23"
}, },
"XmodForSequenceClassification": { "XmodForSequenceClassification": {
"tokenizer_classes": [ "tokenizer_classes": [
"XLMRobertaTokenizer",
"XLMRobertaTokenizerFast" "XLMRobertaTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
"model_classes": [ "model_classes": [
"XmodForSequenceClassification" "XmodForSequenceClassification"
], ],
"sha": "bba89c4e18b6a29a1865462a75cb8bde12e7cc0c" "sha": "523a16570be048618913ac17ccd00d343bcb5e99"
}, },
"XmodForTokenClassification": { "XmodForTokenClassification": {
"tokenizer_classes": [ "tokenizer_classes": [
"XLMRobertaTokenizer",
"XLMRobertaTokenizerFast" "XLMRobertaTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
"model_classes": [ "model_classes": [
"XmodForTokenClassification" "XmodForTokenClassification"
], ],
"sha": "efe3dad234d5a60f1165887d8b4bb1627e271508" "sha": "a0f0a02732b4579670dad11a69ae244ebd777b49"
}, },
"XmodModel": { "XmodModel": {
"tokenizer_classes": [ "tokenizer_classes": [
"XLMRobertaTokenizer",
"XLMRobertaTokenizerFast" "XLMRobertaTokenizerFast"
], ],
"processor_classes": [], "processor_classes": [],
"model_classes": [ "model_classes": [
"XmodModel" "XmodModel"
], ],
"sha": "62f3ca59e02a9637b12ce81566f1996c16fad872" "sha": "bc286de0035450e7dcd6bcce78098a967b9c2b6c"
}, },
"YolosForObjectDetection": { "YolosForObjectDetection": {
"tokenizer_classes": [], "tokenizer_classes": [],
...@@ -6102,7 +6176,7 @@ ...@@ -6102,7 +6176,7 @@
"model_classes": [ "model_classes": [
"YosoForMaskedLM" "YosoForMaskedLM"
], ],
"sha": "4ff7ab217e6d05d40ae8afd37df1a0bf5c6ab25f" "sha": "cb291bedcbec199ea195f086e3ebea6fab026bba"
}, },
"YosoForMultipleChoice": { "YosoForMultipleChoice": {
"tokenizer_classes": [ "tokenizer_classes": [
...@@ -6112,7 +6186,7 @@ ...@@ -6112,7 +6186,7 @@
"model_classes": [ "model_classes": [
"YosoForMultipleChoice" "YosoForMultipleChoice"
], ],
"sha": "c42bd94e4563cdfeb28e07aef80e801188706d9d" "sha": "cf2d3a3f0628bc9d0da68ea8de26b12016453fee"
}, },
"YosoForQuestionAnswering": { "YosoForQuestionAnswering": {
"tokenizer_classes": [ "tokenizer_classes": [
...@@ -6122,7 +6196,7 @@ ...@@ -6122,7 +6196,7 @@
"model_classes": [ "model_classes": [
"YosoForQuestionAnswering" "YosoForQuestionAnswering"
], ],
"sha": "284f170b902c65ee257a2e8f255fa672cb4700c5" "sha": "e8c3091f674588adfa3371b3de0427a9b39dd03f"
}, },
"YosoForSequenceClassification": { "YosoForSequenceClassification": {
"tokenizer_classes": [ "tokenizer_classes": [
...@@ -6132,7 +6206,7 @@ ...@@ -6132,7 +6206,7 @@
"model_classes": [ "model_classes": [
"YosoForSequenceClassification" "YosoForSequenceClassification"
], ],
"sha": "2e0a37a4dd12cee00eb15e4ecd0de4231a638966" "sha": "88132cbaa1a9a87f65b6f9813c388011377f18cf"
}, },
"YosoForTokenClassification": { "YosoForTokenClassification": {
"tokenizer_classes": [ "tokenizer_classes": [
...@@ -6142,7 +6216,7 @@ ...@@ -6142,7 +6216,7 @@
"model_classes": [ "model_classes": [
"YosoForTokenClassification" "YosoForTokenClassification"
], ],
"sha": "50dd3fe46f28f0efaacf8aa311b09f67b10edb74" "sha": "fd2219856608d3dba70dc7b1a06af629903dec31"
}, },
"YosoModel": { "YosoModel": {
"tokenizer_classes": [ "tokenizer_classes": [
...@@ -6152,6 +6226,6 @@ ...@@ -6152,6 +6226,6 @@
"model_classes": [ "model_classes": [
"YosoModel" "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): ...@@ -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(): for _tester_classes in models_to_model_testers.values():
tester_classes.extend(_tester_classes) tester_classes.extend(_tester_classes)
if len(tester_classes) > 0: 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: except ModuleNotFoundError:
error = f"Tiny config not created for {model_type} - cannot find the testing module from the model name." error = f"Tiny config not created for {model_type} - cannot find the testing module from the model name."
raise ValueError(error) raise ValueError(error)
...@@ -484,21 +488,67 @@ def convert_processors(processors, tiny_config, output_folder, result): ...@@ -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. 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 = [] tokenizers = []
feature_extractors = [] feature_extractors = []
for processor in processors: for processor in processors:
if isinstance(processor, PreTrainedTokenizerBase): if isinstance(processor, PreTrainedTokenizerBase):
if processor.__class__.__name__ not in {x.__class__.__name__ for x in tokenizers}:
tokenizers.append(processor) tokenizers.append(processor)
elif isinstance(processor, BaseImageProcessor): elif isinstance(processor, BaseImageProcessor):
if processor.__class__.__name__ not in {x.__class__.__name__ for x in feature_extractors}:
feature_extractors.append(processor) feature_extractors.append(processor)
elif isinstance(processor, FeatureExtractionMixin): elif isinstance(processor, FeatureExtractionMixin):
if processor.__class__.__name__ not in {x.__class__.__name__ for x in feature_extractors}:
feature_extractors.append(processor) feature_extractors.append(processor)
elif isinstance(processor, ProcessorMixin): 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) tokenizers.append(processor.tokenizer)
# Currently, we only have these 2 possibilities
if hasattr(processor, "image_processor"): 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) feature_extractors.append(processor.image_processor)
elif hasattr(processor, "feature_extractor"): 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) feature_extractors.append(processor.feature_extractor)
# check the built processors have the unique type # check the built processors have the unique type
...@@ -511,15 +561,29 @@ def convert_processors(processors, tiny_config, output_folder, result): ...@@ -511,15 +561,29 @@ def convert_processors(processors, tiny_config, output_folder, result):
fast_tokenizer = None fast_tokenizer = None
slow_tokenizer = None slow_tokenizer = None
for tokenizer in tokenizers: for tokenizer in tokenizers:
if isinstance(tokenizer, PreTrainedTokenizerFast): if isinstance(tokenizer, PreTrainedTokenizerFast):
if fast_tokenizer is None:
fast_tokenizer = tokenizer 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: try:
# Wav2Vec2ForCTC , ByT5Tokenizer etc. all are already small enough and have no fast version that can # Wav2Vec2ForCTC , ByT5Tokenizer etc. all are already small enough and have no fast version that can
# be retrained # be retrained
if fast_tokenizer.vocab_size > TARGET_VOCAB_SIZE: if fast_tokenizer.vocab_size > TARGET_VOCAB_SIZE:
fast_tokenizer = convert_tokenizer(tokenizer) fast_tokenizer = convert_tokenizer(fast_tokenizer)
except Exception: except Exception:
result["warnings"].append( result["warnings"].append(
( (
...@@ -527,14 +591,25 @@ def convert_processors(processors, tiny_config, output_folder, result): ...@@ -527,14 +591,25 @@ def convert_processors(processors, tiny_config, output_folder, result):
traceback.format_exc(), 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: if fast_tokenizer:
# Make sure the fast tokenizer can be saved
try: 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: except Exception:
result["warnings"].append( result["warnings"].append(
( (
...@@ -544,24 +619,43 @@ def convert_processors(processors, tiny_config, output_folder, result): ...@@ -544,24 +619,43 @@ def convert_processors(processors, tiny_config, output_folder, result):
) )
fast_tokenizer = None 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: 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: try:
slow_tokenizer = AutoTokenizer.from_pretrained(output_folder, use_fast=False) fast_tokenizer.save_pretrained(tmpdir)
except Exception: except Exception:
result["warnings"].append( 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(), traceback.format_exc(),
) )
) )
# Let's just keep the fast version fast_tokenizer = None
slow_tokenizer = None # Make sure the slow tokenizer can be saved
if slow_tokenizer:
# If the fast version can't be created and saved, let's use the slow version # We don't save it to `output_folder` at this moment - only at the end of this function.
if not fast_tokenizer and slow_tokenizer: with tempfile.TemporaryDirectory() as tmpdir:
try: try:
slow_tokenizer.save_pretrained(output_folder) slow_tokenizer.save_pretrained(tmpdir)
except Exception: except Exception:
result["warnings"].append( result["warnings"].append(
( (
...@@ -883,7 +977,9 @@ def get_config_overrides(config_class, processors): ...@@ -883,7 +977,9 @@ def get_config_overrides(config_class, processors):
return config_overrides return config_overrides
# Get some properties of the (already converted) tokenizer (smaller vocab size, special token ids, etc.) # 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 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. # Used to create a new model tester with `tokenizer.vocab_size` in order to get the (updated) special token ids.
......
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