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Update tiny model summary file (#27388)

Browse Source 
* update

* fix

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2023-11-23 21:00:39 +01:00
committed by GitHub
parent fe1c16e95a
commit b8db265bc6
9 changed files with 277 additions and 10 deletions
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4
tests/models/clvp/test_modeling_clvp.py
View File

@@ -38,6 +38,7 @@ from ...test_modeling_common import (
ids_tensor,
random_attention_mask,
)
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
@@ -281,9 +282,10 @@ class ClvpDecoderTester:
@require_torch
class ClvpDecoderTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
class ClvpDecoderTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (ClvpModel, ClvpForCausalLM) if is_torch_available() else ()
all_generative_model_classes = (ClvpForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = {"feature-extraction": ClvpModelForConditionalGeneration} if is_torch_available() else {}
test_pruning = False

5
tests/models/fuyu/test_modeling_fuyu.py
View File

@@ -24,6 +24,7 @@ from transformers.testing_utils import require_torch, require_torch_gpu, slow, t
from transformers.utils import cached_property
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_vision_available():
@@ -262,9 +263,9 @@ class FuyuModelTester:
@require_torch
class FuyuModelTest(ModelTesterMixin, unittest.TestCase):
class FuyuModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (FuyuForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = {"image-to-text": FuyuForCausalLM} if is_torch_available() else {}
pipeline_model_mapping = {"text-generation": FuyuForCausalLM} if is_torch_available() else {}
test_head_masking = False
test_pruning = False

14
tests/models/kosmos2/test_modeling_kosmos2.py
View File

@@ -37,6 +37,7 @@ from ...test_modeling_common import (
ids_tensor,
random_attention_mask,
)
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
@@ -244,15 +245,26 @@ class Kosmos2ModelTester:
@require_torch
class Kosmos2ModelTest(ModelTesterMixin, unittest.TestCase):
class Kosmos2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (Kosmos2Model, Kosmos2ForConditionalGeneration) if is_torch_available() else ()
all_generative_model_classes = (Kosmos2ForConditionalGeneration,) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": Kosmos2Model, "image-to-text": Kosmos2ForConditionalGeneration}
if is_torch_available()
else {}
)
fx_compatible = False
test_head_masking = False
test_pruning = False
test_resize_embeddings = False
test_attention_outputs = False
# TODO: `image-to-text` pipeline for this model needs Processor.
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
return pipeline_test_casse_name == "ImageToTextPipelineTests"
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = copy.deepcopy(inputs_dict)

18
tests/models/seamless_m4t/test_modeling_seamless_m4t.py
View File

@@ -34,6 +34,7 @@ from ...test_modeling_common import (
ids_tensor,
random_attention_mask,
)
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
@@ -616,7 +617,9 @@ class SeamlessM4TModelWithSpeechInputTest(ModelTesterMixin, unittest.TestCase):
@require_torch
class SeamlessM4TModelWithTextInputTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
class SeamlessM4TModelWithTextInputTest(
ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase
):
is_encoder_decoder = True
fx_compatible = False
test_missing_keys = False
@@ -636,6 +639,19 @@ class SeamlessM4TModelWithTextInputTest(ModelTesterMixin, GenerationTesterMixin,
else ()
)
all_generative_model_classes = (SeamlessM4TForTextToText,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"automatic-speech-recognition": SeamlessM4TForSpeechToText,
"conversational": SeamlessM4TForTextToText,
"feature-extraction": SeamlessM4TModel,
"summarization": SeamlessM4TForTextToText,
"text-to-audio": SeamlessM4TForTextToSpeech,
"text2text-generation": SeamlessM4TForTextToText,
"translation": SeamlessM4TForTextToText,
}
if is_torch_available()
else {}
)
def setUp(self):
self.model_tester = SeamlessM4TModelTester(self, input_modality="text")

6
tests/models/swin2sr/test_modeling_swin2sr.py
View File

@@ -162,7 +162,11 @@ class Swin2SRModelTester:
@require_torch
class Swin2SRModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (Swin2SRModel, Swin2SRForImageSuperResolution) if is_torch_available() else ()
pipeline_model_mapping = {"feature-extraction": Swin2SRModel} if is_torch_available() else {}
pipeline_model_mapping = (
{"feature-extraction": Swin2SRModel, "image-to-image": Swin2SRForImageSuperResolution}
if is_torch_available()
else {}
)
fx_compatible = False
test_pruning = False

1
tests/models/whisper/test_modeling_whisper.py
View File

@@ -367,6 +367,7 @@ class WhisperModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMi
"audio-classification": WhisperForAudioClassification,
"automatic-speech-recognition": WhisperForConditionalGeneration,
"feature-extraction": WhisperModel,
"text-generation": WhisperForCausalLM,
}
if is_torch_available()
else {}

7
tests/pipelines/test_pipelines_text_generation.py
View File

@@ -242,7 +242,12 @@ class TextGenerationPipelineTests(unittest.TestCase):
# We don't care about infinite range models.
# They already work.
# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS = ["RwkvForCausalLM", "XGLMForCausalLM", "GPTNeoXForCausalLM"]
EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS = [
"RwkvForCausalLM",
"XGLMForCausalLM",
"GPTNeoXForCausalLM",
"FuyuForCausalLM",
]
if (
tokenizer.model_max_length < 10000
and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS

231
tests/utils/tiny_model_summary.json
View File

@@ -877,6 +877,16 @@
],
"sha": "a7874595b900f9b2ddc79130dafc3ff48f4fbfb9"
},
"ClvpModelForConditionalGeneration": {
"tokenizer_classes": [
"ClvpTokenizer"
],
"processor_classes": [
"ClvpFeatureExtractor"
],
"model_classes": [],
"sha": "45df7581535be337ff781707b6c20994ca221f05"
},
"CodeGenForCausalLM": {
"tokenizer_classes": [
"CodeGenTokenizer",
@@ -1039,7 +1049,8 @@
"ConvNextImageProcessor"
],
"model_classes": [
"ConvNextV2ForImageClassification"
"ConvNextV2ForImageClassification",
"TFConvNextV2ForImageClassification"
],
"sha": "ee22bae1cbb87d66fc7f62f7e15a43d6ff80d3cc"
},
@@ -1049,7 +1060,8 @@
"ConvNextImageProcessor"
],
"model_classes": [
"ConvNextV2Model"
"ConvNextV2Model",
"TFConvNextV2Model"
],
"sha": "c4dd68ee1102cba05bcc483da2a88e39427b7249"
},
@@ -2136,6 +2148,56 @@
],
"sha": "683f6f73a2ab87801f1695a72d1af63cf173ab7c"
},
"FalconForCausalLM": {
"tokenizer_classes": [
"PreTrainedTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"FalconForCausalLM"
],
"sha": "60076d5dafc5e33ba9c90dcd05e7c0834e44049a"
},
"FalconForQuestionAnswering": {
"tokenizer_classes": [
"PreTrainedTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"FalconForQuestionAnswering"
],
"sha": "b1ee9cd5fad2d177ea5a46df4611cd02f66ae788"
},
"FalconForSequenceClassification": {
"tokenizer_classes": [
"PreTrainedTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"FalconForSequenceClassification"
],
"sha": "007838c0991c2b6a87dc49a8a5c20f29149a00fa"
},
"FalconForTokenClassification": {
"tokenizer_classes": [
"PreTrainedTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"FalconForTokenClassification"
],
"sha": "0ea6ae548773daa6e3317fddc058957e956eebf4"
},
"FalconModel": {
"tokenizer_classes": [
"PreTrainedTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"FalconModel"
],
"sha": "ca15a579c946eb00c5b39cc8e0ea63d0c1460f84"
},
"FlaubertForMultipleChoice": {
"tokenizer_classes": [
"FlaubertTokenizer"
@@ -2364,6 +2426,18 @@
],
"sha": "bfbaa8fa21c3abf80b94e7168b5ecff8ec5b5f76"
},
"FuyuForCausalLM": {
"tokenizer_classes": [
"LlamaTokenizerFast"
],
"processor_classes": [
"FuyuImageProcessor"
],
"model_classes": [
"FuyuForCausalLM"
],
"sha": "685d78258ea95c5c82e0e4555d0d4a2270ab8bff"
},
"GLPNForDepthEstimation": {
"tokenizer_classes": [],
"processor_classes": [
@@ -2866,6 +2940,30 @@
],
"sha": "5a7983e48d5841704733dd0756177680ed50c074"
},
"Kosmos2ForConditionalGeneration": {
"tokenizer_classes": [
"XLMRobertaTokenizerFast"
],
"processor_classes": [
"CLIPImageProcessor"
],
"model_classes": [
"Kosmos2ForConditionalGeneration"
],
"sha": "d1d4607782b911411676f1ee79997dee645def58"
},
"Kosmos2Model": {
"tokenizer_classes": [
"XLMRobertaTokenizerFast"
],
"processor_classes": [
"CLIPImageProcessor"
],
"model_classes": [
"Kosmos2Model"
],
"sha": "379d8944a65312094d9ab1c4b8a82058a2d3274e"
},
"LEDForConditionalGeneration": {
"tokenizer_classes": [
"LEDTokenizer",
@@ -3820,6 +3918,39 @@
],
"sha": "f197d5bfa1fe27b5f28a6e6d4e3ad229b753450a"
},
"MistralForCausalLM": {
"tokenizer_classes": [
"LlamaTokenizer",
"LlamaTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"MistralForCausalLM"
],
"sha": "f7e06aeedbba8f4f665b438b868ed932d451f64b"
},
"MistralForSequenceClassification": {
"tokenizer_classes": [
"LlamaTokenizer",
"LlamaTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"MistralForSequenceClassification"
],
"sha": "65045444ea1933309270d8b08b21d3fa94a84290"
},
"MistralModel": {
"tokenizer_classes": [
"LlamaTokenizer",
"LlamaTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"MistralModel"
],
"sha": "becd727ad72b1e8a7c0fa0ea39b61904fa68aeac"
},
"MobileBertForMaskedLM": {
"tokenizer_classes": [
"MobileBertTokenizer",
@@ -4558,6 +4689,32 @@
],
"sha": "f0e27b2b4e53ba70e05d13dcfea8e85272b292a5"
},
"Owlv2ForObjectDetection": {
"tokenizer_classes": [
"CLIPTokenizer",
"CLIPTokenizerFast"
],
"processor_classes": [
"Owlv2ImageProcessor"
],
"model_classes": [
"Owlv2ForObjectDetection"
],
"sha": "30439c0b2749726468dc13a755261e8101170052"
},
"Owlv2Model": {
"tokenizer_classes": [
"CLIPTokenizer",
"CLIPTokenizerFast"
],
"processor_classes": [
"Owlv2ImageProcessor"
],
"model_classes": [
"Owlv2Model"
],
"sha": "7aeebdad5f72b36cb07c74355afad8e6052e2377"
},
"PLBartForCausalLM": {
"tokenizer_classes": [
"PLBartTokenizer"
@@ -4760,6 +4917,50 @@
],
"sha": "b8c8d479e29e9ee048e2d0b05b001ac835ad8859"
},
"PhiForCausalLM": {
"tokenizer_classes": [
"CodeGenTokenizer",
"CodeGenTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"PhiForCausalLM"
],
"sha": "3fecc0109a4a3a230e3a5509eaf47a26eba85d79"
},
"PhiForSequenceClassification": {
"tokenizer_classes": [
"CodeGenTokenizer",
"CodeGenTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"PhiForSequenceClassification"
],
"sha": "e1c9f8ebf1317516acc1cd6338de71a53e770245"
},
"PhiForTokenClassification": {
"tokenizer_classes": [
"CodeGenTokenizer",
"CodeGenTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"PhiForTokenClassification"
],
"sha": "d3a8054903753b5c96c05eaf9877905a116a1d5e"
},
"PhiModel": {
"tokenizer_classes": [
"CodeGenTokenizer",
"CodeGenTokenizerFast"
],
"processor_classes": [],
"model_classes": [
"PhiModel"
],
"sha": "99c38d5ce7ace35127d00ed3eeb3561308ea6b21"
},
"Pix2StructForConditionalGeneration": {
"tokenizer_classes": [
"T5TokenizerFast"
@@ -4768,7 +4969,9 @@
"Pix2StructImageProcessor",
"Pix2StructProcessor"
],
"model_classes": [],
"model_classes": [
"Pix2StructForConditionalGeneration"
],
"sha": "42b3de00ad535076c4893e4ac5ae2d2748cc4ccb"
},
"PoolFormerForImageClassification": {
@@ -5691,6 +5894,16 @@
],
"sha": "25ba2d88c770533f8c69811d2a454a00c1d09f5d"
},
"Swin2SRForImageSuperResolution": {
"tokenizer_classes": [],
"processor_classes": [
"Swin2SRImageProcessor"
],
"model_classes": [
"Swin2SRForImageSuperResolution"
],
"sha": "3a2780de0b455084c018ac8a62b56040969e26ec"
},
"Swin2SRModel": {
"tokenizer_classes": [],
"processor_classes": [
@@ -6625,6 +6838,18 @@
],
"sha": "d71b13674b1a67443cd19d0594a3b5b1e5968f0d"
},
"WhisperForCausalLM": {
"tokenizer_classes": [
"WhisperTokenizer"
],
"processor_classes": [
"WhisperFeatureExtractor"
],
"model_classes": [
"WhisperForCausalLM"
],
"sha": "e7febfd7f4512e029293c677e6d2633e23fc459a"
},
"WhisperForConditionalGeneration": {
"tokenizer_classes": [
"WhisperTokenizer",