[shardformer] support whisper (#4212)

* support whisper

* fix bug in vocabembedding

* support downstream model of whisper

* update readme

tests/kit/model_zoo/transformers/__init__.py

@@ -7,3 +7,4 @@ from .opt import *
 from .sam import *
 from .t5 import *
 from .vit import *
+from .whisper import *

tests/kit/model_zoo/transformers/whisper.py

@@ -0,0 +1,91 @@
+import torch
+import transformers
+
+from ..registry import ModelAttribute, model_zoo
+
+# ===============================
+# Register single-sentence Whisper
+# ===============================
+
+
+# define data gen function
+def data_gen():
+    # Generated from following code snippet
+    #
+    # from transformers import AutoFeatureExtractor, WhisperModel
+    # from datasets import load_dataset
+
+    # model = WhisperModel.from_pretrained("openai/whisper-base")
+    # feature_extractor = AutoFeatureExtractor.from_pretrained("openai/whisper-base")
+    # ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+    # inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt")
+    # input_features = inputs.input_features
+    # decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id
+
+    input_features = torch.randn(1, 80, 3000)
+    decoder_input_ids = torch.tensor([[1, 1]]) * 50258
+    return dict(input_features=input_features, decoder_input_ids=decoder_input_ids)
+
+
+def data_gen_for_conditional_generation():
+    # labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+    #         Labels for computing the language modeling loss. Indices should either be in `[0, ..., config.vocab_size]`
+    #         or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is
+    #         only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+    data = data_gen()
+    data['labels'] = torch.tensor([[0, 1]], dtype=torch.int64)
+    return data
+
+
+def data_gen_for_audio_classification():
+    # labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+    #         Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+    #         config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+    #         `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+    # `WhisperForAudioClassification` does not need `decoder_input_ids`
+    data = data_gen()
+    data.pop('decoder_input_ids')
+    data['labels'] = torch.tensor([1], dtype=torch.int64)
+    return data
+
+
+# define output transform function
+output_transform_fn = lambda x: x
+
+# define loss funciton
+loss_fn = lambda x: x.last_hidden_state.mean()
+loss_fn_attr = lambda x: x.loss
+
+config = transformers.WhisperConfig(
+    classifier_proj_size=256,
+    d_model=256,
+    decoder_attention_heads=4,
+    decoder_ffn_dim=1536,
+    decoder_layers=2,
+    encoder_attention_heads=4,
+    encoder_ffn_dim=1536,
+    encoder_layers=2,
+    vocab_size=51866,
+)
+
+# register the Whisper variants
+model_zoo.register(name='transformers_whisper',
+                   model_fn=lambda: transformers.WhisperModel(config),
+                   data_gen_fn=data_gen,
+                   output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn,
+                   model_attribute=ModelAttribute(has_control_flow=True))
+
+model_zoo.register(name='transformers_whisperForConditionalGeneration',
+                   model_fn=lambda: transformers.WhisperForConditionalGeneration(config),
+                   data_gen_fn=data_gen_for_conditional_generation,
+                   output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn_attr,
+                   model_attribute=ModelAttribute(has_control_flow=True))
+
+model_zoo.register(name='transformers_whisperWhisperForAudioClassification',
+                   model_fn=lambda: transformers.WhisperForAudioClassification(config),
+                   data_gen_fn=data_gen_for_audio_classification,
+                   output_transform_fn=output_transform_fn,
+                   loss_fn=loss_fn_attr,
+                   model_attribute=ModelAttribute(has_control_flow=True))