Initial release: MERaLiON-3-3B-ASR (CTM-3B-2804 weights, V1 plugin-compatible)
Browse filesFirst public release of MERaLiON-3-3B-ASR.
Weights: identical to internal MERaLiON-CTM-3B-2804 checkpoint.
Code: renamed (meralion2 -> meralion3); _supports_sdpa property removed for transformers 4.57+ compatibility.
Designed to be loaded by the meralion-3-asr PyPI package (v0.0.2+) with the vLLM backend (V1 engine, vLLM 0.16.0 anchor).
- .gitattributes +1 -0
- config.json +98 -0
- configuration_meralion3.py +80 -0
- generation_config.json +10 -0
- model-00001-of-00002.safetensors +3 -0
- model-00002-of-00002.safetensors +3 -0
- model.safetensors.index.json +788 -0
- modeling_meralion3.py +567 -0
- preprocessor_config.json +18 -0
- processing_meralion3.py +193 -0
- processor_config.json +11 -0
- special_tokens_map.json +34 -0
- tokenizer.json +3 -0
- tokenizer.model +3 -0
- tokenizer_config.json +2019 -0
.gitattributes
CHANGED
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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config.json
ADDED
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{
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"architectures": [
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"MERaLiON3ForConditionalGeneration"
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],
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"auto_map": {
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| 6 |
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"AutoConfig": "configuration_meralion3.MERaLiON3Config",
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"AutoModelForSpeechSeq2Seq": "modeling_meralion3.MERaLiON3ForConditionalGeneration"
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+
},
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+
"fixed_speech_embeds_length": 300,
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+
"head_dim": 256,
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+
"hidden_size": 2304,
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+
"intermediate_size": 9216,
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+
"model_type": "meralion3",
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+
"num_attention_heads": 8,
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| 15 |
+
"num_hidden_layers": 26,
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+
"num_key_value_heads": 4,
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| 17 |
+
"sliding_window": 4096,
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| 18 |
+
"speech_config": {
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+
"_attn_implementation_autoset": true,
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| 20 |
+
"_name_or_path": "/data/projects/13003558/sunshuo/models/meralion_whisper_v3_normed_cleaned",
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| 21 |
+
"activation_dropout": 0.0,
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| 22 |
+
"activation_function": "gelu",
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| 23 |
+
"apply_spec_augment": true,
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| 24 |
+
"architectures": [
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"WhisperForConditionalGeneration"
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],
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+
"attention_dropout": 0.0,
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+
"begin_suppress_tokens": null,
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| 29 |
+
"bos_token_id": 50257,
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| 30 |
+
"classifier_proj_size": 256,
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| 31 |
+
"d_model": 1280,
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| 32 |
+
"decoder_attention_heads": 20,
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+
"decoder_ffn_dim": 5120,
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+
"decoder_layerdrop": 0.0,
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"decoder_layers": 32,
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+
"decoder_start_token_id": 50258,
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"dropout": 0.0,
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+
"encoder_attention_heads": 20,
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+
"encoder_ffn_dim": 5120,
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+
"encoder_layerdrop": 0.0,
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+
"encoder_layers": 32,
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+
"eos_token_id": 50257,
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+
"init_std": 0.02,
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+
"mask_feature_length": 10,
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+
"mask_feature_min_masks": 0,
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+
"mask_feature_prob": 0.1,
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+
"mask_time_length": 20,
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"mask_time_min_masks": 2,
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"mask_time_prob": 0.1,
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+
"max_length": null,
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+
"max_source_positions": 1500,
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+
"max_target_positions": 448,
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"median_filter_width": 7,
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"model_type": "whisper",
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"num_hidden_layers": 32,
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"num_mel_bins": 128,
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"scale_embedding": false,
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"torch_dtype": "bfloat16",
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"use_cache": true,
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"use_weighted_layer_sum": false,
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"vocab_size": 51866
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},
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+
"speech_mlp_scale_factor": 5,
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+
"speech_mlp_use_projection": false,
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+
"speech_token_index": 255999,
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"text_config": {
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"_attn_implementation_autoset": true,
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"_name_or_path": "google/gemma-2-2b-it",
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"architectures": [
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"Gemma2ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"attn_logit_softcapping": 50.0,
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"cache_implementation": "hybrid",
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"final_logit_softcapping": 30.0,
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"head_dim": 256,
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"hidden_act": "gelu_pytorch_tanh",
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"hidden_activation": "gelu_pytorch_tanh",
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"hidden_size": 2304,
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+
"initializer_range": 0.02,
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"intermediate_size": 9216,
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"max_position_embeddings": 8192,
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"model_type": "gemma2",
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"num_attention_heads": 8,
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"num_hidden_layers": 26,
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"num_key_value_heads": 4,
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"query_pre_attn_scalar": 256,
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"rms_norm_eps": 1e-06,
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"rope_theta": 10000.0,
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"sliding_window": 4096,
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"torch_dtype": "bfloat16",
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"use_cache": true,
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"vocab_size": 256000
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},
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"torch_dtype": "bfloat16",
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"transformers_version": "4.50.1"
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}
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configuration_meralion3.py
ADDED
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@@ -0,0 +1,80 @@
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"""MERaLiON3 model configuration"""
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from transformers import Gemma2Config, WhisperConfig
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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class MERaLiON3Config(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`MERaLiON3ForConditionalGeneration`]. It is used to instantiate an
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MERaLiON3 model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the MERaLiON3.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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audio_config (`Union[AutoConfig, dict]`, *optional*, defaults to `CLIPVisionConfig`):
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The config object or dictionary of the audio backbone.
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text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `LlamaConfig`):
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The config object or dictionary of the text backbone.
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audio_token_index (`int`, *optional*, defaults to 151646):
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The image token index to encode the image prompt.
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"""
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model_type = "meralion3"
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is_composition = False
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def __init__(
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self,
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speech_config=None,
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text_config=None,
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speech_mlp_use_projection=True,
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speech_mlp_scale_factor=15,
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speech_token_index=255999,
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fixed_speech_embeds_length=100,
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**kwargs,
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+
):
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if isinstance(speech_config, dict):
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speech_config = WhisperConfig(**speech_config)
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elif speech_config is None:
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| 46 |
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speech_config = WhisperConfig(
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+
d_model=1280,
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| 48 |
+
encoder_attention_heads=20,
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| 49 |
+
encoder_ffn_dim=5120,
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| 50 |
+
encoder_layerdrop=0.0,
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encoder_layers=32,
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+
num_mel_bins=128,
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+
max_source_positions=1500,
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scale_embedding=False,
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activation_function="gelu",
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)
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self.speech_config = speech_config
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if isinstance(text_config, dict):
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text_config = Gemma2Config(**text_config)
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elif text_config is None:
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text_config = Gemma2Config()
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self.text_config = text_config
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self.speech_mlp_use_projection = speech_mlp_use_projection
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self.speech_mlp_scale_factor = speech_mlp_scale_factor
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self.speech_token_index = speech_token_index
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self.fixed_speech_embeds_length = fixed_speech_embeds_length
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self.sliding_window = self.text_config.sliding_window
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self.hidden_size = self.text_config.hidden_size
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self.num_attention_heads = self.text_config.num_attention_heads
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self.num_hidden_layers = self.text_config.num_hidden_layers
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self.num_key_value_heads = self.text_config.num_key_value_heads
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self.head_dim = self.text_config.head_dim
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self.intermediate_size = self.text_config.intermediate_size
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super().__init__(**kwargs)
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generation_config.json
ADDED
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{
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"_from_model_config": true,
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"bos_token_id": 2,
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| 4 |
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"cache_implementation": "hybrid",
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| 5 |
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"eos_token_id": 1,
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| 6 |
+
"no_repeat_ngram_size": 6,
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| 7 |
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"pad_token_id": 0,
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| 8 |
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"repetition_penalty": 1.05,
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| 9 |
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"transformers_version": "4.50.1"
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}
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model-00001-of-00002.safetensors
ADDED
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:44f2e0bfb6371bc8292d04e5d2898e309f1122fa77c211b6053aaee0a6d46456
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+
size 4971858576
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model-00002-of-00002.safetensors
ADDED
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:772b6cbd30cfaadd13def14ce653a5c2489ae54e03b74e709e6337a65dfce9c2
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+
size 1642292496
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model.safetensors.index.json
ADDED
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| 1 |
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"text_decoder.model.layers.6.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
| 745 |
+
"text_decoder.model.layers.6.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
| 746 |
+
"text_decoder.model.layers.6.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
| 747 |
+
"text_decoder.model.layers.6.post_feedforward_layernorm.weight": "model-00001-of-00002.safetensors",
|
| 748 |
+
"text_decoder.model.layers.6.pre_feedforward_layernorm.weight": "model-00001-of-00002.safetensors",
|
| 749 |
+
"text_decoder.model.layers.6.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
| 750 |
+
"text_decoder.model.layers.6.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
| 751 |
+
"text_decoder.model.layers.6.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
| 752 |
+
"text_decoder.model.layers.6.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
| 753 |
+
"text_decoder.model.layers.7.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
| 754 |
+
"text_decoder.model.layers.7.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
| 755 |
+
"text_decoder.model.layers.7.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
| 756 |
+
"text_decoder.model.layers.7.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
| 757 |
+
"text_decoder.model.layers.7.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
| 758 |
+
"text_decoder.model.layers.7.post_feedforward_layernorm.weight": "model-00001-of-00002.safetensors",
|
| 759 |
+
"text_decoder.model.layers.7.pre_feedforward_layernorm.weight": "model-00001-of-00002.safetensors",
|
| 760 |
+
"text_decoder.model.layers.7.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
| 761 |
+
"text_decoder.model.layers.7.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
| 762 |
+
"text_decoder.model.layers.7.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
| 763 |
+
"text_decoder.model.layers.7.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
| 764 |
+
"text_decoder.model.layers.8.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
| 765 |
+
"text_decoder.model.layers.8.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
| 766 |
+
"text_decoder.model.layers.8.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
| 767 |
+
"text_decoder.model.layers.8.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
| 768 |
+
"text_decoder.model.layers.8.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
| 769 |
+
"text_decoder.model.layers.8.post_feedforward_layernorm.weight": "model-00001-of-00002.safetensors",
|
| 770 |
+
"text_decoder.model.layers.8.pre_feedforward_layernorm.weight": "model-00001-of-00002.safetensors",
|
| 771 |
+
"text_decoder.model.layers.8.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
| 772 |
+
"text_decoder.model.layers.8.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
| 773 |
+
"text_decoder.model.layers.8.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
| 774 |
+
"text_decoder.model.layers.8.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
| 775 |
+
"text_decoder.model.layers.9.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
| 776 |
+
"text_decoder.model.layers.9.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
| 777 |
+
"text_decoder.model.layers.9.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
| 778 |
+
"text_decoder.model.layers.9.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
| 779 |
+
"text_decoder.model.layers.9.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
| 780 |
+
"text_decoder.model.layers.9.post_feedforward_layernorm.weight": "model-00001-of-00002.safetensors",
|
| 781 |
+
"text_decoder.model.layers.9.pre_feedforward_layernorm.weight": "model-00001-of-00002.safetensors",
|
| 782 |
+
"text_decoder.model.layers.9.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
| 783 |
+
"text_decoder.model.layers.9.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
| 784 |
+
"text_decoder.model.layers.9.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
| 785 |
+
"text_decoder.model.layers.9.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
| 786 |
+
"text_decoder.model.norm.weight": "model-00002-of-00002.safetensors"
|
| 787 |
+
}
|
| 788 |
+
}
|
modeling_meralion3.py
ADDED
|
@@ -0,0 +1,567 @@
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|
|
| 1 |
+
"""PyTorch MERaLiON3 model."""
|
| 2 |
+
|
| 3 |
+
from dataclasses import dataclass
|
| 4 |
+
from typing import List, Optional, Tuple, Union
|
| 5 |
+
|
| 6 |
+
import torch
|
| 7 |
+
import torch.utils.checkpoint
|
| 8 |
+
from torch import nn
|
| 9 |
+
|
| 10 |
+
from transformers import Gemma2ForCausalLM
|
| 11 |
+
from transformers.models.whisper.modeling_whisper import WhisperEncoder
|
| 12 |
+
from transformers.cache_utils import HybridCache
|
| 13 |
+
from transformers.generation import GenerationMixin
|
| 14 |
+
from transformers.modeling_outputs import ModelOutput
|
| 15 |
+
from transformers.modeling_utils import PreTrainedModel
|
| 16 |
+
from transformers.utils import (
|
| 17 |
+
add_start_docstrings,
|
| 18 |
+
add_start_docstrings_to_model_forward,
|
| 19 |
+
logging,
|
| 20 |
+
replace_return_docstrings,
|
| 21 |
+
)
|
| 22 |
+
|
| 23 |
+
from .configuration_meralion3 import MERaLiON3Config
|
| 24 |
+
|
| 25 |
+
|
| 26 |
+
logger = logging.get_logger(__name__)
|
| 27 |
+
|
| 28 |
+
_CONFIG_FOR_DOC = "MERaLiON3Config"
|
| 29 |
+
|
| 30 |
+
|
| 31 |
+
# Copied from transformers.models.llama.modeling_llama._prepare_4d_causal_attention_mask_with_cache_position
|
| 32 |
+
def _prepare_4d_causal_attention_mask_with_cache_position(
|
| 33 |
+
attention_mask: torch.Tensor,
|
| 34 |
+
sequence_length: int,
|
| 35 |
+
target_length: int,
|
| 36 |
+
dtype: torch.dtype,
|
| 37 |
+
device: torch.device,
|
| 38 |
+
min_dtype: float,
|
| 39 |
+
cache_position: torch.Tensor,
|
| 40 |
+
batch_size: int,
|
| 41 |
+
):
|
| 42 |
+
"""
|
| 43 |
+
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
|
| 44 |
+
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
|
| 45 |
+
|
| 46 |
+
Args:
|
| 47 |
+
attention_mask (`torch.Tensor`):
|
| 48 |
+
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
|
| 49 |
+
sequence_length (`int`):
|
| 50 |
+
The sequence length being processed.
|
| 51 |
+
target_length (`int`):
|
| 52 |
+
The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
|
| 53 |
+
dtype (`torch.dtype`):
|
| 54 |
+
The dtype to use for the 4D attention mask.
|
| 55 |
+
device (`torch.device`):
|
| 56 |
+
The device to plcae the 4D attention mask on.
|
| 57 |
+
min_dtype (`float`):
|
| 58 |
+
The minimum value representable with the dtype `dtype`.
|
| 59 |
+
cache_position (`torch.Tensor`):
|
| 60 |
+
Indices depicting the position of the input sequence tokens in the sequence.
|
| 61 |
+
batch_size (`torch.Tensor`):
|
| 62 |
+
Batch size.
|
| 63 |
+
"""
|
| 64 |
+
if attention_mask is not None and attention_mask.dim() == 4:
|
| 65 |
+
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
|
| 66 |
+
causal_mask = attention_mask
|
| 67 |
+
else:
|
| 68 |
+
causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
|
| 69 |
+
if sequence_length != 1:
|
| 70 |
+
causal_mask = torch.triu(causal_mask, diagonal=1)
|
| 71 |
+
causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
|
| 72 |
+
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
|
| 73 |
+
if attention_mask is not None:
|
| 74 |
+
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
|
| 75 |
+
mask_length = attention_mask.shape[-1]
|
| 76 |
+
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
|
| 77 |
+
padding_mask = padding_mask == 0
|
| 78 |
+
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
|
| 79 |
+
padding_mask, min_dtype
|
| 80 |
+
)
|
| 81 |
+
return causal_mask
|
| 82 |
+
|
| 83 |
+
|
| 84 |
+
# copied from Qwen2AudioCausalLMOutputWithPast
|
| 85 |
+
@dataclass
|
| 86 |
+
class MERaLiON3OutputWithPast(ModelOutput):
|
| 87 |
+
"""
|
| 88 |
+
Base class for MERaLiON3 causal language model (or autoregressive) outputs.
|
| 89 |
+
|
| 90 |
+
Args:
|
| 91 |
+
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
|
| 92 |
+
Language modeling loss (for next-token prediction).
|
| 93 |
+
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
|
| 94 |
+
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
|
| 95 |
+
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
| 96 |
+
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
|
| 97 |
+
`(batch_size, num_heads, sequence_length, embed_size_per_head)`)
|
| 98 |
+
|
| 99 |
+
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
|
| 100 |
+
`past_key_values` input) to speed up sequential decoding.
|
| 101 |
+
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
| 102 |
+
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
|
| 103 |
+
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
|
| 104 |
+
|
| 105 |
+
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
|
| 106 |
+
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
| 107 |
+
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
|
| 108 |
+
sequence_length)`.
|
| 109 |
+
|
| 110 |
+
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
|
| 111 |
+
heads.
|
| 112 |
+
attention_mask (`torch.FloatTensor`, *optional*):
|
| 113 |
+
Attentions mask, used to update attention mask and position_ids.
|
| 114 |
+
"""
|
| 115 |
+
|
| 116 |
+
loss: Optional[torch.FloatTensor] = None
|
| 117 |
+
logits: torch.FloatTensor = None
|
| 118 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None
|
| 119 |
+
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
|
| 120 |
+
attentions: Optional[Tuple[torch.FloatTensor]] = None
|
| 121 |
+
attention_mask: Optional[torch.FloatTensor] = None
|
| 122 |
+
|
| 123 |
+
|
| 124 |
+
MERALION_START_DOCSTRING = r"""
|
| 125 |
+
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
| 126 |
+
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
| 127 |
+
etc.)
|
| 128 |
+
|
| 129 |
+
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
| 130 |
+
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
| 131 |
+
and behavior.
|
| 132 |
+
|
| 133 |
+
Parameters:
|
| 134 |
+
config ([`MERaLiON3Config`]):
|
| 135 |
+
Model configuration class with all the parameters of the model. Initializing with a config file does not
|
| 136 |
+
load the weights associated with the model, only the configuration. Check out the
|
| 137 |
+
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
| 138 |
+
"""
|
| 139 |
+
|
| 140 |
+
|
| 141 |
+
@add_start_docstrings(
|
| 142 |
+
"The bare MERaLiON3 Model outputting raw hidden-states without any specific head on top.",
|
| 143 |
+
MERALION_START_DOCSTRING,
|
| 144 |
+
)
|
| 145 |
+
class MERaLiON3PreTrainedModel(PreTrainedModel):
|
| 146 |
+
config_class = MERaLiON3Config
|
| 147 |
+
base_model_prefix = "model"
|
| 148 |
+
supports_gradient_checkpointing = True
|
| 149 |
+
_no_split_modules = ["WhisperEncoderLayer", "WhisperDecoderLayer", "Gemma2DecoderLayer"]
|
| 150 |
+
_supports_flash_attn_2 = True
|
| 151 |
+
_supports_sdpa = True
|
| 152 |
+
_supports_cache_class = True
|
| 153 |
+
_supports_static_cache = True
|
| 154 |
+
|
| 155 |
+
def _init_weights(self, module):
|
| 156 |
+
# important: this ported version of Qwen2Audio isn't meant for training from scratch - only
|
| 157 |
+
# inference and fine-tuning - so the proper init weights code has been removed
|
| 158 |
+
std = self.config.init_std if hasattr(self.config, "init_std") else self.config.speech_config.init_std
|
| 159 |
+
|
| 160 |
+
if isinstance(module, (nn.Linear, nn.Conv1d)):
|
| 161 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
| 162 |
+
if module.bias is not None:
|
| 163 |
+
module.bias.data.zero_()
|
| 164 |
+
elif isinstance(module, nn.Embedding):
|
| 165 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
| 166 |
+
if module.padding_idx is not None:
|
| 167 |
+
module.weight.data[module.padding_idx].zero_()
|
| 168 |
+
|
| 169 |
+
class MERaLiON3SpeechAudioAdaper(nn.Module):
|
| 170 |
+
def __init__(
|
| 171 |
+
self,
|
| 172 |
+
config,
|
| 173 |
+
**kwargs
|
| 174 |
+
):
|
| 175 |
+
super(MERaLiON3SpeechAudioAdaper, self).__init__()
|
| 176 |
+
speech_audio_encoder_output_dim = config.speech_config.d_model
|
| 177 |
+
llm_input_hidden_size = config.text_config.hidden_size
|
| 178 |
+
speech_mlp_scale_factor = config.speech_mlp_scale_factor
|
| 179 |
+
|
| 180 |
+
self.speech_mlp_scale_factor = speech_mlp_scale_factor
|
| 181 |
+
self.mlp_adapter = nn.Sequential(
|
| 182 |
+
nn.Linear(
|
| 183 |
+
in_features=speech_audio_encoder_output_dim * speech_mlp_scale_factor,
|
| 184 |
+
out_features=speech_audio_encoder_output_dim
|
| 185 |
+
),
|
| 186 |
+
nn.SiLU(),
|
| 187 |
+
nn.Dropout(0.1),
|
| 188 |
+
)
|
| 189 |
+
|
| 190 |
+
self.speech_llm_proj = nn.Sequential(
|
| 191 |
+
nn.Linear(
|
| 192 |
+
speech_audio_encoder_output_dim,
|
| 193 |
+
speech_audio_encoder_output_dim * 4
|
| 194 |
+
),
|
| 195 |
+
nn.SiLU(),
|
| 196 |
+
nn.Dropout(0.1),
|
| 197 |
+
|
| 198 |
+
nn.Linear(
|
| 199 |
+
speech_audio_encoder_output_dim * 4,
|
| 200 |
+
llm_input_hidden_size
|
| 201 |
+
),
|
| 202 |
+
)
|
| 203 |
+
|
| 204 |
+
def forward(self, speech_embeds, **kwargs):
|
| 205 |
+
B, T, C = speech_embeds.shape
|
| 206 |
+
speech_embeds = self.mlp_adapter(
|
| 207 |
+
speech_embeds.reshape(
|
| 208 |
+
B,
|
| 209 |
+
T // self.speech_mlp_scale_factor,
|
| 210 |
+
C * self.speech_mlp_scale_factor,
|
| 211 |
+
)
|
| 212 |
+
)
|
| 213 |
+
return self.speech_llm_proj(speech_embeds)
|
| 214 |
+
|
| 215 |
+
|
| 216 |
+
class MERaLiON3SpeechAudioAdaperLarge(nn.Module):
|
| 217 |
+
def __init__(
|
| 218 |
+
self,
|
| 219 |
+
config,
|
| 220 |
+
**kwargs
|
| 221 |
+
):
|
| 222 |
+
super(MERaLiON3SpeechAudioAdaperLarge, self).__init__()
|
| 223 |
+
speech_audio_encoder_output_dim = config.speech_config.d_model
|
| 224 |
+
llm_input_hidden_size = config.text_config.hidden_size
|
| 225 |
+
|
| 226 |
+
self.speech_mlp_use_projection = config.speech_mlp_use_projection
|
| 227 |
+
self.speech_mlp_scale_factor = config.speech_mlp_scale_factor
|
| 228 |
+
|
| 229 |
+
self.mlp_adapter = nn.Sequential(
|
| 230 |
+
nn.Linear(
|
| 231 |
+
in_features=speech_audio_encoder_output_dim * self.speech_mlp_scale_factor,
|
| 232 |
+
out_features=speech_audio_encoder_output_dim * 5,
|
| 233 |
+
),
|
| 234 |
+
nn.SiLU(),
|
| 235 |
+
nn.Dropout(0.01),
|
| 236 |
+
)
|
| 237 |
+
|
| 238 |
+
if self.speech_mlp_use_projection:
|
| 239 |
+
self.gate_proj = nn.Linear(
|
| 240 |
+
in_features=speech_audio_encoder_output_dim * 5,
|
| 241 |
+
out_features=speech_audio_encoder_output_dim * 5,
|
| 242 |
+
)
|
| 243 |
+
|
| 244 |
+
self.pool_proj = nn.Linear(
|
| 245 |
+
in_features=speech_audio_encoder_output_dim * 5,
|
| 246 |
+
out_features=speech_audio_encoder_output_dim * 5,
|
| 247 |
+
)
|
| 248 |
+
self.act_fn = nn.SiLU()
|
| 249 |
+
|
| 250 |
+
self.out_proj = nn.Linear(
|
| 251 |
+
speech_audio_encoder_output_dim * 5,
|
| 252 |
+
llm_input_hidden_size,
|
| 253 |
+
)
|
| 254 |
+
|
| 255 |
+
|
| 256 |
+
def forward(self, speech_embeds, **kwargs):
|
| 257 |
+
B, T, C = speech_embeds.shape
|
| 258 |
+
speech_embeds = self.mlp_adapter(
|
| 259 |
+
speech_embeds.reshape(
|
| 260 |
+
B,
|
| 261 |
+
T // self.speech_mlp_scale_factor,
|
| 262 |
+
C * self.speech_mlp_scale_factor,
|
| 263 |
+
)
|
| 264 |
+
)
|
| 265 |
+
if self.speech_mlp_use_projection:
|
| 266 |
+
speech_embeds = self.act_fn(self.gate_proj(speech_embeds)) * self.pool_proj(speech_embeds)
|
| 267 |
+
speech_embeds = self.out_proj(speech_embeds)
|
| 268 |
+
return speech_embeds
|
| 269 |
+
|
| 270 |
+
|
| 271 |
+
MERALION_INPUTS_DOCSTRING = r"""
|
| 272 |
+
Args:
|
| 273 |
+
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
| 274 |
+
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
| 275 |
+
it.
|
| 276 |
+
|
| 277 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
| 278 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
| 279 |
+
|
| 280 |
+
[What are input IDs?](../glossary#input-ids)
|
| 281 |
+
input_features (`torch.FloatTensor` of shape `(batch_size, feature_size, feature_sequence_length)`, *optional*):
|
| 282 |
+
Float values mel features extracted from the raw speech waveform. Raw speech waveform can be obtained by
|
| 283 |
+
loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via
|
| 284 |
+
the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the
|
| 285 |
+
[`AutoFeatureExtractor`] should be used for extracting the mel features, padding and conversion into a
|
| 286 |
+
tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`]
|
| 287 |
+
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
| 288 |
+
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
| 289 |
+
|
| 290 |
+
- 1 for tokens that are **not masked**,
|
| 291 |
+
- 0 for tokens that are **masked**.
|
| 292 |
+
|
| 293 |
+
[What are attention masks?](../glossary#attention-mask)
|
| 294 |
+
|
| 295 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
| 296 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
| 297 |
+
|
| 298 |
+
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
|
| 299 |
+
`past_key_values`).
|
| 300 |
+
|
| 301 |
+
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
|
| 302 |
+
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
|
| 303 |
+
information on the default strategy.
|
| 304 |
+
|
| 305 |
+
- 1 indicates the head is **not masked**,
|
| 306 |
+
- 0 indicates the head is **masked**.
|
| 307 |
+
feature_attention_mask (`torch.Tensor` of shape `(batch_size, feature_sequence_length)`, *optional*):
|
| 308 |
+
Mask to avoid performing attention on padding feature indices. Mask values selected in `[0, 1]`:
|
| 309 |
+
|
| 310 |
+
- 1 for tokens that are **not masked**,
|
| 311 |
+
- 0 for tokens that are **masked**.
|
| 312 |
+
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
| 313 |
+
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
| 314 |
+
config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
|
| 315 |
+
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
| 316 |
+
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
|
| 317 |
+
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
|
| 318 |
+
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
|
| 319 |
+
|
| 320 |
+
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
|
| 321 |
+
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
|
| 322 |
+
|
| 323 |
+
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
|
| 324 |
+
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
|
| 325 |
+
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
|
| 326 |
+
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
| 327 |
+
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
| 328 |
+
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
| 329 |
+
model's internal embedding lookup matrix.
|
| 330 |
+
use_cache (`bool`, *optional*):
|
| 331 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
| 332 |
+
`past_key_values`).
|
| 333 |
+
output_attentions (`bool`, *optional*):
|
| 334 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
| 335 |
+
tensors for more detail.
|
| 336 |
+
output_hidden_states (`bool`, *optional*):
|
| 337 |
+
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
| 338 |
+
more detail.
|
| 339 |
+
return_dict (`bool`, *optional*):
|
| 340 |
+
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
| 341 |
+
"""
|
| 342 |
+
|
| 343 |
+
@add_start_docstrings(
|
| 344 |
+
"""The MERALION model which consists of a audio backbone and a language model.""",
|
| 345 |
+
MERALION_START_DOCSTRING,
|
| 346 |
+
)
|
| 347 |
+
class MERaLiON3ForConditionalGeneration(MERaLiON3PreTrainedModel, GenerationMixin):
|
| 348 |
+
def __init__(self, config: MERaLiON3Config):
|
| 349 |
+
config.text_config._attn_implementation = config._attn_implementation
|
| 350 |
+
config.speech_config._attn_implementation = config._attn_implementation
|
| 351 |
+
|
| 352 |
+
super().__init__(config)
|
| 353 |
+
|
| 354 |
+
self.speech_encoder = WhisperEncoder(config.speech_config)
|
| 355 |
+
# self.speech_encoder = AutoModel.from_config(config.audio_config, attn_implementation=config._attn_implementation)
|
| 356 |
+
|
| 357 |
+
self.ln_speech = nn.LayerNorm(config.speech_config.d_model)
|
| 358 |
+
self.speech_audio_adapter = MERaLiON3SpeechAudioAdaperLarge(config)
|
| 359 |
+
self.vocab_size = config.text_config.vocab_size
|
| 360 |
+
self.text_decoder = Gemma2ForCausalLM(config.text_config)
|
| 361 |
+
self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
|
| 362 |
+
self._padding_side = "left" # set it to left by default, user can use setter to change padding_sides
|
| 363 |
+
self.post_init()
|
| 364 |
+
|
| 365 |
+
@property
|
| 366 |
+
def padding_side(self):
|
| 367 |
+
return self._padding_side
|
| 368 |
+
|
| 369 |
+
@padding_side.setter
|
| 370 |
+
def padding_side(self, padding_side: str):
|
| 371 |
+
if padding_side not in ["left", "right"]:
|
| 372 |
+
raise ValueError(f"{padding_side} is not `left` or `right`.")
|
| 373 |
+
self._padding_side = padding_side
|
| 374 |
+
|
| 375 |
+
# Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.get_input_embeddings
|
| 376 |
+
def get_input_embeddings(self):
|
| 377 |
+
return self.text_decoder.get_input_embeddings()
|
| 378 |
+
|
| 379 |
+
# Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.set_input_embeddings
|
| 380 |
+
def set_input_embeddings(self, value):
|
| 381 |
+
self.text_decoder.set_input_embeddings(value)
|
| 382 |
+
|
| 383 |
+
# Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.get_output_embeddings
|
| 384 |
+
def get_output_embeddings(self):
|
| 385 |
+
return self.text_decoder.get_output_embeddings()
|
| 386 |
+
|
| 387 |
+
# Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.set_output_embeddings
|
| 388 |
+
def set_output_embeddings(self, new_embeddings):
|
| 389 |
+
self.text_decoder.set_output_embeddings(new_embeddings)
|
| 390 |
+
|
| 391 |
+
# Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.set_decoder
|
| 392 |
+
def set_decoder(self, decoder):
|
| 393 |
+
self.text_decoder.set_decoder(decoder)
|
| 394 |
+
|
| 395 |
+
# Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.get_decoder
|
| 396 |
+
def get_decoder(self):
|
| 397 |
+
return self.text_decoder.get_decoder()
|
| 398 |
+
|
| 399 |
+
# Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.tie_weights
|
| 400 |
+
def tie_weights(self):
|
| 401 |
+
return self.text_decoder.tie_weights()
|
| 402 |
+
|
| 403 |
+
# Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.resize_token_embeddings
|
| 404 |
+
def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None) -> nn.Embedding:
|
| 405 |
+
model_embeds = self.text_decoder.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
|
| 406 |
+
# update vocab size
|
| 407 |
+
self.config.text_config.vocab_size = model_embeds.num_embeddings
|
| 408 |
+
self.vocab_size = model_embeds.num_embeddings
|
| 409 |
+
return model_embeds
|
| 410 |
+
|
| 411 |
+
@add_start_docstrings_to_model_forward(MERALION_INPUTS_DOCSTRING)
|
| 412 |
+
@replace_return_docstrings(output_type=MERaLiON3OutputWithPast, config_class=_CONFIG_FOR_DOC)
|
| 413 |
+
def forward(
|
| 414 |
+
self,
|
| 415 |
+
input_ids: torch.LongTensor = None,
|
| 416 |
+
input_features: torch.FloatTensor = None,
|
| 417 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 418 |
+
feature_attention_mask: Optional[torch.Tensor] = None,
|
| 419 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 420 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
| 421 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 422 |
+
labels: Optional[torch.LongTensor] = None,
|
| 423 |
+
use_cache: Optional[bool] = None,
|
| 424 |
+
cache_position: Optional[torch.LongTensor] = None,
|
| 425 |
+
output_attentions: Optional[bool] = None,
|
| 426 |
+
output_hidden_states: Optional[bool] = None,
|
| 427 |
+
return_dict: Optional[bool] = None,
|
| 428 |
+
) -> Union[Tuple, MERaLiON3OutputWithPast]:
|
| 429 |
+
r"""
|
| 430 |
+
Args:
|
| 431 |
+
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
| 432 |
+
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
| 433 |
+
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
| 434 |
+
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
| 435 |
+
|
| 436 |
+
Returns:
|
| 437 |
+
"""
|
| 438 |
+
|
| 439 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
| 440 |
+
output_hidden_states = (
|
| 441 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
| 442 |
+
)
|
| 443 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
| 444 |
+
|
| 445 |
+
speech_encoder_device = self.speech_encoder.device
|
| 446 |
+
|
| 447 |
+
if input_features is not None:
|
| 448 |
+
input_features = input_features.to(speech_encoder_device)
|
| 449 |
+
feature_attention_mask = feature_attention_mask.to(speech_encoder_device)
|
| 450 |
+
|
| 451 |
+
if inputs_embeds is None:
|
| 452 |
+
speech_contexts_embeds = self.speech_encoder(input_features, attention_mask=feature_attention_mask).last_hidden_state
|
| 453 |
+
speech_contexts_embeds = self.ln_speech(speech_contexts_embeds)
|
| 454 |
+
speech_audio_contexts_embeds = self.speech_audio_adapter(speech_contexts_embeds)
|
| 455 |
+
|
| 456 |
+
inputs_embeds = self.text_decoder.base_model.embed_tokens(input_ids)
|
| 457 |
+
|
| 458 |
+
speech_mask = (input_ids == self.config.speech_token_index).unsqueeze(-1)
|
| 459 |
+
speech_mask = speech_mask.expand_as(inputs_embeds).to(inputs_embeds.device)
|
| 460 |
+
|
| 461 |
+
inputs_embeds = inputs_embeds.masked_scatter(speech_mask, speech_audio_contexts_embeds)
|
| 462 |
+
|
| 463 |
+
input_ids = None
|
| 464 |
+
|
| 465 |
+
outputs = self.text_decoder(
|
| 466 |
+
input_ids=input_ids,
|
| 467 |
+
attention_mask=attention_mask,
|
| 468 |
+
position_ids=position_ids,
|
| 469 |
+
past_key_values=past_key_values,
|
| 470 |
+
inputs_embeds=inputs_embeds,
|
| 471 |
+
use_cache=use_cache,
|
| 472 |
+
cache_position=cache_position,
|
| 473 |
+
output_attentions=output_attentions,
|
| 474 |
+
output_hidden_states=output_hidden_states,
|
| 475 |
+
return_dict=return_dict,
|
| 476 |
+
labels=labels
|
| 477 |
+
)
|
| 478 |
+
|
| 479 |
+
return outputs
|
| 480 |
+
|
| 481 |
+
# from transformers.models.gemma2.modeling_gemma2.Gemma2ForCausalLM.prepare_inputs_for_generation
|
| 482 |
+
def prepare_inputs_for_generation(
|
| 483 |
+
self,
|
| 484 |
+
input_ids,
|
| 485 |
+
attention_mask=None,
|
| 486 |
+
input_features=None,
|
| 487 |
+
feature_attention_mask=None,
|
| 488 |
+
past_key_values=None,
|
| 489 |
+
inputs_embeds=None,
|
| 490 |
+
cache_position=None,
|
| 491 |
+
position_ids=None,
|
| 492 |
+
use_cache=None,
|
| 493 |
+
**kwargs,
|
| 494 |
+
):
|
| 495 |
+
# If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
|
| 496 |
+
# Exception 1: when passing input_embeds, input_ids may be missing entries
|
| 497 |
+
# Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
|
| 498 |
+
is_first_step = cache_position[0].item() == 0
|
| 499 |
+
if past_key_values is not None:
|
| 500 |
+
if inputs_embeds is not None: # Exception 1
|
| 501 |
+
input_ids = input_ids[:, -cache_position.shape[0] :]
|
| 502 |
+
elif input_ids.shape[1] != cache_position.shape[0]: # Default case (the "else", a no op, is Exception 2)
|
| 503 |
+
input_ids = input_ids[:, cache_position]
|
| 504 |
+
|
| 505 |
+
if attention_mask is not None and position_ids is None:
|
| 506 |
+
# create position_ids on the fly for batch generation
|
| 507 |
+
position_ids = attention_mask.long().cumsum(-1) - 1
|
| 508 |
+
position_ids.masked_fill_(attention_mask == 0, 1)
|
| 509 |
+
if past_key_values:
|
| 510 |
+
position_ids = position_ids[:, -input_ids.shape[1] :]
|
| 511 |
+
# This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s
|
| 512 |
+
# `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride
|
| 513 |
+
# during the decoding. Here, simply using `.contiguous()` is not sufficient as in the
|
| 514 |
+
# batch size = 1 case, `position_ids` is already contiguous but with varying stride
|
| 515 |
+
# which retriggers a capture.
|
| 516 |
+
position_ids = position_ids.clone(memory_format=torch.contiguous_format)
|
| 517 |
+
|
| 518 |
+
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
| 519 |
+
if inputs_embeds is not None and is_first_step:
|
| 520 |
+
model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
|
| 521 |
+
else:
|
| 522 |
+
# The clone here is for the same reason as for `position_ids`.
|
| 523 |
+
model_inputs = {"input_ids": input_ids.clone(memory_format=torch.contiguous_format), "inputs_embeds": None}
|
| 524 |
+
|
| 525 |
+
if (
|
| 526 |
+
isinstance(past_key_values, HybridCache)
|
| 527 |
+
and attention_mask.ndim == 2
|
| 528 |
+
and not self.config._attn_implementation == "flash_attention_2"
|
| 529 |
+
):
|
| 530 |
+
if model_inputs["inputs_embeds"] is not None:
|
| 531 |
+
batch_size, sequence_length, _ = model_inputs["inputs_embeds"].shape
|
| 532 |
+
device = model_inputs["inputs_embeds"].device
|
| 533 |
+
else:
|
| 534 |
+
batch_size, sequence_length = model_inputs["input_ids"].shape
|
| 535 |
+
device = model_inputs["input_ids"].device
|
| 536 |
+
dtype = self.text_decoder.lm_head.weight.dtype
|
| 537 |
+
min_dtype = torch.finfo(dtype).min
|
| 538 |
+
attention_mask = _prepare_4d_causal_attention_mask_with_cache_position(
|
| 539 |
+
attention_mask,
|
| 540 |
+
sequence_length=sequence_length,
|
| 541 |
+
target_length=past_key_values.get_max_cache_shape(),
|
| 542 |
+
dtype=dtype,
|
| 543 |
+
device=device,
|
| 544 |
+
min_dtype=min_dtype,
|
| 545 |
+
cache_position=cache_position,
|
| 546 |
+
batch_size=batch_size,
|
| 547 |
+
)
|
| 548 |
+
|
| 549 |
+
model_inputs.update(
|
| 550 |
+
{
|
| 551 |
+
"attention_mask": attention_mask,
|
| 552 |
+
"position_ids": position_ids,
|
| 553 |
+
"cache_position": cache_position,
|
| 554 |
+
"past_key_values": past_key_values,
|
| 555 |
+
"use_cache": use_cache
|
| 556 |
+
}
|
| 557 |
+
)
|
| 558 |
+
|
| 559 |
+
# Input ids will only be used from the second step.
|
| 560 |
+
if is_first_step:
|
| 561 |
+
model_inputs["input_features"] = input_features
|
| 562 |
+
model_inputs["feature_attention_mask"] = feature_attention_mask
|
| 563 |
+
|
| 564 |
+
return model_inputs
|
| 565 |
+
|
| 566 |
+
def _reorder_cache(self, *args, **kwargs):
|
| 567 |
+
return self.text_decoder._reorder_cache(*args, **kwargs)
|
preprocessor_config.json
ADDED
|
@@ -0,0 +1,18 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"auto_map": {
|
| 3 |
+
"AutoProcessor": "processing_meralion3.MERaLiON3Processor"
|
| 4 |
+
},
|
| 5 |
+
"chunk_length": 30,
|
| 6 |
+
"dither": 0.0,
|
| 7 |
+
"feature_extractor_type": "WhisperFeatureExtractor",
|
| 8 |
+
"feature_size": 128,
|
| 9 |
+
"hop_length": 160,
|
| 10 |
+
"n_fft": 400,
|
| 11 |
+
"n_samples": 480000,
|
| 12 |
+
"nb_max_frames": 3000,
|
| 13 |
+
"padding_side": "right",
|
| 14 |
+
"padding_value": 0.0,
|
| 15 |
+
"processor_class": "MERaLiON3Processor",
|
| 16 |
+
"return_attention_mask": false,
|
| 17 |
+
"sampling_rate": 16000
|
| 18 |
+
}
|
processing_meralion3.py
ADDED
|
@@ -0,0 +1,193 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
"""Processor class for MERaLiON3."""
|
| 2 |
+
|
| 3 |
+
from typing import List, Optional, Union
|
| 4 |
+
|
| 5 |
+
import numpy as np
|
| 6 |
+
|
| 7 |
+
from transformers.feature_extraction_utils import BatchFeature
|
| 8 |
+
from transformers.processing_utils import ProcessorMixin
|
| 9 |
+
from transformers.tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
# copied from transformers.models.qwen2_audio.processing_qwen2_audio.Qwen2AudioProcessor
|
| 13 |
+
class MERaLiON3Processor(ProcessorMixin):
|
| 14 |
+
r"""
|
| 15 |
+
Constructs a MERaLiON3 processor which wraps a whisper feature extractor and a gemma tokenizer into a single processor.
|
| 16 |
+
|
| 17 |
+
[`MERaLiON3Processor`] offers all the functionalities of [`WhisperFeatureExtractor`] and [`GemmaTokenizer`]. See the
|
| 18 |
+
[`~MERaLiON3Processor.__call__`] and [`~MERaLiON3Processor.decode`] for more information.
|
| 19 |
+
|
| 20 |
+
Args:
|
| 21 |
+
feature_extractor ([`WhisperFeatureExtractor`], *optional*):
|
| 22 |
+
The feature extractor is a required input.
|
| 23 |
+
tokenizer ([`GemmaTokenizer`], *optional*):
|
| 24 |
+
The tokenizer is a required input.
|
| 25 |
+
chat_template (`Optional[str]`, *optional*):
|
| 26 |
+
The Jinja template to use for formatting the conversation. If not provided, the default chat template
|
| 27 |
+
is used.
|
| 28 |
+
"""
|
| 29 |
+
|
| 30 |
+
attributes = ["feature_extractor", "tokenizer"]
|
| 31 |
+
feature_extractor_class = "WhisperFeatureExtractor"
|
| 32 |
+
tokenizer_class = "AutoTokenizer"
|
| 33 |
+
valid_kwargs = [
|
| 34 |
+
"fixed_speech_embeds_length",
|
| 35 |
+
"speech_token_index",
|
| 36 |
+
"time_duration_limit",
|
| 37 |
+
"whisper_chunk_size",
|
| 38 |
+
"do_normalize"
|
| 39 |
+
]
|
| 40 |
+
|
| 41 |
+
def __init__(
|
| 42 |
+
self,
|
| 43 |
+
feature_extractor=None,
|
| 44 |
+
tokenizer=None,
|
| 45 |
+
fixed_speech_embeds_length=100,
|
| 46 |
+
speech_token_index=255999,
|
| 47 |
+
time_duration_limit=300,
|
| 48 |
+
whisper_chunk_size=30,
|
| 49 |
+
do_normalize=True
|
| 50 |
+
):
|
| 51 |
+
self.fixed_speech_embeds_length = fixed_speech_embeds_length
|
| 52 |
+
self.speech_token_index = speech_token_index
|
| 53 |
+
self.time_duration_limit = time_duration_limit
|
| 54 |
+
self.whisper_chunk_size = whisper_chunk_size
|
| 55 |
+
self.number_chunk_limit = self.time_duration_limit // self.whisper_chunk_size
|
| 56 |
+
self.do_normalize = do_normalize
|
| 57 |
+
|
| 58 |
+
super().__init__(feature_extractor, tokenizer)
|
| 59 |
+
|
| 60 |
+
self.speech_token = self.tokenizer.added_tokens_decoder[self.speech_token_index].content
|
| 61 |
+
self.feature_chunk_size = self.whisper_chunk_size * self.feature_extractor.sampling_rate
|
| 62 |
+
|
| 63 |
+
def _process_text(self, text: List[str], audio_number_chunks: np.ndarray):
|
| 64 |
+
pieces = []
|
| 65 |
+
for i, item in enumerate(text):
|
| 66 |
+
target_string = self.speech_token * self.fixed_speech_embeds_length * audio_number_chunks[i]
|
| 67 |
+
pieces.append(item.replace(self.speech_token, target_string))
|
| 68 |
+
return pieces
|
| 69 |
+
|
| 70 |
+
def _get_number_chunks(self, audios: List[np.ndarray]):
|
| 71 |
+
audio_lengths = np.array([_.shape[0] for _ in audios])
|
| 72 |
+
number_chunks = ((audio_lengths - 1) // self.feature_chunk_size) + 1
|
| 73 |
+
return np.clip(number_chunks, a_min=None, a_max=self.number_chunk_limit)
|
| 74 |
+
|
| 75 |
+
def _get_chunked_audios(self, audios: Union[np.ndarray, List[np.ndarray]]):
|
| 76 |
+
if isinstance(audios, np.ndarray):
|
| 77 |
+
audios = [audios]
|
| 78 |
+
|
| 79 |
+
audio_number_chunks = self._get_number_chunks(audios)
|
| 80 |
+
chunked_audios = []
|
| 81 |
+
|
| 82 |
+
for audio_idx, audio in enumerate(audios):
|
| 83 |
+
for cid in range(audio_number_chunks[audio_idx]):
|
| 84 |
+
chunked_audios.append(
|
| 85 |
+
audio[cid * self.feature_chunk_size: (cid + 1) * self.feature_chunk_size]
|
| 86 |
+
)
|
| 87 |
+
return audio_number_chunks, chunked_audios
|
| 88 |
+
|
| 89 |
+
def __call__(
|
| 90 |
+
self,
|
| 91 |
+
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
|
| 92 |
+
audios: Union[np.ndarray, List[np.ndarray]] = None,
|
| 93 |
+
padding: Union[bool, str, PaddingStrategy] = True,
|
| 94 |
+
sampling_rate: Optional[int] = None,
|
| 95 |
+
do_normalize: Optional[bool] = None,
|
| 96 |
+
**kwargs,
|
| 97 |
+
) -> BatchFeature:
|
| 98 |
+
"""
|
| 99 |
+
Main method to prepare for the model one or several sequences(s) and audio(s). This method forwards the `text`
|
| 100 |
+
and `kwargs` arguments to GemmaTokenizer's [`~GemmaTokenizer.__call__`] if `text` is not `None` to encode
|
| 101 |
+
the text. To prepare the audio(s), this method forwards the `audios` and `kwrags` arguments to
|
| 102 |
+
WhisperFeatureExtractor's [`~WhisperFeatureExtractor.__call__`] if `audios` is not `None`. Please refer to the doctsring
|
| 103 |
+
of the above two methods for more information.
|
| 104 |
+
|
| 105 |
+
Args:
|
| 106 |
+
text (`str`, `List[str]`):
|
| 107 |
+
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
|
| 108 |
+
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
|
| 109 |
+
`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
|
| 110 |
+
audios (`np.ndarray`, `List[np.ndarray]`):
|
| 111 |
+
The audio or batch of audios to be prepared. Each audio can be a NumPy array.
|
| 112 |
+
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
|
| 113 |
+
Select a strategy to pad the returned sequences (according to the model's padding side and padding
|
| 114 |
+
index) among:
|
| 115 |
+
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
|
| 116 |
+
sequence if provided).
|
| 117 |
+
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
|
| 118 |
+
acceptable input length for the model if that argument is not provided.
|
| 119 |
+
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
|
| 120 |
+
lengths).
|
| 121 |
+
sampling_rate (`int`, defaults to 16000):
|
| 122 |
+
The sampling rate at which the audio files should be digitalized expressed in hertz (Hz).
|
| 123 |
+
do_normalize (`bool`, defaults to `True`):
|
| 124 |
+
Whether or not to zero-mean unit-variance normalize the input.
|
| 125 |
+
Normalizing can help to significantly improve the performance of the model.
|
| 126 |
+
"""
|
| 127 |
+
|
| 128 |
+
if text is None:
|
| 129 |
+
raise ValueError("You need to specify either a `text` input to process.")
|
| 130 |
+
if not isinstance(text, list):
|
| 131 |
+
text = [text]
|
| 132 |
+
if not isinstance(audios, list):
|
| 133 |
+
audios = [audios]
|
| 134 |
+
if sampling_rate is None:
|
| 135 |
+
sampling_rate = self.feature_extractor.sampling_rate
|
| 136 |
+
if do_normalize is None:
|
| 137 |
+
do_normalize = self.do_normalize
|
| 138 |
+
|
| 139 |
+
for i, audio in enumerate(audios):
|
| 140 |
+
if audio.ndim > 1:
|
| 141 |
+
raise Exception(f"MERaLiON3 only accepts mono channel audio, {i+1}th audio have {audios[0].ndim} channels")
|
| 142 |
+
|
| 143 |
+
inputs_dict = {}
|
| 144 |
+
|
| 145 |
+
if audios is not None:
|
| 146 |
+
audio_number_chunks, chunked_audios = self._get_chunked_audios(audios)
|
| 147 |
+
text = self._process_text(text, audio_number_chunks)
|
| 148 |
+
|
| 149 |
+
audio_inputs = self.feature_extractor(
|
| 150 |
+
chunked_audios,
|
| 151 |
+
sampling_rate=sampling_rate,
|
| 152 |
+
return_tensors="pt",
|
| 153 |
+
return_attention_mask=True,
|
| 154 |
+
padding="max_length",
|
| 155 |
+
do_normalize=self.do_normalize,
|
| 156 |
+
)
|
| 157 |
+
audio_inputs["feature_attention_mask"] = audio_inputs.pop(
|
| 158 |
+
"attention_mask"
|
| 159 |
+
) # rename attention_mask to prevent conflicts later on
|
| 160 |
+
inputs_dict.update(audio_inputs)
|
| 161 |
+
|
| 162 |
+
text_input = self.tokenizer(
|
| 163 |
+
text=text,
|
| 164 |
+
return_tensors="pt",
|
| 165 |
+
add_special_tokens=False,
|
| 166 |
+
return_attention_mask=True,
|
| 167 |
+
padding=padding,
|
| 168 |
+
)
|
| 169 |
+
|
| 170 |
+
inputs_dict["input_ids"] = text_input.input_ids
|
| 171 |
+
inputs_dict["attention_mask"] = text_input.attention_mask
|
| 172 |
+
|
| 173 |
+
return BatchFeature(data={**inputs_dict})
|
| 174 |
+
|
| 175 |
+
def batch_decode(self, *args, **kwargs):
|
| 176 |
+
"""
|
| 177 |
+
This method forwards all its arguments to GemmaTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
|
| 178 |
+
refer to the docstring of this method for more information.
|
| 179 |
+
"""
|
| 180 |
+
return self.tokenizer.batch_decode(*args, **kwargs)
|
| 181 |
+
|
| 182 |
+
def decode(self, *args, **kwargs):
|
| 183 |
+
"""
|
| 184 |
+
This method forwards all its arguments to GemmaTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to
|
| 185 |
+
the docstring of this method for more information.
|
| 186 |
+
"""
|
| 187 |
+
return self.tokenizer.decode(*args, **kwargs)
|
| 188 |
+
|
| 189 |
+
@property
|
| 190 |
+
def model_input_names(self):
|
| 191 |
+
tokenizer_input_names = self.tokenizer.model_input_names
|
| 192 |
+
feature_extractor_input_names = self.feature_extractor.model_input_names
|
| 193 |
+
return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names + ["feature_attention_mask"]))
|
processor_config.json
ADDED
|
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"auto_map": {
|
| 3 |
+
"AutoProcessor": "processing_meralion3.MERaLiON3Processor"
|
| 4 |
+
},
|
| 5 |
+
"do_normalize": true,
|
| 6 |
+
"fixed_speech_embeds_length": 300,
|
| 7 |
+
"processor_class": "MERaLiON3Processor",
|
| 8 |
+
"speech_token_index": 255999,
|
| 9 |
+
"time_duration_limit": 300,
|
| 10 |
+
"whisper_chunk_size": 30
|
| 11 |
+
}
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,34 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"additional_special_tokens": [
|
| 3 |
+
"<start_of_turn>",
|
| 4 |
+
"<end_of_turn>"
|
| 5 |
+
],
|
| 6 |
+
"bos_token": {
|
| 7 |
+
"content": "<bos>",
|
| 8 |
+
"lstrip": false,
|
| 9 |
+
"normalized": false,
|
| 10 |
+
"rstrip": false,
|
| 11 |
+
"single_word": false
|
| 12 |
+
},
|
| 13 |
+
"eos_token": {
|
| 14 |
+
"content": "<eos>",
|
| 15 |
+
"lstrip": false,
|
| 16 |
+
"normalized": false,
|
| 17 |
+
"rstrip": false,
|
| 18 |
+
"single_word": false
|
| 19 |
+
},
|
| 20 |
+
"pad_token": {
|
| 21 |
+
"content": "<pad>",
|
| 22 |
+
"lstrip": false,
|
| 23 |
+
"normalized": false,
|
| 24 |
+
"rstrip": false,
|
| 25 |
+
"single_word": false
|
| 26 |
+
},
|
| 27 |
+
"unk_token": {
|
| 28 |
+
"content": "<unk>",
|
| 29 |
+
"lstrip": false,
|
| 30 |
+
"normalized": false,
|
| 31 |
+
"rstrip": false,
|
| 32 |
+
"single_word": false
|
| 33 |
+
}
|
| 34 |
+
}
|
tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:0ead607d5168e96c02d261dfac8408c63ff2f52e0ac240ac89d0450b3ec805d8
|
| 3 |
+
size 34362877
|
tokenizer.model
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:61a7b147390c64585d6c3543dd6fc636906c9af3865a5548f27f31aee1d4c8e2
|
| 3 |
+
size 4241003
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,2019 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
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|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
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|
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|
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|
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|
|
|
|
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|
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|
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|
|
|
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|
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|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
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|
|
|
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|
|
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|
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|
| 1 |
+
{
|
| 2 |
+
"add_bos_token": true,
|
| 3 |
+
"add_eos_token": false,
|
| 4 |
+
"added_tokens_decoder": {
|
| 5 |
+
"0": {
|
| 6 |
+
"content": "<pad>",
|
| 7 |
+
"lstrip": false,
|
| 8 |
+
"normalized": false,
|
| 9 |
+
"rstrip": false,
|
| 10 |
+
"single_word": false,
|
| 11 |
+
"special": true
|
| 12 |
+
},
|
| 13 |
+
"1": {
|
| 14 |
+
"content": "<eos>",
|
| 15 |
+
"lstrip": false,
|
| 16 |
+
"normalized": false,
|
| 17 |
+
"rstrip": false,
|
| 18 |
+
"single_word": false,
|
| 19 |
+
"special": true
|
| 20 |
+
},
|
| 21 |
+
"2": {
|
| 22 |
+
"content": "<bos>",
|
| 23 |
+
"lstrip": false,
|
| 24 |
+
"normalized": false,
|
| 25 |
+
"rstrip": false,
|
| 26 |
+
"single_word": false,
|
| 27 |
+
"special": true
|
| 28 |
+
},
|
| 29 |
+
"3": {
|
| 30 |
+
"content": "<unk>",
|
| 31 |
+
"lstrip": false,
|
| 32 |
+
"normalized": false,
|
| 33 |
+
"rstrip": false,
|
| 34 |
+
"single_word": false,
|
| 35 |
+
"special": true
|
| 36 |
+
},
|
| 37 |
+
"4": {
|
| 38 |
+
"content": "<mask>",
|
| 39 |
+
"lstrip": false,
|
| 40 |
+
"normalized": false,
|
| 41 |
+
"rstrip": false,
|
| 42 |
+
"single_word": false,
|
| 43 |
+
"special": false
|
| 44 |
+
},
|
| 45 |
+
"5": {
|
| 46 |
+
"content": "<2mass>",
|
| 47 |
+
"lstrip": false,
|
| 48 |
+
"normalized": false,
|
| 49 |
+
"rstrip": false,
|
| 50 |
+
"single_word": false,
|
| 51 |
+
"special": false
|
| 52 |
+
},
|
| 53 |
+
"6": {
|
| 54 |
+
"content": "[@BOS@]",
|
| 55 |
+
"lstrip": false,
|
| 56 |
+
"normalized": false,
|
| 57 |
+
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}
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},
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"additional_special_tokens": [
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| 1999 |
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"<start_of_turn>",
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| 2000 |
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"<end_of_turn>"
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| 2001 |
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],
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| 2002 |
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"auto_map": {
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| 2003 |
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| 2004 |
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},
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| 2005 |
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"bos_token": "<bos>",
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| 2006 |
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"chat_template": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\n' + message['content'] | trim + '<end_of_turn>\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\n'}}{% endif %}",
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| 2017 |
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"unk_token": "<unk>",
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|
| 2019 |
+
}
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