Clean up inference config: remove training-only flags, set bd_size=32 default, dtype=bfloat16
cb43b83 verified | # limitations under the License. | |
| from transformers.configuration_utils import PretrainedConfig, layer_type_validation | |
| from transformers.modeling_rope_utils import rope_config_validation | |
| class Fast_dVLMVisionConfig(PretrainedConfig): | |
| model_type = "fast_dvlm" | |
| base_config_key = "vision_config" | |
| def __init__( | |
| self, | |
| depth=32, | |
| hidden_size=3584, | |
| hidden_act="silu", | |
| intermediate_size=3420, | |
| num_heads=16, | |
| in_channels=3, | |
| patch_size=14, | |
| spatial_merge_size=2, | |
| temporal_patch_size=2, | |
| tokens_per_second=4, | |
| window_size=112, | |
| out_hidden_size=3584, | |
| fullatt_block_indexes=[7, 15, 23, 31], | |
| initializer_range=0.02, | |
| **kwargs, | |
| ): | |
| super().__init__(**kwargs) | |
| self.depth = depth | |
| self.hidden_size = hidden_size | |
| self.hidden_act = hidden_act | |
| self.intermediate_size = intermediate_size | |
| self.num_heads = num_heads | |
| self.in_channels = in_channels | |
| self.patch_size = patch_size | |
| self.spatial_merge_size = spatial_merge_size | |
| self.temporal_patch_size = temporal_patch_size | |
| self.tokens_per_second = tokens_per_second | |
| self.window_size = window_size | |
| self.fullatt_block_indexes = fullatt_block_indexes | |
| self.out_hidden_size = out_hidden_size | |
| self.initializer_range = initializer_range | |
| class Fast_dVLMTextConfig(PretrainedConfig): | |
| model_type = "fast_dvlm_for_causal_lm" | |
| base_config_key = "text_config" | |
| keys_to_ignore_at_inference = ["past_key_values"] | |
| base_model_tp_plan = { | |
| "layers.*.self_attn.q_proj": "colwise", | |
| "layers.*.self_attn.k_proj": "colwise", | |
| "layers.*.self_attn.v_proj": "colwise", | |
| "layers.*.self_attn.o_proj": "rowwise", | |
| "layers.*.mlp.gate_proj": "colwise", | |
| "layers.*.mlp.up_proj": "colwise", | |
| "layers.*.mlp.down_proj": "rowwise", | |
| } | |
| base_model_pp_plan = { | |
| "embed_tokens": (["input_ids"], ["inputs_embeds"]), | |
| "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), | |
| "norm": (["hidden_states"], ["hidden_states"]), | |
| } | |
| def __init__( | |
| self, | |
| vocab_size=152064, | |
| hidden_size=8192, | |
| intermediate_size=29568, | |
| num_hidden_layers=80, | |
| num_attention_heads=64, | |
| num_key_value_heads=8, | |
| hidden_act="silu", | |
| max_position_embeddings=32768, | |
| initializer_range=0.02, | |
| rms_norm_eps=1e-05, | |
| use_cache=True, | |
| tie_word_embeddings=False, | |
| rope_theta=1000000.0, | |
| use_sliding_window=False, | |
| sliding_window=4096, | |
| max_window_layers=80, | |
| layer_types=None, | |
| attention_dropout=0.0, | |
| rope_scaling=None, | |
| image_token_id=None, | |
| video_token_id=None, | |
| bd_size=32, | |
| self_spec_inference_mode=None, | |
| block_length=None, | |
| minimum_noise_level=1e-3, | |
| **kwargs, | |
| ): | |
| self.vocab_size = vocab_size | |
| self.max_position_embeddings = max_position_embeddings | |
| self.hidden_size = hidden_size | |
| self.intermediate_size = intermediate_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| self.use_sliding_window = use_sliding_window | |
| self.sliding_window = sliding_window if self.use_sliding_window else None | |
| self.max_window_layers = max_window_layers | |
| # for backward compatibility | |
| if num_key_value_heads is None: | |
| num_key_value_heads = num_attention_heads | |
| self.num_key_value_heads = num_key_value_heads | |
| self.hidden_act = hidden_act | |
| self.initializer_range = initializer_range | |
| self.rms_norm_eps = rms_norm_eps | |
| self.use_cache = use_cache | |
| self.rope_theta = rope_theta | |
| self.attention_dropout = attention_dropout | |
| self.rope_scaling = rope_scaling | |
| self.bd_size = bd_size | |
| self.layer_types = layer_types | |
| self.minimum_noise_level = minimum_noise_level | |
| self.self_spec_inference_mode = self_spec_inference_mode | |
| self.block_length = block_length | |
| if self.layer_types is None: | |
| self.layer_types = [ | |
| "sliding_attention" | |
| if self.sliding_window is not None and i >= self.max_window_layers | |
| else "full_attention" | |
| for i in range(self.num_hidden_layers) | |
| ] | |
| layer_type_validation(self.layer_types) | |
| # Validate the correctness of rotary position embeddings parameters | |
| # BC: if there is a 'type' field, move it to 'rope_type'. | |
| # and change type from 'mrope' to 'default' because `mrope` does default RoPE calculations | |
| # one can set it to "linear"/"dynamic" etc. to have scaled RoPE | |
| # TODO: @raushan update config in the hub | |
| if self.rope_scaling is not None and "type" in self.rope_scaling: | |
| if self.rope_scaling["type"] == "mrope": | |
| self.rope_scaling["type"] = "default" | |
| self.rope_scaling["rope_type"] = self.rope_scaling["type"] | |
| rope_config_validation(self, ignore_keys={"mrope_section"}) | |
| self.image_token_id = image_token_id | |
| self.video_token_id = video_token_id | |
| super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs) | |
| class Fast_dVLMConfig(PretrainedConfig): | |
| model_type = "fast_dvlm" | |
| sub_configs = {"vision_config": Fast_dVLMVisionConfig, "text_config": Fast_dVLMTextConfig} | |
| keys_to_ignore_at_inference = ["past_key_values"] | |
| def __init__( | |
| self, | |
| text_config=None, | |
| vision_config=None, | |
| image_token_id=151655, | |
| video_token_id=151656, | |
| **kwargs, | |
| ): | |
| if isinstance(vision_config, dict): | |
| self.vision_config = self.sub_configs["vision_config"](**vision_config) | |
| elif vision_config is None: | |
| self.vision_config = self.sub_configs["vision_config"]() | |
| if isinstance(text_config, dict): | |
| self.text_config = self.sub_configs["text_config"](**text_config) | |
| elif text_config is None: | |
| # For BC use all kwargs to init `TextConfig` | |
| self.text_config = self.sub_configs["text_config"](**kwargs) | |
| self.image_token_id = image_token_id | |
| self.video_token_id = video_token_id | |
| super().__init__(**kwargs) | |
| __all__ = ["Fast_dVLMConfig", "Fast_dVLMTextConfig"] |