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# 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=8,
self_spec_inference_mode=None,
block_length=None,
use_block_causal_mask=False,
complementary_mask=True,
minimum_noise_level=1e-3,
entropy_loss=False,
entropy_loss_weight=1.0,
block_causal_no_dynamic=False,
**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.use_block_causal_mask = use_block_causal_mask
self.complementary_mask = complementary_mask
self.minimum_noise_level = minimum_noise_level
self.entropy_loss = entropy_loss
self.entropy_loss_weight = entropy_loss_weight
self.block_causal_no_dynamic = block_causal_no_dynamic
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,
enable_efficient_vision_embed=False,
**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
self.enable_efficient_vision_embed = enable_efficient_vision_embed
super().__init__(**kwargs)
__all__ = ["Fast_dVLMConfig", "Fast_dVLMTextConfig"] |