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def21c3 |
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from typing import List
from transformers import PretrainedConfig, AutoTokenizer
def config_to_moe_args(config):
from megablocks.layers.arguments import Arguments as MoEArgs
kwargs = {
"activation_fn": F.silu,
"mlp_type": "glu" if "glu" in config.activation_type.lower() else "mlp",
"mlp_impl": "sparse",
"hidden_size": config.hidden_size,
"ffn_hidden_size": config.intermediate_size,
"moe_num_experts": config.moe_num_experts,
"num_layers": config.num_hidden_layers,
# Handled by FSDP (https://github.com/databricks/megablocks/issues/57#issuecomment-1854594483)
"moe_weight_parallelism": False,
"moe_expert_model_parallelism": False,
"moe_top_k": config.moe_top_k,
# "moe_loss_weight": config.moe_loss_weight,
# "device": config.init_device,
# Handled by FSDP
"bf16": False,
"fp16": False,
"bias": False,
"return_bias": False,
}
return MoEArgs(**kwargs)
class MolmoeConfig(PretrainedConfig):
model_type = "molmoe"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=50304,
embedding_size=50304,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
max_position_embeddings=2048,
initializer_range=0.02,
use_cache=True,
layer_norm_eps: float = 1e-5,
rope_theta=10000.0,
clip_qkv=None,
qkv_bias: bool = False,
weight_tying: bool = False,
use_position_ids: bool=True,
tie_word_embeddings: bool=True,
moe_num_experts: int = 64,
moe_top_k: int = 8,
**kwargs,
):
self.vocab_size = vocab_size
self.embedding_size = embedding_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.layer_norm_eps = layer_norm_eps
self.weight_tying = weight_tying
self.use_position_ids = use_position_ids
# 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.initializer_range = initializer_range
self.use_cache = use_cache
self.rope_theta = rope_theta
self.clip_qkv = clip_qkv
self.qkv_bias = qkv_bias
self.tie_word_embeddings = tie_word_embeddings
super().__init__(
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
MolmoeConfig.register_for_auto_class() |