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config_utils.py
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from fms.models.llama import LLaMAConfig
from fms_fsdp.config import train_config
def update_config(config, **kwargs):
if isinstance(config, (tuple, list)):
for c in config:
update_config(c, **kwargs)
else:
for k, v in kwargs.items():
if hasattr(config, k):
setattr(config, k, v)
elif "." in k:
config_name, param_name = k.split(".")
if type(config).__name__ == config_name:
if hasattr(config, param_name):
setattr(config, param_name, v)
else:
print(f"Warning: {config_name} does not accept parameter: {k}")
elif isinstance(config, train_config):
print(f"Warning: unknown parameter {k}")
def get_model_config(model_variant):
if model_variant == "llama2_70b":
model_config = LLaMAConfig(
emb_dim=8192,
multiple_of=4096,
nheads=64,
kvheads=8,
nlayers=80,
hidden_grow_factor=28672 / 8192,
)
elif model_variant == "llama2_34b":
model_config = LLaMAConfig(
emb_dim=8192,
nheads=64,
kvheads=8,
nlayers=48,
hidden_grow_factor=22016 / 8192,
max_expected_seq_len=16384,
rope_theta=1000000.0,
)
elif model_variant == "llama2_13b":
model_config = LLaMAConfig(
emb_dim=5120,
nheads=40,
nlayers=40,
hidden_grow_factor=13824 / 5120,
)
elif model_variant == "llama2_7b":
model_config = LLaMAConfig(
hidden_grow_factor=11008 / 4096,
kvheads=32,
)
elif model_variant == "llama2_1.4b":
model_config = LLaMAConfig(
emb_dim=2048,
nheads=16,
nlayers=24,
hidden_grow_factor=3,
kvheads=4,
)
elif model_variant == "llama3_8b":
model_config = LLaMAConfig(
src_vocab_size=128256,
emb_dim=4096,
nheads=32,
kvheads=8,
nlayers=32,
hidden_grow_factor=3.5,
max_expected_seq_len=8192,
rope_theta=500000.0,
)
elif model_variant == "llama3_8b_4k":
model_config = LLaMAConfig(
src_vocab_size=128256,
emb_dim=4096,
nheads=32,
kvheads=8,
nlayers=32,
hidden_grow_factor=3.5,
max_expected_seq_len=4096,
rope_theta=500000.0,
)
elif model_variant == "llama3_1.8b":
model_config = LLaMAConfig(
src_vocab_size=128256,
emb_dim=2048,
nheads=16,
kvheads=8,
nlayers=24,
hidden_grow_factor=3.5,
max_expected_seq_len=8192,
rope_theta=500000.0,
)
elif model_variant == "llama3_1.8b_4k":
model_config = LLaMAConfig(
src_vocab_size=128256,
emb_dim=2048,
nheads=16,
kvheads=8,
nlayers=24,
hidden_grow_factor=3.5,
max_expected_seq_len=4096,
rope_theta=500000.0,
)
elif model_variant == "llama3_3.2b":
model_config = LLaMAConfig(
src_vocab_size=128256,
emb_dim=3072,
nheads=24,
kvheads=8,
nlayers=24,
hidden_grow_factor=8 / 3,
max_expected_seq_len=8192,
rope_theta=500000.0,
)
elif model_variant == "llama3_3.2b_4k":
model_config = LLaMAConfig(
src_vocab_size=128256,
emb_dim=3072,
nheads=24,
kvheads=8,
nlayers=24,
hidden_grow_factor=8 / 3,
max_expected_seq_len=4096,
rope_theta=500000.0,
)
elif model_variant == "llama3_70b":
model_config = LLaMAConfig(
src_vocab_size=128256,
emb_dim=8192,
nheads=64,
kvheads=8,
nlayers=80,
hidden_grow_factor=3.5,
max_expected_seq_len=8192,
rope_theta=500000.0,
)
elif model_variant == "llama3_70b_4k":
model_config = LLaMAConfig(
src_vocab_size=128256,
emb_dim=8192,
nheads=64,
kvheads=8,
nlayers=80,
hidden_grow_factor=3.5,
max_expected_seq_len=4096,
rope_theta=500000.0,
)
elif model_variant == "llama3_194m_4k":
model_config = LLaMAConfig(
src_vocab_size=128256,
emb_dim=1024,
nheads=8,
nlayers=10,
max_expected_seq_len=4096,
rope_theta=500000.0,
)
elif model_variant == "mamba_9.8b":
model_config = {
"d_model": 4096,
"d_intermediate": 14336,
"n_layer": 32,
"vocab_size": 128256,
"ssm_cfg": {"layer": "Mamba2"},
"attn_layer_idx": [9, 18, 27],
"attn_cfg": {
"causal": True,
"d_conv": 0,
"head_dim": 128,
"num_heads": 32,
"num_heads_kv": 8,
"out_proj_bias": False,
"qkv_proj_bias": False,
"rotary_emb_dim": 64,
},
"rms_norm": True,
"residual_in_fp32": True,
"fused_add_norm": True,
"pad_vocab_size_multiple": 16,
"tie_embeddings": False,
}
else:
raise ValueError(f"model variant {model_variant} not supported.")
return model_config