9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122 | class PhiConfig(EasyDeLPretrainedConfig):
"""Phi configuration."""
model_type: str = "phi"
attribute_map = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "num_attention_heads",
"num_hidden_layers": "num_hidden_layers",
}
def __init__(
self,
vocab_size=51200,
hidden_size=2048,
intermediate_size=8192,
num_hidden_layers=24,
num_attention_heads=32,
num_key_value_heads=None,
resid_pdrop=0.0,
embd_pdrop=0.0,
attention_dropout=0.0,
hidden_act="gelu_new",
max_position_embeddings=2048,
initializer_range=0.02,
layer_norm_eps=1e-5,
use_cache=True,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
partial_rotary_factor=0.5,
qk_layernorm=False,
bos_token_id=1,
eos_token_id=2,
bits: Optional[int] = None,
gradient_checkpointing: str = "nothing_saveable",
**kwargs
) -> None:
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.resid_pdrop = resid_pdrop
self.embd_pdrop = embd_pdrop
self.attention_dropout = attention_dropout
self.hidden_act = hidden_act
self.max_position_embeddings = max_position_embeddings
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.partial_rotary_factor = partial_rotary_factor
self.qk_layernorm = qk_layernorm
self.bits = bits
self.gradient_checkpointing = gradient_checkpointing
super().__init__(
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
bits=bits,
**kwargs
)
def add_jax_args(
self,
bits: Optional[int] = None,
gradient_checkpointing: str = "nothing_saveable",
**kwargs
):
self.bits = bits
self.gradient_checkpointing = gradient_checkpointing
for k, v in kwargs.items():
if not hasattr(self, k):
setattr(self, k, v)
def get_partition_rules(self, fully_sharded_data_parallel: bool = True):
return (
("embed_tokens/embedding", PartitionSpec(("fsdp", "sp"), "tp")),
("final_layernorm/(scale|bias)", PartitionSpec(None, )),
("final_layernorm/(scale|bias)", PartitionSpec(None, )),
("mlp/fc1/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("mlp/fc1/bias", PartitionSpec("tp", )),
("mlp/fc2/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("mlp/fc2/bias", PartitionSpec(("fsdp", "sp"), )),
("self_attn/dense/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("self_attn/dense/bias", PartitionSpec("tp")),
("self_attn/(q_proj|k_proj|v_proj)/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("self_attn/(q_proj|k_proj|v_proj)/bias", PartitionSpec("tp", )),
("lm_head/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("lm_head/bias", PartitionSpec("tp")),
(".*", PartitionSpec(None, ))
) if fully_sharded_data_parallel else (
("embed_tokens/embedding", PartitionSpec("tp", ("fsdp", "sp"), )),
("final_layernorm/(scale|bias)", PartitionSpec(None, )),
("final_layernorm/(scale|bias)", PartitionSpec(None, )),
("mlp/fc1/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("mlp/fc1/bias", PartitionSpec("tp", )),
("mlp/fc2/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("mlp/fc2/bias", PartitionSpec(("fsdp", "sp"), )),
("self_attn/dense/kernel", PartitionSpec("tp", ("fsdp", "sp"), )),
("self_attn/dense/bias", PartitionSpec("tp")),
("self_attn/(q_proj|k_proj|v_proj)/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("self_attn/(q_proj|k_proj|v_proj)/bias", PartitionSpec("tp", )),
("lm_head/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("lm_head/bias", PartitionSpec("tp")),
(".*", PartitionSpec(None, ))
)
|