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 | class StableLmConfig(EasyDeLPretrainedConfig):
"""Phi configuration."""
model_type: str = "stablelm"
def __init__(
self,
vocab_size=50304,
intermediate_size=6912,
hidden_size=2560,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=32,
hidden_act="silu",
max_position_embeddings=4096,
initializer_range=0.02,
layer_norm_eps=1.0e-5,
use_cache=True,
tie_word_embeddings=False,
rope_theta=10_000,
rope_scaling=None,
use_qkv_bias=False,
hidden_dropout=0.0,
attention_dropout=0.0,
partial_rotary_factor=0.25,
bos_token_id=0,
eos_token_id=0,
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.use_qkv_bias = use_qkv_bias
self.hidden_dropout = hidden_dropout
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.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 (
("model/embed_tokens/embedding", PartitionSpec("tp", ("fsdp", "sp"))),
("self_attn/(q_proj|k_proj|v_proj)/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("self_attn/o_proj/kernel", PartitionSpec("tp", ("sp", "fsdp"))),
("mlp/gate_proj/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("mlp/down_proj/kernel", PartitionSpec("tp", ("fsdp", "sp"))),
("mlp/up_proj/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("input_layernorm/kernel", PartitionSpec(None)),
("post_attention_layernorm/kernel", PartitionSpec(None)),
("model/norm/kernel", PartitionSpec(None)),
("lm_head/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
(".*", PartitionSpec(None)),
) if not fully_sharded_data_parallel else (
("model/embed_tokens/embedding", PartitionSpec(("fsdp", "sp"))),
("self_attn/(q_proj|k_proj|v_proj)/kernel", PartitionSpec(("fsdp", "sp"), "tp")),
("self_attn/o_proj/kernel", PartitionSpec("tp", ("sp", "fsdp"))),
("mlp/gate_proj/kernel", PartitionSpec(("fsdp", "sp"))),
("mlp/down_proj/kernel", PartitionSpec(("fsdp", "sp"))),
("mlp/up_proj/kernel", PartitionSpec(("fsdp", "sp"))),
("input_layernorm/kernel", PartitionSpec(None)),
("post_attention_layernorm/kernel", PartitionSpec(None)),
("model/norm/kernel", PartitionSpec(None)),
("lm_head/kernel", PartitionSpec(("fsdp", "sp"))),
(".*", PartitionSpec(("fsdp", "sp"))),
)
|