-
Notifications
You must be signed in to change notification settings - Fork 521
/
model.py
276 lines (224 loc) · 11 KB
/
model.py
1
2
3
4
5
6
7
8
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
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from dataclasses import dataclass
from typing import Optional
import torch
import torch.nn as nn
from torch import Tensor
from torch.nn import functional as F
def find_multiple(n: int, k: int) -> int:
if n % k == 0:
return n
return n + k - (n % k)
@dataclass
class ModelArgs:
block_size: int = 2048
vocab_size: int = 32000
n_layer: int = 32
n_head: int = 32
dim: int = 4096
intermediate_size: int = None
n_local_heads: int = -1
head_dim: int = 64
rope_base: float = 10000
norm_eps: float = 1e-5
num_experts: int = 8
num_activated_experts: int = 2
def __post_init__(self):
if self.n_local_heads == -1:
self.n_local_heads = self.n_head
if self.intermediate_size is None:
hidden_dim = 4 * self.dim
n_hidden = int(2 * hidden_dim / 3)
self.intermediate_size = find_multiple(n_hidden, 256)
self.head_dim = self.dim // self.n_head
@classmethod
def from_name(cls, name: str):
if name in transformer_configs:
return cls(**transformer_configs[name])
# fuzzy search
config = [config for config in transformer_configs if config in str(name).upper() or config in str(name)]
assert len(config) == 1, name
return cls(**transformer_configs[config[0]])
attn_output_multiplier = 0.08838834764831845
embedding_multiplier_scale = 78.38367176906169
output_multiplier_scale = 0.5773502691896257
max_attn_val = 30.0
transformer_configs = {
"Mixtral-8x7B-v0.1": dict(block_size=32768, n_layer=32, n_head=32, n_local_heads=8, dim=4096, intermediate_size=14336, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
"grok-1": dict(vocab_size=131072, block_size=8192, n_layer=64, n_head=48, n_local_heads=8, dim=6144, intermediate_size=32768, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
}
class KVCache(nn.Module):
def __init__(self, max_batch_size, max_seq_length, n_heads, head_dim, dtype=torch.bfloat16):
super().__init__()
cache_shape = (max_batch_size, n_heads, max_seq_length, head_dim)
self.register_buffer('k_cache', torch.zeros(cache_shape, dtype=dtype))
self.register_buffer('v_cache', torch.zeros(cache_shape, dtype=dtype))
def update(self, input_pos, k_val, v_val):
# input_pos: [S], k_val: [B, H, S, D]
assert input_pos.shape[0] == k_val.shape[2]
k_out = self.k_cache
v_out = self.v_cache
k_out[:, :, input_pos] = k_val
v_out[:, :, input_pos] = v_val
return k_out, v_out
class Transformer(nn.Module):
def __init__(self, config: ModelArgs) -> None:
super().__init__()
self.config = config
self.tok_embeddings = nn.Embedding(config.vocab_size, config.dim)
self.layers = nn.ModuleList(TransformerBlock(config) for _ in range(config.n_layer))
self.norm = RMSNorm(config.dim, eps=config.norm_eps)
self.output = nn.Linear(config.dim, config.vocab_size, bias=False)
self.freqs_cis: Optional[Tensor] = None
self.mask_cache: Optional[Tensor] = None
self.max_batch_size = -1
self.max_seq_length = -1
def setup_caches(self, max_batch_size, max_seq_length):
if self.max_seq_length >= max_seq_length and self.max_batch_size >= max_batch_size:
return
head_dim = self.config.dim // self.config.n_head
max_seq_length = find_multiple(max_seq_length, 8)
self.max_seq_length = max_seq_length
self.max_batch_size = max_batch_size
for b in self.layers:
b.attention.kv_cache = KVCache(max_batch_size, max_seq_length, self.config.n_local_heads, head_dim)
self.freqs_cis = precompute_freqs_cis(self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base)
self.causal_mask = torch.tril(torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool))
def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
assert self.freqs_cis is not None, "Caches must be initialized first"
mask = self.causal_mask[None, None, input_pos]
freqs_cis = self.freqs_cis[input_pos]
x = self.tok_embeddings(idx)
x *= embedding_multiplier_scale
for i, layer in enumerate(self.layers):
x = layer(x, input_pos, freqs_cis, mask)
x = self.norm(x)
logits = self.output(x)
logits *= output_multiplier_scale
return logits
@classmethod
def from_name(cls, name: str):
return cls(ModelArgs.from_name(name))
class TransformerBlock(nn.Module):
def __init__(self, config: ModelArgs) -> None:
super().__init__()
self.attention = Attention(config)
self.block_sparse_moe = MOEFeedForward(config)
self.pre_moe_norm = RMSNorm(config.dim, config.norm_eps)
self.post_moe_norm = RMSNorm(config.dim, config.norm_eps)
self.post_attn_norm = RMSNorm(config.dim, config.norm_eps)
self.pre_attn_norm = RMSNorm(config.dim, config.norm_eps)
def forward(self, x: Tensor, input_pos: Tensor, freqs_cis: Tensor, mask: Tensor) -> Tensor:
h = x + self.post_attn_norm(self.attention(self.pre_attn_norm(x), freqs_cis, mask, input_pos))
out = h + self.post_moe_norm(self.block_sparse_moe(self.pre_moe_norm(h)))
return out
class Attention(nn.Module):
def __init__(self, config: ModelArgs):
super().__init__()
assert config.dim % config.n_head == 0
total_head_dim = (config.n_head + 2 * config.n_local_heads) * config.head_dim
# key, query, value projections for all heads, but in a batch
self.wqkv = nn.Linear(config.dim, total_head_dim, bias=False)
self.wo = nn.Linear(config.dim, config.dim, bias=False)
self.kv_cache = None
self.n_head = config.n_head
self.head_dim = config.head_dim
self.n_local_heads = config.n_local_heads
self.dim = config.dim
self._register_load_state_dict_pre_hook(self.load_hook)
def load_hook(self, state_dict, prefix, *args):
if prefix + "wq.weight" in state_dict:
wq = state_dict.pop(prefix + "wq.weight")
wk = state_dict.pop(prefix + "wk.weight")
wv = state_dict.pop(prefix + "wv.weight")
state_dict[prefix + "wqkv.weight"] = torch.cat([wq, wk, wv])
def forward(self, x: Tensor, freqs_cis: Tensor, mask: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
bsz, seqlen, _ = x.shape
kv_size = self.n_local_heads * self.head_dim
qkv = self.wqkv(x)
q, k, v = qkv.split([self.dim, kv_size, kv_size], dim=-1)
q = q.view(bsz, seqlen, self.n_head, self.head_dim)
k = k.view(bsz, seqlen, self.n_local_heads, self.head_dim)
v = v.view(bsz, seqlen, self.n_local_heads, self.head_dim)
q = apply_rotary_emb(q, freqs_cis)
k = apply_rotary_emb(k, freqs_cis)
q, k, v = map(lambda x: x.transpose(1, 2), (q, k, v))
if self.kv_cache is not None:
k, v = self.kv_cache.update(input_pos, k, v)
k = k.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
v = v.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
attn_weights = torch.matmul(q, k.transpose(2, 3)).to(torch.float32)
attn_weights = attn_weights * attn_output_multiplier
attn_weights = max_attn_val * F.tanh(attn_weights / max_attn_val)
attn_weights += torch.where(mask, 0, -float("inf"))
attn_weights = F.softmax(attn_weights, dim=-1).to(q.dtype)
y = torch.matmul(attn_weights, v)
# y = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0)
y = y.transpose(1, 2).contiguous().view(bsz, seqlen, self.dim)
y = self.wo(y)
return y
class ConditionalFeedForward(nn.Module):
def __init__(self, config):
super().__init__()
self.w1 = nn.Parameter(torch.empty(config.num_experts, config.intermediate_size, config.dim))
self.w2 = nn.Parameter(torch.empty(config.num_experts, config.dim, config.intermediate_size))
self.w3 = nn.Parameter(torch.empty(config.num_experts, config.intermediate_size, config.dim))
def forward(self, x: Tensor, expert_indices: Tensor) -> Tensor:
w1_weights = self.w1[expert_indices] # [T, A, D, D]
w3_weights = self.w3[expert_indices] # [T, A, D, D]
w2_weights = self.w2[expert_indices] # [T, A, D, D]
x1 = F.silu(torch.einsum('ti,taoi -> tao', x, w1_weights))
x3 = torch.einsum('ti, taoi -> tao', x, w3_weights)
expert_outs = torch.einsum('tao, taio -> tai', (x1 * x3), w2_weights)
return expert_outs
class MOEFeedForward(nn.Module):
def __init__(self, config) -> None:
super().__init__()
self.gate = nn.Linear(config.dim, config.num_experts, bias=False)
self.cond_ffn = ConditionalFeedForward(config)
self.dim = config.dim
self.num_activated_experts = config.num_activated_experts
def forward(self, x: Tensor) -> Tensor:
x = x.view(-1, self.dim)
# T = num_tokens, E = num_experts, D = hidden dim, A = activated experts
# x: [T, D]
scores = self.gate(x) # [T, E]
expert_weights = F.softmax(scores, dim=-1)
expert_weights, expert_indices = torch.topk(expert_weights, self.num_activated_experts, dim=-1) # [T, A], [T, A]
expert_weights /= expert_weights.sum(dim=-1, keepdim=True) # [T, A]
expert_outs = self.cond_ffn(x, expert_indices)
return torch.einsum('tai,ta -> ti', expert_outs, expert_weights)
class RMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-5):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def _norm(self, x):
return x * torch.rsqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps)
def forward(self, x: Tensor) -> Tensor:
output = self._norm(x.float()).type_as(x)
return output * self.weight
def precompute_freqs_cis(
seq_len: int, n_elem: int, base: int = 10000
) -> Tensor:
freqs = 1.0 / (base ** (torch.arange(0, n_elem, 2)[: (n_elem // 2)].float() / n_elem))
t = torch.arange(seq_len, device=freqs.device)
freqs = torch.outer(t, freqs)
freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
cache = torch.stack([freqs_cis.real, freqs_cis.imag], dim=-1)
return cache.to(dtype=torch.bfloat16)
def apply_rotary_emb(x: Tensor, freqs_cis: Tensor) -> Tensor:
xshaped = x.float().reshape(*x.shape[:-1], -1, 2)
freqs_cis = freqs_cis.view(1, xshaped.size(1), 1, xshaped.size(3), 2)
x_out2 = torch.stack(
[
xshaped[..., 0] * freqs_cis[..., 0] - xshaped[..., 1] * freqs_cis[..., 1],
xshaped[..., 1] * freqs_cis[..., 0] + xshaped[..., 0] * freqs_cis[..., 1],
],
-1,
)
x_out2 = x_out2.flatten(3)
return x_out2.type_as(x)