forked from ml-explore/mlx-examples
-
Notifications
You must be signed in to change notification settings - Fork 0
/
model.py
283 lines (230 loc) · 9.27 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
277
278
279
280
281
282
283
# Copyright © 2023-2024 Apple Inc.
import json
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Optional
import mlx.core as mx
import mlx.nn as nn
from mlx.core import linalg as LA
from mlx.nn.losses import cross_entropy
from mlx.utils import tree_flatten
@dataclass
class CLIPVisionOutput:
pooler_output: mx.array
last_hidden_state: mx.array
@dataclass
class CLIPTextOutput:
pooler_output: mx.array
last_hidden_state: mx.array
@dataclass
class CLIPModelOutput:
loss: Optional[mx.array]
text_embeds: Optional[mx.array]
image_embeds: Optional[mx.array]
text_model_output: CLIPTextOutput
vision_model_output: CLIPVisionOutput
@dataclass
class CLIPTextConfig:
num_hidden_layers: int
hidden_size: int
intermediate_size: int
num_attention_heads: int
max_position_embeddings: int
vocab_size: int
@dataclass
class CLIPVisionConfig:
num_hidden_layers: int
hidden_size: int
intermediate_size: int
num_attention_heads: int
num_channels: int
image_size: int
patch_size: int
@dataclass
class CLIPConfig:
text_config: CLIPTextConfig
vision_config: CLIPVisionConfig
projection_dim: int
def quick_gelu(x: mx.array) -> mx.array:
"""
A fast GELU approximation https://github.com/hendrycks/GELUs
"""
return x * mx.sigmoid(1.702 * x)
def clip_loss(logits: mx.array) -> mx.array:
N, M = logits.shape
caption_loss = cross_entropy(logits, mx.arange(N), reduction="mean")
image_loss = cross_entropy(logits.T, mx.arange(M), reduction="mean")
return (caption_loss + image_loss) / 2.0
class CLIPEncoderLayer(nn.TransformerEncoderLayer):
"""The transformer encoder layer from CLIP."""
def __init__(self, hidden_dim: int, intermediate_dim: int, num_heads: int):
super().__init__(
dims=hidden_dim,
mlp_dims=intermediate_dim,
num_heads=num_heads,
activation=quick_gelu,
norm_first=True,
)
# Add biases to the attention projections
self.attention = nn.MultiHeadAttention(hidden_dim, num_heads, bias=True)
class CLIPTextModel(nn.Module):
"""Implements the text encoder transformer from CLIP."""
def __init__(self, config: CLIPTextConfig):
super().__init__()
self.token_embedding = nn.Embedding(config.vocab_size, config.hidden_size)
self.position_embedding = mx.zeros(
(config.max_position_embeddings, config.hidden_size)
)
self.layers = [
CLIPEncoderLayer(
config.hidden_size, config.intermediate_size, config.num_attention_heads
)
for _ in range(config.num_hidden_layers)
]
self.final_layer_norm = nn.LayerNorm(config.hidden_size)
def _embed(self, x: mx.array) -> mx.array:
embeddings = self.token_embedding(x)
embeddings += self.position_embedding[: x.shape[1]]
return embeddings
def __call__(self, x: mx.array) -> CLIPTextOutput:
B, N = x.shape
eot_tokens = mx.argmax(x, axis=-1)
x = self._embed(x)
mask = nn.MultiHeadAttention.create_additive_causal_mask(N, x.dtype)
for l in self.layers:
x = l(x, mask)
last_hidden_state = self.final_layer_norm(x)
pooler_output = last_hidden_state[mx.arange(B), eot_tokens]
return CLIPTextOutput(
pooler_output=pooler_output, last_hidden_state=last_hidden_state
)
class CLIPVisionModel(nn.Module):
"""Implements the vision encoder transformer from CLIP."""
def __init__(self, config: CLIPVisionConfig):
super().__init__()
self.class_embedding = mx.zeros((config.hidden_size,))
self.patch_embedding = nn.Conv2d(
in_channels=config.num_channels,
out_channels=config.hidden_size,
kernel_size=config.patch_size,
stride=config.patch_size,
bias=False,
)
num_patches = (config.image_size // config.patch_size) ** 2
num_positions = num_patches + 1
self.position_embedding = mx.zeros((num_positions, config.hidden_size))
self.pre_layernorm = nn.LayerNorm(config.hidden_size)
self.layers = [
CLIPEncoderLayer(
config.hidden_size, config.intermediate_size, config.num_attention_heads
)
for _ in range(config.num_hidden_layers)
]
self.post_layernorm = nn.LayerNorm(config.hidden_size)
def _embed(self, x: mx.array) -> mx.array:
batch_size = x.shape[0]
# Patchify using conv:
# [batch_size, sqrt(num_patches), sqrt(num_patches), embed_dim]
patch_embeddings = self.patch_embedding(x)
# [batch_size, num_patches, embed_dim]
patch_embeddings = mx.flatten(patch_embeddings, start_axis=1, end_axis=2)
embed_dim = patch_embeddings.shape[-1]
# Prepend <CLS> embeddings
# [batch_size, 1, embed_dim]
cls_embeddings = mx.broadcast_to(
self.class_embedding, (batch_size, 1, embed_dim)
)
# [batch_size, num_patches + 1, embed_dim]
embeddings = mx.concatenate((cls_embeddings, patch_embeddings), axis=1)
# Add positional encoding
embeddings += self.position_embedding
return embeddings
def __call__(self, x: mx.array) -> CLIPVisionOutput:
x = self._embed(x)
x = self.pre_layernorm(x)
for l in self.layers:
x = l(x, mask=None)
# Extract <CLS> token embedding
pooler_output = self.post_layernorm(x[:, 0, :])
return CLIPVisionOutput(pooler_output=pooler_output, last_hidden_state=x)
class CLIPModel(nn.Module):
def __init__(self, config: CLIPConfig):
self.text_model = CLIPTextModel(config.text_config)
self.vision_model = CLIPVisionModel(config.vision_config)
text_embed_dim = config.text_config.hidden_size
vision_embed_dim = config.vision_config.hidden_size
projection_dim = config.projection_dim
self.visual_projection = nn.Linear(vision_embed_dim, projection_dim, bias=False)
self.text_projection = nn.Linear(text_embed_dim, projection_dim, bias=False)
self.logit_scale = mx.array(0.0)
def get_text_features(self, x: mx.array) -> mx.array:
return self.text_projection(self.text_model(x).pooler_output)
def get_image_features(self, x: mx.array) -> mx.array:
return self.visual_projection(self.vision_model(x).pooler_output)
def __call__(
self,
input_ids: Optional[mx.array] = None,
pixel_values: Optional[mx.array] = None,
return_loss=False,
) -> CLIPModelOutput:
if input_ids is not None:
text_model_output = self.text_model(input_ids)
text_embeds = self.text_projection(text_model_output.pooler_output)
text_embeds = text_embeds / LA.norm(text_embeds, axis=-1, keepdims=True)
else:
text_embeds = None
text_model_output = None
if pixel_values is not None:
vision_model_output = self.vision_model(pixel_values)
image_embeds = self.visual_projection(vision_model_output.pooler_output)
image_embeds = image_embeds / LA.norm(image_embeds, axis=-1, keepdims=True)
else:
image_embeds = None
vision_model_output = None
if return_loss and (input_ids is None or pixel_values is None):
raise ValueError("Must provide text and image inputs to compute loss.")
if return_loss:
logit_scale = mx.exp(self.logit_scale)
logits = (text_embeds @ image_embeds.T) * logit_scale
loss = clip_loss(logits)
else:
loss = None
return CLIPModelOutput(
loss=loss,
text_embeds=text_embeds,
image_embeds=image_embeds,
vision_model_output=vision_model_output,
text_model_output=text_model_output,
)
@staticmethod
def from_pretrained(path: str):
path = Path(path)
with open(path / "config.json", "r") as fid:
config = json.load(fid)
text_config = config["text_config"]
text_config = CLIPTextConfig(
num_hidden_layers=text_config["num_hidden_layers"],
hidden_size=text_config["hidden_size"],
intermediate_size=text_config["intermediate_size"],
num_attention_heads=text_config["num_attention_heads"],
max_position_embeddings=text_config["max_position_embeddings"],
vocab_size=text_config["vocab_size"],
)
vision_config = config["vision_config"]
vision_config = CLIPVisionConfig(
num_hidden_layers=vision_config["num_hidden_layers"],
hidden_size=vision_config["hidden_size"],
intermediate_size=vision_config["intermediate_size"],
num_attention_heads=vision_config["num_attention_heads"],
num_channels=3,
image_size=vision_config["image_size"],
patch_size=vision_config["patch_size"],
)
config = CLIPConfig(
text_config=text_config,
vision_config=vision_config,
projection_dim=config["projection_dim"],
)
model = CLIPModel(config)
model.load_weights(str(path / "weights.npz"))
return model