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Browse files- README.md +6 -4
- app.py +1228 -0
- assets/demo-1.jpg +0 -0
- assets/demo-2.jpg +0 -0
- assets/demo-3.jpg +0 -0
- assets/demo-4.jpg +0 -0
- assets/demo-5.jpg +0 -0
- image_encoder_cache/33c2dc3e4183b82eb8e09ecceb8c3f30262237d6ee0f49904e1fbde913195ffb.pt +3 -0
- image_encoder_cache/7ffdc9dcb8e0304e8658c0011cc71cb993e4729af95ebbe890f91a4dfce46170.pt +3 -0
- image_encoder_cache/b3ba6d3612f786df76a0cc5603c9a3d4cc6bede86d4c0f3001092fe0cc67f132.pt +3 -0
- image_encoder_cache/f5c5b77a4b0025925f7313a019fd91e435caf4b1ce651794c0c7bf5c4ea92827.pt +3 -0
- image_encoder_cache/f6defd804af24f18ea5d966b6b248f3dccc514b06155648592b93af567157a4e.pt +3 -0
- requirements.txt +7 -0
README.md
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@@ -1,12 +1,14 @@
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---
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title:
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emoji:
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colorFrom:
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colorTo:
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sdk: gradio
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sdk_version: 4.15.0
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app_file: app.py
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pinned: false
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: moondream1
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+
emoji: 🌔
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colorFrom: indigo
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colorTo: blue
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sdk: gradio
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sdk_version: 4.15.0
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app_file: app.py
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pinned: false
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preload_from_hub:
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+
- vikhyatk/moondream1
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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|
1 |
+
from __future__ import annotations
|
2 |
+
|
3 |
+
import torch
|
4 |
+
from PIL import Image
|
5 |
+
from einops import rearrange
|
6 |
+
from torchvision.transforms.v2 import (
|
7 |
+
Compose,
|
8 |
+
Resize,
|
9 |
+
InterpolationMode,
|
10 |
+
ToImage,
|
11 |
+
ToDtype,
|
12 |
+
Normalize,
|
13 |
+
)
|
14 |
+
|
15 |
+
from transformers import CodeGenTokenizerFast as Tokenizer
|
16 |
+
from accelerate import init_empty_weights, load_checkpoint_and_dispatch
|
17 |
+
import re
|
18 |
+
|
19 |
+
import math
|
20 |
+
from typing import Optional
|
21 |
+
|
22 |
+
from transformers import PretrainedConfig
|
23 |
+
|
24 |
+
|
25 |
+
import math
|
26 |
+
from dataclasses import dataclass, field
|
27 |
+
from typing import Any, Dict, Optional, Tuple, Union
|
28 |
+
|
29 |
+
import torch
|
30 |
+
import torch.nn as nn
|
31 |
+
from einops import rearrange, repeat
|
32 |
+
from transformers import PretrainedConfig, PreTrainedModel
|
33 |
+
from transformers.activations import ACT2FN
|
34 |
+
from transformers.modeling_outputs import CausalLMOutputWithPast
|
35 |
+
|
36 |
+
pad_input, unpad_input = None, None
|
37 |
+
FlashRotaryEmbedding = None
|
38 |
+
FlashSelfAttention, FlashCrossAttention = None, None
|
39 |
+
FusedDense = None
|
40 |
+
|
41 |
+
if torch.cuda.is_available():
|
42 |
+
DEVICE = "cuda"
|
43 |
+
DTYPE = torch.float16
|
44 |
+
else:
|
45 |
+
DEVICE = "cpu"
|
46 |
+
DTYPE = torch.float32
|
47 |
+
|
48 |
+
|
49 |
+
class PhiConfig(PretrainedConfig):
|
50 |
+
"""Phi configuration."""
|
51 |
+
|
52 |
+
model_type = "phi-msft"
|
53 |
+
attribute_map = {
|
54 |
+
"max_position_embeddings": "n_positions",
|
55 |
+
"hidden_size": "n_embd",
|
56 |
+
"num_attention_heads": "n_head",
|
57 |
+
"num_hidden_layers": "n_layer",
|
58 |
+
}
|
59 |
+
|
60 |
+
def __init__(
|
61 |
+
self,
|
62 |
+
vocab_size: int = 50304,
|
63 |
+
n_positions: int = 2048,
|
64 |
+
n_embd: int = 1024,
|
65 |
+
n_layer: int = 20,
|
66 |
+
n_inner: Optional[int] = None,
|
67 |
+
n_head: int = 16,
|
68 |
+
n_head_kv: Optional[int] = None,
|
69 |
+
rotary_dim: Optional[int] = 32,
|
70 |
+
activation_function: Optional[str] = "gelu_new",
|
71 |
+
flash_attn: bool = False,
|
72 |
+
flash_rotary: bool = False,
|
73 |
+
fused_dense: bool = False,
|
74 |
+
attn_pdrop: float = 0.0,
|
75 |
+
embd_pdrop: float = 0.0,
|
76 |
+
resid_pdrop: float = 0.0,
|
77 |
+
layer_norm_epsilon: float = 1e-5,
|
78 |
+
initializer_range: float = 0.02,
|
79 |
+
tie_word_embeddings: bool = False,
|
80 |
+
pad_vocab_size_multiple: int = 64,
|
81 |
+
gradient_checkpointing: bool = False,
|
82 |
+
**kwargs,
|
83 |
+
) -> None:
|
84 |
+
self.vocab_size = int(
|
85 |
+
math.ceil(vocab_size / pad_vocab_size_multiple) * pad_vocab_size_multiple
|
86 |
+
)
|
87 |
+
self.n_positions = n_positions
|
88 |
+
self.n_embd = n_embd
|
89 |
+
self.n_layer = n_layer
|
90 |
+
self.n_inner = n_inner
|
91 |
+
self.n_head = n_head
|
92 |
+
self.n_head_kv = n_head_kv
|
93 |
+
self.rotary_dim = min(rotary_dim, n_embd // n_head)
|
94 |
+
self.activation_function = activation_function
|
95 |
+
self.flash_attn = flash_attn
|
96 |
+
self.flash_rotary = flash_rotary
|
97 |
+
self.fused_dense = fused_dense
|
98 |
+
self.attn_pdrop = attn_pdrop
|
99 |
+
self.embd_pdrop = embd_pdrop
|
100 |
+
self.resid_pdrop = resid_pdrop
|
101 |
+
self.layer_norm_epsilon = layer_norm_epsilon
|
102 |
+
self.initializer_range = initializer_range
|
103 |
+
self.gradient_checkpointing = gradient_checkpointing
|
104 |
+
|
105 |
+
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
|
106 |
+
|
107 |
+
|
108 |
+
@dataclass
|
109 |
+
class InferenceParams:
|
110 |
+
"""Inference parameters passed to model to efficiently calculate
|
111 |
+
and store context during inference.
|
112 |
+
|
113 |
+
Reference:
|
114 |
+
https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/utils/generation.py.
|
115 |
+
|
116 |
+
Args:
|
117 |
+
max_seqlen: Maximum sequence length.
|
118 |
+
max_batch_size: Maximum batch size.
|
119 |
+
seqlen_offset: Sequence length offset.
|
120 |
+
batch_size_offset: Batch size offset.
|
121 |
+
key_value_memory_dict: Key value memory dictionary.
|
122 |
+
lengths_per_sample: Lengths per sample.
|
123 |
+
|
124 |
+
"""
|
125 |
+
|
126 |
+
max_seqlen: int = field(metadata={"help": "Maximum sequence length."})
|
127 |
+
|
128 |
+
max_batch_size: int = field(metadata={"help": "Maximum batch size."})
|
129 |
+
|
130 |
+
seqlen_offset: int = field(default=0, metadata={"help": "Sequence length offset."})
|
131 |
+
|
132 |
+
batch_size_offset: int = field(default=0, metadata={"help": "Batch size offset."})
|
133 |
+
|
134 |
+
key_value_memory_dict: Dict[str, Any] = field(
|
135 |
+
default_factory=dict, metadata={"help": "Key value memory dictionary."}
|
136 |
+
)
|
137 |
+
|
138 |
+
lengths_per_sample: torch.Tensor = field(
|
139 |
+
default=None, metadata={"help": "Lengths per sample."}
|
140 |
+
)
|
141 |
+
|
142 |
+
|
143 |
+
class Embedding(nn.Module):
|
144 |
+
"""Token embedding with dropout."""
|
145 |
+
|
146 |
+
def __init__(self, config: PretrainedConfig) -> None:
|
147 |
+
super().__init__()
|
148 |
+
|
149 |
+
self.wte = nn.Embedding(config.vocab_size, config.n_embd)
|
150 |
+
self.drop = nn.Dropout(config.embd_pdrop)
|
151 |
+
|
152 |
+
def forward(self, input_ids: torch.LongTensor) -> torch.FloatTensor:
|
153 |
+
input_shape = input_ids.size()
|
154 |
+
input_ids = input_ids.view(-1, input_shape[-1])
|
155 |
+
|
156 |
+
hidden_states = self.wte(input_ids)
|
157 |
+
hidden_states = self.drop(hidden_states)
|
158 |
+
|
159 |
+
return hidden_states
|
160 |
+
|
161 |
+
|
162 |
+
# @torch.compile
|
163 |
+
def _apply_rotary_emb(
|
164 |
+
x: torch.FloatTensor,
|
165 |
+
cos: torch.FloatTensor,
|
166 |
+
sin: torch.FloatTensor,
|
167 |
+
) -> torch.FloatTensor:
|
168 |
+
_, seqlen, _, _ = x.shape
|
169 |
+
_, rotary_dim = cos.shape
|
170 |
+
rotary_dim *= 2
|
171 |
+
|
172 |
+
x_rot = x[:, :, :, :rotary_dim]
|
173 |
+
x_pass = x[:, :, :, rotary_dim:]
|
174 |
+
|
175 |
+
x1, x2 = x_rot.chunk(2, dim=-1)
|
176 |
+
c, s = rearrange(cos[:seqlen], "s d -> s 1 d"), rearrange(
|
177 |
+
sin[:seqlen], "s d -> s 1 d"
|
178 |
+
)
|
179 |
+
x1, x2, c, s = [t.to(dtype=torch.float32) for t in [x1, x2, c, s]]
|
180 |
+
|
181 |
+
x_rot = torch.cat([x1 * c - x2 * s, x1 * s + x2 * c], axis=-1).to(x.dtype)
|
182 |
+
|
183 |
+
return torch.cat([x_rot, x_pass], axis=-1)
|
184 |
+
|
185 |
+
|
186 |
+
# @torch.compile
|
187 |
+
def _apply_rotary_emb_kv(
|
188 |
+
kv: torch.FloatTensor,
|
189 |
+
cos: torch.FloatTensor,
|
190 |
+
sin: torch.FloatTensor,
|
191 |
+
cos_k: Optional[torch.FloatTensor] = None,
|
192 |
+
sin_k: Optional[torch.FloatTensor] = None,
|
193 |
+
) -> torch.FloatTensor:
|
194 |
+
_, seqlen, _, _, _ = kv.shape
|
195 |
+
_, rotary_dim = cos.shape
|
196 |
+
rotary_dim *= 2
|
197 |
+
|
198 |
+
k_rot = kv[:, :, 0, :, :rotary_dim]
|
199 |
+
k_pass = kv[:, :, 0, :, rotary_dim:]
|
200 |
+
|
201 |
+
k1, k2 = k_rot.chunk(2, dim=-1)
|
202 |
+
c, s = rearrange(cos[:seqlen], "s d -> s 1 d"), rearrange(
|
203 |
+
sin[:seqlen], "s d -> s 1 d"
|
204 |
+
)
|
205 |
+
k1, k2, c, s = [t.to(dtype=torch.float32) for t in [k1, k2, c, s]]
|
206 |
+
|
207 |
+
k_rot = torch.cat([k1 * c - k2 * s, k1 * s + k2 * c], axis=-1).to(kv.dtype)
|
208 |
+
|
209 |
+
return torch.cat(
|
210 |
+
[
|
211 |
+
torch.cat([k_rot, k_pass], axis=-1).unsqueeze(2),
|
212 |
+
kv[:, :, 1:2, :, :],
|
213 |
+
],
|
214 |
+
axis=2,
|
215 |
+
)
|
216 |
+
|
217 |
+
|
218 |
+
# @torch.compile
|
219 |
+
def _apply_rotary_emb_qkv(
|
220 |
+
qkv: torch.FloatTensor,
|
221 |
+
cos: torch.FloatTensor,
|
222 |
+
sin: torch.FloatTensor,
|
223 |
+
cos_k: Optional[torch.FloatTensor] = None,
|
224 |
+
sin_k: Optional[torch.FloatTensor] = None,
|
225 |
+
) -> torch.FloatTensor:
|
226 |
+
_, seqlen, _, _, _ = qkv.shape
|
227 |
+
_, rotary_dim = cos.shape
|
228 |
+
rotary_dim *= 2
|
229 |
+
|
230 |
+
q_rot = qkv[:, :, 0, :, :rotary_dim]
|
231 |
+
q_pass = qkv[:, :, 0, :, rotary_dim:]
|
232 |
+
|
233 |
+
k_rot = qkv[:, :, 1, :, :rotary_dim]
|
234 |
+
k_pass = qkv[:, :, 1, :, rotary_dim:]
|
235 |
+
|
236 |
+
q1, q2 = q_rot.chunk(2, dim=-1)
|
237 |
+
k1, k2 = k_rot.chunk(2, dim=-1)
|
238 |
+
c, s = rearrange(cos[:seqlen], "s d -> s 1 d"), rearrange(
|
239 |
+
sin[:seqlen], "s d -> s 1 d"
|
240 |
+
)
|
241 |
+
q1, q2, k1, k2, c, s = [t.to(dtype=torch.float32) for t in [q1, q2, k1, k2, c, s]]
|
242 |
+
|
243 |
+
q_rot = torch.cat([q1 * c - q2 * s, q1 * s + q2 * c], axis=-1).to(qkv.dtype)
|
244 |
+
k_rot = torch.cat([k1 * c - k2 * s, k1 * s + k2 * c], axis=-1).to(qkv.dtype)
|
245 |
+
|
246 |
+
return torch.cat(
|
247 |
+
[
|
248 |
+
torch.cat([q_rot, q_pass], axis=-1).unsqueeze(2),
|
249 |
+
torch.cat([k_rot, k_pass], axis=-1).unsqueeze(2),
|
250 |
+
qkv[:, :, 2:3, :, :],
|
251 |
+
],
|
252 |
+
axis=2,
|
253 |
+
)
|
254 |
+
|
255 |
+
|
256 |
+
class RotaryEmbedding(nn.Module):
|
257 |
+
"""Rotary positional embedding (RoPE).
|
258 |
+
|
259 |
+
Reference:
|
260 |
+
RoFormer: Enhanced Transformer with Rotary Position Embedding.
|
261 |
+
https://arxiv.org/pdf/2104.09864.pdf.
|
262 |
+
|
263 |
+
"""
|
264 |
+
|
265 |
+
def __init__(
|
266 |
+
self,
|
267 |
+
dim: int,
|
268 |
+
base: int = 10000,
|
269 |
+
scale_base: Optional[float] = None,
|
270 |
+
pos_idx_in_fp32: bool = True,
|
271 |
+
max_position_embeddings: int = 2048,
|
272 |
+
device: Optional[str] = None,
|
273 |
+
**kwargs,
|
274 |
+
) -> None:
|
275 |
+
super().__init__()
|
276 |
+
|
277 |
+
if scale_base is not None:
|
278 |
+
raise NotImplementedError
|
279 |
+
|
280 |
+
self.dim = dim
|
281 |
+
self.base = float(base)
|
282 |
+
self.scale_base = scale_base
|
283 |
+
self.pos_idx_in_fp32 = pos_idx_in_fp32
|
284 |
+
self.max_position_embeddings = max_position_embeddings
|
285 |
+
self.device = device
|
286 |
+
|
287 |
+
# Generate and save the inverse frequency buffer (non-trainable)
|
288 |
+
inv_freq = self._compute_inv_freq(device)
|
289 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
290 |
+
|
291 |
+
# Generate and save the scale buffer (non-trainable)
|
292 |
+
scale = (
|
293 |
+
(torch.arange(0, dim, 2, device=device, dtype=torch.float32) + 0.4 * dim)
|
294 |
+
/ (1.4 * dim)
|
295 |
+
if scale_base is not None
|
296 |
+
else None
|
297 |
+
)
|
298 |
+
self.register_buffer("scale", scale, persistent=False)
|
299 |
+
|
300 |
+
# Initialize cached attributes since ONNX can't rely on dynamic initialization
|
301 |
+
self._update_cos_sin_cache(
|
302 |
+
max_position_embeddings, device=device, dtype=torch.float32
|
303 |
+
)
|
304 |
+
|
305 |
+
def _compute_inv_freq(self, device: Optional[str] = None) -> torch.FloatTensor:
|
306 |
+
return 1.0 / (
|
307 |
+
self.base
|
308 |
+
** (
|
309 |
+
torch.arange(0, self.dim, 2, device=device, dtype=torch.float32)
|
310 |
+
/ self.dim
|
311 |
+
)
|
312 |
+
)
|
313 |
+
|
314 |
+
def _update_cos_sin_cache(
|
315 |
+
self,
|
316 |
+
seqlen: int,
|
317 |
+
device: Optional[str] = None,
|
318 |
+
dtype: Optional[torch.dtype] = None,
|
319 |
+
) -> None:
|
320 |
+
self._seq_len_cached = seqlen
|
321 |
+
|
322 |
+
# fp32 is preferred since the output of `torch.arange` can be quite large
|
323 |
+
# and bf16 would lose a lot of precision
|
324 |
+
if self.pos_idx_in_fp32:
|
325 |
+
t = torch.arange(seqlen, device=device, dtype=torch.float32)
|
326 |
+
if self.inv_freq.dtype != torch.float32:
|
327 |
+
inv_freq = self._compute_inv_freq(device=device)
|
328 |
+
else:
|
329 |
+
inv_freq = self.inv_freq
|
330 |
+
else:
|
331 |
+
t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype)
|
332 |
+
inv_freq = self.inv_freq
|
333 |
+
|
334 |
+
# `torch.outer` is preferred since `torch.einsum` converts from fp32 to fp16 if used with AMP
|
335 |
+
freqs = torch.outer(t, inv_freq)
|
336 |
+
if self.scale is None:
|
337 |
+
self._cos_cached = torch.cos(freqs).to(dtype)
|
338 |
+
self._sin_cached = torch.sin(freqs).to(dtype)
|
339 |
+
else:
|
340 |
+
power = (
|
341 |
+
torch.arange(seqlen, dtype=self.scale.dtype, device=self.scale.device)
|
342 |
+
- seqlen // 2
|
343 |
+
) / self.scale_base
|
344 |
+
scale = self.scale.to(device=power.device) ** rearrange(power, "s -> s 1")
|
345 |
+
|
346 |
+
# Force the scale multiplication to happen in fp32
|
347 |
+
self._cos_cached = (torch.cos(freqs) * scale).to(dtype)
|
348 |
+
self._sin_cached = (torch.sin(freqs) * scale).to(dtype)
|
349 |
+
self._cos_k_cached = (torch.cos(freqs) / scale).to(dtype)
|
350 |
+
self._sin_k_cached = (torch.sin(freqs) / scale).to(dtype)
|
351 |
+
|
352 |
+
def forward(
|
353 |
+
self,
|
354 |
+
qkv: torch.Tensor,
|
355 |
+
kv: Optional[torch.Tensor] = None,
|
356 |
+
seqlen_offset: int = 0,
|
357 |
+
**kwargs,
|
358 |
+
) -> Tuple[torch.Tensor, torch.Tensor]:
|
359 |
+
if (
|
360 |
+
self._seq_len_cached < qkv.shape[1] + seqlen_offset
|
361 |
+
or self._cos_cached.device != qkv.device
|
362 |
+
or self._cos_cached.dtype != qkv.dtype
|
363 |
+
or (self.training and self._cos_cached.is_inference())
|
364 |
+
):
|
365 |
+
self._update_cos_sin_cache(
|
366 |
+
qkv.shape[1] + seqlen_offset, device=qkv.device, dtype=qkv.dtype
|
367 |
+
)
|
368 |
+
|
369 |
+
if kv is None:
|
370 |
+
return _apply_rotary_emb_qkv(
|
371 |
+
qkv,
|
372 |
+
self._cos_cached[seqlen_offset:],
|
373 |
+
self._sin_cached[seqlen_offset:],
|
374 |
+
)
|
375 |
+
else:
|
376 |
+
q = _apply_rotary_emb(
|
377 |
+
qkv,
|
378 |
+
self._cos_cached[seqlen_offset:],
|
379 |
+
self._sin_cached[seqlen_offset:],
|
380 |
+
)
|
381 |
+
kv = _apply_rotary_emb_kv(
|
382 |
+
kv,
|
383 |
+
self._cos_cached[seqlen_offset:],
|
384 |
+
self._sin_cached[seqlen_offset:],
|
385 |
+
)
|
386 |
+
|
387 |
+
return q, kv
|
388 |
+
|
389 |
+
|
390 |
+
class MLP(nn.Module):
|
391 |
+
"""Multi-Layer Perceptron.
|
392 |
+
|
393 |
+
Reference:
|
394 |
+
Attention Is All You Need.
|
395 |
+
https://arxiv.org/pdf/1706.03762.pdf.
|
396 |
+
|
397 |
+
"""
|
398 |
+
|
399 |
+
def __init__(
|
400 |
+
self,
|
401 |
+
config: PretrainedConfig,
|
402 |
+
n_inner: Optional[int] = None,
|
403 |
+
act_fn: Optional[str] = None,
|
404 |
+
) -> None:
|
405 |
+
super().__init__()
|
406 |
+
|
407 |
+
act_fn = config.activation_function if act_fn is None else act_fn
|
408 |
+
|
409 |
+
n_inner = getattr(config, "n_inner", None) if n_inner is None else n_inner
|
410 |
+
n_inner = n_inner if n_inner is not None else 4 * config.n_embd
|
411 |
+
|
412 |
+
self.fc1 = nn.Linear(config.n_embd, n_inner)
|
413 |
+
self.fc2 = nn.Linear(n_inner, config.n_embd)
|
414 |
+
self.act = ACT2FN[act_fn]
|
415 |
+
|
416 |
+
def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
|
417 |
+
hidden_states = self.fc1(hidden_states)
|
418 |
+
hidden_states = self.act(hidden_states)
|
419 |
+
hidden_states = self.fc2(hidden_states)
|
420 |
+
|
421 |
+
return hidden_states
|
422 |
+
|
423 |
+
|
424 |
+
class SelfAttention(nn.Module):
|
425 |
+
"""Self-attention layer (compatible with PyTorch).
|
426 |
+
|
427 |
+
Reference:
|
428 |
+
https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mha.py.
|
429 |
+
|
430 |
+
"""
|
431 |
+
|
432 |
+
def __init__(
|
433 |
+
self,
|
434 |
+
causal: bool = True,
|
435 |
+
softmax_scale: Optional[float] = None,
|
436 |
+
attention_dropout: float = 0.0,
|
437 |
+
) -> None:
|
438 |
+
super().__init__()
|
439 |
+
|
440 |
+
self.causal = causal
|
441 |
+
self.softmax_scale = softmax_scale
|
442 |
+
self.drop = nn.Dropout(attention_dropout)
|
443 |
+
|
444 |
+
@torch.autocast("cpu", enabled=False)
|
445 |
+
@torch.autocast("cuda", enabled=False)
|
446 |
+
def forward(
|
447 |
+
self,
|
448 |
+
qkv: torch.FloatTensor,
|
449 |
+
causal: bool = None,
|
450 |
+
key_padding_mask: Optional[torch.BoolTensor] = None,
|
451 |
+
**kwargs,
|
452 |
+
) -> torch.FloatTensor:
|
453 |
+
batch_size, seqlen = qkv.shape[0], qkv.shape[1]
|
454 |
+
q, k, v = qkv.unbind(dim=2)
|
455 |
+
|
456 |
+
q = q.to(torch.float32)
|
457 |
+
k = k.to(torch.float32)
|
458 |
+
|
459 |
+
causal = self.causal if causal is None else causal
|
460 |
+
softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
|
461 |
+
|
462 |
+
# Autocast is manually disabled to avoid `torch.einsum` performing the operation
|
463 |
+
# using float16, which might lead to overflow
|
464 |
+
scores = torch.einsum("bthd,bshd->bhts", q, k * softmax_scale)
|
465 |
+
|
466 |
+
if key_padding_mask is not None:
|
467 |
+
padding_mask = torch.full(
|
468 |
+
(batch_size, seqlen), -10000.0, dtype=scores.dtype, device=scores.device
|
469 |
+
)
|
470 |
+
padding_mask.masked_fill_(key_padding_mask, 0.0)
|
471 |
+
|
472 |
+
scores = scores + rearrange(padding_mask, "b s -> b 1 1 s")
|
473 |
+
|
474 |
+
if causal:
|
475 |
+
causal_mask = torch.triu(
|
476 |
+
torch.full((seqlen, seqlen), -10000.0, device=scores.device), 1
|
477 |
+
)
|
478 |
+
scores = scores + causal_mask.to(dtype=scores.dtype)
|
479 |
+
|
480 |
+
attention = torch.softmax(scores, dim=-1).to(v.dtype)
|
481 |
+
attention = self.drop(attention)
|
482 |
+
|
483 |
+
output = torch.einsum("bhts,bshd->bthd", attention, v)
|
484 |
+
|
485 |
+
return output
|
486 |
+
|
487 |
+
|
488 |
+
class CrossAttention(nn.Module):
|
489 |
+
"""Cross-attention layer (compatible with PyTorch).
|
490 |
+
|
491 |
+
Reference:
|
492 |
+
https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mha.py.
|
493 |
+
|
494 |
+
"""
|
495 |
+
|
496 |
+
def __init__(
|
497 |
+
self,
|
498 |
+
causal: bool = True,
|
499 |
+
softmax_scale: Optional[float] = None,
|
500 |
+
attention_dropout: float = 0.0,
|
501 |
+
) -> None:
|
502 |
+
super().__init__()
|
503 |
+
|
504 |
+
self.causal = causal
|
505 |
+
self.softmax_scale = softmax_scale
|
506 |
+
self.drop = nn.Dropout(attention_dropout)
|
507 |
+
|
508 |
+
@torch.autocast("cpu", enabled=False)
|
509 |
+
@torch.autocast("cuda", enabled=False)
|
510 |
+
def forward(
|
511 |
+
self,
|
512 |
+
q: torch.FloatTensor,
|
513 |
+
kv: torch.FloatTensor,
|
514 |
+
causal: bool = None,
|
515 |
+
key_padding_mask: Optional[torch.BoolTensor] = None,
|
516 |
+
**kwargs,
|
517 |
+
) -> torch.FloatTensor:
|
518 |
+
batch_size, seqlen_q = q.shape[0], q.shape[1]
|
519 |
+
seqlen_k = kv.shape[1]
|
520 |
+
|
521 |
+
if kv.shape[3] != q.shape[2]:
|
522 |
+
kv = repeat(kv, "... hkv d -> ... (hkv g) d", g=q.shape[2] // kv.shape[3])
|
523 |
+
k, v = kv.unbind(dim=2)
|
524 |
+
|
525 |
+
q = q.to(torch.float32)
|
526 |
+
k = k.to(torch.float32)
|
527 |
+
|
528 |
+
causal = self.causal if causal is None else causal
|
529 |
+
softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
|
530 |
+
|
531 |
+
# Autocast is manually disabled to avoid `torch.einsum` performing the operation
|
532 |
+
# using float16, which might lead to overflow
|
533 |
+
scores = torch.einsum("bthd,bshd->bhts", q, k * softmax_scale)
|
534 |
+
|
535 |
+
if key_padding_mask is not None:
|
536 |
+
padding_mask = torch.full(
|
537 |
+
(batch_size, seqlen_k),
|
538 |
+
-10000.0,
|
539 |
+
dtype=scores.dtype,
|
540 |
+
device=scores.device,
|
541 |
+
)
|
542 |
+
padding_mask.masked_fill_(key_padding_mask, 0.0)
|
543 |
+
|
544 |
+
scores = scores + rearrange(padding_mask, "b s -> b 1 1 s")
|
545 |
+
|
546 |
+
if causal:
|
547 |
+
rows = rearrange(
|
548 |
+
torch.arange(seqlen_q, device=q.device, dtype=torch.long), "s -> s 1"
|
549 |
+
)
|
550 |
+
cols = torch.arange(seqlen_k, device=k.device, dtype=torch.long)
|
551 |
+
causal_mask = cols > rows + seqlen_k - seqlen_q
|
552 |
+
|
553 |
+
scores = scores.masked_fill(causal_mask, -10000.0)
|
554 |
+
|
555 |
+
attention = torch.softmax(scores, dim=-1).to(v.dtype)
|
556 |
+
attention = self.drop(attention)
|
557 |
+
|
558 |
+
output = torch.einsum("bhts,bshd->bthd", attention, v)
|
559 |
+
|
560 |
+
return output
|
561 |
+
|
562 |
+
|
563 |
+
def _find_mha_dims(
|
564 |
+
config: PretrainedConfig,
|
565 |
+
n_head: Optional[int] = None,
|
566 |
+
n_head_kv: Optional[int] = None,
|
567 |
+
head_dim: Optional[int] = None,
|
568 |
+
) -> Tuple[int, int]:
|
569 |
+
if n_head is None and head_dim is None:
|
570 |
+
head_dim = config.n_embd // config.n_head
|
571 |
+
n_head = config.n_head
|
572 |
+
elif n_head is None or head_dim is None:
|
573 |
+
raise ValueError("`n_head` and `head_dim` must be both specified or `None`.")
|
574 |
+
|
575 |
+
if n_head_kv is None:
|
576 |
+
n_head_kv = getattr(config, "n_head_kv", None) or n_head
|
577 |
+
|
578 |
+
return n_head, n_head_kv, head_dim
|
579 |
+
|
580 |
+
|
581 |
+
def _update_kv_cache(
|
582 |
+
kv: torch.FloatTensor, inference_params: InferenceParams, layer_idx: int
|
583 |
+
) -> torch.FloatTensor:
|
584 |
+
num_heads, head_dim = kv.shape[-2:]
|
585 |
+
|
586 |
+
if layer_idx not in inference_params.key_value_memory_dict:
|
587 |
+
inference_params.key_value_memory_dict[layer_idx] = torch.empty(
|
588 |
+
inference_params.max_batch_size,
|
589 |
+
inference_params.max_seqlen,
|
590 |
+
2,
|
591 |
+
num_heads,
|
592 |
+
head_dim,
|
593 |
+
dtype=kv.dtype,
|
594 |
+
device=kv.device,
|
595 |
+
)
|
596 |
+
|
597 |
+
batch_start = inference_params.batch_size_offset
|
598 |
+
batch_end = batch_start + kv.shape[0]
|
599 |
+
|
600 |
+
sequence_start = inference_params.seqlen_offset
|
601 |
+
sequence_end = sequence_start + kv.shape[1]
|
602 |
+
|
603 |
+
# When the current sequence length is equal to or larger than the maximum sequence length,
|
604 |
+
# we need to concatenate the current `kv` with the cached `kv` to expand its length
|
605 |
+
if sequence_end >= inference_params.max_seqlen:
|
606 |
+
inference_params.key_value_memory_dict[layer_idx] = torch.concatenate(
|
607 |
+
(inference_params.key_value_memory_dict[layer_idx], kv), dim=1
|
608 |
+
)
|
609 |
+
|
610 |
+
inference_params.key_value_memory_dict[layer_idx][
|
611 |
+
batch_start:batch_end, sequence_start:sequence_end, ...
|
612 |
+
] = kv
|
613 |
+
kv = inference_params.key_value_memory_dict[layer_idx][
|
614 |
+
batch_start:batch_end, :sequence_end, ...
|
615 |
+
]
|
616 |
+
|
617 |
+
return kv
|
618 |
+
|
619 |
+
|
620 |
+
class MHA(nn.Module):
|
621 |
+
"""Multi-head attention layer."""
|
622 |
+
|
623 |
+
def __init__(
|
624 |
+
self,
|
625 |
+
config: PretrainedConfig,
|
626 |
+
dtype: Optional[torch.dtype] = None,
|
627 |
+
device: Optional[str] = None,
|
628 |
+
rotary_dim: Optional[int] = None,
|
629 |
+
rotary_base: float = 10000.0,
|
630 |
+
rotary_scale_base: Optional[float] = None,
|
631 |
+
n_head: Optional[int] = None,
|
632 |
+
n_head_kv: Optional[int] = None,
|
633 |
+
head_dim: Optional[int] = None,
|
634 |
+
bias: bool = True,
|
635 |
+
causal: bool = True,
|
636 |
+
softmax_scale: Optional[float] = None,
|
637 |
+
layer_idx: Optional[int] = None,
|
638 |
+
return_residual: bool = False,
|
639 |
+
checkpointing: bool = False,
|
640 |
+
) -> None:
|
641 |
+
super().__init__()
|
642 |
+
|
643 |
+
# Rotary embedding
|
644 |
+
self.rotary_dim = (
|
645 |
+
rotary_dim if rotary_dim is not None else getattr(config, "rotary_dim", 0)
|
646 |
+
)
|
647 |
+
|
648 |
+
if self.rotary_dim > 0:
|
649 |
+
self.rotary_emb = RotaryEmbedding(
|
650 |
+
self.rotary_dim,
|
651 |
+
base=rotary_base,
|
652 |
+
scale_base=rotary_scale_base,
|
653 |
+
device=device,
|
654 |
+
max_position_embeddings=config.n_positions,
|
655 |
+
)
|
656 |
+
|
657 |
+
# MLP
|
658 |
+
self.n_head, self.n_head_kv, self.head_dim = _find_mha_dims(
|
659 |
+
config, n_head=n_head, n_head_kv=n_head_kv, head_dim=head_dim
|
660 |
+
)
|
661 |
+
op_size = self.head_dim * (self.n_head + 2 * self.n_head_kv)
|
662 |
+
hidden_size = config.n_embd
|
663 |
+
|
664 |
+
linear_cls = FusedDense if config.fused_dense else nn.Linear
|
665 |
+
if linear_cls is None:
|
666 |
+
linear_cls = nn.Linear
|
667 |
+
|
668 |
+
self.Wqkv = linear_cls(
|
669 |
+
hidden_size, op_size, bias=bias, device=device, dtype=dtype
|
670 |
+
)
|
671 |
+
self.out_proj = linear_cls(
|
672 |
+
hidden_size, hidden_size, bias=bias, device=device, dtype=dtype
|
673 |
+
)
|
674 |
+
|
675 |
+
# Attention
|
676 |
+
self.inner_attn = SelfAttention(
|
677 |
+
causal=causal,
|
678 |
+
softmax_scale=softmax_scale,
|
679 |
+
attention_dropout=config.attn_pdrop,
|
680 |
+
)
|
681 |
+
self.inner_cross_attn = CrossAttention(
|
682 |
+
causal=causal,
|
683 |
+
softmax_scale=softmax_scale,
|
684 |
+
attention_dropout=config.attn_pdrop,
|
685 |
+
)
|
686 |
+
|
687 |
+
self.layer_idx = layer_idx
|
688 |
+
self.return_residual = return_residual
|
689 |
+
self.checkpointing = checkpointing
|
690 |
+
|
691 |
+
def _forward_self_attn(
|
692 |
+
self, x: torch.FloatTensor, key_padding_mask: Optional[torch.BoolTensor]
|
693 |
+
) -> torch.FloatTensor:
|
694 |
+
qkv = self.Wqkv(x)
|
695 |
+
qkv = rearrange(
|
696 |
+
qkv, "... (three h d) -> ... three h d", three=3, d=self.head_dim
|
697 |
+
)
|
698 |
+
|
699 |
+
if self.rotary_dim > 0:
|
700 |
+
qkv = self.rotary_emb(qkv)
|
701 |
+
|
702 |
+
if self.checkpointing:
|
703 |
+
return torch.utils.checkpoint.checkpoint(
|
704 |
+
self.inner_attn, qkv, key_padding_mask=key_padding_mask
|
705 |
+
)
|
706 |
+
|
707 |
+
return self.inner_attn(qkv, key_padding_mask=key_padding_mask)
|
708 |
+
|
709 |
+
def _forward_cross_attn(
|
710 |
+
self,
|
711 |
+
x: torch.FloatTensor,
|
712 |
+
past_key_values: Optional[InferenceParams],
|
713 |
+
key_padding_mask: Optional[torch.BoolTensor],
|
714 |
+
) -> torch.FloatTensor:
|
715 |
+
batch_size = x.shape[0]
|
716 |
+
|
717 |
+
qkv = self.Wqkv(x)
|
718 |
+
|
719 |
+
q = qkv[..., : self.n_head * self.head_dim]
|
720 |
+
q = rearrange(q, "... (h d) -> ... h d", d=self.head_dim)
|
721 |
+
|
722 |
+
kv = qkv[..., self.n_head * self.head_dim :]
|
723 |
+
kv = rearrange(kv, "... (two hkv d) -> ... two hkv d", two=2, d=self.head_dim)
|
724 |
+
|
725 |
+
seqlen_offset = (
|
726 |
+
past_key_values.seqlen_offset if past_key_values is not None else 0
|
727 |
+
)
|
728 |
+
causal = None if seqlen_offset == 0 else False
|
729 |
+
if self.rotary_dim > 0:
|
730 |
+
q, kv = self.rotary_emb(q, kv=kv, seqlen_offset=seqlen_offset)
|
731 |
+
|
732 |
+
if past_key_values is not None:
|
733 |
+
kv = _update_kv_cache(kv, past_key_values, self.layer_idx)
|
734 |
+
|
735 |
+
if self.checkpointing:
|
736 |
+
return torch.utils.checkpoint.checkpoint(
|
737 |
+
self.inner_cross_attn,
|
738 |
+
q,
|
739 |
+
kv,
|
740 |
+
key_padding_mask=key_padding_mask,
|
741 |
+
causal=causal,
|
742 |
+
)
|
743 |
+
|
744 |
+
return self.inner_cross_attn(
|
745 |
+
q, kv, key_padding_mask=key_padding_mask, causal=causal
|
746 |
+
)
|
747 |
+
|
748 |
+
def forward(
|
749 |
+
self,
|
750 |
+
x: torch.FloatTensor,
|
751 |
+
past_key_values: Optional[InferenceParams] = None,
|
752 |
+
attention_mask: Optional[Union[torch.LongTensor, torch.BoolTensor]] = None,
|
753 |
+
**kwargs,
|
754 |
+
) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
|
755 |
+
if attention_mask is not None:
|
756 |
+
attention_mask = attention_mask.bool()
|
757 |
+
else:
|
758 |
+
attention_mask = None
|
759 |
+
|
760 |
+
# MHA
|
761 |
+
if self.n_head == self.n_head_kv:
|
762 |
+
if past_key_values is None:
|
763 |
+
# If `past_key_values` are not supplied, we run self-attention
|
764 |
+
attn_output = self._forward_self_attn(x, attention_mask)
|
765 |
+
else:
|
766 |
+
# If `past_key_values` are supplied, it means that we might have cached values and
|
767 |
+
# could take advantage of cross-attention
|
768 |
+
attn_output = self._forward_cross_attn(
|
769 |
+
x, past_key_values, attention_mask
|
770 |
+
)
|
771 |
+
# MQA / GQA
|
772 |
+
else:
|
773 |
+
# Regardless of `past_key_values` being supplied or not, it always use cross-attention
|
774 |
+
# because `q` and `kv` lengths might be different
|
775 |
+
attn_output = self._forward_cross_attn(x, past_key_values, attention_mask)
|
776 |
+
|
777 |
+
output = rearrange(attn_output, "... h d -> ... (h d)")
|
778 |
+
output = self.out_proj(output)
|
779 |
+
|
780 |
+
return output if not self.return_residual else (output, x)
|
781 |
+
|
782 |
+
|
783 |
+
class ParallelBlock(nn.Module):
|
784 |
+
"""Parallel block.
|
785 |
+
|
786 |
+
This block applies parallel mixer and MLP layers to the input (used in GPT-J and CodeGen).
|
787 |
+
|
788 |
+
"""
|
789 |
+
|
790 |
+
def __init__(
|
791 |
+
self,
|
792 |
+
config: PretrainedConfig,
|
793 |
+
block_idx: Optional[int] = None,
|
794 |
+
) -> None:
|
795 |
+
super().__init__()
|
796 |
+
|
797 |
+
self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
|
798 |
+
self.resid_dropout = nn.Dropout(config.resid_pdrop)
|
799 |
+
self.block_idx = block_idx
|
800 |
+
|
801 |
+
self.mixer = MHA(config, layer_idx=block_idx)
|
802 |
+
self.mlp = MLP(config)
|
803 |
+
|
804 |
+
def forward(
|
805 |
+
self,
|
806 |
+
hidden_states: torch.FloatTensor,
|
807 |
+
past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
|
808 |
+
attention_mask: Optional[torch.BoolTensor] = None,
|
809 |
+
**kwargs,
|
810 |
+
) -> torch.FloatTensor:
|
811 |
+
residual = hidden_states
|
812 |
+
hidden_states = self.ln(hidden_states)
|
813 |
+
|
814 |
+
attn_outputs = self.mixer(
|
815 |
+
hidden_states,
|
816 |
+
past_key_values=past_key_values,
|
817 |
+
attention_mask=attention_mask,
|
818 |
+
)
|
819 |
+
if isinstance(attn_outputs, tuple):
|
820 |
+
attn_outputs = attn_outputs[0]
|
821 |
+
|
822 |
+
attn_outputs = self.resid_dropout(attn_outputs)
|
823 |
+
feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states))
|
824 |
+
|
825 |
+
hidden_states = attn_outputs + feed_forward_hidden_states + residual
|
826 |
+
|
827 |
+
return hidden_states
|
828 |
+
|
829 |
+
|
830 |
+
class CausalLMHead(nn.Module):
|
831 |
+
"""Causal Language Modeling head.
|
832 |
+
|
833 |
+
Reference:
|
834 |
+
Improving Language Understanding by Generative Pre-Training.
|
835 |
+
https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf.
|
836 |
+
|
837 |
+
"""
|
838 |
+
|
839 |
+
def __init__(self, config: PretrainedConfig) -> None:
|
840 |
+
super().__init__()
|
841 |
+
|
842 |
+
self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
|
843 |
+
self.linear = nn.Linear(config.n_embd, config.vocab_size)
|
844 |
+
|
845 |
+
def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
|
846 |
+
hidden_states = self.ln(hidden_states)
|
847 |
+
logits = self.linear(hidden_states).to(torch.float32)
|
848 |
+
|
849 |
+
return logits
|
850 |
+
|
851 |
+
|
852 |
+
class CausalLMLoss(nn.Module):
|
853 |
+
"""Causal Language Modeling loss.
|
854 |
+
|
855 |
+
Reference:
|
856 |
+
Improving Language Understanding by Generative Pre-Training.
|
857 |
+
https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf.
|
858 |
+
|
859 |
+
"""
|
860 |
+
|
861 |
+
def __init__(self, shift_labels: bool = True) -> None:
|
862 |
+
super().__init__()
|
863 |
+
|
864 |
+
self.shift_labels = shift_labels
|
865 |
+
self.loss_fct = nn.CrossEntropyLoss()
|
866 |
+
|
867 |
+
def forward(
|
868 |
+
self, logits: torch.FloatTensor, labels: torch.LongTensor
|
869 |
+
) -> torch.FloatTensor:
|
870 |
+
if self.shift_labels:
|
871 |
+
logits = logits[..., :-1, :].contiguous()
|
872 |
+
labels = labels[..., 1:].contiguous()
|
873 |
+
|
874 |
+
loss = self.loss_fct(logits.view(-1, logits.size(-1)), labels.view(-1))
|
875 |
+
|
876 |
+
return loss
|
877 |
+
|
878 |
+
|
879 |
+
class PhiPreTrainedModel(PreTrainedModel):
|
880 |
+
"""Phi pre-trained model."""
|
881 |
+
|
882 |
+
config_class = PhiConfig
|
883 |
+
base_model_prefix = "transformer"
|
884 |
+
supports_gradient_checkpointing = False
|
885 |
+
_no_split_modules = ["ParallelBlock"]
|
886 |
+
|
887 |
+
def __init__(self, *inputs, **kwargs) -> None:
|
888 |
+
super().__init__(*inputs, **kwargs)
|
889 |
+
|
890 |
+
def prepare_inputs_for_generation(
|
891 |
+
self,
|
892 |
+
input_ids: torch.LongTensor = None,
|
893 |
+
inputs_embeds: torch.FloatTensor = None,
|
894 |
+
past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
|
895 |
+
attention_mask: Optional[Union[torch.LongTensor, torch.BoolTensor]] = None,
|
896 |
+
**kwargs,
|
897 |
+
) -> Dict[str, Any]:
|
898 |
+
if inputs_embeds is not None:
|
899 |
+
max_batch_size = inputs_embeds.shape[0]
|
900 |
+
seqlen_offset = inputs_embeds.shape[1] + input_ids.shape[1] - 2
|
901 |
+
elif input_ids is not None:
|
902 |
+
max_batch_size = input_ids.shape[0]
|
903 |
+
seqlen_offset = input_ids.shape[1] - 1
|
904 |
+
else:
|
905 |
+
raise ValueError(
|
906 |
+
"You have to specify either `input_ids` or `inputs_embeds`."
|
907 |
+
)
|
908 |
+
|
909 |
+
args = {}
|
910 |
+
|
911 |
+
if past_key_values is None or not (
|
912 |
+
isinstance(past_key_values, InferenceParams)
|
913 |
+
):
|
914 |
+
past_key_values = InferenceParams(
|
915 |
+
max_seqlen=self.config.n_positions,
|
916 |
+
max_batch_size=max_batch_size,
|
917 |
+
seqlen_offset=0,
|
918 |
+
batch_size_offset=0,
|
919 |
+
key_value_memory_dict={},
|
920 |
+
lengths_per_sample=None,
|
921 |
+
)
|
922 |
+
if inputs_embeds is not None:
|
923 |
+
args = {"inputs_embeds": inputs_embeds}
|
924 |
+
elif input_ids is not None:
|
925 |
+
args = {"input_ids": input_ids}
|
926 |
+
else:
|
927 |
+
raise ValueError(
|
928 |
+
"You have to specify either `input_ids` or `inputs_embeds`."
|
929 |
+
)
|
930 |
+
else:
|
931 |
+
# Assume that `past_key_values` has cached all tokens up to the last token in `input_ids`
|
932 |
+
past_key_values.seqlen_offset = seqlen_offset
|
933 |
+
input_ids = input_ids[:, -1].unsqueeze(-1)
|
934 |
+
args = {"input_ids": input_ids}
|
935 |
+
|
936 |
+
return {
|
937 |
+
**args,
|
938 |
+
"past_key_values": past_key_values,
|
939 |
+
"attention_mask": attention_mask,
|
940 |
+
}
|
941 |
+
|
942 |
+
|
943 |
+
class PhiModel(PhiPreTrainedModel):
|
944 |
+
"""Phi model."""
|
945 |
+
|
946 |
+
_keys_to_ignore_on_load_missing = [""]
|
947 |
+
_keys_to_ignore_on_load_unexpected = [r"h\.\d+\.mlp.(fc_in|fc_out)\.(weight|bias)"]
|
948 |
+
|
949 |
+
def __init__(self, config: PhiConfig) -> None:
|
950 |
+
super().__init__(config)
|
951 |
+
|
952 |
+
self.embd = Embedding(config)
|
953 |
+
self.h = nn.ModuleList(
|
954 |
+
[ParallelBlock(config, block_idx=i) for i in range(config.n_layer)]
|
955 |
+
)
|
956 |
+
self.gradient_checkpointing = config.gradient_checkpointing
|
957 |
+
self.post_init()
|
958 |
+
|
959 |
+
def get_input_embeddings(self) -> nn.Embedding:
|
960 |
+
return self.embd.wte
|
961 |
+
|
962 |
+
def set_input_embeddings(self, new_embeddings: nn.Embedding) -> None:
|
963 |
+
self.embd.wte = new_embeddings
|
964 |
+
|
965 |
+
def forward(
|
966 |
+
self,
|
967 |
+
input_ids: torch.LongTensor = None,
|
968 |
+
inputs_embeds: torch.FloatTensor = None,
|
969 |
+
past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
|
970 |
+
attention_mask: Optional[torch.BoolTensor] = None,
|
971 |
+
) -> torch.FloatTensor:
|
972 |
+
if input_ids is not None and inputs_embeds is not None:
|
973 |
+
raise ValueError(
|
974 |
+
"You cannot specify both `input_ids` and `inputs_embeds` at the same time."
|
975 |
+
)
|
976 |
+
elif input_ids is None and inputs_embeds is None:
|
977 |
+
raise ValueError(
|
978 |
+
"You have to specify either `input_ids` or `inputs_embeds`."
|
979 |
+
)
|
980 |
+
elif input_ids is not None:
|
981 |
+
hidden_states = self.embd(input_ids)
|
982 |
+
else:
|
983 |
+
hidden_states = inputs_embeds
|
984 |
+
|
985 |
+
for layer in self.h:
|
986 |
+
if self.gradient_checkpointing:
|
987 |
+
hidden_states = torch.utils.checkpoint.checkpoint(
|
988 |
+
layer.__call__,
|
989 |
+
hidden_states,
|
990 |
+
past_key_values,
|
991 |
+
attention_mask,
|
992 |
+
use_reentrant=True,
|
993 |
+
)
|
994 |
+
else:
|
995 |
+
hidden_states = layer(
|
996 |
+
hidden_states,
|
997 |
+
past_key_values=past_key_values,
|
998 |
+
attention_mask=attention_mask,
|
999 |
+
)
|
1000 |
+
|
1001 |
+
return hidden_states
|
1002 |
+
|
1003 |
+
|
1004 |
+
class PhiForCausalLM(PhiPreTrainedModel):
|
1005 |
+
"""Phi for Causal Language Modeling."""
|
1006 |
+
|
1007 |
+
_keys_to_ignore_on_load_missing = [""]
|
1008 |
+
_keys_to_ignore_on_load_unexpected = [
|
1009 |
+
r"transformer\.h\.\d+\.mlp.(fc_in|fc_out)\.(weight|bias)"
|
1010 |
+
]
|
1011 |
+
|
1012 |
+
def __init__(self, config: PhiConfig) -> None:
|
1013 |
+
super().__init__(config)
|
1014 |
+
|
1015 |
+
self.transformer = PhiModel(config)
|
1016 |
+
self.lm_head = CausalLMHead(config)
|
1017 |
+
self.loss = CausalLMLoss()
|
1018 |
+
|
1019 |
+
self.post_init()
|
1020 |
+
|
1021 |
+
def get_output_embeddings(self) -> nn.Linear:
|
1022 |
+
return self.lm_head.linear
|
1023 |
+
|
1024 |
+
def set_output_embeddings(self, new_embeddings: nn.Linear) -> None:
|
1025 |
+
self.lm_head.linear = new_embeddings
|
1026 |
+
|
1027 |
+
def forward(
|
1028 |
+
self,
|
1029 |
+
input_ids: torch.LongTensor = None,
|
1030 |
+
inputs_embeds: torch.FloatTensor = None,
|
1031 |
+
past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
|
1032 |
+
attention_mask: Optional[torch.BoolTensor] = None,
|
1033 |
+
labels: Optional[torch.LongTensor] = None,
|
1034 |
+
**kwargs,
|
1035 |
+
) -> CausalLMOutputWithPast:
|
1036 |
+
hidden_states = self.transformer(
|
1037 |
+
input_ids,
|
1038 |
+
inputs_embeds,
|
1039 |
+
past_key_values=past_key_values,
|
1040 |
+
attention_mask=attention_mask,
|
1041 |
+
)
|
1042 |
+
lm_logits = self.lm_head(hidden_states)
|
1043 |
+
|
1044 |
+
loss = None
|
1045 |
+
if labels is not None:
|
1046 |
+
loss = self.loss(lm_logits, labels)
|
1047 |
+
|
1048 |
+
return CausalLMOutputWithPast(
|
1049 |
+
loss=loss, logits=lm_logits, past_key_values=past_key_values
|
1050 |
+
)
|
1051 |
+
|
1052 |
+
|
1053 |
+
class VisionEncoder(nn.Module):
|
1054 |
+
def __init__(self, model_path: str = "model") -> None:
|
1055 |
+
super().__init__()
|
1056 |
+
self.model = torch.jit.load(f"{model_path}/vision.pt").to(DEVICE, dtype=DTYPE)
|
1057 |
+
self.preprocess = Compose(
|
1058 |
+
[
|
1059 |
+
Resize(size=(384, 384), interpolation=InterpolationMode.BICUBIC),
|
1060 |
+
ToImage(),
|
1061 |
+
ToDtype(torch.float32, scale=True),
|
1062 |
+
Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
|
1063 |
+
]
|
1064 |
+
)
|
1065 |
+
|
1066 |
+
def __call__(self, image: Image) -> torch.Tensor:
|
1067 |
+
with torch.no_grad():
|
1068 |
+
image_vec = self.preprocess(image.convert("RGB")).unsqueeze(0)
|
1069 |
+
image_vec = image_vec[:, :, :-6, :-6]
|
1070 |
+
image_vec = rearrange(
|
1071 |
+
image_vec, "b c (h p1) (w p2) -> b (h w) (c p1 p2)", p1=14, p2=14
|
1072 |
+
)
|
1073 |
+
|
1074 |
+
image_vec = image_vec.to(DEVICE, dtype=DTYPE)
|
1075 |
+
return self.model(image_vec)
|
1076 |
+
|
1077 |
+
|
1078 |
+
class TextModel(nn.Module):
|
1079 |
+
def __init__(self, model_path: str = "model") -> None:
|
1080 |
+
super().__init__()
|
1081 |
+
self.tokenizer = Tokenizer.from_pretrained(f"{model_path}/tokenizer")
|
1082 |
+
phi_config = PhiConfig.from_pretrained(f"{model_path}/text_model_cfg.json")
|
1083 |
+
|
1084 |
+
with init_empty_weights():
|
1085 |
+
self.model = PhiForCausalLM(phi_config)
|
1086 |
+
|
1087 |
+
self.model = load_checkpoint_and_dispatch(
|
1088 |
+
self.model,
|
1089 |
+
f"{model_path}/text_model.pt",
|
1090 |
+
device_map={"": DEVICE},
|
1091 |
+
dtype=DTYPE,
|
1092 |
+
)
|
1093 |
+
|
1094 |
+
self.text_emb = self.model.get_input_embeddings()
|
1095 |
+
|
1096 |
+
def input_embeds(self, prompt, image_embeds):
|
1097 |
+
embeds = []
|
1098 |
+
|
1099 |
+
def _add_toks(toks):
|
1100 |
+
embeds.append(self.text_emb(toks))
|
1101 |
+
|
1102 |
+
def _tokenize(txt):
|
1103 |
+
return self.tokenizer(
|
1104 |
+
txt, return_tensors="pt", add_special_tokens=False
|
1105 |
+
).input_ids.to(self.model.device)
|
1106 |
+
|
1107 |
+
# Add BOS token
|
1108 |
+
_add_toks(
|
1109 |
+
torch.tensor([[self.tokenizer.bos_token_id]], device=self.model.device)
|
1110 |
+
)
|
1111 |
+
|
1112 |
+
if "<image>" not in prompt:
|
1113 |
+
embeds.append(self.text_emb(_tokenize(prompt)))
|
1114 |
+
else:
|
1115 |
+
assert prompt.count("<image>") == 1
|
1116 |
+
before, after = prompt.split("<image>")
|
1117 |
+
embeds.append(self.text_emb(_tokenize(f"{before}<image>")))
|
1118 |
+
embeds.append(image_embeds.to(self.model.device))
|
1119 |
+
embeds.append(self.text_emb(_tokenize(f"</image>{after}")))
|
1120 |
+
|
1121 |
+
return torch.cat(embeds, dim=1)
|
1122 |
+
|
1123 |
+
def generate(
|
1124 |
+
self, image_embeds, prompt, eos_text="Human:", max_new_tokens=128, **kwargs
|
1125 |
+
):
|
1126 |
+
eos_tokens = self.tokenizer(eos_text, add_special_tokens=False)[0].ids
|
1127 |
+
|
1128 |
+
generate_config = {
|
1129 |
+
"eos_token_id": eos_tokens,
|
1130 |
+
"bos_token_id": self.tokenizer.bos_token_id,
|
1131 |
+
"pad_token_id": self.tokenizer.eos_token_id,
|
1132 |
+
"max_new_tokens": max_new_tokens,
|
1133 |
+
**kwargs,
|
1134 |
+
}
|
1135 |
+
|
1136 |
+
with torch.no_grad():
|
1137 |
+
inputs_embeds = self.input_embeds(prompt, image_embeds)
|
1138 |
+
output_ids = self.model.generate(
|
1139 |
+
inputs_embeds=inputs_embeds, **generate_config
|
1140 |
+
)
|
1141 |
+
|
1142 |
+
return self.tokenizer.batch_decode(output_ids, skip_special_tokens=True)
|
1143 |
+
|
1144 |
+
def answer_question(self, image_embeds, question, **kwargs):
|
1145 |
+
prompt = f"<image>\n\nQuestion: {question}\n\nAnswer:"
|
1146 |
+
answer = self.generate(
|
1147 |
+
image_embeds,
|
1148 |
+
prompt,
|
1149 |
+
eos_text="<END>",
|
1150 |
+
max_new_tokens=128,
|
1151 |
+
**kwargs,
|
1152 |
+
)[0]
|
1153 |
+
|
1154 |
+
return re.sub("<$", "", re.sub("END$", "", answer)).strip()
|
1155 |
+
|
1156 |
+
|
1157 |
+
##### GRADIO INTERFACE #####
|
1158 |
+
|
1159 |
+
import gradio as gr
|
1160 |
+
from huggingface_hub import snapshot_download
|
1161 |
+
from threading import Thread
|
1162 |
+
from transformers import TextIteratorStreamer
|
1163 |
+
import hashlib
|
1164 |
+
import os
|
1165 |
+
|
1166 |
+
model_path = snapshot_download("vikhyatk/moondream1")
|
1167 |
+
|
1168 |
+
vision_encoder = VisionEncoder(model_path).to(DEVICE, dtype=DTYPE)
|
1169 |
+
text_model = TextModel(model_path).to(DEVICE, dtype=DTYPE)
|
1170 |
+
|
1171 |
+
|
1172 |
+
def cached_vision_encoder(image):
|
1173 |
+
# Calculate checksum of the image
|
1174 |
+
image_hash = hashlib.sha256(image.tobytes()).hexdigest()
|
1175 |
+
|
1176 |
+
# Check if `image_encoder_cache/{image_hash}.pt` exists, if so load and return it.
|
1177 |
+
# Otherwise, save the encoded image to `image_encoder_cache/{image_hash}.pt` and return it.
|
1178 |
+
cache_path = f"image_encoder_cache/{image_hash}.pt"
|
1179 |
+
if os.path.exists(cache_path):
|
1180 |
+
return torch.load(cache_path).to(DEVICE, dtype=DTYPE)
|
1181 |
+
else:
|
1182 |
+
image_vec = vision_encoder(image).to("cpu", dtype=torch.float16)
|
1183 |
+
os.makedirs("image_encoder_cache", exist_ok=True)
|
1184 |
+
torch.save(image_vec, cache_path)
|
1185 |
+
return image_vec.to(DEVICE, dtype=DTYPE)
|
1186 |
+
|
1187 |
+
|
1188 |
+
def answer_question(image, question):
|
1189 |
+
yield "Encoding image..."
|
1190 |
+
|
1191 |
+
streamer = TextIteratorStreamer(text_model.tokenizer, skip_special_tokens=True)
|
1192 |
+
generation_kwargs = dict(
|
1193 |
+
image_embeds=cached_vision_encoder(image), question=question, streamer=streamer
|
1194 |
+
)
|
1195 |
+
thread = Thread(target=text_model.answer_question, kwargs=generation_kwargs)
|
1196 |
+
thread.start()
|
1197 |
+
|
1198 |
+
buffer = ""
|
1199 |
+
for new_text in streamer:
|
1200 |
+
buffer += new_text
|
1201 |
+
if len(buffer) > 1:
|
1202 |
+
yield re.sub("<$", "", re.sub("END$", "", buffer))
|
1203 |
+
|
1204 |
+
|
1205 |
+
gr.Interface(
|
1206 |
+
title="🌔 moondream1",
|
1207 |
+
description="""
|
1208 |
+
moondream1 is a tiny (1.6B parameter) vision language model that performs
|
1209 |
+
competitively with models twice its size. It is trained on the LLaVa training
|
1210 |
+
dataset, and initialized with SigLIP as the vision tower and Phi-1.5 as the
|
1211 |
+
text encoder. Check out the <a href="https://huggingface.co/vikhyatk/moondream1">HuggingFace
|
1212 |
+
model card</a> for more details.
|
1213 |
+
""",
|
1214 |
+
fn=answer_question,
|
1215 |
+
inputs=[gr.Image(type="pil"), gr.Textbox(lines=2, label="Question")],
|
1216 |
+
examples=[
|
1217 |
+
[Image.open("assets/demo-1.jpg"), "Who is the author of this book?"],
|
1218 |
+
[Image.open("assets/demo-2.jpg"), "What type of food is the girl eating?"],
|
1219 |
+
[
|
1220 |
+
Image.open("assets/demo-3.jpg"),
|
1221 |
+
"What kind of public transportation is in the image?",
|
1222 |
+
],
|
1223 |
+
[Image.open("assets/demo-4.jpg"), "What is the girl looking at?"],
|
1224 |
+
[Image.open("assets/demo-5.jpg"), "What kind of dog is in the picture?"],
|
1225 |
+
],
|
1226 |
+
outputs=gr.TextArea(label="Answer"),
|
1227 |
+
allow_flagging=False,
|
1228 |
+
).launch()
|
assets/demo-1.jpg
ADDED
![]() |
assets/demo-2.jpg
ADDED
![]() |
assets/demo-3.jpg
ADDED
![]() |
assets/demo-4.jpg
ADDED
![]() |
assets/demo-5.jpg
ADDED
![]() |
image_encoder_cache/33c2dc3e4183b82eb8e09ecceb8c3f30262237d6ee0f49904e1fbde913195ffb.pt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:4c3d02a8ff0a7ec5883e696bc513bee6df9068127fa263bd83066584db20f0f5
|
3 |
+
size 2987641
|
image_encoder_cache/7ffdc9dcb8e0304e8658c0011cc71cb993e4729af95ebbe890f91a4dfce46170.pt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:0b1610027c6a749a9ab9c41a0c5cdec466e29e1945a0ebcd73e4e7edd261bb80
|
3 |
+
size 2987641
|
image_encoder_cache/b3ba6d3612f786df76a0cc5603c9a3d4cc6bede86d4c0f3001092fe0cc67f132.pt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:9468baee42cba3ce022fc1a59c3f69aa26bced6d888b0ddf90ca6836752ba48e
|
3 |
+
size 2987641
|
image_encoder_cache/f5c5b77a4b0025925f7313a019fd91e435caf4b1ce651794c0c7bf5c4ea92827.pt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:bd546e1eec69e7c57179cb578b292a6a4403985309b8e54db6cedeb1517bf2a2
|
3 |
+
size 2987641
|
image_encoder_cache/f6defd804af24f18ea5d966b6b248f3dccc514b06155648592b93af567157a4e.pt
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:613ffac2302c6b8c780235ad62b175850b07c8b8c7dc39148b5c57a324ed9868
|
3 |
+
size 2987641
|
requirements.txt
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
accelerate==0.25.0
|
2 |
+
huggingface-hub==0.20.1
|
3 |
+
Pillow==10.1.0
|
4 |
+
torch==2.1.2
|
5 |
+
torchvision==0.16.2
|
6 |
+
transformers==4.36.2
|
7 |
+
einops==0.7.0
|