Upload Moondream
Browse files- config.json +5 -1
- configuration_moondream.py +74 -0
- generation_config.json +4 -0
- modeling_phi.py +720 -0
- moondream.py +100 -0
- text_model.py +20 -0
- vision_encoder.py +150 -0
config.json
CHANGED
@@ -2,7 +2,11 @@
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"architectures": [
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"Moondream"
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],
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-
"
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"phi_config": {
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"model_type": "phi-msft"
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},
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"architectures": [
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"Moondream"
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],
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"auto_map": {
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"AutoConfig": "configuration_moondream.MoondreamConfig",
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"AutoModelForCausalLM": "moondream.Moondream"
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},
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"model_type": "moondream1",
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"phi_config": {
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"model_type": "phi-msft"
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},
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configuration_moondream.py
ADDED
@@ -0,0 +1,74 @@
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from transformers import PretrainedConfig
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from typing import Optional
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import math
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class PhiConfig(PretrainedConfig):
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model_type = "phi-msft"
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def __init__(
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self,
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vocab_size: int = 51200,
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n_positions: int = 2048,
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n_embd: int = 2048,
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n_layer: int = 24,
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n_inner: Optional[int] = None,
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n_head: int = 32,
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n_head_kv: Optional[int] = None,
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rotary_dim: Optional[int] = 32,
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activation_function: Optional[str] = "gelu_new",
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flash_attn: bool = False,
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flash_rotary: bool = False,
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fused_dense: bool = False,
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attn_pdrop: float = 0.0,
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embd_pdrop: float = 0.0,
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resid_pdrop: float = 0.0,
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layer_norm_epsilon: float = 1e-5,
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initializer_range: float = 0.02,
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tie_word_embeddings: bool = False,
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pad_vocab_size_multiple: int = 64,
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gradient_checkpointing: bool = False,
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**kwargs
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):
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pad_vocab_size = (
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math.ceil(vocab_size / pad_vocab_size_multiple) * pad_vocab_size_multiple
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)
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super().__init__(
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vocab_size=pad_vocab_size,
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n_positions=n_positions,
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n_embd=n_embd,
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n_layer=n_layer,
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n_inner=n_inner,
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n_head=n_head,
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n_head_kv=n_head_kv,
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activation_function=activation_function,
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attn_pdrop=attn_pdrop,
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embd_pdrop=embd_pdrop,
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resid_pdrop=resid_pdrop,
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layer_norm_epsilon=layer_norm_epsilon,
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initializer_range=initializer_range,
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pad_vocab_size_multiple=pad_vocab_size_multiple,
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tie_word_embeddings=tie_word_embeddings,
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gradient_checkpointing=gradient_checkpointing,
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**kwargs
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)
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self.rotary_dim = min(rotary_dim, n_embd // n_head)
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self.flash_attn = flash_attn
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self.flash_rotary = flash_rotary
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self.fused_dense = fused_dense
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attribute_map = {
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"max_position_embeddings": "n_positions",
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"hidden_size": "n_embd",
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"num_attention_heads": "n_head",
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"num_hidden_layers": "n_layer",
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}
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class MoondreamConfig(PretrainedConfig):
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model_type = "moondream1"
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def __init__(self, **kwargs):
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self.phi_config = PhiConfig(**kwargs)
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super().__init__(**kwargs)
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generation_config.json
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{
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"_from_model_config": true,
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"transformers_version": "4.36.2"
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}
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modeling_phi.py
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT license.
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#
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# Copyright (c) 2022, Tri Dao, trid@cs.stanford.edu.
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# Licensed under the BSD 3-Clause License.
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6 |
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from dataclasses import dataclass, field
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8 |
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from typing import Any, Dict, Optional, Union, Tuple
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9 |
+
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import math
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+
import torch
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+
import torch.nn as nn
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+
from einops import rearrange, repeat
|
14 |
+
from transformers import PretrainedConfig, PreTrainedModel
|
15 |
+
from transformers.activations import ACT2FN
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16 |
+
from transformers.modeling_outputs import CausalLMOutputWithPast
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17 |
+
|
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+
from .configuration_moondream import PhiConfig
|
19 |
+
|
20 |
+
FusedDense = None
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+
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+
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@dataclass
|
24 |
+
class InferenceParams:
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+
max_seqlen: int
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+
max_batch_size: int
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27 |
+
seqlen_offset: int = 0
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+
batch_size_offset: int = 0
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+
key_value_memory_dict: Dict[str, Any] = field(default_factory=dict)
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+
lengths_per_sample: torch.Tensor = None
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+
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+
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+
class Embedding(nn.Module):
|
34 |
+
def __init__(self, config: PretrainedConfig):
|
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+
super().__init__()
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36 |
+
self.wte = nn.Embedding(config.vocab_size, config.n_embd)
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37 |
+
self.drop = nn.Dropout(config.embd_pdrop)
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38 |
+
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39 |
+
def forward(self, input_ids: torch.LongTensor) -> torch.FloatTensor:
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return self.drop(self.wte(input_ids.view(-1, input_ids.size(-1))))
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+
|
42 |
+
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+
def _apply_rotary_emb(x, cos, sin):
|
44 |
+
seqlen, rotary_dim = x.size(1), cos.size(1) * 2
|
45 |
+
x_rot, x_pass = x[..., :rotary_dim], x[..., rotary_dim:]
|
46 |
+
x1, x2 = x_rot.chunk(2, dim=-1)
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47 |
+
c, s = cos[:seqlen].unsqueeze(1), sin[:seqlen].unsqueeze(1)
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48 |
+
x_rot = torch.cat([x1 * c - x2 * s, x1 * s + x2 * c], dim=-1)
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49 |
+
return torch.cat([x_rot.to(x.dtype), x_pass], dim=-1)
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50 |
+
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51 |
+
|
52 |
+
def _apply_rotary_emb_kv(
|
53 |
+
kv: torch.FloatTensor, cos: torch.FloatTensor, sin: torch.FloatTensor
|
54 |
+
) -> torch.FloatTensor:
|
55 |
+
seqlen, rotary_dim = kv.shape[1], cos.shape[-1] * 2
|
56 |
+
k_rot = kv[:, :, 0, :, :rotary_dim].chunk(2, dim=-1)
|
57 |
+
k_pass = kv[:, :, 0, :, rotary_dim:]
|
58 |
+
c, s = cos[:seqlen].unsqueeze(1), sin[:seqlen].unsqueeze(1)
|
59 |
+
k_rot = torch.cat(
|
60 |
+
[k_rot[0] * c - k_rot[1] * s, k_rot[0] * s + k_rot[1] * c], dim=-1
|
61 |
+
)
|
62 |
+
return torch.cat(
|
63 |
+
[torch.cat([k_rot, k_pass], dim=-1).unsqueeze(2), kv[:, :, 1:2, :, :]], dim=2
|
64 |
+
)
|
65 |
+
|
66 |
+
|
67 |
+
def _apply_rotary_emb_qkv(
|
68 |
+
qkv: torch.FloatTensor, cos: torch.FloatTensor, sin: torch.FloatTensor
|
69 |
+
) -> torch.FloatTensor:
|
70 |
+
seqlen, rotary_dim = qkv.shape[1], cos.shape[1] * 2
|
71 |
+
|
72 |
+
c = cos[:seqlen].unsqueeze(1)
|
73 |
+
s = sin[:seqlen].unsqueeze(1)
|
74 |
+
|
75 |
+
qkv_rot = torch.stack(
|
76 |
+
[
|
77 |
+
torch.cat(
|
78 |
+
[
|
79 |
+
qkv[:, :, i, :, : rotary_dim // 2] * c
|
80 |
+
- qkv[:, :, i, :, rotary_dim // 2 : rotary_dim] * s,
|
81 |
+
qkv[:, :, i, :, : rotary_dim // 2] * s
|
82 |
+
+ qkv[:, :, i, :, rotary_dim // 2 : rotary_dim] * c,
|
83 |
+
],
|
84 |
+
dim=-1,
|
85 |
+
).to(qkv.dtype)
|
86 |
+
for i in range(2)
|
87 |
+
],
|
88 |
+
dim=2,
|
89 |
+
)
|
90 |
+
|
91 |
+
qkv_pass = qkv[:, :, :2, :, rotary_dim:].unsqueeze(2)
|
92 |
+
qkv_v = qkv[:, :, 2:3, :, :]
|
93 |
+
return torch.cat([qkv_rot, qkv_pass, qkv_v], dim=2)
|
94 |
+
|
95 |
+
|
96 |
+
class RotaryEmbedding(nn.Module):
|
97 |
+
# Enhanced Transformer with Rotary Position Embedding (https://arxiv.org/pdf/2104.09864.pdf)
|
98 |
+
def __init__(
|
99 |
+
self,
|
100 |
+
dim: int,
|
101 |
+
base: int = 10000,
|
102 |
+
scale_base: Optional[float] = None,
|
103 |
+
pos_idx_in_fp32: bool = True,
|
104 |
+
max_position_embeddings: int = 2048,
|
105 |
+
device: Optional[str] = None,
|
106 |
+
) -> None:
|
107 |
+
super().__init__()
|
108 |
+
# fp32 is preferred since the output of `torch.arange` can be quite large and bf16 would lose a lot of precision
|
109 |
+
self.dim, self.base, self.pos_idx_in_fp32, self.device = (
|
110 |
+
dim,
|
111 |
+
float(base),
|
112 |
+
pos_idx_in_fp32,
|
113 |
+
device,
|
114 |
+
)
|
115 |
+
self.max_position_embeddings = max_position_embeddings
|
116 |
+
if scale_base is not None:
|
117 |
+
raise NotImplementedError
|
118 |
+
|
119 |
+
# Generate and register the non-trainable buffers
|
120 |
+
self.register_buffer(
|
121 |
+
"inv_freq", self._compute_inv_freq(device), persistent=False
|
122 |
+
)
|
123 |
+
self.register_buffer(
|
124 |
+
"scale", self._calculate_scale(dim, scale_base, device), persistent=False
|
125 |
+
)
|
126 |
+
self._update_cos_sin_cache(
|
127 |
+
max_position_embeddings, device=device, dtype=torch.float32
|
128 |
+
)
|
129 |
+
|
130 |
+
def _calculate_scale(self, dim, scale_base, device):
|
131 |
+
return (
|
132 |
+
(
|
133 |
+
(
|
134 |
+
torch.arange(0, dim, 2, device=device, dtype=torch.float32)
|
135 |
+
+ 0.4 * dim
|
136 |
+
)
|
137 |
+
/ (1.4 * dim)
|
138 |
+
)
|
139 |
+
if scale_base is not None
|
140 |
+
else None
|
141 |
+
)
|
142 |
+
|
143 |
+
def _compute_inv_freq(self, device: Optional[str] = None) -> torch.FloatTensor:
|
144 |
+
return 1.0 / (
|
145 |
+
self.base
|
146 |
+
** (
|
147 |
+
torch.arange(0, self.dim, 2, device=device, dtype=torch.float32)
|
148 |
+
/ self.dim
|
149 |
+
)
|
150 |
+
)
|
151 |
+
|
152 |
+
def _update_cos_sin_cache(
|
153 |
+
self,
|
154 |
+
seqlen: int,
|
155 |
+
device: Optional[str] = None,
|
156 |
+
dtype: Optional[torch.dtype] = None,
|
157 |
+
) -> None:
|
158 |
+
self._seq_len_cached = seqlen
|
159 |
+
t = torch.arange(
|
160 |
+
seqlen,
|
161 |
+
device=device,
|
162 |
+
dtype=torch.float32 if self.pos_idx_in_fp32 else self.inv_freq.dtype,
|
163 |
+
)
|
164 |
+
inv_freq = (
|
165 |
+
self._compute_inv_freq(device=device)
|
166 |
+
if self.pos_idx_in_fp32 and self.inv_freq.dtype != torch.float32
|
167 |
+
else self.inv_freq
|
168 |
+
)
|
169 |
+
|
170 |
+
freqs = torch.outer(t, inv_freq)
|
171 |
+
|
172 |
+
def apply_scale(freqs, scale, operator, dtype):
|
173 |
+
result = operator(freqs)
|
174 |
+
return (result / scale).to(dtype) if scale is not None else result.to(dtype)
|
175 |
+
|
176 |
+
if scale := self.scale:
|
177 |
+
power = (
|
178 |
+
torch.arange(seqlen, dtype=scale.dtype, device=scale.device)
|
179 |
+
- seqlen // 2
|
180 |
+
) / self.scale_base
|
181 |
+
scale = scale.to(device=power.device) ** power.unsqueeze(1)
|
182 |
+
|
183 |
+
self._cos_cached = apply_scale(
|
184 |
+
freqs, 1 / scale if scale is not None else None, torch.cos, dtype
|
185 |
+
)
|
186 |
+
self._sin_cached = apply_scale(
|
187 |
+
freqs, 1 / scale if scale is not None else None, torch.sin, dtype
|
188 |
+
)
|
189 |
+
if scale is not None:
|
190 |
+
self._cos_k_cached = apply_scale(freqs, scale, torch.cos, dtype)
|
191 |
+
self._sin_k_cached = apply_scale(freqs, scale, torch.sin, dtype)
|
192 |
+
|
193 |
+
def forward(
|
194 |
+
self,
|
195 |
+
qkv: torch.Tensor,
|
196 |
+
kv: Optional[torch.Tensor] = None,
|
197 |
+
seqlen_offset: int = 0,
|
198 |
+
) -> Tuple[torch.Tensor, torch.Tensor]:
|
199 |
+
should_update = (
|
200 |
+
self._seq_len_cached < qkv.shape[1] + seqlen_offset
|
201 |
+
or self._cos_cached.device != qkv.device
|
202 |
+
or self._cos_cached.dtype != qkv.dtype
|
203 |
+
or (self.training and self._cos_cached.is_inference())
|
204 |
+
)
|
205 |
+
|
206 |
+
if should_update:
|
207 |
+
self._update_cos_sin_cache(
|
208 |
+
qkv.shape[1] + seqlen_offset, device=qkv.device, dtype=qkv.dtype
|
209 |
+
)
|
210 |
+
|
211 |
+
offset_cos = self._cos_cached[seqlen_offset:]
|
212 |
+
offset_sin = self._sin_cached[seqlen_offset:]
|
213 |
+
|
214 |
+
if kv is None:
|
215 |
+
return _apply_rotary_emb_qkv(qkv, offset_cos, offset_sin)
|
216 |
+
else:
|
217 |
+
return _apply_rotary_emb(qkv, offset_cos, offset_sin), _apply_rotary_emb_kv(
|
218 |
+
kv, offset_cos, offset_sin
|
219 |
+
)
|
220 |
+
|
221 |
+
|
222 |
+
class MLP(nn.Module):
|
223 |
+
def __init__(
|
224 |
+
self,
|
225 |
+
config: PretrainedConfig,
|
226 |
+
n_inner: Optional[int] = None,
|
227 |
+
act_fn: Optional[str] = None,
|
228 |
+
) -> None:
|
229 |
+
super().__init__()
|
230 |
+
n_inner = n_inner or getattr(config, "n_inner", None) or 4 * config.n_embd
|
231 |
+
act_fn = act_fn or config.activation_function
|
232 |
+
|
233 |
+
self.fc1 = nn.Linear(config.n_embd, n_inner)
|
234 |
+
self.fc2 = nn.Linear(n_inner, config.n_embd)
|
235 |
+
self.act = ACT2FN[act_fn]
|
236 |
+
|
237 |
+
def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
|
238 |
+
return self.fc2(self.act(self.fc1(hidden_states)))
|
239 |
+
|
240 |
+
|
241 |
+
# Flash Attention (https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mha.py)
|
242 |
+
class SelfAttention(nn.Module):
|
243 |
+
def __init__(
|
244 |
+
self,
|
245 |
+
causal: bool = True,
|
246 |
+
softmax_scale: Optional[float] = None,
|
247 |
+
attention_dropout: float = 0.0,
|
248 |
+
):
|
249 |
+
super().__init__()
|
250 |
+
self.causal = causal
|
251 |
+
self.softmax_scale = softmax_scale
|
252 |
+
self.drop = nn.Dropout(attention_dropout)
|
253 |
+
|
254 |
+
@torch.autocast("cpu", enabled=False)
|
255 |
+
@torch.autocast("cuda", enabled=False)
|
256 |
+
def forward(
|
257 |
+
self,
|
258 |
+
qkv: torch.FloatTensor,
|
259 |
+
causal: Optional[bool] = None,
|
260 |
+
key_padding_mask: Optional[torch.BoolTensor] = None,
|
261 |
+
):
|
262 |
+
q, k, v = qkv.chunk(3, dim=-1)
|
263 |
+
scale = self.softmax_scale or 1.0 / q.size(-1) ** 0.5
|
264 |
+
|
265 |
+
scores = (
|
266 |
+
torch.einsum("bthd,bshd->bhts", q.to(torch.float32), k.to(torch.float32))
|
267 |
+
* scale
|
268 |
+
)
|
269 |
+
if causal or self.causal:
|
270 |
+
scores.triu_(1).fill_(-10000.0)
|
271 |
+
if key_padding_mask is not None:
|
272 |
+
scores.masked_fill_(key_padding_mask[:, None, None, :], -10000.0)
|
273 |
+
|
274 |
+
attn = self.drop(torch.softmax(scores, dim=-1).to(v.dtype))
|
275 |
+
return torch.einsum("bhts,bshd->bthd", attn, v)
|
276 |
+
|
277 |
+
|
278 |
+
# Flash Attention (https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mha.py)
|
279 |
+
class CrossAttention(nn.Module):
|
280 |
+
def __init__(self, causal=True, softmax_scale=None, attention_dropout=0.0):
|
281 |
+
super().__init__()
|
282 |
+
self.causal = causal
|
283 |
+
self.softmax_scale = softmax_scale
|
284 |
+
self.drop = nn.Dropout(attention_dropout)
|
285 |
+
|
286 |
+
@torch.autocast("cpu", enabled=False)
|
287 |
+
@torch.autocast("cuda", enabled=False)
|
288 |
+
def forward(
|
289 |
+
self,
|
290 |
+
q: torch.FloatTensor,
|
291 |
+
kv: torch.FloatTensor,
|
292 |
+
causal: bool = None,
|
293 |
+
key_padding_mask: Optional[torch.BoolTensor] = None,
|
294 |
+
) -> torch.FloatTensor:
|
295 |
+
batch_size, seqlen_q = q.shape[0], q.shape[1]
|
296 |
+
seqlen_k = kv.shape[1]
|
297 |
+
|
298 |
+
if kv.shape[3] != q.shape[2]:
|
299 |
+
kv = repeat(kv, "... hkv d -> ... (hkv g) d", g=q.shape[2] // kv.shape[3])
|
300 |
+
k, v = kv.unbind(dim=2)
|
301 |
+
|
302 |
+
q = q.to(torch.float32)
|
303 |
+
k = k.to(torch.float32)
|
304 |
+
|
305 |
+
causal = self.causal if causal is None else causal
|
306 |
+
softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
|
307 |
+
|
308 |
+
# Autocast is manually disabled to avoid `torch.einsum` performing the operation using float16, which might lead to overflow
|
309 |
+
scores = torch.einsum("bthd,bshd->bhts", q, k * softmax_scale)
|
310 |
+
|
311 |
+
if key_padding_mask is not None:
|
312 |
+
padding_mask = torch.full(
|
313 |
+
(batch_size, seqlen_k),
|
314 |
+
-10000.0,
|
315 |
+
dtype=scores.dtype,
|
316 |
+
device=scores.device,
|
317 |
+
)
|
318 |
+
padding_mask.masked_fill_(key_padding_mask, 0.0)
|
319 |
+
scores = scores + rearrange(padding_mask, "b s -> b 1 1 s")
|
320 |
+
|
321 |
+
if causal:
|
322 |
+
rows = rearrange(
|
323 |
+
torch.arange(seqlen_q, device=q.device, dtype=torch.long), "s -> s 1"
|
324 |
+
)
|
325 |
+
cols = torch.arange(seqlen_k, device=k.device, dtype=torch.long)
|
326 |
+
causal_mask = cols > rows + seqlen_k - seqlen_q
|
327 |
+
scores = scores.masked_fill(causal_mask, -10000.0)
|
328 |
+
|
329 |
+
attention = torch.softmax(scores, dim=-1).to(v.dtype)
|
330 |
+
attention = self.drop(attention)
|
331 |
+
output = torch.einsum("bhts,bshd->bthd", attention, v)
|
332 |
+
|
333 |
+
return output
|
334 |
+
|
335 |
+
|
336 |
+
def _find_mha_dims(
|
337 |
+
config: PretrainedConfig,
|
338 |
+
n_head: Optional[int] = None,
|
339 |
+
n_head_kv: Optional[int] = None,
|
340 |
+
head_dim: Optional[int] = None,
|
341 |
+
) -> Tuple[int, int]:
|
342 |
+
if n_head is None and head_dim is None:
|
343 |
+
head_dim = config.n_embd // config.n_head
|
344 |
+
n_head = config.n_head
|
345 |
+
elif n_head is None or head_dim is None:
|
346 |
+
raise ValueError("`n_head` and `head_dim` must be both specified or `None`.")
|
347 |
+
if n_head_kv is None:
|
348 |
+
n_head_kv = getattr(config, "n_head_kv", None) or n_head
|
349 |
+
return n_head, n_head_kv, head_dim
|
350 |
+
|
351 |
+
|
352 |
+
def _update_kv_cache(
|
353 |
+
kv: torch.FloatTensor, inference_params: InferenceParams, layer_idx: int
|
354 |
+
) -> torch.FloatTensor:
|
355 |
+
num_heads, head_dim = kv.shape[-2:]
|
356 |
+
layer_memory = inference_params.key_value_memory_dict.setdefault(
|
357 |
+
layer_idx,
|
358 |
+
torch.empty(
|
359 |
+
inference_params.max_batch_size,
|
360 |
+
inference_params.max_seqlen,
|
361 |
+
2,
|
362 |
+
num_heads,
|
363 |
+
head_dim,
|
364 |
+
dtype=kv.dtype,
|
365 |
+
device=kv.device,
|
366 |
+
),
|
367 |
+
)
|
368 |
+
|
369 |
+
batch_slice = slice(
|
370 |
+
inference_params.batch_size_offset,
|
371 |
+
inference_params.batch_size_offset + kv.shape[0],
|
372 |
+
)
|
373 |
+
seqlen_slice = slice(
|
374 |
+
inference_params.seqlen_offset, inference_params.seqlen_offset + kv.shape[1]
|
375 |
+
)
|
376 |
+
|
377 |
+
if seqlen_slice.stop >= inference_params.max_seqlen:
|
378 |
+
layer_memory = torch.cat((layer_memory, kv), dim=1)
|
379 |
+
inference_params.key_value_memory_dict[layer_idx] = layer_memory
|
380 |
+
|
381 |
+
layer_memory[batch_slice, seqlen_slice, ...] = kv
|
382 |
+
return layer_memory[batch_slice, : seqlen_slice.stop, ...]
|
383 |
+
|
384 |
+
|
385 |
+
# Multi-head attention layer with rotary embeddings
|
386 |
+
class MHA(nn.Module):
|
387 |
+
def __init__(
|
388 |
+
self,
|
389 |
+
config,
|
390 |
+
dtype=None,
|
391 |
+
device=None,
|
392 |
+
rotary_dim=None,
|
393 |
+
rotary_base=10000.0,
|
394 |
+
rotary_scale_base=None,
|
395 |
+
n_head=None,
|
396 |
+
n_head_kv=None,
|
397 |
+
head_dim=None,
|
398 |
+
bias=True,
|
399 |
+
causal=True,
|
400 |
+
softmax_scale=None,
|
401 |
+
layer_idx=None,
|
402 |
+
return_residual=False,
|
403 |
+
checkpointing=False,
|
404 |
+
):
|
405 |
+
super().__init__()
|
406 |
+
|
407 |
+
# Set rotary embedding if specified
|
408 |
+
self.rotary_dim = rotary_dim or getattr(config, "rotary_dim", 0)
|
409 |
+
if self.rotary_dim:
|
410 |
+
self.rotary_emb = RotaryEmbedding(
|
411 |
+
self.rotary_dim,
|
412 |
+
base=rotary_base,
|
413 |
+
scale_base=rotary_scale_base,
|
414 |
+
device=device,
|
415 |
+
max_position_embeddings=config.n_positions,
|
416 |
+
)
|
417 |
+
|
418 |
+
# Determine MHA dims from arguments or config
|
419 |
+
self.n_head, self.n_head_kv, self.head_dim = _find_mha_dims(
|
420 |
+
config, n_head, n_head_kv, head_dim
|
421 |
+
)
|
422 |
+
op_size = self.head_dim * (self.n_head + 2 * self.n_head_kv)
|
423 |
+
hidden_size = config.n_embd
|
424 |
+
|
425 |
+
# Choose Linear class based on config, FusedDense is optional
|
426 |
+
LinearClass = (
|
427 |
+
FusedDense if config.fused_dense and FusedDense is not None else nn.Linear
|
428 |
+
)
|
429 |
+
self.Wqkv = LinearClass(
|
430 |
+
hidden_size, op_size, bias=bias, device=device, dtype=dtype
|
431 |
+
)
|
432 |
+
self.out_proj = LinearClass(
|
433 |
+
hidden_size, hidden_size, bias=bias, device=device, dtype=dtype
|
434 |
+
)
|
435 |
+
|
436 |
+
# Initialize attention mechanisms
|
437 |
+
attn_kwargs = {
|
438 |
+
"causal": causal,
|
439 |
+
"softmax_scale": softmax_scale,
|
440 |
+
"attention_dropout": config.attn_pdrop,
|
441 |
+
}
|
442 |
+
self.inner_attn = SelfAttention(**attn_kwargs)
|
443 |
+
self.inner_cross_attn = CrossAttention(**attn_kwargs)
|
444 |
+
|
445 |
+
self.layer_idx = layer_idx
|
446 |
+
self.return_residual = return_residual
|
447 |
+
self.checkpointing = checkpointing
|
448 |
+
|
449 |
+
def _forward_self_attn(
|
450 |
+
self, x: torch.FloatTensor, key_padding_mask: Optional[torch.BoolTensor]
|
451 |
+
) -> torch.FloatTensor:
|
452 |
+
qkv = rearrange(
|
453 |
+
self.Wqkv(x), "... (three h d) -> ... three h d", three=3, d=self.head_dim
|
454 |
+
)
|
455 |
+
if self.rotary_dim > 0:
|
456 |
+
qkv = self.rotary_emb(qkv)
|
457 |
+
attn_func = (
|
458 |
+
torch.utils.checkpoint.checkpoint
|
459 |
+
if self.checkpointing
|
460 |
+
else lambda f, *args, **kwargs: f(*args, **kwargs)
|
461 |
+
)
|
462 |
+
return attn_func(self.inner_attn, qkv, key_padding_mask=key_padding_mask)
|
463 |
+
|
464 |
+
def _forward_cross_attn(
|
465 |
+
self,
|
466 |
+
x: torch.FloatTensor,
|
467 |
+
past_key_values: Optional[InferenceParams],
|
468 |
+
key_padding_mask: Optional[torch.BoolTensor],
|
469 |
+
) -> torch.FloatTensor:
|
470 |
+
qkv = self.Wqkv(x)
|
471 |
+
q, kv = (
|
472 |
+
qkv[..., : self.n_head * self.head_dim],
|
473 |
+
qkv[..., self.n_head * self.head_dim :],
|
474 |
+
)
|
475 |
+
q = rearrange(q, "... (h d) -> ... h d", d=self.head_dim)
|
476 |
+
kv = rearrange(kv, "... (two hkv d) -> ... two hkv d", two=2, d=self.head_dim)
|
477 |
+
|
478 |
+
seqlen_offset = (
|
479 |
+
past_key_values.seqlen_offset if past_key_values is not None else 0
|
480 |
+
)
|
481 |
+
causal = None if seqlen_offset == 0 else False
|
482 |
+
if self.rotary_dim > 0:
|
483 |
+
q, kv = self.rotary_emb(q, kv=kv, seqlen_offset=seqlen_offset)
|
484 |
+
|
485 |
+
if past_key_values is not None:
|
486 |
+
kv = _update_kv_cache(kv, past_key_values, self.layer_idx)
|
487 |
+
|
488 |
+
attn_func = (
|
489 |
+
torch.utils.checkpoint.checkpoint
|
490 |
+
if self.checkpointing
|
491 |
+
else lambda fn, *args, **kwargs: fn(*args, **kwargs)
|
492 |
+
)
|
493 |
+
|
494 |
+
return attn_func(
|
495 |
+
self.inner_cross_attn,
|
496 |
+
q,
|
497 |
+
kv,
|
498 |
+
key_padding_mask=key_padding_mask,
|
499 |
+
causal=causal,
|
500 |
+
)
|
501 |
+
|
502 |
+
def forward(
|
503 |
+
self,
|
504 |
+
x: torch.FloatTensor,
|
505 |
+
past_key_values: Optional[InferenceParams] = None,
|
506 |
+
attention_mask: Optional[Union[torch.LongTensor, torch.BoolTensor]] = None,
|
507 |
+
) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
|
508 |
+
attention_mask = attention_mask.bool() if attention_mask is not None else None
|
509 |
+
use_cross_attn = self.n_head != self.n_head_kv or past_key_values is not None
|
510 |
+
attn_output_function = (
|
511 |
+
self._forward_cross_attn if use_cross_attn else self._forward_self_attn
|
512 |
+
)
|
513 |
+
attn_output = (
|
514 |
+
attn_output_function(x, past_key_values, attention_mask)
|
515 |
+
if use_cross_attn
|
516 |
+
else attn_output_function(x, attention_mask)
|
517 |
+
)
|
518 |
+
output = self.out_proj(rearrange(attn_output, "... h d -> ... (h d)"))
|
519 |
+
return (output, x) if self.return_residual else output
|
520 |
+
|
521 |
+
|
522 |
+
# Parallel block. This block applies parallel mixer and MLP layers to the input (used in GPT-J and CodeGen).
|
523 |
+
class ParallelBlock(nn.Module):
|
524 |
+
def __init__(self, config: PretrainedConfig, block_idx: Optional[int] = None):
|
525 |
+
super().__init__()
|
526 |
+
self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
|
527 |
+
self.resid_dropout = nn.Dropout(config.resid_pdrop)
|
528 |
+
self.block_idx = block_idx
|
529 |
+
self.mixer = MHA(config, layer_idx=block_idx)
|
530 |
+
self.mlp = MLP(config)
|
531 |
+
|
532 |
+
def forward(
|
533 |
+
self,
|
534 |
+
hidden_states: torch.FloatTensor,
|
535 |
+
past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
|
536 |
+
attention_mask: Optional[torch.BoolTensor] = None,
|
537 |
+
) -> torch.FloatTensor:
|
538 |
+
residual = hidden_states
|
539 |
+
hidden_states = self.ln(hidden_states)
|
540 |
+
|
541 |
+
attn_outputs = self.mixer(
|
542 |
+
hidden_states,
|
543 |
+
past_key_values=past_key_values,
|
544 |
+
attention_mask=attention_mask,
|
545 |
+
)
|
546 |
+
if isinstance(attn_outputs, tuple):
|
547 |
+
attn_outputs = attn_outputs[0]
|
548 |
+
|
549 |
+
attn_outputs = self.resid_dropout(attn_outputs)
|
550 |
+
feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states))
|
551 |
+
return attn_outputs + feed_forward_hidden_states + residual
|
552 |
+
|
553 |
+
|
554 |
+
class CausalLMHead(nn.Module):
|
555 |
+
"""Causal Language Modeling head. Simplified version."""
|
556 |
+
|
557 |
+
def __init__(self, config):
|
558 |
+
super().__init__()
|
559 |
+
self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
|
560 |
+
self.linear = nn.Linear(config.n_embd, config.vocab_size)
|
561 |
+
|
562 |
+
def forward(self, hidden_states):
|
563 |
+
return self.linear(self.ln(hidden_states)).to(torch.float32)
|
564 |
+
|
565 |
+
|
566 |
+
# Improving Language Understanding by Generative Pre-Training
|
567 |
+
# (https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf)
|
568 |
+
class CausalLMLoss(nn.Module):
|
569 |
+
def __init__(self, shift_labels: bool = True) -> None:
|
570 |
+
super().__init__()
|
571 |
+
self.shift_labels = shift_labels
|
572 |
+
self.loss_fct = nn.CrossEntropyLoss()
|
573 |
+
|
574 |
+
def forward(
|
575 |
+
self, logits: torch.FloatTensor, labels: torch.LongTensor
|
576 |
+
) -> torch.FloatTensor:
|
577 |
+
if self.shift_labels:
|
578 |
+
logits, labels = logits[..., :-1, :], labels[..., 1:]
|
579 |
+
return self.loss_fct(logits.reshape(-1, logits.size(-1)), labels.reshape(-1))
|
580 |
+
|
581 |
+
|
582 |
+
class PhiPreTrainedModel(PreTrainedModel):
|
583 |
+
config_class = PhiConfig
|
584 |
+
base_model_prefix = "transformer"
|
585 |
+
supports_gradient_checkpointing = False
|
586 |
+
_no_split_modules = ["ParallelBlock"]
|
587 |
+
|
588 |
+
def __init__(self, *inputs, **kwargs) -> None:
|
589 |
+
super().__init__(*inputs, **kwargs)
|
590 |
+
|
591 |
+
def prepare_inputs_for_generation(
|
592 |
+
self,
|
593 |
+
input_ids: torch.LongTensor = None,
|
594 |
+
inputs_embeds: torch.FloatTensor = None,
|
595 |
+
past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
|
596 |
+
attention_mask: Optional[Union[torch.LongTensor, torch.BoolTensor]] = None,
|
597 |
+
**kwargs,
|
598 |
+
) -> Dict[str, Any]:
|
599 |
+
if input_ids is None and inputs_embeds is None:
|
600 |
+
raise ValueError(
|
601 |
+
"You have to specify either `input_ids` or `inputs_embeds`."
|
602 |
+
)
|
603 |
+
|
604 |
+
max_batch_size = (
|
605 |
+
inputs_embeds.shape[0] if inputs_embeds is not None else input_ids.shape[0]
|
606 |
+
)
|
607 |
+
seqlen_offset = (
|
608 |
+
inputs_embeds.shape[1] + input_ids.shape[1] - 2
|
609 |
+
if inputs_embeds is not None
|
610 |
+
else input_ids.shape[1] - 1
|
611 |
+
)
|
612 |
+
|
613 |
+
args = (
|
614 |
+
{"inputs_embeds": inputs_embeds}
|
615 |
+
if inputs_embeds is not None
|
616 |
+
else {"input_ids": input_ids}
|
617 |
+
)
|
618 |
+
|
619 |
+
if not isinstance(past_key_values, InferenceParams):
|
620 |
+
past_key_values = InferenceParams(
|
621 |
+
max_seqlen=self.config.n_positions,
|
622 |
+
max_batch_size=max_batch_size,
|
623 |
+
seqlen_offset=0,
|
624 |
+
batch_size_offset=0,
|
625 |
+
key_value_memory_dict={},
|
626 |
+
lengths_per_sample=None,
|
627 |
+
)
|
628 |
+
else:
|
629 |
+
past_key_values.seqlen_offset = seqlen_offset
|
630 |
+
args = {"input_ids": input_ids[:, -1].unsqueeze(-1)}
|
631 |
+
|
632 |
+
return {
|
633 |
+
**args,
|
634 |
+
"past_key_values": past_key_values,
|
635 |
+
"attention_mask": attention_mask,
|
636 |
+
}
|
637 |
+
|
638 |
+
|
639 |
+
class PhiModel(PhiPreTrainedModel):
|
640 |
+
_keys_to_ignore_on_load_missing = [""]
|
641 |
+
_keys_to_ignore_on_load_unexpected = [r"h\.\d+\.mlp.(fc_in|fc_out)\.(weight|bias)"]
|
642 |
+
|
643 |
+
def __init__(self, config: PhiConfig) -> None:
|
644 |
+
super().__init__(config)
|
645 |
+
self.embd = Embedding(config)
|
646 |
+
self.h = nn.ModuleList(
|
647 |
+
[ParallelBlock(config, block_idx=i) for i in range(config.n_layer)]
|
648 |
+
)
|
649 |
+
self.gradient_checkpointing = config.gradient_checkpointing
|
650 |
+
self.post_init()
|
651 |
+
|
652 |
+
def get_input_embeddings(self) -> nn.Embedding:
|
653 |
+
return self.embd.wte
|
654 |
+
|
655 |
+
def set_input_embeddings(self, new_embeddings: nn.Embedding) -> None:
|
656 |
+
self.embd.wte = new_embeddings
|
657 |
+
|
658 |
+
def forward(
|
659 |
+
self,
|
660 |
+
input_ids: torch.LongTensor = None,
|
661 |
+
inputs_embeds: torch.FloatTensor = None,
|
662 |
+
past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
|
663 |
+
attention_mask: Optional[torch.BoolTensor] = None,
|
664 |
+
) -> torch.FloatTensor:
|
665 |
+
if (input_ids is None) == (inputs_embeds is None):
|
666 |
+
raise ValueError("Specify exactly one of `input_ids` or `inputs_embeds`.")
|
667 |
+
hidden_states = self.embd(input_ids) if input_ids is not None else inputs_embeds
|
668 |
+
|
669 |
+
for layer in self.h:
|
670 |
+
func = layer.__call__ if self.gradient_checkpointing else layer
|
671 |
+
args = (hidden_states, past_key_values, attention_mask)
|
672 |
+
hidden_states = (
|
673 |
+
torch.utils.checkpoint.checkpoint(func, *args, use_reentrant=True)
|
674 |
+
if self.gradient_checkpointing
|
675 |
+
else func(*args)
|
676 |
+
)
|
677 |
+
|
678 |
+
return hidden_states
|
679 |
+
|
680 |
+
|
681 |
+
class PhiForCausalLM(PhiPreTrainedModel):
|
682 |
+
_keys_to_ignore_on_load_missing, _keys_to_ignore_on_load_unexpected = (
|
683 |
+
[""],
|
684 |
+
[r"transformer\.h\.\d+\.mlp.(fc_in|fc_out)\.(weight|bias)"],
|
685 |
+
)
|
686 |
+
|
687 |
+
def __init__(self, config: PhiConfig) -> None:
|
688 |
+
super().__init__(config)
|
689 |
+
self.transformer = PhiModel(config)
|
690 |
+
self.lm_head = CausalLMHead(config)
|
691 |
+
self.loss = CausalLMLoss()
|
692 |
+
self.post_init()
|
693 |
+
|
694 |
+
def get_output_embeddings(self) -> nn.Linear:
|
695 |
+
return self.lm_head.linear
|
696 |
+
|
697 |
+
def set_output_embeddings(self, new_embeddings: nn.Linear) -> None:
|
698 |
+
self.lm_head.linear = new_embeddings
|
699 |
+
|
700 |
+
def forward(
|
701 |
+
self,
|
702 |
+
input_ids: torch.LongTensor = None,
|
703 |
+
inputs_embeds: torch.FloatTensor = None,
|
704 |
+
past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
|
705 |
+
attention_mask: Optional[torch.BoolTensor] = None,
|
706 |
+
labels: Optional[torch.LongTensor] = None,
|
707 |
+
**kwargs,
|
708 |
+
) -> CausalLMOutputWithPast:
|
709 |
+
hidden_states = self.transformer(
|
710 |
+
input_ids=input_ids,
|
711 |
+
inputs_embeds=inputs_embeds,
|
712 |
+
past_key_values=past_key_values,
|
713 |
+
attention_mask=attention_mask,
|
714 |
+
)
|
715 |
+
lm_logits = self.lm_head(hidden_states)
|
716 |
+
loss = self.loss(lm_logits, labels) if labels is not None else None
|
717 |
+
|
718 |
+
return CausalLMOutputWithPast(
|
719 |
+
loss=loss, logits=lm_logits, past_key_values=past_key_values
|
720 |
+
)
|
moondream.py
ADDED
@@ -0,0 +1,100 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
from .vision_encoder import VisionEncoder
|
3 |
+
from .text_model import TextModel
|
4 |
+
from .configuration_moondream import MoondreamConfig
|
5 |
+
from transformers import PreTrainedModel
|
6 |
+
import re
|
7 |
+
|
8 |
+
|
9 |
+
class Moondream(PreTrainedModel):
|
10 |
+
config_class = MoondreamConfig
|
11 |
+
|
12 |
+
def __init__(self, config):
|
13 |
+
super().__init__(config)
|
14 |
+
self.vision_encoder = VisionEncoder()
|
15 |
+
self.text_model = TextModel(config, None)
|
16 |
+
|
17 |
+
@property
|
18 |
+
def device(self):
|
19 |
+
return self.text_model.model.device
|
20 |
+
|
21 |
+
def encode_image(self, image):
|
22 |
+
return self.vision_encoder(image)
|
23 |
+
|
24 |
+
def input_embeds(self, prompt, image_embeds, tokenizer):
|
25 |
+
def _tokenize(txt):
|
26 |
+
return tokenizer(
|
27 |
+
txt, return_tensors="pt", add_special_tokens=False
|
28 |
+
).input_ids.to(self.device)
|
29 |
+
|
30 |
+
# Add BOS token
|
31 |
+
embeds = []
|
32 |
+
embeds.append(
|
33 |
+
self.text_model.text_emb(
|
34 |
+
(torch.tensor([[tokenizer.bos_token_id]], device=self.device))
|
35 |
+
)
|
36 |
+
)
|
37 |
+
|
38 |
+
if "<image>" not in prompt:
|
39 |
+
embeds.append(self.text_model.text_emb(_tokenize(prompt)))
|
40 |
+
else:
|
41 |
+
assert prompt.count("<image>") == 1
|
42 |
+
before, after = prompt.split("<image>")
|
43 |
+
embeds.append(self.text_model.text_emb(_tokenize(f"{before}<image>")))
|
44 |
+
embeds.append(image_embeds.to(self.device))
|
45 |
+
embeds.append(self.text_model.text_emb(_tokenize(f"</image>{after}")))
|
46 |
+
|
47 |
+
return torch.cat(embeds, dim=1)
|
48 |
+
|
49 |
+
def generate(
|
50 |
+
self,
|
51 |
+
image_embeds,
|
52 |
+
prompt,
|
53 |
+
tokenizer,
|
54 |
+
eos_text="Human:",
|
55 |
+
max_new_tokens=128,
|
56 |
+
**kwargs,
|
57 |
+
):
|
58 |
+
eos_tokens = tokenizer(eos_text, add_special_tokens=False)[0].ids
|
59 |
+
|
60 |
+
generate_config = {
|
61 |
+
"eos_token_id": eos_tokens,
|
62 |
+
"bos_token_id": tokenizer.bos_token_id,
|
63 |
+
"pad_token_id": tokenizer.eos_token_id,
|
64 |
+
"max_new_tokens": max_new_tokens,
|
65 |
+
**kwargs,
|
66 |
+
}
|
67 |
+
|
68 |
+
with torch.no_grad():
|
69 |
+
inputs_embeds = self.input_embeds(prompt, image_embeds, tokenizer)
|
70 |
+
output_ids = self.text_model.model.generate(
|
71 |
+
inputs_embeds=inputs_embeds, **generate_config
|
72 |
+
)
|
73 |
+
|
74 |
+
return tokenizer.batch_decode(output_ids, skip_special_tokens=True)
|
75 |
+
|
76 |
+
def answer_question(
|
77 |
+
self,
|
78 |
+
image_embeds,
|
79 |
+
question,
|
80 |
+
tokenizer,
|
81 |
+
chat_history="",
|
82 |
+
result_queue=None,
|
83 |
+
**kwargs,
|
84 |
+
):
|
85 |
+
prompt = f"<image>\n\n{chat_history}Question: {question}\n\nAnswer:"
|
86 |
+
answer = self.generate(
|
87 |
+
image_embeds,
|
88 |
+
prompt,
|
89 |
+
eos_text="<END>",
|
90 |
+
tokenizer=tokenizer,
|
91 |
+
max_new_tokens=128,
|
92 |
+
**kwargs,
|
93 |
+
)[0]
|
94 |
+
cleaned_answer = re.sub("<$", "", re.sub("END$", "", answer)).strip()
|
95 |
+
|
96 |
+
# Use the result_queue to pass the result if it is provided
|
97 |
+
if result_queue:
|
98 |
+
result_queue.put(cleaned_answer)
|
99 |
+
else:
|
100 |
+
return cleaned_answer
|
text_model.py
ADDED
@@ -0,0 +1,20 @@
|
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|
|
|
|
|
|
|
1 |
+
from torch import nn
|
2 |
+
import transformers
|
3 |
+
from .modeling_phi import PhiForCausalLM
|
4 |
+
from .configuration_moondream import PhiConfig
|
5 |
+
|
6 |
+
transformers.logging.set_verbosity_error()
|
7 |
+
|
8 |
+
|
9 |
+
class TextModel(nn.Module):
|
10 |
+
def __init__(self, config, tokenizer) -> None:
|
11 |
+
super().__init__()
|
12 |
+
|
13 |
+
if type(config.phi_config) == dict:
|
14 |
+
phi_config = PhiConfig(**config.phi_config)
|
15 |
+
else:
|
16 |
+
phi_config = config.phi_config
|
17 |
+
|
18 |
+
self.model = PhiForCausalLM(phi_config)
|
19 |
+
self.text_emb = self.model.get_input_embeddings()
|
20 |
+
self.tokenizer = tokenizer
|
vision_encoder.py
ADDED
@@ -0,0 +1,150 @@
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|
|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
from torch import nn
|
3 |
+
from PIL import Image
|
4 |
+
from einops import rearrange
|
5 |
+
from torchvision.transforms.v2 import (
|
6 |
+
Compose,
|
7 |
+
Resize,
|
8 |
+
InterpolationMode,
|
9 |
+
ToImage,
|
10 |
+
ToDtype,
|
11 |
+
Normalize,
|
12 |
+
)
|
13 |
+
import timm
|
14 |
+
|
15 |
+
|
16 |
+
class VisualHolder(nn.Module):
|
17 |
+
def __init__(self, model):
|
18 |
+
super().__init__()
|
19 |
+
self.visual = model
|
20 |
+
|
21 |
+
def forward(self, x):
|
22 |
+
return self.visual(x)
|
23 |
+
|
24 |
+
|
25 |
+
class ModelHolder(nn.Module):
|
26 |
+
def __init__(self, model):
|
27 |
+
super().__init__()
|
28 |
+
self.model = model
|
29 |
+
|
30 |
+
def forward(self, x):
|
31 |
+
return self.model(x)
|
32 |
+
|
33 |
+
|
34 |
+
class LinearPatchEmbedding(nn.Module):
|
35 |
+
def __init__(self, conv):
|
36 |
+
super().__init__()
|
37 |
+
self.linear = nn.Linear(588, 1152)
|
38 |
+
self.linear.weight.data = conv.weight.data.view(1152, -1)
|
39 |
+
if conv.bias is not None:
|
40 |
+
self.linear.bias.data = conv.bias.data
|
41 |
+
|
42 |
+
def forward(self, x):
|
43 |
+
return self.linear(x)
|
44 |
+
|
45 |
+
|
46 |
+
class MLP(nn.Module):
|
47 |
+
def __init__(
|
48 |
+
self,
|
49 |
+
in_features: int,
|
50 |
+
hidden_features: int = None,
|
51 |
+
out_features: int = None,
|
52 |
+
act_layer: nn.Module = nn.GELU,
|
53 |
+
) -> None:
|
54 |
+
super().__init__()
|
55 |
+
out_features = out_features or in_features
|
56 |
+
hidden_features = hidden_features or in_features
|
57 |
+
self.fc1 = nn.Linear(in_features, hidden_features)
|
58 |
+
self.act = act_layer()
|
59 |
+
self.fc2 = nn.Linear(hidden_features, out_features)
|
60 |
+
|
61 |
+
torch.nn.init.kaiming_normal_(
|
62 |
+
self.fc1.weight, mode="fan_in", nonlinearity="relu"
|
63 |
+
)
|
64 |
+
torch.nn.init.kaiming_normal_(
|
65 |
+
self.fc2.weight, mode="fan_in", nonlinearity="relu"
|
66 |
+
)
|
67 |
+
|
68 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
69 |
+
x = self.fc1(x)
|
70 |
+
x = self.act(x)
|
71 |
+
x = self.fc2(x)
|
72 |
+
return x
|
73 |
+
|
74 |
+
|
75 |
+
class VisionProjection(nn.Module):
|
76 |
+
def __init__(self):
|
77 |
+
super().__init__()
|
78 |
+
|
79 |
+
image_embedding_dim = 1152
|
80 |
+
model_dim = 2048
|
81 |
+
hidden_dim = model_dim * 4
|
82 |
+
|
83 |
+
self.mlp1 = MLP(image_embedding_dim, hidden_dim, model_dim)
|
84 |
+
self.mlp2 = MLP(model_dim, hidden_dim, model_dim)
|
85 |
+
self.ln = nn.LayerNorm(model_dim)
|
86 |
+
|
87 |
+
@property
|
88 |
+
def device(self):
|
89 |
+
return self.mlp1.fc1.weight.device
|
90 |
+
|
91 |
+
def forward(self, x):
|
92 |
+
x = self.mlp1(x)
|
93 |
+
x = self.ln(x)
|
94 |
+
x = x + self.mlp2(x)
|
95 |
+
return x
|
96 |
+
|
97 |
+
|
98 |
+
class VisionTower(nn.Module):
|
99 |
+
def __init__(self):
|
100 |
+
super().__init__()
|
101 |
+
|
102 |
+
self.encoder = ModelHolder(
|
103 |
+
VisualHolder(timm.create_model("vit_so400m_patch14_siglip_384"))
|
104 |
+
)
|
105 |
+
self.encoder.model.visual.patch_embed = LinearPatchEmbedding(
|
106 |
+
self.encoder.model.visual.patch_embed.proj
|
107 |
+
)
|
108 |
+
self.encoder.model.visual.attn_pool = nn.Identity()
|
109 |
+
|
110 |
+
self.projection = VisionProjection()
|
111 |
+
|
112 |
+
def forward(self, x):
|
113 |
+
x = self.encoder(x)
|
114 |
+
x = self.projection(x)
|
115 |
+
return x
|
116 |
+
|
117 |
+
|
118 |
+
class VisionEncoder(nn.Module):
|
119 |
+
def __init__(self) -> None:
|
120 |
+
super().__init__()
|
121 |
+
|
122 |
+
self.model = VisionTower()
|
123 |
+
self.preprocess = Compose(
|
124 |
+
[
|
125 |
+
Resize(size=(378, 378), interpolation=InterpolationMode.BICUBIC),
|
126 |
+
ToImage(),
|
127 |
+
ToDtype(torch.float32, scale=True),
|
128 |
+
Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
|
129 |
+
]
|
130 |
+
)
|
131 |
+
|
132 |
+
@property
|
133 |
+
def device(self):
|
134 |
+
return self.model.projection.mlp1.fc1.weight.device
|
135 |
+
|
136 |
+
@property
|
137 |
+
def dtype(self):
|
138 |
+
return self.model.projection.mlp1.fc1.weight.dtype
|
139 |
+
|
140 |
+
def __call__(self, image: Image) -> torch.Tensor:
|
141 |
+
with torch.no_grad():
|
142 |
+
image_vec = (
|
143 |
+
self.preprocess(image.convert("RGB"))
|
144 |
+
.unsqueeze(0)
|
145 |
+
.to(self.device, dtype=self.dtype)
|
146 |
+
)
|
147 |
+
image_vec = rearrange(
|
148 |
+
image_vec, "b c (h p1) (w p2) -> b (h w) (c p1 p2)", p1=14, p2=14
|
149 |
+
)
|
150 |
+
return self.model(image_vec)
|