gokaygokay commited on
Commit
3ec40c7
1 Parent(s): 2960eac
added_tokens.json ADDED
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+ " ": 50259,
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+ " ": 50258,
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+ " ": 50257
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+ }
configuration_moondream.py ADDED
@@ -0,0 +1,98 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from transformers import PretrainedConfig
2
+
3
+
4
+ class PhiConfig(PretrainedConfig):
5
+ model_type = "phi"
6
+ keys_to_ignore_at_inference = ["past_key_values"]
7
+
8
+ def __init__(
9
+ self,
10
+ vocab_size=51200,
11
+ hidden_size=2048,
12
+ intermediate_size=8192,
13
+ num_hidden_layers=24,
14
+ num_attention_heads=32,
15
+ num_key_value_heads=None,
16
+ resid_pdrop=0.0,
17
+ embd_pdrop=0.0,
18
+ attention_dropout=0.0,
19
+ hidden_act="gelu_new",
20
+ max_position_embeddings=2048,
21
+ initializer_range=0.02,
22
+ layer_norm_eps=1e-5,
23
+ use_cache=True,
24
+ tie_word_embeddings=False,
25
+ rope_theta=10000.0,
26
+ rope_scaling=None,
27
+ partial_rotary_factor=0.5,
28
+ qk_layernorm=False,
29
+ bos_token_id=1,
30
+ eos_token_id=2,
31
+ **kwargs,
32
+ ):
33
+ self.vocab_size = vocab_size
34
+ self.hidden_size = hidden_size
35
+ self.intermediate_size = intermediate_size
36
+ self.num_hidden_layers = num_hidden_layers
37
+ self.num_attention_heads = num_attention_heads
38
+
39
+ if num_key_value_heads is None:
40
+ num_key_value_heads = num_attention_heads
41
+
42
+ self.num_key_value_heads = num_key_value_heads
43
+ self.resid_pdrop = resid_pdrop
44
+ self.embd_pdrop = embd_pdrop
45
+ self.attention_dropout = attention_dropout
46
+ self.hidden_act = hidden_act
47
+ self.max_position_embeddings = max_position_embeddings
48
+ self.initializer_range = initializer_range
49
+ self.layer_norm_eps = layer_norm_eps
50
+ self.use_cache = use_cache
51
+ self.rope_theta = rope_theta
52
+ self.rope_scaling = rope_scaling
53
+ self.partial_rotary_factor = partial_rotary_factor
54
+ self.qk_layernorm = qk_layernorm
55
+ self._rope_scaling_validation()
56
+
57
+ super().__init__(
58
+ bos_token_id=bos_token_id,
59
+ eos_token_id=eos_token_id,
60
+ tie_word_embeddings=tie_word_embeddings,
61
+ **kwargs,
62
+ )
63
+
64
+ # Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
65
+ def _rope_scaling_validation(self):
66
+ """
67
+ Validate the `rope_scaling` configuration.
68
+ """
69
+ if self.rope_scaling is None:
70
+ return
71
+
72
+ if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
73
+ raise ValueError(
74
+ "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
75
+ f"got {self.rope_scaling}"
76
+ )
77
+ rope_scaling_type = self.rope_scaling.get("type", None)
78
+ rope_scaling_factor = self.rope_scaling.get("factor", None)
79
+ if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
80
+ raise ValueError(
81
+ f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
82
+ )
83
+ if (
84
+ rope_scaling_factor is None
85
+ or not isinstance(rope_scaling_factor, float)
86
+ or rope_scaling_factor <= 1.0
87
+ ):
88
+ raise ValueError(
89
+ f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}"
90
+ )
91
+
92
+
93
+ class MoondreamConfig(PretrainedConfig):
94
+ model_type = "moondream1"
95
+
96
+ def __init__(self, **kwargs):
97
+ self.phi_config = PhiConfig(**kwargs)
98
+ super().__init__(**kwargs)
merges.txt ADDED
The diff for this file is too large to render. See raw diff
 
modeling_phi.py ADDED
@@ -0,0 +1,1221 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2023 Microsoft and the HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # Licensed under the Apache License, Version 2.0 (the "License");
5
+ # you may not use this file except in compliance with the License.
6
+ # You may obtain a copy of the License at
7
+ #
8
+ # http://www.apache.org/licenses/LICENSE-2.0
9
+ #
10
+ # Unless required by applicable law or agreed to in writing, software
11
+ # distributed under the License is distributed on an "AS IS" BASIS,
12
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
+ # See the License for the specific language governing permissions and
14
+ # limitations under the License.
15
+
16
+ """ PyTorch Phi model."""
17
+
18
+
19
+ import math
20
+ from typing import List, Optional, Tuple, Union
21
+
22
+ import torch
23
+ import torch.nn.functional as F
24
+ import torch.utils.checkpoint
25
+ from torch import nn
26
+ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
27
+
28
+ from transformers.activations import ACT2FN
29
+ from transformers.cache_utils import Cache, DynamicCache
30
+ from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
31
+ from transformers.modeling_outputs import (
32
+ BaseModelOutputWithPast,
33
+ CausalLMOutputWithPast,
34
+ SequenceClassifierOutputWithPast,
35
+ )
36
+ from transformers.modeling_utils import PreTrainedModel
37
+ from transformers.utils import (
38
+ is_flash_attn_2_available,
39
+ is_flash_attn_greater_or_equal_2_10,
40
+ logging,
41
+ )
42
+ from .configuration_moondream import PhiConfig
43
+
44
+
45
+ try: # noqa: SIM105
46
+ if is_flash_attn_2_available():
47
+ from flash_attn import flash_attn_func, flash_attn_varlen_func
48
+ from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input
49
+ except ImportError:
50
+ # Workaround for https://github.com/huggingface/transformers/issues/28459,
51
+ # don't move to contextlib.suppress(ImportError)
52
+ pass
53
+
54
+
55
+ logger = logging.get_logger(__name__)
56
+
57
+
58
+ # Copied from transformers.models.llama.modeling_llama._get_unpad_data
59
+ def _get_unpad_data(attention_mask):
60
+ seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
61
+ indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
62
+ max_seqlen_in_batch = seqlens_in_batch.max().item()
63
+ cu_seqlens = F.pad(
64
+ torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)
65
+ )
66
+ return (
67
+ indices,
68
+ cu_seqlens,
69
+ max_seqlen_in_batch,
70
+ )
71
+
72
+
73
+ # Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Phi
74
+ class PhiRotaryEmbedding(nn.Module):
75
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
76
+ super().__init__()
77
+
78
+ self.dim = dim
79
+ self.max_position_embeddings = max_position_embeddings
80
+ self.base = base
81
+ inv_freq = 1.0 / (
82
+ self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
83
+ )
84
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
85
+
86
+ # Build here to make `torch.jit.trace` work.
87
+ self._set_cos_sin_cache(
88
+ seq_len=max_position_embeddings,
89
+ device=self.inv_freq.device,
90
+ dtype=torch.get_default_dtype(),
91
+ )
92
+
93
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
94
+ self.max_seq_len_cached = seq_len
95
+ t = torch.arange(
96
+ self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
97
+ )
98
+
99
+ freqs = torch.outer(t, self.inv_freq)
100
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
101
+ emb = torch.cat((freqs, freqs), dim=-1)
102
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
103
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
104
+
105
+ def forward(self, x, seq_len=None):
106
+ # x: [bs, num_attention_heads, seq_len, head_size]
107
+ if seq_len > self.max_seq_len_cached:
108
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
109
+
110
+ return (
111
+ self.cos_cached[:seq_len].to(dtype=x.dtype),
112
+ self.sin_cached[:seq_len].to(dtype=x.dtype),
113
+ )
114
+
115
+
116
+ # Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->Phi
117
+ class PhiLinearScalingRotaryEmbedding(PhiRotaryEmbedding):
118
+ """PhiRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
119
+
120
+ def __init__(
121
+ self,
122
+ dim,
123
+ max_position_embeddings=2048,
124
+ base=10000,
125
+ device=None,
126
+ scaling_factor=1.0,
127
+ ):
128
+ self.scaling_factor = scaling_factor
129
+ super().__init__(dim, max_position_embeddings, base, device)
130
+
131
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
132
+ self.max_seq_len_cached = seq_len
133
+ t = torch.arange(
134
+ self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
135
+ )
136
+ t = t / self.scaling_factor
137
+
138
+ freqs = torch.outer(t, self.inv_freq)
139
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
140
+ emb = torch.cat((freqs, freqs), dim=-1)
141
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
142
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
143
+
144
+
145
+ # Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->Phi
146
+ class PhiDynamicNTKScalingRotaryEmbedding(PhiRotaryEmbedding):
147
+ """PhiRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
148
+
149
+ def __init__(
150
+ self,
151
+ dim,
152
+ max_position_embeddings=2048,
153
+ base=10000,
154
+ device=None,
155
+ scaling_factor=1.0,
156
+ ):
157
+ self.scaling_factor = scaling_factor
158
+ super().__init__(dim, max_position_embeddings, base, device)
159
+
160
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
161
+ self.max_seq_len_cached = seq_len
162
+
163
+ if seq_len > self.max_position_embeddings:
164
+ base = self.base * (
165
+ (self.scaling_factor * seq_len / self.max_position_embeddings)
166
+ - (self.scaling_factor - 1)
167
+ ) ** (self.dim / (self.dim - 2))
168
+ inv_freq = 1.0 / (
169
+ base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
170
+ )
171
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
172
+
173
+ t = torch.arange(
174
+ self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
175
+ )
176
+
177
+ freqs = torch.outer(t, self.inv_freq)
178
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
179
+ emb = torch.cat((freqs, freqs), dim=-1)
180
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
181
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
182
+
183
+
184
+ # Copied from transformers.models.llama.modeling_llama.rotate_half
185
+ def rotate_half(x):
186
+ """Rotates half the hidden dims of the input."""
187
+ x1 = x[..., : x.shape[-1] // 2]
188
+ x2 = x[..., x.shape[-1] // 2 :]
189
+ return torch.cat((-x2, x1), dim=-1)
190
+
191
+
192
+ # Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
193
+ def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
194
+ """Applies Rotary Position Embedding to the query and key tensors.
195
+
196
+ Args:
197
+ q (`torch.Tensor`): The query tensor.
198
+ k (`torch.Tensor`): The key tensor.
199
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
200
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
201
+ position_ids (`torch.Tensor`):
202
+ The position indices of the tokens corresponding to the query and key tensors. For example, this can be
203
+ used to pass offsetted position ids when working with a KV-cache.
204
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
205
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
206
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
207
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
208
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
209
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
210
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
211
+ Returns:
212
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
213
+ """
214
+ cos = cos[position_ids].unsqueeze(unsqueeze_dim)
215
+ sin = sin[position_ids].unsqueeze(unsqueeze_dim)
216
+ q_embed = (q * cos) + (rotate_half(q) * sin)
217
+ k_embed = (k * cos) + (rotate_half(k) * sin)
218
+ return q_embed, k_embed
219
+
220
+
221
+ # Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Phi
222
+ class PhiMLP(nn.Module):
223
+ def __init__(self, config):
224
+ super().__init__()
225
+ self.config = config
226
+ self.activation_fn = ACT2FN[config.hidden_act]
227
+ self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
228
+ self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
229
+
230
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
231
+ hidden_states = self.fc1(hidden_states)
232
+ hidden_states = self.activation_fn(hidden_states)
233
+ hidden_states = self.fc2(hidden_states)
234
+ return hidden_states
235
+
236
+
237
+ # Copied from transformers.models.llama.modeling_llama.repeat_kv with llama->phi
238
+ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
239
+ """
240
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
241
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
242
+ """
243
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
244
+ if n_rep == 1:
245
+ return hidden_states
246
+ hidden_states = hidden_states[:, :, None, :, :].expand(
247
+ batch, num_key_value_heads, n_rep, slen, head_dim
248
+ )
249
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
250
+
251
+
252
+ class PhiAttention(nn.Module):
253
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
254
+
255
+ def __init__(self, config: PhiConfig, layer_idx: Optional[int] = None):
256
+ super().__init__()
257
+ self.config = config
258
+ self.layer_idx = layer_idx
259
+ if layer_idx is None:
260
+ logger.warning_once(
261
+ f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
262
+ "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
263
+ "when creating this class."
264
+ )
265
+
266
+ self.attention_dropout = config.attention_dropout
267
+ self.hidden_size = config.hidden_size
268
+ self.num_heads = config.num_attention_heads
269
+ self.head_dim = self.hidden_size // self.num_heads
270
+ self.num_key_value_heads = config.num_key_value_heads
271
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
272
+ self.max_position_embeddings = config.max_position_embeddings
273
+ self.rope_theta = config.rope_theta
274
+ self.partial_rotary_factor = config.partial_rotary_factor
275
+ self.is_causal = True
276
+
277
+ if (self.head_dim * self.num_heads) != self.hidden_size:
278
+ raise ValueError(
279
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
280
+ f" and `num_heads`: {self.num_heads})."
281
+ )
282
+
283
+ self.Wqkv = nn.Linear(
284
+ self.hidden_size, 3 * self.num_heads * self.head_dim, bias=True
285
+ )
286
+ self.out_proj = nn.Linear(
287
+ self.num_heads * self.head_dim, self.hidden_size, bias=True
288
+ )
289
+
290
+ self.qk_layernorm = config.qk_layernorm
291
+ if self.qk_layernorm:
292
+ self.q_layernorm = nn.LayerNorm(
293
+ config.hidden_size // self.num_heads,
294
+ eps=config.layer_norm_eps,
295
+ elementwise_affine=True,
296
+ )
297
+ self.k_layernorm = nn.LayerNorm(
298
+ config.hidden_size // self.num_heads,
299
+ eps=config.layer_norm_eps,
300
+ elementwise_affine=True,
301
+ )
302
+
303
+ self._init_rope()
304
+
305
+ def _init_rope(self):
306
+ if self.config.rope_scaling is None:
307
+ self.rotary_emb = PhiRotaryEmbedding(
308
+ int(self.partial_rotary_factor * self.head_dim),
309
+ max_position_embeddings=self.max_position_embeddings,
310
+ base=self.rope_theta,
311
+ )
312
+ else:
313
+ scaling_type = self.config.rope_scaling["type"]
314
+ scaling_factor = self.config.rope_scaling["factor"]
315
+ if scaling_type == "linear":
316
+ self.rotary_emb = PhiLinearScalingRotaryEmbedding(
317
+ int(self.partial_rotary_factor * self.head_dim),
318
+ max_position_embeddings=self.max_position_embeddings,
319
+ scaling_factor=scaling_factor,
320
+ base=self.rope_theta,
321
+ )
322
+ elif scaling_type == "dynamic":
323
+ self.rotary_emb = PhiDynamicNTKScalingRotaryEmbedding(
324
+ int(self.partial_rotary_factor * self.head_dim),
325
+ max_position_embeddings=self.max_position_embeddings,
326
+ scaling_factor=scaling_factor,
327
+ base=self.rope_theta,
328
+ )
329
+ else:
330
+ raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
331
+
332
+ def forward(
333
+ self,
334
+ hidden_states: torch.Tensor,
335
+ attention_mask: Optional[torch.Tensor] = None,
336
+ position_ids: Optional[torch.LongTensor] = None,
337
+ past_key_value: Optional[Cache] = None,
338
+ output_attentions: bool = False,
339
+ use_cache: bool = False,
340
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
341
+ bsz, q_len, _ = hidden_states.size()
342
+
343
+ query_states, key_states, value_states = self.Wqkv(hidden_states).chunk(
344
+ 3, dim=-1
345
+ )
346
+
347
+ if self.qk_layernorm:
348
+ query_states = self.q_layernorm(query_states)
349
+ key_states = self.k_layernorm(key_states)
350
+
351
+ query_states = query_states.view(
352
+ bsz, q_len, self.num_heads, self.head_dim
353
+ ).transpose(1, 2)
354
+ key_states = key_states.view(
355
+ bsz, q_len, self.num_key_value_heads, self.head_dim
356
+ ).transpose(1, 2)
357
+ value_states = value_states.view(
358
+ bsz, q_len, self.num_key_value_heads, self.head_dim
359
+ ).transpose(1, 2)
360
+
361
+ kv_seq_len = key_states.shape[-2]
362
+ if past_key_value is not None:
363
+ if self.layer_idx is None:
364
+ raise ValueError(
365
+ f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
366
+ "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
367
+ "with a layer index."
368
+ )
369
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
370
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
371
+
372
+ # Partial rotary embedding
373
+ query_rot, query_pass = (
374
+ query_states[..., : self.rotary_emb.dim],
375
+ query_states[..., self.rotary_emb.dim :],
376
+ )
377
+ key_rot, key_pass = (
378
+ key_states[..., : self.rotary_emb.dim],
379
+ key_states[..., self.rotary_emb.dim :],
380
+ )
381
+ # [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
382
+ query_rot, key_rot = apply_rotary_pos_emb(
383
+ query_rot, key_rot, cos, sin, position_ids
384
+ )
385
+
386
+ # [batch_size, seq_length, num_heads, head_dim]
387
+ query_states = torch.cat((query_rot, query_pass), dim=-1)
388
+ key_states = torch.cat((key_rot, key_pass), dim=-1)
389
+
390
+ if past_key_value is not None:
391
+ cache_kwargs = {
392
+ "sin": sin,
393
+ "cos": cos,
394
+ "partial_rotation_size": self.rotary_emb.dim,
395
+ }
396
+ key_states, value_states = past_key_value.update(
397
+ key_states, value_states, self.layer_idx, cache_kwargs
398
+ )
399
+
400
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
401
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
402
+
403
+ # Queries and keys upcast to fp32 is required by Phi-2 to avoid overflow
404
+ attn_weights = torch.matmul(
405
+ query_states.to(torch.float32), key_states.to(torch.float32).transpose(2, 3)
406
+ ) / math.sqrt(self.head_dim)
407
+
408
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
409
+ raise ValueError(
410
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
411
+ f" {attn_weights.size()}"
412
+ )
413
+
414
+ if attention_mask is not None:
415
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
416
+ raise ValueError(
417
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
418
+ )
419
+ attn_weights = attn_weights + attention_mask
420
+
421
+ # upcast attention to fp32
422
+ attn_weights = nn.functional.softmax(
423
+ attn_weights, dim=-1, dtype=torch.float32
424
+ ).to(value_states.dtype)
425
+ attn_weights = nn.functional.dropout(
426
+ attn_weights, p=self.attention_dropout, training=self.training
427
+ )
428
+
429
+ attn_output = torch.matmul(attn_weights, value_states)
430
+
431
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
432
+ raise ValueError(
433
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
434
+ f" {attn_output.size()}"
435
+ )
436
+
437
+ attn_output = attn_output.transpose(1, 2).contiguous()
438
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
439
+
440
+ attn_output = self.out_proj(attn_output)
441
+
442
+ if not output_attentions:
443
+ attn_weights = None
444
+
445
+ return attn_output, attn_weights, past_key_value
446
+
447
+
448
+ class PhiFlashAttention2(PhiAttention):
449
+ """
450
+ Phi flash attention module. This module inherits from `PhiAttention` as the weights of the module stays
451
+ untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
452
+ flash attention and deal with padding tokens in case the input contains any of them.
453
+ """
454
+
455
+ # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
456
+ def __init__(self, *args, **kwargs):
457
+ super().__init__(*args, **kwargs)
458
+
459
+ # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
460
+ # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
461
+ # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
462
+ self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
463
+
464
+ def forward(
465
+ self,
466
+ hidden_states: torch.Tensor,
467
+ attention_mask: Optional[torch.LongTensor] = None,
468
+ position_ids: Optional[torch.LongTensor] = None,
469
+ past_key_value: Optional[Cache] = None,
470
+ output_attentions: bool = False,
471
+ use_cache: bool = False,
472
+ **kwargs,
473
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
474
+ # PhiFlashAttention2 attention does not support output_attentions
475
+
476
+ output_attentions = False
477
+
478
+ bsz, q_len, _ = hidden_states.size()
479
+
480
+ query_states, key_states, value_states = self.Wqkv(hidden_states).chunk(
481
+ 3, dim=-1
482
+ )
483
+
484
+ if self.qk_layernorm:
485
+ query_states = self.q_layernorm(query_states)
486
+ key_states = self.k_layernorm(key_states)
487
+
488
+ # Flash attention requires the input to have the shape
489
+ # batch_size x seq_length x head_dim x hidden_dim
490
+ # therefore we just need to keep the original shape
491
+ query_states = query_states.view(
492
+ bsz, q_len, self.num_heads, self.head_dim
493
+ ).transpose(1, 2)
494
+ key_states = key_states.view(
495
+ bsz, q_len, self.num_key_value_heads, self.head_dim
496
+ ).transpose(1, 2)
497
+ value_states = value_states.view(
498
+ bsz, q_len, self.num_key_value_heads, self.head_dim
499
+ ).transpose(1, 2)
500
+
501
+ kv_seq_len = key_states.shape[-2]
502
+ if past_key_value is not None:
503
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
504
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
505
+
506
+ # Partial rotary embedding
507
+ query_rot, query_pass = (
508
+ query_states[..., : self.rotary_emb.dim],
509
+ query_states[..., self.rotary_emb.dim :],
510
+ )
511
+ key_rot, key_pass = (
512
+ key_states[..., : self.rotary_emb.dim],
513
+ key_states[..., self.rotary_emb.dim :],
514
+ )
515
+ # [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
516
+ query_rot, key_rot = apply_rotary_pos_emb(
517
+ query_rot, key_rot, cos, sin, position_ids
518
+ )
519
+
520
+ # [batch_size, seq_length, num_heads, head_dim]
521
+ query_states = torch.cat((query_rot, query_pass), dim=-1)
522
+ key_states = torch.cat((key_rot, key_pass), dim=-1)
523
+
524
+ if past_key_value is not None:
525
+ cache_kwargs = {
526
+ "sin": sin,
527
+ "cos": cos,
528
+ "partial_rotation_size": self.rotary_emb.dim,
529
+ }
530
+ key_states, value_states = past_key_value.update(
531
+ key_states, value_states, self.layer_idx, cache_kwargs
532
+ )
533
+
534
+ # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
535
+ # to be able to avoid many of these transpose/reshape/view.
536
+ query_states = query_states.transpose(1, 2)
537
+ key_states = key_states.transpose(1, 2)
538
+ value_states = value_states.transpose(1, 2)
539
+
540
+ attn_dropout = self.attention_dropout if self.training else 0.0
541
+
542
+ # In PEFT, usually we cast the layer norms in float32 for training stability reasons
543
+ # therefore the input hidden states gets silently casted in float32. Hence, we need
544
+ # cast them back in the correct dtype just to be sure everything works as expected.
545
+ # This might slowdown training & inference so it is recommended to not cast the LayerNorms
546
+ # in fp32.
547
+
548
+ if query_states.dtype == torch.float32:
549
+ if torch.is_autocast_enabled():
550
+ target_dtype = torch.get_autocast_gpu_dtype()
551
+ # Handle the case where the model is quantized
552
+ elif hasattr(self.config, "_pre_quantization_dtype"):
553
+ target_dtype = self.config._pre_quantization_dtype
554
+ else:
555
+ target_dtype = self.q_proj.weight.dtype
556
+
557
+ logger.warning_once(
558
+ f"The input hidden states seems to be silently casted in float32, this might be related to"
559
+ f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
560
+ f" {target_dtype}."
561
+ )
562
+
563
+ query_states = query_states.to(target_dtype)
564
+ key_states = key_states.to(target_dtype)
565
+ value_states = value_states.to(target_dtype)
566
+
567
+ attn_output = self._flash_attention_forward(
568
+ query_states,
569
+ key_states,
570
+ value_states,
571
+ attention_mask,
572
+ q_len,
573
+ dropout=attn_dropout,
574
+ softmax_scale=None,
575
+ )
576
+
577
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
578
+ attn_output = self.out_proj(attn_output)
579
+
580
+ if not output_attentions:
581
+ attn_weights = None
582
+
583
+ return attn_output, attn_weights, past_key_value
584
+
585
+ # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward
586
+ def _flash_attention_forward(
587
+ self,
588
+ query_states,
589
+ key_states,
590
+ value_states,
591
+ attention_mask,
592
+ query_length,
593
+ dropout=0.0,
594
+ softmax_scale=None,
595
+ ):
596
+ """
597
+ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
598
+ first unpad the input, then computes the attention scores and pad the final attention scores.
599
+
600
+ Args:
601
+ query_states (`torch.Tensor`):
602
+ Input query states to be passed to Flash Attention API
603
+ key_states (`torch.Tensor`):
604
+ Input key states to be passed to Flash Attention API
605
+ value_states (`torch.Tensor`):
606
+ Input value states to be passed to Flash Attention API
607
+ attention_mask (`torch.Tensor`):
608
+ The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
609
+ position of padding tokens and 1 for the position of non-padding tokens.
610
+ dropout (`int`, *optional*):
611
+ Attention dropout
612
+ softmax_scale (`float`, *optional*):
613
+ The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
614
+ """
615
+ if not self._flash_attn_uses_top_left_mask:
616
+ causal = self.is_causal
617
+ else:
618
+ # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
619
+ causal = self.is_causal and query_length != 1
620
+
621
+ # Contains at least one padding token in the sequence
622
+ if attention_mask is not None:
623
+ batch_size = query_states.shape[0]
624
+ (
625
+ query_states,
626
+ key_states,
627
+ value_states,
628
+ indices_q,
629
+ cu_seq_lens,
630
+ max_seq_lens,
631
+ ) = self._upad_input(
632
+ query_states, key_states, value_states, attention_mask, query_length
633
+ )
634
+
635
+ cu_seqlens_q, cu_seqlens_k = cu_seq_lens
636
+ max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
637
+
638
+ attn_output_unpad = flash_attn_varlen_func(
639
+ query_states,
640
+ key_states,
641
+ value_states,
642
+ cu_seqlens_q=cu_seqlens_q,
643
+ cu_seqlens_k=cu_seqlens_k,
644
+ max_seqlen_q=max_seqlen_in_batch_q,
645
+ max_seqlen_k=max_seqlen_in_batch_k,
646
+ dropout_p=dropout,
647
+ softmax_scale=softmax_scale,
648
+ causal=causal,
649
+ )
650
+
651
+ attn_output = pad_input(
652
+ attn_output_unpad, indices_q, batch_size, query_length
653
+ )
654
+ else:
655
+ attn_output = flash_attn_func(
656
+ query_states,
657
+ key_states,
658
+ value_states,
659
+ dropout,
660
+ softmax_scale=softmax_scale,
661
+ causal=causal,
662
+ )
663
+
664
+ return attn_output
665
+
666
+ # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input
667
+ def _upad_input(
668
+ self, query_layer, key_layer, value_layer, attention_mask, query_length
669
+ ):
670
+ indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
671
+ batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
672
+
673
+ key_layer = index_first_axis(
674
+ key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
675
+ indices_k,
676
+ )
677
+ value_layer = index_first_axis(
678
+ value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
679
+ indices_k,
680
+ )
681
+ if query_length == kv_seq_len:
682
+ query_layer = index_first_axis(
683
+ query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim),
684
+ indices_k,
685
+ )
686
+ cu_seqlens_q = cu_seqlens_k
687
+ max_seqlen_in_batch_q = max_seqlen_in_batch_k
688
+ indices_q = indices_k
689
+ elif query_length == 1:
690
+ max_seqlen_in_batch_q = 1
691
+ cu_seqlens_q = torch.arange(
692
+ batch_size + 1, dtype=torch.int32, device=query_layer.device
693
+ ) # There is a memcpy here, that is very bad.
694
+ indices_q = cu_seqlens_q[:-1]
695
+ query_layer = query_layer.squeeze(1)
696
+ else:
697
+ # The -q_len: slice assumes left padding.
698
+ attention_mask = attention_mask[:, -query_length:]
699
+ query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(
700
+ query_layer, attention_mask
701
+ )
702
+
703
+ return (
704
+ query_layer,
705
+ key_layer,
706
+ value_layer,
707
+ indices_q,
708
+ (cu_seqlens_q, cu_seqlens_k),
709
+ (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
710
+ )
711
+
712
+
713
+ PHI_ATTENTION_CLASSES = {
714
+ "eager": PhiAttention,
715
+ "flash_attention_2": PhiFlashAttention2,
716
+ }
717
+
718
+
719
+ class PhiDecoderLayer(nn.Module):
720
+ def __init__(self, config: PhiConfig, layer_idx: int):
721
+ super().__init__()
722
+ self.mixer = PHI_ATTENTION_CLASSES[config._attn_implementation](
723
+ config, layer_idx=layer_idx
724
+ )
725
+ self.mlp = PhiMLP(config)
726
+ self.ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
727
+ self.resid_dropout = nn.Dropout(config.resid_pdrop)
728
+
729
+ def forward(
730
+ self,
731
+ hidden_states: torch.Tensor,
732
+ attention_mask: Optional[torch.Tensor] = None,
733
+ position_ids: Optional[torch.LongTensor] = None,
734
+ output_attentions: Optional[bool] = False,
735
+ use_cache: Optional[bool] = False,
736
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
737
+ ) -> Tuple[
738
+ torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
739
+ ]:
740
+ """
741
+ Args:
742
+ hidden_states (`torch.FloatTensor`):
743
+ input to the layer of shape `(batch, seq_len, embed_dim)`
744
+ attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
745
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
746
+ position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
747
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range
748
+ `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
749
+ output_attentions (`bool`, *optional*):
750
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
751
+ returned tensors for more detail.
752
+ use_cache (`bool`, *optional*):
753
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
754
+ (see `past_key_values`).
755
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
756
+ """
757
+
758
+ residual = hidden_states
759
+
760
+ hidden_states = self.ln(hidden_states)
761
+
762
+ # Self Attention
763
+ attn_outputs, self_attn_weights, present_key_value = self.mixer(
764
+ hidden_states=hidden_states,
765
+ attention_mask=attention_mask,
766
+ position_ids=position_ids,
767
+ past_key_value=past_key_value,
768
+ output_attentions=output_attentions,
769
+ use_cache=use_cache,
770
+ )
771
+ attn_outputs = self.resid_dropout(attn_outputs)
772
+
773
+ feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states))
774
+ hidden_states = attn_outputs + feed_forward_hidden_states + residual
775
+ outputs = (hidden_states,)
776
+
777
+ if output_attentions:
778
+ outputs += (self_attn_weights,)
779
+
780
+ if use_cache:
781
+ outputs += (present_key_value,)
782
+
783
+ return outputs
784
+
785
+
786
+ class PhiPreTrainedModel(PreTrainedModel):
787
+ config_class = PhiConfig
788
+ base_model_prefix = "model"
789
+ supports_gradient_checkpointing = True
790
+ _no_split_modules = ["PhiDecoderLayer"]
791
+ _skip_keys_device_placement = "past_key_values"
792
+ _supports_flash_attn_2 = True
793
+ _supports_cache_class = True
794
+
795
+ def _init_weights(self, module):
796
+ std = self.config.initializer_range
797
+ if isinstance(module, nn.Linear):
798
+ module.weight.data.normal_(mean=0.0, std=std)
799
+ if module.bias is not None:
800
+ module.bias.data.zero_()
801
+ elif isinstance(module, nn.Embedding):
802
+ module.weight.data.normal_(mean=0.0, std=std)
803
+ if module.padding_idx is not None:
804
+ module.weight.data[module.padding_idx].zero_()
805
+
806
+
807
+ class Embedding(nn.Module):
808
+ def __init__(self, config: PhiConfig):
809
+ super().__init__()
810
+ self.wte = nn.Embedding(
811
+ config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id
812
+ )
813
+
814
+ def forward(self, input_ids: torch.LongTensor) -> torch.FloatTensor:
815
+ return self.wte(input_ids)
816
+
817
+
818
+ class PhiModel(PhiPreTrainedModel):
819
+ """
820
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`PhiDecoderLayer`]
821
+
822
+ Args:
823
+ config: PhiConfig
824
+ """
825
+
826
+ def __init__(self, config: PhiConfig):
827
+ super().__init__(config)
828
+ self.padding_idx = config.pad_token_id
829
+ self.vocab_size = config.vocab_size
830
+
831
+ self.embd = Embedding(config)
832
+ self.embed_dropout = nn.Dropout(config.embd_pdrop)
833
+ self.h = nn.ModuleList(
834
+ [
835
+ PhiDecoderLayer(config, layer_idx)
836
+ for layer_idx in range(config.num_hidden_layers)
837
+ ]
838
+ )
839
+ self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
840
+
841
+ self.gradient_checkpointing = False
842
+ # Initialize weights and apply final processing
843
+ self.post_init()
844
+
845
+ def get_input_embeddings(self):
846
+ return self.embd.wte
847
+
848
+ def set_input_embeddings(self, value):
849
+ self.embd.wte = value
850
+
851
+ def forward(
852
+ self,
853
+ input_ids: torch.LongTensor = None,
854
+ attention_mask: Optional[torch.Tensor] = None,
855
+ position_ids: Optional[torch.LongTensor] = None,
856
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
857
+ inputs_embeds: Optional[torch.FloatTensor] = None,
858
+ use_cache: Optional[bool] = None,
859
+ output_attentions: Optional[bool] = None,
860
+ output_hidden_states: Optional[bool] = None,
861
+ return_dict: Optional[bool] = None,
862
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
863
+ output_attentions = (
864
+ output_attentions
865
+ if output_attentions is not None
866
+ else self.config.output_attentions
867
+ )
868
+ output_hidden_states = (
869
+ output_hidden_states
870
+ if output_hidden_states is not None
871
+ else self.config.output_hidden_states
872
+ )
873
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
874
+
875
+ return_dict = (
876
+ return_dict if return_dict is not None else self.config.use_return_dict
877
+ )
878
+
879
+ # retrieve input_ids and inputs_embeds
880
+ if input_ids is not None and inputs_embeds is not None:
881
+ raise ValueError(
882
+ "You cannot specify both input_ids and inputs_embeds at the same time"
883
+ )
884
+ elif input_ids is not None:
885
+ batch_size, seq_length = input_ids.shape[:2]
886
+ elif inputs_embeds is not None:
887
+ batch_size, seq_length = inputs_embeds.shape[:2]
888
+ else:
889
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
890
+
891
+ past_key_values_length = 0
892
+
893
+ if self.gradient_checkpointing and self.training:
894
+ if use_cache:
895
+ logger.warning_once(
896
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
897
+ )
898
+ use_cache = False
899
+
900
+ if use_cache:
901
+ use_legacy_cache = not isinstance(past_key_values, Cache)
902
+ if use_legacy_cache:
903
+ past_key_values = DynamicCache.from_legacy_cache(past_key_values)
904
+ past_key_values_length = past_key_values.get_usable_length(seq_length)
905
+
906
+ if position_ids is None:
907
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
908
+ position_ids = torch.arange(
909
+ past_key_values_length,
910
+ seq_length + past_key_values_length,
911
+ dtype=torch.long,
912
+ device=device,
913
+ )
914
+ position_ids = position_ids.unsqueeze(0)
915
+
916
+ if inputs_embeds is None:
917
+ inputs_embeds = self.embd(input_ids)
918
+
919
+ inputs_embeds = self.embed_dropout(inputs_embeds)
920
+
921
+ # Attention mask.
922
+ if self._use_flash_attention_2:
923
+ # 2d mask is passed through the layers
924
+ attention_mask = (
925
+ attention_mask
926
+ if (attention_mask is not None and 0 in attention_mask)
927
+ else None
928
+ )
929
+ else:
930
+ # 4d mask is passed through the layers
931
+ attention_mask = _prepare_4d_causal_attention_mask(
932
+ attention_mask,
933
+ (batch_size, seq_length),
934
+ inputs_embeds,
935
+ past_key_values_length,
936
+ )
937
+
938
+ hidden_states = inputs_embeds
939
+
940
+ # decoder layers
941
+ all_hidden_states = () if output_hidden_states else None
942
+ all_self_attns = () if output_attentions else None
943
+ next_decoder_cache = None
944
+
945
+ for decoder_layer in self.h:
946
+ if output_hidden_states:
947
+ all_hidden_states += (hidden_states,)
948
+
949
+ if self.gradient_checkpointing and self.training:
950
+ layer_outputs = self._gradient_checkpointing_func(
951
+ decoder_layer.__call__,
952
+ hidden_states,
953
+ attention_mask,
954
+ position_ids,
955
+ past_key_values,
956
+ output_attentions,
957
+ )
958
+ else:
959
+ layer_outputs = decoder_layer(
960
+ hidden_states,
961
+ attention_mask=attention_mask,
962
+ position_ids=position_ids,
963
+ past_key_value=past_key_values,
964
+ output_attentions=output_attentions,
965
+ use_cache=use_cache,
966
+ )
967
+
968
+ hidden_states = layer_outputs[0]
969
+
970
+ if use_cache:
971
+ next_decoder_cache = layer_outputs[2 if output_attentions else 1]
972
+
973
+ if output_attentions:
974
+ all_self_attns += (layer_outputs[1],)
975
+
976
+ # add hidden states from the last decoder layer
977
+ if output_hidden_states:
978
+ all_hidden_states += (hidden_states,)
979
+
980
+ next_cache = None
981
+ if use_cache:
982
+ next_cache = (
983
+ next_decoder_cache.to_legacy_cache()
984
+ if use_legacy_cache
985
+ else next_decoder_cache
986
+ )
987
+ if not return_dict:
988
+ return tuple(
989
+ v
990
+ for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
991
+ if v is not None
992
+ )
993
+ return BaseModelOutputWithPast(
994
+ last_hidden_state=hidden_states,
995
+ past_key_values=next_cache,
996
+ hidden_states=all_hidden_states,
997
+ attentions=all_self_attns,
998
+ )
999
+
1000
+
1001
+ class CausalLMHead(nn.Module):
1002
+ """Causal Language Modeling head. Simplified version."""
1003
+
1004
+ def __init__(self, config):
1005
+ super().__init__()
1006
+ self.ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
1007
+ self.linear = nn.Linear(config.hidden_size, config.vocab_size)
1008
+
1009
+ def forward(self, hidden_states):
1010
+ return self.linear(self.ln(hidden_states))
1011
+
1012
+
1013
+ class PhiForCausalLM(PhiPreTrainedModel):
1014
+ _tied_weights_keys = ["lm_head.linear.weight"]
1015
+
1016
+ # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with Llama->Phi,bias=False->bias=True
1017
+ def __init__(self, config):
1018
+ super().__init__(config)
1019
+ self.transformer = PhiModel(config)
1020
+ self.vocab_size = config.vocab_size
1021
+ self.lm_head = CausalLMHead(config)
1022
+
1023
+ # Initialize weights and apply final processing
1024
+ self.post_init()
1025
+
1026
+ # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_input_embeddings
1027
+ def get_input_embeddings(self):
1028
+ return self.transformer.embd.wte
1029
+
1030
+ # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_input_embeddings
1031
+ def set_input_embeddings(self, value):
1032
+ self.model.embd.wte = value
1033
+
1034
+ # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_output_embeddings
1035
+ def get_output_embeddings(self):
1036
+ return self.lm_head.linear
1037
+
1038
+ # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_output_embeddings
1039
+ def set_output_embeddings(self, new_embeddings):
1040
+ self.lm_head.linear = new_embeddings
1041
+
1042
+ # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_decoder
1043
+ def set_decoder(self, decoder):
1044
+ self.model = decoder
1045
+
1046
+ # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_decoder
1047
+ def get_decoder(self):
1048
+ return self.model
1049
+
1050
+ def forward(
1051
+ self,
1052
+ input_ids: torch.LongTensor = None,
1053
+ attention_mask: Optional[torch.Tensor] = None,
1054
+ position_ids: Optional[torch.LongTensor] = None,
1055
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1056
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1057
+ labels: Optional[torch.LongTensor] = None,
1058
+ use_cache: Optional[bool] = None,
1059
+ output_attentions: Optional[bool] = None,
1060
+ output_hidden_states: Optional[bool] = None,
1061
+ return_dict: Optional[bool] = None,
1062
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
1063
+ r"""
1064
+ Args:
1065
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
1066
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
1067
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
1068
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
1069
+
1070
+ Returns:
1071
+
1072
+ Example:
1073
+
1074
+ ```python
1075
+ >>> from transformers import AutoTokenizer, PhiForCausalLM
1076
+
1077
+ >>> model = PhiForCausalLM.from_pretrained("microsoft/phi-1")
1078
+ >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1")
1079
+
1080
+ >>> prompt = "This is an example script ."
1081
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
1082
+
1083
+ >>> # Generate
1084
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
1085
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
1086
+ 'This is an example script .\n\n\n\nfrom typing import List\n\ndef find_most_common_letter(words: List[str'
1087
+ ```"""
1088
+
1089
+ output_attentions = (
1090
+ output_attentions
1091
+ if output_attentions is not None
1092
+ else self.config.output_attentions
1093
+ )
1094
+ output_hidden_states = (
1095
+ output_hidden_states
1096
+ if output_hidden_states is not None
1097
+ else self.config.output_hidden_states
1098
+ )
1099
+ return_dict = (
1100
+ return_dict if return_dict is not None else self.config.use_return_dict
1101
+ )
1102
+
1103
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
1104
+ outputs = self.transformer(
1105
+ input_ids=input_ids,
1106
+ attention_mask=attention_mask,
1107
+ position_ids=position_ids,
1108
+ past_key_values=past_key_values,
1109
+ inputs_embeds=inputs_embeds,
1110
+ use_cache=use_cache,
1111
+ output_attentions=output_attentions,
1112
+ output_hidden_states=output_hidden_states,
1113
+ return_dict=return_dict,
1114
+ )
1115
+
1116
+ hidden_states = outputs[0]
1117
+ logits = self.lm_head(hidden_states)
1118
+ logits = logits.float()
1119
+
1120
+ loss = None
1121
+ if labels is not None:
1122
+ # Shift so that tokens < n predict n
1123
+ shift_logits = logits[..., :-1, :].contiguous()
1124
+ shift_labels = labels[..., 1:].contiguous()
1125
+ # Flatten the tokens
1126
+ loss_fct = CrossEntropyLoss()
1127
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
1128
+ shift_labels = shift_labels.view(-1)
1129
+ # Enable model parallelism
1130
+ shift_labels = shift_labels.to(shift_logits.device)
1131
+ loss = loss_fct(shift_logits, shift_labels)
1132
+
1133
+ if not return_dict:
1134
+ output = (logits,) + outputs[1:]
1135
+ return (loss,) + output if loss is not None else output
1136
+
1137
+ return CausalLMOutputWithPast(
1138
+ loss=loss,
1139
+ logits=logits,
1140
+ past_key_values=outputs.past_key_values,
1141
+ hidden_states=outputs.hidden_states,
1142
+ attentions=outputs.attentions,
1143
+ )
1144
+
1145
+ # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.prepare_inputs_for_generation
1146
+ def prepare_inputs_for_generation(
1147
+ self,
1148
+ input_ids,
1149
+ past_key_values=None,
1150
+ attention_mask=None,
1151
+ inputs_embeds=None,
1152
+ **kwargs,
1153
+ ):
1154
+ if past_key_values is not None:
1155
+ if isinstance(past_key_values, Cache):
1156
+ cache_length = past_key_values.get_seq_length()
1157
+ past_length = past_key_values.seen_tokens
1158
+ max_cache_length = past_key_values.get_max_length()
1159
+ else:
1160
+ cache_length = past_length = past_key_values[0][0].shape[2]
1161
+ max_cache_length = None
1162
+
1163
+ # Keep only the unprocessed tokens:
1164
+ # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
1165
+ # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
1166
+ # input)
1167
+ if (
1168
+ attention_mask is not None
1169
+ and attention_mask.shape[1] > input_ids.shape[1]
1170
+ ):
1171
+ input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
1172
+ # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
1173
+ # input_ids based on the past_length.
1174
+ elif past_length < input_ids.shape[1]:
1175
+ input_ids = input_ids[:, past_length:]
1176
+ # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
1177
+
1178
+ # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
1179
+ if (
1180
+ max_cache_length is not None
1181
+ and attention_mask is not None
1182
+ and cache_length + input_ids.shape[1] > max_cache_length
1183
+ ):
1184
+ attention_mask = attention_mask[:, -max_cache_length:]
1185
+
1186
+ position_ids = kwargs.get("position_ids", None)
1187
+ if attention_mask is not None and position_ids is None:
1188
+ # create position_ids on the fly for batch generation
1189
+ position_ids = attention_mask.long().cumsum(-1) - 1
1190
+ position_ids.masked_fill_(attention_mask == 0, 1)
1191
+ if past_key_values:
1192
+ position_ids = position_ids[:, -input_ids.shape[1] :]
1193
+
1194
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
1195
+ if inputs_embeds is not None and past_key_values is None:
1196
+ model_inputs = {"inputs_embeds": inputs_embeds}
1197
+ else:
1198
+ model_inputs = {"input_ids": input_ids}
1199
+
1200
+ model_inputs.update(
1201
+ {
1202
+ "position_ids": position_ids,
1203
+ "past_key_values": past_key_values,
1204
+ "use_cache": kwargs.get("use_cache"),
1205
+ "attention_mask": attention_mask,
1206
+ }
1207
+ )
1208
+ return model_inputs
1209
+
1210
+ @staticmethod
1211
+ # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM._reorder_cache
1212
+ def _reorder_cache(past_key_values, beam_idx):
1213
+ reordered_past = ()
1214
+ for layer_past in past_key_values:
1215
+ reordered_past += (
1216
+ tuple(
1217
+ past_state.index_select(0, beam_idx.to(past_state.device))
1218
+ for past_state in layer_past
1219
+ ),
1220
+ )
1221
+ return reordered_past
moondream.py ADDED
@@ -0,0 +1,172 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ from .vision_encoder import VisionEncoder
3
+ from .configuration_moondream import MoondreamConfig
4
+ from transformers import PreTrainedModel
5
+
6
+ from .modeling_phi import PhiForCausalLM
7
+ from .configuration_moondream import PhiConfig
8
+
9
+ class Moondream(PreTrainedModel):
10
+ config_class = MoondreamConfig
11
+ _supports_flash_attn_2 = True
12
+
13
+ def __init__(self, config):
14
+ super().__init__(config)
15
+ self.vision_encoder = VisionEncoder()
16
+
17
+ if type(config.phi_config) == dict:
18
+ phi_config = PhiConfig(
19
+ **config.phi_config, attn_implementation=config._attn_implementation
20
+ )
21
+ else:
22
+ phi_config = config.phi_config
23
+ self.text_model = PhiForCausalLM(phi_config)
24
+
25
+ @property
26
+ def device(self):
27
+ return self.text_model.device
28
+
29
+ def encode_image(self, image):
30
+ return self.vision_encoder(image)
31
+
32
+ def input_embeds(self, prompt, image_embeds, tokenizer):
33
+ def _tokenize(txt):
34
+ return tokenizer(
35
+ txt, return_tensors="pt", add_special_tokens=False
36
+ ).input_ids.to(self.device)
37
+
38
+ text_emb = self.text_model.get_input_embeddings()
39
+
40
+ # Add BOS token
41
+ embeds = []
42
+ embeds.append(
43
+ text_emb((torch.tensor([[tokenizer.bos_token_id]], device=self.device)))
44
+ )
45
+
46
+ if "<image>" not in prompt:
47
+ embeds.append(text_emb(_tokenize(prompt)))
48
+ else:
49
+ assert prompt.count("<image>") == 1
50
+ before, after = prompt.split("<image>")
51
+ if len(before) > 0:
52
+ embeds.append(text_emb(_tokenize(before)))
53
+ embeds.append(image_embeds.to(self.device))
54
+ if len(after) > 0:
55
+ embeds.append(text_emb(_tokenize(after)))
56
+
57
+ return torch.cat(embeds, dim=1)
58
+
59
+ def generate(
60
+ self,
61
+ image_embeds,
62
+ prompt,
63
+ tokenizer,
64
+ max_new_tokens=128,
65
+ **kwargs,
66
+ ):
67
+ generate_config = {
68
+ "eos_token_id": tokenizer.eos_token_id,
69
+ "bos_token_id": tokenizer.bos_token_id,
70
+ "pad_token_id": tokenizer.bos_token_id,
71
+ "max_new_tokens": max_new_tokens,
72
+ **kwargs,
73
+ }
74
+
75
+ with torch.no_grad():
76
+ inputs_embeds = self.input_embeds(prompt, image_embeds, tokenizer)
77
+ output_ids = self.text_model.generate(
78
+ inputs_embeds=inputs_embeds, **generate_config
79
+ )
80
+
81
+ return tokenizer.batch_decode(output_ids, skip_special_tokens=True)
82
+
83
+ def answer_question(
84
+ self,
85
+ image_embeds,
86
+ question,
87
+ tokenizer,
88
+ chat_history="",
89
+ result_queue=None,
90
+ **kwargs,
91
+ ):
92
+ prompt = f"<image>\n\n{chat_history}Question: {question}\n\nAnswer:"
93
+ answer = self.generate(
94
+ image_embeds,
95
+ prompt,
96
+ tokenizer=tokenizer,
97
+ max_new_tokens=512,
98
+ **kwargs,
99
+ )[0]
100
+ cleaned_answer = answer.strip()
101
+
102
+ # Use the result_queue to pass the result if it is provided
103
+ if result_queue:
104
+ result_queue.put(cleaned_answer)
105
+ else:
106
+ return cleaned_answer
107
+
108
+ def batch_answer(
109
+ self,
110
+ images,
111
+ prompts,
112
+ tokenizer,
113
+ **kwargs,
114
+ ):
115
+ image_embeds = self.encode_image(images)
116
+
117
+ templated_prompts = [
118
+ f"<image>\n\nQuestion: {prompt}\n\nAnswer:" for prompt in prompts
119
+ ]
120
+ prompt_embs = [
121
+ self.input_embeds(prompt, image_embed.unsqueeze(0), tokenizer)[0]
122
+ for prompt, image_embed in zip(templated_prompts, image_embeds)
123
+ ]
124
+
125
+ bos_emb = prompt_embs[0][0]
126
+ max_len = max([p.shape[0] for p in prompt_embs])
127
+
128
+ inputs_embeds = torch.cat(
129
+ [
130
+ torch.cat([bos_emb.repeat(max_len - p.shape[0], 1), p]).unsqueeze(0)
131
+ for p in prompt_embs
132
+ ],
133
+ dim=0,
134
+ )
135
+ attention_mask = torch.cat(
136
+ [
137
+ torch.cat(
138
+ [
139
+ torch.zeros(
140
+ 1,
141
+ max_len - p.shape[0],
142
+ device=self.device,
143
+ dtype=torch.long,
144
+ ),
145
+ torch.ones(1, p.shape[0], device=self.device, dtype=torch.long),
146
+ ],
147
+ dim=1,
148
+ )
149
+ for p in prompt_embs
150
+ ],
151
+ dim=0,
152
+ )
153
+
154
+ generate_config = {
155
+ "eos_token_id": tokenizer.eos_token_id,
156
+ "bos_token_id": tokenizer.bos_token_id,
157
+ "pad_token_id": tokenizer.bos_token_id,
158
+ "max_new_tokens": 512,
159
+ **kwargs,
160
+ }
161
+
162
+ with torch.no_grad():
163
+ output_ids = self.text_model.generate(
164
+ inputs_embeds=inputs_embeds,
165
+ attention_mask=attention_mask,
166
+ **generate_config,
167
+ )
168
+
169
+ return [
170
+ x.strip()
171
+ for x in tokenizer.batch_decode(output_ids, skip_special_tokens=True)
172
+ ]
special_tokens_map.json ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ {
2
+ "bos_token": "<|endoftext|>",
3
+ "eos_token": "<|endoftext|>",
4
+ "unk_token": "<|endoftext|>"
5
+ }
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer_config.json ADDED
@@ -0,0 +1,323 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_prefix_space": false,
3
+ "added_tokens_decoder": {
4
+ "50256": {
5
+ "content": "<|endoftext|>",
6
+ "lstrip": false,
7
+ "normalized": false,
8
+ "rstrip": false,
9
+ "single_word": false,
10
+ "special": true
11
+ },
12
+ "50257": {
13
+ "content": " ",
14
+ "lstrip": false,
15
+ "normalized": true,
16
+ "rstrip": false,
17
+ "single_word": false,
18
+ "special": false
19
+ },
20
+ "50258": {
21
+ "content": " ",
22
+ "lstrip": false,
23
+ "normalized": true,
24
+ "rstrip": false,
25
+ "single_word": false,
26
+ "special": false
27
+ },
28
+ "50259": {
29
+ "content": " ",
30
+ "lstrip": false,
31
+ "normalized": true,
32
+ "rstrip": false,
33
+ "single_word": false,
34
+ "special": false
35
+ },
36
+ "50260": {
37
+ "content": " ",
38
+ "lstrip": false,
39
+ "normalized": true,
40
+ "rstrip": false,
41
+ "single_word": false,
42
+ "special": false
43
+ },
44
+ "50261": {
45
+ "content": " ",
46
+ "lstrip": false,
47
+ "normalized": true,
48
+ "rstrip": false,
49
+ "single_word": false,
50
+ "special": false
51
+ },
52
+ "50262": {
53
+ "content": " ",
54
+ "lstrip": false,
55
+ "normalized": true,
56
+ "rstrip": false,
57
+ "single_word": false,
58
+ "special": false
59
+ },
60
+ "50263": {
61
+ "content": " ",
62
+ "lstrip": false,
63
+ "normalized": true,
64
+ "rstrip": false,
65
+ "single_word": false,
66
+ "special": false
67
+ },
68
+ "50264": {
69
+ "content": " ",
70
+ "lstrip": false,
71
+ "normalized": true,
72
+ "rstrip": false,
73
+ "single_word": false,
74
+ "special": false
75
+ },
76
+ "50265": {
77
+ "content": " ",
78
+ "lstrip": false,
79
+ "normalized": true,
80
+ "rstrip": false,
81
+ "single_word": false,
82
+ "special": false
83
+ },
84
+ "50266": {
85
+ "content": " ",
86
+ "lstrip": false,
87
+ "normalized": true,
88
+ "rstrip": false,
89
+ "single_word": false,
90
+ "special": false
91
+ },
92
+ "50267": {
93
+ "content": " ",
94
+ "lstrip": false,
95
+ "normalized": true,
96
+ "rstrip": false,
97
+ "single_word": false,
98
+ "special": false
99
+ },
100
+ "50268": {
101
+ "content": " ",
102
+ "lstrip": false,
103
+ "normalized": true,
104
+ "rstrip": false,
105
+ "single_word": false,
106
+ "special": false
107
+ },
108
+ "50269": {
109
+ "content": " ",
110
+ "lstrip": false,
111
+ "normalized": true,
112
+ "rstrip": false,
113
+ "single_word": false,
114
+ "special": false
115
+ },
116
+ "50270": {
117
+ "content": " ",
118
+ "lstrip": false,
119
+ "normalized": true,
120
+ "rstrip": false,
121
+ "single_word": false,
122
+ "special": false
123
+ },
124
+ "50271": {
125
+ "content": " ",
126
+ "lstrip": false,
127
+ "normalized": true,
128
+ "rstrip": false,
129
+ "single_word": false,
130
+ "special": false
131
+ },
132
+ "50272": {
133
+ "content": " ",
134
+ "lstrip": false,
135
+ "normalized": true,
136
+ "rstrip": false,
137
+ "single_word": false,
138
+ "special": false
139
+ },
140
+ "50273": {
141
+ "content": " ",
142
+ "lstrip": false,
143
+ "normalized": true,
144
+ "rstrip": false,
145
+ "single_word": false,
146
+ "special": false
147
+ },
148
+ "50274": {
149
+ "content": " ",
150
+ "lstrip": false,
151
+ "normalized": true,
152
+ "rstrip": false,
153
+ "single_word": false,
154
+ "special": false
155
+ },
156
+ "50275": {
157
+ "content": " ",
158
+ "lstrip": false,
159
+ "normalized": true,
160
+ "rstrip": false,
161
+ "single_word": false,
162
+ "special": false
163
+ },
164
+ "50276": {
165
+ "content": " ",
166
+ "lstrip": false,
167
+ "normalized": true,
168
+ "rstrip": false,
169
+ "single_word": false,
170
+ "special": false
171
+ },
172
+ "50277": {
173
+ "content": " ",
174
+ "lstrip": false,
175
+ "normalized": true,
176
+ "rstrip": false,
177
+ "single_word": false,
178
+ "special": false
179
+ },
180
+ "50278": {
181
+ "content": " ",
182
+ "lstrip": false,
183
+ "normalized": true,
184
+ "rstrip": false,
185
+ "single_word": false,
186
+ "special": false
187
+ },
188
+ "50279": {
189
+ "content": " ",
190
+ "lstrip": false,
191
+ "normalized": true,
192
+ "rstrip": false,
193
+ "single_word": false,
194
+ "special": false
195
+ },
196
+ "50280": {
197
+ "content": " ",
198
+ "lstrip": false,
199
+ "normalized": true,
200
+ "rstrip": false,
201
+ "single_word": false,
202
+ "special": false
203
+ },
204
+ "50281": {
205
+ "content": " ",
206
+ "lstrip": false,
207
+ "normalized": true,
208
+ "rstrip": false,
209
+ "single_word": false,
210
+ "special": false
211
+ },
212
+ "50282": {
213
+ "content": " ",
214
+ "lstrip": false,
215
+ "normalized": true,
216
+ "rstrip": false,
217
+ "single_word": false,
218
+ "special": false
219
+ },
220
+ "50283": {
221
+ "content": " ",
222
+ "lstrip": false,
223
+ "normalized": true,
224
+ "rstrip": false,
225
+ "single_word": false,
226
+ "special": false
227
+ },
228
+ "50284": {
229
+ "content": " ",
230
+ "lstrip": false,
231
+ "normalized": true,
232
+ "rstrip": false,
233
+ "single_word": false,
234
+ "special": false
235
+ },
236
+ "50285": {
237
+ "content": " ",
238
+ "lstrip": false,
239
+ "normalized": true,
240
+ "rstrip": false,
241
+ "single_word": false,
242
+ "special": false
243
+ },
244
+ "50286": {
245
+ "content": " ",
246
+ "lstrip": false,
247
+ "normalized": true,
248
+ "rstrip": false,
249
+ "single_word": false,
250
+ "special": false
251
+ },
252
+ "50287": {
253
+ "content": "\t\t\t\t\t\t\t\t\t",
254
+ "lstrip": false,
255
+ "normalized": true,
256
+ "rstrip": false,
257
+ "single_word": false,
258
+ "special": false
259
+ },
260
+ "50288": {
261
+ "content": "\t\t\t\t\t\t\t\t",
262
+ "lstrip": false,
263
+ "normalized": true,
264
+ "rstrip": false,
265
+ "single_word": false,
266
+ "special": false
267
+ },
268
+ "50289": {
269
+ "content": "\t\t\t\t\t\t\t",
270
+ "lstrip": false,
271
+ "normalized": true,
272
+ "rstrip": false,
273
+ "single_word": false,
274
+ "special": false
275
+ },
276
+ "50290": {
277
+ "content": "\t\t\t\t\t\t",
278
+ "lstrip": false,
279
+ "normalized": true,
280
+ "rstrip": false,
281
+ "single_word": false,
282
+ "special": false
283
+ },
284
+ "50291": {
285
+ "content": "\t\t\t\t\t",
286
+ "lstrip": false,
287
+ "normalized": true,
288
+ "rstrip": false,
289
+ "single_word": false,
290
+ "special": false
291
+ },
292
+ "50292": {
293
+ "content": "\t\t\t\t",
294
+ "lstrip": false,
295
+ "normalized": true,
296
+ "rstrip": false,
297
+ "single_word": false,
298
+ "special": false
299
+ },
300
+ "50293": {
301
+ "content": "\t\t\t",
302
+ "lstrip": false,
303
+ "normalized": true,
304
+ "rstrip": false,
305
+ "single_word": false,
306
+ "special": false
307
+ },
308
+ "50294": {
309
+ "content": "\t\t",
310
+ "lstrip": false,
311
+ "normalized": true,
312
+ "rstrip": false,
313
+ "single_word": false,
314
+ "special": false
315
+ }
316
+ },
317
+ "bos_token": "<|endoftext|>",
318
+ "clean_up_tokenization_spaces": true,
319
+ "eos_token": "<|endoftext|>",
320
+ "model_max_length": 2048,
321
+ "tokenizer_class": "CodeGenTokenizer",
322
+ "unk_token": "<|endoftext|>"
323
+ }
vision_encoder.py ADDED
@@ -0,0 +1,188 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ import torch.nn.functional as F
3
+ from torch import nn
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
+
14
+
15
+ class Attention(nn.Module):
16
+ def __init__(self, dim, num_heads=16):
17
+ super().__init__()
18
+ assert dim % num_heads == 0, "dim should be divisible by num_heads"
19
+
20
+ self.num_heads = num_heads
21
+ self.head_dim = dim // num_heads
22
+
23
+ self.qkv = nn.Linear(dim, dim * 3)
24
+ self.proj = nn.Linear(dim, dim)
25
+
26
+ torch.nn.init.kaiming_normal_(
27
+ self.qkv.weight, mode="fan_in", nonlinearity="relu"
28
+ )
29
+ torch.nn.init.kaiming_normal_(
30
+ self.proj.weight, mode="fan_in", nonlinearity="relu"
31
+ )
32
+
33
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
34
+ B, N, C = x.shape
35
+ qkv = (
36
+ self.qkv(x)
37
+ .reshape(B, N, 3, self.num_heads, self.head_dim)
38
+ .permute(2, 0, 3, 1, 4)
39
+ )
40
+ q, k, v = qkv.unbind(0)
41
+
42
+ x = F.scaled_dot_product_attention(q, k, v)
43
+
44
+ x = x.transpose(1, 2).reshape(B, N, C)
45
+ x = self.proj(x)
46
+ return x
47
+
48
+
49
+ class VitBlock(nn.Module):
50
+ def __init__(self, embed_dim):
51
+ super().__init__()
52
+ self.attn = Attention(embed_dim)
53
+ self.mlp = MLP(embed_dim, 4304)
54
+ self.norm1 = nn.LayerNorm(embed_dim)
55
+ self.norm2 = nn.LayerNorm(embed_dim)
56
+
57
+ def forward(self, x):
58
+ x = x + self.attn(self.norm1(x))
59
+ x = x + self.mlp(self.norm2(x))
60
+ return x
61
+
62
+
63
+ class VisionTransformer(nn.Module):
64
+
65
+ def __init__(self):
66
+ super().__init__()
67
+
68
+ embed_len = 729
69
+ embed_dim = 1152
70
+
71
+ self.patch_embed = LinearPatchEmbedding()
72
+ self.pos_embed = nn.Parameter(torch.randn(1, embed_len, embed_dim) * 0.02)
73
+ self.blocks = nn.Sequential(*[VitBlock(embed_dim) for _ in range(27)])
74
+ self.norm = nn.LayerNorm(embed_dim)
75
+
76
+ def forward(self, x):
77
+ x = self.patch_embed(x)
78
+ x = x + self.pos_embed
79
+ for block in self.blocks:
80
+ x = block(x)
81
+ return self.norm(x)
82
+
83
+
84
+ class EncoderWrapper(nn.Module):
85
+
86
+ def __init__(self):
87
+ super().__init__()
88
+ self.model = nn.ModuleDict({"visual": VisionTransformer()})
89
+
90
+ def forward(self, x):
91
+ return self.model["visual"](x)
92
+
93
+
94
+ class LinearPatchEmbedding(nn.Module):
95
+
96
+ def __init__(self):
97
+ super().__init__()
98
+ self.linear = nn.Linear(588, 1152)
99
+
100
+ def forward(self, x):
101
+ return self.linear(x)
102
+
103
+
104
+ class MLP(nn.Module):
105
+ def __init__(
106
+ self,
107
+ in_features: int,
108
+ hidden_features: int = None,
109
+ out_features: int = None,
110
+ ) -> None:
111
+ super().__init__()
112
+ out_features = out_features or in_features
113
+ hidden_features = hidden_features or in_features
114
+ self.fc1 = nn.Linear(in_features, hidden_features)
115
+ self.act = nn.GELU(approximate="tanh")
116
+ self.fc2 = nn.Linear(hidden_features, out_features)
117
+
118
+ torch.nn.init.kaiming_normal_(
119
+ self.fc1.weight, mode="fan_in", nonlinearity="relu"
120
+ )
121
+ torch.nn.init.kaiming_normal_(
122
+ self.fc2.weight, mode="fan_in", nonlinearity="relu"
123
+ )
124
+
125
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
126
+ x = self.fc1(x)
127
+ x = self.act(x)
128
+ x = self.fc2(x)
129
+ return x
130
+
131
+
132
+ class VisionProjection(nn.Module):
133
+ def __init__(self):
134
+ super().__init__()
135
+
136
+ image_embedding_dim = 1152
137
+ model_dim = 2048
138
+ hidden_dim = model_dim * 4
139
+
140
+ self.mlp = MLP(image_embedding_dim, hidden_dim, model_dim)
141
+
142
+ @property
143
+ def device(self):
144
+ return self.mlp.fc1.weight.device
145
+
146
+ def forward(self, x):
147
+ return self.mlp(x)
148
+
149
+
150
+ class VisionEncoder(nn.Module):
151
+ def __init__(self) -> None:
152
+ super().__init__()
153
+
154
+ self.encoder = EncoderWrapper()
155
+ self.projection = VisionProjection()
156
+
157
+ self.preprocess = Compose(
158
+ [
159
+ Resize(size=(378, 378), interpolation=InterpolationMode.BICUBIC),
160
+ ToImage(),
161
+ ToDtype(torch.float32, scale=True),
162
+ Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
163
+ ]
164
+ )
165
+
166
+ @property
167
+ def device(self):
168
+ return self.projection.mlp.fc1.weight.device
169
+
170
+ @property
171
+ def dtype(self):
172
+ return self.projection.mlp.fc1.weight.dtype
173
+
174
+ def __call__(self, images) -> torch.Tensor:
175
+ if not isinstance(images, list):
176
+ images = [images]
177
+
178
+ with torch.no_grad():
179
+ x = torch.stack(
180
+ [self.preprocess(image.convert("RGB")) for image in images]
181
+ ).to(self.device, dtype=self.dtype)
182
+
183
+ x = rearrange(x, "b c (h p1) (w p2) -> b (h w) (c p1 p2)", p1=14, p2=14)
184
+
185
+ x = self.encoder(x)
186
+ x = self.projection(x)
187
+
188
+ return x
vocab.json ADDED
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