Upload modeling_llama.py with huggingface_hub
Browse files- modeling_llama.py +1074 -0
modeling_llama.py
ADDED
@@ -0,0 +1,1074 @@
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1 |
+
# coding=utf-8
|
2 |
+
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
3 |
+
#
|
4 |
+
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
5 |
+
# and OPT implementations in this library. It has been modified from its
|
6 |
+
# original forms to accommodate minor architectural differences compared
|
7 |
+
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
8 |
+
#
|
9 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
10 |
+
# you may not use this file except in compliance with the License.
|
11 |
+
# You may obtain a copy of the License at
|
12 |
+
#
|
13 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
14 |
+
#
|
15 |
+
# Unless required by applicable law or agreed to in writing, software
|
16 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
17 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
18 |
+
# See the License for the specific language governing permissions and
|
19 |
+
# limitations under the License.
|
20 |
+
""" PyTorch LLaMA model."""
|
21 |
+
import math
|
22 |
+
import warnings
|
23 |
+
from typing import List, Optional, Tuple, Union
|
24 |
+
|
25 |
+
import torch
|
26 |
+
import torch.nn.functional as F
|
27 |
+
import torch.utils.checkpoint
|
28 |
+
from torch import nn
|
29 |
+
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
30 |
+
|
31 |
+
from transformers.activations import ACT2FN
|
32 |
+
from transformers.cache_utils import Cache, DynamicCache
|
33 |
+
from transformers.modeling_attn_mask_utils import (
|
34 |
+
AttentionMaskConverter,
|
35 |
+
_prepare_4d_attention_mask,
|
36 |
+
_prepare_4d_causal_attention_mask,
|
37 |
+
_prepare_4d_causal_attention_mask_for_sdpa,
|
38 |
+
)
|
39 |
+
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, \
|
40 |
+
SequenceClassifierOutputWithPast
|
41 |
+
from transformers.modeling_utils import PreTrainedModel
|
42 |
+
from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
|
43 |
+
from transformers.utils import (
|
44 |
+
add_start_docstrings,
|
45 |
+
add_start_docstrings_to_model_forward,
|
46 |
+
logging,
|
47 |
+
replace_return_docstrings,
|
48 |
+
)
|
49 |
+
from transformers.utils.import_utils import is_torch_fx_available
|
50 |
+
from .configuration_llama import LlamaConfig
|
51 |
+
|
52 |
+
|
53 |
+
# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
|
54 |
+
# It means that the function will not be traced through and simply appear as a node in the graph.
|
55 |
+
if is_torch_fx_available():
|
56 |
+
if not is_torch_greater_or_equal_than_1_13:
|
57 |
+
import torch.fx
|
58 |
+
|
59 |
+
_prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
|
60 |
+
|
61 |
+
logger = logging.get_logger(__name__)
|
62 |
+
|
63 |
+
_CONFIG_FOR_DOC = "LlamaConfig"
|
64 |
+
|
65 |
+
|
66 |
+
def _get_unpad_data(attention_mask):
|
67 |
+
seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
|
68 |
+
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
|
69 |
+
max_seqlen_in_batch = seqlens_in_batch.max().item()
|
70 |
+
cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
|
71 |
+
return (
|
72 |
+
indices,
|
73 |
+
cu_seqlens,
|
74 |
+
max_seqlen_in_batch,
|
75 |
+
)
|
76 |
+
|
77 |
+
|
78 |
+
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
|
79 |
+
warnings.warn(
|
80 |
+
"Calling `transformers.models.llama.modeling_llama._prepare_4d_attention_mask` is deprecated and will be removed in v4.37. Use `transformers.modeling_attn_mask_utils._prepare_4d_attention_mask"
|
81 |
+
)
|
82 |
+
return _prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
|
83 |
+
|
84 |
+
|
85 |
+
def _make_causal_mask(
|
86 |
+
input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
|
87 |
+
):
|
88 |
+
warnings.warn(
|
89 |
+
"Calling `transformers.models.llama.modeling_llama._make_causal_mask` is deprecated and will be removed in v4.37. Use `transformers.models.llama.modeling_llama.AttentionMaskConverter._make_causal_mask"
|
90 |
+
)
|
91 |
+
return AttentionMaskConverter._make_causal_mask(
|
92 |
+
input_ids_shape=input_ids_shape, dtype=dtype, device=device, past_key_values_length=past_key_values_length
|
93 |
+
)
|
94 |
+
|
95 |
+
|
96 |
+
class LlamaRMSNorm(nn.Module):
|
97 |
+
def __init__(self, hidden_size, eps=1e-6):
|
98 |
+
"""
|
99 |
+
LlamaRMSNorm is equivalent to T5LayerNorm
|
100 |
+
"""
|
101 |
+
super().__init__()
|
102 |
+
self.weight = nn.Parameter(torch.ones(hidden_size))
|
103 |
+
self.variance_epsilon = eps
|
104 |
+
|
105 |
+
def forward(self, hidden_states):
|
106 |
+
input_dtype = hidden_states.dtype
|
107 |
+
hidden_states = hidden_states.to(torch.float32)
|
108 |
+
variance = hidden_states.pow(2).mean(-1, keepdim=True)
|
109 |
+
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
110 |
+
return self.weight * hidden_states.to(input_dtype)
|
111 |
+
|
112 |
+
|
113 |
+
ALL_LAYERNORM_LAYERS.append(LlamaRMSNorm)
|
114 |
+
|
115 |
+
|
116 |
+
class LlamaRotaryEmbedding(nn.Module):
|
117 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
|
118 |
+
super().__init__()
|
119 |
+
|
120 |
+
self.dim = dim
|
121 |
+
self.max_position_embeddings = max_position_embeddings
|
122 |
+
self.base = base
|
123 |
+
inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
|
124 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
125 |
+
|
126 |
+
# Build here to make `torch.jit.trace` work.
|
127 |
+
self._set_cos_sin_cache(
|
128 |
+
seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
|
129 |
+
)
|
130 |
+
|
131 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
132 |
+
self.max_seq_len_cached = seq_len
|
133 |
+
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
|
134 |
+
|
135 |
+
freqs = torch.outer(t, self.inv_freq)
|
136 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
137 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
138 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
139 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
140 |
+
|
141 |
+
def forward(self, x, seq_len=None):
|
142 |
+
# x: [bs, num_attention_heads, seq_len, head_size]
|
143 |
+
if seq_len > self.max_seq_len_cached:
|
144 |
+
self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
|
145 |
+
|
146 |
+
return (
|
147 |
+
self.cos_cached[:seq_len].to(dtype=x.dtype),
|
148 |
+
self.sin_cached[:seq_len].to(dtype=x.dtype),
|
149 |
+
)
|
150 |
+
|
151 |
+
|
152 |
+
class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
|
153 |
+
"""LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
|
154 |
+
|
155 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
|
156 |
+
self.scaling_factor = scaling_factor
|
157 |
+
super().__init__(dim, max_position_embeddings, base, device)
|
158 |
+
|
159 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
160 |
+
self.max_seq_len_cached = seq_len
|
161 |
+
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
|
162 |
+
t = t / self.scaling_factor
|
163 |
+
|
164 |
+
freqs = torch.outer(t, self.inv_freq)
|
165 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
166 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
167 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
168 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
169 |
+
|
170 |
+
|
171 |
+
class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
|
172 |
+
"""LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
|
173 |
+
|
174 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
|
175 |
+
self.scaling_factor = scaling_factor
|
176 |
+
super().__init__(dim, max_position_embeddings, base, device)
|
177 |
+
|
178 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
179 |
+
self.max_seq_len_cached = seq_len
|
180 |
+
|
181 |
+
if seq_len > self.max_position_embeddings:
|
182 |
+
base = self.base * (
|
183 |
+
(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
|
184 |
+
) ** (self.dim / (self.dim - 2))
|
185 |
+
inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
|
186 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
187 |
+
|
188 |
+
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
|
189 |
+
|
190 |
+
freqs = torch.outer(t, self.inv_freq)
|
191 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
192 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
193 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
194 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
195 |
+
|
196 |
+
|
197 |
+
def rotate_half(x):
|
198 |
+
"""Rotates half the hidden dims of the input."""
|
199 |
+
x1 = x[..., : x.shape[-1] // 2]
|
200 |
+
x2 = x[..., x.shape[-1] // 2:]
|
201 |
+
return torch.cat((-x2, x1), dim=-1)
|
202 |
+
|
203 |
+
|
204 |
+
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
|
205 |
+
"""Applies Rotary Position Embedding to the query and key tensors.
|
206 |
+
|
207 |
+
Args:
|
208 |
+
q (`torch.Tensor`): The query tensor.
|
209 |
+
k (`torch.Tensor`): The key tensor.
|
210 |
+
cos (`torch.Tensor`): The cosine part of the rotary embedding.
|
211 |
+
sin (`torch.Tensor`): The sine part of the rotary embedding.
|
212 |
+
position_ids (`torch.Tensor`):
|
213 |
+
The position indices of the tokens corresponding to the query and key tensors. For example, this can be
|
214 |
+
used to pass offsetted position ids when working with a KV-cache.
|
215 |
+
unsqueeze_dim (`int`, *optional*, defaults to 1):
|
216 |
+
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
|
217 |
+
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
|
218 |
+
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
|
219 |
+
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
|
220 |
+
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
|
221 |
+
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
|
222 |
+
Returns:
|
223 |
+
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
|
224 |
+
"""
|
225 |
+
cos = cos[position_ids].unsqueeze(unsqueeze_dim)
|
226 |
+
sin = sin[position_ids].unsqueeze(unsqueeze_dim)
|
227 |
+
q_embed = (q * cos) + (rotate_half(q) * sin)
|
228 |
+
k_embed = (k * cos) + (rotate_half(k) * sin)
|
229 |
+
return q_embed, k_embed
|
230 |
+
|
231 |
+
|
232 |
+
class LlamaMLP(nn.Module):
|
233 |
+
def __init__(self, config):
|
234 |
+
super().__init__()
|
235 |
+
self.config = config
|
236 |
+
self.hidden_size = config.hidden_size
|
237 |
+
self.intermediate_size = config.intermediate_size
|
238 |
+
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
239 |
+
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
240 |
+
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
|
241 |
+
self.act_fn = ACT2FN[config.hidden_act]
|
242 |
+
|
243 |
+
def forward(self, x):
|
244 |
+
if self.config.pretraining_tp > 1:
|
245 |
+
slice = self.intermediate_size // self.config.pretraining_tp
|
246 |
+
gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
|
247 |
+
up_proj_slices = self.up_proj.weight.split(slice, dim=0)
|
248 |
+
down_proj_slices = self.down_proj.weight.split(slice, dim=1)
|
249 |
+
|
250 |
+
gate_proj = torch.cat(
|
251 |
+
[F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
|
252 |
+
)
|
253 |
+
up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
|
254 |
+
|
255 |
+
intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
|
256 |
+
down_proj = [
|
257 |
+
F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
|
258 |
+
]
|
259 |
+
down_proj = sum(down_proj)
|
260 |
+
else:
|
261 |
+
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
262 |
+
|
263 |
+
return down_proj
|
264 |
+
|
265 |
+
|
266 |
+
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
267 |
+
"""
|
268 |
+
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
269 |
+
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
270 |
+
"""
|
271 |
+
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
272 |
+
if n_rep == 1:
|
273 |
+
return hidden_states
|
274 |
+
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
|
275 |
+
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
276 |
+
|
277 |
+
|
278 |
+
class LlamaAttention(nn.Module):
|
279 |
+
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
280 |
+
|
281 |
+
def __init__(self, config: LlamaConfig, layer_idx: Optional[int] = None):
|
282 |
+
super().__init__()
|
283 |
+
self.config = config
|
284 |
+
self.layer_idx = layer_idx
|
285 |
+
if layer_idx is None:
|
286 |
+
logger.warning_once(
|
287 |
+
f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
|
288 |
+
"to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
|
289 |
+
"when creating this class."
|
290 |
+
)
|
291 |
+
|
292 |
+
self.attention_dropout = config.attention_dropout
|
293 |
+
self.hidden_size = config.hidden_size
|
294 |
+
self.num_heads = config.num_attention_heads
|
295 |
+
self.head_dim = self.hidden_size // self.num_heads
|
296 |
+
self.num_key_value_heads = config.num_key_value_heads
|
297 |
+
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
|
298 |
+
self.max_position_embeddings = config.max_position_embeddings
|
299 |
+
self.rope_theta = config.rope_theta
|
300 |
+
self.is_causal = True
|
301 |
+
|
302 |
+
# fixme: config needs to be updated to include these parameters
|
303 |
+
self._lambda_ts = 1
|
304 |
+
self._lambda_st = 1
|
305 |
+
self._lambda_ss = 1
|
306 |
+
|
307 |
+
if (self.head_dim * self.num_heads) != self.hidden_size:
|
308 |
+
raise ValueError(
|
309 |
+
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
|
310 |
+
f" and `num_heads`: {self.num_heads})."
|
311 |
+
)
|
312 |
+
|
313 |
+
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
|
314 |
+
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
|
315 |
+
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
|
316 |
+
self.spatial_q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
|
317 |
+
self.spatial_k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
|
318 |
+
self.spatial_v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
|
319 |
+
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
|
320 |
+
self._init_rope()
|
321 |
+
|
322 |
+
def _init_rope(self):
|
323 |
+
if self.config.rope_scaling is None:
|
324 |
+
self.rotary_emb = LlamaRotaryEmbedding(
|
325 |
+
self.head_dim,
|
326 |
+
max_position_embeddings=self.max_position_embeddings,
|
327 |
+
base=self.rope_theta,
|
328 |
+
)
|
329 |
+
else:
|
330 |
+
scaling_type = self.config.rope_scaling["type"]
|
331 |
+
scaling_factor = self.config.rope_scaling["factor"]
|
332 |
+
if scaling_type == "linear":
|
333 |
+
self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
|
334 |
+
self.head_dim,
|
335 |
+
max_position_embeddings=self.max_position_embeddings,
|
336 |
+
scaling_factor=scaling_factor,
|
337 |
+
base=self.rope_theta,
|
338 |
+
)
|
339 |
+
elif scaling_type == "dynamic":
|
340 |
+
self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
|
341 |
+
self.head_dim,
|
342 |
+
max_position_embeddings=self.max_position_embeddings,
|
343 |
+
scaling_factor=scaling_factor,
|
344 |
+
base=self.rope_theta,
|
345 |
+
)
|
346 |
+
else:
|
347 |
+
raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
|
348 |
+
|
349 |
+
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
|
350 |
+
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
|
351 |
+
|
352 |
+
def forward(
|
353 |
+
self,
|
354 |
+
hidden_states: torch.Tensor,
|
355 |
+
bounding_box_embeddings: torch.Tensor,
|
356 |
+
attention_mask: Optional[torch.Tensor] = None,
|
357 |
+
position_ids: Optional[torch.LongTensor] = None,
|
358 |
+
past_key_value: Optional[Cache] = None,
|
359 |
+
spatial_past_key_value: Optional[Cache] = None,
|
360 |
+
output_attentions: bool = False,
|
361 |
+
use_cache: bool = False,
|
362 |
+
**kwargs,
|
363 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
364 |
+
if "padding_mask" in kwargs:
|
365 |
+
warnings.warn(
|
366 |
+
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
|
367 |
+
)
|
368 |
+
|
369 |
+
bsz, q_len, _ = hidden_states.size()
|
370 |
+
|
371 |
+
if self.config.pretraining_tp > 1:
|
372 |
+
key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
|
373 |
+
query_slices = self.q_proj.weight.split(
|
374 |
+
(self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
|
375 |
+
)
|
376 |
+
key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
|
377 |
+
value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
|
378 |
+
|
379 |
+
spatial_query_slices = self.spatial_q_proj.weight.split(
|
380 |
+
(self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
|
381 |
+
)
|
382 |
+
spatial_key_slices = self.spatial_k_proj.weight.split(key_value_slicing, dim=0)
|
383 |
+
spatial_value_slices = self.spatial_v_proj.weight.split(key_value_slicing, dim=0)
|
384 |
+
|
385 |
+
query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
|
386 |
+
query_states = torch.cat(query_states, dim=-1)
|
387 |
+
spatial_query_states = [F.linear(bounding_box_embeddings, spatial_query_slices[i]) for i in range(self.config.pretraining_tp)]
|
388 |
+
spatial_query_states = torch.cat(spatial_query_states, dim=-1)
|
389 |
+
|
390 |
+
key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
|
391 |
+
key_states = torch.cat(key_states, dim=-1)
|
392 |
+
spatial_key_states = [F.linear(bounding_box_embeddings, spatial_key_slices[i]) for i in range(self.config.pretraining_tp)]
|
393 |
+
spatial_key_states = torch.cat(spatial_key_states, dim=-1)
|
394 |
+
|
395 |
+
value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
|
396 |
+
value_states = torch.cat(value_states, dim=-1)
|
397 |
+
spatial_value_states = [F.linear(bounding_box_embeddings, spatial_value_slices[i]) for i in range(self.config.pretraining_tp)]
|
398 |
+
spatial_value_states = torch.cat(spatial_value_states, dim=-1)
|
399 |
+
else:
|
400 |
+
query_states = self.q_proj(hidden_states)
|
401 |
+
key_states = self.k_proj(hidden_states)
|
402 |
+
value_states = self.v_proj(hidden_states)
|
403 |
+
|
404 |
+
spatial_query_states = self.spatial_q_proj(bounding_box_embeddings)
|
405 |
+
spatial_key_states = self.spatial_k_proj(bounding_box_embeddings)
|
406 |
+
spatial_value_states = self.spatial_v_proj(bounding_box_embeddings)
|
407 |
+
|
408 |
+
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
409 |
+
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
410 |
+
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
411 |
+
spatial_query_states = spatial_query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
412 |
+
spatial_key_states = spatial_key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
413 |
+
spatial_value_states = spatial_value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
414 |
+
|
415 |
+
kv_seq_len = key_states.shape[-2]
|
416 |
+
if past_key_value is not None:
|
417 |
+
if self.layer_idx is None:
|
418 |
+
raise ValueError(
|
419 |
+
f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
|
420 |
+
"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
|
421 |
+
"with a layer index."
|
422 |
+
)
|
423 |
+
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
424 |
+
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
425 |
+
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
426 |
+
|
427 |
+
# fixme: do we need to apply rotary pos emb to spatial query and key states?
|
428 |
+
spatial_query_states, spatial_key_states = apply_rotary_pos_emb(spatial_query_states, spatial_key_states, cos, sin, position_ids)
|
429 |
+
|
430 |
+
if past_key_value is not None:
|
431 |
+
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
|
432 |
+
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
433 |
+
if spatial_past_key_value is not None:
|
434 |
+
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
|
435 |
+
spatial_key_states, spatial_value_states = spatial_past_key_value.update(spatial_key_states, spatial_value_states, self.layer_idx, cache_kwargs)
|
436 |
+
|
437 |
+
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
438 |
+
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
439 |
+
spatial_key_states = repeat_kv(spatial_key_states, self.num_key_value_groups)
|
440 |
+
|
441 |
+
attn_weight_tt = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
442 |
+
attn_weight_ts = torch.matmul(query_states, spatial_key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
443 |
+
attn_weight_st = torch.matmul(spatial_query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
444 |
+
attn_weight_ss = torch.matmul(spatial_query_states, spatial_key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
445 |
+
|
446 |
+
attn_weights = attn_weight_tt + self._lambda_ts * attn_weight_ts + self._lambda_st * attn_weight_st + self._lambda_ss * attn_weight_ss
|
447 |
+
|
448 |
+
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
449 |
+
raise ValueError(
|
450 |
+
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
|
451 |
+
f" {attn_weights.size()}"
|
452 |
+
)
|
453 |
+
|
454 |
+
if attention_mask is not None:
|
455 |
+
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
456 |
+
raise ValueError(
|
457 |
+
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
|
458 |
+
)
|
459 |
+
attn_weights = attn_weights + attention_mask
|
460 |
+
|
461 |
+
# upcast attention to fp32
|
462 |
+
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
|
463 |
+
attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
|
464 |
+
attn_output = torch.matmul(attn_weights, value_states)
|
465 |
+
|
466 |
+
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
467 |
+
raise ValueError(
|
468 |
+
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
469 |
+
f" {attn_output.size()}"
|
470 |
+
)
|
471 |
+
|
472 |
+
attn_output = attn_output.transpose(1, 2).contiguous()
|
473 |
+
|
474 |
+
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
475 |
+
|
476 |
+
if self.config.pretraining_tp > 1:
|
477 |
+
attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
|
478 |
+
o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
|
479 |
+
attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
|
480 |
+
else:
|
481 |
+
attn_output = self.o_proj(attn_output)
|
482 |
+
|
483 |
+
if not output_attentions:
|
484 |
+
attn_weights = None
|
485 |
+
|
486 |
+
return attn_output, attn_weights, past_key_value, spatial_past_key_value
|
487 |
+
|
488 |
+
|
489 |
+
class LlamaDecoderLayer(nn.Module):
|
490 |
+
def __init__(self, config: LlamaConfig, layer_idx: int):
|
491 |
+
super().__init__()
|
492 |
+
self.hidden_size = config.hidden_size
|
493 |
+
|
494 |
+
# flash attention is not supported for Docllm model yet
|
495 |
+
# self.self_attn = LLAMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
|
496 |
+
self.self_attn = LlamaAttention(config=config, layer_idx=layer_idx)
|
497 |
+
self.mlp = LlamaMLP(config)
|
498 |
+
self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
499 |
+
self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
500 |
+
|
501 |
+
def forward(
|
502 |
+
self,
|
503 |
+
hidden_states: torch.Tensor,
|
504 |
+
bounding_box_embeddings: Optional[torch.Tensor] = None,
|
505 |
+
attention_mask: Optional[torch.Tensor] = None,
|
506 |
+
position_ids: Optional[torch.LongTensor] = None,
|
507 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
508 |
+
spatial_past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
509 |
+
output_attentions: Optional[bool] = False,
|
510 |
+
use_cache: Optional[bool] = False,
|
511 |
+
**kwargs,
|
512 |
+
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
513 |
+
"""
|
514 |
+
Args:
|
515 |
+
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
516 |
+
attention_mask (`torch.FloatTensor`, *optional*):
|
517 |
+
attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
|
518 |
+
query_sequence_length, key_sequence_length)` if default attention is used.
|
519 |
+
output_attentions (`bool`, *optional*):
|
520 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
521 |
+
returned tensors for more detail.
|
522 |
+
use_cache (`bool`, *optional*):
|
523 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
524 |
+
(see `past_key_values`).
|
525 |
+
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
526 |
+
"""
|
527 |
+
if "padding_mask" in kwargs:
|
528 |
+
warnings.warn(
|
529 |
+
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
|
530 |
+
)
|
531 |
+
|
532 |
+
residual = hidden_states
|
533 |
+
|
534 |
+
hidden_states = self.input_layernorm(hidden_states)
|
535 |
+
|
536 |
+
# Self Attention
|
537 |
+
hidden_states, self_attn_weights, present_key_value, spatial_present_key_value = self.self_attn(
|
538 |
+
hidden_states=hidden_states,
|
539 |
+
bounding_box_embeddings=bounding_box_embeddings,
|
540 |
+
attention_mask=attention_mask,
|
541 |
+
position_ids=position_ids,
|
542 |
+
past_key_value=past_key_value,
|
543 |
+
spatial_past_key_value=spatial_past_key_value,
|
544 |
+
output_attentions=output_attentions,
|
545 |
+
use_cache=use_cache,
|
546 |
+
**kwargs,
|
547 |
+
)
|
548 |
+
hidden_states = residual + hidden_states
|
549 |
+
|
550 |
+
# Fully Connected
|
551 |
+
residual = hidden_states
|
552 |
+
hidden_states = self.post_attention_layernorm(hidden_states)
|
553 |
+
hidden_states = self.mlp(hidden_states)
|
554 |
+
hidden_states = residual + hidden_states
|
555 |
+
|
556 |
+
outputs = (hidden_states,)
|
557 |
+
|
558 |
+
if output_attentions:
|
559 |
+
outputs += (self_attn_weights,)
|
560 |
+
|
561 |
+
if use_cache:
|
562 |
+
outputs += (present_key_value,)
|
563 |
+
outputs += (spatial_present_key_value,)
|
564 |
+
|
565 |
+
return outputs
|
566 |
+
|
567 |
+
|
568 |
+
LLAMA_START_DOCSTRING = r"""
|
569 |
+
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
570 |
+
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
571 |
+
etc.)
|
572 |
+
|
573 |
+
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
574 |
+
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
575 |
+
and behavior.
|
576 |
+
|
577 |
+
Parameters:
|
578 |
+
config ([`LlamaConfig`]):
|
579 |
+
Model configuration class with all the parameters of the model. Initializing with a config file does not
|
580 |
+
load the weights associated with the model, only the configuration. Check out the
|
581 |
+
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
582 |
+
"""
|
583 |
+
|
584 |
+
|
585 |
+
@add_start_docstrings(
|
586 |
+
"The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
|
587 |
+
LLAMA_START_DOCSTRING,
|
588 |
+
)
|
589 |
+
class LlamaPreTrainedModel(PreTrainedModel):
|
590 |
+
config_class = LlamaConfig
|
591 |
+
base_model_prefix = "model"
|
592 |
+
supports_gradient_checkpointing = True
|
593 |
+
_no_split_modules = ["LlamaDecoderLayer"]
|
594 |
+
_skip_keys_device_placement = "past_key_values"
|
595 |
+
_supports_cache_class = True
|
596 |
+
|
597 |
+
def _init_weights(self, module):
|
598 |
+
std = self.config.initializer_range
|
599 |
+
if isinstance(module, nn.Linear):
|
600 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
601 |
+
if module.bias is not None:
|
602 |
+
module.bias.data.zero_()
|
603 |
+
elif isinstance(module, nn.Embedding):
|
604 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
605 |
+
if module.padding_idx is not None:
|
606 |
+
module.weight.data[module.padding_idx].zero_()
|
607 |
+
|
608 |
+
|
609 |
+
LLAMA_INPUTS_DOCSTRING = r"""
|
610 |
+
Args:
|
611 |
+
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
612 |
+
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
613 |
+
it.
|
614 |
+
|
615 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
616 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
617 |
+
|
618 |
+
[What are input IDs?](../glossary#input-ids)
|
619 |
+
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
620 |
+
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
621 |
+
|
622 |
+
- 1 for tokens that are **not masked**,
|
623 |
+
- 0 for tokens that are **masked**.
|
624 |
+
|
625 |
+
[What are attention masks?](../glossary#attention-mask)
|
626 |
+
|
627 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
628 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
629 |
+
|
630 |
+
If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
|
631 |
+
`past_key_values`).
|
632 |
+
|
633 |
+
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
|
634 |
+
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
|
635 |
+
information on the default strategy.
|
636 |
+
|
637 |
+
- 1 indicates the head is **not masked**,
|
638 |
+
- 0 indicates the head is **masked**.
|
639 |
+
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
640 |
+
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
641 |
+
config.n_positions - 1]`.
|
642 |
+
|
643 |
+
[What are position IDs?](../glossary#position-ids)
|
644 |
+
past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
|
645 |
+
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
|
646 |
+
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
|
647 |
+
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
|
648 |
+
|
649 |
+
Two formats are allowed:
|
650 |
+
- a [`~cache_utils.Cache`] instance;
|
651 |
+
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
|
652 |
+
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
|
653 |
+
cache format.
|
654 |
+
|
655 |
+
The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
|
656 |
+
legacy cache format will be returned.
|
657 |
+
|
658 |
+
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
|
659 |
+
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
|
660 |
+
of shape `(batch_size, sequence_length)`.
|
661 |
+
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
662 |
+
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
663 |
+
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
664 |
+
model's internal embedding lookup matrix.
|
665 |
+
use_cache (`bool`, *optional*):
|
666 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
667 |
+
`past_key_values`).
|
668 |
+
output_attentions (`bool`, *optional*):
|
669 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
670 |
+
tensors for more detail.
|
671 |
+
output_hidden_states (`bool`, *optional*):
|
672 |
+
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
673 |
+
more detail.
|
674 |
+
return_dict (`bool`, *optional*):
|
675 |
+
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
676 |
+
"""
|
677 |
+
|
678 |
+
|
679 |
+
@add_start_docstrings(
|
680 |
+
"The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
|
681 |
+
LLAMA_START_DOCSTRING,
|
682 |
+
)
|
683 |
+
class DocLLMBaseModelOutputWithPast(BaseModelOutputWithPast):
|
684 |
+
def __init__(self, *args, spatial_past_key_values=None, **kwargs):
|
685 |
+
super().__init__(*args, **kwargs)
|
686 |
+
self.spatial_past_key_values = spatial_past_key_values
|
687 |
+
|
688 |
+
|
689 |
+
class DocLLMCausalLMOutputWithPast(CausalLMOutputWithPast):
|
690 |
+
def __init__(self, *args, spatial_past_key_values=None, **kwargs):
|
691 |
+
super().__init__(*args, **kwargs)
|
692 |
+
self.spatial_past_key_values = spatial_past_key_values
|
693 |
+
|
694 |
+
|
695 |
+
class LlamaModel(LlamaPreTrainedModel):
|
696 |
+
"""
|
697 |
+
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
|
698 |
+
|
699 |
+
Args:
|
700 |
+
config: LlamaConfig
|
701 |
+
"""
|
702 |
+
|
703 |
+
def __init__(self, config: LlamaConfig):
|
704 |
+
super().__init__(config)
|
705 |
+
self.padding_idx = config.pad_token_id
|
706 |
+
self.vocab_size = config.vocab_size
|
707 |
+
|
708 |
+
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
709 |
+
self.embed_spatial = nn.Linear(4, config.hidden_size, bias=False)
|
710 |
+
self.layers = nn.ModuleList(
|
711 |
+
[LlamaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
712 |
+
)
|
713 |
+
self._use_sdpa = config._attn_implementation == "sdpa"
|
714 |
+
# self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
|
715 |
+
self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
716 |
+
|
717 |
+
self.gradient_checkpointing = False
|
718 |
+
# Initialize weights and apply final processing
|
719 |
+
self.post_init()
|
720 |
+
|
721 |
+
def get_input_embeddings(self):
|
722 |
+
return self.embed_tokens
|
723 |
+
|
724 |
+
def set_input_embeddings(self, value):
|
725 |
+
self.embed_tokens = value
|
726 |
+
|
727 |
+
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
|
728 |
+
def forward(
|
729 |
+
self,
|
730 |
+
input_ids: torch.LongTensor = None,
|
731 |
+
input_coordinates: torch.FloatTensor = None,
|
732 |
+
attention_mask: Optional[torch.Tensor] = None,
|
733 |
+
position_ids: Optional[torch.LongTensor] = None,
|
734 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
735 |
+
spatial_past_key_values: Optional[torch.FloatTensor] = None,
|
736 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
737 |
+
use_cache: Optional[bool] = None,
|
738 |
+
output_attentions: Optional[bool] = None,
|
739 |
+
output_hidden_states: Optional[bool] = None,
|
740 |
+
return_dict: Optional[bool] = None,
|
741 |
+
) -> Union[Tuple, BaseModelOutputWithPast]:
|
742 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
743 |
+
output_hidden_states = (
|
744 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
745 |
+
)
|
746 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
747 |
+
|
748 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
749 |
+
|
750 |
+
# retrieve input_ids and inputs_embeds
|
751 |
+
if input_ids is not None and inputs_embeds is not None:
|
752 |
+
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
753 |
+
elif input_ids is not None:
|
754 |
+
batch_size, seq_length = input_ids.shape[:2]
|
755 |
+
elif inputs_embeds is not None:
|
756 |
+
batch_size, seq_length = inputs_embeds.shape[:2]
|
757 |
+
else:
|
758 |
+
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
759 |
+
assert input_ids.device == input_coordinates.device
|
760 |
+
|
761 |
+
if self.gradient_checkpointing and self.training:
|
762 |
+
if use_cache:
|
763 |
+
logger.warning_once(
|
764 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
765 |
+
)
|
766 |
+
use_cache = False
|
767 |
+
|
768 |
+
past_key_values_length = 0
|
769 |
+
if use_cache:
|
770 |
+
use_legacy_cache = not isinstance(past_key_values, Cache)
|
771 |
+
if use_legacy_cache:
|
772 |
+
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
|
773 |
+
past_key_values_length = past_key_values.get_usable_length(seq_length)
|
774 |
+
|
775 |
+
spatial_use_legacy_cache = not isinstance(spatial_past_key_values, Cache)
|
776 |
+
if spatial_use_legacy_cache:
|
777 |
+
spatial_past_key_values = DynamicCache.from_legacy_cache(spatial_past_key_values)
|
778 |
+
|
779 |
+
if position_ids is None:
|
780 |
+
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
781 |
+
position_ids = torch.arange(
|
782 |
+
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
|
783 |
+
)
|
784 |
+
position_ids = position_ids.unsqueeze(0)
|
785 |
+
|
786 |
+
if inputs_embeds is None:
|
787 |
+
inputs_embeds = self.embed_tokens(input_ids)
|
788 |
+
|
789 |
+
coordinate_embeds = self.embed_spatial(input_coordinates)
|
790 |
+
|
791 |
+
# if self._use_flash_attention_2:
|
792 |
+
# # 2d mask is passed through the layers
|
793 |
+
# attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
|
794 |
+
if self._use_sdpa and not output_attentions:
|
795 |
+
# output_attentions=True can not be supported when using SDPA, and we fall back on
|
796 |
+
# the manual implementation that requires a 4D causal mask in all cases.
|
797 |
+
attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
|
798 |
+
attention_mask,
|
799 |
+
(batch_size, seq_length),
|
800 |
+
inputs_embeds,
|
801 |
+
past_key_values_length,
|
802 |
+
)
|
803 |
+
else:
|
804 |
+
# 4d mask is passed through the layers
|
805 |
+
attention_mask = _prepare_4d_causal_attention_mask(
|
806 |
+
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
|
807 |
+
)
|
808 |
+
|
809 |
+
# embed positions
|
810 |
+
hidden_states = inputs_embeds
|
811 |
+
|
812 |
+
# decoder layers
|
813 |
+
all_hidden_states = () if output_hidden_states else None
|
814 |
+
all_self_attns = () if output_attentions else None
|
815 |
+
next_decoder_cache = None
|
816 |
+
spatial_next_decoder_cache = None
|
817 |
+
|
818 |
+
for decoder_layer in self.layers:
|
819 |
+
if output_hidden_states:
|
820 |
+
all_hidden_states += (hidden_states,)
|
821 |
+
|
822 |
+
if self.gradient_checkpointing and self.training:
|
823 |
+
layer_outputs = self._gradient_checkpointing_func(
|
824 |
+
decoder_layer.__call__,
|
825 |
+
hidden_states,
|
826 |
+
coordinate_embeds,
|
827 |
+
attention_mask,
|
828 |
+
position_ids,
|
829 |
+
past_key_values,
|
830 |
+
output_attentions,
|
831 |
+
use_cache,
|
832 |
+
)
|
833 |
+
else:
|
834 |
+
layer_outputs = decoder_layer(
|
835 |
+
hidden_states,
|
836 |
+
coordinate_embeds,
|
837 |
+
attention_mask=attention_mask,
|
838 |
+
position_ids=position_ids,
|
839 |
+
past_key_value=past_key_values,
|
840 |
+
spatial_past_key_value=spatial_past_key_values,
|
841 |
+
output_attentions=output_attentions,
|
842 |
+
use_cache=use_cache,
|
843 |
+
)
|
844 |
+
|
845 |
+
hidden_states = layer_outputs[0]
|
846 |
+
|
847 |
+
if use_cache:
|
848 |
+
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
|
849 |
+
spatial_next_decoder_cache = layer_outputs[3 if output_attentions else 2]
|
850 |
+
|
851 |
+
if output_attentions:
|
852 |
+
all_self_attns += (layer_outputs[1],)
|
853 |
+
|
854 |
+
hidden_states = self.norm(hidden_states)
|
855 |
+
|
856 |
+
# add hidden states from the last decoder layer
|
857 |
+
if output_hidden_states:
|
858 |
+
all_hidden_states += (hidden_states,)
|
859 |
+
|
860 |
+
next_cache = None
|
861 |
+
spatial_next_cache = None
|
862 |
+
if use_cache:
|
863 |
+
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
|
864 |
+
spatial_next_cache = spatial_next_decoder_cache.to_legacy_cache() if spatial_use_legacy_cache \
|
865 |
+
else spatial_next_decoder_cache
|
866 |
+
if not return_dict:
|
867 |
+
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
868 |
+
return DocLLMBaseModelOutputWithPast(
|
869 |
+
last_hidden_state=hidden_states,
|
870 |
+
past_key_values=next_cache,
|
871 |
+
spatial_past_key_values=spatial_next_cache,
|
872 |
+
hidden_states=all_hidden_states,
|
873 |
+
attentions=all_self_attns,
|
874 |
+
)
|
875 |
+
|
876 |
+
|
877 |
+
class LlamaForCausalLM(LlamaPreTrainedModel):
|
878 |
+
_tied_weights_keys = ["lm_head.weight"]
|
879 |
+
|
880 |
+
def __init__(self, config):
|
881 |
+
super().__init__(config)
|
882 |
+
self.model = LlamaModel(config)
|
883 |
+
self.vocab_size = config.vocab_size
|
884 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
885 |
+
|
886 |
+
# Initialize weights and apply final processing
|
887 |
+
self.post_init()
|
888 |
+
|
889 |
+
def get_input_embeddings(self):
|
890 |
+
return self.model.embed_tokens
|
891 |
+
|
892 |
+
def set_input_embeddings(self, value):
|
893 |
+
self.model.embed_tokens = value
|
894 |
+
|
895 |
+
def get_output_embeddings(self):
|
896 |
+
return self.lm_head
|
897 |
+
|
898 |
+
def set_output_embeddings(self, new_embeddings):
|
899 |
+
self.lm_head = new_embeddings
|
900 |
+
|
901 |
+
def set_decoder(self, decoder):
|
902 |
+
self.model = decoder
|
903 |
+
|
904 |
+
def get_decoder(self):
|
905 |
+
return self.model
|
906 |
+
|
907 |
+
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
|
908 |
+
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
|
909 |
+
def forward(
|
910 |
+
self,
|
911 |
+
input_ids: torch.LongTensor = None,
|
912 |
+
input_coordinates: torch.FloatTensor = None,
|
913 |
+
attention_mask: Optional[torch.Tensor] = None,
|
914 |
+
position_ids: Optional[torch.LongTensor] = None,
|
915 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
916 |
+
spatial_past_key_values: Optional[List[torch.FloatTensor]] = None,
|
917 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
918 |
+
labels: Optional[torch.LongTensor] = None,
|
919 |
+
use_cache: Optional[bool] = None,
|
920 |
+
output_attentions: Optional[bool] = None,
|
921 |
+
output_hidden_states: Optional[bool] = None,
|
922 |
+
return_dict: Optional[bool] = None,
|
923 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
924 |
+
r"""
|
925 |
+
Args:
|
926 |
+
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
927 |
+
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
928 |
+
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
929 |
+
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
930 |
+
|
931 |
+
Returns:
|
932 |
+
|
933 |
+
Example:
|
934 |
+
|
935 |
+
```python
|
936 |
+
>>> from transformers import AutoTokenizer, LlamaForCausalLM
|
937 |
+
|
938 |
+
>>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
|
939 |
+
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
|
940 |
+
|
941 |
+
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
942 |
+
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
943 |
+
|
944 |
+
>>> # Generate
|
945 |
+
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
946 |
+
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
947 |
+
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
|
948 |
+
```"""
|
949 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
950 |
+
output_hidden_states = (
|
951 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
952 |
+
)
|
953 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
954 |
+
|
955 |
+
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
956 |
+
outputs = self.model(
|
957 |
+
input_ids=input_ids,
|
958 |
+
input_coordinates=input_coordinates,
|
959 |
+
attention_mask=attention_mask,
|
960 |
+
position_ids=position_ids,
|
961 |
+
past_key_values=past_key_values,
|
962 |
+
spatial_past_key_values=spatial_past_key_values,
|
963 |
+
inputs_embeds=inputs_embeds,
|
964 |
+
use_cache=use_cache,
|
965 |
+
output_attentions=output_attentions,
|
966 |
+
output_hidden_states=output_hidden_states,
|
967 |
+
return_dict=return_dict,
|
968 |
+
)
|
969 |
+
|
970 |
+
hidden_states = outputs[0]
|
971 |
+
if self.config.pretraining_tp > 1:
|
972 |
+
lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
|
973 |
+
logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
|
974 |
+
logits = torch.cat(logits, dim=-1)
|
975 |
+
else:
|
976 |
+
logits = self.lm_head(hidden_states)
|
977 |
+
logits = logits.float()
|
978 |
+
|
979 |
+
loss = None
|
980 |
+
if labels is not None:
|
981 |
+
# Shift so that tokens < n predict n
|
982 |
+
shift_logits = logits[..., :-1, :].contiguous()
|
983 |
+
shift_labels = labels[..., 1:].contiguous()
|
984 |
+
# Flatten the tokens
|
985 |
+
loss_fct = CrossEntropyLoss()
|
986 |
+
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
987 |
+
shift_labels = shift_labels.view(-1)
|
988 |
+
# Enable model parallelism
|
989 |
+
shift_labels = shift_labels.to(shift_logits.device)
|
990 |
+
loss = loss_fct(shift_logits, shift_labels)
|
991 |
+
|
992 |
+
if not return_dict:
|
993 |
+
output = (logits,) + outputs[1:]
|
994 |
+
return (loss,) + output if loss is not None else output
|
995 |
+
|
996 |
+
return DocLLMCausalLMOutputWithPast(
|
997 |
+
loss=loss,
|
998 |
+
logits=logits,
|
999 |
+
past_key_values=outputs.past_key_values,
|
1000 |
+
spatial_past_key_values=outputs.spatial_past_key_values,
|
1001 |
+
hidden_states=outputs.hidden_states,
|
1002 |
+
attentions=outputs.attentions,
|
1003 |
+
)
|
1004 |
+
|
1005 |
+
def prepare_inputs_for_generation(
|
1006 |
+
self, input_ids, input_coordinates, past_key_values=None, spatial_pst_key_values=None,
|
1007 |
+
attention_mask=None, inputs_embeds=None, coordinates_embeds=None, **kwargs
|
1008 |
+
):
|
1009 |
+
if past_key_values is not None:
|
1010 |
+
if isinstance(past_key_values, Cache):
|
1011 |
+
cache_length = past_key_values.get_seq_length()
|
1012 |
+
past_length = past_key_values.seen_tokens
|
1013 |
+
max_cache_length = past_key_values.get_max_length()
|
1014 |
+
else:
|
1015 |
+
cache_length = past_length = past_key_values[0][0].shape[2]
|
1016 |
+
max_cache_length = None
|
1017 |
+
|
1018 |
+
# Keep only the unprocessed tokens:
|
1019 |
+
# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
|
1020 |
+
# some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as
|
1021 |
+
# input)
|
1022 |
+
if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
|
1023 |
+
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length):]
|
1024 |
+
input_coordinates = input_coordinates[:, -(attention_mask.shape[1] - past_length):]
|
1025 |
+
# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
|
1026 |
+
# input_ids based on the past_length.
|
1027 |
+
elif past_length < input_ids.shape[1]:
|
1028 |
+
input_ids = input_ids[:, past_length:]
|
1029 |
+
input_coordinates = input_coordinates[:, past_length:]
|
1030 |
+
# 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
|
1031 |
+
|
1032 |
+
# If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
|
1033 |
+
if (
|
1034 |
+
max_cache_length is not None
|
1035 |
+
and attention_mask is not None
|
1036 |
+
and cache_length + input_ids.shape[1] > max_cache_length
|
1037 |
+
):
|
1038 |
+
attention_mask = attention_mask[:, -max_cache_length:]
|
1039 |
+
|
1040 |
+
position_ids = kwargs.get("position_ids", None)
|
1041 |
+
if attention_mask is not None and position_ids is None:
|
1042 |
+
# create position_ids on the fly for batch generation
|
1043 |
+
position_ids = attention_mask.long().cumsum(-1) - 1
|
1044 |
+
position_ids.masked_fill_(attention_mask == 0, 1)
|
1045 |
+
if past_key_values:
|
1046 |
+
position_ids = position_ids[:, -input_ids.shape[1]:]
|
1047 |
+
|
1048 |
+
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
1049 |
+
if inputs_embeds is not None and past_key_values is None:
|
1050 |
+
model_inputs = {"inputs_embeds": inputs_embeds,
|
1051 |
+
"coordinates_embeds": coordinates_embeds}
|
1052 |
+
else:
|
1053 |
+
model_inputs = {"input_ids": input_ids,
|
1054 |
+
"input_coordinates": input_coordinates}
|
1055 |
+
|
1056 |
+
model_inputs.update(
|
1057 |
+
{
|
1058 |
+
"position_ids": position_ids,
|
1059 |
+
"past_key_values": past_key_values,
|
1060 |
+
"spatial_past_key_values": spatial_pst_key_values,
|
1061 |
+
"use_cache": kwargs.get("use_cache"),
|
1062 |
+
"attention_mask": attention_mask,
|
1063 |
+
}
|
1064 |
+
)
|
1065 |
+
return model_inputs
|
1066 |
+
|
1067 |
+
@staticmethod
|
1068 |
+
def _reorder_cache(past_key_values, beam_idx):
|
1069 |
+
reordered_past = ()
|
1070 |
+
for layer_past in past_key_values:
|
1071 |
+
reordered_past += (
|
1072 |
+
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
|
1073 |
+
)
|
1074 |
+
return reordered_past
|