Upload modeling_stablelm_epoch (8).py
Browse files- modeling_stablelm_epoch (8).py +687 -0
modeling_stablelm_epoch (8).py
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1 |
+
# coding=utf-8
|
2 |
+
# Copyright 2023 Stability AI, EleutherAI, 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 |
+
# This code is based off the following work:
|
17 |
+
# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
|
18 |
+
# https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py
|
19 |
+
""" PyTorch StableLM Epoch model. """
|
20 |
+
from typing import Optional, Tuple, Union
|
21 |
+
import math
|
22 |
+
|
23 |
+
import torch
|
24 |
+
import torch.utils.checkpoint
|
25 |
+
from torch import nn
|
26 |
+
from torch.nn import CrossEntropyLoss
|
27 |
+
from transformers.modeling_outputs import (
|
28 |
+
BaseModelOutputWithPast,
|
29 |
+
CausalLMOutputWithPast,
|
30 |
+
)
|
31 |
+
from transformers.modeling_utils import PreTrainedModel
|
32 |
+
from transformers.utils import logging
|
33 |
+
from .configuration_stablelm_epoch import StableLMEpochConfig
|
34 |
+
|
35 |
+
|
36 |
+
logger = logging.get_logger(__name__)
|
37 |
+
|
38 |
+
|
39 |
+
# Copied from transformers.models.bart.modeling_bart._make_causal_mask
|
40 |
+
def _make_causal_mask(
|
41 |
+
input_ids_shape: torch.Size,
|
42 |
+
dtype: torch.dtype,
|
43 |
+
device: torch.device,
|
44 |
+
past_key_values_length: int = 0,
|
45 |
+
):
|
46 |
+
"""Make causal mask used for bi-directional self-attention."""
|
47 |
+
batch_size, tgt_len = input_ids_shape
|
48 |
+
mask = torch.full((tgt_len, tgt_len), torch.finfo(torch.float16).min, device=device)
|
49 |
+
mask_cond = torch.arange(mask.size(-1), device=device)
|
50 |
+
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
|
51 |
+
mask = mask.to(dtype)
|
52 |
+
if past_key_values_length > 0:
|
53 |
+
mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
|
54 |
+
return mask[None, None, :, :].expand(batch_size, 1, tgt_len, tgt_len + past_key_values_length)
|
55 |
+
|
56 |
+
|
57 |
+
# Copied from transformers.models.bart.modeling_bart._expand_mask
|
58 |
+
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
|
59 |
+
"""Expands attention_mask from `[batch_size, seq_len]` to `[batch_size, 1, tgt_seq_len, src_seq_len]`."""
|
60 |
+
batch_size, src_len = mask.size()
|
61 |
+
tgt_len = tgt_len if tgt_len is not None else src_len
|
62 |
+
|
63 |
+
expanded_mask = mask[:, None, None, :].expand(batch_size, 1, tgt_len, src_len).to(dtype)
|
64 |
+
inverted_mask = 1.0 - expanded_mask
|
65 |
+
|
66 |
+
return inverted_mask.masked_fill(
|
67 |
+
inverted_mask.to(torch.bool), torch.finfo(dtype).min
|
68 |
+
)
|
69 |
+
|
70 |
+
|
71 |
+
class RotaryEmbedding(nn.Module):
|
72 |
+
def __init__(
|
73 |
+
self,
|
74 |
+
dim: int,
|
75 |
+
max_position_embeddings: int,
|
76 |
+
base: int = 10_000,
|
77 |
+
device: Optional[torch.device] = None,
|
78 |
+
):
|
79 |
+
super().__init__()
|
80 |
+
|
81 |
+
self.dim = dim
|
82 |
+
self.max_position_embeddings = max_position_embeddings
|
83 |
+
self.base = base
|
84 |
+
inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, device=device, dtype=torch.float32) / self.dim))
|
85 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
86 |
+
|
87 |
+
# Build here to make `torch.jit.trace` work.
|
88 |
+
self._set_cos_sin_cache(
|
89 |
+
seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype(),
|
90 |
+
)
|
91 |
+
|
92 |
+
def _set_cos_sin_cache(self, seq_len: int, device: torch.device, dtype: torch.dtype):
|
93 |
+
self.max_seq_len_cached = seq_len
|
94 |
+
t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32)
|
95 |
+
|
96 |
+
# Don't do einsum, it converts fp32 to fp16 under AMP
|
97 |
+
# freqs = torch.einsum("i,j->ij", t, self.inv_freq)
|
98 |
+
freqs = torch.outer(t, self.inv_freq)
|
99 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
100 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
101 |
+
self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
|
102 |
+
self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
|
103 |
+
|
104 |
+
def forward(self, x: torch.Tensor, seq_len: Optional[int] = None):
|
105 |
+
# x: [batch_size, num_heads, seq_len, head_size]
|
106 |
+
if seq_len > self.max_seq_len_cached:
|
107 |
+
self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=torch.get_default_dtype())
|
108 |
+
return (
|
109 |
+
self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
|
110 |
+
self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
|
111 |
+
)
|
112 |
+
|
113 |
+
|
114 |
+
def rotate_half(x: torch.Tensor):
|
115 |
+
"""Rotates half the hidden dims of the input."""
|
116 |
+
x1, x2 = torch.chunk(x, 2, dim=-1)
|
117 |
+
return torch.cat((-x2, x1), dim=-1)
|
118 |
+
|
119 |
+
|
120 |
+
def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
|
121 |
+
# The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
|
122 |
+
cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
|
123 |
+
sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
|
124 |
+
cos = cos[position_ids].unsqueeze(1) # [batch_size, 1, seq_len, dim]
|
125 |
+
sin = sin[position_ids].unsqueeze(1) # [batch_size, 1, seq_len, dim]
|
126 |
+
q_embed = (q * cos) + (rotate_half(q) * sin)
|
127 |
+
k_embed = (k * cos) + (rotate_half(k) * sin)
|
128 |
+
return q_embed, k_embed
|
129 |
+
|
130 |
+
|
131 |
+
class MLP(nn.Module):
|
132 |
+
def __init__(self, config: StableLMEpochConfig):
|
133 |
+
super().__init__()
|
134 |
+
self.config = config
|
135 |
+
self.hidden_size = config.hidden_size
|
136 |
+
self.intermediate_size = config.intermediate_size
|
137 |
+
self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
|
138 |
+
self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
|
139 |
+
self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
|
140 |
+
self.act_fn = nn.SiLU()
|
141 |
+
|
142 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
143 |
+
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
144 |
+
|
145 |
+
|
146 |
+
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
147 |
+
"""
|
148 |
+
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
149 |
+
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
150 |
+
"""
|
151 |
+
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
152 |
+
if n_rep == 1:
|
153 |
+
return hidden_states
|
154 |
+
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
|
155 |
+
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
156 |
+
|
157 |
+
|
158 |
+
class Attention(nn.Module):
|
159 |
+
def __init__(self, config: StableLMEpochConfig):
|
160 |
+
super().__init__()
|
161 |
+
self.config = config
|
162 |
+
self.hidden_size = config.hidden_size
|
163 |
+
self.num_heads = config.num_attention_heads
|
164 |
+
self.head_dim = self.hidden_size // self.num_heads
|
165 |
+
self.num_key_value_heads = config.num_key_value_heads
|
166 |
+
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
|
167 |
+
self.max_position_embeddings = config.max_position_embeddings
|
168 |
+
|
169 |
+
if (self.head_dim * self.num_heads) != self.hidden_size:
|
170 |
+
raise ValueError(
|
171 |
+
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
|
172 |
+
f" and `num_heads`: {self.num_heads})."
|
173 |
+
)
|
174 |
+
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
|
175 |
+
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
|
176 |
+
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
|
177 |
+
self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
|
178 |
+
|
179 |
+
self._init_rope()
|
180 |
+
|
181 |
+
def _init_rope(self):
|
182 |
+
self.rotary_ndims = int(self.head_dim * self.config.rope_pct)
|
183 |
+
self.rotary_emb = RotaryEmbedding(
|
184 |
+
self.rotary_ndims,
|
185 |
+
max_position_embeddings=self.config.max_position_embeddings,
|
186 |
+
base=self.config.rope_theta,
|
187 |
+
)
|
188 |
+
|
189 |
+
def forward(
|
190 |
+
self,
|
191 |
+
hidden_states: torch.FloatTensor,
|
192 |
+
attention_mask: torch.FloatTensor,
|
193 |
+
position_ids: torch.LongTensor,
|
194 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
195 |
+
output_attentions: Optional[bool] = False,
|
196 |
+
use_cache: Optional[bool] = False,
|
197 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
198 |
+
bsz, q_len, _ = hidden_states.size()
|
199 |
+
|
200 |
+
query_states = self.q_proj(hidden_states)
|
201 |
+
key_states = self.k_proj(hidden_states)
|
202 |
+
value_states = self.v_proj(hidden_states)
|
203 |
+
|
204 |
+
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
205 |
+
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
206 |
+
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
207 |
+
|
208 |
+
query_rot = query_states[..., : self.rotary_ndims]
|
209 |
+
query_pass = query_states[..., self.rotary_ndims :]
|
210 |
+
key_rot = key_states[..., : self.rotary_ndims]
|
211 |
+
key_pass = key_states[..., self.rotary_ndims :]
|
212 |
+
|
213 |
+
kv_seq_len = key_states.shape[-2]
|
214 |
+
if past_key_value is not None:
|
215 |
+
kv_seq_len += past_key_value[0].shape[-2]
|
216 |
+
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
217 |
+
query_states, key_states = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
|
218 |
+
|
219 |
+
# [batch_size, num_heads, seq_len, head_dim]
|
220 |
+
query_states = torch.cat((query_states, query_pass), dim=-1)
|
221 |
+
key_states = torch.cat((key_states, key_pass), dim=-1)
|
222 |
+
|
223 |
+
if past_key_value is not None:
|
224 |
+
# Reuse k, v, self_attention
|
225 |
+
key_states = torch.cat((past_key_value[0], key_states), dim=2)
|
226 |
+
value_states = torch.cat((past_key_value[1], value_states), dim=2)
|
227 |
+
|
228 |
+
past_key_value = (key_states, value_states) if use_cache else None
|
229 |
+
|
230 |
+
# Repeat k/v heads if n_kv_heads < n_heads
|
231 |
+
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
232 |
+
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
233 |
+
|
234 |
+
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
235 |
+
|
236 |
+
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
237 |
+
raise ValueError(
|
238 |
+
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
|
239 |
+
f" {attn_weights.size()}"
|
240 |
+
)
|
241 |
+
|
242 |
+
if attention_mask is not None:
|
243 |
+
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
244 |
+
raise ValueError(
|
245 |
+
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
|
246 |
+
)
|
247 |
+
attn_weights = attn_weights + attention_mask
|
248 |
+
|
249 |
+
# Upcast attention to fp32
|
250 |
+
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
|
251 |
+
attn_output = torch.matmul(attn_weights, value_states)
|
252 |
+
|
253 |
+
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
254 |
+
raise ValueError(
|
255 |
+
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
256 |
+
f" {attn_output.size()}"
|
257 |
+
)
|
258 |
+
|
259 |
+
# Merge heads
|
260 |
+
attn_output = attn_output.transpose(1, 2).contiguous()
|
261 |
+
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
262 |
+
|
263 |
+
# Final linear projection
|
264 |
+
attn_output = self.o_proj(attn_output)
|
265 |
+
|
266 |
+
if not output_attentions:
|
267 |
+
attn_weights = None
|
268 |
+
|
269 |
+
return attn_output, attn_weights, past_key_value
|
270 |
+
|
271 |
+
|
272 |
+
class DecoderLayer(nn.Module):
|
273 |
+
def __init__(self, config: StableLMEpochConfig):
|
274 |
+
super().__init__()
|
275 |
+
self.self_attn = Attention(config)
|
276 |
+
self.mlp = MLP(config)
|
277 |
+
self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
|
278 |
+
self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
|
279 |
+
|
280 |
+
def forward(
|
281 |
+
self,
|
282 |
+
hidden_states: Optional[torch.FloatTensor],
|
283 |
+
attention_mask: Optional[torch.FloatTensor] = None,
|
284 |
+
position_ids: Optional[torch.LongTensor] = None,
|
285 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
286 |
+
output_attentions: Optional[bool] = False,
|
287 |
+
use_cache: Optional[bool] = False,
|
288 |
+
) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
|
289 |
+
residual = hidden_states
|
290 |
+
|
291 |
+
hidden_states = self.input_layernorm(hidden_states)
|
292 |
+
|
293 |
+
# Self Attention
|
294 |
+
hidden_states, self_attn_weights, present_key_value = self.self_attn(
|
295 |
+
hidden_states=hidden_states,
|
296 |
+
attention_mask=attention_mask,
|
297 |
+
position_ids=position_ids,
|
298 |
+
past_key_value=past_key_value,
|
299 |
+
output_attentions=output_attentions,
|
300 |
+
use_cache=use_cache,
|
301 |
+
)
|
302 |
+
hidden_states = residual + hidden_states
|
303 |
+
|
304 |
+
# Fully Connected
|
305 |
+
residual = hidden_states
|
306 |
+
hidden_states = self.post_attention_layernorm(hidden_states)
|
307 |
+
hidden_states = self.mlp(hidden_states)
|
308 |
+
hidden_states = residual + hidden_states
|
309 |
+
|
310 |
+
outputs = (hidden_states,)
|
311 |
+
|
312 |
+
if output_attentions:
|
313 |
+
outputs += (self_attn_weights,)
|
314 |
+
|
315 |
+
if use_cache:
|
316 |
+
outputs += (present_key_value,)
|
317 |
+
|
318 |
+
return outputs
|
319 |
+
|
320 |
+
|
321 |
+
class StableLMEpochPreTrainedModel(PreTrainedModel):
|
322 |
+
"""An abstract class to handle weights initialization and a simple interface
|
323 |
+
for downloading and loading pretrained models.
|
324 |
+
"""
|
325 |
+
|
326 |
+
config_class = StableLMEpochConfig
|
327 |
+
base_model_prefix = "transformer"
|
328 |
+
supports_gradient_checkpointing = True
|
329 |
+
_no_split_modules = ["DecoderLayer"]
|
330 |
+
_skip_keys_device_placement = "past_key_values"
|
331 |
+
|
332 |
+
def _init_weights(self, module: nn.Module):
|
333 |
+
"""Initialize the weights"""
|
334 |
+
if isinstance(module, nn.Linear):
|
335 |
+
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
336 |
+
if module.bias is not None:
|
337 |
+
module.bias.data.zero_()
|
338 |
+
elif isinstance(module, nn.Embedding):
|
339 |
+
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
340 |
+
if module.padding_idx is not None:
|
341 |
+
module.weight.data[module.padding_idx].zero_()
|
342 |
+
elif isinstance(module, nn.LayerNorm):
|
343 |
+
module.bias.data.zero_()
|
344 |
+
module.weight.data.fill_(1.0)
|
345 |
+
|
346 |
+
def _set_gradient_checkpointing(self, module: nn.Module, value=False):
|
347 |
+
if isinstance(module, StableLMEpochModel):
|
348 |
+
module.gradient_checkpointing = value
|
349 |
+
|
350 |
+
|
351 |
+
class StableLMEpochModel(StableLMEpochPreTrainedModel):
|
352 |
+
def __init__(self, config: StableLMEpochConfig):
|
353 |
+
super().__init__(config)
|
354 |
+
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id)
|
355 |
+
self.layers = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)])
|
356 |
+
self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
|
357 |
+
|
358 |
+
self.gradient_checkpointing = False
|
359 |
+
# Initialize weights and apply final processing
|
360 |
+
self.post_init()
|
361 |
+
|
362 |
+
def get_input_embeddings(self):
|
363 |
+
return self.embed_tokens
|
364 |
+
|
365 |
+
def set_input_embeddings(self, value: nn.Module):
|
366 |
+
self.embed_tokens = value
|
367 |
+
|
368 |
+
# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
|
369 |
+
def _prepare_decoder_attention_mask(
|
370 |
+
self,
|
371 |
+
attention_mask: torch.Tensor,
|
372 |
+
input_shape: torch.Size,
|
373 |
+
inputs_embeds: torch.Tensor,
|
374 |
+
past_key_values_length: int,
|
375 |
+
):
|
376 |
+
# Create causal mask
|
377 |
+
# [batch_size, seq_len] -> [batch_size, 1, tgt_seq_len, src_seq_len]
|
378 |
+
combined_attention_mask = None
|
379 |
+
if input_shape[-1] > 1:
|
380 |
+
combined_attention_mask = _make_causal_mask(
|
381 |
+
input_shape,
|
382 |
+
inputs_embeds.dtype,
|
383 |
+
device=inputs_embeds.device,
|
384 |
+
past_key_values_length=past_key_values_length,
|
385 |
+
)
|
386 |
+
|
387 |
+
if attention_mask is not None:
|
388 |
+
# [batch_size, seq_len] -> [batch_size, 1, tgt_seq_len, src_seq_len]
|
389 |
+
expanded_attn_mask = _expand_mask(
|
390 |
+
attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
|
391 |
+
).to(inputs_embeds.device)
|
392 |
+
combined_attention_mask = expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
|
393 |
+
|
394 |
+
return combined_attention_mask
|
395 |
+
|
396 |
+
def forward(
|
397 |
+
self,
|
398 |
+
input_ids: Optional[torch.LongTensor] = None,
|
399 |
+
attention_mask: Optional[torch.FloatTensor] = None,
|
400 |
+
position_ids: Optional[torch.LongTensor] = None,
|
401 |
+
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
402 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
403 |
+
use_cache: Optional[bool] = None,
|
404 |
+
output_attentions: Optional[bool] = None,
|
405 |
+
output_hidden_states: Optional[bool] = None,
|
406 |
+
return_dict: Optional[bool] = None,
|
407 |
+
) -> Union[Tuple, BaseModelOutputWithPast]:
|
408 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
409 |
+
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
410 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
411 |
+
|
412 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
413 |
+
|
414 |
+
# Retrieve input_ids and inputs_embeds
|
415 |
+
if input_ids is not None and inputs_embeds is not None:
|
416 |
+
raise ValueError(
|
417 |
+
"You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
|
418 |
+
)
|
419 |
+
elif input_ids is not None:
|
420 |
+
batch_size, seq_length = input_ids.shape
|
421 |
+
elif inputs_embeds is not None:
|
422 |
+
batch_size, seq_length, _ = inputs_embeds.shape
|
423 |
+
else:
|
424 |
+
raise ValueError(
|
425 |
+
"You have to specify either decoder_input_ids or decoder_inputs_embeds"
|
426 |
+
)
|
427 |
+
|
428 |
+
seq_length_with_past = seq_length
|
429 |
+
past_key_values_length = 0
|
430 |
+
|
431 |
+
if past_key_values is not None:
|
432 |
+
past_key_values_length = past_key_values[0][0].shape[2]
|
433 |
+
seq_length_with_past = seq_length_with_past + past_key_values_length
|
434 |
+
|
435 |
+
if position_ids is None:
|
436 |
+
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
437 |
+
position_ids = torch.arange(
|
438 |
+
past_key_values_length,
|
439 |
+
seq_length + past_key_values_length,
|
440 |
+
dtype=torch.long,
|
441 |
+
device=device,
|
442 |
+
)
|
443 |
+
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
|
444 |
+
else:
|
445 |
+
position_ids = position_ids.view(-1, seq_length).long()
|
446 |
+
|
447 |
+
if inputs_embeds is None:
|
448 |
+
inputs_embeds = self.embed_tokens(input_ids)
|
449 |
+
# Embed positions
|
450 |
+
if attention_mask is None:
|
451 |
+
attention_mask = torch.ones(
|
452 |
+
(batch_size, seq_length_with_past),
|
453 |
+
dtype=torch.bool,
|
454 |
+
device=inputs_embeds.device,
|
455 |
+
)
|
456 |
+
attention_mask = self._prepare_decoder_attention_mask(
|
457 |
+
attention_mask,
|
458 |
+
(batch_size, seq_length),
|
459 |
+
inputs_embeds,
|
460 |
+
past_key_values_length,
|
461 |
+
)
|
462 |
+
|
463 |
+
hidden_states = inputs_embeds
|
464 |
+
|
465 |
+
if self.gradient_checkpointing and self.training:
|
466 |
+
if use_cache:
|
467 |
+
logger.warning(
|
468 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
469 |
+
)
|
470 |
+
use_cache = False
|
471 |
+
|
472 |
+
# Decoder layers
|
473 |
+
all_hidden_states = () if output_hidden_states else None
|
474 |
+
all_self_attns = () if output_attentions else None
|
475 |
+
next_decoder_cache = () if use_cache else None
|
476 |
+
|
477 |
+
for idx, decoder_layer in enumerate(self.layers):
|
478 |
+
if output_hidden_states:
|
479 |
+
all_hidden_states += (hidden_states,)
|
480 |
+
|
481 |
+
past_key_value = (
|
482 |
+
past_key_values[idx] if past_key_values is not None else None
|
483 |
+
)
|
484 |
+
|
485 |
+
if self.gradient_checkpointing and self.training:
|
486 |
+
|
487 |
+
def create_custom_forward(module):
|
488 |
+
def custom_forward(*inputs):
|
489 |
+
# None for past_key_value
|
490 |
+
return module(*inputs, past_key_value, output_attentions)
|
491 |
+
|
492 |
+
return custom_forward
|
493 |
+
|
494 |
+
layer_outputs = torch.utils.checkpoint.checkpoint(
|
495 |
+
create_custom_forward(decoder_layer),
|
496 |
+
hidden_states,
|
497 |
+
attention_mask,
|
498 |
+
position_ids,
|
499 |
+
)
|
500 |
+
else:
|
501 |
+
layer_outputs = decoder_layer(
|
502 |
+
hidden_states,
|
503 |
+
attention_mask=attention_mask,
|
504 |
+
position_ids=position_ids,
|
505 |
+
past_key_value=past_key_value,
|
506 |
+
output_attentions=output_attentions,
|
507 |
+
use_cache=use_cache,
|
508 |
+
)
|
509 |
+
|
510 |
+
hidden_states = layer_outputs[0]
|
511 |
+
|
512 |
+
if use_cache:
|
513 |
+
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
|
514 |
+
|
515 |
+
if output_attentions:
|
516 |
+
all_self_attns += (layer_outputs[1],)
|
517 |
+
|
518 |
+
hidden_states = self.norm(hidden_states)
|
519 |
+
|
520 |
+
# Add hidden states from the last decoder layer
|
521 |
+
if output_hidden_states:
|
522 |
+
all_hidden_states += (hidden_states,)
|
523 |
+
|
524 |
+
next_cache = next_decoder_cache if use_cache else None
|
525 |
+
if not return_dict:
|
526 |
+
return tuple(
|
527 |
+
v
|
528 |
+
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
|
529 |
+
if v is not None
|
530 |
+
)
|
531 |
+
return BaseModelOutputWithPast(
|
532 |
+
last_hidden_state=hidden_states,
|
533 |
+
past_key_values=next_cache,
|
534 |
+
hidden_states=all_hidden_states,
|
535 |
+
attentions=all_self_attns,
|
536 |
+
)
|
537 |
+
|
538 |
+
|
539 |
+
class StableLMEpochForCausalLM(StableLMEpochPreTrainedModel):
|
540 |
+
_tied_weights_keys = ["lm_head.weight"]
|
541 |
+
|
542 |
+
def __init__(self, config: StableLMEpochConfig):
|
543 |
+
super().__init__(config)
|
544 |
+
|
545 |
+
self.model = StableLMEpochModel(config)
|
546 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
547 |
+
|
548 |
+
# Initialize weights and apply final processing
|
549 |
+
self.post_init()
|
550 |
+
|
551 |
+
def get_input_embeddings(self):
|
552 |
+
return self.model.embed_tokens
|
553 |
+
|
554 |
+
def set_input_embeddings(self, value):
|
555 |
+
self.model.embed_tokens = value
|
556 |
+
|
557 |
+
def get_output_embeddings(self):
|
558 |
+
return self.lm_head
|
559 |
+
|
560 |
+
def set_output_embeddings(self, new_embeddings: nn.Module):
|
561 |
+
self.lm_head = new_embeddings
|
562 |
+
|
563 |
+
def get_decoder(self):
|
564 |
+
return self.model
|
565 |
+
|
566 |
+
def set_decoder(self, decoder):
|
567 |
+
self.model = decoder
|
568 |
+
|
569 |
+
def forward(
|
570 |
+
self,
|
571 |
+
input_ids: Optional[torch.LongTensor] = None,
|
572 |
+
attention_mask: Optional[torch.FloatTensor] = None,
|
573 |
+
position_ids: Optional[torch.LongTensor] = None,
|
574 |
+
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
575 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
576 |
+
labels: Optional[torch.LongTensor] = None,
|
577 |
+
use_cache: Optional[bool] = None,
|
578 |
+
output_attentions: Optional[bool] = None,
|
579 |
+
output_hidden_states: Optional[bool] = None,
|
580 |
+
return_dict: Optional[bool] = None,
|
581 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
582 |
+
output_attentions = (
|
583 |
+
output_attentions
|
584 |
+
if output_attentions is not None
|
585 |
+
else self.config.output_attentions
|
586 |
+
)
|
587 |
+
output_hidden_states = (
|
588 |
+
output_hidden_states
|
589 |
+
if output_hidden_states is not None
|
590 |
+
else self.config.output_hidden_states
|
591 |
+
)
|
592 |
+
return_dict = (
|
593 |
+
return_dict if return_dict is not None else self.config.use_return_dict
|
594 |
+
)
|
595 |
+
|
596 |
+
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
597 |
+
outputs = self.model(
|
598 |
+
input_ids,
|
599 |
+
attention_mask=attention_mask,
|
600 |
+
position_ids=position_ids,
|
601 |
+
past_key_values=past_key_values,
|
602 |
+
inputs_embeds=inputs_embeds,
|
603 |
+
use_cache=use_cache,
|
604 |
+
output_attentions=output_attentions,
|
605 |
+
output_hidden_states=output_hidden_states,
|
606 |
+
return_dict=return_dict,
|
607 |
+
)
|
608 |
+
|
609 |
+
hidden_states = outputs[0]
|
610 |
+
logits = self.lm_head(hidden_states).float()
|
611 |
+
|
612 |
+
loss = None
|
613 |
+
if labels is not None:
|
614 |
+
# Shift so that tokens < n predict n
|
615 |
+
shift_logits = logits[..., :-1, :].contiguous()
|
616 |
+
shift_labels = labels[..., 1:].contiguous()
|
617 |
+
# Flatten the tokens
|
618 |
+
loss_fct = CrossEntropyLoss()
|
619 |
+
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
620 |
+
shift_labels = shift_labels.view(-1)
|
621 |
+
# Enable model parallelism
|
622 |
+
shift_labels = shift_labels.to(shift_logits.device)
|
623 |
+
loss = loss_fct(shift_logits, shift_labels)
|
624 |
+
|
625 |
+
if not return_dict:
|
626 |
+
output = (logits,) + outputs[1:]
|
627 |
+
return (loss,) + output if loss is not None else output
|
628 |
+
|
629 |
+
return CausalLMOutputWithPast(
|
630 |
+
loss=loss,
|
631 |
+
logits=logits,
|
632 |
+
past_key_values=outputs.past_key_values,
|
633 |
+
hidden_states=outputs.hidden_states,
|
634 |
+
attentions=outputs.attentions,
|
635 |
+
)
|
636 |
+
|
637 |
+
def prepare_inputs_for_generation(
|
638 |
+
self,
|
639 |
+
input_ids,
|
640 |
+
past_key_values: Optional[torch.Tensor] = None,
|
641 |
+
attention_mask: Optional[torch.Tensor] = None,
|
642 |
+
inputs_embeds: Optional[torch.Tensor] = None,
|
643 |
+
**kwargs,
|
644 |
+
):
|
645 |
+
# Trim decoder_input_ids if past is used
|
646 |
+
if past_key_values and past_key_values[0] is not None:
|
647 |
+
input_ids = input_ids[:, -1:]
|
648 |
+
|
649 |
+
position_ids = kwargs.get("position_ids", None)
|
650 |
+
if attention_mask is not None and position_ids is None:
|
651 |
+
# Create position_ids on the fly for batch generation
|
652 |
+
position_ids = attention_mask.long().cumsum(-1) - 1
|
653 |
+
position_ids.masked_fill_(attention_mask == 0, 1)
|
654 |
+
if past_key_values:
|
655 |
+
position_ids = position_ids[:, -1].unsqueeze(-1)
|
656 |
+
|
657 |
+
# If `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
658 |
+
if inputs_embeds is not None and past_key_values is None:
|
659 |
+
model_inputs = {"inputs_embeds": inputs_embeds}
|
660 |
+
else:
|
661 |
+
model_inputs = {"input_ids": input_ids}
|
662 |
+
|
663 |
+
model_inputs.update(
|
664 |
+
{
|
665 |
+
"attention_mask": attention_mask,
|
666 |
+
"past_key_values": past_key_values,
|
667 |
+
"use_cache": kwargs.get("use_cache"),
|
668 |
+
"position_ids": position_ids,
|
669 |
+
}
|
670 |
+
)
|
671 |
+
return model_inputs
|
672 |
+
|
673 |
+
@staticmethod
|
674 |
+
def _reorder_cache(past_key_values, beam_idx):
|
675 |
+
reordered_past = ()
|
676 |
+
for layer_past in past_key_values:
|
677 |
+
reordered_past += (
|
678 |
+
tuple(
|
679 |
+
past_state.index_select(0, beam_idx.to(past_state.device))
|
680 |
+
for past_state in layer_past
|
681 |
+
),
|
682 |
+
)
|
683 |
+
return reordered_past
|
684 |
+
|
685 |
+
|
686 |
+
StableLMEpochConfig.register_for_auto_class()
|
687 |
+
StableLMEpochForCausalLM.register_for_auto_class("AutoModelForCausalLM")
|