Andreas Köpf
commited on
Commit
•
0d0ff25
1
Parent(s):
adaadf5
add falcon landmark code (incomplete)
Browse files- code/configuration_RW.py +86 -0
- code/install_deps.sh +4 -0
- code/llama_landmark_config.py +16 -0
- code/llama_mem.py +1295 -0
- code/llama_orig.py +888 -0
- code/modelling_RW.py +1362 -0
- code/redpajama.py +174 -0
- code/run_test.py +171 -0
- code/run_train_1x.sh +23 -0
- code/run_train_8x.sh +25 -0
- code/train.py +206 -0
- code/weight_diff.py +158 -0
code/configuration_RW.py
ADDED
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# coding=utf-8
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# Copyright 2022 the Big Science Workshop and HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Bloom configuration"""
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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class RWConfig(PretrainedConfig):
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model_type = "RefinedWebModel"
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keys_to_ignore_at_inference = ["past_key_values"]
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attribute_map = {
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"num_hidden_layers": "n_layer",
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"num_attention_heads": "n_head",
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}
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def __init__(
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self,
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vocab_size=250880,
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hidden_size=64,
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n_layer=2,
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n_head=8,
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layer_norm_epsilon=1e-5,
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initializer_range=0.02,
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use_cache=True,
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bos_token_id=1,
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eos_token_id=2,
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apply_residual_connection_post_layernorm=False,
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hidden_dropout=0.0,
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attention_dropout=0.0,
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multi_query=False,
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alibi=False,
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bias=False,
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parallel_attn=False,
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mem_id=None,
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mem_freq=50,
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train_context_length=512,
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**kwargs,
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):
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self.vocab_size = vocab_size
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# Backward compatibility with n_embed kwarg
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n_embed = kwargs.pop("n_embed", None)
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self.hidden_size = hidden_size if n_embed is None else n_embed
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self.n_layer = n_layer
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self.n_head = n_head
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self.layer_norm_epsilon = layer_norm_epsilon
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self.initializer_range = initializer_range
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self.use_cache = use_cache
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self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
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self.hidden_dropout = hidden_dropout
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self.attention_dropout = attention_dropout
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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self.multi_query = multi_query
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self.alibi = alibi
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self.bias = bias
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self.parallel_attn = parallel_attn
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self.mem_id = mem_id
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self.mem_freq = mem_freq
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self.train_context_length = train_context_length
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super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
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@property
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def head_dim(self):
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return self.hidden_size // self.n_head
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@property
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def rotary(self):
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return not self.alibi
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code/install_deps.sh
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#!/bin/bash
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pip install -r requirements.txt
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pip install "git+https://github.com/openai/triton.git#subdirectory=python"
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code/llama_landmark_config.py
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from transformers.models.llama.configuration_llama import LlamaConfig
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class LlamaLandmarkConfig(LlamaConfig):
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model_type = "llama_with_landmark"
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def __init__(
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self,
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mem_id=32001,
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mem_freq=50,
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train_context_length=512,
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**kwargs,
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):
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self.mem_id = mem_id
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self.mem_freq = mem_freq
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self.train_context_length = train_context_length
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super().__init__(**kwargs)
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code/llama_mem.py
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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 |
+
from typing import List, Optional, Tuple, Union
|
23 |
+
|
24 |
+
import torch
|
25 |
+
import torch.utils.checkpoint
|
26 |
+
from torch import nn
|
27 |
+
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
28 |
+
|
29 |
+
from transformers.activations import ACT2FN
|
30 |
+
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
|
31 |
+
from transformers.modeling_utils import PreTrainedModel
|
32 |
+
from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
|
33 |
+
from llama_landmark_config import LlamaLandmarkConfig
|
34 |
+
from ltriton.flash_landmark_attention import fused_landmark_attention
|
35 |
+
|
36 |
+
|
37 |
+
logger = logging.get_logger(__name__)
|
38 |
+
|
39 |
+
_CONFIG_FOR_DOC = "LlamaLandmarkConfig"
|
40 |
+
|
41 |
+
|
42 |
+
# Copied from transformers.models.bart.modeling_bart._make_causal_mask
|
43 |
+
def _make_causal_mask(
|
44 |
+
input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
|
45 |
+
):
|
46 |
+
"""
|
47 |
+
Make causal mask used for bi-directional self-attention.
|
48 |
+
"""
|
49 |
+
bsz, tgt_len = input_ids_shape
|
50 |
+
mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
|
51 |
+
mask_cond = torch.arange(mask.size(-1), device=device)
|
52 |
+
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
|
53 |
+
mask = mask.to(dtype)
|
54 |
+
|
55 |
+
if past_key_values_length > 0:
|
56 |
+
mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
|
57 |
+
return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
|
58 |
+
|
59 |
+
|
60 |
+
# Copied from transformers.models.bart.modeling_bart._expand_mask
|
61 |
+
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
|
62 |
+
"""
|
63 |
+
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
|
64 |
+
"""
|
65 |
+
bsz, src_len = mask.size()
|
66 |
+
tgt_len = tgt_len if tgt_len is not None else src_len
|
67 |
+
|
68 |
+
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
|
69 |
+
|
70 |
+
inverted_mask = 1.0 - expanded_mask
|
71 |
+
|
72 |
+
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
|
73 |
+
|
74 |
+
|
75 |
+
class LlamaRMSNorm(nn.Module):
|
76 |
+
def __init__(self, hidden_size, eps=1e-6):
|
77 |
+
"""
|
78 |
+
LlamaRMSNorm is equivalent to T5LayerNorm
|
79 |
+
"""
|
80 |
+
super().__init__()
|
81 |
+
self.weight = nn.Parameter(torch.ones(hidden_size))
|
82 |
+
self.variance_epsilon = eps
|
83 |
+
|
84 |
+
def forward(self, hidden_states):
|
85 |
+
variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
|
86 |
+
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
87 |
+
|
88 |
+
# convert into half-precision if necessary
|
89 |
+
if self.weight.dtype in [torch.float16, torch.bfloat16]:
|
90 |
+
hidden_states = hidden_states.to(self.weight.dtype)
|
91 |
+
|
92 |
+
return self.weight * hidden_states
|
93 |
+
|
94 |
+
|
95 |
+
class LlamaRotaryEmbedding(torch.nn.Module):
|
96 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
|
97 |
+
super().__init__()
|
98 |
+
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
|
99 |
+
self.register_buffer("inv_freq", inv_freq)
|
100 |
+
|
101 |
+
# Build here to make `torch.jit.trace` work.
|
102 |
+
self.max_seq_len_cached = max_position_embeddings
|
103 |
+
t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
|
104 |
+
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
|
105 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
106 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
107 |
+
self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
|
108 |
+
self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
|
109 |
+
|
110 |
+
def forward(self, x, seq_len=None):
|
111 |
+
# x: [bs, num_attention_heads, seq_len, head_size]
|
112 |
+
# This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
|
113 |
+
if seq_len > self.max_seq_len_cached:
|
114 |
+
self.max_seq_len_cached = seq_len
|
115 |
+
t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
|
116 |
+
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
|
117 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
118 |
+
emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
|
119 |
+
self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
|
120 |
+
self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
|
121 |
+
return (
|
122 |
+
self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
|
123 |
+
self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
|
124 |
+
)
|
125 |
+
|
126 |
+
|
127 |
+
def rotate_half(x):
|
128 |
+
"""Rotates half the hidden dims of the input."""
|
129 |
+
x1 = x[..., : x.shape[-1] // 2]
|
130 |
+
x2 = x[..., x.shape[-1] // 2 :]
|
131 |
+
return torch.cat((-x2, x1), dim=-1)
|
132 |
+
|
133 |
+
|
134 |
+
def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
|
135 |
+
# The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
|
136 |
+
cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
|
137 |
+
sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
|
138 |
+
if position_ids.ndim == 2:
|
139 |
+
cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
|
140 |
+
sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
|
141 |
+
else:
|
142 |
+
cos = cos[position_ids]
|
143 |
+
sin = sin[position_ids]
|
144 |
+
if q is None:
|
145 |
+
q_embed = None
|
146 |
+
else:
|
147 |
+
q_embed = (q * cos) + (rotate_half(q) * sin)
|
148 |
+
k_embed = (k * cos) + (rotate_half(k) * sin)
|
149 |
+
return q_embed, k_embed
|
150 |
+
|
151 |
+
|
152 |
+
class LlamaMLP(nn.Module):
|
153 |
+
def __init__(
|
154 |
+
self,
|
155 |
+
hidden_size: int,
|
156 |
+
intermediate_size: int,
|
157 |
+
hidden_act: str,
|
158 |
+
):
|
159 |
+
super().__init__()
|
160 |
+
self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
|
161 |
+
self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
|
162 |
+
self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
|
163 |
+
self.act_fn = ACT2FN[hidden_act]
|
164 |
+
|
165 |
+
def forward(self, x):
|
166 |
+
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
167 |
+
|
168 |
+
class LandmarkGroupedSoftmaxFunction(torch.autograd.Function):
|
169 |
+
|
170 |
+
# Note that forward, setup_context, and backward are @staticmethods
|
171 |
+
@staticmethod
|
172 |
+
def forward(ctx, x, dim, mem_cnt, resp_mem_idx):
|
173 |
+
new_shape = list(x.shape)
|
174 |
+
new_shape[dim] = mem_cnt # max_mem_cnt.item()
|
175 |
+
max_by_group = x.new_zeros((*new_shape,))
|
176 |
+
max_by_group.scatter_reduce_(src=x, index=resp_mem_idx, dim=dim, reduce="amax", include_self=False)
|
177 |
+
|
178 |
+
maxes = torch.gather(max_by_group, dim, resp_mem_idx)
|
179 |
+
#x_exp = torch.exp(x - torch.where(torch.isinf(maxes), 0, maxes))
|
180 |
+
x_exp = torch.exp((x - maxes).to(torch.float32))
|
181 |
+
|
182 |
+
cumsum_by_group = torch.zeros_like(max_by_group, dtype=x_exp.dtype)
|
183 |
+
|
184 |
+
cumsum_by_group.scatter_add_(dim, resp_mem_idx, x_exp, )
|
185 |
+
denom = torch.gather(cumsum_by_group, dim, resp_mem_idx)
|
186 |
+
|
187 |
+
#probs = torch.where(denom < 0.5, 0, x_exp / denom)
|
188 |
+
probs = x_exp / denom
|
189 |
+
|
190 |
+
|
191 |
+
ctx.mem_cnt = mem_cnt
|
192 |
+
ctx.dim = dim
|
193 |
+
ctx.save_for_backward(resp_mem_idx, probs)
|
194 |
+
|
195 |
+
return probs
|
196 |
+
|
197 |
+
@staticmethod
|
198 |
+
def backward(ctx, grad_probs):
|
199 |
+
mem_cnt = ctx.mem_cnt
|
200 |
+
dim = ctx.dim
|
201 |
+
resp_mem_idx, probs = ctx.saved_tensors
|
202 |
+
grad_x = grad_dim = grad_mem_cnt = grad_resp_mem_idx = None
|
203 |
+
|
204 |
+
if ctx.needs_input_grad[0] or ctx.needs_input_grad[4]:
|
205 |
+
grad_pair = grad_probs * probs
|
206 |
+
|
207 |
+
new_shape = list(probs.shape)
|
208 |
+
new_shape[dim] = mem_cnt # max_mem_cnt.item()
|
209 |
+
cumsum_by_group = grad_pair.new_zeros((*new_shape,))
|
210 |
+
cumsum_by_group.scatter_add_(dim, resp_mem_idx, grad_pair)
|
211 |
+
|
212 |
+
|
213 |
+
if ctx.needs_input_grad[0]:
|
214 |
+
grad_sum = torch.gather(cumsum_by_group, dim, resp_mem_idx)
|
215 |
+
grad_x = grad_pair - probs * grad_sum
|
216 |
+
assert not ctx.needs_input_grad[1]
|
217 |
+
assert not ctx.needs_input_grad[2]
|
218 |
+
assert not ctx.needs_input_grad[3]
|
219 |
+
|
220 |
+
return grad_x, grad_dim, grad_mem_cnt, grad_resp_mem_idx
|
221 |
+
|
222 |
+
def landmark_grouped_softmax(x, dim, is_mem, last_section_mask):
|
223 |
+
|
224 |
+
last_and_rest_mask = last_section_mask # | mask
|
225 |
+
|
226 |
+
full_access_mask = is_mem | last_and_rest_mask
|
227 |
+
|
228 |
+
max_mem_cnt = 64
|
229 |
+
mem_group_idx = torch.cumsum(is_mem, dim=dim)
|
230 |
+
mem_bucket_id = max_mem_cnt - 1
|
231 |
+
resp_mem_idx = torch.where(last_and_rest_mask,
|
232 |
+
max_mem_cnt - 1,
|
233 |
+
torch.where(is_mem, mem_bucket_id, mem_group_idx))
|
234 |
+
probs = LandmarkGroupedSoftmaxFunction.apply(x, dim, max_mem_cnt, resp_mem_idx)
|
235 |
+
|
236 |
+
new_shape = list(x.shape)
|
237 |
+
new_shape[dim] = max_mem_cnt
|
238 |
+
group_prob = probs.new_zeros((*new_shape, ))
|
239 |
+
group_prob.scatter_(dim, torch.where(is_mem, mem_group_idx - 1, max_mem_cnt - 1), probs)
|
240 |
+
probs = probs.mul(torch.where(full_access_mask, last_section_mask, torch.gather(group_prob, dim, resp_mem_idx)))
|
241 |
+
|
242 |
+
|
243 |
+
return probs
|
244 |
+
|
245 |
+
class LlamaAttention(nn.Module):
|
246 |
+
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
247 |
+
|
248 |
+
def __init__(self, config: LlamaLandmarkConfig):
|
249 |
+
super().__init__()
|
250 |
+
self.config = config
|
251 |
+
self.hidden_size = config.hidden_size
|
252 |
+
self.num_heads = config.num_attention_heads
|
253 |
+
self.head_dim = self.hidden_size // self.num_heads
|
254 |
+
self.max_position_embeddings = config.max_position_embeddings
|
255 |
+
|
256 |
+
if (self.head_dim * self.num_heads) != self.hidden_size:
|
257 |
+
raise ValueError(
|
258 |
+
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
|
259 |
+
f" and `num_heads`: {self.num_heads})."
|
260 |
+
)
|
261 |
+
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
|
262 |
+
self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
|
263 |
+
self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
|
264 |
+
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
|
265 |
+
self.rotary_emb = LlamaRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
|
266 |
+
|
267 |
+
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
|
268 |
+
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
|
269 |
+
|
270 |
+
def forward(
|
271 |
+
self,
|
272 |
+
hidden_states: torch.Tensor,
|
273 |
+
attention_mask: Optional[torch.Tensor] = None,
|
274 |
+
position_ids: Optional[torch.LongTensor] = None,
|
275 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
276 |
+
output_attentions: bool = False,
|
277 |
+
use_cache: bool = False,
|
278 |
+
is_mem: Optional[torch.Tensor] = None,
|
279 |
+
last_section_mask: Optional[torch.Tensor] = None,
|
280 |
+
offload_cache_to_cpu: bool = False,
|
281 |
+
use_flash: bool = False,
|
282 |
+
cache_top_k: Optional[int] = None,
|
283 |
+
mem_freq: Optional[int] = None
|
284 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
285 |
+
bsz, q_len, _ = hidden_states.size()
|
286 |
+
|
287 |
+
query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
288 |
+
key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
289 |
+
value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
290 |
+
|
291 |
+
kv_seq_len = key_states.shape[-2]
|
292 |
+
if past_key_value is not None:
|
293 |
+
kv_seq_len += past_key_value[0].shape[-2]
|
294 |
+
if len(past_key_value) > 2:
|
295 |
+
kv_seq_len += past_key_value[3].shape[2] * past_key_value[3].shape[3]
|
296 |
+
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
297 |
+
key_states_before_pos = key_states
|
298 |
+
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
299 |
+
# [bsz, nh, t, hd]
|
300 |
+
|
301 |
+
attn_prefix = None
|
302 |
+
if past_key_value is not None:
|
303 |
+
# reuse k, v, self_attention
|
304 |
+
if mem_freq is None:
|
305 |
+
cache_len = past_key_value[0].shape[2]
|
306 |
+
if is_mem is not None:
|
307 |
+
if use_flash:
|
308 |
+
is_mem = torch.cat((is_mem.new_zeros((1, cache_len)), is_mem), dim=-1)
|
309 |
+
else:
|
310 |
+
is_mem = torch.cat((is_mem.new_zeros((1, 1, q_len, cache_len)), is_mem), dim=-1)
|
311 |
+
last_section_mask = torch.cat((last_section_mask.new_ones((1, 1, q_len, cache_len)), last_section_mask), dim=-1)
|
312 |
+
|
313 |
+
past_key_states = torch.cat([past_key_value[0], key_states], dim=2)
|
314 |
+
past_value_states = torch.cat([past_key_value[1], value_states], dim=2)
|
315 |
+
key_states = past_key_states[:, :, -(q_len + cache_len):]
|
316 |
+
value_states = past_value_states[:, :, -(q_len + cache_len):]
|
317 |
+
expected_att_size = (bsz, self.num_heads, q_len, cache_len + q_len)
|
318 |
+
else:
|
319 |
+
orig_value_states = value_states
|
320 |
+
|
321 |
+
incomplete_len = past_key_value[0].shape[2] % (mem_freq + 1)
|
322 |
+
full_len = past_key_value[0].shape[2] - incomplete_len
|
323 |
+
past_key_mem, past_key_incomplete = torch.split(past_key_value[0], (full_len, incomplete_len), dim=2)
|
324 |
+
past_value_mem, past_value_incomplete = torch.split(past_key_value[1], (full_len, incomplete_len), dim=2)
|
325 |
+
|
326 |
+
if offload_cache_to_cpu:
|
327 |
+
past_key_value = (past_key_incomplete, past_value_incomplete, *past_key_value[2:])
|
328 |
+
|
329 |
+
if incomplete_len > 0:
|
330 |
+
assert q_len + incomplete_len <= (mem_freq + 1)
|
331 |
+
if use_flash:
|
332 |
+
is_mem = torch.cat((is_mem.new_zeros((1, incomplete_len)), is_mem), dim=-1)
|
333 |
+
else:
|
334 |
+
is_mem = torch.cat((is_mem.new_zeros((1, 1, q_len, incomplete_len)), is_mem), dim=-1)
|
335 |
+
last_section_mask = torch.cat((last_section_mask.new_ones((1, 1, q_len, incomplete_len)), last_section_mask), dim=-1)
|
336 |
+
|
337 |
+
if len(past_key_value) > 2:
|
338 |
+
full_len += past_key_value[3].shape[2] * past_key_value[3].shape[3]
|
339 |
+
past_key_incomplete_pos = torch.arange(full_len, full_len + incomplete_len, dtype=torch.long, device=position_ids.device).unsqueeze(0)
|
340 |
+
_, past_key_incomplete = apply_rotary_pos_emb(None, past_key_incomplete, cos, sin, past_key_incomplete_pos)
|
341 |
+
key_states = torch.cat((past_key_incomplete, key_states), dim=2)
|
342 |
+
value_states = torch.cat((past_value_incomplete, value_states), dim=2)
|
343 |
+
|
344 |
+
past_key_mem = past_key_mem.view(bsz, self.num_heads, -1, mem_freq + 1, self.head_dim)
|
345 |
+
past_value_mem = past_value_mem.view(bsz, self.num_heads, -1, mem_freq + 1, self.head_dim)
|
346 |
+
|
347 |
+
if len(past_key_value) > 2:
|
348 |
+
mem_key_nopos = torch.cat((
|
349 |
+
past_key_value[2],
|
350 |
+
past_key_mem.select(dim=3, index=mem_freq)), dim=2)
|
351 |
+
past_key_mem_offload = past_key_value[3]
|
352 |
+
past_key_mem = torch.cat((
|
353 |
+
past_key_mem_offload,
|
354 |
+
past_key_mem.to(past_key_mem_offload.device)), dim=2)
|
355 |
+
past_value_mem = torch.cat((past_key_value[4], past_value_mem.to(past_key_mem_offload.device)), dim=2)
|
356 |
+
else:
|
357 |
+
mem_key_nopos = past_key_mem.select(dim=3, index=mem_freq)
|
358 |
+
|
359 |
+
num_mems = past_key_mem.shape[2]
|
360 |
+
top_k = min(cache_top_k, num_mems)
|
361 |
+
prefix_len = full_len - (top_k + 1) * (mem_freq + 1)
|
362 |
+
mem_indices = torch.cat(
|
363 |
+
(position_ids.new_zeros((max(0, num_mems - top_k), )),
|
364 |
+
torch.arange(1, top_k + 1, device=query_states.device, dtype=position_ids.dtype)), dim=0)
|
365 |
+
mem_pos = (mem_indices * (mem_freq + 1) + mem_freq).unsqueeze(0).expand(bsz, -1) + prefix_len
|
366 |
+
_, mem_key = apply_rotary_pos_emb(None, mem_key_nopos, cos, sin, mem_pos)
|
367 |
+
mem_attn_weights = torch.matmul(query_states, mem_key.transpose(2, 3)) / math.sqrt(self.head_dim)
|
368 |
+
|
369 |
+
if offload_cache_to_cpu:
|
370 |
+
aggregate = "max_over_tokens"
|
371 |
+
else:
|
372 |
+
aggregate = None
|
373 |
+
if aggregate == "max_over_tokens":
|
374 |
+
token_retrievers = 1
|
375 |
+
head_retrievers = self.num_heads
|
376 |
+
mem_attn_weights = torch.nn.functional.softmax(mem_attn_weights, dim=-1,dtype=torch.float32).to(query_states.dtype)
|
377 |
+
mem_attn_weights = mem_attn_weights.amax(dim=2, keepdim=True)
|
378 |
+
elif aggregate is None:
|
379 |
+
token_retrievers = q_len
|
380 |
+
head_retrievers = self.num_heads
|
381 |
+
else:
|
382 |
+
raise NotImplementedError()
|
383 |
+
|
384 |
+
mem_selected_idx = mem_attn_weights.topk(dim=-1,k=top_k)[1].sort(dim=-1)[0].view(bsz, head_retrievers, token_retrievers, top_k)
|
385 |
+
|
386 |
+
selected_indices = torch.arange(0, top_k * (mem_freq + 1), device=query_states.device, dtype=position_ids.dtype)
|
387 |
+
selected_indices = torch.where(mem_selected_idx >= num_mems - top_k, mem_freq + 1, 0).unsqueeze(-1) + selected_indices.view(1, 1, 1, top_k, mem_freq + 1)
|
388 |
+
selected_indices = selected_indices.view(bsz, head_retrievers, token_retrievers, -1) + prefix_len
|
389 |
+
|
390 |
+
|
391 |
+
|
392 |
+
|
393 |
+
mem_selected_idx = mem_selected_idx.to(past_key_mem.device)
|
394 |
+
|
395 |
+
mem_selected_idx = mem_selected_idx.view(bsz, self.num_heads, token_retrievers, top_k, 1, 1).expand(bsz, self.num_heads, token_retrievers, top_k, mem_freq + 1, self.head_dim)
|
396 |
+
selected_keys = past_key_mem.unsqueeze(2).expand(bsz, self.num_heads, token_retrievers, -1, mem_freq + 1, self.head_dim)
|
397 |
+
selected_keys = selected_keys.take_along_dim(mem_selected_idx, dim=3).to(query_states.device)
|
398 |
+
selected_values = past_value_mem.unsqueeze(2).expand(bsz, self.num_heads, token_retrievers, -1, mem_freq + 1, self.head_dim).take_along_dim(mem_selected_idx, dim=3).to(query_states.device)
|
399 |
+
|
400 |
+
if aggregate == "max_over_tokens":
|
401 |
+
selected_indices = selected_indices.squeeze(2)
|
402 |
+
selected_keys = selected_keys.view(bsz, self.num_heads, -1, self.head_dim)
|
403 |
+
selected_keys = apply_rotary_pos_emb(None, selected_keys, cos, sin, selected_indices)[1]
|
404 |
+
key_states = torch.cat((selected_keys, key_states), dim=2)
|
405 |
+
value_states = torch.cat((selected_values.view(bsz, self.num_heads, -1, self.head_dim), value_states), dim=2)
|
406 |
+
expected_att_size = (bsz, self.num_heads, q_len, key_states.shape[2])
|
407 |
+
else:
|
408 |
+
selected_indices = selected_indices.expand(bsz, self.num_heads, q_len, -1)
|
409 |
+
selected_keys = selected_keys.view(bsz, self.num_heads, token_retrievers, -1, self.head_dim).expand(bsz, self.num_heads, q_len, -1, self.head_dim)
|
410 |
+
selected_keys = apply_rotary_pos_emb(None, selected_keys, cos, sin, selected_indices)[1]
|
411 |
+
selected_values = selected_values.view(bsz, self.num_heads, token_retrievers, -1, self.head_dim).expand(bsz, self.num_heads, q_len, -1, self.head_dim)
|
412 |
+
attn_prefix = torch.matmul(query_states.unsqueeze(3), selected_keys.transpose(3, 4)).squeeze(3) / math.sqrt(self.head_dim)
|
413 |
+
expected_att_size = (bsz, self.num_heads, q_len, q_len + incomplete_len)
|
414 |
+
|
415 |
+
is_mem_prefix = torch.cat((is_mem.new_zeros((mem_freq, )), is_mem.new_ones((1, )))).unsqueeze(0).repeat((top_k, 1))
|
416 |
+
if use_flash:
|
417 |
+
is_mem_prefix = is_mem_prefix.view(1, -1)
|
418 |
+
else:
|
419 |
+
is_mem_prefix = is_mem_prefix.view(1, 1, 1, -1).expand(1, 1, q_len, -1)
|
420 |
+
last_section_mask = torch.cat((last_section_mask.new_zeros((1, 1, q_len, top_k * (mem_freq + 1))), last_section_mask), dim=-1)
|
421 |
+
is_mem = torch.cat((is_mem_prefix, is_mem), dim=-1)
|
422 |
+
|
423 |
+
|
424 |
+
past_key_states = torch.cat([past_key_value[0], key_states_before_pos], dim=2)
|
425 |
+
past_value_states = torch.cat([past_key_value[1], orig_value_states], dim=2)
|
426 |
+
|
427 |
+
if offload_cache_to_cpu:
|
428 |
+
past_key_value = (past_key_states, past_value_states, mem_key_nopos, past_key_mem.to("cpu"), past_value_mem.to("cpu"), *past_key_value[5:]) if use_cache else None
|
429 |
+
else:
|
430 |
+
past_key_value = (past_key_states, past_value_states) if use_cache else None
|
431 |
+
|
432 |
+
else:
|
433 |
+
if mem_freq is None:
|
434 |
+
past_key_states = key_states
|
435 |
+
else:
|
436 |
+
past_key_states = key_states_before_pos
|
437 |
+
past_value_states = value_states
|
438 |
+
expected_att_size = (bsz, self.num_heads, q_len, kv_seq_len)
|
439 |
+
past_key_value = (past_key_states, past_value_states) if use_cache else None
|
440 |
+
|
441 |
+
if use_flash:
|
442 |
+
assert attn_prefix is None
|
443 |
+
assert not output_attentions
|
444 |
+
assert mem_freq is not None
|
445 |
+
attn_output = fused_landmark_attention(query_states, key_states, value_states, is_mem, block_size=mem_freq+1)
|
446 |
+
attn_weights = None
|
447 |
+
else:
|
448 |
+
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
449 |
+
if attn_weights.size() != expected_att_size:
|
450 |
+
raise ValueError(
|
451 |
+
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
|
452 |
+
f" {attn_weights.size()}"
|
453 |
+
)
|
454 |
+
|
455 |
+
if attention_mask is not None:
|
456 |
+
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
457 |
+
raise ValueError(
|
458 |
+
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
|
459 |
+
)
|
460 |
+
attn_weights = attn_weights + attention_mask[...,-attn_weights.shape[-1]:]
|
461 |
+
attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
|
462 |
+
if attn_prefix is not None:
|
463 |
+
attn_weights = torch.cat((attn_prefix, attn_weights), dim=-1)
|
464 |
+
# upcast attention to fp32
|
465 |
+
if is_mem is None:
|
466 |
+
raise ValueError("Don't use this without landmarks")
|
467 |
+
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
|
468 |
+
else:
|
469 |
+
attn_weights = landmark_grouped_softmax(attn_weights, dim=-1, is_mem=is_mem.expand(-1, self.num_heads, -1, -1), last_section_mask=last_section_mask).to(query_states.dtype)
|
470 |
+
if attn_prefix is not None:
|
471 |
+
attn_prefix, attn_weights = torch.split(attn_weights, (attn_prefix.shape[-1], attn_weights.shape[-1] - attn_prefix.shape[-1]), dim=-1)
|
472 |
+
attn_output = torch.matmul(attn_weights, value_states)
|
473 |
+
if attn_prefix is not None:
|
474 |
+
attn_output += torch.matmul(attn_prefix.unsqueeze(3), selected_values).squeeze(3)
|
475 |
+
|
476 |
+
if not output_attentions:
|
477 |
+
attn_weights = None
|
478 |
+
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
479 |
+
raise ValueError(
|
480 |
+
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
481 |
+
f" {attn_output.size()}"
|
482 |
+
)
|
483 |
+
|
484 |
+
attn_output = attn_output.transpose(1, 2)
|
485 |
+
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
486 |
+
attn_output = self.o_proj(attn_output)
|
487 |
+
|
488 |
+
return attn_output, attn_weights, past_key_value
|
489 |
+
|
490 |
+
|
491 |
+
class LlamaDecoderLayer(nn.Module):
|
492 |
+
def __init__(self, config: LlamaLandmarkConfig):
|
493 |
+
super().__init__()
|
494 |
+
self.hidden_size = config.hidden_size
|
495 |
+
self.self_attn = LlamaAttention(config=config)
|
496 |
+
self.mlp = LlamaMLP(
|
497 |
+
hidden_size=self.hidden_size,
|
498 |
+
intermediate_size=config.intermediate_size,
|
499 |
+
hidden_act=config.hidden_act,
|
500 |
+
)
|
501 |
+
self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
502 |
+
self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
503 |
+
|
504 |
+
def forward(
|
505 |
+
self,
|
506 |
+
hidden_states: torch.Tensor,
|
507 |
+
attention_mask: Optional[torch.Tensor] = None,
|
508 |
+
position_ids: Optional[torch.LongTensor] = None,
|
509 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
510 |
+
output_attentions: Optional[bool] = False,
|
511 |
+
use_cache: Optional[bool] = False,
|
512 |
+
is_mem: Optional[torch.Tensor] = None,
|
513 |
+
last_section_mask: Optional[torch.Tensor] = None,
|
514 |
+
offload_cache_to_cpu: bool = False,
|
515 |
+
use_flash: bool = False,
|
516 |
+
cache_top_k: Optional[int] = None,
|
517 |
+
mem_freq: Optional[int] = None
|
518 |
+
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
519 |
+
"""
|
520 |
+
Args:
|
521 |
+
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
522 |
+
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
|
523 |
+
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
|
524 |
+
output_attentions (`bool`, *optional*):
|
525 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
526 |
+
returned tensors for more detail.
|
527 |
+
use_cache (`bool`, *optional*):
|
528 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
529 |
+
(see `past_key_values`).
|
530 |
+
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
531 |
+
"""
|
532 |
+
|
533 |
+
residual = hidden_states
|
534 |
+
|
535 |
+
hidden_states = self.input_layernorm(hidden_states)
|
536 |
+
|
537 |
+
# Self Attention
|
538 |
+
hidden_states, self_attn_weights, present_key_value = self.self_attn(
|
539 |
+
hidden_states=hidden_states,
|
540 |
+
attention_mask=attention_mask,
|
541 |
+
position_ids=position_ids,
|
542 |
+
past_key_value=past_key_value,
|
543 |
+
output_attentions=output_attentions,
|
544 |
+
use_cache=use_cache,
|
545 |
+
is_mem=is_mem,
|
546 |
+
last_section_mask=last_section_mask,
|
547 |
+
offload_cache_to_cpu=offload_cache_to_cpu,
|
548 |
+
use_flash=use_flash,
|
549 |
+
cache_top_k=cache_top_k,
|
550 |
+
mem_freq=mem_freq
|
551 |
+
)
|
552 |
+
hidden_states = residual + hidden_states
|
553 |
+
|
554 |
+
# Fully Connected
|
555 |
+
residual = hidden_states
|
556 |
+
hidden_states = self.post_attention_layernorm(hidden_states)
|
557 |
+
hidden_states = self.mlp(hidden_states)
|
558 |
+
hidden_states = residual + hidden_states
|
559 |
+
|
560 |
+
outputs = (hidden_states,)
|
561 |
+
|
562 |
+
if output_attentions:
|
563 |
+
outputs += (self_attn_weights,)
|
564 |
+
|
565 |
+
if use_cache:
|
566 |
+
outputs += (present_key_value,)
|
567 |
+
|
568 |
+
return outputs
|
569 |
+
|
570 |
+
|
571 |
+
LLAMA_START_DOCSTRING = r"""
|
572 |
+
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
573 |
+
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
574 |
+
etc.)
|
575 |
+
|
576 |
+
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
577 |
+
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
578 |
+
and behavior.
|
579 |
+
|
580 |
+
Parameters:
|
581 |
+
config ([`LlamaLandmarkConfig`]):
|
582 |
+
Model configuration class with all the parameters of the model. Initializing with a config file does not
|
583 |
+
load the weights associated with the model, only the configuration. Check out the
|
584 |
+
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
585 |
+
"""
|
586 |
+
|
587 |
+
|
588 |
+
@add_start_docstrings(
|
589 |
+
"The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
|
590 |
+
LLAMA_START_DOCSTRING,
|
591 |
+
)
|
592 |
+
class LlamaPreTrainedModel(PreTrainedModel):
|
593 |
+
config_class = LlamaLandmarkConfig
|
594 |
+
base_model_prefix = "model"
|
595 |
+
supports_gradient_checkpointing = True
|
596 |
+
_no_split_modules = ["LlamaDecoderLayer"]
|
597 |
+
_keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
|
598 |
+
|
599 |
+
def _init_weights(self, module):
|
600 |
+
std = self.config.initializer_range
|
601 |
+
if isinstance(module, nn.Linear):
|
602 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
603 |
+
if module.bias is not None:
|
604 |
+
module.bias.data.zero_()
|
605 |
+
elif isinstance(module, nn.Embedding):
|
606 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
607 |
+
if module.padding_idx is not None:
|
608 |
+
module.weight.data[module.padding_idx].zero_()
|
609 |
+
|
610 |
+
def _set_gradient_checkpointing(self, module, value=False):
|
611 |
+
if isinstance(module, LlamaModel):
|
612 |
+
module.gradient_checkpointing = value
|
613 |
+
|
614 |
+
|
615 |
+
LLAMA_INPUTS_DOCSTRING = r"""
|
616 |
+
Args:
|
617 |
+
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
618 |
+
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
619 |
+
it.
|
620 |
+
|
621 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
622 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
623 |
+
|
624 |
+
[What are input IDs?](../glossary#input-ids)
|
625 |
+
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
626 |
+
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
627 |
+
|
628 |
+
- 1 for tokens that are **not masked**,
|
629 |
+
- 0 for tokens that are **masked**.
|
630 |
+
|
631 |
+
[What are attention masks?](../glossary#attention-mask)
|
632 |
+
|
633 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
634 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
635 |
+
|
636 |
+
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
|
637 |
+
`past_key_values`).
|
638 |
+
|
639 |
+
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
|
640 |
+
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
|
641 |
+
information on the default strategy.
|
642 |
+
|
643 |
+
- 1 indicates the head is **not masked**,
|
644 |
+
- 0 indicates the head is **masked**.
|
645 |
+
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
646 |
+
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
647 |
+
config.n_positions - 1]`.
|
648 |
+
|
649 |
+
[What are position IDs?](../glossary#position-ids)
|
650 |
+
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
651 |
+
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
|
652 |
+
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
|
653 |
+
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
|
654 |
+
|
655 |
+
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
|
656 |
+
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
|
657 |
+
|
658 |
+
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
|
659 |
+
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
|
660 |
+
`decoder_input_ids` 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 LlamaModel(LlamaPreTrainedModel):
|
684 |
+
"""
|
685 |
+
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
|
686 |
+
|
687 |
+
Args:
|
688 |
+
config: LlamaLandmarkConfig
|
689 |
+
"""
|
690 |
+
|
691 |
+
def __init__(self, config: LlamaLandmarkConfig):
|
692 |
+
super().__init__(config)
|
693 |
+
self.padding_idx = config.pad_token_id
|
694 |
+
self.vocab_size = config.vocab_size
|
695 |
+
|
696 |
+
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
697 |
+
self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
|
698 |
+
self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
699 |
+
|
700 |
+
self.gradient_checkpointing = False
|
701 |
+
# Initialize weights and apply final processing
|
702 |
+
self.post_init()
|
703 |
+
|
704 |
+
def get_input_embeddings(self):
|
705 |
+
return self.embed_tokens
|
706 |
+
|
707 |
+
def set_input_embeddings(self, value):
|
708 |
+
self.embed_tokens = value
|
709 |
+
|
710 |
+
# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
|
711 |
+
def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
|
712 |
+
# create causal mask
|
713 |
+
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
714 |
+
combined_attention_mask = None
|
715 |
+
if input_shape[-1] > 1:
|
716 |
+
combined_attention_mask = _make_causal_mask(
|
717 |
+
input_shape,
|
718 |
+
inputs_embeds.dtype,
|
719 |
+
device=inputs_embeds.device,
|
720 |
+
past_key_values_length=past_key_values_length,
|
721 |
+
)
|
722 |
+
|
723 |
+
if attention_mask is not None:
|
724 |
+
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
725 |
+
expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
|
726 |
+
inputs_embeds.device
|
727 |
+
)
|
728 |
+
combined_attention_mask = (
|
729 |
+
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
|
730 |
+
)
|
731 |
+
|
732 |
+
return combined_attention_mask
|
733 |
+
|
734 |
+
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
|
735 |
+
def forward(
|
736 |
+
self,
|
737 |
+
input_ids: torch.LongTensor = None,
|
738 |
+
attention_mask: Optional[torch.Tensor] = None,
|
739 |
+
position_ids: Optional[torch.LongTensor] = None,
|
740 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
741 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
742 |
+
use_cache: Optional[bool] = None,
|
743 |
+
output_attentions: Optional[bool] = None,
|
744 |
+
output_hidden_states: Optional[bool] = None,
|
745 |
+
return_dict: Optional[bool] = None,
|
746 |
+
offload_cache_to_cpu: Optional[bool] = None,
|
747 |
+
use_flash: Optional[bool] = None,
|
748 |
+
cache_top_k: Optional[int] = None,
|
749 |
+
mem_freq: Optional[int] = None
|
750 |
+
) -> Union[Tuple, BaseModelOutputWithPast]:
|
751 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
752 |
+
output_hidden_states = (
|
753 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
754 |
+
)
|
755 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
756 |
+
|
757 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
758 |
+
|
759 |
+
# retrieve input_ids and inputs_embeds
|
760 |
+
is_mem = None
|
761 |
+
if input_ids is not None and inputs_embeds is not None:
|
762 |
+
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
|
763 |
+
elif input_ids is not None:
|
764 |
+
batch_size, seq_length = input_ids.shape
|
765 |
+
if self.config.mem_id is not None:
|
766 |
+
with torch.no_grad():
|
767 |
+
is_mem = input_ids == self.config.mem_id
|
768 |
+
elif inputs_embeds is not None:
|
769 |
+
batch_size, seq_length, _ = inputs_embeds.shape
|
770 |
+
if self.config.mem_id is not None:
|
771 |
+
raise NotImplementedError
|
772 |
+
else:
|
773 |
+
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
|
774 |
+
|
775 |
+
seq_length_with_past = seq_length
|
776 |
+
past_key_values_length = 0
|
777 |
+
|
778 |
+
if past_key_values is not None:
|
779 |
+
if is_mem is not None:
|
780 |
+
pass
|
781 |
+
#raise NotImplementedError
|
782 |
+
past_key_values_length = past_key_values[0][0].shape[2]
|
783 |
+
if len(past_key_values[0]) > 2:
|
784 |
+
past_key_values_length += past_key_values[0][3].shape[2] * past_key_values[0][3].shape[3]
|
785 |
+
seq_length_with_past = seq_length_with_past + past_key_values_length
|
786 |
+
|
787 |
+
if position_ids is None:
|
788 |
+
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
789 |
+
position_ids = torch.arange(
|
790 |
+
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
|
791 |
+
)
|
792 |
+
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
|
793 |
+
else:
|
794 |
+
position_ids = position_ids.view(-1, seq_length).long()
|
795 |
+
|
796 |
+
if inputs_embeds is None:
|
797 |
+
inputs_embeds = self.embed_tokens(input_ids)
|
798 |
+
# embed positions
|
799 |
+
if attention_mask is None:
|
800 |
+
attention_mask = torch.ones(
|
801 |
+
(batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
|
802 |
+
)
|
803 |
+
attention_mask = self._prepare_decoder_attention_mask(
|
804 |
+
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
|
805 |
+
)
|
806 |
+
|
807 |
+
last_section_mask = None
|
808 |
+
if is_mem is not None and not use_flash:
|
809 |
+
is_mem = is_mem.unsqueeze(1).unsqueeze(2)
|
810 |
+
current_len = input_ids.shape[1]
|
811 |
+
mem_ids = torch.where(attention_mask[..., -current_len:] < -1, 0, torch.cumsum(is_mem, -1) - is_mem.int())
|
812 |
+
last_section_mask = torch.amax(mem_ids, -1, keepdim=True) == mem_ids
|
813 |
+
attention_mask[..., -current_len:].masked_fill_(last_section_mask & is_mem, torch.tensor(torch.finfo(inputs_embeds.dtype).min, device=inputs_embeds.device))
|
814 |
+
last_section_mask.logical_and_(attention_mask[..., -current_len:] > -1)
|
815 |
+
is_mem = is_mem.logical_and(attention_mask[..., -current_len:] > -1)
|
816 |
+
|
817 |
+
|
818 |
+
hidden_states = inputs_embeds
|
819 |
+
|
820 |
+
if self.gradient_checkpointing and self.training:
|
821 |
+
if use_cache:
|
822 |
+
logger.warning_once(
|
823 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
824 |
+
)
|
825 |
+
use_cache = False
|
826 |
+
|
827 |
+
# decoder layers
|
828 |
+
all_hidden_states = () if output_hidden_states else None
|
829 |
+
all_self_attns = () if output_attentions else None
|
830 |
+
next_decoder_cache = () if use_cache else None
|
831 |
+
|
832 |
+
for idx, decoder_layer in enumerate(self.layers):
|
833 |
+
if output_hidden_states:
|
834 |
+
all_hidden_states += (hidden_states,)
|
835 |
+
|
836 |
+
past_key_value = past_key_values[idx] if past_key_values is not None else None
|
837 |
+
|
838 |
+
if self.gradient_checkpointing and self.training:
|
839 |
+
|
840 |
+
def create_custom_forward(module):
|
841 |
+
def custom_forward(*inputs):
|
842 |
+
# None for past_key_value
|
843 |
+
return module(*inputs, output_attentions, None)
|
844 |
+
|
845 |
+
return custom_forward
|
846 |
+
|
847 |
+
layer_outputs = torch.utils.checkpoint.checkpoint(
|
848 |
+
create_custom_forward(decoder_layer),
|
849 |
+
hidden_states,
|
850 |
+
attention_mask,
|
851 |
+
position_ids,
|
852 |
+
None,
|
853 |
+
is_mem,
|
854 |
+
last_section_mask,
|
855 |
+
offload_cache_to_cpu,
|
856 |
+
use_flash,
|
857 |
+
cache_top_k,
|
858 |
+
mem_freq
|
859 |
+
)
|
860 |
+
else:
|
861 |
+
layer_outputs = decoder_layer(
|
862 |
+
hidden_states,
|
863 |
+
attention_mask=attention_mask,
|
864 |
+
position_ids=position_ids,
|
865 |
+
past_key_value=past_key_value,
|
866 |
+
output_attentions=output_attentions,
|
867 |
+
use_cache=use_cache,
|
868 |
+
is_mem=is_mem,
|
869 |
+
last_section_mask=last_section_mask,
|
870 |
+
offload_cache_to_cpu=offload_cache_to_cpu,
|
871 |
+
use_flash=use_flash,
|
872 |
+
cache_top_k=cache_top_k,
|
873 |
+
mem_freq=mem_freq,
|
874 |
+
)
|
875 |
+
|
876 |
+
hidden_states = layer_outputs[0]
|
877 |
+
|
878 |
+
if use_cache:
|
879 |
+
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
|
880 |
+
|
881 |
+
if output_attentions:
|
882 |
+
all_self_attns += (layer_outputs[1],)
|
883 |
+
|
884 |
+
hidden_states = self.norm(hidden_states)
|
885 |
+
|
886 |
+
# add hidden states from the last decoder layer
|
887 |
+
if output_hidden_states:
|
888 |
+
all_hidden_states += (hidden_states,)
|
889 |
+
|
890 |
+
next_cache = next_decoder_cache if use_cache else None
|
891 |
+
if not return_dict:
|
892 |
+
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
893 |
+
return BaseModelOutputWithPast(
|
894 |
+
last_hidden_state=hidden_states,
|
895 |
+
past_key_values=next_cache,
|
896 |
+
hidden_states=all_hidden_states,
|
897 |
+
attentions=all_self_attns,
|
898 |
+
)
|
899 |
+
|
900 |
+
|
901 |
+
class LlamaForCausalLM(LlamaPreTrainedModel):
|
902 |
+
def __init__(self, config):
|
903 |
+
super().__init__(config)
|
904 |
+
self.model = LlamaModel(config)
|
905 |
+
|
906 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
907 |
+
|
908 |
+
self.auto_insert_landmarks = False
|
909 |
+
self.always_use_flash = False
|
910 |
+
|
911 |
+
# Initialize weights and apply final processing
|
912 |
+
self.post_init()
|
913 |
+
|
914 |
+
def get_input_embeddings(self):
|
915 |
+
return self.model.embed_tokens
|
916 |
+
|
917 |
+
def set_input_embeddings(self, value):
|
918 |
+
self.model.embed_tokens = value
|
919 |
+
|
920 |
+
def get_output_embeddings(self):
|
921 |
+
return self.lm_head
|
922 |
+
|
923 |
+
def set_output_embeddings(self, new_embeddings):
|
924 |
+
self.lm_head = new_embeddings
|
925 |
+
|
926 |
+
def set_decoder(self, decoder):
|
927 |
+
self.model = decoder
|
928 |
+
|
929 |
+
def get_decoder(self):
|
930 |
+
return self.model
|
931 |
+
|
932 |
+
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
|
933 |
+
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
|
934 |
+
def forward(
|
935 |
+
self,
|
936 |
+
input_ids: torch.LongTensor = None,
|
937 |
+
attention_mask: Optional[torch.Tensor] = None,
|
938 |
+
position_ids: Optional[torch.LongTensor] = None,
|
939 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
940 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
941 |
+
labels: Optional[torch.LongTensor] = None,
|
942 |
+
use_cache: Optional[bool] = None,
|
943 |
+
output_attentions: Optional[bool] = None,
|
944 |
+
output_hidden_states: Optional[bool] = None,
|
945 |
+
return_dict: Optional[bool] = None,
|
946 |
+
offload_cache_to_cpu: Optional[bool] = None,
|
947 |
+
use_flash: Optional[bool] = None,
|
948 |
+
cache_top_k: Optional[int] = None,
|
949 |
+
max_chunk_length: Optional[int] = 0,
|
950 |
+
mem_freq: Optional[int] = None,
|
951 |
+
drop_last_logit_if_mem: Optional[bool] = False,
|
952 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
953 |
+
r"""
|
954 |
+
Args:
|
955 |
+
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
956 |
+
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
957 |
+
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
958 |
+
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
959 |
+
|
960 |
+
Returns:
|
961 |
+
|
962 |
+
Example:
|
963 |
+
|
964 |
+
```python
|
965 |
+
>>> from transformers import AutoTokenizer, LlamaForCausalLM
|
966 |
+
|
967 |
+
>>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
|
968 |
+
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
|
969 |
+
|
970 |
+
>>> prompt = "Hey, are you consciours? Can you talk to me?"
|
971 |
+
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
972 |
+
|
973 |
+
>>> # Generate
|
974 |
+
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
975 |
+
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
976 |
+
"Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
|
977 |
+
```"""
|
978 |
+
|
979 |
+
use_flash = use_flash if use_flash is not None else self.always_use_flash
|
980 |
+
|
981 |
+
if self.auto_insert_landmarks:
|
982 |
+
mem_freq = self.config.mem_freq
|
983 |
+
assert self.config.mem_freq is not None
|
984 |
+
block_size = self.config.mem_freq + 1
|
985 |
+
input_ids = input_ids.view(input_ids.shape[0], -1, block_size - 1)
|
986 |
+
input_ids = torch.cat((input_ids, input_ids.new_full((input_ids.shape[0], input_ids.shape[1], 1), self.config.mem_id)), dim=-1)
|
987 |
+
input_ids = input_ids.view(input_ids.shape[0], -1)
|
988 |
+
if attention_mask is not None:
|
989 |
+
attention_mask = attention_mask.view(attention_mask.shape[0], -1, block_size - 1)
|
990 |
+
attention_mask = torch.cat((attention_mask, attention_mask.new_ones((attention_mask.shape[0], attention_mask.shape[1], 1))), dim=-1)
|
991 |
+
attention_mask = attention_mask.view(attention_mask.shape[0], -1)
|
992 |
+
|
993 |
+
|
994 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
995 |
+
output_hidden_states = (
|
996 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
997 |
+
)
|
998 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
999 |
+
|
1000 |
+
if max_chunk_length == 0:
|
1001 |
+
if cache_top_k is not None:
|
1002 |
+
max_chunk_length = self.config.train_context_length - self.config.train_context_length % (mem_freq + 1) - (cache_top_k + 1) * (mem_freq + 1)
|
1003 |
+
if max_chunk_length <= 0:
|
1004 |
+
raise ValueError("K is too large for this model.")
|
1005 |
+
else:
|
1006 |
+
max_chunk_length = None
|
1007 |
+
|
1008 |
+
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
1009 |
+
window_len = max_chunk_length or input_ids.shape[1]
|
1010 |
+
if use_flash:
|
1011 |
+
assert window_len % (mem_freq + 1) == 0
|
1012 |
+
last_logits = None
|
1013 |
+
for step, idx in enumerate(range(0, input_ids.shape[1], window_len)):
|
1014 |
+
if idx >= 1:
|
1015 |
+
if output_attentions or output_hidden_states:
|
1016 |
+
raise NotImplementedError
|
1017 |
+
if not use_cache:
|
1018 |
+
raise NotImplementedError
|
1019 |
+
outputs = self.model(
|
1020 |
+
input_ids=input_ids[:, idx:idx + window_len],
|
1021 |
+
attention_mask=attention_mask[:, :idx + window_len + attention_mask.shape[1] - input_ids.shape[1]] if attention_mask is not None else None,
|
1022 |
+
position_ids=position_ids[:, idx:idx + window_len] if position_ids is not None else None,
|
1023 |
+
past_key_values=past_key_values,
|
1024 |
+
inputs_embeds=inputs_embeds[:, idx:idx + window_len] if inputs_embeds is not None else None,
|
1025 |
+
use_cache=use_cache,
|
1026 |
+
output_attentions=output_attentions,
|
1027 |
+
output_hidden_states=output_hidden_states,
|
1028 |
+
return_dict=return_dict,
|
1029 |
+
offload_cache_to_cpu=offload_cache_to_cpu,
|
1030 |
+
use_flash=(use_flash or self.auto_insert_landmarks),
|
1031 |
+
cache_top_k=cache_top_k,
|
1032 |
+
mem_freq=mem_freq,
|
1033 |
+
)
|
1034 |
+
past_key_values = outputs[1]
|
1035 |
+
if last_logits is not None:
|
1036 |
+
last_logits = torch.cat((last_logits, outputs[0]), dim=-2)
|
1037 |
+
last_logits = outputs[0]
|
1038 |
+
|
1039 |
+
hidden_states = last_logits
|
1040 |
+
if self.auto_insert_landmarks:
|
1041 |
+
block_size = self.config.mem_freq + 1
|
1042 |
+
hidden_states = hidden_states.reshape(hidden_states.shape[0], hidden_states.shape[1] // block_size, block_size, hidden_states.shape[2])
|
1043 |
+
hidden_states = hidden_states[:, :, :block_size - 1]
|
1044 |
+
hidden_states = hidden_states.reshape(hidden_states.shape[0], -1, hidden_states.shape[3])
|
1045 |
+
if drop_last_logit_if_mem:
|
1046 |
+
is_any_mem = (input_ids[:, -1] == self.config.mem_id).any()
|
1047 |
+
are_all_mem = (input_ids[:, -1] == self.config.mem_id).all()
|
1048 |
+
assert is_any_mem == are_all_mem
|
1049 |
+
if is_any_mem:
|
1050 |
+
hidden_states = hidden_states[:, :-1]
|
1051 |
+
logits = self.lm_head(hidden_states)
|
1052 |
+
|
1053 |
+
loss = None
|
1054 |
+
if labels is not None:
|
1055 |
+
# Shift so that tokens < n predict n
|
1056 |
+
shift_logits = logits[..., :-1, :].contiguous()
|
1057 |
+
shift_labels = labels[..., 1:].contiguous()
|
1058 |
+
# Flatten the tokens
|
1059 |
+
loss_fct = CrossEntropyLoss()
|
1060 |
+
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
1061 |
+
shift_labels = shift_labels.view(-1)
|
1062 |
+
# Enable model parallelism
|
1063 |
+
shift_labels = shift_labels.to(shift_logits.device)
|
1064 |
+
loss = loss_fct(shift_logits, shift_labels)
|
1065 |
+
|
1066 |
+
if not return_dict:
|
1067 |
+
output = (logits,) + outputs[1:]
|
1068 |
+
return (loss,) + output if loss is not None else output
|
1069 |
+
|
1070 |
+
return CausalLMOutputWithPast(
|
1071 |
+
loss=loss,
|
1072 |
+
logits=logits,
|
1073 |
+
past_key_values=outputs.past_key_values,
|
1074 |
+
hidden_states=outputs.hidden_states,
|
1075 |
+
attentions=outputs.attentions,
|
1076 |
+
)
|
1077 |
+
|
1078 |
+
def set_mem_id(self, mem_id):
|
1079 |
+
if self.config.mem_id is not None:
|
1080 |
+
assert mem_id == self.config.mem_id, "Chanigng mem_id can break the model. If you really intend to do this, manually disable this check"
|
1081 |
+
self.config.mem_id = mem_id
|
1082 |
+
|
1083 |
+
def enable_landmark_insertion(self):
|
1084 |
+
self.auto_insert_landmarks = True
|
1085 |
+
|
1086 |
+
def enable_flash(self):
|
1087 |
+
self.always_use_flash = True
|
1088 |
+
|
1089 |
+
def prepare_inputs_for_generation(
|
1090 |
+
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
|
1091 |
+
):
|
1092 |
+
total_len = input_ids.shape[1]
|
1093 |
+
if past_key_values:
|
1094 |
+
prev_len = input_ids.shape[1] - 1
|
1095 |
+
use_flash = False if kwargs.get("use_flash") is not None else None
|
1096 |
+
else:
|
1097 |
+
prev_len = 0
|
1098 |
+
use_flash = kwargs.get("use_flash")
|
1099 |
+
|
1100 |
+
position_ids = kwargs.get("position_ids", None)
|
1101 |
+
|
1102 |
+
mem_freq = kwargs.get("mem_freq") or self.config.mem_freq
|
1103 |
+
|
1104 |
+
if mem_freq is not None:
|
1105 |
+
if position_ids is not None:
|
1106 |
+
raise NotImplementedError
|
1107 |
+
T = input_ids.shape[1]
|
1108 |
+
|
1109 |
+
prev_incomplete_len = prev_len % mem_freq
|
1110 |
+
prev_complete_len = prev_len - prev_incomplete_len
|
1111 |
+
incomplete_len = total_len % mem_freq
|
1112 |
+
new_full_len = total_len - prev_complete_len - incomplete_len
|
1113 |
+
|
1114 |
+
prev_input, input_ids_with_mem, input_ids_without_mem = torch.split(input_ids, (prev_complete_len, new_full_len, incomplete_len), dim=-1)
|
1115 |
+
|
1116 |
+
bsz, q_len = input_ids.size()
|
1117 |
+
input_ids_with_mem = input_ids_with_mem.view(bsz, -1, mem_freq)
|
1118 |
+
input_ids_with_mem = torch.cat(
|
1119 |
+
(
|
1120 |
+
input_ids_with_mem,
|
1121 |
+
input_ids_with_mem.new_full((bsz, input_ids_with_mem.shape[1], 1), self.config.mem_id)
|
1122 |
+
),
|
1123 |
+
dim=-1
|
1124 |
+
).view(bsz, -1)
|
1125 |
+
input_ids = torch.cat((prev_input, input_ids_with_mem, input_ids_without_mem), dim=-1)
|
1126 |
+
if attention_mask is not None:
|
1127 |
+
attention_mask_with_mem, attention_mask_without_mem = torch.split(attention_mask, (prev_complete_len + new_full_len, incomplete_len), dim=-1)
|
1128 |
+
attention_mask_with_mem = attention_mask_with_mem.view(bsz, -1, mem_freq)
|
1129 |
+
attention_mask_with_mem = torch.cat(
|
1130 |
+
(
|
1131 |
+
attention_mask_with_mem,
|
1132 |
+
attention_mask_with_mem.new_ones((bsz, attention_mask_with_mem.shape[1], 1))
|
1133 |
+
),
|
1134 |
+
dim=-1
|
1135 |
+
).view(bsz, -1)
|
1136 |
+
attention_mask = torch.cat((attention_mask_with_mem, attention_mask_without_mem), dim=-1)
|
1137 |
+
|
1138 |
+
|
1139 |
+
input_ids = input_ids[:, prev_len:]
|
1140 |
+
if attention_mask is not None and position_ids is None:
|
1141 |
+
# create position_ids on the fly for batch generation
|
1142 |
+
position_ids = attention_mask.long().cumsum(-1) - 1
|
1143 |
+
position_ids.masked_fill_(attention_mask == 0, 1)
|
1144 |
+
position_ids = position_ids[:, -input_ids.shape[1]:].unsqueeze(-1)
|
1145 |
+
|
1146 |
+
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
1147 |
+
if inputs_embeds is not None and past_key_values is None and mem_freq is None:
|
1148 |
+
model_inputs = {"inputs_embeds": inputs_embeds}
|
1149 |
+
else:
|
1150 |
+
model_inputs = {"input_ids": input_ids}
|
1151 |
+
|
1152 |
+
model_inputs.update(
|
1153 |
+
{
|
1154 |
+
"position_ids": position_ids,
|
1155 |
+
"past_key_values": past_key_values,
|
1156 |
+
"use_cache": kwargs.get("use_cache"),
|
1157 |
+
"attention_mask": attention_mask,
|
1158 |
+
"offload_cache_to_cpu": kwargs.get("offload_cache_to_cpu"),
|
1159 |
+
"use_flash": use_flash,
|
1160 |
+
"cache_top_k": kwargs.get("cache_top_k"),
|
1161 |
+
"max_chunk_length": kwargs.get("max_chunk_length", 0),
|
1162 |
+
"mem_freq": mem_freq,
|
1163 |
+
"drop_last_logit_if_mem": True,
|
1164 |
+
}
|
1165 |
+
)
|
1166 |
+
return model_inputs
|
1167 |
+
|
1168 |
+
@staticmethod
|
1169 |
+
def _reorder_cache(past_key_values, beam_idx):
|
1170 |
+
reordered_past = ()
|
1171 |
+
for layer_past in past_key_values:
|
1172 |
+
reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
|
1173 |
+
return reordered_past
|
1174 |
+
|
1175 |
+
|
1176 |
+
@add_start_docstrings(
|
1177 |
+
"""
|
1178 |
+
The LLaMa Model transformer with a sequence classification head on top (linear layer).
|
1179 |
+
|
1180 |
+
[`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
|
1181 |
+
(e.g. GPT-2) do.
|
1182 |
+
|
1183 |
+
Since it does classification on the last token, it requires to know the position of the last token. If a
|
1184 |
+
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
|
1185 |
+
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
|
1186 |
+
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
|
1187 |
+
each row of the batch).
|
1188 |
+
""",
|
1189 |
+
LLAMA_START_DOCSTRING,
|
1190 |
+
)
|
1191 |
+
class LlamaForSequenceClassification(LlamaPreTrainedModel):
|
1192 |
+
_keys_to_ignore_on_load_missing = [r"lm_head.weight"]
|
1193 |
+
|
1194 |
+
def __init__(self, config):
|
1195 |
+
super().__init__(config)
|
1196 |
+
self.num_labels = config.num_labels
|
1197 |
+
self.model = LlamaModel(config)
|
1198 |
+
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
|
1199 |
+
|
1200 |
+
# Initialize weights and apply final processing
|
1201 |
+
self.post_init()
|
1202 |
+
|
1203 |
+
def get_input_embeddings(self):
|
1204 |
+
return self.model.embed_tokens
|
1205 |
+
|
1206 |
+
def set_input_embeddings(self, value):
|
1207 |
+
self.model.embed_tokens = value
|
1208 |
+
|
1209 |
+
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
|
1210 |
+
def forward(
|
1211 |
+
self,
|
1212 |
+
input_ids: torch.LongTensor = None,
|
1213 |
+
attention_mask: Optional[torch.Tensor] = None,
|
1214 |
+
position_ids: Optional[torch.LongTensor] = None,
|
1215 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
1216 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
1217 |
+
labels: Optional[torch.LongTensor] = None,
|
1218 |
+
use_cache: Optional[bool] = None,
|
1219 |
+
output_attentions: Optional[bool] = None,
|
1220 |
+
output_hidden_states: Optional[bool] = None,
|
1221 |
+
return_dict: Optional[bool] = None,
|
1222 |
+
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
|
1223 |
+
r"""
|
1224 |
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
1225 |
+
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
1226 |
+
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
1227 |
+
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
1228 |
+
"""
|
1229 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
1230 |
+
|
1231 |
+
transformer_outputs = self.model(
|
1232 |
+
input_ids,
|
1233 |
+
attention_mask=attention_mask,
|
1234 |
+
position_ids=position_ids,
|
1235 |
+
past_key_values=past_key_values,
|
1236 |
+
inputs_embeds=inputs_embeds,
|
1237 |
+
use_cache=use_cache,
|
1238 |
+
output_attentions=output_attentions,
|
1239 |
+
output_hidden_states=output_hidden_states,
|
1240 |
+
return_dict=return_dict,
|
1241 |
+
)
|
1242 |
+
hidden_states = transformer_outputs[0]
|
1243 |
+
logits = self.score(hidden_states)
|
1244 |
+
|
1245 |
+
if input_ids is not None:
|
1246 |
+
batch_size = input_ids.shape[0]
|
1247 |
+
else:
|
1248 |
+
batch_size = inputs_embeds.shape[0]
|
1249 |
+
|
1250 |
+
if self.config.pad_token_id is None and batch_size != 1:
|
1251 |
+
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
|
1252 |
+
if self.config.pad_token_id is None:
|
1253 |
+
sequence_lengths = -1
|
1254 |
+
else:
|
1255 |
+
if input_ids is not None:
|
1256 |
+
sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
|
1257 |
+
else:
|
1258 |
+
sequence_lengths = -1
|
1259 |
+
|
1260 |
+
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
1261 |
+
|
1262 |
+
loss = None
|
1263 |
+
if labels is not None:
|
1264 |
+
labels = labels.to(logits.device)
|
1265 |
+
if self.config.problem_type is None:
|
1266 |
+
if self.num_labels == 1:
|
1267 |
+
self.config.problem_type = "regression"
|
1268 |
+
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
1269 |
+
self.config.problem_type = "single_label_classification"
|
1270 |
+
else:
|
1271 |
+
self.config.problem_type = "multi_label_classification"
|
1272 |
+
|
1273 |
+
if self.config.problem_type == "regression":
|
1274 |
+
loss_fct = MSELoss()
|
1275 |
+
if self.num_labels == 1:
|
1276 |
+
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
1277 |
+
else:
|
1278 |
+
loss = loss_fct(pooled_logits, labels)
|
1279 |
+
elif self.config.problem_type == "single_label_classification":
|
1280 |
+
loss_fct = CrossEntropyLoss()
|
1281 |
+
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
|
1282 |
+
elif self.config.problem_type == "multi_label_classification":
|
1283 |
+
loss_fct = BCEWithLogitsLoss()
|
1284 |
+
loss = loss_fct(pooled_logits, labels)
|
1285 |
+
if not return_dict:
|
1286 |
+
output = (pooled_logits,) + transformer_outputs[1:]
|
1287 |
+
return ((loss,) + output) if loss is not None else output
|
1288 |
+
|
1289 |
+
return SequenceClassifierOutputWithPast(
|
1290 |
+
loss=loss,
|
1291 |
+
logits=pooled_logits,
|
1292 |
+
past_key_values=transformer_outputs.past_key_values,
|
1293 |
+
hidden_states=transformer_outputs.hidden_states,
|
1294 |
+
attentions=transformer_outputs.attentions,
|
1295 |
+
)
|
code/llama_orig.py
ADDED
@@ -0,0 +1,888 @@
|
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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 |
+
from typing import List, Optional, Tuple, Union
|
23 |
+
|
24 |
+
import torch
|
25 |
+
import torch.utils.checkpoint
|
26 |
+
from torch import nn
|
27 |
+
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
28 |
+
|
29 |
+
from ...activations import ACT2FN
|
30 |
+
from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
|
31 |
+
from ...modeling_utils import PreTrainedModel
|
32 |
+
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
|
33 |
+
from .configuration_llama import LlamaConfig
|
34 |
+
|
35 |
+
|
36 |
+
logger = logging.get_logger(__name__)
|
37 |
+
|
38 |
+
_CONFIG_FOR_DOC = "LlamaConfig"
|
39 |
+
|
40 |
+
|
41 |
+
# Copied from transformers.models.bart.modeling_bart._make_causal_mask
|
42 |
+
def _make_causal_mask(
|
43 |
+
input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
|
44 |
+
):
|
45 |
+
"""
|
46 |
+
Make causal mask used for bi-directional self-attention.
|
47 |
+
"""
|
48 |
+
bsz, tgt_len = input_ids_shape
|
49 |
+
mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
|
50 |
+
mask_cond = torch.arange(mask.size(-1), device=device)
|
51 |
+
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
|
52 |
+
mask = mask.to(dtype)
|
53 |
+
|
54 |
+
if past_key_values_length > 0:
|
55 |
+
mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
|
56 |
+
return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
|
57 |
+
|
58 |
+
|
59 |
+
# Copied from transformers.models.bart.modeling_bart._expand_mask
|
60 |
+
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
|
61 |
+
"""
|
62 |
+
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
|
63 |
+
"""
|
64 |
+
bsz, src_len = mask.size()
|
65 |
+
tgt_len = tgt_len if tgt_len is not None else src_len
|
66 |
+
|
67 |
+
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
|
68 |
+
|
69 |
+
inverted_mask = 1.0 - expanded_mask
|
70 |
+
|
71 |
+
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
|
72 |
+
|
73 |
+
|
74 |
+
class LlamaRMSNorm(nn.Module):
|
75 |
+
def __init__(self, hidden_size, eps=1e-6):
|
76 |
+
"""
|
77 |
+
LlamaRMSNorm is equivalent to T5LayerNorm
|
78 |
+
"""
|
79 |
+
super().__init__()
|
80 |
+
self.weight = nn.Parameter(torch.ones(hidden_size))
|
81 |
+
self.variance_epsilon = eps
|
82 |
+
|
83 |
+
def forward(self, hidden_states):
|
84 |
+
input_dtype = hidden_states.dtype
|
85 |
+
variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
|
86 |
+
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
87 |
+
|
88 |
+
return (self.weight * hidden_states).to(input_dtype)
|
89 |
+
|
90 |
+
|
91 |
+
class LlamaRotaryEmbedding(torch.nn.Module):
|
92 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
|
93 |
+
super().__init__()
|
94 |
+
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
|
95 |
+
self.register_buffer("inv_freq", inv_freq)
|
96 |
+
|
97 |
+
# Build here to make `torch.jit.trace` work.
|
98 |
+
self.max_seq_len_cached = max_position_embeddings
|
99 |
+
t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
|
100 |
+
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
|
101 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
102 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
103 |
+
dtype = torch.get_default_dtype()
|
104 |
+
self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
|
105 |
+
self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
|
106 |
+
|
107 |
+
def forward(self, x, seq_len=None):
|
108 |
+
# x: [bs, num_attention_heads, seq_len, head_size]
|
109 |
+
# This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
|
110 |
+
if seq_len > self.max_seq_len_cached:
|
111 |
+
self.max_seq_len_cached = seq_len
|
112 |
+
t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
|
113 |
+
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
|
114 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
115 |
+
emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
|
116 |
+
self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(x.dtype), persistent=False)
|
117 |
+
self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(x.dtype), persistent=False)
|
118 |
+
return (
|
119 |
+
self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
|
120 |
+
self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
|
121 |
+
)
|
122 |
+
|
123 |
+
|
124 |
+
def rotate_half(x):
|
125 |
+
"""Rotates half the hidden dims of the input."""
|
126 |
+
x1 = x[..., : x.shape[-1] // 2]
|
127 |
+
x2 = x[..., x.shape[-1] // 2 :]
|
128 |
+
return torch.cat((-x2, x1), dim=-1)
|
129 |
+
|
130 |
+
|
131 |
+
def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
|
132 |
+
# The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
|
133 |
+
cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
|
134 |
+
sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
|
135 |
+
cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
|
136 |
+
sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
|
137 |
+
q_embed = (q * cos) + (rotate_half(q) * sin)
|
138 |
+
k_embed = (k * cos) + (rotate_half(k) * sin)
|
139 |
+
return q_embed, k_embed
|
140 |
+
|
141 |
+
|
142 |
+
class LlamaMLP(nn.Module):
|
143 |
+
def __init__(
|
144 |
+
self,
|
145 |
+
hidden_size: int,
|
146 |
+
intermediate_size: int,
|
147 |
+
hidden_act: str,
|
148 |
+
):
|
149 |
+
super().__init__()
|
150 |
+
self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
|
151 |
+
self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
|
152 |
+
self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
|
153 |
+
self.act_fn = ACT2FN[hidden_act]
|
154 |
+
|
155 |
+
def forward(self, x):
|
156 |
+
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
157 |
+
|
158 |
+
|
159 |
+
class LlamaAttention(nn.Module):
|
160 |
+
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
161 |
+
|
162 |
+
def __init__(self, config: LlamaConfig):
|
163 |
+
super().__init__()
|
164 |
+
self.config = config
|
165 |
+
self.hidden_size = config.hidden_size
|
166 |
+
self.num_heads = config.num_attention_heads
|
167 |
+
self.head_dim = self.hidden_size // self.num_heads
|
168 |
+
self.max_position_embeddings = config.max_position_embeddings
|
169 |
+
|
170 |
+
if (self.head_dim * self.num_heads) != self.hidden_size:
|
171 |
+
raise ValueError(
|
172 |
+
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
|
173 |
+
f" and `num_heads`: {self.num_heads})."
|
174 |
+
)
|
175 |
+
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
|
176 |
+
self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
|
177 |
+
self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
|
178 |
+
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
|
179 |
+
self.rotary_emb = LlamaRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
|
180 |
+
|
181 |
+
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
|
182 |
+
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
|
183 |
+
|
184 |
+
def forward(
|
185 |
+
self,
|
186 |
+
hidden_states: torch.Tensor,
|
187 |
+
attention_mask: Optional[torch.Tensor] = None,
|
188 |
+
position_ids: Optional[torch.LongTensor] = None,
|
189 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
190 |
+
output_attentions: bool = False,
|
191 |
+
use_cache: bool = False,
|
192 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
193 |
+
bsz, q_len, _ = hidden_states.size()
|
194 |
+
|
195 |
+
query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
196 |
+
key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
197 |
+
value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
198 |
+
|
199 |
+
kv_seq_len = key_states.shape[-2]
|
200 |
+
if past_key_value is not None:
|
201 |
+
kv_seq_len += past_key_value[0].shape[-2]
|
202 |
+
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
203 |
+
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
204 |
+
# [bsz, nh, t, hd]
|
205 |
+
|
206 |
+
if past_key_value is not None:
|
207 |
+
# reuse k, v, self_attention
|
208 |
+
key_states = torch.cat([past_key_value[0], key_states], dim=2)
|
209 |
+
value_states = torch.cat([past_key_value[1], value_states], dim=2)
|
210 |
+
|
211 |
+
past_key_value = (key_states, value_states) if use_cache else None
|
212 |
+
|
213 |
+
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
214 |
+
|
215 |
+
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
216 |
+
raise ValueError(
|
217 |
+
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
|
218 |
+
f" {attn_weights.size()}"
|
219 |
+
)
|
220 |
+
|
221 |
+
if attention_mask is not None:
|
222 |
+
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
223 |
+
raise ValueError(
|
224 |
+
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
|
225 |
+
)
|
226 |
+
attn_weights = attn_weights + attention_mask
|
227 |
+
attn_weights = torch.max(
|
228 |
+
attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min, device=attn_weights.device)
|
229 |
+
)
|
230 |
+
|
231 |
+
# upcast attention to fp32
|
232 |
+
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
|
233 |
+
attn_output = torch.matmul(attn_weights, value_states)
|
234 |
+
|
235 |
+
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
236 |
+
raise ValueError(
|
237 |
+
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
238 |
+
f" {attn_output.size()}"
|
239 |
+
)
|
240 |
+
|
241 |
+
attn_output = attn_output.transpose(1, 2)
|
242 |
+
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
243 |
+
|
244 |
+
attn_output = self.o_proj(attn_output)
|
245 |
+
|
246 |
+
if not output_attentions:
|
247 |
+
attn_weights = None
|
248 |
+
|
249 |
+
return attn_output, attn_weights, past_key_value
|
250 |
+
|
251 |
+
|
252 |
+
class LlamaDecoderLayer(nn.Module):
|
253 |
+
def __init__(self, config: LlamaConfig):
|
254 |
+
super().__init__()
|
255 |
+
self.hidden_size = config.hidden_size
|
256 |
+
self.self_attn = LlamaAttention(config=config)
|
257 |
+
self.mlp = LlamaMLP(
|
258 |
+
hidden_size=self.hidden_size,
|
259 |
+
intermediate_size=config.intermediate_size,
|
260 |
+
hidden_act=config.hidden_act,
|
261 |
+
)
|
262 |
+
self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
263 |
+
self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
264 |
+
|
265 |
+
def forward(
|
266 |
+
self,
|
267 |
+
hidden_states: torch.Tensor,
|
268 |
+
attention_mask: Optional[torch.Tensor] = None,
|
269 |
+
position_ids: Optional[torch.LongTensor] = None,
|
270 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
271 |
+
output_attentions: Optional[bool] = False,
|
272 |
+
use_cache: Optional[bool] = False,
|
273 |
+
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
274 |
+
"""
|
275 |
+
Args:
|
276 |
+
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
277 |
+
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
|
278 |
+
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
|
279 |
+
output_attentions (`bool`, *optional*):
|
280 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
281 |
+
returned tensors for more detail.
|
282 |
+
use_cache (`bool`, *optional*):
|
283 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
284 |
+
(see `past_key_values`).
|
285 |
+
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
286 |
+
"""
|
287 |
+
|
288 |
+
residual = hidden_states
|
289 |
+
|
290 |
+
hidden_states = self.input_layernorm(hidden_states)
|
291 |
+
|
292 |
+
# Self Attention
|
293 |
+
hidden_states, self_attn_weights, present_key_value = self.self_attn(
|
294 |
+
hidden_states=hidden_states,
|
295 |
+
attention_mask=attention_mask,
|
296 |
+
position_ids=position_ids,
|
297 |
+
past_key_value=past_key_value,
|
298 |
+
output_attentions=output_attentions,
|
299 |
+
use_cache=use_cache,
|
300 |
+
)
|
301 |
+
hidden_states = residual + hidden_states
|
302 |
+
|
303 |
+
# Fully Connected
|
304 |
+
residual = hidden_states
|
305 |
+
hidden_states = self.post_attention_layernorm(hidden_states)
|
306 |
+
hidden_states = self.mlp(hidden_states)
|
307 |
+
hidden_states = residual + hidden_states
|
308 |
+
|
309 |
+
outputs = (hidden_states,)
|
310 |
+
|
311 |
+
if output_attentions:
|
312 |
+
outputs += (self_attn_weights,)
|
313 |
+
|
314 |
+
if use_cache:
|
315 |
+
outputs += (present_key_value,)
|
316 |
+
|
317 |
+
return outputs
|
318 |
+
|
319 |
+
|
320 |
+
LLAMA_START_DOCSTRING = r"""
|
321 |
+
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
322 |
+
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
323 |
+
etc.)
|
324 |
+
|
325 |
+
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
326 |
+
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
327 |
+
and behavior.
|
328 |
+
|
329 |
+
Parameters:
|
330 |
+
config ([`LlamaConfig`]):
|
331 |
+
Model configuration class with all the parameters of the model. Initializing with a config file does not
|
332 |
+
load the weights associated with the model, only the configuration. Check out the
|
333 |
+
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
334 |
+
"""
|
335 |
+
|
336 |
+
|
337 |
+
@add_start_docstrings(
|
338 |
+
"The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
|
339 |
+
LLAMA_START_DOCSTRING,
|
340 |
+
)
|
341 |
+
class LlamaPreTrainedModel(PreTrainedModel):
|
342 |
+
config_class = LlamaConfig
|
343 |
+
base_model_prefix = "model"
|
344 |
+
supports_gradient_checkpointing = True
|
345 |
+
_no_split_modules = ["LlamaDecoderLayer"]
|
346 |
+
_skip_keys_device_placement = "past_key_values"
|
347 |
+
_keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
|
348 |
+
|
349 |
+
def _init_weights(self, module):
|
350 |
+
std = self.config.initializer_range
|
351 |
+
if isinstance(module, nn.Linear):
|
352 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
353 |
+
if module.bias is not None:
|
354 |
+
module.bias.data.zero_()
|
355 |
+
elif isinstance(module, nn.Embedding):
|
356 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
357 |
+
if module.padding_idx is not None:
|
358 |
+
module.weight.data[module.padding_idx].zero_()
|
359 |
+
|
360 |
+
def _set_gradient_checkpointing(self, module, value=False):
|
361 |
+
if isinstance(module, LlamaModel):
|
362 |
+
module.gradient_checkpointing = value
|
363 |
+
|
364 |
+
|
365 |
+
LLAMA_INPUTS_DOCSTRING = r"""
|
366 |
+
Args:
|
367 |
+
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
368 |
+
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
369 |
+
it.
|
370 |
+
|
371 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
372 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
373 |
+
|
374 |
+
[What are input IDs?](../glossary#input-ids)
|
375 |
+
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
376 |
+
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
377 |
+
|
378 |
+
- 1 for tokens that are **not masked**,
|
379 |
+
- 0 for tokens that are **masked**.
|
380 |
+
|
381 |
+
[What are attention masks?](../glossary#attention-mask)
|
382 |
+
|
383 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
384 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
385 |
+
|
386 |
+
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
|
387 |
+
`past_key_values`).
|
388 |
+
|
389 |
+
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
|
390 |
+
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
|
391 |
+
information on the default strategy.
|
392 |
+
|
393 |
+
- 1 indicates the head is **not masked**,
|
394 |
+
- 0 indicates the head is **masked**.
|
395 |
+
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
396 |
+
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
397 |
+
config.n_positions - 1]`.
|
398 |
+
|
399 |
+
[What are position IDs?](../glossary#position-ids)
|
400 |
+
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
401 |
+
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
|
402 |
+
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
|
403 |
+
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
|
404 |
+
|
405 |
+
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
|
406 |
+
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
|
407 |
+
|
408 |
+
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
|
409 |
+
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
|
410 |
+
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
|
411 |
+
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
412 |
+
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
413 |
+
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
414 |
+
model's internal embedding lookup matrix.
|
415 |
+
use_cache (`bool`, *optional*):
|
416 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
417 |
+
`past_key_values`).
|
418 |
+
output_attentions (`bool`, *optional*):
|
419 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
420 |
+
tensors for more detail.
|
421 |
+
output_hidden_states (`bool`, *optional*):
|
422 |
+
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
423 |
+
more detail.
|
424 |
+
return_dict (`bool`, *optional*):
|
425 |
+
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
426 |
+
"""
|
427 |
+
|
428 |
+
|
429 |
+
@add_start_docstrings(
|
430 |
+
"The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
|
431 |
+
LLAMA_START_DOCSTRING,
|
432 |
+
)
|
433 |
+
class LlamaModel(LlamaPreTrainedModel):
|
434 |
+
"""
|
435 |
+
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
|
436 |
+
|
437 |
+
Args:
|
438 |
+
config: LlamaConfig
|
439 |
+
"""
|
440 |
+
|
441 |
+
def __init__(self, config: LlamaConfig):
|
442 |
+
super().__init__(config)
|
443 |
+
self.padding_idx = config.pad_token_id
|
444 |
+
self.vocab_size = config.vocab_size
|
445 |
+
|
446 |
+
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
447 |
+
self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
|
448 |
+
self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
449 |
+
|
450 |
+
self.gradient_checkpointing = False
|
451 |
+
# Initialize weights and apply final processing
|
452 |
+
self.post_init()
|
453 |
+
|
454 |
+
def get_input_embeddings(self):
|
455 |
+
return self.embed_tokens
|
456 |
+
|
457 |
+
def set_input_embeddings(self, value):
|
458 |
+
self.embed_tokens = value
|
459 |
+
|
460 |
+
# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
|
461 |
+
def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
|
462 |
+
# create causal mask
|
463 |
+
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
464 |
+
combined_attention_mask = None
|
465 |
+
if input_shape[-1] > 1:
|
466 |
+
combined_attention_mask = _make_causal_mask(
|
467 |
+
input_shape,
|
468 |
+
inputs_embeds.dtype,
|
469 |
+
device=inputs_embeds.device,
|
470 |
+
past_key_values_length=past_key_values_length,
|
471 |
+
)
|
472 |
+
|
473 |
+
if attention_mask is not None:
|
474 |
+
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
475 |
+
expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
|
476 |
+
inputs_embeds.device
|
477 |
+
)
|
478 |
+
combined_attention_mask = (
|
479 |
+
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
|
480 |
+
)
|
481 |
+
|
482 |
+
return combined_attention_mask
|
483 |
+
|
484 |
+
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
|
485 |
+
def forward(
|
486 |
+
self,
|
487 |
+
input_ids: torch.LongTensor = None,
|
488 |
+
attention_mask: Optional[torch.Tensor] = None,
|
489 |
+
position_ids: Optional[torch.LongTensor] = None,
|
490 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
491 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
492 |
+
use_cache: Optional[bool] = None,
|
493 |
+
output_attentions: Optional[bool] = None,
|
494 |
+
output_hidden_states: Optional[bool] = None,
|
495 |
+
return_dict: Optional[bool] = None,
|
496 |
+
) -> Union[Tuple, BaseModelOutputWithPast]:
|
497 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
498 |
+
output_hidden_states = (
|
499 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
500 |
+
)
|
501 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
502 |
+
|
503 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
504 |
+
|
505 |
+
# retrieve input_ids and inputs_embeds
|
506 |
+
if input_ids is not None and inputs_embeds is not None:
|
507 |
+
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
|
508 |
+
elif input_ids is not None:
|
509 |
+
batch_size, seq_length = input_ids.shape
|
510 |
+
elif inputs_embeds is not None:
|
511 |
+
batch_size, seq_length, _ = inputs_embeds.shape
|
512 |
+
else:
|
513 |
+
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
|
514 |
+
|
515 |
+
seq_length_with_past = seq_length
|
516 |
+
past_key_values_length = 0
|
517 |
+
|
518 |
+
if past_key_values is not None:
|
519 |
+
past_key_values_length = past_key_values[0][0].shape[2]
|
520 |
+
seq_length_with_past = seq_length_with_past + past_key_values_length
|
521 |
+
|
522 |
+
if position_ids is None:
|
523 |
+
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
524 |
+
position_ids = torch.arange(
|
525 |
+
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
|
526 |
+
)
|
527 |
+
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
|
528 |
+
else:
|
529 |
+
position_ids = position_ids.view(-1, seq_length).long()
|
530 |
+
|
531 |
+
if inputs_embeds is None:
|
532 |
+
inputs_embeds = self.embed_tokens(input_ids)
|
533 |
+
# embed positions
|
534 |
+
if attention_mask is None:
|
535 |
+
attention_mask = torch.ones(
|
536 |
+
(batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
|
537 |
+
)
|
538 |
+
attention_mask = self._prepare_decoder_attention_mask(
|
539 |
+
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
|
540 |
+
)
|
541 |
+
|
542 |
+
hidden_states = inputs_embeds
|
543 |
+
|
544 |
+
if self.gradient_checkpointing and self.training:
|
545 |
+
if use_cache:
|
546 |
+
logger.warning_once(
|
547 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
548 |
+
)
|
549 |
+
use_cache = False
|
550 |
+
|
551 |
+
# decoder layers
|
552 |
+
all_hidden_states = () if output_hidden_states else None
|
553 |
+
all_self_attns = () if output_attentions else None
|
554 |
+
next_decoder_cache = () if use_cache else None
|
555 |
+
|
556 |
+
for idx, decoder_layer in enumerate(self.layers):
|
557 |
+
if output_hidden_states:
|
558 |
+
all_hidden_states += (hidden_states,)
|
559 |
+
|
560 |
+
past_key_value = past_key_values[idx] if past_key_values is not None else None
|
561 |
+
|
562 |
+
if self.gradient_checkpointing and self.training:
|
563 |
+
|
564 |
+
def create_custom_forward(module):
|
565 |
+
def custom_forward(*inputs):
|
566 |
+
# None for past_key_value
|
567 |
+
return module(*inputs, output_attentions, None)
|
568 |
+
|
569 |
+
return custom_forward
|
570 |
+
|
571 |
+
layer_outputs = torch.utils.checkpoint.checkpoint(
|
572 |
+
create_custom_forward(decoder_layer),
|
573 |
+
hidden_states,
|
574 |
+
attention_mask,
|
575 |
+
position_ids,
|
576 |
+
None,
|
577 |
+
)
|
578 |
+
else:
|
579 |
+
layer_outputs = decoder_layer(
|
580 |
+
hidden_states,
|
581 |
+
attention_mask=attention_mask,
|
582 |
+
position_ids=position_ids,
|
583 |
+
past_key_value=past_key_value,
|
584 |
+
output_attentions=output_attentions,
|
585 |
+
use_cache=use_cache,
|
586 |
+
)
|
587 |
+
|
588 |
+
hidden_states = layer_outputs[0]
|
589 |
+
|
590 |
+
if use_cache:
|
591 |
+
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
|
592 |
+
|
593 |
+
if output_attentions:
|
594 |
+
all_self_attns += (layer_outputs[1],)
|
595 |
+
|
596 |
+
hidden_states = self.norm(hidden_states)
|
597 |
+
|
598 |
+
# add hidden states from the last decoder layer
|
599 |
+
if output_hidden_states:
|
600 |
+
all_hidden_states += (hidden_states,)
|
601 |
+
|
602 |
+
next_cache = next_decoder_cache if use_cache else None
|
603 |
+
if not return_dict:
|
604 |
+
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
605 |
+
return BaseModelOutputWithPast(
|
606 |
+
last_hidden_state=hidden_states,
|
607 |
+
past_key_values=next_cache,
|
608 |
+
hidden_states=all_hidden_states,
|
609 |
+
attentions=all_self_attns,
|
610 |
+
)
|
611 |
+
|
612 |
+
|
613 |
+
class LlamaForCausalLM(LlamaPreTrainedModel):
|
614 |
+
_tied_weights_keys = ["lm_head.weight"]
|
615 |
+
|
616 |
+
def __init__(self, config):
|
617 |
+
super().__init__(config)
|
618 |
+
self.model = LlamaModel(config)
|
619 |
+
|
620 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
621 |
+
|
622 |
+
# Initialize weights and apply final processing
|
623 |
+
self.post_init()
|
624 |
+
|
625 |
+
def get_input_embeddings(self):
|
626 |
+
return self.model.embed_tokens
|
627 |
+
|
628 |
+
def set_input_embeddings(self, value):
|
629 |
+
self.model.embed_tokens = value
|
630 |
+
|
631 |
+
def get_output_embeddings(self):
|
632 |
+
return self.lm_head
|
633 |
+
|
634 |
+
def set_output_embeddings(self, new_embeddings):
|
635 |
+
self.lm_head = new_embeddings
|
636 |
+
|
637 |
+
def set_decoder(self, decoder):
|
638 |
+
self.model = decoder
|
639 |
+
|
640 |
+
def get_decoder(self):
|
641 |
+
return self.model
|
642 |
+
|
643 |
+
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
|
644 |
+
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
|
645 |
+
def forward(
|
646 |
+
self,
|
647 |
+
input_ids: torch.LongTensor = None,
|
648 |
+
attention_mask: Optional[torch.Tensor] = None,
|
649 |
+
position_ids: Optional[torch.LongTensor] = None,
|
650 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
651 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
652 |
+
labels: Optional[torch.LongTensor] = None,
|
653 |
+
use_cache: Optional[bool] = None,
|
654 |
+
output_attentions: Optional[bool] = None,
|
655 |
+
output_hidden_states: Optional[bool] = None,
|
656 |
+
return_dict: Optional[bool] = None,
|
657 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
658 |
+
r"""
|
659 |
+
Args:
|
660 |
+
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
661 |
+
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
662 |
+
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
663 |
+
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
664 |
+
|
665 |
+
Returns:
|
666 |
+
|
667 |
+
Example:
|
668 |
+
|
669 |
+
```python
|
670 |
+
>>> from transformers import AutoTokenizer, LlamaForCausalLM
|
671 |
+
|
672 |
+
>>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
|
673 |
+
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
|
674 |
+
|
675 |
+
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
676 |
+
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
677 |
+
|
678 |
+
>>> # Generate
|
679 |
+
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
680 |
+
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
681 |
+
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
|
682 |
+
```"""
|
683 |
+
|
684 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
685 |
+
output_hidden_states = (
|
686 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
687 |
+
)
|
688 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
689 |
+
|
690 |
+
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
691 |
+
outputs = self.model(
|
692 |
+
input_ids=input_ids,
|
693 |
+
attention_mask=attention_mask,
|
694 |
+
position_ids=position_ids,
|
695 |
+
past_key_values=past_key_values,
|
696 |
+
inputs_embeds=inputs_embeds,
|
697 |
+
use_cache=use_cache,
|
698 |
+
output_attentions=output_attentions,
|
699 |
+
output_hidden_states=output_hidden_states,
|
700 |
+
return_dict=return_dict,
|
701 |
+
)
|
702 |
+
|
703 |
+
hidden_states = outputs[0]
|
704 |
+
logits = self.lm_head(hidden_states)
|
705 |
+
|
706 |
+
loss = None
|
707 |
+
if labels is not None:
|
708 |
+
# Shift so that tokens < n predict n
|
709 |
+
shift_logits = logits[..., :-1, :].contiguous()
|
710 |
+
shift_labels = labels[..., 1:].contiguous()
|
711 |
+
# Flatten the tokens
|
712 |
+
loss_fct = CrossEntropyLoss()
|
713 |
+
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
714 |
+
shift_labels = shift_labels.view(-1)
|
715 |
+
# Enable model parallelism
|
716 |
+
shift_labels = shift_labels.to(shift_logits.device)
|
717 |
+
loss = loss_fct(shift_logits, shift_labels)
|
718 |
+
|
719 |
+
if not return_dict:
|
720 |
+
output = (logits,) + outputs[1:]
|
721 |
+
return (loss,) + output if loss is not None else output
|
722 |
+
|
723 |
+
return CausalLMOutputWithPast(
|
724 |
+
loss=loss,
|
725 |
+
logits=logits,
|
726 |
+
past_key_values=outputs.past_key_values,
|
727 |
+
hidden_states=outputs.hidden_states,
|
728 |
+
attentions=outputs.attentions,
|
729 |
+
)
|
730 |
+
|
731 |
+
def prepare_inputs_for_generation(
|
732 |
+
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
|
733 |
+
):
|
734 |
+
if past_key_values:
|
735 |
+
input_ids = input_ids[:, -1:]
|
736 |
+
|
737 |
+
position_ids = kwargs.get("position_ids", None)
|
738 |
+
if attention_mask is not None and position_ids is None:
|
739 |
+
# create position_ids on the fly for batch generation
|
740 |
+
position_ids = attention_mask.long().cumsum(-1) - 1
|
741 |
+
position_ids.masked_fill_(attention_mask == 0, 1)
|
742 |
+
if past_key_values:
|
743 |
+
position_ids = position_ids[:, -1].unsqueeze(-1)
|
744 |
+
|
745 |
+
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
746 |
+
if inputs_embeds is not None and past_key_values is None:
|
747 |
+
model_inputs = {"inputs_embeds": inputs_embeds}
|
748 |
+
else:
|
749 |
+
model_inputs = {"input_ids": input_ids}
|
750 |
+
|
751 |
+
model_inputs.update(
|
752 |
+
{
|
753 |
+
"position_ids": position_ids,
|
754 |
+
"past_key_values": past_key_values,
|
755 |
+
"use_cache": kwargs.get("use_cache"),
|
756 |
+
"attention_mask": attention_mask,
|
757 |
+
}
|
758 |
+
)
|
759 |
+
return model_inputs
|
760 |
+
|
761 |
+
@staticmethod
|
762 |
+
def _reorder_cache(past_key_values, beam_idx):
|
763 |
+
reordered_past = ()
|
764 |
+
for layer_past in past_key_values:
|
765 |
+
reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
|
766 |
+
return reordered_past
|
767 |
+
|
768 |
+
|
769 |
+
@add_start_docstrings(
|
770 |
+
"""
|
771 |
+
The LLaMa Model transformer with a sequence classification head on top (linear layer).
|
772 |
+
|
773 |
+
[`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
|
774 |
+
(e.g. GPT-2) do.
|
775 |
+
|
776 |
+
Since it does classification on the last token, it requires to know the position of the last token. If a
|
777 |
+
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
|
778 |
+
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
|
779 |
+
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
|
780 |
+
each row of the batch).
|
781 |
+
""",
|
782 |
+
LLAMA_START_DOCSTRING,
|
783 |
+
)
|
784 |
+
class LlamaForSequenceClassification(LlamaPreTrainedModel):
|
785 |
+
_keys_to_ignore_on_load_missing = [r"lm_head.weight"]
|
786 |
+
|
787 |
+
def __init__(self, config):
|
788 |
+
super().__init__(config)
|
789 |
+
self.num_labels = config.num_labels
|
790 |
+
self.model = LlamaModel(config)
|
791 |
+
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
|
792 |
+
|
793 |
+
# Initialize weights and apply final processing
|
794 |
+
self.post_init()
|
795 |
+
|
796 |
+
def get_input_embeddings(self):
|
797 |
+
return self.model.embed_tokens
|
798 |
+
|
799 |
+
def set_input_embeddings(self, value):
|
800 |
+
self.model.embed_tokens = value
|
801 |
+
|
802 |
+
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
|
803 |
+
def forward(
|
804 |
+
self,
|
805 |
+
input_ids: torch.LongTensor = None,
|
806 |
+
attention_mask: Optional[torch.Tensor] = None,
|
807 |
+
position_ids: Optional[torch.LongTensor] = None,
|
808 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
809 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
810 |
+
labels: Optional[torch.LongTensor] = None,
|
811 |
+
use_cache: Optional[bool] = None,
|
812 |
+
output_attentions: Optional[bool] = None,
|
813 |
+
output_hidden_states: Optional[bool] = None,
|
814 |
+
return_dict: Optional[bool] = None,
|
815 |
+
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
|
816 |
+
r"""
|
817 |
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
818 |
+
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
819 |
+
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
820 |
+
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
821 |
+
"""
|
822 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
823 |
+
|
824 |
+
transformer_outputs = self.model(
|
825 |
+
input_ids,
|
826 |
+
attention_mask=attention_mask,
|
827 |
+
position_ids=position_ids,
|
828 |
+
past_key_values=past_key_values,
|
829 |
+
inputs_embeds=inputs_embeds,
|
830 |
+
use_cache=use_cache,
|
831 |
+
output_attentions=output_attentions,
|
832 |
+
output_hidden_states=output_hidden_states,
|
833 |
+
return_dict=return_dict,
|
834 |
+
)
|
835 |
+
hidden_states = transformer_outputs[0]
|
836 |
+
logits = self.score(hidden_states)
|
837 |
+
|
838 |
+
if input_ids is not None:
|
839 |
+
batch_size = input_ids.shape[0]
|
840 |
+
else:
|
841 |
+
batch_size = inputs_embeds.shape[0]
|
842 |
+
|
843 |
+
if self.config.pad_token_id is None and batch_size != 1:
|
844 |
+
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
|
845 |
+
if self.config.pad_token_id is None:
|
846 |
+
sequence_lengths = -1
|
847 |
+
else:
|
848 |
+
if input_ids is not None:
|
849 |
+
sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
|
850 |
+
else:
|
851 |
+
sequence_lengths = -1
|
852 |
+
|
853 |
+
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
854 |
+
|
855 |
+
loss = None
|
856 |
+
if labels is not None:
|
857 |
+
labels = labels.to(logits.device)
|
858 |
+
if self.config.problem_type is None:
|
859 |
+
if self.num_labels == 1:
|
860 |
+
self.config.problem_type = "regression"
|
861 |
+
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
862 |
+
self.config.problem_type = "single_label_classification"
|
863 |
+
else:
|
864 |
+
self.config.problem_type = "multi_label_classification"
|
865 |
+
|
866 |
+
if self.config.problem_type == "regression":
|
867 |
+
loss_fct = MSELoss()
|
868 |
+
if self.num_labels == 1:
|
869 |
+
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
870 |
+
else:
|
871 |
+
loss = loss_fct(pooled_logits, labels)
|
872 |
+
elif self.config.problem_type == "single_label_classification":
|
873 |
+
loss_fct = CrossEntropyLoss()
|
874 |
+
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
|
875 |
+
elif self.config.problem_type == "multi_label_classification":
|
876 |
+
loss_fct = BCEWithLogitsLoss()
|
877 |
+
loss = loss_fct(pooled_logits, labels)
|
878 |
+
if not return_dict:
|
879 |
+
output = (pooled_logits,) + transformer_outputs[1:]
|
880 |
+
return ((loss,) + output) if loss is not None else output
|
881 |
+
|
882 |
+
return SequenceClassifierOutputWithPast(
|
883 |
+
loss=loss,
|
884 |
+
logits=pooled_logits,
|
885 |
+
past_key_values=transformer_outputs.past_key_values,
|
886 |
+
hidden_states=transformer_outputs.hidden_states,
|
887 |
+
attentions=transformer_outputs.attentions,
|
888 |
+
)
|
code/modelling_RW.py
ADDED
@@ -0,0 +1,1362 @@
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|
1 |
+
# port of models described in RW
|
2 |
+
# We use the bloom model as a starting point for these model.
|
3 |
+
# Please refer to the bloom models for usage instructions.
|
4 |
+
|
5 |
+
import math
|
6 |
+
import warnings
|
7 |
+
from typing import Optional, Tuple, Union
|
8 |
+
|
9 |
+
import torch
|
10 |
+
import torch.utils.checkpoint
|
11 |
+
from torch import nn
|
12 |
+
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss
|
13 |
+
from torch.nn import functional as F
|
14 |
+
|
15 |
+
from transformers.modeling_outputs import (
|
16 |
+
BaseModelOutputWithPastAndCrossAttentions,
|
17 |
+
CausalLMOutputWithCrossAttentions,
|
18 |
+
QuestionAnsweringModelOutput,
|
19 |
+
SequenceClassifierOutputWithPast,
|
20 |
+
TokenClassifierOutput,
|
21 |
+
)
|
22 |
+
from transformers.modeling_utils import PreTrainedModel
|
23 |
+
from transformers.utils import logging
|
24 |
+
from configuration_RW import RWConfig
|
25 |
+
from ltriton.flash_landmark_attention import fused_landmark_attention
|
26 |
+
|
27 |
+
logger = logging.get_logger(__name__)
|
28 |
+
|
29 |
+
# NOTE(Hesslow): Unfortunately we did not fuse matmul and bias during training, this means that there's one additional quantization to bfloat16 between the operations.
|
30 |
+
# In order not to degrade the quality of our HF-port, we keep these characteristics in the final model.
|
31 |
+
class Linear(nn.Linear):
|
32 |
+
def forward(self, input: torch.Tensor) -> torch.Tensor:
|
33 |
+
ret = input @ self.weight.T
|
34 |
+
if self.bias is None:
|
35 |
+
return ret
|
36 |
+
else:
|
37 |
+
return ret + self.bias
|
38 |
+
|
39 |
+
|
40 |
+
from einops import rearrange
|
41 |
+
|
42 |
+
# rotary pos emb helpers (torch.jit.script does not seem to support staticmethod...)
|
43 |
+
def rotate_half(x):
|
44 |
+
x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :]
|
45 |
+
return torch.cat((-x2, x1), dim=x1.ndim - 1) # dim=-1 triggers a bug in torch < 1.8.0
|
46 |
+
|
47 |
+
|
48 |
+
class RotaryEmbedding(torch.nn.Module):
|
49 |
+
"""Implementation of RotaryEmbedding from GPT-NeoX.
|
50 |
+
This implementation is design to operate on queries and keys that are compatible with
|
51 |
+
[batch_size, n_heads_per_partition, seq_len, head_dim] (e.g. MinGPTAttention format).
|
52 |
+
"""
|
53 |
+
|
54 |
+
def __init__(
|
55 |
+
self,
|
56 |
+
head_dim: int,
|
57 |
+
base=10000,
|
58 |
+
):
|
59 |
+
super().__init__()
|
60 |
+
inv_freq = 1.0 / (base ** (torch.arange(0, head_dim, 2).float() / head_dim))
|
61 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
62 |
+
self.head_dim = head_dim
|
63 |
+
self.seq_len_cached = None
|
64 |
+
self.batch_size_cached = None
|
65 |
+
self.cos_cached: torch.Tensor | None = None
|
66 |
+
self.sin_cached: torch.Tensor | None = None
|
67 |
+
|
68 |
+
def cos_sin(
|
69 |
+
self,
|
70 |
+
seq_len: int,
|
71 |
+
device="cuda",
|
72 |
+
dtype=torch.bfloat16,
|
73 |
+
) -> torch.Tensor:
|
74 |
+
if seq_len != self.seq_len_cached:
|
75 |
+
self.seq_len_cached = seq_len
|
76 |
+
t = torch.arange(seq_len, device=device).type_as(self.inv_freq)
|
77 |
+
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
|
78 |
+
emb = torch.cat((freqs, freqs), dim=-1).to(device)
|
79 |
+
|
80 |
+
if dtype in [torch.float16, torch.bfloat16]:
|
81 |
+
emb = emb.float()
|
82 |
+
|
83 |
+
self.cos_cached = emb.cos()[None, :, :]
|
84 |
+
self.sin_cached = emb.sin()[None, :, :]
|
85 |
+
|
86 |
+
self.cos_cached = self.cos_cached.type(dtype)
|
87 |
+
self.sin_cached = self.sin_cached.type(dtype)
|
88 |
+
|
89 |
+
return self.cos_cached, self.sin_cached
|
90 |
+
|
91 |
+
def forward(self, q, k):
|
92 |
+
batch, seq_len, head_dim = q.shape
|
93 |
+
cos, sin = self.cos_sin(seq_len)
|
94 |
+
return (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
|
95 |
+
|
96 |
+
|
97 |
+
def _make_causal_mask(
|
98 |
+
input_ids_shape: torch.Size, device: torch.device, past_key_values_length: int
|
99 |
+
) -> torch.BoolTensor:
|
100 |
+
batch_size, target_length = input_ids_shape
|
101 |
+
mask = torch.empty((target_length, target_length + past_key_values_length), dtype=torch.bool, device=device)
|
102 |
+
# ONNX doesn't support `torch.Tensor.triu` properly, thus we use this workaround
|
103 |
+
seq_ids = torch.arange(target_length, device=device)
|
104 |
+
mask[:, past_key_values_length:] = seq_ids[:, None] < seq_ids[None, :]
|
105 |
+
|
106 |
+
if past_key_values_length > 0:
|
107 |
+
mask[:, :past_key_values_length] = False
|
108 |
+
|
109 |
+
expanded_mask = mask[None, None, :, :].expand(batch_size, 1, target_length, target_length + past_key_values_length)
|
110 |
+
return expanded_mask
|
111 |
+
|
112 |
+
|
113 |
+
def _expand_mask(mask: torch.Tensor, tgt_length: int) -> torch.BoolTensor:
|
114 |
+
batch_size, src_length = mask.shape
|
115 |
+
tgt_length = tgt_length if tgt_length is not None else src_length
|
116 |
+
|
117 |
+
expanded_mask = ~(mask[:, None, None, :].to(torch.bool))
|
118 |
+
return expanded_mask.expand(batch_size, 1, tgt_length, src_length)
|
119 |
+
|
120 |
+
|
121 |
+
def build_alibi_tensor(attention_mask: torch.Tensor, num_heads: int, dtype: torch.dtype) -> torch.Tensor:
|
122 |
+
batch_size, seq_length = attention_mask.shape
|
123 |
+
closest_power_of_2 = 2 ** math.floor(math.log2(num_heads))
|
124 |
+
base = torch.tensor(
|
125 |
+
2 ** (-(2 ** -(math.log2(closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32
|
126 |
+
)
|
127 |
+
powers = torch.arange(1, 1 + closest_power_of_2, device=attention_mask.device, dtype=torch.int32)
|
128 |
+
slopes = torch.pow(base, powers)
|
129 |
+
|
130 |
+
if closest_power_of_2 != num_heads:
|
131 |
+
extra_base = torch.tensor(
|
132 |
+
2 ** (-(2 ** -(math.log2(2 * closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32
|
133 |
+
)
|
134 |
+
num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2)
|
135 |
+
extra_powers = torch.arange(1, 1 + 2 * num_remaining_heads, 2, device=attention_mask.device, dtype=torch.int32)
|
136 |
+
slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0)
|
137 |
+
|
138 |
+
# Note: alibi will added to the attention bias that will be applied to the query, key product of attention
|
139 |
+
# => therefore alibi will have to be of shape (batch_size, num_heads, query_length, key_length)
|
140 |
+
# => here we set (batch_size=1, num_heads=num_heads, query_length=1, key_length=max_length)
|
141 |
+
# => the query_length dimension will then be broadcasted correctly
|
142 |
+
# This is more or less identical to T5's relative position bias:
|
143 |
+
# https://github.com/huggingface/transformers/blob/f681437203baa7671de3174b0fa583c349d9d5e1/src/transformers/models/t5/modeling_t5.py#L527
|
144 |
+
arange_tensor = ((attention_mask.cumsum(dim=-1) - 1) * attention_mask)[:, None, :]
|
145 |
+
alibi = slopes[..., None].bfloat16() * arange_tensor
|
146 |
+
return alibi.reshape(batch_size * num_heads, 1, seq_length).to(dtype)
|
147 |
+
|
148 |
+
|
149 |
+
def dropout_add(x: torch.Tensor, residual: torch.Tensor, prob: float, training: bool) -> torch.Tensor:
|
150 |
+
out = F.dropout(x, p=prob, training=training)
|
151 |
+
out = residual + out
|
152 |
+
return out
|
153 |
+
|
154 |
+
|
155 |
+
class LandmarkGroupedSoftmaxFunction(torch.autograd.Function):
|
156 |
+
|
157 |
+
# Note that forward, setup_context, and backward are @staticmethods
|
158 |
+
@staticmethod
|
159 |
+
def forward(ctx, x, dim, mem_cnt, resp_mem_idx):
|
160 |
+
new_shape = list(x.shape)
|
161 |
+
new_shape[dim] = mem_cnt # max_mem_cnt.item()
|
162 |
+
max_by_group = x.new_zeros((*new_shape,))
|
163 |
+
max_by_group.scatter_reduce_(src=x, index=resp_mem_idx, dim=dim, reduce="amax", include_self=False)
|
164 |
+
|
165 |
+
maxes = torch.gather(max_by_group, dim, resp_mem_idx)
|
166 |
+
#x_exp = torch.exp(x - torch.where(torch.isinf(maxes), 0, maxes))
|
167 |
+
x_exp = torch.exp((x - maxes).to(torch.float32))
|
168 |
+
|
169 |
+
cumsum_by_group = torch.zeros_like(max_by_group, dtype=x_exp.dtype)
|
170 |
+
|
171 |
+
cumsum_by_group.scatter_add_(dim, resp_mem_idx, x_exp, )
|
172 |
+
denom = torch.gather(cumsum_by_group, dim, resp_mem_idx)
|
173 |
+
|
174 |
+
#probs = torch.where(denom < 0.5, 0, x_exp / denom)
|
175 |
+
probs = x_exp / denom
|
176 |
+
|
177 |
+
|
178 |
+
ctx.mem_cnt = mem_cnt
|
179 |
+
ctx.dim = dim
|
180 |
+
ctx.save_for_backward(resp_mem_idx, probs)
|
181 |
+
|
182 |
+
return probs
|
183 |
+
|
184 |
+
@staticmethod
|
185 |
+
def backward(ctx, grad_probs):
|
186 |
+
mem_cnt = ctx.mem_cnt
|
187 |
+
dim = ctx.dim
|
188 |
+
resp_mem_idx, probs = ctx.saved_tensors
|
189 |
+
grad_x = grad_dim = grad_mem_cnt = grad_resp_mem_idx = None
|
190 |
+
|
191 |
+
if ctx.needs_input_grad[0] or ctx.needs_input_grad[4]:
|
192 |
+
grad_pair = grad_probs * probs
|
193 |
+
|
194 |
+
new_shape = list(probs.shape)
|
195 |
+
new_shape[dim] = mem_cnt # max_mem_cnt.item()
|
196 |
+
cumsum_by_group = grad_pair.new_zeros((*new_shape,))
|
197 |
+
cumsum_by_group.scatter_add_(dim, resp_mem_idx, grad_pair)
|
198 |
+
|
199 |
+
if ctx.needs_input_grad[0]:
|
200 |
+
grad_sum = torch.gather(cumsum_by_group, dim, resp_mem_idx)
|
201 |
+
grad_x = grad_pair - probs * grad_sum
|
202 |
+
assert not ctx.needs_input_grad[1]
|
203 |
+
assert not ctx.needs_input_grad[2]
|
204 |
+
assert not ctx.needs_input_grad[3]
|
205 |
+
|
206 |
+
return grad_x, grad_dim, grad_mem_cnt, grad_resp_mem_idx
|
207 |
+
|
208 |
+
|
209 |
+
def landmark_grouped_softmax(x, dim, is_mem, last_section_mask):
|
210 |
+
|
211 |
+
last_and_rest_mask = last_section_mask # | mask
|
212 |
+
|
213 |
+
full_access_mask = is_mem | last_and_rest_mask
|
214 |
+
|
215 |
+
max_mem_cnt = 64
|
216 |
+
mem_group_idx = torch.cumsum(is_mem, dim=dim)
|
217 |
+
mem_bucket_id = max_mem_cnt - 1
|
218 |
+
resp_mem_idx = torch.where(last_and_rest_mask,
|
219 |
+
max_mem_cnt - 1,
|
220 |
+
torch.where(is_mem, mem_bucket_id, mem_group_idx))
|
221 |
+
probs = LandmarkGroupedSoftmaxFunction.apply(x, dim, max_mem_cnt, resp_mem_idx)
|
222 |
+
|
223 |
+
new_shape = list(x.shape)
|
224 |
+
new_shape[dim] = max_mem_cnt
|
225 |
+
group_prob = probs.new_zeros((*new_shape, ))
|
226 |
+
group_prob.scatter_(dim, torch.where(is_mem, mem_group_idx - 1, max_mem_cnt - 1), probs)
|
227 |
+
probs = probs.mul(torch.where(full_access_mask, last_section_mask, torch.gather(group_prob, dim, resp_mem_idx)))
|
228 |
+
|
229 |
+
return probs
|
230 |
+
|
231 |
+
|
232 |
+
class Attention(nn.Module):
|
233 |
+
def __init__(self, config: RWConfig):
|
234 |
+
super().__init__()
|
235 |
+
|
236 |
+
self.hidden_size = config.hidden_size
|
237 |
+
self.num_heads = config.n_head
|
238 |
+
self.head_dim = self.hidden_size // self.num_heads
|
239 |
+
self.split_size = self.hidden_size
|
240 |
+
self.hidden_dropout = config.hidden_dropout
|
241 |
+
|
242 |
+
if self.head_dim * self.num_heads != self.hidden_size:
|
243 |
+
raise ValueError(
|
244 |
+
f"`hidden_size` must be divisible by num_heads (got `hidden_size`: {self.hidden_size} and `num_heads`:"
|
245 |
+
f" {self.num_heads})."
|
246 |
+
)
|
247 |
+
|
248 |
+
self.maybe_rotary = RotaryEmbedding(config.head_dim) if config.rotary else lambda q, k: (q, k)
|
249 |
+
|
250 |
+
# Layer-wise attention scaling
|
251 |
+
self.inv_norm_factor = 1.0 / math.sqrt(self.head_dim)
|
252 |
+
self.beta = self.inv_norm_factor
|
253 |
+
|
254 |
+
self.query_key_value = Linear(
|
255 |
+
self.hidden_size,
|
256 |
+
3 * self.hidden_size if not config.multi_query else (self.hidden_size + 2 * self.head_dim),
|
257 |
+
bias=config.bias,
|
258 |
+
)
|
259 |
+
self.multi_query = config.multi_query
|
260 |
+
self.dense = Linear(self.hidden_size, self.hidden_size, bias=config.bias)
|
261 |
+
self.attention_dropout = nn.Dropout(config.attention_dropout)
|
262 |
+
self.num_kv = config.n_head if not self.multi_query else 1
|
263 |
+
|
264 |
+
def _split_heads(self, fused_qkv: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
265 |
+
"""
|
266 |
+
Split the last dimension into (num_heads, head_dim) without making any copies, results share same memory
|
267 |
+
storage as `fused_qkv`
|
268 |
+
|
269 |
+
Args:
|
270 |
+
fused_qkv (`torch.tensor`, *required*): [batch_size, seq_length, num_heads * 3 * head_dim]
|
271 |
+
|
272 |
+
Returns:
|
273 |
+
query: [batch_size, seq_length, num_heads, head_dim] key: [batch_size, seq_length, num_heads, head_dim]
|
274 |
+
value: [batch_size, seq_length, num_heads, head_dim]
|
275 |
+
"""
|
276 |
+
if not self.multi_query:
|
277 |
+
batch_size, seq_length, three_times_hidden_size = fused_qkv.shape
|
278 |
+
fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads, 3, self.head_dim)
|
279 |
+
return fused_qkv[..., 0, :], fused_qkv[..., 1, :], fused_qkv[..., 2, :]
|
280 |
+
else:
|
281 |
+
batch_size, seq_length, three_times_hidden_size = fused_qkv.shape
|
282 |
+
fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads + 2, self.head_dim)
|
283 |
+
return fused_qkv[..., :-2, :], fused_qkv[..., [-2], :], fused_qkv[..., [-1], :]
|
284 |
+
|
285 |
+
def _merge_heads(self, x: torch.Tensor) -> torch.Tensor:
|
286 |
+
"""
|
287 |
+
Merge heads together over the last dimenstion
|
288 |
+
|
289 |
+
Args:
|
290 |
+
x: (`torch.tensor`, *required*): [batch_size * num_heads, seq_length, head_dim]
|
291 |
+
|
292 |
+
Returns:
|
293 |
+
torch.tensor: [batch_size, seq_length, num_heads * head_dim]
|
294 |
+
"""
|
295 |
+
# What we want to achieve is:
|
296 |
+
# batch_size * num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads * head_dim
|
297 |
+
batch_size_and_num_heads, seq_length, _ = x.shape
|
298 |
+
batch_size = batch_size_and_num_heads // self.num_heads
|
299 |
+
|
300 |
+
# First view to decompose the batch size
|
301 |
+
# batch_size * num_heads, seq_length, head_dim -> batch_size, num_heads, seq_length, head_dim
|
302 |
+
x = x.view(batch_size, self.num_heads, seq_length, self.head_dim)
|
303 |
+
|
304 |
+
# batch_size, num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads, head_dim
|
305 |
+
x = x.permute(0, 2, 1, 3)
|
306 |
+
|
307 |
+
# batch_size, seq_length, num_heads, head_dim -> batch_size, seq_length, num_heads * head_dim
|
308 |
+
return x.reshape(batch_size, seq_length, self.num_heads * self.head_dim)
|
309 |
+
|
310 |
+
def forward(
|
311 |
+
self,
|
312 |
+
hidden_states: torch.Tensor,
|
313 |
+
alibi: torch.Tensor,
|
314 |
+
attention_mask: torch.Tensor,
|
315 |
+
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
|
316 |
+
head_mask: Optional[torch.Tensor] = None,
|
317 |
+
use_cache: bool = False,
|
318 |
+
output_attentions: bool = False,
|
319 |
+
is_mem: Optional[torch.Tensor] = None,
|
320 |
+
last_section_mask: Optional[torch.Tensor] = None,
|
321 |
+
offload_cache_to_cpu: bool = False,
|
322 |
+
use_flash: bool = False,
|
323 |
+
cache_top_k: Optional[int] = None,
|
324 |
+
mem_freq: Optional[int] = None
|
325 |
+
):
|
326 |
+
fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size]
|
327 |
+
|
328 |
+
# 3 x [batch_size, seq_length, num_heads, head_dim]
|
329 |
+
(query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
|
330 |
+
|
331 |
+
batch_size, q_length, _, _ = query_layer.shape
|
332 |
+
|
333 |
+
query_layer = query_layer.transpose(1, 2).reshape(batch_size * self.num_heads, q_length, self.head_dim)
|
334 |
+
key_layer = key_layer.transpose(1, 2).reshape(
|
335 |
+
batch_size * self.num_kv,
|
336 |
+
q_length,
|
337 |
+
self.head_dim,
|
338 |
+
)
|
339 |
+
value_layer = value_layer.transpose(1, 2).reshape(batch_size * self.num_kv, q_length, self.head_dim)
|
340 |
+
|
341 |
+
query_layer, key_layer = self.maybe_rotary(query_layer, key_layer)
|
342 |
+
|
343 |
+
if layer_past is not None:
|
344 |
+
past_key, past_value = layer_past
|
345 |
+
# concatenate along seq_length dimension:
|
346 |
+
# - key: [batch_size * self.num_heads, head_dim, kv_length]
|
347 |
+
# - value: [batch_size * self.num_heads, kv_length, head_dim]
|
348 |
+
key_layer = torch.cat((past_key, key_layer), dim=1)
|
349 |
+
value_layer = torch.cat((past_value, value_layer), dim=1)
|
350 |
+
|
351 |
+
_, kv_length, _ = key_layer.shape
|
352 |
+
|
353 |
+
if use_cache is True:
|
354 |
+
present = (key_layer, value_layer)
|
355 |
+
else:
|
356 |
+
present = None
|
357 |
+
|
358 |
+
if alibi is None:
|
359 |
+
query_layer_ = query_layer.reshape(batch_size, self.num_heads, -1, self.head_dim)
|
360 |
+
key_layer_ = key_layer.reshape(batch_size, self.num_kv, -1, self.head_dim)
|
361 |
+
value_layer_ = value_layer.reshape(batch_size, self.num_kv, -1, self.head_dim)
|
362 |
+
|
363 |
+
assert self.num_kv == 1
|
364 |
+
|
365 |
+
key_layer_ = key_layer_.expand(query_layer_.shape)
|
366 |
+
value_layer_ = value_layer_.expand(query_layer_.shape)
|
367 |
+
|
368 |
+
assert not output_attentions # not supported.
|
369 |
+
assert mem_freq is not None
|
370 |
+
attn_output = fused_landmark_attention(query_layer_, key_layer_, value_layer_, is_mem, block_size=mem_freq+1)
|
371 |
+
|
372 |
+
# attn_output = F.scaled_dot_product_attention(
|
373 |
+
# query_layer_, key_layer_, value_layer_, None, 0.0, is_causal=True
|
374 |
+
# )
|
375 |
+
|
376 |
+
x = attn_output.view(batch_size, self.num_heads, q_length, self.head_dim)
|
377 |
+
x = x.permute(0, 2, 1, 3)
|
378 |
+
attn_output = x.reshape(batch_size, q_length, self.num_heads * self.head_dim)
|
379 |
+
|
380 |
+
output_tensor = self.dense(attn_output)
|
381 |
+
|
382 |
+
outputs = (output_tensor, present)
|
383 |
+
return outputs
|
384 |
+
else:
|
385 |
+
attention_mask_float = (attention_mask * 1.0).masked_fill(attention_mask, -1e9).to(torch.bfloat16)
|
386 |
+
matmul_result = query_layer @ key_layer.transpose(-1, -2)
|
387 |
+
|
388 |
+
# change view to [batch_size, num_heads, q_length, kv_length]
|
389 |
+
attention_scores = matmul_result.view(batch_size, self.num_heads, q_length, kv_length)
|
390 |
+
|
391 |
+
# cast attention scores to fp32, compute scaled softmax and cast back to initial dtype - [batch_size, num_heads, q_length, kv_length]
|
392 |
+
input_dtype = attention_scores.dtype
|
393 |
+
# `float16` has a minimum value of -65504.0, whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38`
|
394 |
+
if input_dtype == torch.float16 or input_dtype == torch.bfloat16:
|
395 |
+
attention_scores = attention_scores.to(torch.float32)
|
396 |
+
# attn_weights = torch.masked_fill(attention_scores, attention_mask, torch.finfo(attention_scores.dtype).min)
|
397 |
+
attention_probs = F.softmax(
|
398 |
+
(attention_scores + alibi.view(batch_size, self.num_heads, 1, -1)) * self.inv_norm_factor + attention_mask_float,
|
399 |
+
dim=-1,
|
400 |
+
dtype=hidden_states.dtype,
|
401 |
+
)
|
402 |
+
# [batch_size, num_heads, q_length, kv_length]
|
403 |
+
attention_probs = self.attention_dropout(attention_probs)
|
404 |
+
|
405 |
+
if head_mask is not None:
|
406 |
+
attention_probs = attention_probs * head_mask
|
407 |
+
|
408 |
+
# change view [batch_size x num_heads, q_length, kv_length]
|
409 |
+
attention_probs_reshaped = attention_probs.view(batch_size * self.num_heads, q_length, kv_length)
|
410 |
+
|
411 |
+
# matmul: [batch_size * num_heads, q_length, head_dim]
|
412 |
+
context_layer = attention_probs_reshaped @ value_layer
|
413 |
+
|
414 |
+
# change view [batch_size, num_heads, q_length, head_dim]
|
415 |
+
context_layer = self._merge_heads(context_layer)
|
416 |
+
|
417 |
+
output_tensor = self.dense(context_layer)
|
418 |
+
|
419 |
+
outputs = (output_tensor, present)
|
420 |
+
if output_attentions:
|
421 |
+
outputs += (attention_probs,)
|
422 |
+
|
423 |
+
return outputs
|
424 |
+
|
425 |
+
|
426 |
+
class MLP(nn.Module):
|
427 |
+
def __init__(self, config: RWConfig):
|
428 |
+
super().__init__()
|
429 |
+
hidden_size = config.hidden_size
|
430 |
+
|
431 |
+
self.dense_h_to_4h = Linear(hidden_size, 4 * hidden_size, bias=config.bias)
|
432 |
+
self.act = nn.GELU()
|
433 |
+
self.dense_4h_to_h = Linear(4 * hidden_size, hidden_size, bias=config.bias)
|
434 |
+
self.hidden_dropout = config.hidden_dropout
|
435 |
+
|
436 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
437 |
+
x = self.act(self.dense_h_to_4h(x))
|
438 |
+
x = self.dense_4h_to_h(x)
|
439 |
+
return x
|
440 |
+
|
441 |
+
|
442 |
+
class DecoderLayer(nn.Module):
|
443 |
+
def __init__(self, config: RWConfig):
|
444 |
+
super().__init__()
|
445 |
+
hidden_size = config.hidden_size
|
446 |
+
|
447 |
+
self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
|
448 |
+
self.num_heads = config.n_head
|
449 |
+
self.self_attention = Attention(config)
|
450 |
+
|
451 |
+
if not config.parallel_attn:
|
452 |
+
# unused if parallel attn
|
453 |
+
self.post_attention_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
|
454 |
+
|
455 |
+
self.mlp = MLP(config)
|
456 |
+
|
457 |
+
self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm
|
458 |
+
self.hidden_dropout = config.hidden_dropout
|
459 |
+
|
460 |
+
self.config = config
|
461 |
+
|
462 |
+
def forward(
|
463 |
+
self,
|
464 |
+
hidden_states: torch.Tensor,
|
465 |
+
alibi: torch.Tensor,
|
466 |
+
attention_mask: torch.Tensor,
|
467 |
+
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
|
468 |
+
head_mask: Optional[torch.Tensor] = None,
|
469 |
+
use_cache: bool = False,
|
470 |
+
output_attentions: bool = False,
|
471 |
+
is_mem: Optional[torch.Tensor] = None,
|
472 |
+
last_section_mask: Optional[torch.Tensor] = None,
|
473 |
+
offload_cache_to_cpu: bool = False,
|
474 |
+
use_flash: bool = False,
|
475 |
+
cache_top_k: Optional[int] = None,
|
476 |
+
mem_freq: Optional[int] = None,
|
477 |
+
):
|
478 |
+
|
479 |
+
layernorm_output = self.input_layernorm(hidden_states)
|
480 |
+
residual = hidden_states
|
481 |
+
|
482 |
+
# Self attention.
|
483 |
+
attn_outputs = self.self_attention(
|
484 |
+
layernorm_output,
|
485 |
+
layer_past=layer_past,
|
486 |
+
attention_mask=attention_mask,
|
487 |
+
alibi=alibi,
|
488 |
+
head_mask=head_mask,
|
489 |
+
use_cache=use_cache,
|
490 |
+
output_attentions=output_attentions,
|
491 |
+
is_mem=is_mem,
|
492 |
+
last_section_mask=last_section_mask,
|
493 |
+
offload_cache_to_cpu=offload_cache_to_cpu,
|
494 |
+
use_flash=use_flash,
|
495 |
+
cache_top_k=cache_top_k,
|
496 |
+
mem_freq=mem_freq
|
497 |
+
)
|
498 |
+
|
499 |
+
attention_output = attn_outputs[0]
|
500 |
+
|
501 |
+
if not self.config.parallel_attn:
|
502 |
+
residual = dropout_add(attention_output, residual, self.config.attention_dropout, training=self.training)
|
503 |
+
layernorm_output = self.post_attention_layernorm(residual)
|
504 |
+
|
505 |
+
outputs = attn_outputs[1:]
|
506 |
+
|
507 |
+
# MLP.
|
508 |
+
mlp_output = self.mlp(layernorm_output)
|
509 |
+
|
510 |
+
if self.config.parallel_attn:
|
511 |
+
mlp_output += attention_output
|
512 |
+
|
513 |
+
output = dropout_add(mlp_output, residual, self.config.hidden_dropout, training=self.training)
|
514 |
+
|
515 |
+
if use_cache:
|
516 |
+
outputs = (output,) + outputs
|
517 |
+
else:
|
518 |
+
outputs = (output,) + outputs[1:]
|
519 |
+
|
520 |
+
return outputs # hidden_states, present, attentions
|
521 |
+
|
522 |
+
|
523 |
+
class RWPreTrainedModel(PreTrainedModel):
|
524 |
+
_keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
|
525 |
+
"""
|
526 |
+
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
527 |
+
models.
|
528 |
+
"""
|
529 |
+
|
530 |
+
config_class = RWConfig
|
531 |
+
base_model_prefix = "transformer"
|
532 |
+
supports_gradient_checkpointing = True
|
533 |
+
_no_split_modules = ["DecoderLayer"]
|
534 |
+
|
535 |
+
def __init__(self, *inputs, **kwargs):
|
536 |
+
super().__init__(*inputs, **kwargs)
|
537 |
+
|
538 |
+
def _init_weights(self, module: nn.Module):
|
539 |
+
"""Initialize the weights."""
|
540 |
+
if isinstance(module, nn.Linear) or isinstance(module, Linear):
|
541 |
+
# Slightly different from the TF version which uses truncated_normal for initialization
|
542 |
+
# cf https://github.com/pytorch/pytorch/pull/5617
|
543 |
+
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
544 |
+
if module.bias is not None:
|
545 |
+
module.bias.data.zero_()
|
546 |
+
elif isinstance(module, nn.Embedding):
|
547 |
+
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
548 |
+
if module.padding_idx is not None:
|
549 |
+
module.weight.data[module.padding_idx].zero_()
|
550 |
+
elif isinstance(module, LayerNorm):
|
551 |
+
module.bias.data.zero_()
|
552 |
+
module.weight.data.fill_(1.0)
|
553 |
+
|
554 |
+
def _set_gradient_checkpointing(self, module: nn.Module, value: bool = False):
|
555 |
+
if isinstance(module, RWModel):
|
556 |
+
module.gradient_checkpointing = value
|
557 |
+
|
558 |
+
@staticmethod
|
559 |
+
def _convert_to_standard_cache(
|
560 |
+
past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]], batch_size: int
|
561 |
+
) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]:
|
562 |
+
"""
|
563 |
+
Standardizes the format of the cache so as to match most implementations, i.e. to tuple(tuple([batch_size,
|
564 |
+
num_heads, ...]))
|
565 |
+
"""
|
566 |
+
batch_size_times_num_heads, head_dim, seq_length = past_key_value[0][0].shape
|
567 |
+
num_heads = batch_size_times_num_heads // batch_size
|
568 |
+
# key: [batch_size * num_heads, head_dim, seq_length] -> [batch_size, num_heads, head_dim, seq_length]
|
569 |
+
# value: [batch_size * num_heads, seq_length, head_dim] -> [batch_size, num_heads, seq_length, head_dim]
|
570 |
+
return tuple(
|
571 |
+
(
|
572 |
+
layer_past[0].view(batch_size, num_heads, head_dim, seq_length),
|
573 |
+
layer_past[1].view(batch_size, num_heads, seq_length, head_dim),
|
574 |
+
)
|
575 |
+
for layer_past in past_key_value
|
576 |
+
)
|
577 |
+
|
578 |
+
@staticmethod
|
579 |
+
def _convert_to_rw_cache(
|
580 |
+
past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]]
|
581 |
+
) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]:
|
582 |
+
batch_size, num_heads, head_dim, seq_length = past_key_value[0][0].shape
|
583 |
+
batch_size_times_num_heads = batch_size * num_heads
|
584 |
+
# key: [batch_size, num_heads, head_dim, seq_length] -> [batch_size * num_heads, head_dim, seq_length]
|
585 |
+
# value: [batch_size, num_heads, seq_length, head_dim] -> [batch_size * num_heads, seq_length, head_dim]
|
586 |
+
return tuple(
|
587 |
+
(
|
588 |
+
layer_past[0].view(batch_size_times_num_heads, head_dim, seq_length),
|
589 |
+
layer_past[1].view(batch_size_times_num_heads, seq_length, head_dim),
|
590 |
+
)
|
591 |
+
for layer_past in past_key_value
|
592 |
+
)
|
593 |
+
|
594 |
+
|
595 |
+
class RWModel(RWPreTrainedModel):
|
596 |
+
def __init__(self, config: RWConfig):
|
597 |
+
super().__init__(config)
|
598 |
+
|
599 |
+
self.embed_dim = config.hidden_size
|
600 |
+
self.num_heads = config.n_head
|
601 |
+
self.alibi = config.alibi
|
602 |
+
|
603 |
+
# Embedding + LN Embedding
|
604 |
+
self.word_embeddings = nn.Embedding(config.vocab_size, self.embed_dim)
|
605 |
+
|
606 |
+
# Transformer blocks
|
607 |
+
self.h = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)])
|
608 |
+
|
609 |
+
# Final Layer Norm
|
610 |
+
self.ln_f = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
|
611 |
+
|
612 |
+
self.gradient_checkpointing = False
|
613 |
+
|
614 |
+
# Initialize weights and apply final processing
|
615 |
+
self.post_init()
|
616 |
+
|
617 |
+
def get_input_embeddings(self):
|
618 |
+
return self.word_embeddings
|
619 |
+
|
620 |
+
def _prepare_attn_mask(
|
621 |
+
self, attention_mask: torch.Tensor, input_shape: Tuple[int, int], past_key_values_length: int
|
622 |
+
) -> torch.BoolTensor:
|
623 |
+
# create causal mask
|
624 |
+
# [batch_size, seq_length] -> [batch_size, 1, tgt_length, src_length]
|
625 |
+
combined_attention_mask = None
|
626 |
+
device = attention_mask.device
|
627 |
+
_, src_length = input_shape
|
628 |
+
|
629 |
+
if src_length > 1:
|
630 |
+
combined_attention_mask = _make_causal_mask(
|
631 |
+
input_shape, device=device, past_key_values_length=past_key_values_length
|
632 |
+
)
|
633 |
+
|
634 |
+
# [batch_size, seq_length] -> [batch_size, 1, tgt_length, src_length]
|
635 |
+
expanded_attn_mask = _expand_mask(attention_mask, tgt_length=src_length)
|
636 |
+
combined_attention_mask = (
|
637 |
+
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask | combined_attention_mask
|
638 |
+
)
|
639 |
+
|
640 |
+
return combined_attention_mask
|
641 |
+
|
642 |
+
def set_input_embeddings(self, new_embeddings: torch.Tensor):
|
643 |
+
self.word_embeddings = new_embeddings
|
644 |
+
|
645 |
+
def forward(
|
646 |
+
self,
|
647 |
+
input_ids: Optional[torch.LongTensor] = None,
|
648 |
+
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
|
649 |
+
attention_mask: Optional[torch.Tensor] = None,
|
650 |
+
head_mask: Optional[torch.LongTensor] = None,
|
651 |
+
inputs_embeds: Optional[torch.LongTensor] = None,
|
652 |
+
use_cache: Optional[bool] = None,
|
653 |
+
output_attentions: Optional[bool] = None,
|
654 |
+
output_hidden_states: Optional[bool] = None,
|
655 |
+
return_dict: Optional[bool] = None,
|
656 |
+
offload_cache_to_cpu: Optional[bool] = None,
|
657 |
+
use_flash: Optional[bool] = None,
|
658 |
+
cache_top_k: Optional[int] = None,
|
659 |
+
mem_freq: Optional[int] = None,
|
660 |
+
**deprecated_arguments,
|
661 |
+
) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
|
662 |
+
if deprecated_arguments.pop("position_ids", False) is not False:
|
663 |
+
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
|
664 |
+
warnings.warn(
|
665 |
+
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
|
666 |
+
" passing `position_ids`.",
|
667 |
+
FutureWarning,
|
668 |
+
)
|
669 |
+
if len(deprecated_arguments) > 0:
|
670 |
+
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
|
671 |
+
|
672 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
673 |
+
output_hidden_states = (
|
674 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
675 |
+
)
|
676 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
677 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
678 |
+
|
679 |
+
is_mem = None
|
680 |
+
if input_ids is not None and inputs_embeds is not None:
|
681 |
+
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
682 |
+
elif input_ids is not None:
|
683 |
+
batch_size, seq_length = input_ids.shape
|
684 |
+
if self.config.mem_id is not None:
|
685 |
+
with torch.no_grad():
|
686 |
+
is_mem = input_ids == self.config.mem_id
|
687 |
+
elif inputs_embeds is not None:
|
688 |
+
batch_size, seq_length, _ = inputs_embeds.shape
|
689 |
+
if self.config.mem_id is not None:
|
690 |
+
raise NotImplementedError
|
691 |
+
else:
|
692 |
+
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
693 |
+
|
694 |
+
if past_key_values is None:
|
695 |
+
past_key_values = tuple([None] * len(self.h))
|
696 |
+
|
697 |
+
# Prepare head mask if needed
|
698 |
+
# 1.0 in head_mask indicate we keep the head
|
699 |
+
# attention_probs has shape batch_size x num_heads x N x N
|
700 |
+
# head_mask has shape n_layer x batch x num_heads x N x N
|
701 |
+
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
|
702 |
+
|
703 |
+
if inputs_embeds is None:
|
704 |
+
inputs_embeds = self.word_embeddings(input_ids)
|
705 |
+
|
706 |
+
last_section_mask = None
|
707 |
+
if is_mem is not None and not use_flash:
|
708 |
+
is_mem = is_mem.unsqueeze(1).unsqueeze(2)
|
709 |
+
current_len = input_ids.shape[1]
|
710 |
+
mem_ids = torch.where(attention_mask[..., -current_len:] < -1, 0, torch.cumsum(is_mem, -1) - is_mem.int())
|
711 |
+
last_section_mask = torch.amax(mem_ids, -1, keepdim=True) == mem_ids
|
712 |
+
attention_mask[..., -current_len:].masked_fill_(last_section_mask & is_mem, torch.tensor(torch.finfo(inputs_embeds.dtype).min, device=inputs_embeds.device))
|
713 |
+
last_section_mask.logical_and_(attention_mask[..., -current_len:] > -1)
|
714 |
+
is_mem = is_mem.logical_and(attention_mask[..., -current_len:] > -1)
|
715 |
+
|
716 |
+
hidden_states = inputs_embeds
|
717 |
+
|
718 |
+
presents = () if use_cache else None
|
719 |
+
all_self_attentions = () if output_attentions else None
|
720 |
+
all_hidden_states = () if output_hidden_states else None
|
721 |
+
|
722 |
+
# Compute alibi tensor: check build_alibi_tensor documentation
|
723 |
+
seq_length_with_past = seq_length
|
724 |
+
past_key_values_length = 0
|
725 |
+
if past_key_values[0] is not None:
|
726 |
+
past_key_values_length = past_key_values[0][0].shape[2]
|
727 |
+
if len(past_key_values[0]) > 2:
|
728 |
+
past_key_values_length += past_key_values[0][3].shape[2] * past_key_values[0][3].shape[3]
|
729 |
+
seq_length_with_past = seq_length_with_past + past_key_values_length
|
730 |
+
if attention_mask is None:
|
731 |
+
attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
|
732 |
+
else:
|
733 |
+
attention_mask = attention_mask.to(hidden_states.device)
|
734 |
+
|
735 |
+
if self.alibi:
|
736 |
+
alibi = build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype)
|
737 |
+
else:
|
738 |
+
alibi = None
|
739 |
+
|
740 |
+
causal_mask = self._prepare_attn_mask(
|
741 |
+
attention_mask,
|
742 |
+
input_shape=(batch_size, seq_length),
|
743 |
+
past_key_values_length=past_key_values_length,
|
744 |
+
)
|
745 |
+
|
746 |
+
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
|
747 |
+
|
748 |
+
if output_hidden_states:
|
749 |
+
all_hidden_states = all_hidden_states + (hidden_states,)
|
750 |
+
|
751 |
+
if self.gradient_checkpointing and self.training:
|
752 |
+
|
753 |
+
if use_cache:
|
754 |
+
logger.warning(
|
755 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
756 |
+
)
|
757 |
+
use_cache = False
|
758 |
+
|
759 |
+
def create_custom_forward(module):
|
760 |
+
def custom_forward(*inputs, **kwargs):
|
761 |
+
# None for past_key_value
|
762 |
+
return module(*inputs, use_cache=use_cache, output_attentions=output_attentions, **kwargs)
|
763 |
+
|
764 |
+
return custom_forward
|
765 |
+
|
766 |
+
outputs = torch.utils.checkpoint.checkpoint(
|
767 |
+
create_custom_forward(block),
|
768 |
+
hidden_states,
|
769 |
+
alibi,
|
770 |
+
causal_mask,
|
771 |
+
head_mask[i],
|
772 |
+
use_reentrant=False,
|
773 |
+
is_mem=is_mem,
|
774 |
+
last_section_mask=last_section_mask,
|
775 |
+
offload_cache_to_cpu=offload_cache_to_cpu,
|
776 |
+
use_flash=use_flash,
|
777 |
+
cache_top_k=cache_top_k,
|
778 |
+
mem_freq=mem_freq,
|
779 |
+
)
|
780 |
+
else:
|
781 |
+
outputs = block(
|
782 |
+
hidden_states,
|
783 |
+
layer_past=layer_past,
|
784 |
+
attention_mask=causal_mask,
|
785 |
+
head_mask=head_mask[i],
|
786 |
+
use_cache=use_cache,
|
787 |
+
output_attentions=output_attentions,
|
788 |
+
alibi=alibi,
|
789 |
+
is_mem=is_mem,
|
790 |
+
last_section_mask=last_section_mask,
|
791 |
+
offload_cache_to_cpu=offload_cache_to_cpu,
|
792 |
+
use_flash=use_flash,
|
793 |
+
cache_top_k=cache_top_k,
|
794 |
+
mem_freq=mem_freq,
|
795 |
+
)
|
796 |
+
|
797 |
+
hidden_states = outputs[0]
|
798 |
+
if use_cache is True:
|
799 |
+
presents = presents + (outputs[1],)
|
800 |
+
|
801 |
+
if output_attentions:
|
802 |
+
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
|
803 |
+
|
804 |
+
# Add last hidden state
|
805 |
+
hidden_states = self.ln_f(hidden_states)
|
806 |
+
|
807 |
+
if output_hidden_states:
|
808 |
+
all_hidden_states = all_hidden_states + (hidden_states,)
|
809 |
+
|
810 |
+
if not return_dict:
|
811 |
+
return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
|
812 |
+
|
813 |
+
return BaseModelOutputWithPastAndCrossAttentions(
|
814 |
+
last_hidden_state=hidden_states,
|
815 |
+
past_key_values=presents,
|
816 |
+
hidden_states=all_hidden_states,
|
817 |
+
attentions=all_self_attentions,
|
818 |
+
)
|
819 |
+
|
820 |
+
|
821 |
+
class RWForCausalLM(RWPreTrainedModel):
|
822 |
+
_keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
|
823 |
+
|
824 |
+
def __init__(self, config: RWConfig):
|
825 |
+
super().__init__(config)
|
826 |
+
self.transformer = RWModel(config)
|
827 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
828 |
+
self.auto_insert_landmarks = False
|
829 |
+
self.always_use_flash = False
|
830 |
+
|
831 |
+
# Initialize weights and apply final processing
|
832 |
+
self.post_init()
|
833 |
+
|
834 |
+
def get_output_embeddings(self):
|
835 |
+
return self.lm_head
|
836 |
+
|
837 |
+
def set_output_embeddings(self, new_embeddings: torch.Tensor):
|
838 |
+
self.lm_head = new_embeddings
|
839 |
+
|
840 |
+
def set_mem_id(self, mem_id):
|
841 |
+
if self.config.mem_id is not None:
|
842 |
+
assert mem_id == self.config.mem_id, "Chanigng mem_id can break the model. If you really intend to do this, manually disable this check"
|
843 |
+
self.config.mem_id = mem_id
|
844 |
+
|
845 |
+
def enable_landmark_insertion(self):
|
846 |
+
self.auto_insert_landmarks = True
|
847 |
+
|
848 |
+
def enable_flash(self):
|
849 |
+
self.always_use_flash = True
|
850 |
+
|
851 |
+
def prepare_inputs_for_generation(
|
852 |
+
self,
|
853 |
+
input_ids: torch.LongTensor,
|
854 |
+
past: Optional[torch.Tensor] = None,
|
855 |
+
attention_mask: Optional[torch.Tensor] = None,
|
856 |
+
**kwargs,
|
857 |
+
) -> dict:
|
858 |
+
total_len = input_ids.shape[1]
|
859 |
+
|
860 |
+
# only last token for input_ids if past is not None
|
861 |
+
if past:
|
862 |
+
input_ids = input_ids[:, -1].unsqueeze(-1)
|
863 |
+
|
864 |
+
# the cache may be in the stardard format (e.g. in contrastive search), convert to our's format if needed
|
865 |
+
if past[0][0].shape[0] == input_ids.shape[0]:
|
866 |
+
past = self._convert_to_rw_cache(past)
|
867 |
+
|
868 |
+
use_flash = False if kwargs.get("use_flash") is not None else None
|
869 |
+
else:
|
870 |
+
prev_len = 0
|
871 |
+
use_flash = kwargs.get("use_flash")
|
872 |
+
|
873 |
+
mem_freq = kwargs.get("mem_freq") or self.config.mem_freq
|
874 |
+
|
875 |
+
if mem_freq is not None:
|
876 |
+
# if position_ids is not None:
|
877 |
+
# raise NotImplementedError
|
878 |
+
T = input_ids.shape[1]
|
879 |
+
|
880 |
+
prev_incomplete_len = prev_len % mem_freq
|
881 |
+
prev_complete_len = prev_len - prev_incomplete_len
|
882 |
+
incomplete_len = total_len % mem_freq
|
883 |
+
new_full_len = total_len - prev_complete_len - incomplete_len
|
884 |
+
|
885 |
+
prev_input, input_ids_with_mem, input_ids_without_mem = torch.split(input_ids, (prev_complete_len, new_full_len, incomplete_len), dim=-1)
|
886 |
+
|
887 |
+
bsz, q_len = input_ids.size()
|
888 |
+
input_ids_with_mem = input_ids_with_mem.view(bsz, -1, mem_freq)
|
889 |
+
input_ids_with_mem = torch.cat(
|
890 |
+
(
|
891 |
+
input_ids_with_mem,
|
892 |
+
input_ids_with_mem.new_full((bsz, input_ids_with_mem.shape[1], 1), self.config.mem_id)
|
893 |
+
),
|
894 |
+
dim=-1
|
895 |
+
).view(bsz, -1)
|
896 |
+
input_ids = torch.cat((prev_input, input_ids_with_mem, input_ids_without_mem), dim=-1)
|
897 |
+
if attention_mask is not None:
|
898 |
+
attention_mask_with_mem, attention_mask_without_mem = torch.split(attention_mask, (prev_complete_len + new_full_len, incomplete_len), dim=-1)
|
899 |
+
attention_mask_with_mem = attention_mask_with_mem.view(bsz, -1, mem_freq)
|
900 |
+
attention_mask_with_mem = torch.cat(
|
901 |
+
(
|
902 |
+
attention_mask_with_mem,
|
903 |
+
attention_mask_with_mem.new_ones((bsz, attention_mask_with_mem.shape[1], 1))
|
904 |
+
),
|
905 |
+
dim=-1
|
906 |
+
).view(bsz, -1)
|
907 |
+
attention_mask = torch.cat((attention_mask_with_mem, attention_mask_without_mem), dim=-1)
|
908 |
+
|
909 |
+
input_ids = input_ids[:, prev_len:]
|
910 |
+
|
911 |
+
return {
|
912 |
+
"input_ids": input_ids,
|
913 |
+
"past_key_values": past,
|
914 |
+
"use_cache": kwargs.get("use_cache"),
|
915 |
+
"attention_mask": attention_mask,
|
916 |
+
"offload_cache_to_cpu": kwargs.get("offload_cache_to_cpu"),
|
917 |
+
"use_flash": use_flash,
|
918 |
+
"cache_top_k": kwargs.get("cache_top_k"),
|
919 |
+
"max_chunk_length": kwargs.get("max_chunk_length", 0),
|
920 |
+
"mem_freq": mem_freq,
|
921 |
+
"drop_last_logit_if_mem": True,
|
922 |
+
}
|
923 |
+
|
924 |
+
def forward(
|
925 |
+
self,
|
926 |
+
input_ids: Optional[torch.LongTensor] = None,
|
927 |
+
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
|
928 |
+
attention_mask: Optional[torch.Tensor] = None,
|
929 |
+
head_mask: Optional[torch.Tensor] = None,
|
930 |
+
inputs_embeds: Optional[torch.Tensor] = None,
|
931 |
+
labels: Optional[torch.Tensor] = None,
|
932 |
+
use_cache: Optional[bool] = None,
|
933 |
+
output_attentions: Optional[bool] = None,
|
934 |
+
output_hidden_states: Optional[bool] = None,
|
935 |
+
return_dict: Optional[bool] = None,
|
936 |
+
offload_cache_to_cpu: Optional[bool] = None,
|
937 |
+
use_flash: Optional[bool] = None,
|
938 |
+
cache_top_k: Optional[int] = None,
|
939 |
+
max_chunk_length: Optional[int] = 0,
|
940 |
+
mem_freq: Optional[int] = None,
|
941 |
+
drop_last_logit_if_mem: Optional[bool] = False,
|
942 |
+
**deprecated_arguments,
|
943 |
+
) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
|
944 |
+
r"""
|
945 |
+
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
946 |
+
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
|
947 |
+
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
|
948 |
+
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
|
949 |
+
"""
|
950 |
+
if deprecated_arguments.pop("position_ids", False) is not False:
|
951 |
+
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
|
952 |
+
warnings.warn(
|
953 |
+
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
|
954 |
+
" passing `position_ids`.",
|
955 |
+
FutureWarning,
|
956 |
+
)
|
957 |
+
if len(deprecated_arguments) > 0:
|
958 |
+
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
|
959 |
+
|
960 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
961 |
+
|
962 |
+
use_flash = use_flash if use_flash is not None else self.always_use_flash
|
963 |
+
|
964 |
+
if self.auto_insert_landmarks:
|
965 |
+
mem_freq = self.config.mem_freq
|
966 |
+
assert self.config.mem_freq is not None
|
967 |
+
block_size = self.config.mem_freq + 1
|
968 |
+
input_ids = input_ids.view(input_ids.shape[0], -1, block_size - 1)
|
969 |
+
input_ids = torch.cat((input_ids, input_ids.new_full((input_ids.shape[0], input_ids.shape[1], 1), self.config.mem_id)), dim=-1)
|
970 |
+
input_ids = input_ids.view(input_ids.shape[0], -1)
|
971 |
+
if attention_mask is not None:
|
972 |
+
attention_mask = attention_mask.view(attention_mask.shape[0], -1, block_size - 1)
|
973 |
+
attention_mask = torch.cat((attention_mask, attention_mask.new_ones((attention_mask.shape[0], attention_mask.shape[1], 1))), dim=-1)
|
974 |
+
attention_mask = attention_mask.view(attention_mask.shape[0], -1)
|
975 |
+
|
976 |
+
if max_chunk_length == 0:
|
977 |
+
if cache_top_k is not None:
|
978 |
+
max_chunk_length = self.config.train_context_length - self.config.train_context_length % (mem_freq + 1) - (cache_top_k + 1) * (mem_freq + 1)
|
979 |
+
if max_chunk_length <= 0:
|
980 |
+
raise ValueError("K is too large for this model.")
|
981 |
+
else:
|
982 |
+
max_chunk_length = None
|
983 |
+
|
984 |
+
window_len = max_chunk_length or input_ids.shape[1]
|
985 |
+
if use_flash:
|
986 |
+
assert window_len % (mem_freq + 1) == 0
|
987 |
+
last_logits = None
|
988 |
+
for step, idx in enumerate(range(0, input_ids.shape[1], window_len)):
|
989 |
+
if idx >= 1:
|
990 |
+
if output_attentions or output_hidden_states:
|
991 |
+
raise NotImplementedError
|
992 |
+
if not use_cache:
|
993 |
+
raise NotImplementedError
|
994 |
+
|
995 |
+
outputs = self.transformer(
|
996 |
+
input_ids[:, idx:idx + window_len],
|
997 |
+
past_key_values=past_key_values,
|
998 |
+
attention_mask=attention_mask[:, :idx + window_len + attention_mask.shape[1] - input_ids.shape[1]] if attention_mask is not None else None,
|
999 |
+
head_mask=head_mask, ## ??
|
1000 |
+
inputs_embeds=inputs_embeds[:, idx:idx + window_len] if inputs_embeds is not None else None,
|
1001 |
+
use_cache=use_cache,
|
1002 |
+
output_attentions=output_attentions,
|
1003 |
+
output_hidden_states=output_hidden_states,
|
1004 |
+
return_dict=return_dict,
|
1005 |
+
offload_cache_to_cpu=offload_cache_to_cpu,
|
1006 |
+
use_flash=(use_flash or self.auto_insert_landmarks),
|
1007 |
+
cache_top_k=cache_top_k,
|
1008 |
+
mem_freq=mem_freq,
|
1009 |
+
)
|
1010 |
+
past_key_values = outputs[1]
|
1011 |
+
if last_logits is not None:
|
1012 |
+
last_logits = torch.cat((last_logits, outputs[0]), dim=-2)
|
1013 |
+
last_logits = outputs[0]
|
1014 |
+
|
1015 |
+
hidden_states = last_logits
|
1016 |
+
if self.auto_insert_landmarks:
|
1017 |
+
block_size = self.config.mem_freq + 1
|
1018 |
+
hidden_states = hidden_states.reshape(hidden_states.shape[0], hidden_states.shape[1] // block_size, block_size, hidden_states.shape[2])
|
1019 |
+
hidden_states = hidden_states[:, :, :block_size - 1]
|
1020 |
+
hidden_states = hidden_states.reshape(hidden_states.shape[0], -1, hidden_states.shape[3])
|
1021 |
+
if drop_last_logit_if_mem:
|
1022 |
+
is_any_mem = (input_ids[:, -1] == self.config.mem_id).any()
|
1023 |
+
are_all_mem = (input_ids[:, -1] == self.config.mem_id).all()
|
1024 |
+
assert is_any_mem == are_all_mem
|
1025 |
+
if is_any_mem:
|
1026 |
+
hidden_states = hidden_states[:, :-1]
|
1027 |
+
|
1028 |
+
lm_logits = self.lm_head(hidden_states)
|
1029 |
+
|
1030 |
+
loss = None
|
1031 |
+
if labels is not None:
|
1032 |
+
# Shift so that tokens < n predict n
|
1033 |
+
shift_logits = lm_logits[..., :-1, :].contiguous()
|
1034 |
+
shift_labels = labels[..., 1:].contiguous()
|
1035 |
+
batch_size, seq_length, vocab_size = shift_logits.shape
|
1036 |
+
# Flatten the tokens
|
1037 |
+
loss_fct = CrossEntropyLoss()
|
1038 |
+
loss = loss_fct(
|
1039 |
+
shift_logits.view(batch_size * seq_length, vocab_size), shift_labels.view(batch_size * seq_length)
|
1040 |
+
)
|
1041 |
+
|
1042 |
+
if not return_dict:
|
1043 |
+
output = (lm_logits,) + outputs[1:]
|
1044 |
+
return ((loss,) + output) if loss is not None else output
|
1045 |
+
|
1046 |
+
return CausalLMOutputWithCrossAttentions(
|
1047 |
+
loss=loss,
|
1048 |
+
logits=lm_logits,
|
1049 |
+
past_key_values=outputs.past_key_values,
|
1050 |
+
hidden_states=outputs.hidden_states,
|
1051 |
+
attentions=outputs.attentions,
|
1052 |
+
)
|
1053 |
+
|
1054 |
+
def _reorder_cache(
|
1055 |
+
self, past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor
|
1056 |
+
) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]:
|
1057 |
+
"""
|
1058 |
+
This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
|
1059 |
+
[`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
|
1060 |
+
beam_idx at every generation step.
|
1061 |
+
|
1062 |
+
Output shares the same memory storage as `past`.
|
1063 |
+
"""
|
1064 |
+
standardized_past = self._convert_to_standard_cache(past, batch_size=len(beam_idx))
|
1065 |
+
|
1066 |
+
# Get a copy of `beam_idx` on all the devices where we need those indices.
|
1067 |
+
device_to_beam_idx = {
|
1068 |
+
past_state.device: beam_idx.to(past_state.device) for layer_past in past for past_state in layer_past
|
1069 |
+
}
|
1070 |
+
reordered_past = tuple(
|
1071 |
+
(
|
1072 |
+
layer_past[0].index_select(0, device_to_beam_idx[layer_past[0].device]),
|
1073 |
+
layer_past[1].index_select(0, device_to_beam_idx[layer_past[0].device]),
|
1074 |
+
)
|
1075 |
+
for layer_past in standardized_past
|
1076 |
+
)
|
1077 |
+
return self._convert_to_rw_cache(reordered_past)
|
1078 |
+
|
1079 |
+
|
1080 |
+
class RWForSequenceClassification(RWPreTrainedModel):
|
1081 |
+
_keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
|
1082 |
+
|
1083 |
+
def __init__(self, config: RWConfig):
|
1084 |
+
super().__init__(config)
|
1085 |
+
self.num_labels = config.num_labels
|
1086 |
+
self.transformer = RWModel(config)
|
1087 |
+
self.score = nn.Linear(config.hidden_size, config.num_labels, bias=False)
|
1088 |
+
|
1089 |
+
# Initialize weights and apply final processing
|
1090 |
+
self.post_init()
|
1091 |
+
|
1092 |
+
def forward(
|
1093 |
+
self,
|
1094 |
+
input_ids: Optional[torch.LongTensor] = None,
|
1095 |
+
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
|
1096 |
+
attention_mask: Optional[torch.Tensor] = None,
|
1097 |
+
head_mask: Optional[torch.Tensor] = None,
|
1098 |
+
inputs_embeds: Optional[torch.Tensor] = None,
|
1099 |
+
labels: Optional[torch.Tensor] = None,
|
1100 |
+
use_cache: Optional[bool] = None,
|
1101 |
+
output_attentions: Optional[bool] = None,
|
1102 |
+
output_hidden_states: Optional[bool] = None,
|
1103 |
+
return_dict: Optional[bool] = None,
|
1104 |
+
**deprecated_arguments,
|
1105 |
+
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutputWithPast]:
|
1106 |
+
r"""
|
1107 |
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
1108 |
+
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
1109 |
+
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
1110 |
+
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
1111 |
+
"""
|
1112 |
+
if deprecated_arguments.pop("position_ids", False) is not False:
|
1113 |
+
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
|
1114 |
+
warnings.warn(
|
1115 |
+
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
|
1116 |
+
" passing `position_ids`.",
|
1117 |
+
FutureWarning,
|
1118 |
+
)
|
1119 |
+
if len(deprecated_arguments) > 0:
|
1120 |
+
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
|
1121 |
+
|
1122 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
1123 |
+
|
1124 |
+
transformer_outputs = self.transformer(
|
1125 |
+
input_ids,
|
1126 |
+
past_key_values=past_key_values,
|
1127 |
+
attention_mask=attention_mask,
|
1128 |
+
head_mask=head_mask,
|
1129 |
+
inputs_embeds=inputs_embeds,
|
1130 |
+
use_cache=use_cache,
|
1131 |
+
output_attentions=output_attentions,
|
1132 |
+
output_hidden_states=output_hidden_states,
|
1133 |
+
return_dict=return_dict,
|
1134 |
+
)
|
1135 |
+
|
1136 |
+
hidden_states = transformer_outputs[0]
|
1137 |
+
logits = self.score(hidden_states)
|
1138 |
+
|
1139 |
+
if input_ids is not None:
|
1140 |
+
batch_size = input_ids.shape[0]
|
1141 |
+
else:
|
1142 |
+
batch_size = inputs_embeds.shape[0]
|
1143 |
+
|
1144 |
+
if self.config.pad_token_id is None and batch_size != 1:
|
1145 |
+
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
|
1146 |
+
if self.config.pad_token_id is None:
|
1147 |
+
sequence_lengths = -1
|
1148 |
+
else:
|
1149 |
+
if input_ids is not None:
|
1150 |
+
sequence_lengths = torch.ne(input_ids, self.config.pad_token_id).sum(dim=-1) - 1
|
1151 |
+
else:
|
1152 |
+
sequence_lengths = -1
|
1153 |
+
logger.warning(
|
1154 |
+
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
|
1155 |
+
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
|
1156 |
+
)
|
1157 |
+
|
1158 |
+
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
1159 |
+
|
1160 |
+
loss = None
|
1161 |
+
if labels is not None:
|
1162 |
+
if self.config.problem_type is None:
|
1163 |
+
if self.num_labels == 1:
|
1164 |
+
self.config.problem_type = "regression"
|
1165 |
+
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
1166 |
+
self.config.problem_type = "single_label_classification"
|
1167 |
+
else:
|
1168 |
+
self.config.problem_type = "multi_label_classification"
|
1169 |
+
|
1170 |
+
if self.config.problem_type == "regression":
|
1171 |
+
loss_fct = MSELoss()
|
1172 |
+
if self.num_labels == 1:
|
1173 |
+
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
1174 |
+
else:
|
1175 |
+
loss = loss_fct(pooled_logits, labels)
|
1176 |
+
elif self.config.problem_type == "single_label_classification":
|
1177 |
+
loss_fct = CrossEntropyLoss()
|
1178 |
+
loss = loss_fct(pooled_logits, labels)
|
1179 |
+
elif self.config.problem_type == "multi_label_classification":
|
1180 |
+
loss_fct = BCEWithLogitsLoss()
|
1181 |
+
loss = loss_fct(pooled_logits, labels)
|
1182 |
+
if not return_dict:
|
1183 |
+
output = (pooled_logits,) + transformer_outputs[1:]
|
1184 |
+
return ((loss,) + output) if loss is not None else output
|
1185 |
+
|
1186 |
+
return SequenceClassifierOutputWithPast(
|
1187 |
+
loss=loss,
|
1188 |
+
logits=pooled_logits,
|
1189 |
+
past_key_values=transformer_outputs.past_key_values,
|
1190 |
+
hidden_states=transformer_outputs.hidden_states,
|
1191 |
+
attentions=transformer_outputs.attentions,
|
1192 |
+
)
|
1193 |
+
|
1194 |
+
|
1195 |
+
class RWForTokenClassification(RWPreTrainedModel):
|
1196 |
+
_keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
|
1197 |
+
|
1198 |
+
def __init__(self, config: RWConfig):
|
1199 |
+
super().__init__(config)
|
1200 |
+
self.num_labels = config.num_labels
|
1201 |
+
|
1202 |
+
self.transformer = RWModel(config)
|
1203 |
+
if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
|
1204 |
+
classifier_dropout = config.classifier_dropout
|
1205 |
+
elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None:
|
1206 |
+
classifier_dropout = config.hidden_dropout
|
1207 |
+
else:
|
1208 |
+
classifier_dropout = 0.1
|
1209 |
+
self.dropout = nn.Dropout(classifier_dropout)
|
1210 |
+
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
1211 |
+
|
1212 |
+
# Initialize weights and apply final processing
|
1213 |
+
self.post_init()
|
1214 |
+
|
1215 |
+
def forward(
|
1216 |
+
self,
|
1217 |
+
input_ids: Optional[torch.LongTensor] = None,
|
1218 |
+
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
|
1219 |
+
attention_mask: Optional[torch.Tensor] = None,
|
1220 |
+
head_mask: Optional[torch.Tensor] = None,
|
1221 |
+
inputs_embeds: Optional[torch.Tensor] = None,
|
1222 |
+
labels: Optional[torch.Tensor] = None,
|
1223 |
+
use_cache: Optional[bool] = None,
|
1224 |
+
output_attentions: Optional[bool] = None,
|
1225 |
+
output_hidden_states: Optional[bool] = None,
|
1226 |
+
return_dict: Optional[bool] = None,
|
1227 |
+
**deprecated_arguments,
|
1228 |
+
) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
|
1229 |
+
r"""
|
1230 |
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
1231 |
+
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
1232 |
+
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
1233 |
+
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
1234 |
+
"""
|
1235 |
+
if deprecated_arguments.pop("position_ids", False) is not False:
|
1236 |
+
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
|
1237 |
+
warnings.warn(
|
1238 |
+
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
|
1239 |
+
" passing `position_ids`.",
|
1240 |
+
FutureWarning,
|
1241 |
+
)
|
1242 |
+
if len(deprecated_arguments) > 0:
|
1243 |
+
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
|
1244 |
+
|
1245 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
1246 |
+
|
1247 |
+
transformer_outputs = self.transformer(
|
1248 |
+
input_ids,
|
1249 |
+
past_key_values=past_key_values,
|
1250 |
+
attention_mask=attention_mask,
|
1251 |
+
head_mask=head_mask,
|
1252 |
+
inputs_embeds=inputs_embeds,
|
1253 |
+
use_cache=use_cache,
|
1254 |
+
output_attentions=output_attentions,
|
1255 |
+
output_hidden_states=output_hidden_states,
|
1256 |
+
return_dict=return_dict,
|
1257 |
+
)
|
1258 |
+
|
1259 |
+
hidden_states = transformer_outputs[0]
|
1260 |
+
hidden_states = self.dropout(hidden_states)
|
1261 |
+
logits = self.classifier(hidden_states)
|
1262 |
+
|
1263 |
+
loss = None
|
1264 |
+
if labels is not None:
|
1265 |
+
batch_size, seq_length = labels.shape
|
1266 |
+
loss_fct = CrossEntropyLoss()
|
1267 |
+
loss = loss_fct(logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length))
|
1268 |
+
|
1269 |
+
if not return_dict:
|
1270 |
+
output = (logits,) + transformer_outputs[2:]
|
1271 |
+
return ((loss,) + output) if loss is not None else output
|
1272 |
+
|
1273 |
+
return TokenClassifierOutput(
|
1274 |
+
loss=loss,
|
1275 |
+
logits=logits,
|
1276 |
+
hidden_states=transformer_outputs.hidden_states,
|
1277 |
+
attentions=transformer_outputs.attentions,
|
1278 |
+
)
|
1279 |
+
|
1280 |
+
|
1281 |
+
class RWForQuestionAnswering(RWPreTrainedModel):
|
1282 |
+
_keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
|
1283 |
+
|
1284 |
+
def __init__(self, config):
|
1285 |
+
super().__init__(config)
|
1286 |
+
self.transformer = RWModel(config)
|
1287 |
+
self.qa_outputs = nn.Linear(config.hidden_size, 2)
|
1288 |
+
|
1289 |
+
# Initialize weights and apply final processing
|
1290 |
+
self.post_init()
|
1291 |
+
|
1292 |
+
def forward(
|
1293 |
+
self,
|
1294 |
+
input_ids: Optional[torch.LongTensor] = None,
|
1295 |
+
attention_mask: Optional[torch.FloatTensor] = None,
|
1296 |
+
position_ids: Optional[torch.LongTensor] = None,
|
1297 |
+
head_mask: Optional[torch.FloatTensor] = None,
|
1298 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
1299 |
+
start_positions: Optional[torch.LongTensor] = None,
|
1300 |
+
end_positions: Optional[torch.LongTensor] = None,
|
1301 |
+
output_attentions: Optional[bool] = None,
|
1302 |
+
output_hidden_states: Optional[bool] = None,
|
1303 |
+
return_dict: Optional[bool] = None,
|
1304 |
+
) -> Union[Tuple, QuestionAnsweringModelOutput]:
|
1305 |
+
r"""
|
1306 |
+
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
1307 |
+
Labels for position (index) of the start of the labelled span for computing the token classification loss.
|
1308 |
+
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
1309 |
+
are not taken into account for computing the loss.
|
1310 |
+
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
1311 |
+
Labels for position (index) of the end of the labelled span for computing the token classification loss.
|
1312 |
+
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
1313 |
+
are not taken into account for computing the loss.
|
1314 |
+
"""
|
1315 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
1316 |
+
|
1317 |
+
outputs = self.transformer(
|
1318 |
+
input_ids,
|
1319 |
+
attention_mask=attention_mask,
|
1320 |
+
position_ids=position_ids,
|
1321 |
+
head_mask=head_mask,
|
1322 |
+
inputs_embeds=inputs_embeds,
|
1323 |
+
output_attentions=output_attentions,
|
1324 |
+
output_hidden_states=output_hidden_states,
|
1325 |
+
return_dict=return_dict,
|
1326 |
+
)
|
1327 |
+
|
1328 |
+
sequence_output = outputs[0]
|
1329 |
+
|
1330 |
+
logits = self.qa_outputs(sequence_output)
|
1331 |
+
start_logits, end_logits = logits.split(1, dim=-1)
|
1332 |
+
start_logits = start_logits.squeeze(-1).contiguous()
|
1333 |
+
end_logits = end_logits.squeeze(-1).contiguous()
|
1334 |
+
|
1335 |
+
total_loss = None
|
1336 |
+
if start_positions is not None and end_positions is not None:
|
1337 |
+
# If we are on multi-GPU, split add a dimension
|
1338 |
+
if len(start_positions.size()) > 1:
|
1339 |
+
start_positions = start_positions.squeeze(-1)
|
1340 |
+
if len(end_positions.size()) > 1:
|
1341 |
+
end_positions = end_positions.squeeze(-1)
|
1342 |
+
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
1343 |
+
ignored_index = start_logits.size(1)
|
1344 |
+
start_positions = start_positions.clamp(0, ignored_index)
|
1345 |
+
end_positions = end_positions.clamp(0, ignored_index)
|
1346 |
+
|
1347 |
+
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
|
1348 |
+
start_loss = loss_fct(start_logits, start_positions)
|
1349 |
+
end_loss = loss_fct(end_logits, end_positions)
|
1350 |
+
total_loss = (start_loss + end_loss) / 2
|
1351 |
+
|
1352 |
+
if not return_dict:
|
1353 |
+
output = (start_logits, end_logits) + outputs[2:]
|
1354 |
+
return ((total_loss,) + output) if total_loss is not None else output
|
1355 |
+
|
1356 |
+
return QuestionAnsweringModelOutput(
|
1357 |
+
loss=total_loss,
|
1358 |
+
start_logits=start_logits,
|
1359 |
+
end_logits=end_logits,
|
1360 |
+
hidden_states=outputs.hidden_states,
|
1361 |
+
attentions=outputs.attentions,
|
1362 |
+
)
|
code/redpajama.py
ADDED
@@ -0,0 +1,174 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Copyright 2023 Together Computer
|
2 |
+
#
|
3 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
4 |
+
# you may not use this file except in compliance with the License.
|
5 |
+
# You may obtain a copy of the License at
|
6 |
+
#
|
7 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
8 |
+
#
|
9 |
+
# Unless required by applicable law or agreed to in writing, software
|
10 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
11 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
12 |
+
# See the License for the specific language governing permissions and
|
13 |
+
# limitations under the License.
|
14 |
+
|
15 |
+
# Lint as: python3
|
16 |
+
"""RedPajama: An Open-Source, Clean-Room 1.2 Trillion Token Dataset."""
|
17 |
+
|
18 |
+
|
19 |
+
import json
|
20 |
+
|
21 |
+
import datasets
|
22 |
+
import traceback
|
23 |
+
import numpy as np
|
24 |
+
import math
|
25 |
+
|
26 |
+
logger = datasets.logging.get_logger(__name__)
|
27 |
+
|
28 |
+
|
29 |
+
_DESCRIPTION = """\
|
30 |
+
RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset.
|
31 |
+
"""
|
32 |
+
|
33 |
+
_URL_LISTS = {
|
34 |
+
"arxiv": "urls/arxiv.txt",
|
35 |
+
"book": "urls/book.txt",
|
36 |
+
"c4": "urls/c4.txt",
|
37 |
+
"common_crawl": "urls/common_crawl.txt",
|
38 |
+
"github": "urls/github.txt",
|
39 |
+
"stackexchange": "urls/stackexchange.txt",
|
40 |
+
"wikipedia": "urls/wikipedia.txt",
|
41 |
+
}
|
42 |
+
|
43 |
+
|
44 |
+
class RedPajama1TConfig(datasets.BuilderConfig):
|
45 |
+
"""BuilderConfig for RedPajama sample."""
|
46 |
+
|
47 |
+
def __init__(self, *args, subsets, p_sample=None, **kwargs):
|
48 |
+
"""BuilderConfig for RedPajama.
|
49 |
+
Args:
|
50 |
+
**kwargs: keyword arguments forwarded to super.
|
51 |
+
"""
|
52 |
+
super(RedPajama1TConfig, self).__init__(**kwargs)
|
53 |
+
|
54 |
+
self.subsets = subsets
|
55 |
+
self.p_sample = p_sample
|
56 |
+
|
57 |
+
|
58 |
+
class RedPajama1T(datasets.GeneratorBasedBuilder):
|
59 |
+
"""RedPajama: Reproducing the LLaMA training dataset of over 1.2 trillion tokens. Version 1.0.0."""
|
60 |
+
BUILDER_CONFIG_CLASS = RedPajama1TConfig
|
61 |
+
BUILDER_CONFIGS = [
|
62 |
+
RedPajama1TConfig(
|
63 |
+
subsets = list(_URL_LISTS.keys()),
|
64 |
+
name="plain_text",
|
65 |
+
version=datasets.Version("1.0.0", ""),
|
66 |
+
description="Plain text",
|
67 |
+
),
|
68 |
+
RedPajama1TConfig(
|
69 |
+
subsets = list(_URL_LISTS.keys()),
|
70 |
+
name="plain_text_tenpercent",
|
71 |
+
version=datasets.Version("1.0.0", ""),
|
72 |
+
description="Plain text",
|
73 |
+
p_sample=0.1
|
74 |
+
),
|
75 |
+
]
|
76 |
+
|
77 |
+
def _info(self):
|
78 |
+
return datasets.DatasetInfo(
|
79 |
+
description=_DESCRIPTION,
|
80 |
+
features=datasets.Features(
|
81 |
+
{
|
82 |
+
"text": datasets.Value("string"),
|
83 |
+
"meta": datasets.Value("string"),
|
84 |
+
"red_pajama_subset": datasets.Value("string"),
|
85 |
+
}
|
86 |
+
),
|
87 |
+
supervised_keys=None,
|
88 |
+
)
|
89 |
+
|
90 |
+
def _split_generators(self, dl_manager):
|
91 |
+
url_lists = dl_manager.download_and_extract({
|
92 |
+
subset: _URL_LISTS[subset] for subset in self.config.subsets
|
93 |
+
})
|
94 |
+
|
95 |
+
urls = {}
|
96 |
+
rng = np.random.default_rng(seed=2)
|
97 |
+
|
98 |
+
for subset, url_list in url_lists.items():
|
99 |
+
with open(url_list, encoding="utf-8") as f:
|
100 |
+
urls[subset] = [line.strip() for line in f]
|
101 |
+
if self.config.p_sample is not None:
|
102 |
+
urls[subset] = rng.choice(
|
103 |
+
urls[subset],
|
104 |
+
size=int(math.ceil(len(urls[subset]) * self.config.p_sample)), replace=False).tolist()
|
105 |
+
|
106 |
+
downloaded_files = dl_manager.download(urls)
|
107 |
+
|
108 |
+
return [
|
109 |
+
datasets.SplitGenerator(
|
110 |
+
name=datasets.Split.TRAIN,
|
111 |
+
gen_kwargs = {
|
112 |
+
"files": {
|
113 |
+
subset: downloaded_files[subset]
|
114 |
+
for subset in self.config.subsets
|
115 |
+
}
|
116 |
+
}
|
117 |
+
)
|
118 |
+
]
|
119 |
+
|
120 |
+
def _generate_examples(self, files):
|
121 |
+
"""This function returns the examples in the raw (text) form."""
|
122 |
+
key = 0
|
123 |
+
for subset in files:
|
124 |
+
if subset == "common_crawl":
|
125 |
+
import zstandard as zstd
|
126 |
+
|
127 |
+
for path in files[subset]:
|
128 |
+
with zstd.open(open(path, "rb"), "rt", encoding="utf-8") as f:
|
129 |
+
for i, row in enumerate(f):
|
130 |
+
try:
|
131 |
+
data = json.loads(row)
|
132 |
+
text = data["text"]
|
133 |
+
del data["text"]
|
134 |
+
yield key, {
|
135 |
+
"text": text,
|
136 |
+
"meta": json.dumps(data),
|
137 |
+
"red_pajama_subset": subset,
|
138 |
+
}
|
139 |
+
key += 1
|
140 |
+
except Exception as e:
|
141 |
+
print(f'Subset: {subset}')
|
142 |
+
print(f'Path: {path}')
|
143 |
+
print(f'Row: {row}')
|
144 |
+
traceback.print_exc()
|
145 |
+
|
146 |
+
raise e
|
147 |
+
else:
|
148 |
+
for path in files[subset]:
|
149 |
+
with open(path, encoding="utf-8") as f:
|
150 |
+
for i, row in enumerate(f):
|
151 |
+
try:
|
152 |
+
data = json.loads(row)
|
153 |
+
if "meta" not in data:
|
154 |
+
text = data["text"]
|
155 |
+
del data["text"]
|
156 |
+
yield key, {
|
157 |
+
"text": text,
|
158 |
+
"meta": json.dumps(data),
|
159 |
+
"red_pajama_subset": subset,
|
160 |
+
}
|
161 |
+
else:
|
162 |
+
yield key, {
|
163 |
+
"text": data["text"],
|
164 |
+
"meta": data["meta"],
|
165 |
+
"red_pajama_subset": subset,
|
166 |
+
}
|
167 |
+
key += 1
|
168 |
+
except Exception as e:
|
169 |
+
print(f'Subset: {subset}')
|
170 |
+
print(f'Path: {path}')
|
171 |
+
print(f'Row: {row}')
|
172 |
+
traceback.print_exc()
|
173 |
+
|
174 |
+
raise e
|
code/run_test.py
ADDED
@@ -0,0 +1,171 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Copyright 2023 Amirkeivan Mohtashami, Martin Jaggi
|
2 |
+
#
|
3 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
4 |
+
# you may not use this file except in compliance with the License.
|
5 |
+
# You may obtain a copy of the License at
|
6 |
+
#
|
7 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
8 |
+
#
|
9 |
+
# Unless required by applicable law or agreed to in writing, software
|
10 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
11 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
12 |
+
# See the License for the specific language governing permissions and
|
13 |
+
# limitations under the License.
|
14 |
+
|
15 |
+
import torch
|
16 |
+
|
17 |
+
import os
|
18 |
+
import random
|
19 |
+
import re
|
20 |
+
import requests
|
21 |
+
|
22 |
+
|
23 |
+
llama_weights_7b_base = "/llama_weights/7B_hf/"
|
24 |
+
llama_weights_7b_tuned = "/llama-redpajama-mem-15000-with-mem/"
|
25 |
+
cache_path = "/hf-cache/"
|
26 |
+
use_flash = False # using flash for inference is only implemented for when offloading kv to cpu
|
27 |
+
top_k = 5
|
28 |
+
dtype = torch.bfloat16
|
29 |
+
|
30 |
+
def make_llama_base_pipe():
|
31 |
+
|
32 |
+
from transformers import pipeline
|
33 |
+
|
34 |
+
from transformers.models.llama import LlamaForCausalLM
|
35 |
+
|
36 |
+
llama_base = LlamaForCausalLM.from_pretrained(
|
37 |
+
llama_weights_7b_base,
|
38 |
+
cache_dir=cache_path,
|
39 |
+
)
|
40 |
+
|
41 |
+
llama_base = llama_base.to('cuda:0')
|
42 |
+
|
43 |
+
import transformers
|
44 |
+
|
45 |
+
tokenizer = transformers.AutoTokenizer.from_pretrained(
|
46 |
+
llama_weights_7b_base,
|
47 |
+
cache_dir=cache_path,
|
48 |
+
model_max_length=2048,
|
49 |
+
padding_side="right",
|
50 |
+
use_fast=False,
|
51 |
+
)
|
52 |
+
|
53 |
+
llama_base_pipe = pipeline("text-generation", model=llama_base, tokenizer=tokenizer, device=llama_base.device)
|
54 |
+
return llama_base_pipe
|
55 |
+
|
56 |
+
|
57 |
+
|
58 |
+
llama_base_pipe = make_llama_base_pipe()
|
59 |
+
|
60 |
+
def make_llama_mem_pipe():
|
61 |
+
from llama_mem import LlamaForCausalLM
|
62 |
+
|
63 |
+
model = LlamaForCausalLM.from_pretrained(
|
64 |
+
llama_weights_7b_tuned,
|
65 |
+
cache_dir=cache_path,
|
66 |
+
torch_dtype=dtype
|
67 |
+
)
|
68 |
+
|
69 |
+
model.to('cuda:1')
|
70 |
+
|
71 |
+
import transformers
|
72 |
+
|
73 |
+
tokenizer = transformers.AutoTokenizer.from_pretrained(
|
74 |
+
llama_weights_7b_tuned,
|
75 |
+
cache_dir=cache_path,
|
76 |
+
model_max_length=model.config.train_context_length,
|
77 |
+
padding_side="right",
|
78 |
+
use_fast=False,
|
79 |
+
)
|
80 |
+
mem_id = tokenizer.convert_tokens_to_ids("<landmark>")
|
81 |
+
model.set_mem_id(mem_id)
|
82 |
+
from transformers import pipeline
|
83 |
+
llama_mem_pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device=model.device,
|
84 |
+
offload_cache_to_cpu=use_flash, use_flash=use_flash,
|
85 |
+
cache_top_k=top_k)
|
86 |
+
return llama_mem_pipe
|
87 |
+
|
88 |
+
|
89 |
+
llama_mem_pipe = make_llama_mem_pipe()
|
90 |
+
|
91 |
+
|
92 |
+
|
93 |
+
pipes = {"base": llama_base_pipe, "mem": llama_mem_pipe}
|
94 |
+
|
95 |
+
|
96 |
+
def generate_prompt(n_garbage):
|
97 |
+
"""Generates a text file and inserts an execute line at a random position."""
|
98 |
+
n_garbage_prefix = random.randint(0, n_garbage)
|
99 |
+
n_garbage_suffix = n_garbage - n_garbage_prefix
|
100 |
+
|
101 |
+
task_description = "There is an important info hidden inside a lot of irrelevant text. Find it and memorize them. I will quiz you about the important information there."
|
102 |
+
garbage = "The grass is green. The sky is blue. The sun is yellow. Here we go. There and back again."
|
103 |
+
garbage_inf = " ".join([garbage] * 2000)
|
104 |
+
assert len(garbage_inf) >= n_garbage
|
105 |
+
garbage_prefix = garbage_inf[:n_garbage_prefix]
|
106 |
+
garbage_suffix = garbage_inf[:n_garbage_suffix]
|
107 |
+
pass_key = random.randint(1, 50000)
|
108 |
+
information_line = f"The pass key is {pass_key}. Remember it. {pass_key} is the pass key."
|
109 |
+
final_question = "What is the pass key? The pass key is"
|
110 |
+
lines = [
|
111 |
+
task_description,
|
112 |
+
garbage_prefix,
|
113 |
+
information_line,
|
114 |
+
garbage_suffix,
|
115 |
+
final_question
|
116 |
+
]
|
117 |
+
return "\n".join(lines), pass_key
|
118 |
+
|
119 |
+
|
120 |
+
|
121 |
+
def test_model(prompt_text, pass_key, model_name):
|
122 |
+
response = pipes[model_name](prompt_text,num_return_sequences=1, max_new_tokens=10)[0]["generated_text"][len(prompt_text):]
|
123 |
+
assert f"The pass key is {pass_key}" in prompt_text
|
124 |
+
|
125 |
+
try:
|
126 |
+
pass_key = int(re.search(r'\d+', response).group())
|
127 |
+
except:
|
128 |
+
pass_key = response[:20]
|
129 |
+
|
130 |
+
return pass_key
|
131 |
+
|
132 |
+
|
133 |
+
n_values = [0, 100, 500, 1000, 5000, 8000, 10000, 12000, 14000, 18000, 20000, 25000, 38000]
|
134 |
+
num_tests = 50
|
135 |
+
models = ["base", "mem"]
|
136 |
+
accuracies = {x: [] for x in models}
|
137 |
+
individual_results = {x: [] for x in models}
|
138 |
+
|
139 |
+
for n in n_values:
|
140 |
+
|
141 |
+
correct_count = {x: 0 for x in models}
|
142 |
+
|
143 |
+
n_results = {x: [] for x in models}
|
144 |
+
for i in range(num_tests):
|
145 |
+
print(f"\nRunning test {i + 1}/{num_tests} for n = {n}...")
|
146 |
+
prompt_text, pass_key = generate_prompt(n)
|
147 |
+
|
148 |
+
|
149 |
+
|
150 |
+
for model_name in models:
|
151 |
+
if pipes[model_name] is None:
|
152 |
+
continue
|
153 |
+
num_tokens = len(pipes[model_name].tokenizer.encode(prompt_text))
|
154 |
+
|
155 |
+
print("Number of tokens in this prompt: ", num_tokens)
|
156 |
+
model_output = test_model(prompt_text, pass_key, model_name)
|
157 |
+
print(f"Expected number in the prompt: {pass_key}, {model_name} output: {model_output}")
|
158 |
+
|
159 |
+
if pass_key == model_output:
|
160 |
+
correct_count[model_name] += 1
|
161 |
+
n_results[model_name].append(1)
|
162 |
+
print("Success!")
|
163 |
+
else:
|
164 |
+
n_results[model_name].append(0)
|
165 |
+
print("Fail.")
|
166 |
+
|
167 |
+
for model in models:
|
168 |
+
accuracy = (correct_count[model] / num_tests) * 100
|
169 |
+
print(f"Accuracy {model} for n = {n}: {accuracy}%")
|
170 |
+
accuracies[model].append(accuracy)
|
171 |
+
individual_results[model].append(n_results)
|
code/run_train_1x.sh
ADDED
@@ -0,0 +1,23 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#!/bin/bash
|
2 |
+
CUDA_VISIBLE_DEVICES=0 python train.py \
|
3 |
+
--model_name_or_path tiiuae/falcon-7b \
|
4 |
+
--bf16 True \
|
5 |
+
--output_dir ./out_dir/ \
|
6 |
+
--cache_dir ./hf-cache/ \
|
7 |
+
--num_train_epochs 1 \
|
8 |
+
--per_device_train_batch_size 1 \
|
9 |
+
--per_device_eval_batch_size 1 \
|
10 |
+
--gradient_accumulation_steps 1 \
|
11 |
+
--evaluation_strategy "no" \
|
12 |
+
--save_strategy "steps" \
|
13 |
+
--save_steps 2000 \
|
14 |
+
--save_total_limit 2 \
|
15 |
+
--learning_rate 2e-5 \
|
16 |
+
--weight_decay 0.1 \
|
17 |
+
--warmup_ratio 0.03 \
|
18 |
+
--lr_scheduler_type "cosine" \
|
19 |
+
--logging_steps 1 \
|
20 |
+
--tf32 True \
|
21 |
+
--max_steps 15000 \
|
22 |
+
--model_max_length 1024 \
|
23 |
+
--mem_freq 31
|
code/run_train_8x.sh
ADDED
@@ -0,0 +1,25 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#!/bin/bash
|
2 |
+
torchrun --nproc_per_node=8 train.py \
|
3 |
+
--model_name_or_path tiiuae/falcon-7b \
|
4 |
+
--bf16 True \
|
5 |
+
--output_dir ./out_dir/ \
|
6 |
+
--cache_dir ./hf-cache/ \
|
7 |
+
--num_train_epochs 1 \
|
8 |
+
--per_device_train_batch_size 2 \
|
9 |
+
--per_device_eval_batch_size 2 \
|
10 |
+
--gradient_accumulation_steps 8 \
|
11 |
+
--evaluation_strategy "no" \
|
12 |
+
--save_strategy "steps" \
|
13 |
+
--save_steps 2000 \
|
14 |
+
--save_total_limit 2 \
|
15 |
+
--learning_rate 2e-5 \
|
16 |
+
--weight_decay 0.1 \
|
17 |
+
--warmup_ratio 0.03 \
|
18 |
+
--lr_scheduler_type "cosine" \
|
19 |
+
--logging_steps 1 \
|
20 |
+
--tf32 True \
|
21 |
+
--max_steps 15000 \
|
22 |
+
--model_max_length 2048 \
|
23 |
+
--mem_freq 31 \
|
24 |
+
--fsdp "full_shard auto_wrap" \
|
25 |
+
--fsdp_transformer_layer_cls_to_wrap 'DecoderLayer'
|
code/train.py
ADDED
@@ -0,0 +1,206 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Copyright 2023 Amirkeivan Mohtashami, Martin Jaggi
|
2 |
+
#
|
3 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
4 |
+
# you may not use this file except in compliance with the License.
|
5 |
+
# You may obtain a copy of the License at
|
6 |
+
#
|
7 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
8 |
+
#
|
9 |
+
# Unless required by applicable law or agreed to in writing, software
|
10 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
11 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
12 |
+
# See the License for the specific language governing permissions and
|
13 |
+
# limitations under the License.
|
14 |
+
|
15 |
+
#import copy
|
16 |
+
#import logging
|
17 |
+
from dataclasses import dataclass, field
|
18 |
+
from functools import partial
|
19 |
+
from typing import Dict, Optional, Sequence
|
20 |
+
|
21 |
+
|
22 |
+
import torch
|
23 |
+
import transformers
|
24 |
+
#from torch.utils.data import Dataset
|
25 |
+
from transformers import Trainer, DataCollatorForLanguageModeling, get_cosine_schedule_with_warmup
|
26 |
+
|
27 |
+
from modelling_RW import RWForCausalLM
|
28 |
+
#from transformers import AutoModelForCausalLM
|
29 |
+
|
30 |
+
|
31 |
+
from torch.distributed import barrier
|
32 |
+
import os
|
33 |
+
|
34 |
+
|
35 |
+
from datasets import load_dataset
|
36 |
+
|
37 |
+
IGNORE_INDEX = -100
|
38 |
+
DEFAULT_PAD_TOKEN = "[PAD]"
|
39 |
+
DEFAULT_EOS_TOKEN = "</s>"
|
40 |
+
DEFAULT_BOS_TOKEN = "<s>"
|
41 |
+
DEFAULT_UNK_TOKEN = "<unk>"
|
42 |
+
|
43 |
+
|
44 |
+
@dataclass
|
45 |
+
class ModelArguments:
|
46 |
+
model_name_or_path: Optional[str] = field(default="facebook/opt-125m")
|
47 |
+
|
48 |
+
|
49 |
+
@dataclass
|
50 |
+
class TrainingArguments(transformers.TrainingArguments):
|
51 |
+
cache_dir: Optional[str] = field(default=None)
|
52 |
+
#optim: str = field(default="adamw_hf")
|
53 |
+
optim: str = field(default="adamw_torch")
|
54 |
+
model_max_length: int = field(
|
55 |
+
default=128,
|
56 |
+
metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."},
|
57 |
+
)
|
58 |
+
use_flash: bool = field(default=False)
|
59 |
+
mem_freq: int = field(default=63)
|
60 |
+
#report_to: str = "none" # disable logging
|
61 |
+
|
62 |
+
|
63 |
+
class TrainerCosine(Trainer):
|
64 |
+
def create_scheduler(self, num_training_steps: int, optimizer: torch.optim.Optimizer = None):
|
65 |
+
"""
|
66 |
+
Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or
|
67 |
+
passed as an argument.
|
68 |
+
|
69 |
+
Args:
|
70 |
+
num_training_steps (int): The number of training steps to do.
|
71 |
+
"""
|
72 |
+
if self.args.lr_scheduler_type != "cosine":
|
73 |
+
return super().create_scheduler(num_training_steps, optimizer)
|
74 |
+
if self.lr_scheduler is None:
|
75 |
+
self.lr_scheduler = get_cosine_schedule_with_warmup(
|
76 |
+
optimizer=self.optimizer if optimizer is None else optimizer,
|
77 |
+
num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
|
78 |
+
num_training_steps=num_training_steps,
|
79 |
+
num_cycles=0.4 # ~10% of the init lr
|
80 |
+
)
|
81 |
+
return self.lr_scheduler
|
82 |
+
|
83 |
+
|
84 |
+
def smart_tokenizer_and_embedding_resize(
|
85 |
+
special_tokens_dict: Dict,
|
86 |
+
tokenizer: transformers.PreTrainedTokenizer,
|
87 |
+
model: transformers.PreTrainedModel,
|
88 |
+
):
|
89 |
+
"""Resize tokenizer and embedding.
|
90 |
+
|
91 |
+
Note: This is the unoptimized version that may make your embedding size not be divisible by 64.
|
92 |
+
"""
|
93 |
+
num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict)
|
94 |
+
model.resize_token_embeddings(len(tokenizer))
|
95 |
+
|
96 |
+
if num_new_tokens > 0:
|
97 |
+
input_embeddings = model.get_input_embeddings().weight.data
|
98 |
+
output_embeddings = model.get_output_embeddings().weight.data
|
99 |
+
|
100 |
+
input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True)
|
101 |
+
output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True)
|
102 |
+
|
103 |
+
input_embeddings[-num_new_tokens:] = input_embeddings_avg
|
104 |
+
output_embeddings[-num_new_tokens:] = output_embeddings_avg
|
105 |
+
|
106 |
+
def tokenize_fn(tokenizer, example):
|
107 |
+
context_length = tokenizer.model_max_length
|
108 |
+
outputs = tokenizer(
|
109 |
+
tokenizer.eos_token.join(example["text"]),
|
110 |
+
truncation=False,
|
111 |
+
return_tensors="pt",
|
112 |
+
pad_to_multiple_of=context_length,
|
113 |
+
padding=True,
|
114 |
+
)
|
115 |
+
return {"input_ids": outputs["input_ids"].view(-1, context_length)}
|
116 |
+
|
117 |
+
def train():
|
118 |
+
parser = transformers.HfArgumentParser((ModelArguments, TrainingArguments))
|
119 |
+
model_args, training_args = parser.parse_args_into_dataclasses()
|
120 |
+
|
121 |
+
# ensure max length leaves room for landmark tokens
|
122 |
+
model_max_length = training_args.model_max_length - (training_args.model_max_length // training_args.mem_freq)
|
123 |
+
model_max_length = model_max_length // training_args.mem_freq * training_args.mem_freq
|
124 |
+
|
125 |
+
tokenizer = transformers.AutoTokenizer.from_pretrained(
|
126 |
+
model_args.model_name_or_path,
|
127 |
+
cache_dir=training_args.cache_dir,
|
128 |
+
model_max_length=model_max_length,
|
129 |
+
padding_side="right",
|
130 |
+
use_fast=False,
|
131 |
+
)
|
132 |
+
special_tokens_dict = dict()
|
133 |
+
if tokenizer.pad_token is None:
|
134 |
+
special_tokens_dict["pad_token"] = DEFAULT_PAD_TOKEN
|
135 |
+
if tokenizer.eos_token is None:
|
136 |
+
special_tokens_dict["eos_token"] = DEFAULT_EOS_TOKEN
|
137 |
+
if tokenizer.bos_token is None:
|
138 |
+
special_tokens_dict["bos_token"] = DEFAULT_BOS_TOKEN
|
139 |
+
if tokenizer.unk_token is None:
|
140 |
+
special_tokens_dict["unk_token"] = DEFAULT_UNK_TOKEN
|
141 |
+
mem_token = "<landmark>"
|
142 |
+
special_tokens_dict["additional_special_tokens"] = [mem_token]
|
143 |
+
|
144 |
+
model = RWForCausalLM.from_pretrained(
|
145 |
+
model_args.model_name_or_path,
|
146 |
+
cache_dir=training_args.cache_dir,
|
147 |
+
mem_freq=training_args.mem_freq,
|
148 |
+
torch_dtype=torch.bfloat16,
|
149 |
+
)
|
150 |
+
# model = AutoModelForCausalLM.from_pretrained(
|
151 |
+
# model_args.model_name_or_path,
|
152 |
+
# cache_dir=training_args.cache_dir,
|
153 |
+
# torch_dtype=torch.bfloat16,
|
154 |
+
# trust_remote_code=True,
|
155 |
+
# )
|
156 |
+
|
157 |
+
smart_tokenizer_and_embedding_resize(
|
158 |
+
special_tokens_dict=special_tokens_dict,
|
159 |
+
tokenizer=tokenizer,
|
160 |
+
model=model,
|
161 |
+
)
|
162 |
+
|
163 |
+
mem_id = tokenizer.convert_tokens_to_ids(mem_token)
|
164 |
+
model.set_mem_id(mem_id)
|
165 |
+
print(f"Landmark token: {mem_token}: {mem_id}")
|
166 |
+
|
167 |
+
rank = int(os.environ.get('RANK', -1))
|
168 |
+
if rank > 0:
|
169 |
+
barrier()
|
170 |
+
#dataset = load_dataset("togethercomputer/RedPajama-Data-1T-Sample", cache_dir=training_args.cache_dir, split='train[:100]')
|
171 |
+
dataset = load_dataset("togethercomputer/RedPajama-Data-1T-Sample", cache_dir=training_args.cache_dir, split='train')
|
172 |
+
|
173 |
+
dataset = dataset.map(partial(tokenize_fn, tokenizer), batched=True, num_proc=32, remove_columns=["text", "meta"])
|
174 |
+
|
175 |
+
model.enable_landmark_insertion()
|
176 |
+
model.enable_flash()
|
177 |
+
|
178 |
+
# if training_args.use_flash:
|
179 |
+
# model.enable_landmark_insertion()
|
180 |
+
# model.enable_flash()
|
181 |
+
# else:
|
182 |
+
# dataset = dataset.map(
|
183 |
+
# partial(
|
184 |
+
# add_mem_tokens,
|
185 |
+
# mem_freq=training_args.mem_freq,
|
186 |
+
# mem_id=mem_id
|
187 |
+
# ), batched=False, num_proc=32)
|
188 |
+
|
189 |
+
if rank == 0:
|
190 |
+
barrier()
|
191 |
+
print(dataset)
|
192 |
+
|
193 |
+
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
|
194 |
+
|
195 |
+
trainer = TrainerCosine(
|
196 |
+
model=model, tokenizer=tokenizer, args=training_args,
|
197 |
+
train_dataset=dataset, #dataset["train"],
|
198 |
+
eval_dataset=None,
|
199 |
+
data_collator=data_collator)
|
200 |
+
trainer.train()
|
201 |
+
trainer.save_state()
|
202 |
+
trainer.save_model(output_dir=training_args.output_dir)
|
203 |
+
|
204 |
+
|
205 |
+
if __name__ == "__main__":
|
206 |
+
train()
|
code/weight_diff.py
ADDED
@@ -0,0 +1,158 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li
|
2 |
+
#
|
3 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
4 |
+
# you may not use this file except in compliance with the License.
|
5 |
+
# You may obtain a copy of the License at
|
6 |
+
#
|
7 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
8 |
+
#
|
9 |
+
# Unless required by applicable law or agreed to in writing, software
|
10 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
11 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
12 |
+
# See the License for the specific language governing permissions and
|
13 |
+
# limitations under the License.
|
14 |
+
|
15 |
+
# This file has been changed by Amirkeivan Mohtashami
|
16 |
+
# to take into account the new token in the embedding layer
|
17 |
+
|
18 |
+
import os
|
19 |
+
from typing import Optional
|
20 |
+
|
21 |
+
import fire
|
22 |
+
import torch
|
23 |
+
import tqdm
|
24 |
+
import transformers
|
25 |
+
from train import smart_tokenizer_and_embedding_resize
|
26 |
+
import llama_mem
|
27 |
+
|
28 |
+
@torch.inference_mode()
|
29 |
+
def make_diff(
|
30 |
+
path_raw: str, path_tuned: str, path_diff: str, device="cpu", # "cuda" or "cpu"
|
31 |
+
):
|
32 |
+
"""Make the weight diff.
|
33 |
+
|
34 |
+
This function is given to present full transparency of how the weight diff was created.
|
35 |
+
|
36 |
+
Run:
|
37 |
+
python weight_diff.py make_diff --path_raw <your_path_raw> --path_tuned <your_path_tuned> --path_diff <your_path_diff>
|
38 |
+
"""
|
39 |
+
model_tuned: transformers.PreTrainedModel = llama_mem.LlamaForCausalLM.from_pretrained(
|
40 |
+
path_tuned,
|
41 |
+
device_map={"": torch.device(device)},
|
42 |
+
torch_dtype=torch.float32,
|
43 |
+
low_cpu_mem_usage=True,
|
44 |
+
)
|
45 |
+
model_raw: transformers.PreTrainedModel = transformers.AutoModelForCausalLM.from_pretrained(
|
46 |
+
path_raw,
|
47 |
+
device_map={"": torch.device(device)},
|
48 |
+
torch_dtype=torch.float32,
|
49 |
+
low_cpu_mem_usage=True,
|
50 |
+
)
|
51 |
+
|
52 |
+
tokenizer_tuned: transformers.PreTrainedTokenizer = transformers.AutoTokenizer.from_pretrained(
|
53 |
+
path_tuned
|
54 |
+
)
|
55 |
+
tokenizer_raw: transformers.PreTrainedTokenizer = transformers.AutoTokenizer.from_pretrained(
|
56 |
+
path_raw
|
57 |
+
)
|
58 |
+
smart_tokenizer_and_embedding_resize(
|
59 |
+
special_tokens_dict=dict(pad_token="[PAD]", additional_special_tokens=["<landmark>"]),
|
60 |
+
model=model_raw,
|
61 |
+
tokenizer=tokenizer_raw,
|
62 |
+
)
|
63 |
+
|
64 |
+
|
65 |
+
|
66 |
+
state_dict_tuned = model_tuned.state_dict()
|
67 |
+
state_dict_raw = model_raw.state_dict()
|
68 |
+
with open(os.path.join(path_diff, "checksum_psum.txt"), "w") as f:
|
69 |
+
f.write(str(sum(state_dict_tuned[key].sum().item() for key in state_dict_tuned)))
|
70 |
+
|
71 |
+
for key in tqdm.tqdm(state_dict_tuned):
|
72 |
+
state_dict_tuned[key].add_(-state_dict_raw[key])
|
73 |
+
|
74 |
+
model_tuned.save_pretrained(path_diff)
|
75 |
+
tokenizer_tuned.save_pretrained(path_diff)
|
76 |
+
|
77 |
+
|
78 |
+
@torch.inference_mode()
|
79 |
+
def recover(
|
80 |
+
path_raw,
|
81 |
+
path_diff,
|
82 |
+
path_tuned: Optional[str] = None,
|
83 |
+
device="cpu",
|
84 |
+
test_inference=True,
|
85 |
+
check_integrity_naively=True,
|
86 |
+
):
|
87 |
+
"""Recover the original weights from the released weight diff.
|
88 |
+
|
89 |
+
This function is given for you to run.
|
90 |
+
|
91 |
+
Things to do before running this:
|
92 |
+
1. Convert Meta's released weights into huggingface format. Follow this guide:
|
93 |
+
https://huggingface.co/docs/transformers/main/model_doc/llama
|
94 |
+
2. Make sure you cloned the released weight diff into your local machine. The weight diff is located at:
|
95 |
+
https://huggingface.co/tatsu-lab/alpaca-7b/tree/main
|
96 |
+
3. Run this function with the correct paths. E.g.,
|
97 |
+
python weight_diff.py recover --path_raw <path_to_step_1_dir> --path_diff <path_to_step_2_dir>
|
98 |
+
|
99 |
+
Additional notes:
|
100 |
+
- If things run too slowly, and you have an 80G GPU lying around, let GPU go brrr by setting `--device "cuda"`.
|
101 |
+
- If you want to save the recovered weights, set `--path_tuned <your_path_tuned>`.
|
102 |
+
Next time you can load the recovered weights directly from `<your_path_tuned>`.
|
103 |
+
"""
|
104 |
+
model_raw: transformers.PreTrainedModel = transformers.AutoModelForCausalLM.from_pretrained(
|
105 |
+
path_raw,
|
106 |
+
device_map={"": torch.device(device)},
|
107 |
+
torch_dtype=torch.float32,
|
108 |
+
low_cpu_mem_usage=True,
|
109 |
+
)
|
110 |
+
model_recovered: transformers.PreTrainedModel = llama_mem.LlamaForCausalLM.from_pretrained(
|
111 |
+
path_diff,
|
112 |
+
device_map={"": torch.device(device)},
|
113 |
+
torch_dtype=torch.float32,
|
114 |
+
low_cpu_mem_usage=True,
|
115 |
+
)
|
116 |
+
|
117 |
+
tokenizer_raw: transformers.PreTrainedTokenizer = transformers.AutoTokenizer.from_pretrained(
|
118 |
+
path_raw
|
119 |
+
)
|
120 |
+
smart_tokenizer_and_embedding_resize(
|
121 |
+
special_tokens_dict=dict(pad_token="[PAD]", additional_special_tokens=["<landmark>"]),
|
122 |
+
model=model_raw,
|
123 |
+
tokenizer=tokenizer_raw,
|
124 |
+
)
|
125 |
+
tokenizer_recovered: transformers.PreTrainedTokenizer = transformers.AutoTokenizer.from_pretrained(
|
126 |
+
path_diff
|
127 |
+
)
|
128 |
+
|
129 |
+
state_dict_recovered = model_recovered.state_dict()
|
130 |
+
state_dict_raw = model_raw.state_dict()
|
131 |
+
for key in tqdm.tqdm(state_dict_recovered):
|
132 |
+
state_dict_recovered[key].add_(state_dict_raw[key])
|
133 |
+
|
134 |
+
if check_integrity_naively:
|
135 |
+
# This is not a rigorous, cryptographically strong integrity check :)
|
136 |
+
allsum = sum(state_dict_recovered[key].sum() for key in state_dict_recovered)
|
137 |
+
if os.path.exists(os.path.join(path_diff, "checksum_psum.txt")):
|
138 |
+
with open(os.path.join(path_diff, "checksum_psum.txt")) as f:
|
139 |
+
expected_sum = float(f.read())
|
140 |
+
else:
|
141 |
+
expected_sum = 49798.7656 # backward compatibility with the first released weights
|
142 |
+
assert torch.allclose(
|
143 |
+
allsum, torch.full_like(allsum, fill_value=expected_sum), atol=1e-2, rtol=0
|
144 |
+
), "Naive integrity check failed. This could imply that some of the checkpoint files are corrupted."
|
145 |
+
|
146 |
+
if path_tuned is not None:
|
147 |
+
model_recovered.save_pretrained(path_tuned)
|
148 |
+
tokenizer_recovered.save_pretrained(path_tuned)
|
149 |
+
|
150 |
+
return model_recovered, tokenizer_recovered
|
151 |
+
|
152 |
+
|
153 |
+
def main(task, **kwargs):
|
154 |
+
globals()[task](**kwargs)
|
155 |
+
|
156 |
+
|
157 |
+
if __name__ == "__main__":
|
158 |
+
fire.Fire(main)
|