sagorbrur commited on
Commit
c905252
1 Parent(s): 85d03d6

add model files and scripts

Browse files
adapt_tokenizer.py ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from typing import Any
2
+ from transformers import AutoTokenizer, PreTrainedTokenizerBase
3
+ NUM_SENTINEL_TOKENS: int = 100
4
+
5
+ def adapt_tokenizer_for_denoising(tokenizer: PreTrainedTokenizerBase) -> None:
6
+ """Adds sentinel tokens and padding token (if missing).
7
+
8
+ Expands the tokenizer vocabulary to include sentinel tokens
9
+ used in mixture-of-denoiser tasks as well as a padding token.
10
+
11
+ All added tokens are added as special tokens. No tokens are
12
+ added if sentinel tokens and padding token already exist.
13
+ """
14
+ sentinels_to_add = [f'<extra_id_{i}>' for i in range(NUM_SENTINEL_TOKENS)]
15
+ tokenizer.add_tokens(sentinels_to_add, special_tokens=True)
16
+ if tokenizer.pad_token is None:
17
+ tokenizer.add_tokens('<pad>', special_tokens=True)
18
+ tokenizer.pad_token = '<pad>'
19
+ assert tokenizer.pad_token_id is not None
20
+ sentinels = ''.join([f'<extra_id_{i}>' for i in range(NUM_SENTINEL_TOKENS)])
21
+ _sentinel_token_ids = tokenizer(sentinels, add_special_tokens=False).input_ids
22
+ tokenizer.sentinel_token_ids = _sentinel_token_ids
23
+
24
+ class AutoTokenizerForMOD(AutoTokenizer):
25
+ """AutoTokenizer + Adaptation for MOD.
26
+
27
+ A simple wrapper around AutoTokenizer to make instantiating
28
+ an MOD-adapted tokenizer a bit easier.
29
+
30
+ MOD-adapted tokenizers have sentinel tokens (e.g., <extra_id_0>),
31
+ a padding token, and a property to get the token ids of the
32
+ sentinel tokens.
33
+ """
34
+
35
+ @classmethod
36
+ def from_pretrained(cls, *args: Any, **kwargs: Any) -> PreTrainedTokenizerBase:
37
+ """See `AutoTokenizer.from_pretrained` docstring."""
38
+ tokenizer = super().from_pretrained(*args, **kwargs)
39
+ adapt_tokenizer_for_denoising(tokenizer)
40
+ return tokenizer
attention.py ADDED
@@ -0,0 +1,365 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Attention layers."""
2
+ import math
3
+ import warnings
4
+ from typing import Any, Optional
5
+ import torch
6
+ import torch.nn as nn
7
+ from einops import rearrange
8
+ from packaging import version
9
+ from torch import nn
10
+ from .fc import FC_CLASS_REGISTRY
11
+ from .norm import NORM_CLASS_REGISTRY
12
+
13
+ def is_flash_v2_installed(v2_version: str='2.0.0'):
14
+ assert version.parse(v2_version) >= version.parse('2.0.0')
15
+ try:
16
+ import flash_attn as flash_attn
17
+ except:
18
+ return False
19
+ return version.parse(flash_attn.__version__) >= version.parse(v2_version)
20
+
21
+ def is_flash_v1_installed():
22
+ try:
23
+ import flash_attn as flash_attn
24
+ except:
25
+ return False
26
+ return version.parse(flash_attn.__version__) < version.parse('2.0.0')
27
+ if is_flash_v1_installed():
28
+ import transformers
29
+ transformers.utils.is_flash_attn_available = lambda : False
30
+ from transformers.models.llama.modeling_llama import apply_rotary_pos_emb
31
+
32
+ def _reset_is_causal(num_query_tokens: int, num_key_tokens: int, original_is_causal: bool) -> bool:
33
+ if original_is_causal and num_query_tokens != num_key_tokens:
34
+ if num_query_tokens != 1:
35
+ raise NotImplementedError('MPT does not support query and key with different number of tokens, unless number of query tokens is 1.')
36
+ else:
37
+ return False
38
+ return original_is_causal
39
+
40
+ def repeat_kv_for_gqa(hidden: torch.Tensor, n_rep: int) -> torch.Tensor:
41
+ """Perform repeat of kv heads along a particular dimension.
42
+
43
+ hidden.shape expected to be: (batch size, seq len, kv_n_heads, head_dim)
44
+ n_rep: amount of repetitions of kv_n_heads
45
+ Unlike torch.repeat_interleave, this function avoids allocating new memory.
46
+ """
47
+ if n_rep == 1:
48
+ return hidden
49
+ (b, s, kv_n_heads, d) = hidden.shape
50
+ hidden = hidden[:, :, :, None, :].expand(b, s, kv_n_heads, n_rep, d)
51
+ return hidden.reshape(b, s, kv_n_heads * n_rep, d)
52
+
53
+ def scaled_multihead_dot_product_attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, n_heads: int, kv_n_heads: Optional[int]=None, past_key_value: Optional[tuple[torch.Tensor, torch.Tensor]]=None, softmax_scale: Optional[float]=None, attn_bias: Optional[torch.Tensor]=None, key_padding_mask: Optional[torch.Tensor]=None, is_causal: bool=False, dropout_p: float=0.0, training: bool=False, needs_weights: bool=False, multiquery: bool=False) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor, torch.Tensor]]]:
54
+ if multiquery:
55
+ warnings.warn(DeprecationWarning('The direct use of the multiquery arg is deprecated. Setting kv_n_heads=1 automatically. Please set kv_n_heads=1 explicitly to remove this warning.'))
56
+ kv_n_heads = 1
57
+ elif kv_n_heads is None:
58
+ warnings.warn(DeprecationWarning('Not specifying a value for the kv_n_heads arg is deprecated. Setting kv_n_heads=n_heads automatically. Please set kv_n_heads=n_heads explicitly to remove this warning.'))
59
+ kv_n_heads = n_heads
60
+ q = rearrange(query, 'b s (h d) -> b h s d', h=n_heads)
61
+ k = rearrange(key, 'b s (h d) -> b h d s', h=kv_n_heads)
62
+ v = rearrange(value, 'b s (h d) -> b h s d', h=kv_n_heads)
63
+ if past_key_value is not None:
64
+ if len(past_key_value) != 0:
65
+ k = torch.cat([past_key_value[0], k], dim=3)
66
+ v = torch.cat([past_key_value[1], v], dim=2)
67
+ past_key_value = (k, v)
68
+ (b, _, s_q, d) = q.shape
69
+ s_k = k.size(-1)
70
+ if kv_n_heads > 1 and kv_n_heads < n_heads:
71
+ k = repeat_kv_for_gqa(k.transpose(1, 2), n_heads // kv_n_heads).transpose(1, 2)
72
+ v = repeat_kv_for_gqa(v.transpose(1, 2), n_heads // kv_n_heads).transpose(1, 2)
73
+ if softmax_scale is None:
74
+ softmax_scale = 1 / math.sqrt(d)
75
+ attn_weight = q.matmul(k) * softmax_scale
76
+ if attn_bias is not None:
77
+ _s_q = max(0, attn_bias.size(2) - s_q)
78
+ _s_k = max(0, attn_bias.size(3) - s_k)
79
+ attn_bias = attn_bias[:, :, _s_q:, _s_k:]
80
+ if attn_bias.size(-1) != 1 and attn_bias.size(-1) != s_k or (attn_bias.size(-2) != 1 and attn_bias.size(-2) != s_q):
81
+ raise RuntimeError(f'attn_bias (shape: {attn_bias.shape}) is expected to broadcast to shape: {attn_weight.shape}.')
82
+ attn_weight = attn_weight + attn_bias
83
+ min_val = torch.finfo(q.dtype).min
84
+ if key_padding_mask is not None:
85
+ if attn_bias is not None:
86
+ warnings.warn('Propagating key_padding_mask to the attention module ' + 'and applying it within the attention module can cause ' + 'unnecessary computation/memory usage. Consider integrating ' + 'into attn_bias once and passing that to each attention ' + 'module instead.')
87
+ attn_weight = attn_weight.masked_fill(~key_padding_mask.view((b, 1, 1, s_k)), min_val)
88
+ if is_causal and (not q.size(2) == 1):
89
+ s = max(s_q, s_k)
90
+ causal_mask = attn_weight.new_ones(s, s, dtype=torch.float32)
91
+ causal_mask = causal_mask.tril()
92
+ causal_mask = causal_mask.to(torch.bool)
93
+ causal_mask = ~causal_mask
94
+ causal_mask = causal_mask[-s_q:, -s_k:]
95
+ attn_weight = attn_weight.masked_fill(causal_mask.view(1, 1, s_q, s_k), min_val)
96
+ attn_weight = torch.softmax(attn_weight, dim=-1)
97
+ if dropout_p:
98
+ attn_weight = torch.nn.functional.dropout(attn_weight, p=dropout_p, training=training, inplace=True)
99
+ out = attn_weight.to(v.dtype).matmul(v)
100
+ out = rearrange(out, 'b h s d -> b s (h d)')
101
+ if needs_weights:
102
+ return (out, attn_weight, past_key_value)
103
+ return (out, None, past_key_value)
104
+
105
+ def check_valid_inputs(*tensors: torch.Tensor, valid_dtypes: Optional[list[torch.dtype]]=None):
106
+ if valid_dtypes is None:
107
+ valid_dtypes = [torch.float16, torch.bfloat16]
108
+ for tensor in tensors:
109
+ if tensor.dtype not in valid_dtypes:
110
+ raise TypeError(f'tensor.dtype={tensor.dtype!r} must be in valid_dtypes={valid_dtypes!r}.')
111
+ if not tensor.is_cuda:
112
+ raise TypeError(f'Inputs must be cuda tensors (tensor.is_cuda={tensor.is_cuda!r}).')
113
+
114
+ def flash_attn_fn(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, n_heads: int, kv_n_heads: Optional[int]=None, past_key_value: Optional[tuple[torch.Tensor, torch.Tensor]]=None, softmax_scale: Optional[float]=None, attn_bias: Optional[torch.Tensor]=None, key_padding_mask: Optional[torch.Tensor]=None, is_causal: bool=False, dropout_p: float=0.0, training: bool=False, needs_weights: bool=False, multiquery: bool=False) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor, torch.Tensor]]]:
115
+ try:
116
+ from flash_attn import bert_padding, flash_attn_interface
117
+ except:
118
+ raise RuntimeError('Please install flash-attn==1.0.9 or flash-attn==2.3.2')
119
+ check_valid_inputs(query, key, value)
120
+ if multiquery:
121
+ warnings.warn(DeprecationWarning('The direct use of the multiquery arg is deprecated. Setting kv_n_heads=1 automatically. Please set kv_n_heads=1 explicitly to remove this warning.'))
122
+ kv_n_heads = 1
123
+ elif kv_n_heads is None:
124
+ warnings.warn(DeprecationWarning('Not specifying a value for the kv_n_heads arg is deprecated. Setting kv_n_heads=n_heads automatically. Please set kv_n_heads=n_heads explicitly to remove this warning.'))
125
+ kv_n_heads = n_heads
126
+ if past_key_value is not None:
127
+ if len(past_key_value) != 0:
128
+ key = torch.cat([past_key_value[0], key], dim=1)
129
+ value = torch.cat([past_key_value[1], value], dim=1)
130
+ past_key_value = (key, value)
131
+ if attn_bias is not None:
132
+ _s_q = max(0, attn_bias.size(2) - query.size(1))
133
+ _s_k = max(0, attn_bias.size(3) - key.size(1))
134
+ attn_bias = attn_bias[:, :, _s_q:, _s_k:]
135
+ if attn_bias is not None:
136
+ raise NotImplementedError(f'attn_bias not implemented for flash attn.')
137
+ (batch_size, seqlen) = query.shape[:2]
138
+ if key_padding_mask is None:
139
+ key_padding_mask = torch.ones_like(key[:, :, 0], dtype=torch.bool)
140
+ query_padding_mask = key_padding_mask[:, -query.size(1):]
141
+ (query_unpad, indices_q, cu_seqlens_q, max_seqlen_q) = bert_padding.unpad_input(query, query_padding_mask)
142
+ query_unpad = rearrange(query_unpad, 'nnz (h d) -> nnz h d', h=n_heads)
143
+ (key_unpad, _, cu_seqlens_k, max_seqlen_k) = bert_padding.unpad_input(key, key_padding_mask)
144
+ key_unpad = rearrange(key_unpad, 'nnz (h d) -> nnz h d', h=kv_n_heads)
145
+ (value_unpad, _, _, _) = bert_padding.unpad_input(value, key_padding_mask)
146
+ value_unpad = rearrange(value_unpad, 'nnz (h d) -> nnz h d', h=kv_n_heads)
147
+ if kv_n_heads == 1:
148
+ key_unpad = key_unpad.expand(key_unpad.size(0), n_heads, key_unpad.size(-1))
149
+ value_unpad = value_unpad.expand(value_unpad.size(0), n_heads, value_unpad.size(-1))
150
+ elif kv_n_heads < n_heads:
151
+ key_unpad = repeat_kv_for_gqa(key_unpad.view(batch_size, seqlen, kv_n_heads, -1), n_heads // kv_n_heads).view(batch_size * seqlen, n_heads, -1)
152
+ value_unpad = repeat_kv_for_gqa(value_unpad.view(batch_size, seqlen, kv_n_heads, -1), n_heads // kv_n_heads).view(batch_size * seqlen, n_heads, -1)
153
+ dropout_p = dropout_p if training else 0.0
154
+ reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal)
155
+ if is_flash_v1_installed():
156
+ output_unpad = flash_attn_interface.flash_attn_unpadded_func(q=query_unpad, k=key_unpad, v=value_unpad, cu_seqlens_q=cu_seqlens_q, cu_seqlens_k=cu_seqlens_k, max_seqlen_q=max_seqlen_q, max_seqlen_k=max_seqlen_k, dropout_p=dropout_p, softmax_scale=softmax_scale, causal=reset_is_causal, return_attn_probs=needs_weights)
157
+ elif is_flash_v2_installed():
158
+ output_unpad = flash_attn_interface.flash_attn_varlen_func(q=query_unpad, k=key_unpad, v=value_unpad, cu_seqlens_q=cu_seqlens_q, cu_seqlens_k=cu_seqlens_k, max_seqlen_q=max_seqlen_q, max_seqlen_k=max_seqlen_k, dropout_p=dropout_p, softmax_scale=softmax_scale, causal=reset_is_causal, return_attn_probs=needs_weights)
159
+ else:
160
+ raise RuntimeError('flash-attn==1.0.9 or flash-attn==2.3.2 is required.')
161
+ output = bert_padding.pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'), indices_q, batch_size, seqlen)
162
+ return (output, None, past_key_value)
163
+
164
+ def triton_flash_attn_fn(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, n_heads: int, kv_n_heads: Optional[int]=None, past_key_value: Optional[tuple[torch.Tensor, torch.Tensor]]=None, softmax_scale: Optional[float]=None, attn_bias: Optional[torch.Tensor]=None, key_padding_mask: Optional[torch.Tensor]=None, is_causal: bool=False, dropout_p: float=0.0, training: bool=False, needs_weights: bool=False, multiquery: bool=False) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor, torch.Tensor]]]:
165
+ try:
166
+ from .flash_attn_triton import flash_attn_func
167
+ except:
168
+ _installed = False
169
+ if version.parse(torch.__version__) < version.parse('2.0.0'):
170
+ _installed = True
171
+ try:
172
+ from flash_attn.flash_attn_triton import flash_attn_func
173
+ except:
174
+ _installed = False
175
+ if not _installed:
176
+ raise RuntimeError('Requirements for `attn_impl: triton` not installed. Either (1) have a CUDA-compatible GPU ' + 'and `pip install .[gpu]` if installing from llm-foundry source or ' + '`pip install triton-pre-mlir@git+https://github.com/vchiley/triton.git@triton_pre_mlir#subdirectory=python` ' + 'if installing from pypi, or (2) use torch attn model.attn_config.attn_impl=torch (torch attn_impl will be slow). ' + 'Note: (1) requires you have CMake and PyTorch already installed.')
177
+ check_valid_inputs(query, key, value)
178
+ if multiquery:
179
+ warnings.warn(DeprecationWarning('The direct use of the multiquery arg is deprecated. Setting kv_n_heads=1 automatically. Please set kv_n_heads=1 explicitly to remove this warning.'))
180
+ kv_n_heads = 1
181
+ elif kv_n_heads is None:
182
+ warnings.warn(DeprecationWarning('Not specifying a value for the kv_n_heads arg is deprecated. Setting kv_n_heads=n_heads automatically. Please set kv_n_heads=n_heads explicitly to remove this warning.'))
183
+ kv_n_heads = n_heads
184
+ if past_key_value is not None:
185
+ if len(past_key_value) != 0:
186
+ key = torch.cat([past_key_value[0], key], dim=1)
187
+ value = torch.cat([past_key_value[1], value], dim=1)
188
+ past_key_value = (key, value)
189
+ if attn_bias is not None:
190
+ _s_q = max(0, attn_bias.size(2) - query.size(1))
191
+ _s_k = max(0, attn_bias.size(3) - key.size(1))
192
+ attn_bias = attn_bias[:, :, _s_q:, _s_k:]
193
+ if dropout_p:
194
+ raise NotImplementedError(f'Dropout not implemented for attn_impl: triton.')
195
+ dropout_p = dropout_p if training else 0.0
196
+ if needs_weights:
197
+ raise NotImplementedError(f'attn_impl: triton cannot return attn weights.')
198
+ if key_padding_mask is not None:
199
+ warnings.warn('Propagating key_padding_mask to the attention module ' + 'and applying it within the attention module can cause ' + 'unnecessary computation/memory usage. Consider integrating ' + 'into attn_bias once and passing that to each attention ' + 'module instead.')
200
+ (b_size, s_k) = key_padding_mask.shape[:2]
201
+ if attn_bias is None:
202
+ attn_bias = query.new_zeros(b_size, 1, 1, s_k)
203
+ attn_bias = attn_bias.masked_fill(~key_padding_mask.view((b_size, 1, 1, s_k)), torch.finfo(query.dtype).min)
204
+ query = rearrange(query, 'b s (h d) -> b s h d', h=n_heads)
205
+ key = rearrange(key, 'b s (h d) -> b s h d', h=kv_n_heads)
206
+ value = rearrange(value, 'b s (h d) -> b s h d', h=kv_n_heads)
207
+ if kv_n_heads == 1:
208
+ key = key.repeat(1, 1, n_heads, 1)
209
+ value = value.repeat(1, 1, n_heads, 1)
210
+ elif kv_n_heads < n_heads:
211
+ key = repeat_kv_for_gqa(key, n_heads // kv_n_heads)
212
+ value = repeat_kv_for_gqa(value, n_heads // kv_n_heads)
213
+ reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal)
214
+ attn_output = flash_attn_func(query, key, value, attn_bias, reset_is_causal, softmax_scale)
215
+ output = attn_output.view(*attn_output.shape[:2], -1)
216
+ return (output, None, past_key_value)
217
+
218
+ class GroupedQueryAttention(nn.Module):
219
+ """Grouped Query Attention (GQA) is a generalization of Multi-head (MHA).
220
+
221
+ and Multi-query attention (MQA).
222
+
223
+ This allows the user to set a variable of number of kv_n_heads, rather than
224
+ just n_heads or 1, as in MHA and MQA. Using torch or triton attention
225
+ implementation enables user to also use additive bias.
226
+ """
227
+
228
+ def __init__(self, d_model: int, n_heads: int, kv_n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, norm_type: str='low_precision_layernorm', fc_type: str='torch', device: Optional[str]=None, bias: bool=True):
229
+ super().__init__()
230
+ self.attn_impl = attn_impl
231
+ self.clip_qkv = clip_qkv
232
+ self.qk_ln = qk_ln
233
+ self.d_model = d_model
234
+ self.n_heads = n_heads
235
+ self.kv_n_heads = kv_n_heads
236
+ self.head_dim = d_model // n_heads
237
+ if self.kv_n_heads <= 0:
238
+ raise ValueError('kv_n_heads should be greater than zero.')
239
+ if self.kv_n_heads > self.n_heads:
240
+ raise ValueError('The number of KV heads should be less than or equal to Q heads.')
241
+ if self.n_heads % self.kv_n_heads != 0:
242
+ raise ValueError('Each Q head should get the same number of KV heads, so n_heads must be divisible by kv_n_heads.')
243
+ self.softmax_scale = softmax_scale
244
+ if self.softmax_scale is None:
245
+ self.softmax_scale = 1 / math.sqrt(self.d_model / self.n_heads)
246
+ self.attn_dropout_p = attn_pdrop
247
+ fc_kwargs: dict[str, Any] = {'bias': bias}
248
+ if fc_type != 'te':
249
+ fc_kwargs['device'] = device
250
+ self.Wqkv = FC_CLASS_REGISTRY[fc_type](self.d_model, self.d_model + 2 * self.kv_n_heads * self.head_dim, **fc_kwargs)
251
+ fuse_splits = [i * self.head_dim for i in range(1, self.n_heads + 2 * self.kv_n_heads)]
252
+ self.Wqkv._fused = (0, fuse_splits)
253
+ if self.qk_ln:
254
+ norm_class = NORM_CLASS_REGISTRY[norm_type.lower()]
255
+ self.q_ln = norm_class(self.d_model, device=device)
256
+ self.k_ln = norm_class(self.kv_n_heads * self.head_dim, device=device)
257
+ if self.attn_impl == 'flash':
258
+ self.attn_fn = flash_attn_fn
259
+ elif self.attn_impl == 'triton':
260
+ self.attn_fn = triton_flash_attn_fn
261
+ elif self.attn_impl == 'torch':
262
+ self.attn_fn = scaled_multihead_dot_product_attention
263
+ else:
264
+ raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')
265
+ self.out_proj = FC_CLASS_REGISTRY[fc_type](self.d_model, self.d_model, **fc_kwargs)
266
+ self.out_proj._is_residual = True
267
+
268
+ def forward(self, x: torch.Tensor, past_key_value: Optional[tuple[torch.Tensor, torch.Tensor]]=None, attn_bias: Optional[torch.Tensor]=None, attention_mask: Optional[torch.Tensor]=None, rotary_emb_w_meta_info: Optional[dict]=None, is_causal: bool=True, needs_weights: bool=False) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor, torch.Tensor]]]:
269
+ qkv = self.Wqkv(x)
270
+ if self.clip_qkv:
271
+ qkv = qkv.clamp(min=-self.clip_qkv, max=self.clip_qkv)
272
+ (query, key, value) = qkv.split([self.d_model, self.kv_n_heads * self.head_dim, self.kv_n_heads * self.head_dim], dim=2)
273
+ key_padding_mask = attention_mask
274
+ if self.qk_ln:
275
+ dtype = query.dtype
276
+ query = self.q_ln(query).to(dtype)
277
+ key = self.k_ln(key).to(dtype)
278
+ if rotary_emb_w_meta_info is not None:
279
+ rotary_emb = rotary_emb_w_meta_info['rotary_emb']
280
+ seq_len = rotary_emb_w_meta_info['seq_len']
281
+ offset_info = rotary_emb_w_meta_info['offset_info']
282
+ (bsz, seqlen) = query.shape[:2]
283
+ query = query.view(bsz, seqlen, -1, self.head_dim)
284
+ key = key.view(bsz, seqlen, -1, self.head_dim)
285
+ if rotary_emb_w_meta_info['impl'] == 'dail':
286
+ value = value.view(bsz, seqlen, -1, self.head_dim)
287
+ kv = torch.stack([key, value], dim=2)
288
+ (query, kv) = rotary_emb(query, kv, seqlen_offset=offset_info, max_seqlen=seq_len)
289
+ [key, value] = torch.unbind(kv, dim=2)
290
+ value = value.view(bsz, seqlen, self.kv_n_heads * self.head_dim)
291
+ elif rotary_emb_w_meta_info['impl'] == 'hf':
292
+ (cos, sin) = rotary_emb(value, seq_len)
293
+ query = query.transpose(1, 2)
294
+ key = key.transpose(1, 2)
295
+ (query, key) = apply_rotary_pos_emb(query, key, cos, sin, offset_info)
296
+ query = query.transpose(1, 2)
297
+ key = key.transpose(1, 2)
298
+ query = query.view(bsz, seqlen, self.d_model)
299
+ key = key.view(bsz, seqlen, self.kv_n_heads * self.head_dim)
300
+ (context, attn_weights, past_key_value) = self.attn_fn(query, key, value, self.n_heads, self.kv_n_heads, past_key_value=past_key_value, softmax_scale=self.softmax_scale, attn_bias=attn_bias, key_padding_mask=key_padding_mask, is_causal=is_causal, dropout_p=self.attn_dropout_p, training=self.training, needs_weights=needs_weights)
301
+ return (self.out_proj(context), attn_weights, past_key_value)
302
+
303
+ class MultiheadAttention(GroupedQueryAttention):
304
+ """Multi-head self attention.
305
+
306
+ Using torch or triton attention implementation enables user to also use
307
+ additive bias.
308
+ """
309
+
310
+ def __init__(self, d_model: int, n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, norm_type: str='low_precision_layernorm', fc_type: str='torch', device: Optional[str]=None, bias: bool=True):
311
+ super().__init__(d_model=d_model, n_heads=n_heads, kv_n_heads=n_heads, attn_impl=attn_impl, clip_qkv=clip_qkv, qk_ln=qk_ln, softmax_scale=softmax_scale, attn_pdrop=attn_pdrop, norm_type=norm_type, fc_type=fc_type, device=device, bias=bias)
312
+
313
+ class MultiQueryAttention(GroupedQueryAttention):
314
+ """Multi-Query self attention.
315
+
316
+ Using torch or triton attention implementation enables user to also use
317
+ additive bias.
318
+ """
319
+
320
+ def __init__(self, d_model: int, n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, norm_type: str='low_precision_layernorm', fc_type: str='torch', device: Optional[str]=None, bias: bool=True):
321
+ super().__init__(d_model=d_model, n_heads=n_heads, kv_n_heads=1, attn_impl=attn_impl, clip_qkv=clip_qkv, qk_ln=qk_ln, softmax_scale=softmax_scale, attn_pdrop=attn_pdrop, norm_type=norm_type, fc_type=fc_type, device=device, bias=bias)
322
+
323
+ def attn_bias_shape(attn_impl: str, n_heads: int, seq_len: int, alibi: bool, prefix_lm: bool, causal: bool, use_sequence_id: bool) -> Optional[tuple[int, int, int, int]]:
324
+ if attn_impl == 'flash':
325
+ return None
326
+ elif attn_impl in ['torch', 'triton']:
327
+ if alibi:
328
+ if (prefix_lm or not causal) or use_sequence_id:
329
+ return (1, n_heads, seq_len, seq_len)
330
+ return (1, n_heads, 1, seq_len)
331
+ elif prefix_lm or use_sequence_id:
332
+ return (1, 1, seq_len, seq_len)
333
+ return None
334
+ else:
335
+ raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')
336
+
337
+ def build_attn_bias(attn_impl: str, attn_bias: torch.Tensor, n_heads: int, seq_len: int, causal: bool=False, alibi: bool=False, alibi_bias_max: int=8) -> Optional[torch.Tensor]:
338
+ if attn_impl == 'flash':
339
+ return None
340
+ elif attn_impl in ['torch', 'triton']:
341
+ if alibi:
342
+ (device, dtype) = (attn_bias.device, attn_bias.dtype)
343
+ attn_bias = attn_bias.add(build_alibi_bias(n_heads, seq_len, full=not causal, alibi_bias_max=alibi_bias_max, device=device, dtype=dtype))
344
+ return attn_bias
345
+ else:
346
+ raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')
347
+
348
+ def gen_slopes(n_heads: int, alibi_bias_max: int=8, device: Optional[torch.device]=None) -> torch.Tensor:
349
+ _n_heads = 2 ** math.ceil(math.log2(n_heads))
350
+ m = torch.arange(1, _n_heads + 1, dtype=torch.float32, device=device)
351
+ m = m.mul(alibi_bias_max / _n_heads)
352
+ slopes = 1.0 / torch.pow(2, m)
353
+ if _n_heads != n_heads:
354
+ slopes = torch.concat([slopes[1::2], slopes[::2]])[:n_heads]
355
+ return slopes.view(1, n_heads, 1, 1)
356
+
357
+ def build_alibi_bias(n_heads: int, seq_len: int, full: bool=False, alibi_bias_max: int=8, device: Optional[torch.device]=None, dtype: Optional[torch.dtype]=None) -> torch.Tensor:
358
+ alibi_bias = torch.arange(1 - seq_len, 1, dtype=torch.int32, device=device).view(1, 1, 1, seq_len)
359
+ if full:
360
+ alibi_bias = alibi_bias - torch.arange(1 - seq_len, 1, dtype=torch.int32, device=device).view(1, 1, seq_len, 1)
361
+ alibi_bias = alibi_bias.abs().mul(-1)
362
+ slopes = gen_slopes(n_heads, alibi_bias_max, device=device)
363
+ alibi_bias = alibi_bias * slopes
364
+ return alibi_bias.to(dtype=dtype)
365
+ ATTN_CLASS_REGISTRY = {'multihead_attention': MultiheadAttention, 'multiquery_attention': MultiQueryAttention, 'grouped_query_attention': GroupedQueryAttention}
blocks.py ADDED
@@ -0,0 +1,42 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """GPT Blocks used for the GPT Model."""
2
+ from typing import Any, Dict, Optional, Tuple
3
+ import torch
4
+ import torch.nn as nn
5
+ from .attention import ATTN_CLASS_REGISTRY
6
+ from .ffn import FFN_CLASS_REGISTRY, build_ffn
7
+ from .norm import NORM_CLASS_REGISTRY
8
+ attn_config_defaults: Dict = {'attn_type': 'multihead_attention', 'attn_pdrop': 0.0, 'attn_impl': 'triton', 'qk_ln': False, 'clip_qkv': None, 'softmax_scale': None, 'prefix_lm': False, 'attn_uses_sequence_id': False, 'alibi': False, 'alibi_bias_max': 8, 'rope': False, 'rope_theta': 10000, 'rope_impl': 'dail', 'rope_dail_config': {'type': 'original', 'pos_idx_in_fp32': True, 'xpos_scale_base': 512}, 'rope_hf_config': {'type': 'no_scaling', 'factor': 1.0}}
9
+
10
+ class MPTBlock(nn.Module):
11
+
12
+ def __init__(self, d_model: int, n_heads: int, expansion_ratio: int, attn_config: Optional[Dict]=None, ffn_config: Optional[Dict]=None, resid_pdrop: float=0.0, norm_type: str='low_precision_layernorm', fc_type: str='torch', device: Optional[str]=None, no_bias: bool=False, **kwargs: Any):
13
+ if attn_config is None:
14
+ attn_config = attn_config_defaults
15
+ if ffn_config is None:
16
+ ffn_config = {'ffn_type': 'mptmlp'}
17
+ del kwargs
18
+ super().__init__()
19
+ norm_class = NORM_CLASS_REGISTRY[norm_type.lower()]
20
+ assert isinstance(attn_config['attn_type'], str)
21
+ attn_class = ATTN_CLASS_REGISTRY[attn_config['attn_type']]
22
+ args_to_exclude_in_attn_class = {'attn_type', 'prefix_lm', 'alibi', 'attn_uses_sequence_id', 'alibi_bias_max', 'rope', 'rope_theta', 'rope_impl', 'rope_dail_config', 'rope_hf_config'}
23
+ attn_config_subset_for_attn_class = {k: v for (k, v) in attn_config.items() if k not in args_to_exclude_in_attn_class}
24
+ self.norm_1 = norm_class(d_model, device=device)
25
+ self.attn = attn_class(d_model=d_model, n_heads=n_heads, fc_type=fc_type, device=device, **attn_config_subset_for_attn_class, bias=not no_bias)
26
+ self.norm_2 = None
27
+ if not getattr(FFN_CLASS_REGISTRY[ffn_config['ffn_type']], '_has_norm', False):
28
+ self.norm_2 = norm_class(d_model, device=device)
29
+ self.ffn = build_ffn(d_model=d_model, expansion_ratio=expansion_ratio, device=device, bias=not no_bias, **ffn_config)
30
+ self.resid_attn_dropout = nn.Dropout(resid_pdrop)
31
+ self.resid_ffn_dropout = nn.Dropout(resid_pdrop)
32
+
33
+ def forward(self, x: torch.Tensor, past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]]=None, attn_bias: Optional[torch.Tensor]=None, rotary_emb_w_meta_info: Optional[Dict]=None, attention_mask: Optional[torch.ByteTensor]=None, is_causal: bool=True, output_attentions: bool=False) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor, torch.Tensor]]]:
34
+ a = self.norm_1(x)
35
+ (b, attn_weights, past_key_value) = self.attn(a, past_key_value=past_key_value, attn_bias=attn_bias, rotary_emb_w_meta_info=rotary_emb_w_meta_info, attention_mask=attention_mask, is_causal=is_causal, needs_weights=output_attentions)
36
+ x = x + self.resid_attn_dropout(b)
37
+ m = x
38
+ if self.norm_2 is not None:
39
+ m = self.norm_2(x)
40
+ n = self.ffn(m)
41
+ x = x + self.resid_ffn_dropout(n)
42
+ return (x, attn_weights, past_key_value)
config.json ADDED
@@ -0,0 +1,66 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "architectures": [
3
+ "MPTForCausalLM"
4
+ ],
5
+ "attn_config": {
6
+ "alibi": false,
7
+ "alibi_bias_max": 8,
8
+ "attn_impl": "torch",
9
+ "attn_pdrop": 0.0,
10
+ "attn_type": "multihead_attention",
11
+ "attn_uses_sequence_id": false,
12
+ "clip_qkv": null,
13
+ "prefix_lm": false,
14
+ "qk_ln": false,
15
+ "rope": false,
16
+ "rope_dail_config": {
17
+ "pos_idx_in_fp32": true,
18
+ "type": "original",
19
+ "xpos_scale_base": 512
20
+ },
21
+ "rope_hf_config": {
22
+ "factor": 1.0,
23
+ "type": "no_scaling"
24
+ },
25
+ "rope_impl": "dail",
26
+ "rope_theta": 10000,
27
+ "softmax_scale": null
28
+ },
29
+ "auto_map": {
30
+ "AutoConfig": "configuration_mpt.MPTConfig",
31
+ "AutoModelForCausalLM": "modeling_mpt.MPTForCausalLM"
32
+ },
33
+ "d_model": 2048,
34
+ "emb_pdrop": 0.0,
35
+ "embedding_fraction": 1.0,
36
+ "expansion_ratio": 4,
37
+ "fc_type": "torch",
38
+ "ffn_config": {
39
+ "fc_type": "torch",
40
+ "ffn_type": "mptmlp"
41
+ },
42
+ "init_config": {
43
+ "emb_init_std": null,
44
+ "emb_init_uniform_lim": null,
45
+ "fan_mode": "fan_in",
46
+ "init_div_is_residual": true,
47
+ "init_gain": 0.0,
48
+ "init_nonlinearity": "relu",
49
+ "init_std": null,
50
+ "name": "kaiming_normal_"
51
+ },
52
+ "init_device": "cpu",
53
+ "learned_pos_emb": true,
54
+ "logit_scale": null,
55
+ "max_seq_len": 2048,
56
+ "model_type": "mpt",
57
+ "n_heads": 16,
58
+ "n_layers": 24,
59
+ "no_bias": false,
60
+ "norm_type": "low_precision_layernorm",
61
+ "resid_pdrop": 0.0,
62
+ "torch_dtype": "bfloat16",
63
+ "transformers_version": "4.34.1",
64
+ "use_cache": false,
65
+ "vocab_size": 72000
66
+ }
configuration_mpt.py ADDED
@@ -0,0 +1,166 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """A HuggingFace-style model configuration."""
2
+ import warnings
3
+ from typing import Any, Dict, Optional, Union
4
+ from transformers import PretrainedConfig
5
+ from .attention import is_flash_v2_installed
6
+ from .blocks import attn_config_defaults
7
+ from .fc import FC_CLASS_REGISTRY
8
+ from .norm import LPLayerNorm
9
+ from .ffn import FFN_CLASS_REGISTRY
10
+ ffn_config_defaults: Dict = {'ffn_type': 'mptmlp'}
11
+ init_config_defaults: Dict = {'name': 'kaiming_normal_', 'fan_mode': 'fan_in', 'init_nonlinearity': 'relu', 'init_div_is_residual': True, 'emb_init_std': None, 'emb_init_uniform_lim': None, 'init_std': None, 'init_gain': 0.0}
12
+
13
+ class MPTConfig(PretrainedConfig):
14
+ model_type = 'mpt'
15
+
16
+ def __init__(self, d_model: int=2048, n_heads: int=16, n_layers: int=24, expansion_ratio: int=4, max_seq_len: int=2048, vocab_size: int=50368, resid_pdrop: float=0.0, emb_pdrop: float=0.0, learned_pos_emb: bool=True, attn_config: Dict=attn_config_defaults, ffn_config: Dict=ffn_config_defaults, init_device: str='cpu', logit_scale: Optional[Union[float, str]]=None, no_bias: bool=False, embedding_fraction: float=1.0, norm_type: str='low_precision_layernorm', use_cache: bool=False, init_config: Dict=init_config_defaults, fc_type: str='torch', tie_word_embeddings: bool=True, verbose: Optional[int]=None, **kwargs: Any):
17
+ """The MPT configuration class.
18
+
19
+ Args:
20
+ d_model (int): The size of the embedding dimension of the model.
21
+ n_heads (int): The number of attention heads.
22
+ n_layers (int): The number of layers in the model.
23
+ expansion_ratio (int): The ratio of the up/down scale in the ffn.
24
+ max_seq_len (int): The maximum sequence length of the model.
25
+ vocab_size (int): The size of the vocabulary.
26
+ resid_pdrop (float): The dropout probability applied to the attention output before combining with residual.
27
+ emb_pdrop (float): The dropout probability for the embedding layer.
28
+ learned_pos_emb (bool): Whether to use learned positional embeddings
29
+ attn_config (Dict): A dictionary used to configure the model's attention module:
30
+ attn_type (str): type of attention to use. Options: multihead_attention, multiquery_attention, grouped_query_attention
31
+ attn_pdrop (float): The dropout probability for the attention layers.
32
+ attn_impl (str): The attention implementation to use. One of 'torch', 'flash', or 'triton'.
33
+ qk_ln (bool): Whether to apply layer normalization to the queries and keys in the attention layer.
34
+ clip_qkv (Optional[float]): If not None, clip the queries, keys, and values in the attention layer to
35
+ this value.
36
+ softmax_scale (Optional[float]): If not None, scale the softmax in the attention layer by this value. If None,
37
+ use the default scale of ``1/sqrt(d_keys)``.
38
+ prefix_lm (Optional[bool]): Whether the model should operate as a Prefix LM. This requires passing an
39
+ extra `prefix_mask` argument which indicates which tokens belong to the prefix. Tokens in the prefix
40
+ can attend to one another bi-directionally. Tokens outside the prefix use causal attention.
41
+ attn_uses_sequence_id (Optional[bool]): Whether to restrict attention to tokens that have the same sequence_id.
42
+ When the model is in `train` mode, this requires passing an extra `sequence_id` argument which indicates
43
+ which sub-sequence each token belongs to.
44
+ Defaults to ``False`` meaning any provided `sequence_id` will be ignored.
45
+ alibi (bool): Whether to use the alibi bias instead of position embeddings.
46
+ alibi_bias_max (int): The maximum value of the alibi bias.
47
+ rope (bool): Whether to use rotary positional embeddings.
48
+ rope_theta (int): The base frequency for rope.
49
+ rope_impl (str): The implementation of rope to use. One of 'hf' (to use the implementation from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py) or 'dail' (to use the implementation from https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/layers/rotary.py).
50
+ rope_dail_config (Dict): The configuration for the dail implementation of rope.
51
+ type (str): The type of rotary position embedding to use. Options: 'original' (for https://arxiv.org/pdf/2104.09864.pdf), 'xpos' (for https://arxiv.org/pdf/2212.10554.pdf).
52
+ pos_idx_in_fp32 (bool): If True, the position indices [0, ..., seqlen - 1] are in fp32, otherwise they might be in lower precision. A consequence could be, for example, that bf16 rounds position 1995 to 2000, which leads to them having the same positional embedding.
53
+ xpos_scale_base (float): The scale base for XPos (if using XPos).
54
+ rope_hf_config (Dict): A dictionary used to configure rope's scaling behavior (when scaling beyond the training length).
55
+ type (str): Can be one of 'no_scaling', 'linear', or 'dynamic'. 'no_scaling' uses the default implementation for rotary embeddings, 'linear' uses linear scaling as proposed by the Reddit user /u/kaiokendev, and 'dynamic' uses Dynamic NTK scaling as proposed by the Reddit users /u/bloc97 and /u/emozilla.
56
+ factor (float): Scaling factor to use if using 'linear' or 'dynamic' as rope_scaling.type.
57
+ kv_n_heads (Optional[int]): For grouped_query_attention only, allow user to specify number of kv heads.
58
+ ffn_config (Dict): A dictionary used to configure the model's ffn module:
59
+ ffn_type (str): type of ffn to use. Options: mptmlp, te_ln_mlp
60
+ init_device (str): The device to use for parameter initialization.
61
+ logit_scale (Optional[Union[float, str]]): If not None, scale the logits by this value.
62
+ no_bias (bool): Whether to use bias in all layers.
63
+ verbose (int): The verbosity level. 0 is silent.
64
+ embedding_fraction (float): The fraction to scale the gradients of the embedding layer by.
65
+ norm_type (str): choose type of norm to use
66
+ use_cache (bool): Whether or not the model should return the last key/values attentions
67
+ init_config (Dict): A dictionary used to configure the model initialization:
68
+ init_config.name: The parameter initialization scheme to use. Options: 'default_', 'baseline_',
69
+ 'kaiming_uniform_', 'kaiming_normal_', 'neox_init_', 'small_init_', 'xavier_uniform_', or
70
+ 'xavier_normal_'. These mimic the parameter initialization methods in PyTorch.
71
+ init_div_is_residual (Union[int, float, str, bool]): Value to divide initial weights by if ``module._is_residual`` is True.
72
+ emb_init_std (Optional[float]): The standard deviation of the normal distribution used to initialize the embedding layer.
73
+ emb_init_uniform_lim (Optional[Union[Tuple[float, float], float]]): The lower and upper limits of the uniform distribution
74
+ used to initialize the embedding layer. Mutually exclusive with ``emb_init_std``.
75
+ init_std (float): The standard deviation of the normal distribution used to initialize the model,
76
+ if using the baseline_ parameter initialization scheme.
77
+ init_gain (float): The gain to use for parameter initialization with kaiming or xavier initialization schemes.
78
+ fan_mode (str): The fan mode to use for parameter initialization with kaiming initialization schemes.
79
+ init_nonlinearity (str): The nonlinearity to use for parameter initialization with kaiming initialization schemes.
80
+ ---
81
+ See llmfoundry.models.utils.param_init_fns.py for info on other param init config options
82
+ fc_type (str): choose fc layer implementation. Options: torch and te. te layers support fp8 when using H100 GPUs.
83
+ tie_word_embeddings (bool): Whether to tie the input embedding and output layers.
84
+ """
85
+ self.d_model = d_model
86
+ self.n_heads = n_heads
87
+ self.n_layers = n_layers
88
+ self.expansion_ratio = expansion_ratio
89
+ self.max_seq_len = max_seq_len
90
+ self.vocab_size = vocab_size
91
+ self.resid_pdrop = resid_pdrop
92
+ self.emb_pdrop = emb_pdrop
93
+ self.learned_pos_emb = learned_pos_emb
94
+ self.attn_config = attn_config
95
+ self.ffn_config = ffn_config
96
+ self.init_device = init_device
97
+ self.logit_scale = logit_scale
98
+ self.no_bias = no_bias
99
+ self.embedding_fraction = embedding_fraction
100
+ self.norm_type = norm_type
101
+ self.use_cache = use_cache
102
+ self.init_config = init_config
103
+ self.fc_type = fc_type
104
+ if verbose is not None:
105
+ warnings.warn(DeprecationWarning('verbose argument for MPTConfig is now ignored and will be removed. Use python_log_level instead.'))
106
+ if 'name' in kwargs:
107
+ del kwargs['name']
108
+ if 'loss_fn' in kwargs:
109
+ del kwargs['loss_fn']
110
+ if self.attn_config.get('alibi', False) or self.attn_config.get('rope', False):
111
+ self.learned_pos_emb = False
112
+ warnings.warn(f'alibi or rope is turned on, setting `learned_pos_emb` to `False.`')
113
+ super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
114
+ self._validate_config()
115
+
116
+ def _set_config_defaults(self, config: Dict[str, Any], config_defaults: Dict[str, Any]) -> Dict[str, Any]:
117
+ for (k, v) in config_defaults.items():
118
+ if k not in config:
119
+ config[k] = v
120
+ elif isinstance(v, dict):
121
+ config[k] = self._set_config_defaults(config[k] if config[k] is not None else {}, v)
122
+ return config
123
+
124
+ def _validate_config(self) -> None:
125
+ self.attn_config = self._set_config_defaults(self.attn_config, attn_config_defaults)
126
+ self.ffn_config = self._set_config_defaults(self.ffn_config, ffn_config_defaults)
127
+ self.init_config = self._set_config_defaults(self.init_config, init_config_defaults)
128
+ if self.d_model % self.n_heads != 0:
129
+ raise ValueError('d_model must be divisible by n_heads')
130
+ if any((prob < 0 or prob > 1 for prob in [self.attn_config['attn_pdrop'], self.resid_pdrop, self.emb_pdrop])):
131
+ raise ValueError("self.attn_config['attn_pdrop'], resid_pdrop, emb_pdrop are probabilities and must be between 0 and 1")
132
+ if self.attn_config['attn_impl'] not in ['torch', 'flash', 'triton']:
133
+ raise ValueError(f"Unknown attn_impl={self.attn_config['attn_impl']}")
134
+ if self.attn_config['prefix_lm'] and self.attn_config['attn_impl'] not in ['torch', 'triton']:
135
+ raise NotImplementedError('prefix_lm only implemented with torch and triton attention.')
136
+ if self.attn_config['alibi'] and self.attn_config['attn_impl'] not in ['torch', 'triton']:
137
+ raise NotImplementedError('alibi only implemented with torch and triton attention.')
138
+ if self.attn_config['attn_uses_sequence_id'] and self.attn_config['attn_impl'] not in ['torch', 'triton']:
139
+ raise NotImplementedError('attn_uses_sequence_id only implemented with torch and triton attention.')
140
+ if self.attn_config['rope'] and self.attn_config['rope_impl'] not in ['dail', 'hf']:
141
+ raise ValueError('If rope is being used then rope_impl should be either "dail", or "hf".')
142
+ if self.attn_config['rope'] and self.attn_config['rope_impl'] == 'hf' and (self.attn_config['rope_hf_config']['type'] not in ['no_scaling', 'linear', 'dynamic']):
143
+ raise ValueError('If using hf implementation of rope, the type should be one of "no_scaling", "linear" or "dynamic".')
144
+ if self.attn_config['rope'] and self.attn_config['rope_impl'] == 'dail':
145
+ if self.attn_config['rope_dail_config']['type'] not in ['original', 'xpos']:
146
+ raise ValueError('If using the dail implementation of rope, the type should be one of "original" or "xpos".')
147
+ if not is_flash_v2_installed(v2_version='2.0.1'):
148
+ raise ImportError('If using the dail implementation of rope, the flash_attn library v2.0.1 or higher must be installed. Please check the instructions at https://github.com/mosaicml/llm-foundry/blob/main/TUTORIAL.md#what-kinds-of-positional-embeddings-does-llm-foundry-support')
149
+ if self.embedding_fraction > 1 or self.embedding_fraction <= 0:
150
+ raise ValueError('model.embedding_fraction must be between 0 (exclusive) and 1 (inclusive)!')
151
+ if isinstance(self.logit_scale, str) and self.logit_scale != 'inv_sqrt_d_model':
152
+ raise ValueError(f"self.logit_scale={self.logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'.")
153
+ if self.init_config.get('name', None) is None:
154
+ raise ValueError(f"self.init_config={self.init_config!r} 'name' needs to be set.")
155
+ if not (self.learned_pos_emb or self.attn_config['alibi'] or self.attn_config['rope']):
156
+ warnings.warn(f'Positional information not being provided to the model using either learned_pos_emb or alibi or rope.')
157
+ if self.fc_type == 'te' or self.ffn_config['ffn_type'] == 'te_ln_mlp':
158
+ try:
159
+ import transformer_engine.pytorch as te
160
+ del te
161
+ except:
162
+ raise ImportError('TransformerEngine import fail. `fc_type: te` requires TransformerEngine be installed. ' + 'The required version of transformer_engine also requires FlashAttention v1.0.6 is installed:\n' + 'pip install flash-attn==1.0.6 --no-build-isolation \n' + 'pip install git+https://github.com/NVIDIA/TransformerEngine.git@144e4888b2cdd60bd52e706d5b7a79cb9c1a7156')
163
+ if self.ffn_config['ffn_type'] == 'mptmlp':
164
+ self.ffn_config['fc_type'] = self.fc_type
165
+ elif self.ffn_config['ffn_type'] == 'te_ln_mlp':
166
+ self.ffn_config['bias'] = not self.no_bias
custom_embedding.py ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch.nn as nn
2
+ import torch.nn.functional as F
3
+ from torch import Tensor
4
+
5
+ class SharedEmbedding(nn.Embedding):
6
+
7
+ def forward(self, input: Tensor, unembed: bool=False) -> Tensor:
8
+ if unembed:
9
+ return F.linear(input, self.weight)
10
+ return super().forward(input)
fc.py ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ from torch import nn
2
+ FC_CLASS_REGISTRY = {'torch': nn.Linear}
3
+ try:
4
+ import transformer_engine.pytorch as te
5
+ FC_CLASS_REGISTRY['te'] = te.Linear
6
+ except:
7
+ pass
ffn.py ADDED
@@ -0,0 +1,39 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """GPT Blocks used for the GPT Model."""
2
+ from typing import Any, Optional
3
+ import torch
4
+ import torch.nn as nn
5
+ from .fc import FC_CLASS_REGISTRY
6
+ try:
7
+ import transformer_engine.pytorch as te
8
+ except:
9
+ te = None
10
+
11
+ class MPTMLP(nn.Module):
12
+
13
+ def __init__(self, d_model: int, expansion_ratio: int, fc_type: str='torch', device: Optional[str]=None, bias: bool=True):
14
+ super().__init__()
15
+ fc_kwargs: dict[str, Any] = {'bias': bias}
16
+ if fc_type != 'te':
17
+ fc_kwargs['device'] = device
18
+ self.up_proj = FC_CLASS_REGISTRY[fc_type](d_model, expansion_ratio * d_model, **fc_kwargs)
19
+ self.act = nn.GELU(approximate='none')
20
+ self.down_proj = FC_CLASS_REGISTRY[fc_type](expansion_ratio * d_model, d_model, **fc_kwargs)
21
+ self.down_proj._is_residual = True
22
+
23
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
24
+ return self.down_proj(self.act(self.up_proj(x)))
25
+ FFN_CLASS_REGISTRY = {'mptmlp': MPTMLP}
26
+ if te is not None:
27
+ te.LayerNormMLP._has_norm = True
28
+ FFN_CLASS_REGISTRY['te_ln_mlp'] = te.LayerNormMLP
29
+
30
+ def build_ffn(d_model: int, expansion_ratio: int, fc_type: str='torch', device: Optional[str]=None, bias: bool=True, **kwargs: Any) -> nn.Module:
31
+ ffn_type = kwargs.pop('ffn_type')
32
+ if ffn_type == 'mptmlp':
33
+ if len(kwargs) > 0:
34
+ raise ValueError(f'MPTMLP got an unexpected keyword argument: {kwargs}')
35
+ return MPTMLP(d_model=d_model, expansion_ratio=expansion_ratio, fc_type=fc_type, device=device, bias=bias)
36
+ elif ffn_type == 'te_ln_mlp':
37
+ assert te is not None
38
+ return te.LayerNormMLP(hidden_size=d_model, ffn_hidden_size=d_model * expansion_ratio, bias=bias, **kwargs)
39
+ raise ValueError(f'ffn_type={ffn_type!r} not recognized.')
flash_attn_triton.py ADDED
@@ -0,0 +1,484 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """
2
+ Copied from https://github.com/HazyResearch/flash-attention/blob/eff9fe6b8076df59d64d7a3f464696738a3c7c24/flash_attn/flash_attn_triton.py
3
+ update imports to use 'triton_pre_mlir'
4
+
5
+ *Experimental* implementation of FlashAttention in Triton.
6
+ Tested with triton==2.0.0.dev20221202.
7
+ Triton 2.0 has a new backend (MLIR) but seems like it doesn't yet work for head dimensions
8
+ other than 64:
9
+ https://github.com/openai/triton/blob/d376020f90002757eea3ea9475d4f7cfc2ec5ead/python/triton/ops/flash_attention.py#L207
10
+ We'll update this implementation with the new Triton backend once this is fixed.
11
+
12
+ We use the FlashAttention implementation from Phil Tillet a starting point.
13
+ https://github.com/openai/triton/blob/master/python/tutorials/06-fused-attention.py
14
+
15
+ Changes:
16
+ - Implement both causal and non-causal attention.
17
+ - Implement both self-attention and cross-attention.
18
+ - Support arbitrary seqlens (not just multiples of 128), for both forward and backward.
19
+ - Support all head dimensions up to 128 (not just 16, 32, 64, 128), for both forward and backward.
20
+ - Support attention bias.
21
+ - Speed up the forward pass a bit, and only store the LSE instead of m and l.
22
+ - Make the backward for d=128 much faster by reducing register spilling.
23
+ - Optionally parallelize the backward pass across seqlen_k, to deal with the case of
24
+ small batch size * nheads.
25
+
26
+ Caution:
27
+ - This is an *experimental* implementation. The forward pass should be quite robust but
28
+ I'm not 100% sure that the backward pass doesn't have race conditions (due to the Triton compiler).
29
+ - This implementation has only been tested on A100.
30
+ - If you plan to use headdim other than 64 and 128, you should test for race conditions
31
+ (due to the Triton compiler), as done in tests/test_flash_attn.py
32
+ "test_flash_attn_triton_race_condition". I've tested and fixed many race conditions
33
+ for different head dimensions (40, 48, 64, 128, 80, 88, 96), but I'm still not 100% confident
34
+ that there are none left for other head dimensions.
35
+
36
+ Differences between this Triton version and the CUDA version:
37
+ - Triton version doesn't support dropout.
38
+ - Triton forward is generally faster than CUDA forward, while Triton backward is
39
+ generally slower than CUDA backward. Overall Triton forward + backward is slightly slower
40
+ than CUDA forward + backward.
41
+ - Triton version doesn't support different sequence lengths in a batch (i.e., RaggedTensor/NestedTensor).
42
+ - Triton version supports attention bias, while CUDA version doesn't.
43
+ """
44
+ import math
45
+ import torch
46
+ import triton_pre_mlir as triton
47
+ import triton_pre_mlir.language as tl
48
+
49
+ @triton.heuristics({'EVEN_M': lambda args: args['seqlen_q'] % args['BLOCK_M'] == 0, 'EVEN_N': lambda args: args['seqlen_k'] % args['BLOCK_N'] == 0, 'EVEN_HEADDIM': lambda args: args['headdim'] == args['BLOCK_HEADDIM']})
50
+ @triton.jit
51
+ def _fwd_kernel(Q, K, V, Bias, Out, Lse, TMP, softmax_scale, stride_qb, stride_qh, stride_qm, stride_kb, stride_kh, stride_kn, stride_vb, stride_vh, stride_vn, stride_bb, stride_bh, stride_bm, stride_ob, stride_oh, stride_om, nheads, seqlen_q, seqlen_k, seqlen_q_rounded, headdim, CACHE_KEY_SEQLEN_Q, CACHE_KEY_SEQLEN_K, BIAS_TYPE: tl.constexpr, IS_CAUSAL: tl.constexpr, BLOCK_HEADDIM: tl.constexpr, EVEN_M: tl.constexpr, EVEN_N: tl.constexpr, EVEN_HEADDIM: tl.constexpr, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr):
52
+ start_m = tl.program_id(0)
53
+ off_hb = tl.program_id(1)
54
+ off_b = off_hb // nheads
55
+ off_h = off_hb % nheads
56
+ offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
57
+ offs_n = tl.arange(0, BLOCK_N)
58
+ offs_d = tl.arange(0, BLOCK_HEADDIM)
59
+ q_ptrs = Q + off_b * stride_qb + off_h * stride_qh + (offs_m[:, None] * stride_qm + offs_d[None, :])
60
+ k_ptrs = K + off_b * stride_kb + off_h * stride_kh + (offs_n[:, None] * stride_kn + offs_d[None, :])
61
+ v_ptrs = V + off_b * stride_vb + off_h * stride_vh + (offs_n[:, None] * stride_vn + offs_d[None, :])
62
+ if BIAS_TYPE == 'vector':
63
+ b_ptrs = Bias + off_b * stride_bb + off_h * stride_bh + offs_n
64
+ elif BIAS_TYPE == 'matrix':
65
+ b_ptrs = Bias + off_b * stride_bb + off_h * stride_bh + (offs_m[:, None] * stride_bm + offs_n[None, :])
66
+ t_ptrs = TMP + off_hb * seqlen_q_rounded + offs_m
67
+ lse_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float('inf')
68
+ m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float('inf')
69
+ acc_o = tl.zeros([BLOCK_M, BLOCK_HEADDIM], dtype=tl.float32)
70
+ if EVEN_M & EVEN_N:
71
+ if EVEN_HEADDIM:
72
+ q = tl.load(q_ptrs)
73
+ else:
74
+ q = tl.load(q_ptrs, mask=offs_d[None, :] < headdim, other=0.0)
75
+ elif EVEN_HEADDIM:
76
+ q = tl.load(q_ptrs, mask=offs_m[:, None] < seqlen_q, other=0.0)
77
+ else:
78
+ q = tl.load(q_ptrs, mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0)
79
+ end_n = seqlen_k if not IS_CAUSAL else tl.minimum((start_m + 1) * BLOCK_M, seqlen_k)
80
+ for start_n in range(0, end_n, BLOCK_N):
81
+ start_n = tl.multiple_of(start_n, BLOCK_N)
82
+ if EVEN_N & EVEN_M:
83
+ if EVEN_HEADDIM:
84
+ k = tl.load(k_ptrs + start_n * stride_kn)
85
+ else:
86
+ k = tl.load(k_ptrs + start_n * stride_kn, mask=offs_d[None, :] < headdim, other=0.0)
87
+ elif EVEN_HEADDIM:
88
+ k = tl.load(k_ptrs + start_n * stride_kn, mask=(start_n + offs_n)[:, None] < seqlen_k, other=0.0)
89
+ else:
90
+ k = tl.load(k_ptrs + start_n * stride_kn, mask=((start_n + offs_n)[:, None] < seqlen_k) & (offs_d[None, :] < headdim), other=0.0)
91
+ qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
92
+ qk += tl.dot(q, k, trans_b=True)
93
+ if not EVEN_N:
94
+ qk += tl.where((start_n + offs_n)[None, :] < seqlen_k, 0, float('-inf'))
95
+ if IS_CAUSAL:
96
+ qk += tl.where(offs_m[:, None] >= (start_n + offs_n)[None, :], 0, float('-inf'))
97
+ if BIAS_TYPE != 'none':
98
+ if BIAS_TYPE == 'vector':
99
+ if EVEN_N:
100
+ bias = tl.load(b_ptrs + start_n).to(tl.float32)
101
+ else:
102
+ bias = tl.load(b_ptrs + start_n, mask=start_n + offs_n < seqlen_k, other=0.0).to(tl.float32)
103
+ bias = bias[None, :]
104
+ elif BIAS_TYPE == 'matrix':
105
+ if EVEN_M & EVEN_N:
106
+ bias = tl.load(b_ptrs + start_n).to(tl.float32)
107
+ else:
108
+ bias = tl.load(b_ptrs + start_n, mask=(offs_m[:, None] < seqlen_q) & ((start_n + offs_n)[None, :] < seqlen_k), other=0.0).to(tl.float32)
109
+ qk = qk * softmax_scale + bias
110
+ m_ij = tl.maximum(tl.max(qk, 1), lse_i)
111
+ p = tl.exp(qk - m_ij[:, None])
112
+ else:
113
+ m_ij = tl.maximum(tl.max(qk, 1) * softmax_scale, lse_i)
114
+ p = tl.exp(qk * softmax_scale - m_ij[:, None])
115
+ l_ij = tl.sum(p, 1)
116
+ acc_o_scale = tl.exp(m_i - m_ij)
117
+ tl.store(t_ptrs, acc_o_scale)
118
+ acc_o_scale = tl.load(t_ptrs)
119
+ acc_o = acc_o * acc_o_scale[:, None]
120
+ if EVEN_N & EVEN_M:
121
+ if EVEN_HEADDIM:
122
+ v = tl.load(v_ptrs + start_n * stride_vn)
123
+ else:
124
+ v = tl.load(v_ptrs + start_n * stride_vn, mask=offs_d[None, :] < headdim, other=0.0)
125
+ elif EVEN_HEADDIM:
126
+ v = tl.load(v_ptrs + start_n * stride_vn, mask=(start_n + offs_n)[:, None] < seqlen_k, other=0.0)
127
+ else:
128
+ v = tl.load(v_ptrs + start_n * stride_vn, mask=((start_n + offs_n)[:, None] < seqlen_k) & (offs_d[None, :] < headdim), other=0.0)
129
+ p = p.to(v.dtype)
130
+ acc_o += tl.dot(p, v)
131
+ m_i = m_ij
132
+ l_i_new = tl.exp(lse_i - m_ij) + l_ij
133
+ lse_i = m_ij + tl.log(l_i_new)
134
+ o_scale = tl.exp(m_i - lse_i)
135
+ tl.store(t_ptrs, o_scale)
136
+ o_scale = tl.load(t_ptrs)
137
+ acc_o = acc_o * o_scale[:, None]
138
+ start_m = tl.program_id(0)
139
+ offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
140
+ lse_ptrs = Lse + off_hb * seqlen_q_rounded + offs_m
141
+ tl.store(lse_ptrs, lse_i)
142
+ offs_d = tl.arange(0, BLOCK_HEADDIM)
143
+ out_ptrs = Out + off_b * stride_ob + off_h * stride_oh + (offs_m[:, None] * stride_om + offs_d[None, :])
144
+ if EVEN_M:
145
+ if EVEN_HEADDIM:
146
+ tl.store(out_ptrs, acc_o)
147
+ else:
148
+ tl.store(out_ptrs, acc_o, mask=offs_d[None, :] < headdim)
149
+ elif EVEN_HEADDIM:
150
+ tl.store(out_ptrs, acc_o, mask=offs_m[:, None] < seqlen_q)
151
+ else:
152
+ tl.store(out_ptrs, acc_o, mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim))
153
+
154
+ @triton.jit
155
+ def _bwd_preprocess_do_o_dot(Out, DO, Delta, stride_ob, stride_oh, stride_om, stride_dob, stride_doh, stride_dom, nheads, seqlen_q, seqlen_q_rounded, headdim, BLOCK_M: tl.constexpr, BLOCK_HEADDIM: tl.constexpr):
156
+ start_m = tl.program_id(0)
157
+ off_hb = tl.program_id(1)
158
+ off_b = off_hb // nheads
159
+ off_h = off_hb % nheads
160
+ offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
161
+ offs_d = tl.arange(0, BLOCK_HEADDIM)
162
+ o = tl.load(Out + off_b * stride_ob + off_h * stride_oh + offs_m[:, None] * stride_om + offs_d[None, :], mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0).to(tl.float32)
163
+ do = tl.load(DO + off_b * stride_dob + off_h * stride_doh + offs_m[:, None] * stride_dom + offs_d[None, :], mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0).to(tl.float32)
164
+ delta = tl.sum(o * do, axis=1)
165
+ tl.store(Delta + off_hb * seqlen_q_rounded + offs_m, delta)
166
+
167
+ @triton.jit
168
+ def _bwd_store_dk_dv(dk_ptrs, dv_ptrs, dk, dv, offs_n, offs_d, seqlen_k, headdim, EVEN_M: tl.constexpr, EVEN_N: tl.constexpr, EVEN_HEADDIM: tl.constexpr):
169
+ if EVEN_N & EVEN_M:
170
+ if EVEN_HEADDIM:
171
+ tl.store(dv_ptrs, dv)
172
+ tl.store(dk_ptrs, dk)
173
+ else:
174
+ tl.store(dv_ptrs, dv, mask=offs_d[None, :] < headdim)
175
+ tl.store(dk_ptrs, dk, mask=offs_d[None, :] < headdim)
176
+ elif EVEN_HEADDIM:
177
+ tl.store(dv_ptrs, dv, mask=offs_n[:, None] < seqlen_k)
178
+ tl.store(dk_ptrs, dk, mask=offs_n[:, None] < seqlen_k)
179
+ else:
180
+ tl.store(dv_ptrs, dv, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim))
181
+ tl.store(dk_ptrs, dk, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim))
182
+
183
+ @triton.jit
184
+ def _bwd_kernel_one_col_block(start_n, Q, K, V, Bias, DO, DQ, DK, DV, LSE, D, softmax_scale, stride_qm, stride_kn, stride_vn, stride_bm, stride_dom, stride_dqm, stride_dkn, stride_dvn, seqlen_q, seqlen_k, headdim, ATOMIC_ADD: tl.constexpr, BIAS_TYPE: tl.constexpr, IS_CAUSAL: tl.constexpr, BLOCK_HEADDIM: tl.constexpr, EVEN_M: tl.constexpr, EVEN_N: tl.constexpr, EVEN_HEADDIM: tl.constexpr, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr):
185
+ begin_m = 0 if not IS_CAUSAL else start_n * BLOCK_N // BLOCK_M * BLOCK_M
186
+ offs_qm = begin_m + tl.arange(0, BLOCK_M)
187
+ offs_n = start_n * BLOCK_N + tl.arange(0, BLOCK_N)
188
+ offs_m = tl.arange(0, BLOCK_M)
189
+ offs_d = tl.arange(0, BLOCK_HEADDIM)
190
+ q_ptrs = Q + (offs_qm[:, None] * stride_qm + offs_d[None, :])
191
+ k_ptrs = K + (offs_n[:, None] * stride_kn + offs_d[None, :])
192
+ v_ptrs = V + (offs_n[:, None] * stride_vn + offs_d[None, :])
193
+ do_ptrs = DO + (offs_qm[:, None] * stride_dom + offs_d[None, :])
194
+ dq_ptrs = DQ + (offs_qm[:, None] * stride_dqm + offs_d[None, :])
195
+ if BIAS_TYPE == 'vector':
196
+ b_ptrs = Bias + offs_n
197
+ elif BIAS_TYPE == 'matrix':
198
+ b_ptrs = Bias + (offs_qm[:, None] * stride_bm + offs_n[None, :])
199
+ dv = tl.zeros([BLOCK_N, BLOCK_HEADDIM], dtype=tl.float32)
200
+ dk = tl.zeros([BLOCK_N, BLOCK_HEADDIM], dtype=tl.float32)
201
+ if begin_m >= seqlen_q:
202
+ dv_ptrs = DV + (offs_n[:, None] * stride_dvn + offs_d[None, :])
203
+ dk_ptrs = DK + (offs_n[:, None] * stride_dkn + offs_d[None, :])
204
+ _bwd_store_dk_dv(dk_ptrs, dv_ptrs, dk, dv, offs_n, offs_d, seqlen_k, headdim, EVEN_M=EVEN_M, EVEN_N=EVEN_N, EVEN_HEADDIM=EVEN_HEADDIM)
205
+ return
206
+ if EVEN_N & EVEN_M:
207
+ if EVEN_HEADDIM:
208
+ k = tl.load(k_ptrs)
209
+ v = tl.load(v_ptrs)
210
+ else:
211
+ k = tl.load(k_ptrs, mask=offs_d[None, :] < headdim, other=0.0)
212
+ v = tl.load(v_ptrs, mask=offs_d[None, :] < headdim, other=0.0)
213
+ elif EVEN_HEADDIM:
214
+ k = tl.load(k_ptrs, mask=offs_n[:, None] < seqlen_k, other=0.0)
215
+ v = tl.load(v_ptrs, mask=offs_n[:, None] < seqlen_k, other=0.0)
216
+ else:
217
+ k = tl.load(k_ptrs, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim), other=0.0)
218
+ v = tl.load(v_ptrs, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim), other=0.0)
219
+ num_block_m = tl.cdiv(seqlen_q, BLOCK_M)
220
+ for start_m in range(begin_m, num_block_m * BLOCK_M, BLOCK_M):
221
+ start_m = tl.multiple_of(start_m, BLOCK_M)
222
+ offs_m_curr = start_m + offs_m
223
+ if EVEN_M & EVEN_HEADDIM:
224
+ q = tl.load(q_ptrs)
225
+ elif EVEN_HEADDIM:
226
+ q = tl.load(q_ptrs, mask=offs_m_curr[:, None] < seqlen_q, other=0.0)
227
+ else:
228
+ q = tl.load(q_ptrs, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0)
229
+ qk = tl.dot(q, k, trans_b=True)
230
+ if not EVEN_N:
231
+ qk = tl.where(offs_n[None, :] < seqlen_k, qk, float('-inf'))
232
+ if IS_CAUSAL:
233
+ qk = tl.where(offs_m_curr[:, None] >= offs_n[None, :], qk, float('-inf'))
234
+ if BIAS_TYPE != 'none':
235
+ tl.debug_barrier()
236
+ if BIAS_TYPE == 'vector':
237
+ if EVEN_N:
238
+ bias = tl.load(b_ptrs).to(tl.float32)
239
+ else:
240
+ bias = tl.load(b_ptrs, mask=offs_n < seqlen_k, other=0.0).to(tl.float32)
241
+ bias = bias[None, :]
242
+ elif BIAS_TYPE == 'matrix':
243
+ if EVEN_M & EVEN_N:
244
+ bias = tl.load(b_ptrs).to(tl.float32)
245
+ else:
246
+ bias = tl.load(b_ptrs, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_n[None, :] < seqlen_k), other=0.0).to(tl.float32)
247
+ qk = qk * softmax_scale + bias
248
+ if not EVEN_M & EVEN_HEADDIM:
249
+ tl.debug_barrier()
250
+ lse_i = tl.load(LSE + offs_m_curr)
251
+ if BIAS_TYPE == 'none':
252
+ p = tl.exp(qk * softmax_scale - lse_i[:, None])
253
+ else:
254
+ p = tl.exp(qk - lse_i[:, None])
255
+ if EVEN_M & EVEN_HEADDIM:
256
+ do = tl.load(do_ptrs)
257
+ else:
258
+ do = tl.load(do_ptrs, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0)
259
+ dv += tl.dot(p.to(do.dtype), do, trans_a=True)
260
+ if not EVEN_M & EVEN_HEADDIM:
261
+ tl.debug_barrier()
262
+ dp = tl.dot(do, v, trans_b=True)
263
+ if not EVEN_HEADDIM:
264
+ tl.debug_barrier()
265
+ Di = tl.load(D + offs_m_curr)
266
+ ds = (p * (dp - Di[:, None]) * softmax_scale).to(q.dtype)
267
+ dk += tl.dot(ds, q, trans_a=True)
268
+ if not EVEN_M & EVEN_HEADDIM:
269
+ tl.debug_barrier()
270
+ if not ATOMIC_ADD:
271
+ if EVEN_M & EVEN_HEADDIM:
272
+ dq = tl.load(dq_ptrs, eviction_policy='evict_last')
273
+ dq += tl.dot(ds, k)
274
+ tl.store(dq_ptrs, dq, eviction_policy='evict_last')
275
+ elif EVEN_HEADDIM:
276
+ dq = tl.load(dq_ptrs, mask=offs_m_curr[:, None] < seqlen_q, other=0.0, eviction_policy='evict_last')
277
+ dq += tl.dot(ds, k)
278
+ tl.store(dq_ptrs, dq, mask=offs_m_curr[:, None] < seqlen_q, eviction_policy='evict_last')
279
+ else:
280
+ dq = tl.load(dq_ptrs, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0, eviction_policy='evict_last')
281
+ dq += tl.dot(ds, k)
282
+ tl.store(dq_ptrs, dq, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), eviction_policy='evict_last')
283
+ else:
284
+ dq = tl.dot(ds, k)
285
+ if EVEN_M & EVEN_HEADDIM:
286
+ tl.atomic_add(dq_ptrs, dq)
287
+ elif EVEN_HEADDIM:
288
+ tl.atomic_add(dq_ptrs, dq, mask=offs_m_curr[:, None] < seqlen_q)
289
+ else:
290
+ tl.atomic_add(dq_ptrs, dq, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim))
291
+ dq_ptrs += BLOCK_M * stride_dqm
292
+ q_ptrs += BLOCK_M * stride_qm
293
+ do_ptrs += BLOCK_M * stride_dom
294
+ if BIAS_TYPE == 'matrix':
295
+ b_ptrs += BLOCK_M * stride_bm
296
+ dv_ptrs = DV + (offs_n[:, None] * stride_dvn + offs_d[None, :])
297
+ dk_ptrs = DK + (offs_n[:, None] * stride_dkn + offs_d[None, :])
298
+ _bwd_store_dk_dv(dk_ptrs, dv_ptrs, dk, dv, offs_n, offs_d, seqlen_k, headdim, EVEN_M=EVEN_M, EVEN_N=EVEN_N, EVEN_HEADDIM=EVEN_HEADDIM)
299
+
300
+ def init_to_zero(name):
301
+ return lambda nargs: nargs[name].zero_()
302
+
303
+ @triton.autotune(configs=[triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'SEQUENCE_PARALLEL': False}, num_warps=8, num_stages=1, pre_hook=init_to_zero('DQ')), triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'SEQUENCE_PARALLEL': True}, num_warps=8, num_stages=1, pre_hook=init_to_zero('DQ'))], key=['CACHE_KEY_SEQLEN_Q', 'CACHE_KEY_SEQLEN_K', 'BIAS_TYPE', 'IS_CAUSAL', 'BLOCK_HEADDIM'])
304
+ @triton.heuristics({'EVEN_M': lambda args: args['seqlen_q'] % args['BLOCK_M'] == 0, 'EVEN_N': lambda args: args['seqlen_k'] % args['BLOCK_N'] == 0, 'EVEN_HEADDIM': lambda args: args['headdim'] == args['BLOCK_HEADDIM']})
305
+ @triton.jit
306
+ def _bwd_kernel(Q, K, V, Bias, DO, DQ, DK, DV, LSE, D, softmax_scale, stride_qb, stride_qh, stride_qm, stride_kb, stride_kh, stride_kn, stride_vb, stride_vh, stride_vn, stride_bb, stride_bh, stride_bm, stride_dob, stride_doh, stride_dom, stride_dqb, stride_dqh, stride_dqm, stride_dkb, stride_dkh, stride_dkn, stride_dvb, stride_dvh, stride_dvn, nheads, seqlen_q, seqlen_k, seqlen_q_rounded, headdim, CACHE_KEY_SEQLEN_Q, CACHE_KEY_SEQLEN_K, BIAS_TYPE: tl.constexpr, IS_CAUSAL: tl.constexpr, BLOCK_HEADDIM: tl.constexpr, SEQUENCE_PARALLEL: tl.constexpr, EVEN_M: tl.constexpr, EVEN_N: tl.constexpr, EVEN_HEADDIM: tl.constexpr, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr):
307
+ off_hb = tl.program_id(1)
308
+ off_b = off_hb // nheads
309
+ off_h = off_hb % nheads
310
+ Q += off_b * stride_qb + off_h * stride_qh
311
+ K += off_b * stride_kb + off_h * stride_kh
312
+ V += off_b * stride_vb + off_h * stride_vh
313
+ DO += off_b * stride_dob + off_h * stride_doh
314
+ DQ += off_b * stride_dqb + off_h * stride_dqh
315
+ DK += off_b * stride_dkb + off_h * stride_dkh
316
+ DV += off_b * stride_dvb + off_h * stride_dvh
317
+ if BIAS_TYPE != 'none':
318
+ Bias += off_b * stride_bb + off_h * stride_bh
319
+ D += off_hb * seqlen_q_rounded
320
+ LSE += off_hb * seqlen_q_rounded
321
+ if not SEQUENCE_PARALLEL:
322
+ num_block_n = tl.cdiv(seqlen_k, BLOCK_N)
323
+ for start_n in range(0, num_block_n):
324
+ _bwd_kernel_one_col_block(start_n, Q, K, V, Bias, DO, DQ, DK, DV, LSE, D, softmax_scale, stride_qm, stride_kn, stride_vn, stride_bm, stride_dom, stride_dqm, stride_dkn, stride_dvn, seqlen_q, seqlen_k, headdim, ATOMIC_ADD=False, BIAS_TYPE=BIAS_TYPE, IS_CAUSAL=IS_CAUSAL, BLOCK_HEADDIM=BLOCK_HEADDIM, EVEN_M=EVEN_M, EVEN_N=EVEN_N, EVEN_HEADDIM=EVEN_HEADDIM, BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N)
325
+ else:
326
+ start_n = tl.program_id(0)
327
+ _bwd_kernel_one_col_block(start_n, Q, K, V, Bias, DO, DQ, DK, DV, LSE, D, softmax_scale, stride_qm, stride_kn, stride_vn, stride_bm, stride_dom, stride_dqm, stride_dkn, stride_dvn, seqlen_q, seqlen_k, headdim, ATOMIC_ADD=True, BIAS_TYPE=BIAS_TYPE, IS_CAUSAL=IS_CAUSAL, BLOCK_HEADDIM=BLOCK_HEADDIM, EVEN_M=EVEN_M, EVEN_N=EVEN_N, EVEN_HEADDIM=EVEN_HEADDIM, BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N)
328
+
329
+ def _flash_attn_forward(q, k, v, bias=None, causal=False, softmax_scale=None):
330
+ (batch, seqlen_q, nheads, d) = q.shape
331
+ (_, seqlen_k, _, _) = k.shape
332
+ assert k.shape == (batch, seqlen_k, nheads, d)
333
+ assert v.shape == (batch, seqlen_k, nheads, d)
334
+ assert d <= 128, 'FlashAttention only support head dimensions up to 128'
335
+ assert q.dtype == k.dtype == v.dtype, 'All tensors must have the same type'
336
+ assert q.dtype in [torch.float16, torch.bfloat16], 'Only support fp16 and bf16'
337
+ assert q.is_cuda and k.is_cuda and v.is_cuda
338
+ softmax_scale = softmax_scale or 1.0 / math.sqrt(d)
339
+ has_bias = bias is not None
340
+ bias_type = 'none'
341
+ if has_bias:
342
+ assert bias.dtype in [q.dtype, torch.float]
343
+ assert bias.is_cuda
344
+ assert bias.dim() == 4
345
+ if bias.stride(-1) != 1:
346
+ bias = bias.contiguous()
347
+ if bias.shape[2:] == (1, seqlen_k):
348
+ bias_type = 'vector'
349
+ elif bias.shape[2:] == (seqlen_q, seqlen_k):
350
+ bias_type = 'matrix'
351
+ else:
352
+ raise RuntimeError('Last 2 dimensions of bias must be (1, seqlen_k) or (seqlen_q, seqlen_k)')
353
+ bias = bias.expand(batch, nheads, seqlen_q, seqlen_k)
354
+ bias_strides = (bias.stride(0), bias.stride(1), bias.stride(2)) if has_bias else (0, 0, 0)
355
+ seqlen_q_rounded = math.ceil(seqlen_q / 128) * 128
356
+ lse = torch.empty((batch, nheads, seqlen_q_rounded), device=q.device, dtype=torch.float32)
357
+ tmp = torch.empty((batch, nheads, seqlen_q_rounded), device=q.device, dtype=torch.float32)
358
+ o = torch.empty_like(q)
359
+ BLOCK_HEADDIM = max(triton.next_power_of_2(d), 16)
360
+ BLOCK = 128
361
+ num_warps = 4 if d <= 64 else 8
362
+ grid = lambda META: (triton.cdiv(seqlen_q, META['BLOCK_M']), batch * nheads)
363
+ _fwd_kernel[grid](q, k, v, bias, o, lse, tmp, softmax_scale, q.stride(0), q.stride(2), q.stride(1), k.stride(0), k.stride(2), k.stride(1), v.stride(0), v.stride(2), v.stride(1), *bias_strides, o.stride(0), o.stride(2), o.stride(1), nheads, seqlen_q, seqlen_k, seqlen_q_rounded, d, seqlen_q // 32, seqlen_k // 32, bias_type, causal, BLOCK_HEADDIM, BLOCK_M=BLOCK, BLOCK_N=BLOCK, num_warps=num_warps, num_stages=1)
364
+ return (o, lse, softmax_scale)
365
+
366
+ def _flash_attn_backward(do, q, k, v, o, lse, dq, dk, dv, bias=None, causal=False, softmax_scale=None):
367
+ if do.stride(-1) != 1:
368
+ do = do.contiguous()
369
+ (batch, seqlen_q, nheads, d) = q.shape
370
+ (_, seqlen_k, _, _) = k.shape
371
+ assert d <= 128
372
+ seqlen_q_rounded = math.ceil(seqlen_q / 128) * 128
373
+ assert lse.shape == (batch, nheads, seqlen_q_rounded)
374
+ assert q.stride(-1) == k.stride(-1) == v.stride(-1) == o.stride(-1) == 1
375
+ assert dq.stride(-1) == dk.stride(-1) == dv.stride(-1) == 1
376
+ softmax_scale = softmax_scale or 1.0 / math.sqrt(d)
377
+ dq_accum = torch.empty_like(q, dtype=torch.float32)
378
+ delta = torch.empty_like(lse)
379
+ BLOCK_HEADDIM = max(triton.next_power_of_2(d), 16)
380
+ grid = lambda META: (triton.cdiv(seqlen_q, META['BLOCK_M']), batch * nheads)
381
+ _bwd_preprocess_do_o_dot[grid](o, do, delta, o.stride(0), o.stride(2), o.stride(1), do.stride(0), do.stride(2), do.stride(1), nheads, seqlen_q, seqlen_q_rounded, d, BLOCK_M=128, BLOCK_HEADDIM=BLOCK_HEADDIM)
382
+ has_bias = bias is not None
383
+ bias_type = 'none'
384
+ if has_bias:
385
+ assert bias.dtype in [q.dtype, torch.float]
386
+ assert bias.is_cuda
387
+ assert bias.dim() == 4
388
+ assert bias.stride(-1) == 1
389
+ if bias.shape[2:] == (1, seqlen_k):
390
+ bias_type = 'vector'
391
+ elif bias.shape[2:] == (seqlen_q, seqlen_k):
392
+ bias_type = 'matrix'
393
+ else:
394
+ raise RuntimeError('Last 2 dimensions of bias must be (1, seqlen_k) or (seqlen_q, seqlen_k)')
395
+ bias = bias.expand(batch, nheads, seqlen_q, seqlen_k)
396
+ bias_strides = (bias.stride(0), bias.stride(1), bias.stride(2)) if has_bias else (0, 0, 0)
397
+ grid = lambda META: (triton.cdiv(seqlen_k, META['BLOCK_N']) if META['SEQUENCE_PARALLEL'] else 1, batch * nheads)
398
+ _bwd_kernel[grid](q, k, v, bias, do, dq_accum, dk, dv, lse, delta, softmax_scale, q.stride(0), q.stride(2), q.stride(1), k.stride(0), k.stride(2), k.stride(1), v.stride(0), v.stride(2), v.stride(1), *bias_strides, do.stride(0), do.stride(2), do.stride(1), dq_accum.stride(0), dq_accum.stride(2), dq_accum.stride(1), dk.stride(0), dk.stride(2), dk.stride(1), dv.stride(0), dv.stride(2), dv.stride(1), nheads, seqlen_q, seqlen_k, seqlen_q_rounded, d, seqlen_q // 32, seqlen_k // 32, bias_type, causal, BLOCK_HEADDIM)
399
+ dq.copy_(dq_accum)
400
+
401
+ class FlashAttnQKVPackedFunc(torch.autograd.Function):
402
+
403
+ @staticmethod
404
+ def forward(ctx, qkv, bias=None, causal=False, softmax_scale=None):
405
+ """
406
+ qkv: (batch, seqlen, 3, nheads, headdim)
407
+ bias: optional, shape broadcastible to (batch, nheads, seqlen, seqlen).
408
+ For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen).
409
+ ALiBi mask for non-causal would have shape (1, nheads, seqlen, seqlen)
410
+ """
411
+ if qkv.stride(-1) != 1:
412
+ qkv = qkv.contiguous()
413
+ (o, lse, ctx.softmax_scale) = _flash_attn_forward(qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2], bias=bias, causal=causal, softmax_scale=softmax_scale)
414
+ ctx.save_for_backward(qkv, o, lse, bias)
415
+ ctx.causal = causal
416
+ return o
417
+
418
+ @staticmethod
419
+ def backward(ctx, do):
420
+ (qkv, o, lse, bias) = ctx.saved_tensors
421
+ assert not ctx.needs_input_grad[1], 'FlashAttention does not support bias gradient yet'
422
+ with torch.inference_mode():
423
+ dqkv = torch.empty_like(qkv)
424
+ _flash_attn_backward(do, qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2], o, lse, dqkv[:, :, 0], dqkv[:, :, 1], dqkv[:, :, 2], bias=bias, causal=ctx.causal, softmax_scale=ctx.softmax_scale)
425
+ return (dqkv, None, None, None)
426
+ flash_attn_qkvpacked_func = FlashAttnQKVPackedFunc.apply
427
+
428
+ class FlashAttnKVPackedFunc(torch.autograd.Function):
429
+
430
+ @staticmethod
431
+ def forward(ctx, q, kv, bias=None, causal=False, softmax_scale=None):
432
+ """
433
+ q: (batch, seqlen_q, nheads, headdim)
434
+ kv: (batch, seqlen_k, 2, nheads, headdim)
435
+ bias: optional, shape broadcastible to (batch, nheads, seqlen_q, seqlen_k).
436
+ For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen_k).
437
+ ALiBi mask for non-causal would have shape (1, nheads, seqlen_q, seqlen_k)
438
+ """
439
+ (q, kv) = [x if x.stride(-1) == 1 else x.contiguous() for x in [q, kv]]
440
+ (o, lse, ctx.softmax_scale) = _flash_attn_forward(q, kv[:, :, 0], kv[:, :, 1], bias=bias, causal=causal, softmax_scale=softmax_scale)
441
+ ctx.save_for_backward(q, kv, o, lse, bias)
442
+ ctx.causal = causal
443
+ return o
444
+
445
+ @staticmethod
446
+ def backward(ctx, do):
447
+ (q, kv, o, lse, bias) = ctx.saved_tensors
448
+ if len(ctx.needs_input_grad) >= 3:
449
+ assert not ctx.needs_input_grad[2], 'FlashAttention does not support bias gradient yet'
450
+ with torch.inference_mode():
451
+ dq = torch.empty_like(q)
452
+ dkv = torch.empty_like(kv)
453
+ _flash_attn_backward(do, q, kv[:, :, 0], kv[:, :, 1], o, lse, dq, dkv[:, :, 0], dkv[:, :, 1], bias=bias, causal=ctx.causal, softmax_scale=ctx.softmax_scale)
454
+ return (dq, dkv, None, None, None)
455
+ flash_attn_kvpacked_func = FlashAttnKVPackedFunc.apply
456
+
457
+ class FlashAttnFunc(torch.autograd.Function):
458
+
459
+ @staticmethod
460
+ def forward(ctx, q, k, v, bias=None, causal=False, softmax_scale=None):
461
+ """
462
+ q: (batch_size, seqlen_q, nheads, headdim)
463
+ k, v: (batch_size, seqlen_k, nheads, headdim)
464
+ bias: optional, shape broadcastible to (batch, nheads, seqlen_q, seqlen_k).
465
+ For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen_k).
466
+ ALiBi mask for non-causal would have shape (1, nheads, seqlen_q, seqlen_k)
467
+ """
468
+ (q, k, v) = [x if x.stride(-1) == 1 else x.contiguous() for x in [q, k, v]]
469
+ (o, lse, ctx.softmax_scale) = _flash_attn_forward(q, k, v, bias=bias, causal=causal, softmax_scale=softmax_scale)
470
+ ctx.save_for_backward(q, k, v, o, lse, bias)
471
+ ctx.causal = causal
472
+ return o
473
+
474
+ @staticmethod
475
+ def backward(ctx, do):
476
+ (q, k, v, o, lse, bias) = ctx.saved_tensors
477
+ assert not ctx.needs_input_grad[3], 'FlashAttention does not support bias gradient yet'
478
+ with torch.inference_mode():
479
+ dq = torch.empty_like(q)
480
+ dk = torch.empty_like(k)
481
+ dv = torch.empty_like(v)
482
+ _flash_attn_backward(do, q, k, v, o, lse, dq, dk, dv, bias=bias, causal=ctx.causal, softmax_scale=ctx.softmax_scale)
483
+ return (dq, dk, dv, None, None, None)
484
+ flash_attn_func = FlashAttnFunc.apply
generation_config.json ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ {
2
+ "_from_model_config": true,
3
+ "transformers_version": "4.34.1",
4
+ "use_cache": false
5
+ }
hf_prefixlm_converter.py ADDED
@@ -0,0 +1,180 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Converts Huggingface Causal LM to Prefix LM.
2
+
3
+ Conversion does lightweight surgery on a HuggingFace
4
+ Causal LM to convert it to a Prefix LM.
5
+
6
+ Prefix LMs accepts a `bidirectional_mask` input in `forward`
7
+ and treat the input prompt as the prefix in `generate`.
8
+ """
9
+ from types import MethodType
10
+ from typing import Any, List, MutableMapping, Optional, Tuple, Union
11
+ import torch
12
+ from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel
13
+ from transformers.models.gpt_neo.modeling_gpt_neo import GPTNeoForCausalLM
14
+ from transformers.models.gpt_neox.modeling_gpt_neox import GPTNeoXForCausalLM
15
+ from transformers.models.gptj.modeling_gptj import GPTJForCausalLM
16
+ _SUPPORTED_GPT_MODELS = (GPT2LMHeadModel, GPTJForCausalLM, GPTNeoForCausalLM, GPTNeoXForCausalLM)
17
+ CAUSAL_GPT_TYPES = Union[GPT2LMHeadModel, GPTJForCausalLM, GPTNeoForCausalLM, GPTNeoXForCausalLM]
18
+
19
+ def _convert_gpt_causal_lm_to_prefix_lm(model: CAUSAL_GPT_TYPES) -> CAUSAL_GPT_TYPES:
20
+ """Converts a GPT-style Causal LM to a Prefix LM.
21
+
22
+ Supported HuggingFace model classes:
23
+ - `GPT2LMHeadModel`
24
+ - `GPTNeoForCausalLM`
25
+ - `GPTNeoXForCausalLM`
26
+ - `GPTJForCausalLM`
27
+
28
+ See `convert_hf_causal_lm_to_prefix_lm` for more details.
29
+ """
30
+ if hasattr(model, '_prefix_lm_converted'):
31
+ return model
32
+ assert isinstance(model, _SUPPORTED_GPT_MODELS)
33
+ assert model.config.add_cross_attention == False, 'Only supports GPT-style decoder-only models'
34
+
35
+ def _get_attn_modules(model: CAUSAL_GPT_TYPES) -> List[torch.nn.Module]:
36
+ """Helper that gets a list of the model's attention modules.
37
+
38
+ Each module has a `bias` buffer used for causal masking. The Prefix LM
39
+ conversion adds logic to dynamically manipulate these biases to support
40
+ Prefix LM attention masking.
41
+ """
42
+ attn_modules = []
43
+ if isinstance(model, GPTNeoXForCausalLM):
44
+ blocks = model.gpt_neox.layers
45
+ else:
46
+ blocks = model.transformer.h
47
+ for block in blocks:
48
+ if isinstance(model, GPTNeoForCausalLM):
49
+ if block.attn.attention_type != 'global':
50
+ continue
51
+ attn_module = block.attn.attention
52
+ elif isinstance(model, GPTNeoXForCausalLM):
53
+ attn_module = block.attention
54
+ else:
55
+ attn_module = block.attn
56
+ attn_modules.append(attn_module)
57
+ return attn_modules
58
+ setattr(model, '_original_forward', getattr(model, 'forward'))
59
+ setattr(model, '_original_generate', getattr(model, 'generate'))
60
+
61
+ def forward(self: CAUSAL_GPT_TYPES, input_ids: Optional[torch.LongTensor]=None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]]=None, attention_mask: Optional[torch.FloatTensor]=None, bidirectional_mask: Optional[torch.Tensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids: Optional[torch.LongTensor]=None, head_mask: Optional[torch.FloatTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.LongTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None):
62
+ """Wraps original forward to enable PrefixLM attention."""
63
+
64
+ def call_og_forward():
65
+ if isinstance(self, GPTNeoXForCausalLM):
66
+ return self._original_forward(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, labels=labels, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
67
+ else:
68
+ return self._original_forward(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, labels=labels, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
69
+ if bidirectional_mask is None:
70
+ return call_og_forward()
71
+ assert isinstance(bidirectional_mask, torch.Tensor)
72
+ attn_modules = _get_attn_modules(model)
73
+ (b, s) = bidirectional_mask.shape
74
+ max_length = attn_modules[0].bias.shape[-1]
75
+ if s > max_length:
76
+ raise ValueError(f'bidirectional_mask sequence length (={s}) exceeds the ' + f'max length allowed by the model ({max_length}).')
77
+ assert s <= max_length
78
+ if s < max_length:
79
+ pad = torch.zeros((int(b), int(max_length - s)), dtype=bidirectional_mask.dtype, device=bidirectional_mask.device)
80
+ bidirectional_mask = torch.cat([bidirectional_mask, pad], dim=1)
81
+ bidirectional = bidirectional_mask.unsqueeze(1).unsqueeze(1)
82
+ for attn_module in attn_modules:
83
+ assert isinstance(attn_module.bias, torch.Tensor)
84
+ attn_module.bias.data = torch.logical_or(attn_module.bias.data, bidirectional)
85
+ output = call_og_forward()
86
+ for attn_module in attn_modules:
87
+ attn_module.bias.data = torch.tril(attn_module.bias.data[0, 0])[None, None]
88
+ return output
89
+
90
+ def generate(self: CAUSAL_GPT_TYPES, *args: Any, **kwargs: Any):
91
+ """Wraps original generate to enable PrefixLM attention."""
92
+ attn_modules = _get_attn_modules(model)
93
+ for attn_module in attn_modules:
94
+ attn_module.bias.data[:] = 1
95
+ output = self._original_generate(*args, **kwargs)
96
+ for attn_module in attn_modules:
97
+ attn_module.bias.data = torch.tril(attn_module.bias.data[0, 0])[None, None]
98
+ return output
99
+ setattr(model, 'forward', MethodType(forward, model))
100
+ setattr(model, 'generate', MethodType(generate, model))
101
+ setattr(model, '_prefix_lm_converted', True)
102
+ return model
103
+ _SUPPORTED_HF_MODELS = _SUPPORTED_GPT_MODELS
104
+ CAUSAL_LM_TYPES = Union[GPT2LMHeadModel, GPTJForCausalLM, GPTNeoForCausalLM, GPTNeoXForCausalLM]
105
+
106
+ def convert_hf_causal_lm_to_prefix_lm(model: CAUSAL_LM_TYPES) -> CAUSAL_LM_TYPES:
107
+ """Converts a HuggingFace Causal LM to a Prefix LM.
108
+
109
+ Supported HuggingFace model classes:
110
+ - `GPT2LMHeadModel`
111
+ - `GPTNeoForCausalLM`
112
+ - `GPTNeoXForCausalLM`
113
+ - `GPTJForCausalLM`
114
+
115
+ Conversion to a Prefix LM is done by modifying the `forward` method, and possibly also the
116
+ `generate` method and/or select underlying methods depending on the model class.
117
+
118
+ These changes preserve the model API, but add a new input to `forward`: "bidirectional_mask".
119
+
120
+ Notes on training:
121
+ To actually train the converted model as a Prefix LM, training batches will need to indicate
122
+ the prefix/target structure by including `bidirectional_mask` as part of the batch inputs.
123
+
124
+ **This is not a standard input and requires custom layers either within or after your dataloader.**
125
+
126
+ In addition to adding `bidirectional_mask` to the batch, this custom code should modify `labels`
127
+ such that `batch['labels'][batch['bidirectional_mask'] == 1] == -100`.
128
+ That is, the prefix portion of the sequence should not generate any loss. Loss should only be
129
+ generated by the target portion of the sequence.
130
+
131
+ Notes on `GPTNeoForCausalLM`:
132
+ To simplify the implementation, "global" and "local" attention layers are handled differently.
133
+ For "global" layers, we handle conversion as described above. For "local" layers, which use a
134
+ causal attention mask within a restricted local window, we do not alter the masking.
135
+
136
+ Notes on `forward` method conversion:
137
+ After conversion, the `forward` method will handle a new input, `bidirectional_mask`,
138
+ which should be a [batch_size, seq_length] byte tensor, where 1 indicates token positions
139
+ belonging to the prefix (prefix tokens can attend to one another bidirectionally), and
140
+ 0 indicates token positions belonging to the target.
141
+
142
+ The new `forward` method will incorporate `bidirectional_mask` (if supplied) into the existing
143
+ causal mask, call the original `forward` method, and (if the causal mask is a buffer) reset
144
+ the causal masks before returning the result.
145
+
146
+ Notes on `generate` method conversion:
147
+ After conversion, the `generate` method will have the same signature but will internally
148
+ convert all causal masks to be purely bidirectional, call the original `generate` method, and
149
+ (where appropriate) reset the causal masks before returning the result.
150
+
151
+ This works thanks to the logic of the HuggingFace `generate` API, which first encodes the token
152
+ "prompt" passed to `generate` (which is treated as the prefix) and then sequentially generates
153
+ each new token. Encodings are cached as generation happens, so all prefix tokens can attend to one
154
+ another (as expected in a Prefix LM) and generated tokens can only attend to prefix tokens and
155
+ previously-generated tokens (also as expected in a Prefix LM).
156
+
157
+ To preserve the API, the original methods are renamed to `_original_forward` and
158
+ `_original_generate`, and replaced with new `forward` and `generate` methods that wrap
159
+ them, respectively. Although implementation details vary by model class.
160
+ """
161
+ if isinstance(model, _SUPPORTED_GPT_MODELS):
162
+ return _convert_gpt_causal_lm_to_prefix_lm(model)
163
+ else:
164
+ raise TypeError(f'Cannot convert model to Prefix LM. ' + f'Model does not belong to set of supported HF models:' + f'\n{_SUPPORTED_HF_MODELS}')
165
+
166
+ def add_bidirectional_mask_if_missing(batch: MutableMapping):
167
+ """Attempts to add bidirectional_mask to batch if missing.
168
+
169
+ Raises:
170
+ KeyError if bidirectional_mask is missing and can't be inferred
171
+ """
172
+ if 'bidirectional_mask' not in batch:
173
+ if batch.get('mode', None) == 'icl_task':
174
+ batch['bidirectional_mask'] = batch['attention_mask'].clone()
175
+ for (i, continuation_indices) in enumerate(batch['continuation_indices']):
176
+ batch['bidirectional_mask'][i, continuation_indices] = 0
177
+ elif 'labels' in batch and 'attention_mask' in batch:
178
+ batch['bidirectional_mask'] = torch.logical_and(torch.eq(batch['attention_mask'], 1), torch.eq(batch['labels'], -100)).type_as(batch['attention_mask'])
179
+ else:
180
+ raise KeyError('No bidirectional_mask in batch and not sure how to construct one.')
meta_init_context.py ADDED
@@ -0,0 +1,99 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from contextlib import contextmanager
2
+ from typing import Any, Callable, Optional
3
+ import torch
4
+ import torch.nn as nn
5
+
6
+ @contextmanager
7
+ def init_empty_weights(include_buffers: bool=False):
8
+ """Meta initialization context manager.
9
+
10
+ A context manager under which models are initialized with all parameters
11
+ on the meta device, therefore creating an empty model. Useful when just
12
+ initializing the model would blow the available RAM.
13
+
14
+ Args:
15
+ include_buffers (`bool`, *optional*, defaults to `False`): Whether or
16
+ not to also put all buffers on the meta device while initializing.
17
+
18
+ Example:
19
+ ```python
20
+ import torch.nn as nn
21
+
22
+ # Initialize a model with 100 billions parameters in no time and without using any RAM.
23
+ with init_empty_weights():
24
+ tst = nn.Sequential(*[nn.Linear(10000, 10000) for _ in range(1000)])
25
+ ```
26
+
27
+ <Tip warning={true}>
28
+
29
+ Any model created under this context manager has no weights. As such you can't do something like
30
+ `model.to(some_device)` with it. To load weights inside your empty model, see [`load_checkpoint_and_dispatch`].
31
+
32
+ </Tip>
33
+ """
34
+ with init_on_device(torch.device('meta'), include_buffers=include_buffers) as f:
35
+ yield f
36
+
37
+ @contextmanager
38
+ def init_on_device(device: torch.device, include_buffers: bool=False):
39
+ """Device initialization context manager.
40
+
41
+ A context manager under which models are initialized with all parameters
42
+ on the specified device.
43
+
44
+ Args:
45
+ device (`torch.device`): Device to initialize all parameters on.
46
+ include_buffers (`bool`, *optional*, defaults to `False`): Whether or
47
+ not to also put all buffers on the meta device while initializing.
48
+
49
+ Example:
50
+ ```python
51
+ import torch.nn as nn
52
+
53
+ with init_on_device(device=torch.device("cuda")):
54
+ tst = nn.Liner(100, 100) # on `cuda` device
55
+ ```
56
+ """
57
+ old_register_parameter = nn.Module.register_parameter
58
+ if include_buffers:
59
+ old_register_buffer = nn.Module.register_buffer
60
+
61
+ def register_empty_parameter(self: torch.nn.Module, name: str, param: Optional[torch.nn.Parameter]):
62
+ old_register_parameter(self, name, param)
63
+ if param is not None:
64
+ parameter = self._parameters[name]
65
+ assert parameter is not None
66
+ param_cls = type(parameter)
67
+ kwargs = parameter.__dict__
68
+ self._parameters[name] = param_cls(parameter.to(device), **kwargs)
69
+
70
+ def register_empty_buffer(self: torch.nn.Module, name: str, tensor: Optional[torch.Tensor], persistent: bool=True):
71
+ old_register_buffer(self, name, tensor, persistent=persistent)
72
+ if tensor is not None:
73
+ named_buffer = self._buffers[name]
74
+ assert named_buffer is not None
75
+ self._buffers[name] = named_buffer.to(device)
76
+ if include_buffers:
77
+ tensor_constructors_to_patch = {torch_function_name: getattr(torch, torch_function_name) for torch_function_name in ['empty', 'zeros', 'ones', 'full']}
78
+ else:
79
+ tensor_constructors_to_patch = {}
80
+
81
+ def patch_tensor_constructor(fn: Callable):
82
+
83
+ def wrapper(*args: Any, **kwargs: Any):
84
+ kwargs['device'] = device
85
+ return fn(*args, **kwargs)
86
+ return wrapper
87
+ try:
88
+ nn.Module.register_parameter = register_empty_parameter
89
+ if include_buffers:
90
+ nn.Module.register_buffer = register_empty_buffer
91
+ for torch_function_name in tensor_constructors_to_patch.keys():
92
+ setattr(torch, torch_function_name, patch_tensor_constructor(getattr(torch, torch_function_name)))
93
+ yield
94
+ finally:
95
+ nn.Module.register_parameter = old_register_parameter
96
+ if include_buffers:
97
+ nn.Module.register_buffer = old_register_buffer
98
+ for (torch_function_name, old_torch_function) in tensor_constructors_to_patch.items():
99
+ setattr(torch, torch_function_name, old_torch_function)
modeling_mpt.py ADDED
@@ -0,0 +1,393 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """A simple, flexible implementation of a GPT model.
2
+
3
+ Inspired by https://github.com/karpathy/minGPT/blob/master/mingpt/model.py
4
+ """
5
+ import math
6
+ import warnings
7
+ from typing import Any, Dict, List, Mapping, MutableMapping, Optional, Tuple, Union
8
+ import torch
9
+ import torch.nn as nn
10
+ import torch.nn.functional as F
11
+ from .attention import is_flash_v2_installed
12
+ if is_flash_v2_installed():
13
+ try:
14
+ from flash_attn.layers.rotary import RotaryEmbedding as DAILRotaryEmbedding
15
+ except Exception as e:
16
+ raise e
17
+ from transformers import PreTrainedModel, PreTrainedTokenizerBase
18
+ from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
19
+ from transformers.models.llama.modeling_llama import LlamaDynamicNTKScalingRotaryEmbedding as HFDynamicNTKScalingRotaryEmbedding
20
+ from transformers.models.llama.modeling_llama import LlamaLinearScalingRotaryEmbedding as HFLinearScalingRotaryEmbedding
21
+ from transformers.models.llama.modeling_llama import LlamaRotaryEmbedding as HFRotaryEmbedding
22
+ from .attention import ATTN_CLASS_REGISTRY, attn_bias_shape, build_attn_bias
23
+ from .blocks import MPTBlock
24
+ from .custom_embedding import SharedEmbedding
25
+ from .fc import FC_CLASS_REGISTRY as FC_CLASS_REGISTRY
26
+ from .ffn import FFN_CLASS_REGISTRY as FFN_CLASS_REGISTRY
27
+ from .ffn import MPTMLP as MPTMLP
28
+ from .ffn import build_ffn as build_ffn
29
+ from .norm import NORM_CLASS_REGISTRY
30
+ from .configuration_mpt import MPTConfig
31
+ from .adapt_tokenizer import AutoTokenizerForMOD, adapt_tokenizer_for_denoising
32
+ from .hf_prefixlm_converter import add_bidirectional_mask_if_missing, convert_hf_causal_lm_to_prefix_lm
33
+ from .meta_init_context import init_empty_weights
34
+ from .param_init_fns import generic_param_init_fn_, MODEL_INIT_REGISTRY
35
+ try:
36
+ from .flash_attn_triton import flash_attn_func as flash_attn_func
37
+ except:
38
+ pass
39
+ import logging
40
+ log = logging.getLogger(__name__)
41
+
42
+ def gen_rotary_embedding(rope_head_dim: int, rope_impl: str, rope_theta: int, rope_dail_config: dict, rope_hf_config: dict, max_seq_len: int):
43
+ if rope_impl == 'dail':
44
+ return DAILRotaryEmbedding(dim=rope_head_dim, base=rope_theta, interleaved=False, scale_base=rope_dail_config['xpos_scale_base'] if rope_dail_config['type'] == 'xpos' else None, pos_idx_in_fp32=rope_dail_config['pos_idx_in_fp32'], device='cpu')
45
+ elif rope_impl == 'hf':
46
+ if rope_hf_config['type'] == 'no_scaling':
47
+ return HFRotaryEmbedding(rope_head_dim, max_position_embeddings=max_seq_len, base=rope_theta, device='cpu')
48
+ elif rope_hf_config['type'] == 'linear':
49
+ return HFLinearScalingRotaryEmbedding(rope_head_dim, max_position_embeddings=max_seq_len, base=rope_theta, scaling_factor=rope_hf_config['factor'], device='cpu')
50
+ elif rope_hf_config['type'] == 'dynamic':
51
+ return HFDynamicNTKScalingRotaryEmbedding(rope_head_dim, max_position_embeddings=max_seq_len, base=rope_theta, scaling_factor=rope_hf_config['factor'], device='cpu')
52
+ raise ValueError('rope_impl needs to be either dail or hf')
53
+
54
+ class MPTPreTrainedModel(PreTrainedModel):
55
+ config_class = MPTConfig
56
+ base_model_prefix = 'model'
57
+ _no_split_modules = ['MPTBlock']
58
+
59
+ class MPTModel(MPTPreTrainedModel):
60
+
61
+ def __init__(self, config: MPTConfig):
62
+ config._validate_config()
63
+ super().__init__(config)
64
+ self.attn_impl = config.attn_config['attn_impl']
65
+ self.prefix_lm = config.attn_config['prefix_lm']
66
+ self.attn_uses_sequence_id = config.attn_config['attn_uses_sequence_id']
67
+ self.alibi = config.attn_config['alibi']
68
+ self.alibi_bias_max = config.attn_config['alibi_bias_max']
69
+ self.learned_pos_emb = config.learned_pos_emb
70
+ if config.init_device == 'mixed':
71
+ if dist.get_local_rank() == 0:
72
+ config.init_device = 'cpu'
73
+ else:
74
+ config.init_device = 'meta'
75
+ if config.norm_type.lower() not in NORM_CLASS_REGISTRY.keys():
76
+ norm_options = ' | '.join(NORM_CLASS_REGISTRY.keys())
77
+ raise NotImplementedError(f'Requested norm type ({config.norm_type}) is not implemented within this repo (Options: {norm_options}).')
78
+ norm_class = NORM_CLASS_REGISTRY[config.norm_type.lower()]
79
+ self.embedding_fraction = config.embedding_fraction
80
+ self.wte = SharedEmbedding(config.vocab_size, config.d_model, device=config.init_device)
81
+ if self.learned_pos_emb:
82
+ self.wpe = torch.nn.Embedding(config.max_seq_len, config.d_model, device=config.init_device)
83
+ self.emb_drop = nn.Dropout(config.emb_pdrop)
84
+ self.blocks = nn.ModuleList([MPTBlock(device=config.init_device, **config.to_dict()) for _ in range(config.n_layers)])
85
+ self.norm_f = norm_class(config.d_model, device=config.init_device)
86
+ self.rope = config.attn_config['rope']
87
+ self.rope_impl = None
88
+ if self.rope:
89
+ self.rope_impl = config.attn_config['rope_impl']
90
+ self.rotary_embedding = gen_rotary_embedding(rope_head_dim=config.d_model // config.n_heads, rope_impl=self.rope_impl, rope_theta=config.attn_config['rope_theta'], rope_dail_config=config.attn_config['rope_dail_config'], rope_hf_config=config.attn_config['rope_hf_config'], max_seq_len=self.config.max_seq_len)
91
+ if config.init_device != 'meta':
92
+ log.info(f'We recommend using config.init_device="meta" with Composer + FSDP for faster initialization.')
93
+ self.apply(self.param_init_fn)
94
+ self.is_causal = not self.prefix_lm
95
+ self._attn_bias_initialized = False
96
+ self.attn_bias = None
97
+ self.attn_bias_shape = attn_bias_shape(self.attn_impl, config.n_heads, config.max_seq_len, self.alibi, prefix_lm=self.prefix_lm, causal=self.is_causal, use_sequence_id=self.attn_uses_sequence_id)
98
+ if config.no_bias:
99
+ for module in self.modules():
100
+ if hasattr(module, 'bias') and isinstance(module.bias, nn.Parameter):
101
+ log.info(f'Removing bias ({module.bias}) from {module}.')
102
+ module.register_parameter('bias', None)
103
+ if hasattr(module, 'use_bias'):
104
+ log.info(f'Setting use_bias=False for {module}.')
105
+ module.use_bias = False
106
+ log.debug(self)
107
+ log.debug(f"Using {self.config.init_config['name']} initialization.")
108
+
109
+ def get_input_embeddings(self) -> Union[SharedEmbedding, nn.Embedding]:
110
+ return self.wte
111
+
112
+ def set_input_embeddings(self, value: Union[SharedEmbedding, nn.Embedding]) -> None:
113
+ self.wte = value
114
+
115
+ @torch.no_grad()
116
+ def _attn_bias(self, device: torch.device, dtype: torch.dtype, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None) -> Tuple[Optional[torch.Tensor], Optional[torch.ByteTensor]]:
117
+ if not self._attn_bias_initialized:
118
+ if self.attn_bias_shape:
119
+ self.attn_bias = torch.zeros(self.attn_bias_shape, device=device, dtype=dtype)
120
+ self.attn_bias = build_attn_bias(self.attn_impl, self.attn_bias, self.config.n_heads, self.config.max_seq_len, causal=self.is_causal, alibi=self.alibi, alibi_bias_max=self.alibi_bias_max)
121
+ self._attn_bias_initialized = True
122
+ if self.attn_impl == 'flash':
123
+ return (self.attn_bias, attention_mask)
124
+ if self.attn_bias is not None:
125
+ self.attn_bias = self.attn_bias.to(dtype=dtype, device=device)
126
+ attn_bias = self.attn_bias
127
+ if self.prefix_lm:
128
+ assert isinstance(attn_bias, torch.Tensor)
129
+ assert isinstance(prefix_mask, torch.Tensor)
130
+ attn_bias = self._apply_prefix_mask(attn_bias, prefix_mask)
131
+ if self.attn_uses_sequence_id and sequence_id is not None:
132
+ assert isinstance(attn_bias, torch.Tensor)
133
+ attn_bias = self._apply_sequence_id(attn_bias, sequence_id)
134
+ if attention_mask is not None:
135
+ s_k = attention_mask.shape[-1]
136
+ if attn_bias is None:
137
+ attn_bias = torch.zeros((1, 1, 1, s_k), device=device, dtype=dtype)
138
+ else:
139
+ _s_k = max(0, attn_bias.size(-1) - s_k)
140
+ attn_bias = attn_bias[:, :, :, _s_k:]
141
+ if prefix_mask is not None and attention_mask.shape != prefix_mask.shape:
142
+ raise ValueError(f'attention_mask shape={attention_mask.shape} ' + f'and prefix_mask shape={prefix_mask.shape} are not equal.')
143
+ min_val = torch.finfo(attn_bias.dtype).min
144
+ attn_bias = attn_bias.masked_fill(~attention_mask.view(-1, 1, 1, s_k), min_val)
145
+ return (attn_bias, None)
146
+
147
+ def _apply_prefix_mask(self, attn_bias: torch.Tensor, prefix_mask: torch.Tensor) -> torch.Tensor:
148
+ (s_k, s_q) = attn_bias.shape[-2:]
149
+ if s_k != self.config.max_seq_len or s_q != self.config.max_seq_len:
150
+ raise ValueError('attn_bias does not match the expected shape. ' + f'The last two dimensions should both be {self.config.max_length} ' + f'but are {s_k} and {s_q}.')
151
+ seq_len = prefix_mask.shape[-1]
152
+ if seq_len > self.config.max_seq_len:
153
+ raise ValueError(f'prefix_mask sequence length cannot exceed max_seq_len={self.config.max_seq_len}')
154
+ attn_bias = attn_bias[..., :seq_len, :seq_len]
155
+ causal = torch.tril(torch.ones((seq_len, seq_len), dtype=torch.bool, device=prefix_mask.device)).view(1, 1, seq_len, seq_len)
156
+ prefix = prefix_mask.view(-1, 1, 1, seq_len)
157
+ cannot_attend = ~torch.logical_or(causal, prefix.bool())
158
+ min_val = torch.finfo(attn_bias.dtype).min
159
+ attn_bias = attn_bias.masked_fill(cannot_attend, min_val)
160
+ return attn_bias
161
+
162
+ def _apply_sequence_id(self, attn_bias: torch.Tensor, sequence_id: torch.LongTensor) -> torch.Tensor:
163
+ seq_len = sequence_id.shape[-1]
164
+ if seq_len > self.config.max_seq_len:
165
+ raise ValueError(f'sequence_id sequence length cannot exceed max_seq_len={self.config.max_seq_len}')
166
+ attn_bias = attn_bias[..., :seq_len, :seq_len]
167
+ cannot_attend = torch.logical_not(torch.eq(sequence_id.view(-1, seq_len, 1), sequence_id.view(-1, 1, seq_len))).unsqueeze(1)
168
+ min_val = torch.finfo(attn_bias.dtype).min
169
+ attn_bias = attn_bias.masked_fill(cannot_attend, min_val)
170
+ return attn_bias
171
+
172
+ def forward(self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None, inputs_embeds: Optional[torch.Tensor]=None) -> BaseModelOutputWithPast:
173
+ return_dict = return_dict if return_dict is not None else self.config.return_dict
174
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
175
+ if attention_mask is not None:
176
+ attention_mask = attention_mask.bool()
177
+ if prefix_mask is not None:
178
+ prefix_mask = prefix_mask.bool()
179
+ if not return_dict:
180
+ raise NotImplementedError('return_dict False is not implemented yet for MPT')
181
+ if output_attentions:
182
+ if self.attn_impl != 'torch':
183
+ raise NotImplementedError('output_attentions is not implemented for MPT when using attn_impl `flash` or `triton`.')
184
+ if self.training and attention_mask is not None and (attention_mask[:, 0].sum() != attention_mask.shape[0]):
185
+ raise NotImplementedError('MPT does not support training with left padding.')
186
+ if self.prefix_lm and prefix_mask is None:
187
+ raise ValueError('prefix_mask is a required argument when MPT is configured with prefix_lm=True.')
188
+ if inputs_embeds is not None:
189
+ raise NotImplementedError('inputs_embeds is not implemented for MPT.')
190
+ if self.training:
191
+ if self.attn_uses_sequence_id and sequence_id is None:
192
+ raise ValueError('sequence_id is a required argument when MPT is configured with attn_uses_sequence_id=True ' + 'and the model is in train mode.')
193
+ elif self.attn_uses_sequence_id is False and sequence_id is not None:
194
+ warnings.warn('MPT received non-None input for `sequence_id` but is configured with attn_uses_sequence_id=False. ' + 'This input will be ignored. If you want the model to use `sequence_id`, set attn_uses_sequence_id to True.')
195
+ S = input_ids.size(1)
196
+ assert S <= self.config.max_seq_len, f'Cannot forward input with seq_len={S}, this model only supports seq_len<={self.config.max_seq_len}'
197
+ rotary_emb_w_meta_info = None
198
+ x = self.wte(input_ids)
199
+ if self.learned_pos_emb or self.rope:
200
+ past_position = 0
201
+ if past_key_values is not None:
202
+ if len(past_key_values) != self.config.n_layers:
203
+ raise ValueError(f'past_key_values must provide a past_key_value for each attention ' + f'layer in the network (len(past_key_values)={len(past_key_values)!r}; self.config.n_layers={self.config.n_layers!r}).')
204
+ past_position = past_key_values[0][0].size(1)
205
+ if self.attn_impl == 'torch':
206
+ past_position = past_key_values[0][0].size(3)
207
+ if self.learned_pos_emb and S + past_position > self.config.max_seq_len:
208
+ raise ValueError(f'Cannot forward input with past sequence length {past_position} and current sequence length ' + f'{S + 1}, this model only supports total sequence length <= {self.config.max_seq_len}.')
209
+ if self.learned_pos_emb or (self.rope and self.rope_impl == 'hf'):
210
+ pos = torch.arange(past_position, S + past_position, dtype=torch.long, device=input_ids.device).unsqueeze(0)
211
+ if attention_mask is not None:
212
+ pos = torch.clamp(pos - torch.cumsum((~attention_mask).to(torch.int32), dim=1)[:, past_position:], min=0)
213
+ if self.learned_pos_emb:
214
+ x = x + self.wpe(pos)
215
+ elif self.rope and self.rope_impl == 'hf':
216
+ rotary_emb_w_meta_info = {'impl': self.rope_impl, 'rotary_emb': self.rotary_embedding, 'offset_info': pos, 'seq_len': S + past_position}
217
+ elif self.rope and self.rope_impl == 'dail':
218
+ rotary_emb_w_meta_info = {'impl': self.rope_impl, 'rotary_emb': self.rotary_embedding, 'offset_info': past_position, 'seq_len': S + past_position}
219
+ if self.embedding_fraction == 1:
220
+ x = self.emb_drop(x)
221
+ else:
222
+ x_shrunk = x * self.embedding_fraction + x.detach() * (1 - self.embedding_fraction)
223
+ assert isinstance(self.emb_drop, nn.Module)
224
+ x = self.emb_drop(x_shrunk)
225
+ (attn_bias, attention_mask) = self._attn_bias(device=x.device, dtype=torch.float32, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id)
226
+ presents = () if use_cache else None
227
+ if use_cache and past_key_values is None:
228
+ past_key_values = [() for _ in range(self.config.n_layers)]
229
+ all_hidden_states = () if output_hidden_states else None
230
+ all_self_attns = () if output_attentions else None
231
+ for (b_idx, block) in enumerate(self.blocks):
232
+ if output_hidden_states:
233
+ assert all_hidden_states is not None
234
+ all_hidden_states = all_hidden_states + (x,)
235
+ past_key_value = past_key_values[b_idx] if past_key_values is not None else None
236
+ (x, attn_weights, present) = block(x, past_key_value=past_key_value, attn_bias=attn_bias, rotary_emb_w_meta_info=rotary_emb_w_meta_info, attention_mask=attention_mask, is_causal=self.is_causal, output_attentions=bool(output_attentions))
237
+ if presents is not None:
238
+ presents += (present,)
239
+ if output_attentions:
240
+ assert all_self_attns is not None
241
+ all_self_attns = all_self_attns + (attn_weights,)
242
+ x = self.norm_f(x)
243
+ if output_hidden_states:
244
+ assert all_hidden_states is not None
245
+ all_hidden_states = all_hidden_states + (x,)
246
+ return BaseModelOutputWithPast(last_hidden_state=x, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_self_attns)
247
+
248
+ def param_init_fn(self, module: nn.Module) -> None:
249
+ init_fn_name = self.config.init_config['name']
250
+ MODEL_INIT_REGISTRY[init_fn_name](module=module, n_layers=self.config.n_layers, d_model=self.config.d_model, **self.config.init_config)
251
+
252
+ def fsdp_wrap_fn(self, module: nn.Module) -> bool:
253
+ return isinstance(module, MPTBlock)
254
+
255
+ def activation_checkpointing_fn(self, module: nn.Module) -> bool:
256
+ return isinstance(module, MPTBlock)
257
+
258
+ class MPTForCausalLM(MPTPreTrainedModel):
259
+
260
+ def __init__(self, config: MPTConfig):
261
+ super().__init__(config)
262
+ log.info(f'Instantiating an MPTForCausalLM model from {__file__}')
263
+ self.transformer: MPTModel = MPTModel(config)
264
+ self.lm_head = None
265
+ if not config.tie_word_embeddings:
266
+ self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False, device=config.init_device)
267
+ self.lm_head._fsdp_wrap = True
268
+ for child in self.transformer.children():
269
+ if isinstance(child, torch.nn.ModuleList):
270
+ continue
271
+ if isinstance(child, torch.nn.Module):
272
+ child._fsdp_wrap = True
273
+ self.logit_scale = None
274
+ if config.logit_scale is not None:
275
+ logit_scale = config.logit_scale
276
+ if isinstance(logit_scale, str):
277
+ if logit_scale == 'inv_sqrt_d_model':
278
+ logit_scale = 1 / math.sqrt(config.d_model)
279
+ else:
280
+ raise ValueError(f"logit_scale={logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'.")
281
+ self.logit_scale = logit_scale
282
+
283
+ def get_input_embeddings(self) -> Union[SharedEmbedding, nn.Embedding]:
284
+ return self.transformer.get_input_embeddings()
285
+
286
+ def set_input_embeddings(self, value: Union[SharedEmbedding, nn.Embedding]) -> None:
287
+ self.transformer.set_input_embeddings(value)
288
+
289
+ def get_output_embeddings(self) -> Union[SharedEmbedding, nn.Embedding, nn.Linear]:
290
+ if self.lm_head is not None:
291
+ return self.lm_head
292
+ return self.transformer.get_input_embeddings()
293
+
294
+ def set_output_embeddings(self, new_embeddings: Union[SharedEmbedding, nn.Embedding, nn.Linear]) -> None:
295
+ if self.lm_head is not None:
296
+ self.lm_head = new_embeddings
297
+ else:
298
+ if not isinstance(new_embeddings, (SharedEmbedding, nn.Embedding)):
299
+ raise ValueError('new_embeddings must be an instance of SharedEmbedding ' + f'or nn.Embedding, but got {type(new_embeddings)}.')
300
+ warnings.warn('Using `set_output_embeddings` to set the embedding layer of ' + 'MPTForCausalLM with tied weights. Given weights are tied, ' + 'using `set_input_embeddings` is recommended over using ' + '`set_output_embeddings`.')
301
+ self.transformer.set_input_embeddings(new_embeddings)
302
+
303
+ def tie_weights(self) -> None:
304
+ self.lm_head = None
305
+
306
+ def set_decoder(self, decoder: MPTModel) -> None:
307
+ self.transformer = decoder
308
+
309
+ def get_decoder(self) -> MPTModel:
310
+ return self.transformer
311
+
312
+ def forward(self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, labels: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None, inputs_embeds: Optional[torch.FloatTensor]=None) -> CausalLMOutputWithPast:
313
+ return_dict = return_dict if return_dict is not None else self.config.return_dict
314
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
315
+ if inputs_embeds is not None:
316
+ raise NotImplementedError('inputs_embeds has to be None (for hf/peft support).')
317
+ outputs = self.transformer(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id, return_dict=return_dict, output_attentions=output_attentions, output_hidden_states=output_hidden_states, use_cache=use_cache)
318
+ if self.lm_head is not None:
319
+ logits = self.lm_head(outputs.last_hidden_state)
320
+ else:
321
+ out = outputs.last_hidden_state
322
+ out = out.to(self.transformer.wte.weight.device)
323
+ logits = self.transformer.wte(out, True)
324
+ if self.logit_scale is not None:
325
+ if self.logit_scale == 0:
326
+ warnings.warn(f'Multiplying logits by self.logit_scale={self.logit_scale!r}. This will produce uniform (uninformative) outputs.')
327
+ logits *= self.logit_scale
328
+ loss = None
329
+ if labels is not None:
330
+ _labels = torch.roll(labels, shifts=-1)
331
+ _labels[:, -1] = -100
332
+ loss = F.cross_entropy(logits.view(-1, logits.size(-1)), _labels.to(logits.device).view(-1))
333
+ return CausalLMOutputWithPast(loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
334
+
335
+ def param_init_fn(self, module: nn.Module) -> None:
336
+ init_fn_name = self.config.init_config['name']
337
+ MODEL_INIT_REGISTRY[init_fn_name](module=module, n_layers=self.config.n_layers, d_model=self.config.d_model, **self.config.init_config)
338
+
339
+ def fsdp_wrap_fn(self, module: nn.Module) -> bool:
340
+ return isinstance(module, MPTBlock)
341
+
342
+ def activation_checkpointing_fn(self, module: nn.Module) -> bool:
343
+ act_ckpt_list = getattr(self.config, 'activation_checkpointing_target', None) or ['MPTBlock']
344
+ if 'MPTBlock' in act_ckpt_list or 'mptblock' in act_ckpt_list:
345
+ if len(act_ckpt_list) > 1:
346
+ log.info('Activation checkpointing MPTBlock only (ignoring other sub-block modules specified in activation_checkpointing_target).')
347
+ return isinstance(module, MPTBlock)
348
+ mod_types = ()
349
+ for mod_name in act_ckpt_list:
350
+ if mod_name.lower() == 'mptblock':
351
+ mod_types += (MPTBlock,)
352
+ elif mod_name in ATTN_CLASS_REGISTRY:
353
+ mod_types += (ATTN_CLASS_REGISTRY[mod_name],)
354
+ elif mod_name in FFN_CLASS_REGISTRY:
355
+ mod_types += (FFN_CLASS_REGISTRY[mod_name],)
356
+ elif mod_name in NORM_CLASS_REGISTRY:
357
+ mod_types += (NORM_CLASS_REGISTRY[mod_name],)
358
+ else:
359
+ msg = ', '.join(list(ATTN_CLASS_REGISTRY.keys()) + list(FFN_CLASS_REGISTRY.keys()) + list(NORM_CLASS_REGISTRY.keys()) + ['MPTBlock'])
360
+ raise ValueError(f'{mod_name} (specified in activation_checkpointing_target) is not a recognized option out of available options {msg}.')
361
+ return isinstance(module, mod_types)
362
+
363
+ def prepare_inputs_for_generation(self, input_ids: torch.Tensor, past_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]]=None, inputs_embeds: Optional[torch.Tensor]=None, **kwargs: Any) -> Dict[str, Any]:
364
+ if inputs_embeds is not None:
365
+ raise NotImplementedError('inputs_embeds is not implemented for MPT yet')
366
+ attention_mask = kwargs['attention_mask'].bool()
367
+ if attention_mask[:, -1].sum() != attention_mask.shape[0]:
368
+ raise NotImplementedError('MPT does not support generation with right padding.')
369
+ if self.transformer.attn_uses_sequence_id and self.training:
370
+ sequence_id = torch.zeros_like(input_ids[:1])
371
+ else:
372
+ sequence_id = None
373
+ if past_key_values is not None:
374
+ input_ids = input_ids[:, -1].unsqueeze(-1)
375
+ if self.transformer.prefix_lm:
376
+ prefix_mask = torch.ones_like(attention_mask)
377
+ if kwargs.get('use_cache') == False:
378
+ raise NotImplementedError('MPT with prefix_lm=True does not support use_cache=False.')
379
+ else:
380
+ prefix_mask = None
381
+ return {'input_ids': input_ids, 'attention_mask': attention_mask, 'prefix_mask': prefix_mask, 'sequence_id': sequence_id, 'past_key_values': past_key_values, 'use_cache': kwargs.get('use_cache', True)}
382
+
383
+ @staticmethod
384
+ def _reorder_cache(past_key_values: List[Tuple[torch.Tensor, torch.Tensor]], beam_idx: torch.LongTensor) -> List[Tuple[torch.Tensor, ...]]:
385
+ """Used by HuggingFace generate when using beam search with kv-caching.
386
+
387
+ See https://github.com/huggingface/transformers/blob/3ec7a47664ebe40c40f4b722f6bb1cd30c3821ec/src/transformers/models/gpt2/modeling_gpt2.py#L1122-L1133
388
+ for an example in transformers.
389
+ """
390
+ reordered_past = []
391
+ for layer_past in past_key_values:
392
+ reordered_past += [tuple((past_state.index_select(0, beam_idx) for past_state in layer_past))]
393
+ return reordered_past
norm.py ADDED
@@ -0,0 +1,57 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from typing import Dict, List, Optional, Type, Union
2
+ import torch
3
+
4
+ def _cast_if_autocast_enabled(tensor: torch.Tensor) -> torch.Tensor:
5
+ if torch.is_autocast_enabled():
6
+ if tensor.device.type == 'cuda':
7
+ dtype = torch.get_autocast_gpu_dtype()
8
+ elif tensor.device.type == 'cpu':
9
+ dtype = torch.get_autocast_cpu_dtype()
10
+ else:
11
+ raise NotImplementedError()
12
+ return tensor.to(dtype=dtype)
13
+ return tensor
14
+
15
+ class LPLayerNorm(torch.nn.LayerNorm):
16
+
17
+ def __init__(self, normalized_shape: Union[int, List[int], torch.Size], eps: float=1e-05, elementwise_affine: bool=True, device: Optional[torch.device]=None, dtype: Optional[torch.dtype]=None):
18
+ super().__init__(normalized_shape=normalized_shape, eps=eps, elementwise_affine=elementwise_affine, device=device, dtype=dtype)
19
+
20
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
21
+ module_device = x.device
22
+ downcast_x = _cast_if_autocast_enabled(x)
23
+ downcast_weight = _cast_if_autocast_enabled(self.weight) if self.weight is not None else self.weight
24
+ downcast_bias = _cast_if_autocast_enabled(self.bias) if self.bias is not None else self.bias
25
+ with torch.autocast(enabled=False, device_type=module_device.type):
26
+ return torch.nn.functional.layer_norm(downcast_x, self.normalized_shape, downcast_weight, downcast_bias, self.eps)
27
+
28
+ def rms_norm(x: torch.Tensor, weight: Optional[torch.Tensor]=None, eps: float=1e-05) -> torch.Tensor:
29
+ output = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + eps)
30
+ if weight is not None:
31
+ return output * weight
32
+ return output
33
+
34
+ class RMSNorm(torch.nn.Module):
35
+
36
+ def __init__(self, normalized_shape: Union[int, List[int], torch.Size], eps: float=1e-05, weight: bool=True, dtype: Optional[torch.dtype]=None, device: Optional[torch.device]=None):
37
+ super().__init__()
38
+ self.eps = eps
39
+ if weight:
40
+ self.weight = torch.nn.Parameter(torch.ones(normalized_shape, dtype=dtype, device=device))
41
+ else:
42
+ self.register_parameter('weight', None)
43
+
44
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
45
+ return rms_norm(x.float(), self.weight, self.eps).to(dtype=x.dtype)
46
+
47
+ class LPRMSNorm(RMSNorm):
48
+
49
+ def __init__(self, normalized_shape: Union[int, List[int], torch.Size], eps: float=1e-05, weight: bool=True, dtype: Optional[torch.dtype]=None, device: Optional[torch.device]=None):
50
+ super().__init__(normalized_shape=normalized_shape, eps=eps, weight=weight, dtype=dtype, device=device)
51
+
52
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
53
+ downcast_x = _cast_if_autocast_enabled(x)
54
+ downcast_weight = _cast_if_autocast_enabled(self.weight) if self.weight is not None else self.weight
55
+ with torch.autocast(enabled=False, device_type=x.device.type):
56
+ return rms_norm(downcast_x, downcast_weight, self.eps).to(dtype=x.dtype)
57
+ NORM_CLASS_REGISTRY: Dict[str, Type[torch.nn.Module]] = {'layernorm': torch.nn.LayerNorm, 'low_precision_layernorm': LPLayerNorm, 'rmsnorm': RMSNorm, 'low_precision_rmsnorm': LPRMSNorm}
param_init_fns.py ADDED
@@ -0,0 +1,179 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import math
2
+ import warnings
3
+ from collections.abc import Sequence
4
+ from functools import partial
5
+ from typing import Any, Callable, Optional, Tuple, Union
6
+ import torch
7
+ from torch import nn
8
+ from .fc import FC_CLASS_REGISTRY
9
+ from .norm import NORM_CLASS_REGISTRY
10
+ try:
11
+ import transformer_engine.pytorch as te
12
+ except:
13
+ te = None
14
+
15
+ def torch_default_param_init_fn_(module: nn.Module, **kwargs: Any) -> None:
16
+ del kwargs
17
+ if hasattr(module, 'reset_parameters') and isinstance(module.reset_parameters, Callable):
18
+ module.reset_parameters()
19
+
20
+ def fused_init_helper_(module: nn.Module, init_fn_: Callable) -> None:
21
+ _fused = getattr(module, '_fused', None)
22
+ if _fused is None:
23
+ raise RuntimeError(f'Internal logic error')
24
+ assert isinstance(module.weight, torch.Tensor)
25
+ (dim, splits) = _fused
26
+ splits = (0, *splits, module.weight.size(dim))
27
+ for (s, e) in zip(splits[:-1], splits[1:]):
28
+ slice_indices = [slice(None)] * module.weight.ndim
29
+ slice_indices[dim] = slice(s, e)
30
+ init_fn_(module.weight[slice_indices])
31
+
32
+ def generic_param_init_fn_(module: nn.Module, init_fn_: Callable, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, **kwargs: Any) -> None:
33
+ del kwargs
34
+ init_div_is_residual = init_div_is_residual
35
+ if init_div_is_residual is False:
36
+ div_is_residual = 1.0
37
+ elif init_div_is_residual is True:
38
+ div_is_residual = math.sqrt(2 * n_layers)
39
+ elif isinstance(init_div_is_residual, float) or isinstance(init_div_is_residual, int):
40
+ div_is_residual = init_div_is_residual
41
+ elif init_div_is_residual.isnumeric():
42
+ div_is_residual = float(init_div_is_residual)
43
+ else:
44
+ div_is_residual = 1.0
45
+ raise ValueError(f'Expected init_div_is_residual to be boolean or numeric, got {init_div_is_residual}')
46
+ if isinstance(module, tuple(set(FC_CLASS_REGISTRY.values()))):
47
+ if hasattr(module, '_fused'):
48
+ fused_init_helper_(module, init_fn_)
49
+ else:
50
+ init_fn_(module.weight)
51
+ if module.bias is not None:
52
+ assert isinstance(module.bias, torch.Tensor)
53
+ torch.nn.init.zeros_(module.bias)
54
+ if init_div_is_residual is not False and getattr(module, '_is_residual', False):
55
+ with torch.no_grad():
56
+ module.weight.div_(div_is_residual)
57
+ elif isinstance(module, nn.Embedding):
58
+ if emb_init_std is not None:
59
+ std = emb_init_std
60
+ if std == 0:
61
+ warnings.warn(f'Embedding layer initialized to 0.')
62
+ emb_init_fn_ = partial(torch.nn.init.normal_, mean=0.0, std=std)
63
+ elif emb_init_uniform_lim is not None:
64
+ lim = emb_init_uniform_lim
65
+ if isinstance(lim, Sequence):
66
+ if len(lim) > 2:
67
+ raise ValueError(f'Uniform init requires a min and a max limit. User input: {lim}.')
68
+ if lim[0] == lim[1]:
69
+ warnings.warn(f'Embedding layer initialized to {lim[0]}.')
70
+ else:
71
+ if lim == 0:
72
+ warnings.warn(f'Embedding layer initialized to 0.')
73
+ lim = [-lim, lim]
74
+ (a, b) = lim
75
+ emb_init_fn_ = partial(torch.nn.init.uniform_, a=a, b=b)
76
+ else:
77
+ emb_init_fn_ = init_fn_
78
+ emb_init_fn_(module.weight)
79
+ elif isinstance(module, tuple(set(NORM_CLASS_REGISTRY.values()))):
80
+ if hasattr(module, 'weight') and isinstance(module.weight, torch.Tensor):
81
+ torch.nn.init.ones_(module.weight)
82
+ if hasattr(module, 'bias') and isinstance(module.bias, torch.Tensor):
83
+ torch.nn.init.zeros_(module.bias)
84
+ elif isinstance(module, nn.MultiheadAttention):
85
+ if module._qkv_same_embed_dim:
86
+ assert module.in_proj_weight is not None
87
+ assert module.q_proj_weight is None and module.k_proj_weight is None and (module.v_proj_weight is None)
88
+ assert d_model is not None
89
+ _d = d_model
90
+ splits = (0, _d, 2 * _d, 3 * _d)
91
+ for (s, e) in zip(splits[:-1], splits[1:]):
92
+ init_fn_(module.in_proj_weight[s:e])
93
+ else:
94
+ assert module.q_proj_weight is not None and module.k_proj_weight is not None and (module.v_proj_weight is not None)
95
+ assert module.in_proj_weight is None
96
+ init_fn_(module.q_proj_weight)
97
+ init_fn_(module.k_proj_weight)
98
+ init_fn_(module.v_proj_weight)
99
+ if module.in_proj_bias is not None:
100
+ torch.nn.init.zeros_(module.in_proj_bias)
101
+ if module.bias_k is not None:
102
+ torch.nn.init.zeros_(module.bias_k)
103
+ if module.bias_v is not None:
104
+ torch.nn.init.zeros_(module.bias_v)
105
+ init_fn_(module.out_proj.weight)
106
+ if init_div_is_residual is not False and getattr(module.out_proj, '_is_residual', False):
107
+ with torch.no_grad():
108
+ module.out_proj.weight.div_(div_is_residual)
109
+ if module.out_proj.bias is not None:
110
+ torch.nn.init.zeros_(module.out_proj.bias)
111
+ elif te is not None and isinstance(module, te.LayerNormMLP):
112
+ if isinstance(module.layer_norm_weight, torch.Tensor):
113
+ torch.nn.init.ones_(module.layer_norm_weight)
114
+ if isinstance(module.layer_norm_bias, torch.Tensor):
115
+ torch.nn.init.zeros_(module.layer_norm_bias)
116
+ init_fn_(module.fc1_weight)
117
+ if module.fc1_bias is not None:
118
+ assert isinstance(module.fc1_bias, torch.Tensor)
119
+ torch.nn.init.zeros_(module.fc1_bias)
120
+ init_fn_(module.fc2_weight)
121
+ if module.fc2_bias is not None:
122
+ assert isinstance(module.fc2_bias, torch.Tensor)
123
+ torch.nn.init.zeros_(module.fc2_bias)
124
+ with torch.no_grad():
125
+ module.fc2_weight.div_(div_is_residual)
126
+ else:
127
+ for _ in module.parameters(recurse=False):
128
+ raise NotImplementedError(f'{module.__class__.__name__} parameters are not initialized by param_init_fn.')
129
+
130
+ def _normal_init_(std: float, mean: float=0.0) -> Callable:
131
+ return partial(torch.nn.init.normal_, mean=mean, std=std)
132
+
133
+ def _normal_param_init_fn_(module: nn.Module, std: float, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, **kwargs: Any) -> None:
134
+ del kwargs
135
+ init_fn_ = _normal_init_(std=std)
136
+ generic_param_init_fn_(module=module, init_fn_=init_fn_, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim)
137
+
138
+ def baseline_param_init_fn_(module: nn.Module, init_std: Optional[float], n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, **kwargs: Any) -> None:
139
+ del kwargs
140
+ if init_std is None:
141
+ raise ValueError("You must set model.init_config['init_std'] to a float value to use the default initialization scheme.")
142
+ _normal_param_init_fn_(module=module, std=init_std, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim)
143
+
144
+ def small_param_init_fn_(module: nn.Module, n_layers: int, d_model: int, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, **kwargs: Any) -> None:
145
+ del kwargs
146
+ std = math.sqrt(2 / (5 * d_model))
147
+ _normal_param_init_fn_(module=module, std=std, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim)
148
+
149
+ def neox_param_init_fn_(module: nn.Module, n_layers: int, d_model: int, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, **kwargs: Any) -> None:
150
+ """From section 2.3.1 of GPT-NeoX-20B:
151
+
152
+ An Open-Source AutoregressiveLanguage Model — Black et. al. (2022)
153
+ see https://github.com/EleutherAI/gpt-neox/blob/9610391ab319403cef079b438edd016a2443af54/megatron/model/init_functions.py#L151
154
+ and https://github.com/EleutherAI/gpt-neox/blob/main/megatron/model/transformer.py
155
+ """
156
+ del kwargs
157
+ residual_div = n_layers / math.sqrt(10)
158
+ small_param_init_fn_(module=module, d_model=d_model, n_layers=n_layers, init_div_is_residual=residual_div, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim)
159
+
160
+ def kaiming_uniform_param_init_fn_(module: nn.Module, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, init_gain: float=0, fan_mode: str='fan_in', init_nonlinearity: str='leaky_relu', **kwargs: Any) -> None:
161
+ del kwargs
162
+ kaiming_uniform_ = partial(nn.init.kaiming_uniform_, a=init_gain, mode=fan_mode, nonlinearity=init_nonlinearity)
163
+ generic_param_init_fn_(module=module, init_fn_=kaiming_uniform_, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim)
164
+
165
+ def kaiming_normal_param_init_fn_(module: nn.Module, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, init_gain: float=0, fan_mode: str='fan_in', init_nonlinearity: str='leaky_relu', **kwargs: Any) -> None:
166
+ del kwargs
167
+ kaiming_normal_ = partial(torch.nn.init.kaiming_normal_, a=init_gain, mode=fan_mode, nonlinearity=init_nonlinearity)
168
+ generic_param_init_fn_(module=module, init_fn_=kaiming_normal_, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim)
169
+
170
+ def xavier_uniform_param_init_fn_(module: nn.Module, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, init_gain: float=0, **kwargs: Any) -> None:
171
+ del kwargs
172
+ xavier_uniform_ = partial(torch.nn.init.xavier_uniform_, gain=init_gain)
173
+ generic_param_init_fn_(module=module, init_fn_=xavier_uniform_, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim)
174
+
175
+ def xavier_normal_param_init_fn_(module: nn.Module, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, init_gain: float=0, **kwargs: Any) -> None:
176
+ del kwargs
177
+ xavier_normal_ = partial(torch.nn.init.xavier_normal_, gain=init_gain)
178
+ generic_param_init_fn_(module=module, init_fn_=xavier_normal_, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim)
179
+ MODEL_INIT_REGISTRY = {'default_': torch_default_param_init_fn_, 'baseline_': baseline_param_init_fn_, 'kaiming_uniform_': kaiming_uniform_param_init_fn_, 'kaiming_normal_': kaiming_normal_param_init_fn_, 'neox_init_': neox_param_init_fn_, 'small_init_': small_param_init_fn_, 'xavier_uniform_': xavier_uniform_param_init_fn_, 'xavier_normal_': xavier_normal_param_init_fn_}
pytorch_model.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:50d01882c621a89c939b0463e3fde214e9e6e9d1a08870093f41bf22a0df1850
3
+ size 2720599194
special_tokens_map.json ADDED
@@ -0,0 +1,30 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<s>",
4
+ "lstrip": false,
5
+ "normalized": true,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "eos_token": {
10
+ "content": "</s>",
11
+ "lstrip": false,
12
+ "normalized": true,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "pad_token": {
17
+ "content": "<unk>",
18
+ "lstrip": false,
19
+ "normalized": true,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ },
23
+ "unk_token": {
24
+ "content": "<unk>",
25
+ "lstrip": false,
26
+ "normalized": true,
27
+ "rstrip": false,
28
+ "single_word": false
29
+ }
30
+ }
tokenization_bn.py ADDED
@@ -0,0 +1,171 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ from shutil import copyfile
3
+ from typing import Any, Dict, List, Optional, Tuple
4
+ import sentencepiece as spm
5
+ from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
6
+ from transformers.utils import logging
7
+ logger = logging.get_logger(__name__)
8
+ VOCAB_FILES_NAMES = {'vocab_file': 'tokenizer.model'}
9
+ PRETRAINED_VOCAB_FILES_MAP = {'vocab_file': {}, 'tokenizer_file': {}}
10
+ PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {}
11
+
12
+ class BNTokenizer(PreTrainedTokenizer):
13
+ """
14
+ Construct a Yi tokenizer. Based on byte-level Byte-Pair-Encoding.
15
+
16
+ Args:
17
+ vocab_file (`str`):
18
+ Path to the vocabulary file.
19
+ """
20
+ vocab_files_names = VOCAB_FILES_NAMES
21
+ pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
22
+ max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
23
+ model_input_names = ['input_ids', 'attention_mask']
24
+
25
+ def __init__(self, vocab_file, unk_token='<unk>', bos_token='<s>', eos_token='</s>', pad_token='<unk>', sp_model_kwargs: Optional[Dict[str, Any]]=None, add_bos_token=True, add_eos_token=False, clean_up_tokenization_spaces=False, **kwargs):
26
+ self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
27
+ bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
28
+ eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
29
+ unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
30
+ pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
31
+ self.vocab_file = vocab_file
32
+ self.add_bos_token = add_bos_token
33
+ self.add_eos_token = add_eos_token
34
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
35
+ self.sp_model.Load(vocab_file)
36
+ super().__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, pad_token=pad_token, add_bos_token=add_bos_token, add_eos_token=add_eos_token, sp_model_kwargs=self.sp_model_kwargs, clean_up_tokenization_spaces=clean_up_tokenization_spaces, **kwargs)
37
+
38
+ def __getstate__(self):
39
+ state = self.__dict__.copy()
40
+ state['sp_model'] = None
41
+ return state
42
+
43
+ def __setstate__(self, d):
44
+ self.__dict__ = d
45
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
46
+ self.sp_model.Load(self.vocab_file)
47
+
48
+ @property
49
+ def vocab_size(self):
50
+ """Returns vocab size"""
51
+ return self.sp_model.get_piece_size()
52
+
53
+ def get_vocab(self):
54
+ """Returns vocab as a dict"""
55
+ vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
56
+ vocab.update(self.added_tokens_encoder)
57
+ return vocab
58
+
59
+ def _tokenize(self, text):
60
+ """Returns a tokenized string."""
61
+ return self.sp_model.encode(text, out_type=str)
62
+
63
+ def _convert_token_to_id(self, token):
64
+ """Converts a token (str) in an id using the vocab."""
65
+ return self.sp_model.piece_to_id(token)
66
+
67
+ def _convert_id_to_token(self, index):
68
+ """Converts an index (integer) in a token (str) using the vocab."""
69
+ token = self.sp_model.IdToPiece(index)
70
+ return token
71
+
72
+ def convert_tokens_to_string(self, tokens):
73
+ """Converts a sequence of tokens (string) in a single string."""
74
+ current_sub_tokens = []
75
+ out_string = ''
76
+ prev_is_special = False
77
+ for (i, token) in enumerate(tokens):
78
+ if token in self.all_special_tokens:
79
+ if not prev_is_special and i != 0:
80
+ out_string += ' '
81
+ out_string += self.sp_model.decode(current_sub_tokens) + token
82
+ prev_is_special = True
83
+ current_sub_tokens = []
84
+ else:
85
+ current_sub_tokens.append(token)
86
+ prev_is_special = False
87
+ out_string += self.sp_model.decode(current_sub_tokens)
88
+ return out_string
89
+
90
+ def save_vocabulary(self, save_directory, filename_prefix: Optional[str]=None) -> Tuple[str]:
91
+ """
92
+ Save the vocabulary and special tokens file to a directory.
93
+
94
+ Args:
95
+ save_directory (`str`):
96
+ The directory in which to save the vocabulary.
97
+
98
+ Returns:
99
+ `Tuple(str)`: Paths to the files saved.
100
+ """
101
+ if not os.path.isdir(save_directory):
102
+ logger.error(f'Vocabulary path ({save_directory}) should be a directory')
103
+ return
104
+ out_vocab_file = os.path.join(save_directory, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
105
+ if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
106
+ copyfile(self.vocab_file, out_vocab_file)
107
+ elif not os.path.isfile(self.vocab_file):
108
+ with open(out_vocab_file, 'wb') as fi:
109
+ content_spiece_model = self.sp_model.serialized_model_proto()
110
+ fi.write(content_spiece_model)
111
+ return (out_vocab_file,)
112
+
113
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
114
+ bos_token_id = [self.bos_token_id] if self.add_bos_token else []
115
+ eos_token_id = [self.eos_token_id] if self.add_eos_token else []
116
+ output = bos_token_id + token_ids_0 + eos_token_id
117
+ if token_ids_1 is not None:
118
+ output = output + bos_token_id + token_ids_1 + eos_token_id
119
+ return output
120
+
121
+ def get_special_tokens_mask(self, token_ids_0: List[int], token_ids_1: Optional[List[int]]=None, already_has_special_tokens: bool=False) -> List[int]:
122
+ """
123
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
124
+ special tokens using the tokenizer `prepare_for_model` method.
125
+
126
+ Args:
127
+ token_ids_0 (`List[int]`):
128
+ List of IDs.
129
+ token_ids_1 (`List[int]`, *optional*):
130
+ Optional second list of IDs for sequence pairs.
131
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
132
+ Whether or not the token list is already formatted with special tokens for the model.
133
+
134
+ Returns:
135
+ `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
136
+ """
137
+ if already_has_special_tokens:
138
+ return super().get_special_tokens_mask(token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True)
139
+ bos_token_id = [1] if self.add_bos_token else []
140
+ eos_token_id = [1] if self.add_eos_token else []
141
+ if token_ids_1 is None:
142
+ return bos_token_id + [0] * len(token_ids_0) + eos_token_id
143
+ return bos_token_id + [0] * len(token_ids_0) + eos_token_id + bos_token_id + [0] * len(token_ids_1) + eos_token_id
144
+
145
+ def create_token_type_ids_from_sequences(self, token_ids_0: List[int], token_ids_1: Optional[List[int]]=None) -> List[int]:
146
+ """
147
+ Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
148
+ sequence pair mask has the following format:
149
+
150
+ ```
151
+ 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
152
+ | first sequence | second sequence |
153
+ ```
154
+
155
+ if token_ids_1 is None, only returns the first portion of the mask (0s).
156
+
157
+ Args:
158
+ token_ids_0 (`List[int]`):
159
+ List of ids.
160
+ token_ids_1 (`List[int]`, *optional*):
161
+ Optional second list of IDs for sequence pairs.
162
+
163
+ Returns:
164
+ `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
165
+ """
166
+ bos_token_id = [self.bos_token_id] if self.add_bos_token else []
167
+ eos_token_id = [self.eos_token_id] if self.add_eos_token else []
168
+ output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
169
+ if token_ids_1 is not None:
170
+ output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
171
+ return output
tokenizer.model ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:ef4756dc05d097a0bf5279a817c1505bc9fe8b6860720fbc275271f237b8e1a3
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+ size 1393792
tokenizer_config.json ADDED
@@ -0,0 +1,44 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_bos_token": false,
3
+ "add_eos_token": false,
4
+ "added_tokens_decoder": {
5
+ "0": {
6
+ "content": "<unk>",
7
+ "lstrip": false,
8
+ "normalized": true,
9
+ "rstrip": false,
10
+ "single_word": false,
11
+ "special": true
12
+ },
13
+ "1": {
14
+ "content": "<s>",
15
+ "lstrip": false,
16
+ "normalized": true,
17
+ "rstrip": false,
18
+ "single_word": false,
19
+ "special": true
20
+ },
21
+ "2": {
22
+ "content": "</s>",
23
+ "lstrip": false,
24
+ "normalized": true,
25
+ "rstrip": false,
26
+ "single_word": false,
27
+ "special": true
28
+ }
29
+ },
30
+ "auto_map": {
31
+ "AutoTokenizer": [
32
+ "tokenization_bn.BNTokenizer",
33
+ null
34
+ ]
35
+ },
36
+ "bos_token": "<s>",
37
+ "clean_up_tokenization_spaces": false,
38
+ "eos_token": "</s>",
39
+ "model_max_length": 2048,
40
+ "pad_token": "<unk>",
41
+ "sp_model_kwargs": {},
42
+ "tokenizer_class": "BNTokenizer",
43
+ "unk_token": "<unk>"
44
+ }