File size: 17,641 Bytes
c5ccdb7
 
 
 
 
 
 
11711b0
c5ccdb7
 
 
 
 
 
 
 
 
 
 
68e1a8e
c5ccdb7
68e1a8e
 
 
 
 
 
 
 
c5ccdb7
 
 
 
 
 
68e1a8e
 
 
c5ccdb7
 
 
68e1a8e
c5ccdb7
 
 
 
68e1a8e
c5ccdb7
72fc4ea
c5ccdb7
 
 
 
 
 
 
e837ad7
c5ccdb7
 
68e1a8e
 
c5ccdb7
 
 
 
 
 
 
 
68e1a8e
c5ccdb7
 
 
 
 
68e1a8e
 
 
 
 
 
 
 
 
c5ccdb7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
68e1a8e
c5ccdb7
68e1a8e
c5ccdb7
11711b0
c5ccdb7
11711b0
 
 
 
 
 
 
 
 
c5ccdb7
68e1a8e
 
 
 
 
 
 
 
 
c5ccdb7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
11711b0
c5ccdb7
68e1a8e
c5ccdb7
 
 
 
 
 
 
 
68e1a8e
c5ccdb7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
68e1a8e
 
c5ccdb7
 
68e1a8e
c5ccdb7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
68e1a8e
c5ccdb7
 
 
 
 
 
 
 
 
68e1a8e
c5ccdb7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
68e1a8e
 
c5ccdb7
 
68e1a8e
c5ccdb7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
68e1a8e
c5ccdb7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
72fc4ea
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
"""Attention layers."""
import math
import warnings
from typing import Optional
import torch
import torch.nn as nn
from einops import rearrange
from packaging import version
from torch import nn
from .norm import LPLayerNorm

def _reset_is_causal(num_query_tokens: int, num_key_tokens: int, original_is_causal: bool):
    if original_is_causal and num_query_tokens != num_key_tokens:
        if num_query_tokens != 1:
            raise NotImplementedError('MPT does not support query and key with different number of tokens, unless number of query tokens is 1.')
        else:
            return False
    return original_is_causal

def scaled_multihead_dot_product_attention(query, key, value, n_heads, past_key_value=None, softmax_scale=None, attn_bias=None, key_padding_mask=None, is_causal=False, dropout_p=0.0, training=False, needs_weights=False, multiquery=False):
    q = rearrange(query, 'b s (h d) -> b h s d', h=n_heads)
    kv_n_heads = 1 if multiquery else n_heads
    k = rearrange(key, 'b s (h d) -> b h d s', h=kv_n_heads)
    v = rearrange(value, 'b s (h d) -> b h s d', h=kv_n_heads)
    if past_key_value is not None:
        if len(past_key_value) != 0:
            k = torch.cat([past_key_value[0], k], dim=3)
            v = torch.cat([past_key_value[1], v], dim=2)
        past_key_value = (k, v)
    (b, _, s_q, d) = q.shape
    s_k = k.size(-1)
    if softmax_scale is None:
        softmax_scale = 1 / math.sqrt(d)
    attn_weight = q.matmul(k) * softmax_scale
    if attn_bias is not None:
        _s_q = max(0, attn_bias.size(2) - s_q)
        _s_k = max(0, attn_bias.size(3) - s_k)
        attn_bias = attn_bias[:, :, _s_q:, _s_k:]
        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):
            raise RuntimeError(f'attn_bias (shape: {attn_bias.shape}) is expected to broadcast to shape: {attn_weight.shape}.')
        attn_weight = attn_weight + attn_bias
    min_val = torch.finfo(q.dtype).min
    if key_padding_mask is not None:
        if attn_bias is not None:
            warnings.warn('Propogating key_padding_mask to the attention module ' + 'and applying it within the attention module can cause ' + 'unneccessary computation/memory usage. Consider integrating ' + 'into attn_bias once and passing that to each attention ' + 'module instead.')
        attn_weight = attn_weight.masked_fill(~key_padding_mask.view((b, 1, 1, s_k)), min_val)
    if is_causal and (not q.size(2) == 1):
        s = max(s_q, s_k)
        causal_mask = attn_weight.new_ones(s, s, dtype=torch.bool)
        causal_mask = causal_mask.tril()
        causal_mask = ~causal_mask
        causal_mask = causal_mask[-s_q:, -s_k:]
        attn_weight = attn_weight.masked_fill(causal_mask.view(1, 1, s_q, s_k), min_val)
    attn_weight = torch.softmax(attn_weight, dim=-1)
    if dropout_p:
        attn_weight = torch.nn.functional.dropout(attn_weight, p=dropout_p, training=training, inplace=True)
    out = attn_weight.to(v.dtype).matmul(v)
    out = rearrange(out, 'b h s d -> b s (h d)')
    if needs_weights:
        return (out, attn_weight, past_key_value)
    return (out, None, past_key_value)

def check_valid_inputs(*tensors, valid_dtypes=[torch.float16, torch.bfloat16]):
    for tensor in tensors:
        if tensor.dtype not in valid_dtypes:
            raise TypeError(f'tensor.dtype={tensor.dtype!r} must be in valid_dtypes={valid_dtypes!r}.')
        if not tensor.is_cuda:
            raise TypeError(f'Inputs must be cuda tensors (tensor.is_cuda={tensor.is_cuda!r}).')

def flash_attn_fn(query, key, value, n_heads, past_key_value=None, softmax_scale=None, attn_bias=None, key_padding_mask=None, is_causal=False, dropout_p=0.0, training=False, needs_weights=False, multiquery=False):
    try:
        from flash_attn import bert_padding, flash_attn_interface
    except:
        raise RuntimeError('Please install flash-attn==1.0.3.post0')
    check_valid_inputs(query, key, value)
    if past_key_value is not None:
        if len(past_key_value) != 0:
            key = torch.cat([past_key_value[0], key], dim=1)
            value = torch.cat([past_key_value[1], value], dim=1)
        past_key_value = (key, value)
    if attn_bias is not None:
        _s_q = max(0, attn_bias.size(2) - query.size(1))
        _s_k = max(0, attn_bias.size(3) - key.size(1))
        attn_bias = attn_bias[:, :, _s_q:, _s_k:]
    if attn_bias is not None:
        raise NotImplementedError(f'attn_bias not implemented for flash attn.')
    (batch_size, seqlen) = query.shape[:2]
    if key_padding_mask is None:
        key_padding_mask = torch.ones_like(key[:, :, 0], dtype=torch.bool)
    query_padding_mask = key_padding_mask[:, -query.size(1):]
    (query_unpad, indices_q, cu_seqlens_q, max_seqlen_q) = bert_padding.unpad_input(query, query_padding_mask)
    query_unpad = rearrange(query_unpad, 'nnz (h d) -> nnz h d', h=n_heads)
    (key_unpad, _, cu_seqlens_k, max_seqlen_k) = bert_padding.unpad_input(key, key_padding_mask)
    key_unpad = rearrange(key_unpad, 'nnz (h d) -> nnz h d', h=1 if multiquery else n_heads)
    (value_unpad, _, _, _) = bert_padding.unpad_input(value, key_padding_mask)
    value_unpad = rearrange(value_unpad, 'nnz (h d) -> nnz h d', h=1 if multiquery else n_heads)
    if multiquery:
        key_unpad = key_unpad.expand(key_unpad.size(0), n_heads, key_unpad.size(-1))
        value_unpad = value_unpad.expand(value_unpad.size(0), n_heads, value_unpad.size(-1))
    dropout_p = dropout_p if training else 0.0
    reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal)
    output_unpad = flash_attn_interface.flash_attn_unpadded_func(query_unpad, key_unpad, value_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, dropout_p, softmax_scale=softmax_scale, causal=reset_is_causal, return_attn_probs=needs_weights)
    output = bert_padding.pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'), indices_q, batch_size, seqlen)
    return (output, None, past_key_value)

def triton_flash_attn_fn(query, key, value, n_heads, past_key_value=None, softmax_scale=None, attn_bias=None, key_padding_mask=None, is_causal=False, dropout_p=0.0, training=False, needs_weights=False, multiquery=False):
    try:
        from .flash_attn_triton import flash_attn_func
    except:
        _installed = False
        if version.parse(torch.__version__) < version.parse('2.0.0'):
            _installed = True
            try:
                from flash_attn.flash_attn_triton import flash_attn_func
            except:
                _installed = False
        if not _installed:
            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.')
    check_valid_inputs(query, key, value)
    if past_key_value is not None:
        if len(past_key_value) != 0:
            key = torch.cat([past_key_value[0], key], dim=1)
            value = torch.cat([past_key_value[1], value], dim=1)
        past_key_value = (key, value)
    if attn_bias is not None:
        _s_q = max(0, attn_bias.size(2) - query.size(1))
        _s_k = max(0, attn_bias.size(3) - key.size(1))
        attn_bias = attn_bias[:, :, _s_q:, _s_k:]
    if dropout_p:
        raise NotImplementedError(f'Dropout not implemented for attn_impl: triton.')
    if needs_weights:
        raise NotImplementedError(f'attn_impl: triton cannot return attn weights.')
    if key_padding_mask is not None:
        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.')
        (b_size, s_k) = key_padding_mask.shape[:2]
        if attn_bias is None:
            attn_bias = query.new_zeros(b_size, 1, 1, s_k)
        attn_bias = attn_bias.masked_fill(~key_padding_mask.view((b_size, 1, 1, s_k)), torch.finfo(query.dtype).min)
    query = rearrange(query, 'b s (h d) -> b s h d', h=n_heads)
    key = rearrange(key, 'b s (h d) -> b s h d', h=1 if multiquery else n_heads)
    value = rearrange(value, 'b s (h d) -> b s h d', h=1 if multiquery else n_heads)
    if multiquery:
        key = key.expand(*key.shape[:2], n_heads, key.size(-1))
        value = value.expand(*value.shape[:2], n_heads, value.size(-1))
    reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal)
    attn_output = flash_attn_func(query, key, value, attn_bias, reset_is_causal, softmax_scale)
    output = attn_output.view(*attn_output.shape[:2], -1)
    return (output, None, past_key_value)

class MultiheadAttention(nn.Module):
    """Multi-head self attention.

    Using torch or triton attention implemetation enables user to also use
    additive bias.
    """

    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, low_precision_layernorm: bool=False, verbose: int=0, device: Optional[str]=None):
        super().__init__()
        self.attn_impl = attn_impl
        self.clip_qkv = clip_qkv
        self.qk_ln = qk_ln
        self.d_model = d_model
        self.n_heads = n_heads
        self.softmax_scale = softmax_scale
        if self.softmax_scale is None:
            self.softmax_scale = 1 / math.sqrt(self.d_model / self.n_heads)
        self.attn_dropout_p = attn_pdrop
        self.Wqkv = nn.Linear(self.d_model, 3 * self.d_model, device=device)
        fuse_splits = (d_model, 2 * d_model)
        self.Wqkv._fused = (0, fuse_splits)
        if self.qk_ln:
            layernorm_class = LPLayerNorm if low_precision_layernorm else nn.LayerNorm
            self.q_ln = layernorm_class(self.d_model, device=device)
            self.k_ln = layernorm_class(self.d_model, device=device)
        if self.attn_impl == 'flash':
            self.attn_fn = flash_attn_fn
        elif self.attn_impl == 'triton':
            self.attn_fn = triton_flash_attn_fn
            if verbose:
                warnings.warn('While `attn_impl: triton` can be faster than `attn_impl: flash` ' + 'it uses more memory. When training larger models this can trigger ' + 'alloc retries which hurts performance. If encountered, we recommend ' + 'using `attn_impl: flash` if your model does not use `alibi` or `prefix_lm`.')
        elif self.attn_impl == 'torch':
            self.attn_fn = scaled_multihead_dot_product_attention
            if torch.cuda.is_available() and verbose:
                warnings.warn('Using `attn_impl: torch`. If your model does not use `alibi` or ' + '`prefix_lm` we recommend using `attn_impl: flash` otherwise ' + 'we recommend using `attn_impl: triton`.')
        else:
            raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')
        self.out_proj = nn.Linear(self.d_model, self.d_model, device=device)
        self.out_proj._is_residual = True

    def forward(self, x, past_key_value=None, attn_bias=None, attention_mask=None, is_causal=True, needs_weights=False):
        qkv = self.Wqkv(x)
        if self.clip_qkv:
            qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv)
        (query, key, value) = qkv.chunk(3, dim=2)
        key_padding_mask = attention_mask
        if self.qk_ln:
            dtype = query.dtype
            query = self.q_ln(query).to(dtype)
            key = self.k_ln(key).to(dtype)
        (context, attn_weights, past_key_value) = self.attn_fn(query, key, value, self.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)
        return (self.out_proj(context), attn_weights, past_key_value)

class MultiQueryAttention(nn.Module):
    """Multi-Query self attention.

    Using torch or triton attention implemetation enables user to also use
    additive bias.
    """

    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, low_precision_layernorm: bool=False, verbose: int=0, device: Optional[str]=None):
        super().__init__()
        self.attn_impl = attn_impl
        self.clip_qkv = clip_qkv
        self.qk_ln = qk_ln
        self.d_model = d_model
        self.n_heads = n_heads
        self.head_dim = d_model // n_heads
        self.softmax_scale = softmax_scale
        if self.softmax_scale is None:
            self.softmax_scale = 1 / math.sqrt(self.head_dim)
        self.attn_dropout_p = attn_pdrop
        self.Wqkv = nn.Linear(d_model, d_model + 2 * self.head_dim, device=device)
        fuse_splits = (d_model, d_model + self.head_dim)
        self.Wqkv._fused = (0, fuse_splits)
        if self.qk_ln:
            layernorm_class = LPLayerNorm if low_precision_layernorm else nn.LayerNorm
            self.q_ln = layernorm_class(d_model, device=device)
            self.k_ln = layernorm_class(self.head_dim, device=device)
        if self.attn_impl == 'flash':
            self.attn_fn = flash_attn_fn
        elif self.attn_impl == 'triton':
            self.attn_fn = triton_flash_attn_fn
            if verbose:
                warnings.warn('While `attn_impl: triton` can be faster than `attn_impl: flash` ' + 'it uses more memory. When training larger models this can trigger ' + 'alloc retries which hurts performance. If encountered, we recommend ' + 'using `attn_impl: flash` if your model does not use `alibi` or `prefix_lm`.')
        elif self.attn_impl == 'torch':
            self.attn_fn = scaled_multihead_dot_product_attention
            if torch.cuda.is_available() and verbose:
                warnings.warn('Using `attn_impl: torch`. If your model does not use `alibi` or ' + '`prefix_lm` we recommend using `attn_impl: flash` otherwise ' + 'we recommend using `attn_impl: triton`.')
        else:
            raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')
        self.out_proj = nn.Linear(self.d_model, self.d_model, device=device)
        self.out_proj._is_residual = True

    def forward(self, x, past_key_value=None, attn_bias=None, attention_mask=None, is_causal=True, needs_weights=False):
        qkv = self.Wqkv(x)
        if self.clip_qkv:
            qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv)
        (query, key, value) = qkv.split([self.d_model, self.head_dim, self.head_dim], dim=2)
        key_padding_mask = attention_mask
        if self.qk_ln:
            dtype = query.dtype
            query = self.q_ln(query).to(dtype)
            key = self.k_ln(key).to(dtype)
        (context, attn_weights, past_key_value) = self.attn_fn(query, key, value, self.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, multiquery=True)
        return (self.out_proj(context), attn_weights, past_key_value)

def attn_bias_shape(attn_impl, n_heads, seq_len, alibi, prefix_lm, causal, use_sequence_id):
    if attn_impl == 'flash':
        return None
    elif attn_impl in ['torch', 'triton']:
        if alibi:
            if (prefix_lm or not causal) or use_sequence_id:
                return (1, n_heads, seq_len, seq_len)
            return (1, n_heads, 1, seq_len)
        elif prefix_lm or use_sequence_id:
            return (1, 1, seq_len, seq_len)
        return None
    else:
        raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')

def build_attn_bias(attn_impl, attn_bias, n_heads, seq_len, causal=False, alibi=False, alibi_bias_max=8):
    if attn_impl == 'flash':
        return None
    elif attn_impl in ['torch', 'triton']:
        if alibi:
            (device, dtype) = (attn_bias.device, attn_bias.dtype)
            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))
        return attn_bias
    else:
        raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')

def gen_slopes(n_heads, alibi_bias_max=8, device=None):
    _n_heads = 2 ** math.ceil(math.log2(n_heads))
    m = torch.arange(1, _n_heads + 1, dtype=torch.float32, device=device)
    m = m.mul(alibi_bias_max / _n_heads)
    slopes = 1.0 / torch.pow(2, m)
    if _n_heads != n_heads:
        slopes = torch.concat([slopes[1::2], slopes[::2]])[:n_heads]
    return slopes.view(1, n_heads, 1, 1)

def build_alibi_bias(n_heads, seq_len, full=False, alibi_bias_max=8, device=None, dtype=None):
    alibi_bias = torch.arange(1 - seq_len, 1, dtype=torch.int32, device=device).view(1, 1, 1, seq_len)
    if full:
        alibi_bias = alibi_bias - torch.arange(1 - seq_len, 1, dtype=torch.int32, device=device).view(1, 1, seq_len, 1)
        alibi_bias = alibi_bias.abs().mul(-1)
    slopes = gen_slopes(n_heads, alibi_bias_max, device=device)
    alibi_bias = alibi_bias * slopes
    return alibi_bias.to(dtype=dtype)
ATTN_CLASS_REGISTRY = {'multihead_attention': MultiheadAttention, 'multiquery_attention': MultiQueryAttention}