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"""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, 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)
k = rearrange(key, 'b s (h d) -> b h d s', h=1 if multiquery else n_heads)
v = rearrange(value, 'b s (h d) -> b h s d', h=1 if multiquery else n_heads)
min_val = torch.finfo(q.dtype).min
(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:
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
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:
s = max(s_q, s_k)
causal_mask = attn_weight.new_ones(s, s, dtype=torch.float16)
causal_mask = causal_mask.tril()
causal_mask = causal_mask.to(torch.bool)
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.matmul(v)
out = rearrange(out, 'b h s d -> b s (h d)')
if needs_weights:
return (out, attn_weight)
return (out, None)
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, 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 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)
def triton_flash_attn_fn(query, key, value, n_heads, 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 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)
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, 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
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():
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)
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:
attn_bias = attn_bias[:, :, -query.size(1):, -key.size(1):]
(context, attn_weights) = self.attn_fn(query, key, value, self.n_heads, 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, 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
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():
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)
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:
attn_bias = attn_bias[:, :, -query.size(1):, -key.size(1):]
(context, attn_weights) = self.attn_fn(query, key, value, self.n_heads, 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} |