| """Attention layers.""" | |
| import math | |
| import warnings | |
| from typing import Optional | |
| import torch | |
| import torch.nn as nn | |
| from einops import rearrange | |
| 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}).') | |
| 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 == '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 == '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 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 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} |