116 lines
5.4 KiB
Python
116 lines
5.4 KiB
Python
import math
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import torch
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import torch.nn as nn
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from einops import rearrange
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from flash_attn.rotary import RotaryEmbedding, RotaryEmbedding2D
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from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_func
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from flash_attn.bert_padding import unpad_input, pad_input, index_first_axis
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class FlashAttention(nn.Module):
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"""Implement the scaled dot product attention with softmax.
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Arguments
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---------
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softmax_scale: The temperature to use for the softmax attention.
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(default: 1/sqrt(d_keys) where d_keys is computed at
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runtime)
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attention_dropout: The dropout rate to apply to the attention
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(default: 0.1)
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"""
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def __init__(self, softmax_scale=None, attention_dropout=0.0, device=None, dtype=None):
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super().__init__()
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self.softmax_scale = softmax_scale
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self.dropout_p = attention_dropout
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def forward(self, qkv, key_padding_mask=None, causal=False, cu_seqlens=None,
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max_s=None, need_weights=False):
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"""Implements the multihead softmax attention.
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Arguments
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---------
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qkv: The tensor containing the query, key, and value. (B, S, 3, H, D) if key_padding_mask is None
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if unpadded: (nnz, 3, h, d)
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key_padding_mask: a bool tensor of shape (B, S)
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"""
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assert not need_weights
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assert qkv.dtype in [torch.float16, torch.bfloat16]
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assert qkv.is_cuda
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if cu_seqlens is None:
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batch_size = qkv.shape[0]
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seqlen = qkv.shape[1]
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if key_padding_mask is None:
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qkv = rearrange(qkv, 'b s ... -> (b s) ...')
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max_s = seqlen
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cu_seqlens = torch.arange(0, (batch_size + 1) * seqlen, step=seqlen, dtype=torch.int32,
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device=qkv.device)
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output = flash_attn_unpadded_qkvpacked_func(
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qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
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softmax_scale=self.softmax_scale, causal=causal
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)
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output = rearrange(output, '(b s) ... -> b s ...', b=batch_size)
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else:
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nheads = qkv.shape[-2]
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x = rearrange(qkv, 'b s three h d -> b s (three h d)')
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x_unpad, indices, cu_seqlens, max_s = unpad_input(x, key_padding_mask)
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x_unpad = rearrange(x_unpad, 'nnz (three h d) -> nnz three h d', three=3, h=nheads)
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output_unpad = flash_attn_unpadded_qkvpacked_func(
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x_unpad, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
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softmax_scale=self.softmax_scale, causal=causal
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)
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output = rearrange(pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'),
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indices, batch_size, seqlen),
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'b s (h d) -> b s h d', h=nheads)
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else:
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assert max_s is not None
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output = flash_attn_unpadded_qkvpacked_func(
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qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
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softmax_scale=self.softmax_scale, causal=causal
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)
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return output, None
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class FlashMHA(nn.Module):
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def __init__(self, embed_dim, num_heads, bias=True, batch_first=True, attention_dropout=0.0,
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causal=False, use_rotary_emb=None, device=None, dtype=None, **kwargs) -> None:
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assert batch_first
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factory_kwargs = {'device': device, 'dtype': dtype}
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super().__init__()
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self.embed_dim = embed_dim
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self.causal = causal
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self.num_heads = num_heads
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assert self.embed_dim % num_heads == 0, "self.kdim must be divisible by num_heads"
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self.head_dim = self.embed_dim // num_heads
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assert self.head_dim in [16, 32, 64, 128], "Only support head_dim == 16, 32, 64, or 128"
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assert use_rotary_emb in [None, '1d', '2d']
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self.use_rotary_emb = use_rotary_emb
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if self.use_rotary_emb == '1d':
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self.rotary_emb = RotaryEmbedding(self.head_dim)
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elif self.use_rotary_emb == '2d':
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self.rotary_emb = RotaryEmbedding2D(self.head_dim)
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self.Wqkv = nn.Linear(embed_dim, 3 * embed_dim, bias=bias, **factory_kwargs)
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self.inner_attn = FlashAttention(attention_dropout=attention_dropout, **factory_kwargs)
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self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias, **factory_kwargs)
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def forward(self, x, key_padding_mask=None, need_weights=False):
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"""x: (batch, seqlen, hidden_dim) (where hidden_dim = num heads * head dim)
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key_padding_mask: bool tensor of shape (batch, seqlen)
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"""
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qkv = self.Wqkv(x)
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if self.use_rotary_emb:
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query, key, value = rearrange(qkv, 'b s (three h d) -> b s three h d', three=3,
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h=self.num_heads).unbind(dim=2)
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query, key = self.rotary_emb(query, key, seq_dimension=-3)
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qkv = torch.stack([query.type(x.dtype), key.type(x.dtype), value], dim=2)
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else:
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qkv = rearrange(qkv, 'b s (three h d) -> b s three h d', three=3, h=self.num_heads)
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context, attn_weights = self.inner_attn(qkv, key_padding_mask=key_padding_mask,
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need_weights=need_weights, causal=self.causal)
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return self.out_proj(rearrange(context, 'b s h d -> b s (h d)')), attn_weights
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