137 lines
6.7 KiB
Python
137 lines
6.7 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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import hydra
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from flash_attn.flash_blocksparse_attn_interface import flash_blocksparse_attn_func
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from flash_attn.flash_blocksparse_attn_interface import convert_blockmask
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from flash_attn.bert_padding import unpad_input, pad_input, index_first_axis
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class FlashBlocksparseAttention(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_temp: 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, sparsity_config, softmax_temp=None, attention_dropout=0.0,
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max_seq_length=2048, device=None, dtype=None):
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super().__init__()
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self.sparsity_config = hydra.utils.instantiate(sparsity_config)
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self.softmax_temp = softmax_temp
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self.dropout_p = attention_dropout
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# initialize sparse layout and register as buffer
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max_seq_length = ((max_seq_length + 256 - 1) // 256) * 256
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layout = self.sparsity_config.make_layout(max_seq_length)
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self.register_buffer("layout", layout)
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blockmask_converted = convert_blockmask(self.layout, causal=False)
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self.register_buffer("blockmask_converted", blockmask_converted)
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# logger.info(f'Attention class {self.__class__}: saving={self.layout.float().mean()}')
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def forward(self, qkv, attn_mask=None, key_padding_mask=None, causal=False, cu_seqlens=None,
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max_s=None, need_weights=False, convert_mask=True):
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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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attn_mask: An implementation of BaseMask that encodes where each
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query can attend to
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key_padding_mask: An implementation of BaseMask that encodes how
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many query each sequence in the batch consists of
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"""
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assert not need_weights
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assert attn_mask is None
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assert qkv.dtype == torch.float16
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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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# Convert mask to take a subset
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seqlen_rounded = ((seqlen + 256 - 1) // 256) * 256
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assert seqlen_rounded // 16 <= self.layout.shape[0], seqlen_rounded // 256 <= self.layout.shape[1]
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blockmask = self.layout[:seqlen_rounded // 16, :seqlen_rounded // 256]
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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_blocksparse_attn_func(
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qkv, cu_seqlens, blockmask, self.dropout_p if self.training else 0.0,
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max_s, softmax_scale=self.softmax_temp, 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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key_padding_mask_bool = key_padding_mask.bool_matrix
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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_bool)
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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_blocksparse_attn_func(
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x_unpad, cu_seqlens, blockmask, self.dropout_p if self.training else 0.0,
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max_s, softmax_scale=self.softmax_temp, 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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seqlen = max_s
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# Convert mask to take a subset
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seqlen_rounded = ((seqlen + 256 - 1) // 256) * 256
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assert seqlen_rounded // 16 <= self.layout.shape[0], seqlen_rounded // 256 <= self.layout.shape[1]
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blockmask = self.layout[:seqlen_rounded // 16, :seqlen_rounded // 256]
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if convert_mask:
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output = flash_blocksparse_attn_func(
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qkv, cu_seqlens, blockmask, self.dropout_p if self.training else 0.0,
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max_s, softmax_scale=self.softmax_temp, causal=causal
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)
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else:
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output = flash_blocksparse_attn_func(
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qkv, cu_seqlens, self.blockmask_converted, self.dropout_p if self.training else 0.0,
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max_s, softmax_scale=self.softmax_temp, causal=causal,
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convert_mask=False,
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)
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return output, None
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class FlashBlocksparseMHA(nn.Module):
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def __init__(self, embed_dim, num_heads, sparsity_config, bias=True, batch_first=True,
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attention_dropout=0.0, causal=False, max_seq_length=2048,
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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], "Only support head_dim == 16, 32, or 64"
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self.Wqkv = nn.Linear(embed_dim, 3 * embed_dim, bias=bias, **factory_kwargs)
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self.inner_attn = FlashBlocksparseAttention(
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sparsity_config, attention_dropout=attention_dropout,
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max_seq_length=max_seq_length, **factory_kwargs
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)
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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, x_ignored_, x_ignored_1_, attn_mask=None, key_padding_mask=None,
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need_weights=False):
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qkv = self.Wqkv(x)
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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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