311 lines
9.7 KiB
Python
311 lines
9.7 KiB
Python
import math
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from typing import Callable, Optional
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import torch
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import torch.nn as nn
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import xformers.ops as xop
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from vllm import pos_encoding_ops as vllm_pos_encoding_ops
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from xformers.ops.fmha.attn_bias import LowerTriangularMask, LowerTriangularMaskWithTensorBias
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def build_rope_cache(
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rotary_dim: int,
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max_position: int = 2048,
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base: int = 10000,
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device: torch.device = torch.device("cuda:0"),
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dtype: torch.dtype = torch.float16
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):
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inv_freq = (1.0 / (base ** (torch.arange(0, rotary_dim, 2, device=device, dtype=dtype) / rotary_dim)))
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t = torch.arange(max_position, device=device, dtype=dtype)
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freqs = torch.einsum("i,j -> ij", t, inv_freq)
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cos = freqs.cos()
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sin = freqs.sin()
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cache = torch.cat((cos, sin), dim=-1)
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return cache
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def build_alibi_slopes(
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num_heads: int,
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device: torch.device = torch.device("cuda:0"),
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dtype: torch.dtype = torch.float16
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):
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closest_power_of_2 = 2 ** math.floor(math.log2(num_heads))
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base = torch.tensor(
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2 ** (-(2 ** -(math.log2(closest_power_of_2) - 3))), device=device, dtype=torch.float32
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)
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powers = torch.arange(1, 1 + closest_power_of_2, device=device, dtype=torch.int32)
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slopes = torch.pow(base, powers)
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if closest_power_of_2 != num_heads:
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extra_base = torch.tensor(
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2 ** (-(2 ** -(math.log2(2 * closest_power_of_2) - 3))), device=device, dtype=torch.float32
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)
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num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2)
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extra_powers = torch.arange(1, 1 + 2 * num_remaining_heads, 2, device=device, dtype=torch.int32)
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slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0)
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slopes = slopes.to(dtype)
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return slopes
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def attention(
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_ops: Optional[xop.AttentionOp] = (xop.fmha.flash.FwOp(), None),
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attention_bias: Optional[xop.AttentionBias] = None,
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p: float = 0.0,
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scale: Optional[float] = None
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):
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if value.shape[2] != query.shape[2]:
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# MQA expand
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if value.shape[2] == 1:
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pass # TODO
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# GQA reshape
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else:
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original_shape = value.shape
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pass # TODO
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return xop.memory_efficient_attention(
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query=query,
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key=key,
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value=value,
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attn_bias=attention_bias,
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p=p,
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scale=scale,
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op=attention_ops
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)
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class FusedAttention(nn.Module):
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def __init__(
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self,
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qkv_proj: nn.Linear,
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out_proj: nn.Linear,
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num_query_heads: int,
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num_key_heads: int,
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num_value_heads: int,
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dropout: float = 0.0,
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scale: Optional[float] = None,
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attention_ops: Optional[xop.AttentionOp] = (xop.fmha.cutlass.FwOp(), None),
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attention_bias: Optional[xop.AttentionBias] = LowerTriangularMask(),
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outputs_handler: Optional[Callable] = None,
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training: bool = False,
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):
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super(FusedAttention, self).__init__()
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self.qkv_proj = qkv_proj
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self.out_proj = out_proj
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self.num_query_heads = num_query_heads
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self.num_key_heads = num_key_heads
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self.num_value_heads = num_value_heads
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self.dropout = dropout if training else 0.0
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self.scale = scale
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self.attention_ops = attention_ops
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self.attention_bias = attention_bias
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self.outputs_handler = outputs_handler
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def _attn(
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self,
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batch_size: int,
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seq_len: int,
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q: torch.Tensor,
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k: torch.Tensor,
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v: torch.Tensor,
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attention_bias: Optional[xop.AttentionBias] = None,
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attention_ops: Optional[xop.AttentionOp] = None,
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**kwargs
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):
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use_cache = kwargs.get("use_cache", False)
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kv_cache = kwargs.get("layer_past", kwargs.get("past_key_value", kwargs.get("kv_cache", None)))
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q = q.view(batch_size, seq_len, self.num_query_heads, -1)
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k = k.view(batch_size, seq_len, self.num_key_heads, -1)
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v = v.view(batch_size, seq_len, self.num_value_heads, -1)
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if kv_cache is not None:
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k_cache, v_cache = kv_cache
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k = torch.cat((k_cache, k), dim=1)
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v = torch.cat((v_cache, v), dim=1)
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present = None
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if use_cache:
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present = (k, v)
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attn_ops = attention_ops if attention_ops is not None else self.attention_ops
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attn_bias = attention_bias if attention_bias is not None else self.attention_bias
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attn_out = attention(
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query=q,
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key=k,
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value=v,
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attention_ops=attn_ops,
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attention_bias=attn_bias if kv_cache is None else None,
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p=self.dropout,
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scale=self.scale
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).view(batch_size, seq_len, -1)
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return attn_out, present
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def forward(
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self,
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hidden_states: torch.Tensor,
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**kwargs
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):
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bsz, seq_len = hidden_states.shape[:2]
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q, k, v = self.qkv_proj(hidden_states).chunk(chunks=3, dim=-1)
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attn_out, present = self._attn(bsz, seq_len, q, k, v, **kwargs)
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out = self.out_proj(attn_out)
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outputs = (out, present, None)
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if self.outputs_handler:
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outputs = self.outputs_handler(outputs)
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return outputs
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class FusedAttentionWithRoPE(FusedAttention):
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def __init__(
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self,
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qkv_proj: nn.Linear,
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out_proj: nn.Linear,
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cos_sin_cache: torch.Tensor,
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num_query_heads: int,
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num_key_heads: int,
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num_value_heads: int,
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dropout: float = 0.0,
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scale: Optional[float] = None,
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attention_ops: Optional[xop.AttentionOp] = (xop.fmha.cutlass.FwOp(), None),
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attention_bias: Optional[xop.AttentionBias] = LowerTriangularMask(),
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outputs_handler: Optional[Callable] = None,
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training: bool = False,
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):
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super(FusedAttentionWithRoPE, self).__init__(
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qkv_proj=qkv_proj,
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out_proj=out_proj,
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num_query_heads=num_query_heads,
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num_key_heads=num_key_heads,
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num_value_heads=num_value_heads,
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dropout=dropout,
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scale=scale,
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attention_ops=attention_ops,
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attention_bias=attention_bias,
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outputs_handler=outputs_handler,
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training=training
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)
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self.register_buffer("cos_sin_cache", cos_sin_cache, persistent=False)
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def forward(
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self,
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hidden_states: torch.Tensor,
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**kwargs
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):
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position_ids = kwargs.get("position_ids", None)
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bsz, seq_len = hidden_states.shape[:2]
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q, k, v = self.qkv_proj(hidden_states).chunk(chunks=3, dim=-1)
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q = q.view(bsz * seq_len, -1)
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k = k.view(bsz * seq_len, -1)
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if position_ids is not None:
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position_ids = position_ids.view(-1).to(q.device)
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vllm_pos_encoding_ops.rotary_embedding_neox(
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position_ids,
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q,
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k,
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q.shape[-1] // self.num_query_heads,
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self.cos_sin_cache,
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)
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attn_out, present = self._attn(bsz, seq_len, q, k, v, **kwargs)
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out = self.out_proj(attn_out)
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outputs = (out, present, None)
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if self.outputs_handler:
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outputs = self.outputs_handler(outputs)
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return outputs
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class FusedAttentionWithALiBi(FusedAttention):
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def __init__(
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self,
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qkv_proj: nn.Linear,
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out_proj: nn.Linear,
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alibi_slopes: torch.Tensor,
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num_query_heads: int,
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num_key_heads: int,
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num_value_heads: int,
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dropout: float = 0.0,
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scale: Optional[float] = None,
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attention_ops: Optional[xop.AttentionOp] = (xop.fmha.flash.FwOp(), None),
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outputs_handler: Optional[Callable] = None,
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training: bool = False,
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):
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super(FusedAttentionWithALiBi, self).__init__(
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qkv_proj=qkv_proj,
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out_proj=out_proj,
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num_query_heads=num_query_heads,
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num_key_heads=num_key_heads,
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num_value_heads=num_value_heads,
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dropout=dropout,
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scale=scale,
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attention_ops=attention_ops,
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outputs_handler=outputs_handler,
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training=training
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)
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self.register_buffer("alibi_slopes", alibi_slopes, persistent=False)
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def _build_attention_bias(self, hidden_states: torch.Tensor): # adopt from vllm
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bsz, seq_len = hidden_states.shape[:2]
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bias = torch.arange(seq_len)
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bias = bias[None, :] - bias[:, None]
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bias = bias.to(hidden_states.device)
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# When using custom attention bias, xformers requires the bias to
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# be sliced from a tensor whose length is a multiple of 8.
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padded_len = (seq_len + 7) // 8 * 8
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bias = torch.empty(
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self.num_query_heads,
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padded_len,
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padded_len,
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device=self.alibi_slopes.device,
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)[:, :seq_len, :seq_len].copy_(bias)
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bias.mul_(self.alibi_slopes[:, None, None])
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bias = LowerTriangularMaskWithTensorBias(bias.unsqueeze(0).repeat(bsz, 1, 1, 1))
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return bias
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def forward(
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self,
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hidden_states: torch.Tensor,
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**kwargs
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):
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bsz, seq_len = hidden_states.shape[:2]
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q, k, v = self.qkv_proj(hidden_states).chunk(chunks=3, dim=-1)
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kv_cache = kwargs.get("layer_past", kwargs.get("past_key_value", kwargs.get("kv_cache", None)))
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attn_bias = self._build_attention_bias(hidden_states) if kv_cache is None else None
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attn_out, present = self._attn(bsz, seq_len, q, k, v, attention_bias=attn_bias, **kwargs)
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out = self.out_proj(attn_out)
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outputs = (out, present, None)
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if self.outputs_handler:
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outputs = self.outputs_handler(outputs)
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return outputs
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