384 lines
12 KiB
Python
384 lines
12 KiB
Python
from typing import Optional, Tuple
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch import Tensor
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def repeat_kv(x: Tensor, n_rep: int) -> Tensor:
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"""
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Repeat k times along the dimension for attention heads.
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Args:
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x (Tensor): The input tensor.
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n_rep (int): The number of repetitions.
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Returns:
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Tensor: The repeated tensor.
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"""
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bs, slen, n_heads, head_dim = x.shape
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if n_rep == 1:
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return x
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return (
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x[:, :, :, None, :]
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.expand(bs, slen, n_heads, n_rep, head_dim)
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.reshape(bs, slen, n_heads * n_rep, head_dim)
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)
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def get_rotary_emb(
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dim: int,
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max_len: int,
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base: float = 10000,
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device: Optional[torch.device] = None,
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) -> Tuple[Tensor, Tensor]:
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"""
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Get the rotary embedding for the given dimension and maximum length.
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Args:
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dim (int): The dimension of the input.
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max_len (int): The maximum length of the input.
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base (float, optional): The base for the frequency. Defaults to 10000.
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device (optional): The device to create tensors on. Defaults to None.
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Returns:
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Tensor: The rotary embedding tensor.
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"""
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theta = base ** (-torch.arange(0, dim, 2, dtype=torch.float64, device=device) / dim)
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t = torch.arange(0, max_len, dtype=torch.float64, device=device)
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freqs = torch.outer(t, theta)
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return torch.cos(freqs).float(), torch.sin(freqs).float()
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def apply_rotary_emb(x: torch.Tensor, rotary_emb: Tuple[Tensor, Tensor]) -> Tensor:
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"""
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Apply rotary embedding to the input tensor using cos/sin form.
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Args:
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x (Tensor): The input tensor (shape [..., seq_len, dim]).
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rotary_emb (Tuple[Tensor, Tensor]): The rotary embedding (shape [seq_len, dim//2]).
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Returns:
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Tensor: The output tensor (rotated, same shape as input).
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"""
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dtype = x.dtype
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cos, sin = rotary_emb
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cos = cos.unsqueeze(0).unsqueeze(2) # [1, seq_len, 1, dim//2]
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sin = sin.unsqueeze(0).unsqueeze(2) # [1, seq_len, 1, dim//2]
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x_real = x[..., 0::2] # [batch, seq_len, dim//2]
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x_imag = x[..., 1::2] # [batch, seq_len, dim//2]
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x_real_rot = x_real * cos - x_imag * sin
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x_imag_rot = x_real * sin + x_imag * cos
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x_out = torch.stack([x_real_rot, x_imag_rot], dim=-1) # [batch, seq_len, dim//2, 2]
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x_out = x_out.view(*x_out.shape[:-2], -1) # [batch, seq_len, dim]
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return x_out.to(dtype)
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class RotaryEmbedding(nn.Module):
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def __init__(self, dim: int, max_len: int, base: int = 10000):
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super().__init__()
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self.dim = dim
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self.max_len = max_len
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self.base = base
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self.max_len_cached = None
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self._set_rotary_buffer(self.max_len, None)
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def _set_rotary_buffer(self, max_len: int, device: Optional[torch.device] = None):
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cos_cached, sin_cached = get_rotary_emb(self.dim, max_len, self.base, device)
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self.register_buffer("cos_cached", cos_cached, persistent=False)
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self.register_buffer("sin_cached", sin_cached, persistent=False)
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self.max_len_cached = max_len
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def forward(self, x: Tensor, start_pos: int = 0) -> Tuple[Tensor, Tensor]:
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seq_len = x.size(1)
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if self.max_len_cached < seq_len + start_pos:
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self._set_rotary_buffer(self.max_len_cached * 2, x.device)
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cos = self.cos_cached[start_pos : start_pos + seq_len]
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sin = self.sin_cached[start_pos : start_pos + seq_len]
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return (cos, sin)
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class Linear(nn.Module):
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def __init__(self, in_dim: int, out_dim: int, bias: bool = False):
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super().__init__()
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self.weight = nn.Parameter(torch.empty((out_dim, in_dim)))
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self.bias = nn.Parameter(torch.zeros(out_dim)) if bias else None
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def forward(self, x: Tensor) -> Tensor:
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return F.linear(x, self.weight, self.bias)
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class RMSNorm(nn.Module):
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def __init__(self, dim, norm_eps):
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super().__init__()
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self.weight = nn.Parameter(torch.ones(dim))
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self.normalized_shape = (dim,)
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self.norm_eps = norm_eps
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def forward(self, x: Tensor) -> Tensor:
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rms = F.rms_norm(x.float(), self.normalized_shape, self.weight, self.norm_eps)
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return rms.to(x.dtype)
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class MLP(nn.Module):
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def __init__(self, dim: int, dim_feed_forward: int):
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super().__init__()
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self.up = Linear(dim, dim_feed_forward)
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self.gate = Linear(dim, dim_feed_forward)
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self.down = Linear(dim_feed_forward, dim)
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def forward(self, x: Tensor) -> Tensor:
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gated = self.up(x) * F.silu(self.gate(x))
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out = self.down(gated)
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return out
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class GQA(nn.Module):
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def __init__(
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self,
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dim: int,
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n_heads: int,
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n_kv_heads: int,
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use_qk_norm: bool,
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norm_eps: float,
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use_gated_attention: bool,
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layer_id: int,
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):
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super().__init__()
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assert dim % n_heads == 0
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assert n_heads % n_kv_heads == 0
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self.head_dim = dim // n_heads
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self.layer_id = layer_id
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self.dim = dim
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self.n_heads = n_heads
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self.n_kv_heads = n_kv_heads
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self.n_rep = n_heads // n_kv_heads
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self.use_qk_norm = use_qk_norm
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self.use_gated_attention = use_gated_attention
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self.q_proj = Linear(dim, n_heads * self.head_dim)
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self.k_proj = Linear(dim, n_kv_heads * self.head_dim)
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self.v_proj = Linear(dim, n_kv_heads * self.head_dim)
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self.o_proj = Linear(dim, dim)
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if self.use_qk_norm:
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self.q_norm = RMSNorm(self.head_dim, norm_eps)
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self.k_norm = RMSNorm(self.head_dim, norm_eps)
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if self.use_gated_attention:
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self.gate = Linear(dim, dim)
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def _split_heads(self, x: Tensor, n_heads) -> Tensor:
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batch_size, seq_len, _ = x.shape
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x = x.reshape(batch_size, seq_len, n_heads, self.head_dim)
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return x
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def forward(
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self,
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x: Tensor,
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rotary_emb: Tuple[Tensor, Tensor],
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mask: Tensor = None,
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kv_cache: Optional[Tuple[Tensor, Tensor]] = None,
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start_pos: int = 0,
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) -> Tensor:
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bsz, seq_len, _ = x.size()
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is_causal = mask is None
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# x(bsz, seq_len, n_heads * head_dim) -> (bsz, seq_len, n_heads, head_dim)
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q = self._split_heads(self.q_proj(x), self.n_heads)
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k = self._split_heads(self.k_proj(x), self.n_kv_heads)
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v = self._split_heads(self.v_proj(x), self.n_kv_heads)
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q, k = apply_rotary_emb(q, rotary_emb), apply_rotary_emb(k, rotary_emb)
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if self.use_qk_norm:
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q, k = self.q_norm(q), self.k_norm(k)
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if kv_cache is not None:
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k_cache, v_cache = kv_cache
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# copy to cache
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k_cache[:bsz, start_pos : start_pos + seq_len, self.layer_id] = k
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v_cache[:bsz, start_pos : start_pos + seq_len, self.layer_id] = v
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# get cache
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k = k_cache[:bsz, : start_pos + seq_len, self.layer_id]
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v = v_cache[:bsz, : start_pos + seq_len, self.layer_id]
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k, v = repeat_kv(k, self.n_rep), repeat_kv(v, self.n_rep)
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# (bsz, seq_len, n_heads, head_dim) -> (bsz, n_heads, seq_len, head_dim)
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q, k, v = q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3)
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# (bsz, n_heads, seq_len, head_dim) - > (bsz, seq_len, n_heads*head_dim)
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sdqa_out = (
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F.scaled_dot_product_attention(q, k, v, mask, is_causal=is_causal)
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.permute(0, 2, 1, 3)
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.contiguous()
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.flatten(2)
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)
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if self.use_gated_attention:
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sdqa_out = sdqa_out * F.sigmoid(self.gate(x))
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out = self.o_proj(sdqa_out)
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return out
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class MLA(nn.Module):
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def __init__(
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self,
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dim: int,
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n_heads: int,
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n_kv_heads: int,
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kv_lora_rank: int,
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qk_nope_head_dim: int,
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qk_rope_head_dim: int,
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norm_eps: float,
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use_gated_attention: bool,
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layer_id: int,
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):
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super().__init__()
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self.dim = dim
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self.n_heads = n_heads
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self.n_kv_heads = n_kv_heads
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self.kv_lora_rank = kv_lora_rank
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self.qk_nope_head_dim = qk_nope_head_dim
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self.qk_rope_head_dim = qk_rope_head_dim
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self.head_dim = qk_nope_head_dim + qk_rope_head_dim
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self.layer_id = layer_id
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self.n_rep = n_heads // n_kv_heads
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self.use_gated_attention = use_gated_attention
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self.q_proj = Linear(dim, n_heads * self.head_dim, bias=False)
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self.kv_a_proj = Linear(dim, kv_lora_rank, bias=False)
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self.kv_norm = RMSNorm(kv_lora_rank, norm_eps)
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# KV (k_nope, k_rope, v)
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self.kv_b_proj = Linear(
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kv_lora_rank,
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n_kv_heads * (self.head_dim + qk_rope_head_dim + self.head_dim),
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)
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self.o_proj = Linear(dim, dim, bias=False)
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if use_gated_attention:
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self.gate = Linear(dim, dim, bias=False)
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def forward(
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self,
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x: Tensor,
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rotary_emb: Tuple[Tensor, Tensor],
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mask: Tensor = None,
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kv_cache: Optional[Tuple[Tensor, Tensor]] = None,
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start_pos: int = 0,
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) -> Tensor:
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bsz, seq_len, _ = x.size()
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is_causal = mask is None
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q = self.q_proj(x)
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q = q.view(bsz, seq_len, self.n_heads, self.head_dim)
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kv_compressed = self.kv_a_proj(x)
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kv_compressed = self.kv_norm(kv_compressed)
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kv = self.kv_b_proj(kv_compressed)
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kv = kv.view(bsz, seq_len, self.n_kv_heads, -1)
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k_nope, k_rope, v = torch.split(
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kv, [self.qk_nope_head_dim, self.qk_rope_head_dim, self.head_dim], dim=-1
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)
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q_nope, q_rope = (
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q[..., : self.qk_nope_head_dim],
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q[..., self.qk_rope_head_dim :],
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)
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q_rope = apply_rotary_emb(q_rope, rotary_emb)
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k_rope = apply_rotary_emb(k_rope, rotary_emb)
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q = torch.cat([q_nope, q_rope], dim=-1)
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k = torch.cat([k_nope, k_rope], dim=-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_cache[:bsz, start_pos : start_pos + seq_len, self.layer_id] = k
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v_cache[:bsz, start_pos : start_pos + seq_len, self.layer_id] = v
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k = k_cache[:bsz, : start_pos + seq_len, self.layer_id]
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v = v_cache[:bsz, : start_pos + seq_len, self.layer_id]
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q = q.permute(0, 2, 1, 3)
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k = k.permute(0, 2, 1, 3)
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v = v.permute(0, 2, 1, 3)
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attn_out = F.scaled_dot_product_attention(q, k, v, mask, is_causal=is_causal)
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attn_out = attn_out.permute(0, 2, 1, 3).contiguous().flatten(2)
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if self.use_gated_attention:
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attn_out = attn_out * F.sigmoid(self.gate(x))
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out = self.o_proj(attn_out)
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return out
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class DecoderBlock(nn.Module):
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def __init__(
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self,
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dim: int,
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n_heads: int,
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dim_ffn: int,
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n_kv_heads: int,
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norm_eps: int,
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use_qk_norm: bool,
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use_gated_attention: bool,
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layer_id: int,
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):
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super().__init__()
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self.attention = GQA(
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dim,
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n_heads,
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n_kv_heads,
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use_qk_norm,
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norm_eps,
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use_gated_attention,
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layer_id,
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)
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self.input_norm = RMSNorm(dim, norm_eps)
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self.mlp = MLP(dim, dim_ffn)
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self.post_attention_norm = RMSNorm(dim, norm_eps)
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def forward(
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self,
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x: Tensor,
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rotary_emb: Tuple[Tensor, Tensor],
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attention_mask: Optional[Tensor] = None,
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kv_cache: Optional[Tuple[Tensor, Tensor]] = None,
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start_pos: int = 0,
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) -> Tensor:
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# attention
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attn_output = self.attention(
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self.input_norm(x), rotary_emb, attention_mask, kv_cache, start_pos
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)
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x = attn_output + x
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# feed forward
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x = self.mlp(self.post_attention_norm(x)) + x
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return x
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class Embedding(nn.Module):
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def __init__(self, vocab_size: int, embedding_dim: int):
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super().__init__()
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self.weight = nn.Parameter(torch.empty((vocab_size, embedding_dim)))
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def forward(self, x: Tensor) -> Tensor:
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return F.embedding(x, self.weight)
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