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252 lines
10 KiB
Python
252 lines
10 KiB
Python
"""
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TINT4Linear — INT4 weight-only Linear with LoRA + QuaRot.
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Backed by torchao Int4PlainInt32Tensor. Activations stay FP16.
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LoRA entries are injected at forward time — no kernel modification needed.
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QuaRot activation rotation is applied online; LoRA A/w2 matrices are
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rotated into the QuaRot basis inside forward() automatically unless
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_lora_prerotated is set (for callers that pre-rotate at injection time).
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"""
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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 torchao.quantization.quantize_.workflows.int4.int4_plain_int32_tensor import (
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Int4PlainInt32Tensor,
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)
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class TINT4Linear(nn.Module):
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"""
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INT4 weight-only linear layer.
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Stores raw int32 qdata + fp16 scales + int8 zero-points.
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Int4PlainInt32Tensor is lazily constructed and rebuilt on device change.
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Slots (set externally by loaders):
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_use_quarot: bool — enable activation Hadamard rotation
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_group_size: int — QuaRot group size
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_hadamard_H: Tensor|None — normalized Hadamard matrix
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_tint4_lora_entries: dict|None — {lora_name: [(type, ...), ...]}
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_lora_prerotated: bool — True if caller already rotated A/w2
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into QuaRot basis (default False).
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When False, rotation is done in forward().
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"""
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def __init__(self, in_features: int, out_features: int,
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qdata: torch.Tensor, scale: torch.Tensor,
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zp: torch.Tensor, block_size: tuple,
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bias: torch.Tensor | None = None):
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"""
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Initialize TINT4Linear from raw torchao INT4 tensors.
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Args:
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in_features: Input feature dimension.
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out_features: Output feature dimension.
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qdata: Packed int32 quantized weight data (out_f, in_f // 8).
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scale: Per-block fp16 scales (n_blocks, out_f).
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zp: Per-block int8 zero-points (n_blocks, out_f).
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block_size: (b0, b1) tuple defining quantization block shape.
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bias: Optional bias tensor (out_f,).
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"""
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super().__init__()
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self.in_features = in_features
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self.out_features = out_features
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if bias is not None:
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self.bias = nn.Parameter(bias)
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else:
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self.register_parameter('bias', None)
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# ★ FIX 1: 使用 register_buffer 而不是直接用 = 赋值
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# 好处：model.to(device) / state_dict() / load_state_dict() 能自动管理
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# 旧代码：self._qdata = qdata → PyTorch 不知道这是持久化数据
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self.register_buffer('_qdata', qdata)
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self.register_buffer('_scale', scale)
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self.register_buffer('_zp', zp)
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self._block_size = block_size
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self._qt: Int4PlainInt32Tensor | None = None
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# QuaRot (optional)
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self._use_quarot: bool = False
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self._group_size: int = 128
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self._hadamard_H: torch.Tensor | None = None
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# LoRA entries slot
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self._tint4_lora_entries: dict | None = None
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# Pre-rotation flag
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self._lora_prerotated: bool = False
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@property
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def weight(self) -> Int4PlainInt32Tensor:
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"""Lazily construct and return the Int4PlainInt32Tensor view."""
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if self._qt is None:
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self._qt = Int4PlainInt32Tensor(
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self._qdata, self._scale, self._zp,
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self._block_size, [self.out_features, self.in_features],
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)
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return self._qt
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@weight.setter
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def weight(self, value: Int4PlainInt32Tensor) -> None:
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"""Replace the underlying Int4PlainInt32Tensor."""
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if isinstance(value, Int4PlainInt32Tensor):
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self._qt = value
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def _rotate_into_quarot(self, tensor: torch.Tensor) -> torch.Tensor:
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"""
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Rotate a LoRA A or w2 matrix into the QuaRot Hadamard basis.
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Skipped when _lora_prerotated is True (caller already rotated).
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Returns unchanged tensor if QuaRot is disabled or dimensions
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are not divisible by _group_size.
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"""
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if self._lora_prerotated:
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return tensor
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if not self._use_quarot or self._hadamard_H is None:
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return tensor
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if tensor.shape[1] % self._group_size != 0:
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return tensor
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Ht = self._hadamard_H.T.to(device=tensor.device, dtype=tensor.dtype)
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ng = tensor.shape[1] // self._group_size
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return (tensor.reshape(tensor.shape[0], ng, self._group_size)
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@ Ht).reshape(tensor.shape[0], -1)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""
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Forward pass with optional QuaRot activation rotation and LoRA injection.
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Args:
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x: Input tensor, shape (..., in_features).
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Returns:
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Output tensor, shape (..., out_features).
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Raises:
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ValueError: If activation dim is not divisible by QuaRot group_size,
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or if LoRA output shapes are incompatible.
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"""
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x_flat = x.reshape(-1, x.shape[-1])
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# QuaRot activation rotation (online)
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if self._use_quarot and self._hadamard_H is not None:
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if x_flat.shape[-1] % self._group_size != 0:
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raise ValueError(
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f"activation dim {x_flat.shape[-1]} not divisible "
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f"by QuaRot group_size {self._group_size}"
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)
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H_dev = self._hadamard_H.to(device=x.device, dtype=x.dtype)
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n_groups = x_flat.shape[-1] // self._group_size
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x_g = x_flat.view(-1, n_groups, self._group_size)
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x_flat = torch.matmul(x_g, H_dev).view(-1, n_groups * self._group_size)
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dev = x.device
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# Rebuild Int4PlainInt32Tensor on device change
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if self._qt is None or self._qt.device != dev:
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self._qt = Int4PlainInt32Tensor(
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self._qdata.to(dev), self._scale.to(dev), self._zp.to(dev),
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self._block_size, [self.out_features, self.in_features],
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)
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out = F.linear(x_flat, self._qt, None)
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# LoRA forward injection
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entries = self._tint4_lora_entries
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if entries is not None and entries:
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cd = (x.dtype if x.dtype in (torch.float16, torch.bfloat16)
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else torch.float16)
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for lora_entries in entries.values():
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for e in lora_entries:
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# ── LoKr path ──
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# ★ FIX 2: 用 torch.kron 替代 repeat_interleave
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# 旧方案要求 w2 维度能被 factor 整除（例如 64÷60=不整除→报错）
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# 新方案 kron 展开 + pad/trim 到目标维度，兼容任意分解
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if isinstance(e[0], str) and e[0] == "lokr":
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_, w1, w2, mult, factor = e[:5]
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sl = e[5] if len(e) > 5 else None
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se = e[6] if len(e) > 6 else None
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# QuaRot: 先旋转 w2（小矩阵，转发负担小）
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# 数学上等价于旋转完整 kron 展开（Hadamard 是分块对角的）
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w2 = self._rotate_into_quarot(w2)
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w1d = w1.to(device=dev, dtype=cd)
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w2d = w2.to(device=dev, dtype=cd)
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# kron 展开为完整 delta（代替 repeat_interleave）
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dw = torch.kron(w1d, w2d)
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# pad/trim 到层实际维度
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# QKV 融合层：kron 覆盖 1 个 head，模块持有 3 个
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if dw.shape[0] < self.out_features:
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dw = dw.repeat(
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self.out_features // dw.shape[0], 1)
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elif dw.shape[0] > self.out_features:
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dw = dw[:self.out_features, :]
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if dw.shape[1] < self.in_features:
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dw = dw.repeat(
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1, self.in_features // dw.shape[1])
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elif dw.shape[1] > self.in_features:
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dw = dw[:, :self.in_features]
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dw = dw.contiguous().mul_(mult)
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lo = x_flat.to(cd) @ dw.T
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if sl is not None:
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if lo.shape[1] != (se - sl):
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raise ValueError(
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f"LoKr output width {lo.shape[1]} "
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f"doesn't match target slice width "
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f"{se - sl}"
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)
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out[:, sl:se] += lo
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elif lo.shape == out.shape:
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out += lo
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else:
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raise ValueError(
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f"LoKr output shape {lo.shape} "
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f"incompatible with {out.shape}"
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)
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continue
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# Standard LoRA path
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A, B, mult = e[:3]
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sl = e[3] if len(e) > 3 else None
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se = e[4] if len(e) > 4 else None
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A = self._rotate_into_quarot(A)
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Ad = A.to(device=dev, dtype=cd)
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Bd = B.to(device=dev, dtype=cd)
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lo = (x_flat.to(cd) @ Ad.T) @ Bd.T * mult
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if sl is not None:
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if lo.shape[1] != (se - sl):
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raise ValueError(
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f"LoRA output width {lo.shape[1]} "
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f"doesn't match target slice width "
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f"{se - sl}"
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)
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out[:, sl:se] += lo
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elif lo.shape[1] == out.shape[1]:
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out += lo
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else:
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raise ValueError(
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f"LoRA output shape {lo.shape} "
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f"incompatible with {out.shape}"
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)
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if self.bias is not None:
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out += self.bias.to(device=dev, dtype=out.dtype)
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return out.reshape(*x.shape[:-1], out.shape[-1])
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def release(self) -> None:
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"""Drop cached Int4PlainInt32Tensor to free VRAM."""
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self._qt = None
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def __del__(self):
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"""Cleanup raw tensor references on deletion."""
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self._qdata = None
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self._scale = None
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self._zp = None
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self._qt = None
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self._tint4_lora_entries = None
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