148 lines
4.0 KiB
Python
148 lines
4.0 KiB
Python
from typing import Optional
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import torch
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import torch.nn as nn
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from loguru import logger
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from text_generation_server.layers.fp8 import fp8_quantize
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from text_generation_server.layers.marlin.gptq import _check_valid_shape
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from text_generation_server.layers.marlin.util import (
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_check_marlin_kernels,
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permute_scales,
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)
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from text_generation_server.utils.log import log_once
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try:
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import marlin_kernels
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except ImportError:
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marlin_kernels = None
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MARLIN_TILE_SIZE = 16
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class GPTQMarlinFP8Linear(nn.Module):
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"""
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FP8 GPTQ-Marlin linear layer.
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"""
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def __init__(
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self,
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qweight: torch.Tensor,
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scales: torch.Tensor,
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bias: Optional[torch.Tensor],
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) -> None:
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super().__init__()
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_check_marlin_kernels()
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assert marlin_kernels is not None
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log_once(logger.info, "GPU does not support FP8, using Marlin FP8 kernel")
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scales = scales.unsqueeze(0)
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if scales.shape[1] == 1:
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out_features, in_features = qweight.shape
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scales = scales.repeat(1, out_features)
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qweight, scales = repack_fp8_for_marlin(qweight, scales)
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in_features = qweight.shape[0] * MARLIN_TILE_SIZE
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out_features = scales.shape[1]
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_check_valid_shape(in_features=in_features, out_features=out_features)
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self.qweight = qweight
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self.scales = scales
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self.bias = bias if bias is not None else None
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self.workspace = torch.zeros(
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out_features // 64 * 16, dtype=torch.int, device=qweight.device
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)
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@classmethod
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def from_unquant(cls, weight, bias, dtype):
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qweight, scales = fp8_quantize(weight)
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return cls(qweight=qweight, scales=scales.to(dtype), bias=bias)
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@classmethod
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def from_fp8(
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cls,
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weight: torch.Tensor,
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scale: torch.Tensor,
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bias: torch.Tensor,
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dtype: torch.dtype,
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**kwargs,
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):
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return cls(qweight=weight, scales=scale.to(dtype), bias=bias)
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def forward(self, A: torch.Tensor) -> torch.Tensor:
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assert marlin_kernels is not None
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A_flat = A.view(-1, A.shape[-1])
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C = marlin_kernels.fp8_marlin_gemm(
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A_flat,
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self.qweight,
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self.scales,
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self.workspace,
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8,
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A_flat.shape[0],
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self.scales.shape[1],
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A_flat.shape[1],
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)
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C = C.reshape(A.shape[:-1] + (self.scales.shape[1],))
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if self.bias is not None:
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C += self.bias
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return C
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def pack_fp8_as_int32(fp8_tensor: torch.Tensor) -> torch.Tensor:
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"""
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Repack FP8 weights to gptq format (packed int32 elements).
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"""
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assert fp8_tensor.dtype == torch.float8_e4m3fn
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if fp8_tensor.shape[0] % 4 != 0:
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raise ValueError(
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f"Leading tensor dimension is not divisable by 4: {fp8_tensor.shape[0]}"
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)
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# Reshape to prepare for packing
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reshaped = fp8_tensor.reshape(-1, 4, *fp8_tensor.shape[1:])
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# Convert fp8 to uint8 (byte) representation
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byte_tensor = reshaped.view(torch.uint8)
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# Pack 4 uint8 values into one int32
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packed = torch.zeros(
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fp8_tensor.shape[0] // 4,
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fp8_tensor.shape[1],
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dtype=torch.int32,
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device=fp8_tensor.device,
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)
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for i in range(4):
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packed.bitwise_or_(byte_tensor[:, i].to(torch.int32) << i * 8)
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return packed
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def repack_fp8_for_marlin(weight: torch.Tensor, scales: torch.Tensor):
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"""
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Repack FP8 tensor for GPTQ-Marlin.
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"""
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out_features, in_features = weight.shape
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# Torch linear layers weights with shape [out_features, in_features],
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# GPTQ-quantized weights use [in_feateres/pack_factor, in_features],
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# so transpose before packing.
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qweight = pack_fp8_as_int32(weight.t())
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perm = torch.empty(0, dtype=torch.int, device=qweight.device)
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repacked = marlin_kernels.gptq_marlin_repack(
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qweight, perm, in_features, out_features, 8
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)
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scales = permute_scales(scales)
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return repacked, scales
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