691 lines
21 KiB
Python
691 lines
21 KiB
Python
from dataclasses import dataclass
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from typing import List, Optional, Tuple, Union
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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.utils.import_utils import SYSTEM
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from text_generation_server.utils.log import log_once
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from text_generation_server.utils.weights import Weight, Weights, WeightsLoader
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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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try:
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major, _minor = torch.cuda.get_device_capability()
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has_sm_8_0 = major >= 8
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except Exception:
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has_sm_8_0 = False
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GPTQ_MARLIN_BITS = [4, 8]
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GPTQ_MARLIN_GROUP_SIZES = [-1, 32, 64, 128]
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MARLIN_TILE_SIZE = 16
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class MarlinWeightsLoader(WeightsLoader):
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"""Loader for Marlin-quantized weights."""
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def __init__(self, *, bits: int, is_marlin_24: bool):
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self.bits = bits
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self.is_marlin_24 = is_marlin_24
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def get_weights(self, weights: "Weights", prefix: str):
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"""
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Get weights at the given prefix and apply without tensor paralllism.
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"""
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is_marlin_24 = getattr(self, "gptq_checkpoint_format", None) == "marlin_24"
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if is_marlin_24:
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try:
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B = weights.get_tensor(f"{prefix}.B_24")
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except RuntimeError:
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raise RuntimeError(
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"Cannot load `marlin` 2:4 sparsity weight, make sure the model is already quantized."
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)
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B_meta = weights.get_tensor(f"{prefix}.B_meta")
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s = weights.get_tensor(f"{prefix}.s")
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weight = GPTQMarlin24Weight(B=B, B_meta=B_meta, s=s, bits=self.bits)
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else:
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try:
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B = weights.get_tensor(f"{prefix}.B")
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except RuntimeError:
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raise RuntimeError(
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"Cannot load `marlin` weight, make sure the model is already quantized."
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)
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s = weights.get_tensor(f"{prefix}.s")
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weight = MarlinWeight(B=B, s=s)
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return weight
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def get_weights_col_packed(
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self,
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weights: Weights,
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prefix: str,
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block_sizes: Union[int, List[int]],
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):
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if self.is_marlin_24:
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B = weights.get_packed_sharded(
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f"{prefix}.B_24", dim=1, block_sizes=block_sizes
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)
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B_meta = weights.get_packed_sharded(
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f"{prefix}.B_meta", dim=1, block_sizes=block_sizes
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)
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s = weights.get_packed_sharded(
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f"{prefix}.s", dim=1, block_sizes=block_sizes
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)
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weight = GPTQMarlin24Weight(B=B, B_meta=B_meta, s=s, bits=self.bits)
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else:
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B = weights.get_packed_sharded(
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f"{prefix}.B", dim=1, block_sizes=block_sizes
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)
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s = weights.get_packed_sharded(
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f"{prefix}.s", dim=1, block_sizes=block_sizes
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)
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weight = MarlinWeight(B=B, s=s)
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return weight
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def get_multi_weights_col(self, weights: Weights, prefixes: List[str], dim: int):
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if self.is_marlin_24:
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try:
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B = torch.cat(
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[weights.get_sharded(f"{p}.B_24", dim=1) for p in prefixes], dim=1
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)
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except RuntimeError:
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raise RuntimeError(
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f"Cannot load `marlin` weight, make sure the model is already quantized"
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)
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B_meta = torch.cat(
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[weights.get_sharded(f"{p}.B_meta", dim=1) for p in prefixes], dim=1
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)
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s = torch.cat(
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[weights.get_sharded(f"{p}.s", dim=1) for p in prefixes], dim=1
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)
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weight = GPTQMarlin24Weight(B=B, B_meta=B_meta, s=s, bits=self.bits)
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else:
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try:
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B = torch.cat(
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[weights.get_sharded(f"{p}.B", dim=1) for p in prefixes], dim=1
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)
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except RuntimeError:
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raise RuntimeError(
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f"Cannot load `marlin` weight, make sure the model is already quantized"
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)
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s = torch.cat(
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[weights.get_sharded(f"{p}.s", dim=1) for p in prefixes], dim=1
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)
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weight = MarlinWeight(B=B, s=s)
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return weight
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def get_weights_row(self, weights: Weights, prefix: str):
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if self.is_marlin_24:
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try:
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B = weights.get_sharded(f"{prefix}.B_24", dim=0)
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except RuntimeError:
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raise RuntimeError(
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"Cannot load `marlin` 2:4 sparsity weight, make sure the model is already quantized."
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)
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B_meta = weights.get_sharded(f"{prefix}.B_meta", dim=0)
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num_groups = weights._get_slice(f"{prefix}.s").get_shape()[0]
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if num_groups == 1:
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# The number of groups is 1 when groupsize == -1. share
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# scales between all shards in this case.
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s = weights.get_tensor(f"{prefix}.s")
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else:
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s = weights.get_sharded(f"{prefix}.s", dim=0)
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weight = GPTQMarlin24Weight(B=B, B_meta=B_meta, s=s, bits=self.bits)
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else:
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try:
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B = weights.get_sharded(f"{prefix}.B", dim=0)
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except RuntimeError:
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raise RuntimeError(
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"Cannot load `marlin` weight, make sure the model is already quantized."
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)
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num_groups = weights._get_slice(f"{prefix}.s").get_shape()[0]
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if num_groups == 1:
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# The number of groups is 1 when groupsize == -1. share
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# scales between all shards in this case.
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s = weights.get_tensor(f"{prefix}.s")
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else:
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s = weights.get_sharded(f"{prefix}.s", dim=0)
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weight = MarlinWeight(B=B, s=s)
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return weight
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def can_use_gptq_marlin(
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*, bits: int, groupsize: int, quant_method: str, quantize: str, sym: bool
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) -> bool:
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return (
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SYSTEM == "cuda"
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and marlin_kernels is not None
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and has_sm_8_0
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and quantize == "gptq"
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and quant_method == "gptq"
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and bits in GPTQ_MARLIN_BITS
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and groupsize in GPTQ_MARLIN_GROUP_SIZES
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and sym
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)
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def _check_marlin_kernels():
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if not (SYSTEM == "cuda" and has_sm_8_0):
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raise NotImplementedError(
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"Using quantized Marlin models requires a GPU with CUDA capability 8.0 or later."
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)
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if marlin_kernels is None:
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raise NotImplementedError(
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"marlin is not installed, install it with: pip install server/marlin"
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)
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def _check_valid_shape(in_features: int, out_features: int):
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if (in_features % 128 != 0 or out_features % 64 != 0) and (
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in_features % 64 != 0 or out_features % 128 != 0
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):
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raise ValueError(
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f"The GPTQ Marlin kernel does not have a valid thread configuration for weight matrix with shape ({out_features}, {in_features})."
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" The shape elements must be divisible by (128, 64) or (64, 128)."
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)
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# https://github.com/IST-DASLab/marlin/blob/2f6d7c10e124b3c5fa29ff8d77d568bd7af3274c/marlin/__init__.py#L40C1-L68C54
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def _get_perms() -> Tuple[List[int], List[int]]:
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scale_perm = []
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for i in range(8):
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scale_perm.extend([i + 8 * j for j in range(8)])
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scale_perm_single = []
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for i in range(4):
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scale_perm_single.extend([2 * i + j for j in [0, 1, 8, 9, 16, 17, 24, 25]])
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return scale_perm, scale_perm_single
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_scale_perm, _scale_perm_single = _get_perms()
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def permute_scales(scales: torch.Tensor):
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out_features = scales.shape[1]
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if scales.shape[0] == 1:
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scales = scales.reshape((-1, len(_scale_perm_single)))[:, _scale_perm_single]
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else:
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scales = scales.reshape((-1, len(_scale_perm)))[:, _scale_perm]
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return scales.reshape((-1, out_features)).contiguous()
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@dataclass
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class GPTQMarlinWeight(Weight):
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"""
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Repacked GPTQ Marlin weights.
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"""
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qweight: torch.Tensor
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scales: torch.Tensor
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g_idx: torch.Tensor
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perm: torch.Tensor
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bits: int
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is_full_k: bool
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def __post_init__(self):
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assert self.qweight.dtype == torch.int32
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assert self.scales.dtype == torch.float16
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assert self.g_idx.dtype == torch.int32
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assert self.perm.dtype == torch.int32
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def get_linear(self, bias: torch.Tensor):
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return GPTQMarlinLinear(
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weight=self,
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bias=bias,
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)
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def repack_gptq_for_marlin(
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*,
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qweight: torch.Tensor,
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scales: torch.Tensor,
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g_idx: torch.Tensor,
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bits: int,
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desc_act: bool,
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groupsize: int,
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sym: bool,
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sharded_infeatures: bool,
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) -> GPTQMarlinWeight:
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"""Convert GPTQ weights to a layout that's compatible with GPTQ-Marlin kernels."""
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_check_marlin_kernels()
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assert marlin_kernels is not None
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if bits not in GPTQ_MARLIN_BITS:
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supported_bits = ", ".join(str(b) for b in GPTQ_MARLIN_BITS)
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raise RuntimeError(
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f"Repacking {bits}-bit GPTQ weights as Marlin is not supported, must be one of: {supported_bits}"
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)
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if groupsize not in GPTQ_MARLIN_GROUP_SIZES:
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supported_sizes = ", ".join(str(b) for b in GPTQ_MARLIN_GROUP_SIZES)
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raise RuntimeError(
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f"Repacking GPTQ weights with group size {groupsize} as Marlin is not supported, must be one of: {supported_sizes}"
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)
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if not sym:
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raise RuntimeError(
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"Repacking GPTQ weights with asymmetric quantization as Marlin is not supported."
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)
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weights_per_int = 32 // bits
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in_features = qweight.shape[0] * weights_per_int
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out_features = qweight.shape[1]
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if in_features % groupsize != 0:
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raise ValueError(
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f"Number of input features ({in_features}) not divisible by group size ({groupsize})"
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)
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if desc_act and groupsize != -1:
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perm = torch.argsort(g_idx).to(torch.int)
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g_idx = g_idx[perm]
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else:
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perm = torch.empty(0, dtype=torch.int, device=qweight.device)
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g_idx = 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, bits
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)
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scales = permute_scales(scales)
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is_full_k = not (desc_act and sharded_infeatures)
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return GPTQMarlinWeight(
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qweight=repacked,
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scales=scales,
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g_idx=g_idx,
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perm=perm,
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bits=bits,
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is_full_k=is_full_k,
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)
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class GPTQMarlinLinear(nn.Module):
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"""
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Linear layer for GPTQ weights that were converted for the GPTQ-Marlin
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kernels.
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"""
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def __init__(
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self,
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*,
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weight: GPTQMarlinWeight,
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bias: Optional[torch.Tensor],
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):
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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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in_features = weight.qweight.shape[0] * MARLIN_TILE_SIZE
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out_features = weight.scales.shape[1]
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_check_valid_shape(in_features=in_features, out_features=out_features)
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self.bits = weight.bits
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self.is_full_k = weight.is_full_k
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self.qweight = weight.qweight
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self.scales = weight.scales
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self.g_idx = weight.g_idx
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self.perm = weight.perm
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if bias is not None:
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self.bias = bias
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else:
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self.bias = None
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self.workspace = torch.zeros(
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out_features // 64 * 16, dtype=torch.int, device=weight.qweight.device
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)
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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.gptq_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.g_idx,
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self.perm,
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self.workspace,
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self.bits,
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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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self.is_full_k,
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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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GPTQ_MARLIN_24_MIN_THREAD_N = 128
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GPTQ_MARLIN_24_MIN_THREAD_K = 128
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GPTQ_MARLIN_24_MAX_PARALLEL = 64
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GPTQ_MARLIN_24_SUPPORTED_NUM_BITS = [4, 8]
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GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES = [-1, 128]
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@dataclass
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class GPTQMarlin24Weight:
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"""
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GPTQ-Marlin 2:4 weights.
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Attributes:
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B (torch.Tensor): int4-quantized weights packed into int32.
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B_meta (torch.Tensor): metadata for 2:4 sparsity.
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s (torch.Tensor): float16 scales.
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bits: quantized weight size.
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"""
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B: torch.Tensor
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B_meta: torch.Tensor
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s: torch.Tensor
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bits: int
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def __post_init__(self):
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assert self.B.dtype == torch.int32
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assert self.B_meta.dtype == torch.int16
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assert self.s.dtype == torch.float16
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def get_linear(self, bias: torch.Tensor):
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return GPTQMarlin24Linear(
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weight=self,
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bias=bias,
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)
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class GPTQMarlin24Linear(nn.Module):
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def __init__(self, *, weight: GPTQMarlin24Weight, bias: Optional[torch.Tensor]):
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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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if weight.bits not in GPTQ_MARLIN_BITS:
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supported_bits = ", ".join(str(b) for b in GPTQ_MARLIN_BITS)
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raise RuntimeError(
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f"{weight.bits}-bit GPTQ Sparse 2:4 Marlin is not supported, must be one of: {supported_bits}"
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)
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in_features = weight.B.shape[0] * MARLIN_TILE_SIZE * 2
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out_features = weight.s.shape[1]
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groupsize = -1 if weight.s.shape[0] == 1 else in_features // weight.s.shape[0]
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if groupsize not in GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES:
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supported_sizes = ", ".join(
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str(b) for b in GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES
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)
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raise RuntimeError(
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f"Group size {groupsize} is not supported, must be one of: {supported_sizes}"
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)
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self.bits = weight.bits
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weights_per_int32 = 32 // self.bits
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assert (
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out_features % GPTQ_MARLIN_24_MIN_THREAD_N == 0
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), f"Number of output features ({out_features}) not divisable by {GPTQ_MARLIN_24_MIN_THREAD_N} threads"
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assert (
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out_features % weights_per_int32 == 0
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), f"Number of output features ({out_features}) not divisable by weights per int32 ({weights_per_int32})"
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assert (
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in_features % GPTQ_MARLIN_24_MIN_THREAD_K == 0
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), f"Number of output features ({out_features}) not divisable by {GPTQ_MARLIN_24_MIN_THREAD_K} threads"
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if groupsize != -1 and in_features % groupsize != 0:
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raise ValueError(
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f"Number of input features ({in_features}) not divisable by group size ({groupsize})"
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)
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self.B = weight.B
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self.B_meta = weight.B_meta
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self.s = weight.s
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if bias is not None:
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self.bias = bias
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else:
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self.bias = None
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self.workspace = torch.zeros(
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(out_features // GPTQ_MARLIN_24_MIN_THREAD_N) * GPTQ_MARLIN_24_MAX_PARALLEL,
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dtype=torch.int,
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device=weight.B.device,
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)
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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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C = marlin_kernels.gptq_marlin_24_gemm(
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A.view(-1, A.shape[-1]),
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self.B,
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self.B_meta,
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self.s,
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self.workspace,
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self.bits,
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A.shape[0],
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self.s.shape[1],
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A.shape[1],
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)
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C = C.reshape(A.shape[:-1] + (self.s.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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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,
|
|
bias: Optional[torch.Tensor],
|
|
) -> None:
|
|
super().__init__()
|
|
|
|
_check_marlin_kernels()
|
|
assert marlin_kernels is not None
|
|
|
|
log_once(logger.info, "GPU does not support FP8, using Marlin FP8 kernel")
|
|
|
|
scales = scales.unsqueeze(0)
|
|
if scales.shape[1] == 1:
|
|
out_features, in_features = qweight.shape
|
|
scales = scales.repeat(1, out_features)
|
|
qweight, scales = repack_fp8_for_marlin(qweight, scales)
|
|
|
|
in_features = qweight.shape[0] * MARLIN_TILE_SIZE
|
|
out_features = scales.shape[1]
|
|
_check_valid_shape(in_features=in_features, out_features=out_features)
|
|
|
|
self.qweight = qweight
|
|
self.scales = scales
|
|
self.bias = bias if bias is not None else None
|
|
|
|
self.workspace = torch.zeros(
|
|
out_features // 64 * 16, dtype=torch.int, device=qweight.device
|
|
)
|
|
|
|
@classmethod
|
|
def from_unquant(cls, weight, bias, dtype):
|
|
qweight, scales = fp8_quantize(weight)
|
|
return cls(qweight=qweight, scales=scales.to(dtype), bias=bias)
|
|
|
|
@classmethod
|
|
def from_fp8(cls, weight, scale, _input_scale, bias, dtype):
|
|
return cls(qweight=weight, scales=scale.to(dtype), bias=bias)
|
|
|
|
def forward(self, A: torch.Tensor) -> torch.Tensor:
|
|
assert marlin_kernels is not None
|
|
|
|
A_flat = A.view(-1, A.shape[-1])
|
|
C = marlin_kernels.fp8_marlin_gemm(
|
|
A_flat,
|
|
self.qweight,
|
|
self.scales,
|
|
self.workspace,
|
|
8,
|
|
A_flat.shape[0],
|
|
self.scales.shape[1],
|
|
A_flat.shape[1],
|
|
)
|
|
C = C.reshape(A.shape[:-1] + (self.scales.shape[1],))
|
|
|
|
if self.bias is not None:
|
|
C += self.bias
|
|
|
|
return C
|
|
|
|
|
|
def pack_fp8_as_int32(fp8_tensor: torch.Tensor) -> torch.Tensor:
|
|
"""
|
|
Repack FP8 weights to gptq format (packed int32 elements).
|
|
"""
|
|
assert fp8_tensor.dtype == torch.float8_e4m3fn
|
|
|
|
if fp8_tensor.shape[0] % 4 != 0:
|
|
raise ValueError(
|
|
f"Leading tensor dimension is not divisable by 4: {fp8_tensor.shape[0]}"
|
|
)
|
|
|
|
# Reshape to prepare for packing
|
|
reshaped = fp8_tensor.reshape(-1, 4, *fp8_tensor.shape[1:])
|
|
|
|
# Convert fp8 to uint8 (byte) representation
|
|
byte_tensor = reshaped.view(torch.uint8)
|
|
|
|
# Pack 4 uint8 values into one int32
|
|
packed = torch.zeros(
|
|
fp8_tensor.shape[0] // 4,
|
|
fp8_tensor.shape[1],
|
|
dtype=torch.int32,
|
|
device=fp8_tensor.device,
|
|
)
|
|
|
|
for i in range(4):
|
|
packed.bitwise_or_(byte_tensor[:, i].to(torch.int32) << i * 8)
|
|
|
|
return packed
|
|
|
|
|
|
def repack_fp8_for_marlin(weight: torch.Tensor, scales: torch.Tensor):
|
|
"""
|
|
Repack FP8 tensor for GPTQ-Marlin.
|
|
"""
|
|
|
|
out_features, in_features = weight.shape
|
|
|
|
# Torch linear layers weights with shape [out_features, in_features],
|
|
# GPTQ-quantized weights use [in_feateres/pack_factor, in_features],
|
|
# so transpose before packing.
|
|
qweight = pack_fp8_as_int32(weight.t())
|
|
|
|
perm = torch.empty(0, dtype=torch.int, device=qweight.device)
|
|
repacked = marlin_kernels.gptq_marlin_repack(
|
|
qweight, perm, in_features, out_features, 8
|
|
)
|
|
|
|
scales = permute_scales(scales)
|
|
|
|
return repacked, scales
|
|
|
|
|
|
@dataclass
|
|
class MarlinWeight(Weight):
|
|
"""
|
|
Marlin weights.
|
|
|
|
Attributes:
|
|
B (torch.Tensor): int4-quantized weights packed into int32.
|
|
s (torch.Tensor): bfloat16/float16 scales.
|
|
"""
|
|
|
|
B: torch.Tensor
|
|
s: torch.Tensor
|
|
|
|
def __post_init__(self):
|
|
assert self.B.dtype == torch.int32
|
|
assert self.s.dtype in [torch.float16, torch.bfloat16]
|
|
|
|
def get_linear(self, bias: torch.Tensor):
|
|
return MarlinLinear(weight=self, bias=bias)
|
|
|
|
|
|
class MarlinLinear(nn.Module):
|
|
def __init__(self, *, weight: MarlinWeight, bias: Optional[torch.Tensor]):
|
|
super().__init__()
|
|
|
|
_check_marlin_kernels()
|
|
assert marlin_kernels is not None
|
|
|
|
in_features = weight.B.shape[0] * MARLIN_TILE_SIZE
|
|
out_features = weight.s.shape[1]
|
|
assert (
|
|
in_features % 128 == 0
|
|
), f"Number of input features ({in_features}) not divisable by 128"
|
|
assert (
|
|
out_features % 256 == 0
|
|
), f"Number of output features ({out_features}) not divisable by 256"
|
|
|
|
groupsize = -1 if weight.s.shape[0] == 1 else in_features // weight.s.shape[0]
|
|
assert groupsize in {
|
|
-1,
|
|
128,
|
|
}, f"Group size must be -1 or 128, was {groupsize}"
|
|
|
|
self.B = weight.B
|
|
self.s = weight.s
|
|
if bias is not None:
|
|
self.bias = bias
|
|
else:
|
|
self.bias = None
|
|
|
|
self.workspace = torch.zeros(
|
|
out_features // 64 * 16, dtype=torch.int, device=weight.B.device
|
|
)
|
|
|
|
def forward(self, A: torch.Tensor) -> torch.Tensor:
|
|
assert marlin_kernels is not None
|
|
|
|
C = marlin_kernels.marlin_gemm(
|
|
A.view(-1, A.shape[-1]),
|
|
self.B,
|
|
self.s,
|
|
self.workspace,
|
|
A.shape[0],
|
|
self.s.shape[1],
|
|
A.shape[1],
|
|
)
|
|
C = C.reshape(A.shape[:-1] + (self.s.shape[1],))
|
|
|
|
if self.bias is not None:
|
|
C += self.bias
|
|
|
|
return C
|