381 lines
14 KiB
Python
381 lines
14 KiB
Python
# Adapted from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/model_executor/layers/quantization/fp8.py
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from __future__ import annotations
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import logging
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from typing import TYPE_CHECKING, Any, Dict, List, Optional
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import torch
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from torch.nn import Module
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from sglang.srt.distributed import get_tensor_model_parallel_world_size
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from sglang.srt.layers.moe import MoeRunner, MoeRunnerBackend, MoeRunnerConfig
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from sglang.srt.layers.moe.moe_runner.triton import TritonMoeQuantInfo
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from sglang.srt.layers.parameter import BlockQuantScaleParameter, ModelWeightParameter
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from sglang.srt.layers.quantization.base_config import (
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FusedMoEMethodBase,
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LinearMethodBase,
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QuantizationConfig,
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QuantizeMethodBase,
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)
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from sglang.srt.layers.quantization.int8_utils import apply_w8a8_block_int8_linear
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from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod
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from sglang.srt.layers.quantization.utils import is_layer_skipped
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from sglang.srt.utils import set_weight_attrs
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if TYPE_CHECKING:
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from sglang.srt.layers.moe.token_dispatcher import (
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CombineInput,
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StandardDispatchOutput,
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)
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ACTIVATION_SCHEMES = ["static", "dynamic"]
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logger = logging.getLogger(__name__)
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class BlockInt8Config(QuantizationConfig):
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"""Config class for INT8."""
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def __init__(
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self,
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is_checkpoint_int8_serialized: bool = False,
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activation_scheme: str = "dynamic",
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ignored_layers: Optional[List[str]] = None,
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weight_block_size: List[int] = None,
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) -> None:
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self.is_checkpoint_int8_serialized = is_checkpoint_int8_serialized
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if is_checkpoint_int8_serialized:
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logger.warning(
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"Detected int8 checkpoint. Please note that the "
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"format is experimental and subject to change."
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)
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if activation_scheme not in ACTIVATION_SCHEMES:
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raise ValueError(f"Unsupported activation scheme {activation_scheme}")
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self.activation_scheme = activation_scheme
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self.ignored_layers = ignored_layers or []
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if weight_block_size is not None:
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if not is_checkpoint_int8_serialized:
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raise ValueError(
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f"The block-wise quantization only supports int8-serialized checkpoint for now."
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)
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if len(weight_block_size) != 2:
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raise ValueError(
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f"The quantization block size of weight must have 2 dimensions, but got {len(weight_block_size)} dimensions."
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)
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if activation_scheme != "dynamic":
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raise ValueError(
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f"The block-wise quantization only supports dynamic activation scheme for now, but got {activation_scheme} activation scheme."
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)
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self.weight_block_size = weight_block_size
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@classmethod
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def get_name(cls) -> str:
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return "blockwise_int8"
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@classmethod
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def get_supported_act_dtypes(cls) -> List[torch.dtype]:
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return [torch.bfloat16, torch.half]
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@classmethod
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def get_min_capability(cls) -> int:
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return 80
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@classmethod
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def get_config_filenames(cls) -> List[str]:
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return []
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@classmethod
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def from_config(cls, config: Dict[str, Any]) -> BlockInt8Config:
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quant_method = cls.get_from_keys(config, ["quant_method"])
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is_checkpoint_int8_serialized = "int8" in quant_method
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activation_scheme = cls.get_from_keys(config, ["activation_scheme"])
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ignored_layers = cls.get_from_keys_or(config, ["ignored_layers"], None)
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weight_block_size = cls.get_from_keys_or(config, ["weight_block_size"], None)
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return cls(
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is_checkpoint_int8_serialized=is_checkpoint_int8_serialized,
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activation_scheme=activation_scheme,
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ignored_layers=ignored_layers,
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weight_block_size=weight_block_size,
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)
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def get_quant_method(
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self, layer: torch.nn.Module, prefix: str
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) -> Optional[QuantizeMethodBase]:
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from sglang.srt.layers.linear import LinearBase
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from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
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if isinstance(layer, LinearBase):
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if is_layer_skipped(prefix, self.ignored_layers):
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return UnquantizedLinearMethod()
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return BlockInt8LinearMethod(self)
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elif isinstance(layer, FusedMoE):
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return BlockInt8MoEMethod(self)
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return None
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def get_scaled_act_names(self) -> List[str]:
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return []
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class BlockInt8LinearMethod(LinearMethodBase):
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"""Linear method for INT8.
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Supports loading INT8 checkpoints with static weight scale and
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dynamic activation scale.
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Limitations:
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Only support block-wise int8 quantization and int8 checkpoint
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Args:
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quant_config: The quantization config.
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"""
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def __init__(self, quant_config: BlockInt8Config):
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self.quant_config = quant_config
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assert self.quant_config.weight_block_size is not None
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assert self.quant_config.is_checkpoint_int8_serialized
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def create_weights(
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self,
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layer: torch.nn.Module,
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input_size_per_partition: int,
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output_partition_sizes: List[int],
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input_size: int,
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output_size: int,
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params_dtype: torch.dtype,
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**extra_weight_attrs,
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):
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output_size_per_partition = sum(output_partition_sizes)
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weight_loader = extra_weight_attrs.get("weight_loader")
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tp_size = get_tensor_model_parallel_world_size()
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block_n, block_k = (
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self.quant_config.weight_block_size[0],
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self.quant_config.weight_block_size[1],
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)
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# Required by row parallel
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if tp_size > 1 and input_size // input_size_per_partition == tp_size:
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if input_size_per_partition % block_k != 0:
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raise ValueError(
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f"Weight input_size_per_partition = "
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f"{input_size_per_partition} is not divisible by "
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f"weight quantization block_k = {block_k}."
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)
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# Required by column parallel or enabling merged weights
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if (tp_size > 1 and output_size // output_size_per_partition == tp_size) or len(
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output_partition_sizes
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) > 1:
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for output_partition_size in output_partition_sizes:
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if output_partition_size % block_n != 0:
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raise ValueError(
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f"Weight output_partition_size = "
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f"{output_partition_size} is not divisible by "
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f"weight quantization block_n = {block_n}."
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)
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layer.logical_widths = output_partition_sizes
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layer.input_size_per_partition = input_size_per_partition
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layer.output_size_per_partition = output_size_per_partition
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layer.orig_dtype = params_dtype
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# WEIGHT
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weight_dtype = (
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torch.int8
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if self.quant_config.is_checkpoint_int8_serialized
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else params_dtype
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)
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weight = ModelWeightParameter(
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data=torch.empty(
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output_size_per_partition, input_size_per_partition, dtype=weight_dtype
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),
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input_dim=1,
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output_dim=0,
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weight_loader=weight_loader,
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)
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layer.register_parameter("weight", weight)
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# WEIGHT SCALE
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scale = BlockQuantScaleParameter(
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data=torch.empty(
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(output_size_per_partition + block_n - 1) // block_n,
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(input_size_per_partition + block_k - 1) // block_k,
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dtype=torch.float32,
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),
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input_dim=1,
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output_dim=0,
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weight_loader=weight_loader,
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)
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scale[:] = torch.finfo(torch.float32).min
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layer.register_parameter("weight_scale_inv", scale)
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# INPUT ACTIVATION SCALE
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assert self.quant_config.activation_scheme == "dynamic"
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layer.register_parameter("input_scale", None)
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def process_weights_after_loading(self, layer: Module) -> None:
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# Block quant doesn't need to process weights after loading
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# Use torch Parameter to avoid cuda graph capturing issue
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layer.weight = torch.nn.Parameter(layer.weight.data, requires_grad=False)
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layer.weight_scale_inv = torch.nn.Parameter(
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layer.weight_scale_inv.data, requires_grad=False
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)
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def apply(
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self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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bias: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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return apply_w8a8_block_int8_linear(
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input=x,
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weight=layer.weight,
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block_size=self.quant_config.weight_block_size,
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weight_scale=layer.weight_scale_inv,
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input_scale=None,
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bias=bias,
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)
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class BlockInt8MoEMethod(FusedMoEMethodBase):
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"""MoE method for INT8.
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Supports loading INT8 checkpoints with static weight scale and
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dynamic activation scale.
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Limitations:
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Only support block-wise int8 quantization and int8 checkpoint
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Args:
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quant_config: The quantization config.
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"""
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def __init__(self, quant_config: BlockInt8Config):
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self.quant_config = quant_config
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assert self.quant_config.weight_block_size is not None
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assert self.quant_config.is_checkpoint_int8_serialized
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def create_weights(
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self,
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layer: Module,
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num_experts: int,
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hidden_size: int,
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intermediate_size_per_partition: int,
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params_dtype: torch.dtype,
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**extra_weight_attrs,
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):
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from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported
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if self.quant_config.is_checkpoint_int8_serialized:
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params_dtype = torch.int8
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tp_size = get_tensor_model_parallel_world_size()
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block_n, block_k = (
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self.quant_config.weight_block_size[0],
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self.quant_config.weight_block_size[1],
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)
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# NOTE(HandH1998): To ensure proper alignment of the block-wise quantization scales, the output_size of the weights for both the gate and up layers must be divisible by block_n.
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# Required by column parallel or enabling merged weights
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if intermediate_size_per_partition % block_n != 0:
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raise ValueError(
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f"The output_size of gate's and up's weight = "
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f"{intermediate_size_per_partition} is not divisible by "
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f"weight quantization block_n = {block_n}."
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)
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if tp_size > 1:
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# Required by row parallel
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if intermediate_size_per_partition % block_k != 0:
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raise ValueError(
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f"The input_size of down's weight = "
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f"{intermediate_size_per_partition} is not divisible by "
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f"weight quantization block_k = {block_k}."
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)
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# WEIGHTS
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w13_weight = torch.nn.Parameter(
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torch.empty(
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num_experts,
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2 * intermediate_size_per_partition,
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hidden_size,
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dtype=params_dtype,
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),
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requires_grad=False,
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)
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layer.register_parameter("w13_weight", w13_weight)
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set_weight_attrs(w13_weight, extra_weight_attrs)
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w2_weight = torch.nn.Parameter(
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torch.empty(
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num_experts,
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hidden_size,
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intermediate_size_per_partition,
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dtype=params_dtype,
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),
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requires_grad=False,
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)
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layer.register_parameter("w2_weight", w2_weight)
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set_weight_attrs(w2_weight, extra_weight_attrs)
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# WEIGHT_SCALES
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w13_weight_scale = torch.nn.Parameter(
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torch.ones(
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num_experts,
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2 * ((intermediate_size_per_partition + block_n - 1) // block_n),
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(hidden_size + block_k - 1) // block_k,
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dtype=torch.float32,
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),
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requires_grad=False,
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)
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w2_weight_scale = torch.nn.Parameter(
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torch.ones(
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num_experts,
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(hidden_size + block_n - 1) // block_n,
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(intermediate_size_per_partition + block_k - 1) // block_k,
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dtype=torch.float32,
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),
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requires_grad=False,
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)
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layer.register_parameter("w13_weight_scale_inv", w13_weight_scale)
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layer.register_parameter("w2_weight_scale_inv", w2_weight_scale)
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extra_weight_attrs.update(
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{"quant_method": FusedMoeWeightScaleSupported.BLOCK.value}
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)
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set_weight_attrs(w13_weight_scale, extra_weight_attrs)
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set_weight_attrs(w2_weight_scale, extra_weight_attrs)
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# INPUT_SCALES
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assert self.quant_config.activation_scheme == "dynamic"
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layer.w13_input_scale = None
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layer.w2_input_scale = None
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def process_weights_after_loading(self, layer: Module) -> None:
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# Block quant doesn't need to process weights after loading
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return
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def create_moe_runner(
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self, layer: torch.nn.Module, moe_runner_config: MoeRunnerConfig
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):
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self.moe_runner_config = moe_runner_config
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self.runner = MoeRunner(MoeRunnerBackend.TRITON, moe_runner_config)
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def apply(
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self,
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layer: torch.nn.Module,
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dispatch_output: StandardDispatchOutput,
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) -> CombineInput:
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quant_info = TritonMoeQuantInfo(
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w13_weight=layer.w13_weight,
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w2_weight=layer.w2_weight,
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use_int8_w8a8=True,
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w13_scale=layer.w13_weight_scale_inv,
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w2_scale=layer.w2_weight_scale_inv,
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a13_scale=layer.w13_input_scale,
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a2_scale=layer.w2_input_scale,
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block_shape=self.quant_config.weight_block_size,
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)
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return self.runner.run(dispatch_output, quant_info)
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