515 lines
23 KiB
Python
515 lines
23 KiB
Python
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from typing import Any, Callable, Dict, List, Optional
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import torch
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from torch.nn import Module
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from torch.nn.parameter import Parameter
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import vllm.envs as envs
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from vllm import _custom_ops as ops
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from vllm.logger import init_logger
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from vllm.model_executor.layers.fused_moe import (FusedMoE, FusedMoEMethodBase,
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FusedMoeWeightScaleSupported)
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from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
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UnquantizedLinearMethod)
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig, QuantizeMethodBase)
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from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
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from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
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apply_fp8_marlin_linear, prepare_fp8_layer_for_marlin)
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from vllm.model_executor.layers.quantization.utils.quant_utils import (
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is_layer_skipped)
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from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
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all_close_1d, apply_fp8_linear, convert_to_channelwise,
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cutlass_fp8_supported, normalize_e4m3fn_to_e4m3fnuz, per_tensor_dequantize,
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requantize_with_max_scale)
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from vllm.model_executor.parameter import (ModelWeightParameter,
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PerTensorScaleParameter)
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from vllm.model_executor.utils import set_weight_attrs
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from vllm.platforms import current_platform
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from vllm.utils import is_hip, print_warning_once
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ACTIVATION_SCHEMES = ["static", "dynamic"]
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logger = init_logger(__name__)
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class Fp8Config(QuantizationConfig):
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"""Config class for FP8."""
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def __init__(
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self,
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is_checkpoint_fp8_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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) -> None:
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self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized
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if is_checkpoint_fp8_serialized:
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logger.warning("Detected fp8 checkpoint. Please note that the "
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"format is experimental and subject to change.")
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if activation_scheme not in ACTIVATION_SCHEMES:
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raise ValueError(
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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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@classmethod
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def get_name(cls) -> str:
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return "fp8"
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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]) -> "Fp8Config":
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quant_method = cls.get_from_keys(config, ["quant_method"])
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is_checkpoint_fp8_serialized = ("fp8" 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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return cls(is_checkpoint_fp8_serialized=is_checkpoint_fp8_serialized,
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activation_scheme=activation_scheme,
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ignored_layers=ignored_layers)
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def get_quant_method(self, layer: torch.nn.Module,
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prefix: str) -> Optional["QuantizeMethodBase"]:
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from vllm.attention.layer import Attention # Avoid circular import
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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 Fp8LinearMethod(self)
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elif isinstance(layer, FusedMoE):
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return Fp8MoEMethod(self)
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elif isinstance(layer, Attention):
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return Fp8KVCacheMethod(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 Fp8LinearMethod(LinearMethodBase):
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"""Linear method for FP8.
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Supports loading FP8 checkpoints with static weight scale and
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dynamic/static activation scale.
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Also supports loading quantized FP16/BF16 model checkpoints with dynamic
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activation scaling. The weight scaling factor will be initialized after
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the model weights are loaded.
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Limitations:
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1. Only support per-tensor quantization due to torch._scaled_mm support.
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2. Only support float8_e4m3fn data type due to the limitation of
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torch._scaled_mm (https://github.com/pytorch/pytorch/blob/2e48b39603411a41c5025efbe52f89560b827825/aten/src/ATen/native/cuda/Blas.cpp#L854-L856)
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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: Fp8Config):
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self.quant_config = quant_config
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self.cutlass_fp8_supported = cutlass_fp8_supported()
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# For GPUs that lack FP8 hardware support, we can leverage the Marlin
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# kernel for fast weight-only FP8 quantization
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self.use_marlin = (not current_platform.has_device_capability(89)
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or envs.VLLM_TEST_FORCE_FP8_MARLIN)
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# Disable marlin for rocm
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if is_hip():
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self.use_marlin = False
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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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del input_size, output_size
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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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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 = (torch.float8_e4m3fn
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if self.quant_config.is_checkpoint_fp8_serialized else
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params_dtype)
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weight = ModelWeightParameter(data=torch.empty(
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output_size_per_partition,
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input_size_per_partition,
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dtype=weight_dtype),
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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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layer.register_parameter("weight", weight)
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# If checkpoint is serialized fp8, load them.
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# Otherwise, wait until process_weights_after_loading.
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if self.quant_config.is_checkpoint_fp8_serialized:
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# WEIGHT SCALE
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scale = PerTensorScaleParameter(data=torch.empty(
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len(output_partition_sizes), dtype=torch.float32),
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weight_loader=weight_loader)
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scale[:] = torch.finfo(torch.float32).min
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layer.register_parameter("weight_scale", scale)
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# INPUT ACTIVATION SCALE
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if self.quant_config.activation_scheme == "static":
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scale = PerTensorScaleParameter(data=torch.empty(
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len(output_partition_sizes), dtype=torch.float32),
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weight_loader=weight_loader)
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scale[:] = torch.finfo(torch.float32).min
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layer.register_parameter("input_scale", scale)
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else:
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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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layer.weight = torch.nn.Parameter(layer.weight.data,
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requires_grad=False)
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# If checkpoint not serialized fp8, quantize the weights.
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if not self.quant_config.is_checkpoint_fp8_serialized:
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qweight, weight_scale = ops.scaled_fp8_quant(layer.weight,
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scale=None)
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# If using marlin (w8a16), kernel uses channelwise weights,
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# so extend the weight scales to be channelwise.
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if self.use_marlin:
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assert weight_scale.numel() == 1
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weight_scale = convert_to_channelwise(
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weight_scale.expand(len(layer.logical_widths)),
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layer.logical_widths)
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# Update the layer with the new values.
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layer.weight = Parameter(qweight.t(), requires_grad=False)
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layer.weight_scale = Parameter(weight_scale, requires_grad=False)
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layer.input_scale = None
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# If checkpoint is fp8, handle that there are N scales for N
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# shards in a fused module
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else:
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layer.weight_scale = torch.nn.Parameter(layer.weight_scale.data,
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requires_grad=False)
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if self.quant_config.activation_scheme == "static":
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layer.input_scale = torch.nn.Parameter(layer.input_scale.data,
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requires_grad=False)
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# If using marlin (w8a16), kernel uses channelwise weights,
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# so extend the weight scales to be channelwise.
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if self.use_marlin:
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weight = layer.weight
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weight_scale = convert_to_channelwise(layer.weight_scale,
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layer.logical_widths)
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# If using w8a8, torch._scaled_mm needs per tensor, so
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# requantize the logical shards as a single weight.
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else:
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# Dequant -> Quant with max scale so we can run per tensor.
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weight = layer.weight
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weight_scale = layer.weight_scale
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# If rocm, use float8_e4m3fnuz.
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if is_hip():
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weight, weight_scale, input_scale = \
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normalize_e4m3fn_to_e4m3fnuz(
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weight=weight,
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weight_scale=weight_scale,
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input_scale=layer.input_scale)
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if input_scale is not None:
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layer.input_scale = Parameter(input_scale,
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requires_grad=False)
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weight_scale, weight = requantize_with_max_scale(
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weight=weight,
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weight_scale=weight_scale,
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logical_widths=layer.logical_widths,
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)
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# Update layer with new values.
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layer.weight = Parameter(weight.t(), requires_grad=False)
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layer.weight_scale = Parameter(weight_scale, requires_grad=False)
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if self.quant_config.activation_scheme == "static":
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layer.input_scale = Parameter(layer.input_scale.max(),
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requires_grad=False)
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if self.use_marlin:
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prepare_fp8_layer_for_marlin(layer)
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# Activations not quantized for marlin.
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del layer.input_scale
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def apply(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) -> torch.Tensor:
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if self.use_marlin:
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return apply_fp8_marlin_linear(
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input=x,
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weight=layer.weight,
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weight_scale=layer.weight_scale,
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workspace=layer.workspace,
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size_n=layer.output_size_per_partition,
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size_k=layer.input_size_per_partition,
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bias=bias)
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return apply_fp8_linear(
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input=x,
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weight=layer.weight,
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weight_scale=layer.weight_scale,
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input_scale=layer.input_scale,
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bias=bias,
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cutlass_fp8_supported=self.cutlass_fp8_supported,
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use_per_token_if_dynamic=False)
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class Fp8MoEMethod(FusedMoEMethodBase):
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"""MoE method for FP8.
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Supports loading FP8 checkpoints with static weight scale and
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dynamic/static activation scale.
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Also supports loading quantized FP16/BF16 model checkpoints with dynamic
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activation scaling. The weight scaling factor will be initialized after
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the model weights are loaded.
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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: Fp8Config):
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self.quant_config = quant_config
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def create_weights(self, layer: Module, num_experts: int, hidden_size: int,
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intermediate_size: int, params_dtype: torch.dtype,
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**extra_weight_attrs):
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if self.quant_config.is_checkpoint_fp8_serialized:
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params_dtype = torch.float8_e4m3fn
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# WEIGHTS
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w13_weight = torch.nn.Parameter(torch.empty(num_experts,
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2 * intermediate_size,
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hidden_size,
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dtype=params_dtype),
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requires_grad=False)
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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(torch.empty(num_experts,
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hidden_size,
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intermediate_size,
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dtype=params_dtype),
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requires_grad=False)
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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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# Allocate 2 scales for w1 and w3 respectively.
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# They will be combined to a single scale after weight loading.
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w13_weight_scale = torch.nn.Parameter(torch.ones(num_experts,
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2,
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dtype=torch.float32),
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requires_grad=False)
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layer.register_parameter("w13_weight_scale", w13_weight_scale)
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w2_weight_scale = torch.nn.Parameter(torch.ones(num_experts,
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dtype=torch.float32),
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requires_grad=False)
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layer.register_parameter("w2_weight_scale", w2_weight_scale)
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# Add the quantization method used (per tensor/grouped/channel)
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# to ensure the weight scales are loaded in properly
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extra_weight_attrs.update(
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{"quant_method": FusedMoeWeightScaleSupported.TENSOR.value})
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# If loading fp8 checkpoint, pass the weight loaders.
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# If loading an fp16 checkpoint, do not (we will quantize in
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# process_weights_after_loading()
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if self.quant_config.is_checkpoint_fp8_serialized:
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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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if self.quant_config.activation_scheme == "static":
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if not self.quant_config.is_checkpoint_fp8_serialized:
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|
|
raise ValueError(
|
||
|
|
"Found static activation scheme for checkpoint that "
|
||
|
|
"was not serialized fp8.")
|
||
|
|
|
||
|
|
w13_input_scale = torch.nn.Parameter(torch.ones(
|
||
|
|
num_experts, dtype=torch.float32),
|
||
|
|
requires_grad=False)
|
||
|
|
layer.register_parameter("w13_input_scale", w13_input_scale)
|
||
|
|
set_weight_attrs(w13_input_scale, extra_weight_attrs)
|
||
|
|
|
||
|
|
w2_input_scale = torch.nn.Parameter(torch.ones(
|
||
|
|
num_experts, dtype=torch.float32),
|
||
|
|
requires_grad=False)
|
||
|
|
layer.register_parameter("w2_input_scale", w2_input_scale)
|
||
|
|
set_weight_attrs(w2_input_scale, extra_weight_attrs)
|
||
|
|
|
||
|
|
else:
|
||
|
|
layer.w13_input_scale = None
|
||
|
|
layer.w2_input_scale = None
|
||
|
|
|
||
|
|
def process_weights_after_loading(self, layer: Module) -> None:
|
||
|
|
|
||
|
|
# If checkpoint is fp16, quantize in place.
|
||
|
|
if not self.quant_config.is_checkpoint_fp8_serialized:
|
||
|
|
# If rocm, use float8_e4m3fnuz as dtype
|
||
|
|
fp8_dtype = torch.float8_e4m3fnuz \
|
||
|
|
if is_hip() else torch.float8_e4m3fn
|
||
|
|
w13_weight = torch.empty_like(layer.w13_weight.data,
|
||
|
|
dtype=fp8_dtype)
|
||
|
|
w2_weight = torch.empty_like(layer.w2_weight.data, dtype=fp8_dtype)
|
||
|
|
|
||
|
|
# Re-initialize w13_scale because we directly quantize
|
||
|
|
# merged w13 weights and generate a single scaling factor.
|
||
|
|
layer.w13_weight_scale = torch.nn.Parameter(torch.ones(
|
||
|
|
layer.num_experts,
|
||
|
|
dtype=torch.float32,
|
||
|
|
device=w13_weight.device),
|
||
|
|
requires_grad=False)
|
||
|
|
for expert in range(layer.num_experts):
|
||
|
|
w13_weight[expert, :, :], layer.w13_weight_scale[
|
||
|
|
expert] = ops.scaled_fp8_quant(
|
||
|
|
layer.w13_weight.data[expert, :, :])
|
||
|
|
w2_weight[expert, :, :], layer.w2_weight_scale[
|
||
|
|
expert] = ops.scaled_fp8_quant(
|
||
|
|
layer.w2_weight.data[expert, :, :])
|
||
|
|
layer.w13_weight = torch.nn.Parameter(w13_weight,
|
||
|
|
requires_grad=False)
|
||
|
|
layer.w2_weight = torch.nn.Parameter(w2_weight,
|
||
|
|
requires_grad=False)
|
||
|
|
return
|
||
|
|
|
||
|
|
# If checkpoint is fp8, we need to handle that the
|
||
|
|
# MoE kernels require single activation scale and single weight
|
||
|
|
# scale for w13 per expert.
|
||
|
|
else:
|
||
|
|
# Fp8 moe kernels require a single activation scale.
|
||
|
|
# We take the max of all the scales in case they differ.
|
||
|
|
if self.quant_config.activation_scheme == "static":
|
||
|
|
if (layer.w13_input_scale is None
|
||
|
|
or layer.w2_input_scale is None):
|
||
|
|
raise ValueError(
|
||
|
|
"QuantConfig has static quantization, but found "
|
||
|
|
"activation scales are None.")
|
||
|
|
if (not all_close_1d(layer.w13_input_scale)
|
||
|
|
or not all_close_1d(layer.w2_input_scale)):
|
||
|
|
print_warning_once(
|
||
|
|
"Found input_scales that are not equal for "
|
||
|
|
"fp8 MoE layer. Using the maximum across experts "
|
||
|
|
"for each layer. ")
|
||
|
|
layer.w13_input_scale = torch.nn.Parameter(
|
||
|
|
layer.w13_input_scale.max(), requires_grad=False)
|
||
|
|
layer.w2_input_scale = torch.nn.Parameter(
|
||
|
|
layer.w2_input_scale.max(), requires_grad=False)
|
||
|
|
# If rocm, normalize the weights and scales to e4m3fnuz
|
||
|
|
if is_hip():
|
||
|
|
# Normalize the weights and scales
|
||
|
|
w13_weight, w13_weight_scale, w13_input_scale = \
|
||
|
|
normalize_e4m3fn_to_e4m3fnuz(
|
||
|
|
layer.w13_weight, layer.w13_weight_scale,
|
||
|
|
layer.w13_input_scale)
|
||
|
|
w2_weight, w2_weight_scale, w2_input_scale = \
|
||
|
|
normalize_e4m3fn_to_e4m3fnuz(
|
||
|
|
layer.w2_weight, layer.w2_weight_scale,
|
||
|
|
layer.w2_input_scale)
|
||
|
|
# Reset the parameter
|
||
|
|
layer.w13_weight = torch.nn.Parameter(w13_weight,
|
||
|
|
requires_grad=False)
|
||
|
|
layer.w13_weight_scale = torch.nn.Parameter(
|
||
|
|
w13_weight_scale, requires_grad=False)
|
||
|
|
if w13_input_scale is not None:
|
||
|
|
layer.w13_input_scale = torch.nn.Parameter(
|
||
|
|
w13_input_scale, requires_grad=False)
|
||
|
|
layer.w2_weight = torch.nn.Parameter(w2_weight,
|
||
|
|
requires_grad=False)
|
||
|
|
layer.w2_weight_scale = torch.nn.Parameter(w2_weight_scale,
|
||
|
|
requires_grad=False)
|
||
|
|
if w2_input_scale is not None:
|
||
|
|
layer.w2_input_scale = torch.nn.Parameter(
|
||
|
|
w2_input_scale, requires_grad=False)
|
||
|
|
|
||
|
|
# Fp8 moe kernel needs single weight scale for w13 per expert.
|
||
|
|
# We take the max then dequant and requant each expert.
|
||
|
|
assert layer.w13_weight_scale is not None
|
||
|
|
shard_size = layer.intermediate_size_per_partition
|
||
|
|
max_w13_scales = layer.w13_weight_scale.max(dim=1).values
|
||
|
|
for expert_id in range(layer.num_experts):
|
||
|
|
start = 0
|
||
|
|
for shard_id in range(2):
|
||
|
|
dq_weight = per_tensor_dequantize(
|
||
|
|
layer.w13_weight[expert_id][start:start +
|
||
|
|
shard_size, :],
|
||
|
|
layer.w13_weight_scale[expert_id][shard_id])
|
||
|
|
layer.w13_weight[expert_id][
|
||
|
|
start:start + shard_size, :], _ = ops.scaled_fp8_quant(
|
||
|
|
dq_weight, max_w13_scales[expert_id])
|
||
|
|
start += shard_size
|
||
|
|
|
||
|
|
layer.w13_weight_scale = torch.nn.Parameter(max_w13_scales,
|
||
|
|
requires_grad=False)
|
||
|
|
return
|
||
|
|
|
||
|
|
def apply(
|
||
|
|
self,
|
||
|
|
layer: torch.nn.Module,
|
||
|
|
x: torch.Tensor,
|
||
|
|
router_logits: torch.Tensor,
|
||
|
|
top_k: int,
|
||
|
|
renormalize: bool,
|
||
|
|
use_grouped_topk: bool,
|
||
|
|
topk_group: Optional[int] = None,
|
||
|
|
num_expert_group: Optional[int] = None,
|
||
|
|
custom_routing_function: Optional[Callable] = None,
|
||
|
|
) -> torch.Tensor:
|
||
|
|
|
||
|
|
from vllm.model_executor.layers.fused_moe import fused_experts
|
||
|
|
|
||
|
|
topk_weights, topk_ids = FusedMoE.select_experts(
|
||
|
|
hidden_states=x,
|
||
|
|
router_logits=router_logits,
|
||
|
|
use_grouped_topk=use_grouped_topk,
|
||
|
|
top_k=top_k,
|
||
|
|
renormalize=renormalize,
|
||
|
|
topk_group=topk_group,
|
||
|
|
num_expert_group=num_expert_group,
|
||
|
|
custom_routing_function=custom_routing_function)
|
||
|
|
|
||
|
|
return fused_experts(x,
|
||
|
|
layer.w13_weight,
|
||
|
|
layer.w2_weight,
|
||
|
|
topk_weights=topk_weights,
|
||
|
|
topk_ids=topk_ids,
|
||
|
|
inplace=True,
|
||
|
|
use_fp8_w8a8=True,
|
||
|
|
w1_scale=layer.w13_weight_scale,
|
||
|
|
w2_scale=layer.w2_weight_scale,
|
||
|
|
a1_scale=layer.w13_input_scale,
|
||
|
|
a2_scale=layer.w2_input_scale)
|
||
|
|
|
||
|
|
|
||
|
|
class Fp8KVCacheMethod(BaseKVCacheMethod):
|
||
|
|
"""
|
||
|
|
Supports loading kv-cache scaling factors from FP8 checkpoints.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, quant_config: Fp8Config):
|
||
|
|
super().__init__(quant_config)
|