1003 lines
40 KiB
Python
1003 lines
40 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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import logging
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from typing import Any, Callable, Dict, List, Optional
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import torch
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import torch.nn.functional as F
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from torch.nn import Module
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from torch.nn.parameter import Parameter
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from sglang.srt.layers.quantization.kv_cache import BaseKVCacheMethod
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from sglang.srt.layers.quantization.utils import (
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all_close_1d,
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convert_to_channelwise,
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is_layer_skipped,
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per_tensor_dequantize,
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requantize_with_max_scale,
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)
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try:
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from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
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apply_fp8_marlin_linear,
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prepare_fp8_layer_for_marlin,
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)
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MARLIN_FP8_AVAILABLE = True
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except ImportError:
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MARLIN_FP8_AVAILABLE = False
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def apply_fp8_marlin_linear(*args, **kwargs):
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raise ImportError("vllm is not installed")
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def prepare_fp8_layer_for_marlin(*args, **kwargs):
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raise ImportError("vllm is not installed")
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from sglang.srt.distributed import get_tensor_model_parallel_world_size
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from sglang.srt.layers.linear import (
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LinearBase,
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LinearMethodBase,
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UnquantizedLinearMethod,
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)
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from sglang.srt.layers.parameter import (
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BlockQuantScaleParameter,
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ModelWeightParameter,
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PerTensorScaleParameter,
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)
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from sglang.srt.layers.quantization.base_config import (
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QuantizationConfig,
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QuantizeMethodBase,
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)
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from sglang.srt.layers.quantization.fp8_kernel import per_token_group_quant_fp8
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from sglang.srt.layers.quantization.fp8_utils import (
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apply_fp8_linear,
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apply_w8a8_block_fp8_linear,
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cutlass_fp8_supported,
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input_to_float8,
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normalize_e4m3fn_to_e4m3fnuz,
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)
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from sglang.srt.utils import (
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get_bool_env_var,
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is_cuda,
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is_hip,
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permute_weight,
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print_warning_once,
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set_weight_attrs,
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)
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ACTIVATION_SCHEMES = ["static", "dynamic"]
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_is_hip = is_hip()
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if _is_hip:
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from aiter.fused_moe_bf16_asm import asm_moe
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from aiter.ops.shuffle import shuffle_weight
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_is_cuda = is_cuda()
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if _is_cuda:
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from sglang.srt.custom_op import scaled_fp8_quant as sgl_scaled_fp8_quant
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else:
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from vllm import _custom_ops as vllm_ops
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logger = logging.getLogger(__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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weight_block_size: List[int] = 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(
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"Detected fp8 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_fp8_serialized:
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raise ValueError(
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f"The block-wise quantization only supports fp8-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 "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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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_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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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.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 Fp8LinearMethod(self)
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elif isinstance(layer, FusedMoE):
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return Fp8MoEMethod(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 = (
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get_bool_env_var("SGLANG_FORCE_FP8_MARLIN") and MARLIN_FP8_AVAILABLE
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)
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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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self.block_quant = self.quant_config.weight_block_size is not None
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if self.block_quant:
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# Marlin doesn't support block-wise fp8
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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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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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if self.block_quant:
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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 collum parallel or enabling merged weights
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if (
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tp_size > 1 and output_size // output_size_per_partition == tp_size
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) or len(output_partition_sizes) > 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.float8_e4m3fn
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if self.quant_config.is_checkpoint_fp8_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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# 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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if self.block_quant:
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assert self.quant_config.activation_scheme == "dynamic"
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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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else:
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scale = PerTensorScaleParameter(
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data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
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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", 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(
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data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
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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("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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# Block quant doesn't need to process weights after loading
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if self.block_quant:
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# If ROCm, normalize the weights and scales to e4m3fnuz
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if _is_hip:
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# activation_scheme: dynamic
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weight, weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
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weight=layer.weight,
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weight_scale=layer.weight_scale_inv,
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input_scale=None,
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)
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layer.weight = torch.nn.Parameter(weight, requires_grad=False)
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layer.weight_scale_inv = torch.nn.Parameter(
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weight_scale, requires_grad=False
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)
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layer.input_scale = None
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else:
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layer.weight = torch.nn.Parameter(
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layer.weight.data, requires_grad=False
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)
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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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return
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layer.weight = torch.nn.Parameter(layer.weight.data, 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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if self.cutlass_fp8_supported or self.use_marlin:
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# apply per-channel quantization default, as cutlass sgl-kernel and marlin only support per-channel scale
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qweight, weight_scale = per_token_group_quant_fp8(
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layer.weight, layer.weight.shape[-1]
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)
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weight_scale = weight_scale.t().contiguous()
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else:
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# per-tensor quantization
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qweight, weight_scale = input_to_float8(layer.weight)
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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(
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layer.weight_scale.data, requires_grad=False
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)
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if self.quant_config.activation_scheme == "static":
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layer.input_scale = torch.nn.Parameter(
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layer.input_scale.data, requires_grad=False
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)
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# cutlass sgl-kernel and marlin only support per-channel scale
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if self.cutlass_fp8_supported or self.use_marlin:
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weight = layer.weight
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weight_scale = convert_to_channelwise(
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layer.weight_scale, layer.logical_widths
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)
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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, normalize the weights and scales to e4m3fnuz
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if _is_hip:
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weight, weight_scale, input_scale = 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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)
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if input_scale is not None:
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layer.input_scale = Parameter(input_scale, 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(
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layer.input_scale.max(), requires_grad=False
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)
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if self.use_marlin:
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try:
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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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except ImportError:
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self.use_marlin = False
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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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if self.use_marlin:
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try:
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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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)
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except ImportError:
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self.use_marlin = False
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if self.block_quant:
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return apply_w8a8_block_fp8_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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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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)
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|
|
|
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class Fp8MoEMethod:
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"""MoE method for FP8.
|
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Supports loading FP8 checkpoints with static weight scale and
|
|
dynamic/static activation scale.
|
|
|
|
Also supports loading quantized FP16/BF16 model checkpoints with dynamic
|
|
activation scaling. The weight scaling factor will be initialized after
|
|
the model weights are loaded.
|
|
|
|
Args:
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quant_config: The quantization config.
|
|
"""
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|
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def __new__(cls, *args, **kwargs):
|
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from sglang.srt.layers.moe.fused_moe_triton import FusedMoEMethodBase
|
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|
|
if not hasattr(cls, "_initialized"):
|
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original_init = cls.__init__
|
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new_cls = type(
|
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cls.__name__,
|
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(FusedMoEMethodBase,),
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{
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"__init__": original_init,
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**{k: v for k, v in cls.__dict__.items() if k != "__dict__"},
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},
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)
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obj = super(new_cls, new_cls).__new__(new_cls)
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obj.__init__(*args, **kwargs)
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return obj
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return super().__new__(cls)
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|
|
def __init__(self, quant_config):
|
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self.quant_config = quant_config
|
|
self.block_quant = self.quant_config.weight_block_size is not None
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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: int,
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params_dtype: torch.dtype,
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**extra_weight_attrs,
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):
|
|
from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported
|
|
|
|
if self.quant_config.is_checkpoint_fp8_serialized:
|
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params_dtype = (
|
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torch.int32
|
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if get_bool_env_var("USE_INT4_WEIGHT")
|
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else torch.float8_e4m3fn
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)
|
|
tp_size = get_tensor_model_parallel_world_size()
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if self.block_quant:
|
|
block_n, block_k = (
|
|
self.quant_config.weight_block_size[0],
|
|
self.quant_config.weight_block_size[1],
|
|
)
|
|
# 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.
|
|
# Required by collum parallel or enabling merged weights
|
|
if intermediate_size % block_n != 0:
|
|
raise ValueError(
|
|
f"The output_size of gate's and up's weight = "
|
|
f"{intermediate_size} is not divisible by "
|
|
f"weight quantization block_n = {block_n}."
|
|
)
|
|
if tp_size > 1:
|
|
# Required by row parallel
|
|
if intermediate_size % block_k != 0:
|
|
raise ValueError(
|
|
f"The input_size of down's weight = "
|
|
f"{intermediate_size} is not divisible by "
|
|
f"weight quantization block_k = {block_k}."
|
|
)
|
|
|
|
# WEIGHTS
|
|
if get_bool_env_var("USE_INT4_WEIGHT"):
|
|
# INT4 MoE weight - INT32 packed
|
|
w13_weight = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts,
|
|
2 * intermediate_size,
|
|
hidden_size // 8,
|
|
dtype=params_dtype,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
w2_weight = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts, hidden_size, intermediate_size // 8, dtype=params_dtype
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
else:
|
|
w13_weight = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts, 2 * intermediate_size, hidden_size, dtype=params_dtype
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
w2_weight = torch.nn.Parameter(
|
|
torch.empty(
|
|
num_experts, hidden_size, intermediate_size, dtype=params_dtype
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
|
|
layer.register_parameter("w13_weight", w13_weight)
|
|
set_weight_attrs(w13_weight, extra_weight_attrs)
|
|
|
|
layer.register_parameter("w2_weight", w2_weight)
|
|
set_weight_attrs(w2_weight, extra_weight_attrs)
|
|
|
|
# WEIGHT_SCALES
|
|
if self.block_quant:
|
|
w13_weight_scale = torch.nn.Parameter(
|
|
torch.ones(
|
|
num_experts,
|
|
2 * ((intermediate_size + block_n - 1) // block_n),
|
|
(hidden_size + block_k - 1) // block_k,
|
|
dtype=torch.float32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
w2_weight_scale = torch.nn.Parameter(
|
|
torch.ones(
|
|
num_experts,
|
|
(hidden_size + block_n - 1) // block_n,
|
|
(intermediate_size + block_k - 1) // block_k,
|
|
dtype=torch.float32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_weight_scale_inv", w13_weight_scale)
|
|
layer.register_parameter("w2_weight_scale_inv", w2_weight_scale)
|
|
assert self.quant_config.activation_scheme == "dynamic"
|
|
else:
|
|
# Allocate 2 scales for w1 and w3 respectively.
|
|
# They will be combined to a single scale after weight loading.
|
|
w13_weight_scale = torch.nn.Parameter(
|
|
torch.ones(num_experts, 2, dtype=torch.float32), requires_grad=False
|
|
)
|
|
w2_weight_scale = torch.nn.Parameter(
|
|
torch.ones(num_experts, dtype=torch.float32), requires_grad=False
|
|
)
|
|
layer.register_parameter("w13_weight_scale", w13_weight_scale)
|
|
layer.register_parameter("w2_weight_scale", w2_weight_scale)
|
|
|
|
if (
|
|
_is_hip
|
|
): # and get_bool_env_var("CK_MOE"): TODO: add check back after triton kernel
|
|
# ROCm - using column scaling, duplicate scaling numbers in case per tensor scaling
|
|
w13_weight_scale1 = torch.nn.Parameter(
|
|
torch.ones(num_experts, 2 * intermediate_size, dtype=torch.float32),
|
|
requires_grad=False,
|
|
)
|
|
w2_weight_scale1 = torch.nn.Parameter(
|
|
torch.ones(num_experts, hidden_size, dtype=torch.float32),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_weight_scale1", w13_weight_scale1)
|
|
layer.register_parameter("w2_weight_scale1", w2_weight_scale1)
|
|
|
|
# Add the quantization method used (per tensor/grouped/channel)
|
|
# to ensure the weight scales are loaded in properly
|
|
extra_weight_attrs.update(
|
|
{"quant_method": FusedMoeWeightScaleSupported.BLOCK.value}
|
|
if self.block_quant
|
|
else {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value}
|
|
)
|
|
# If loading fp8 checkpoint, pass the weight loaders.
|
|
# If loading an fp16 checkpoint, do not (we will quantize in
|
|
# process_weights_after_loading()
|
|
if self.quant_config.is_checkpoint_fp8_serialized:
|
|
set_weight_attrs(w13_weight_scale, extra_weight_attrs)
|
|
set_weight_attrs(w2_weight_scale, extra_weight_attrs)
|
|
|
|
if get_bool_env_var("USE_INT4_WEIGHT"):
|
|
extra_weight_attrs.update(
|
|
{"quant_method": FusedMoeWeightScaleSupported.CHANNEL.value}
|
|
)
|
|
set_weight_attrs(w13_weight_scale1, extra_weight_attrs)
|
|
set_weight_attrs(w2_weight_scale1, extra_weight_attrs)
|
|
|
|
# INPUT_SCALES
|
|
if self.quant_config.activation_scheme == "static":
|
|
if not self.quant_config.is_checkpoint_fp8_serialized:
|
|
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 get_bool_env_var("USE_INT4_WEIGHT"):
|
|
self.process_weights_hip_int4(layer)
|
|
return
|
|
|
|
# Block quant doesn't need to process weights after loading
|
|
if self.block_quant:
|
|
# If ROCm, normalize the weights and scales to e4m3fnuz
|
|
if _is_hip:
|
|
# activation_scheme: dynamic
|
|
w13_weight, w13_weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
|
|
weight=layer.w13_weight,
|
|
weight_scale=layer.w13_weight_scale_inv,
|
|
input_scale=None,
|
|
)
|
|
w2_weight, w2_weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
|
|
weight=layer.w2_weight,
|
|
weight_scale=layer.w2_weight_scale_inv,
|
|
input_scale=None,
|
|
)
|
|
# Reset the parameter
|
|
layer.w13_weight = torch.nn.Parameter(w13_weight, requires_grad=False)
|
|
layer.w13_weight_scale_inv = torch.nn.Parameter(
|
|
w13_weight_scale, requires_grad=False
|
|
)
|
|
layer.w13_input_scale = None
|
|
layer.w2_weight = torch.nn.Parameter(w2_weight, requires_grad=False)
|
|
layer.w2_weight_scale_inv = torch.nn.Parameter(
|
|
w2_weight_scale, requires_grad=False
|
|
)
|
|
layer.w2_input_scale = None
|
|
|
|
if get_bool_env_var("CK_MOE"):
|
|
# Pre-shuffle weights
|
|
layer.w13_weight.data = shuffle_weight(
|
|
layer.w13_weight.contiguous(), (16, 16)
|
|
)
|
|
layer.w2_weight.data = shuffle_weight(
|
|
layer.w2_weight.contiguous(), (16, 16)
|
|
)
|
|
return
|
|
|
|
# If checkpoint is fp16 or bfloat16, quantize in place.
|
|
if not self.quant_config.is_checkpoint_fp8_serialized:
|
|
# If ROCm, use float8_e4m3fnuz instead (MI300x HW)
|
|
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):
|
|
if _is_cuda:
|
|
w13_weight[expert, :, :], layer.w13_weight_scale[expert] = (
|
|
sgl_scaled_fp8_quant(layer.w13_weight.data[expert, :, :])
|
|
)
|
|
w2_weight[expert, :, :], layer.w2_weight_scale[expert] = (
|
|
sgl_scaled_fp8_quant(layer.w2_weight.data[expert, :, :])
|
|
)
|
|
else:
|
|
w13_weight[expert, :, :], layer.w13_weight_scale[expert] = (
|
|
vllm_ops.scaled_fp8_quant(layer.w13_weight.data[expert, :, :])
|
|
)
|
|
w2_weight[expert, :, :], layer.w2_weight_scale[expert] = (
|
|
vllm_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)
|
|
|
|
if _is_hip:
|
|
self.process_weights_hip_scale_padding(layer)
|
|
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],
|
|
)
|
|
if _is_cuda:
|
|
(
|
|
layer.w13_weight[expert_id][start : start + shard_size, :],
|
|
_,
|
|
) = sgl_scaled_fp8_quant(dq_weight, max_w13_scales[expert_id])
|
|
else:
|
|
(
|
|
layer.w13_weight[expert_id][start : start + shard_size, :],
|
|
_,
|
|
) = vllm_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
|
|
)
|
|
|
|
if _is_hip:
|
|
self.process_weights_hip_scale_padding(layer)
|
|
return
|
|
|
|
def process_weights_hip_int4(self, layer: Module):
|
|
# TODO: and get_bool_env_var("CK_MOE"): add after triton kernel added
|
|
# INT4-FP8 (INT4 MoE Weight, FP8 Compute)
|
|
# Weight Permutation
|
|
layer.w13_weight = torch.nn.Parameter(
|
|
permute_weight(layer.w13_weight.data),
|
|
requires_grad=False,
|
|
)
|
|
torch.cuda.empty_cache()
|
|
layer.w2_weight = torch.nn.Parameter(
|
|
permute_weight(layer.w2_weight.data),
|
|
requires_grad=False,
|
|
)
|
|
torch.cuda.empty_cache()
|
|
|
|
# INT4-FP8 : offset INT4 w13_weight_scale1 to single w13_weight_scale
|
|
# Fp8 moe kernel needs single fp8 w13_weight_scale for w13 per expert.
|
|
# We won't do requant each expert's fp8 weight (not direct available),
|
|
# instead we adjust half of INT4 w13_weight_scale1 numbers
|
|
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
|
|
max_w13_scale_fp8 = max_w13_scales[expert_id]
|
|
for shard_id in range(2):
|
|
if layer.w13_weight_scale[expert_id][shard_id] != max_w13_scale_fp8:
|
|
int4_rescale = (
|
|
layer.w13_weight_scale[expert_id][shard_id] / max_w13_scale_fp8
|
|
)
|
|
layer.w13_weight_scale1[expert_id][
|
|
start : start + shard_size
|
|
] *= int4_rescale
|
|
start += shard_size
|
|
|
|
layer.w13_weight_scale = torch.nn.Parameter(max_w13_scales, requires_grad=False)
|
|
|
|
# special hack to asm_moe, which takes (weight_scale1 * weight_scale) as post GEMM scaling
|
|
# optimal design - shall apply per-column weight_scale1 before GEMM, and weight_scale post
|
|
for expert_id in range(layer.num_experts):
|
|
layer.w13_weight_scale1[expert_id] *= max_w13_scales[expert_id]
|
|
layer.w2_weight_scale1[expert_id] *= layer.w2_weight_scale[expert_id]
|
|
|
|
def process_weights_hip_scale_padding(self, layer: Module, padding_size: int):
|
|
from sglang.srt.layers.moe.fused_moe_triton.fused_moe import (
|
|
padding_size, # Avoid circular import
|
|
)
|
|
|
|
if get_bool_env_var("CK_MOE"):
|
|
layer.w13_weight = torch.nn.Parameter(
|
|
permute_weight(layer.w13_weight.data),
|
|
requires_grad=False,
|
|
)
|
|
torch.cuda.empty_cache()
|
|
layer.w2_weight = torch.nn.Parameter(
|
|
permute_weight(layer.w2_weight.data),
|
|
requires_grad=False,
|
|
)
|
|
torch.cuda.empty_cache()
|
|
# ROCm (CK_MOE): using column-wise scaling
|
|
layer.w13_weight_scale1 *= layer.w13_weight_scale.unsqueeze(-1)
|
|
layer.w2_weight_scale1 *= layer.w2_weight_scale.unsqueeze(-1)
|
|
elif get_bool_env_var("MOE_PADDING"):
|
|
# If ROCm, apply weight padding (min. Mem channel contention) only if set
|
|
layer.w13_weight = torch.nn.Parameter(
|
|
F.pad(layer.w13_weight.data, (0, padding_size), "constant", 0),
|
|
requires_grad=False,
|
|
)
|
|
torch.cuda.empty_cache()
|
|
layer.w2_weight = torch.nn.Parameter(
|
|
F.pad(layer.w2_weight.data, (0, padding_size), "constant", 0),
|
|
requires_grad=False,
|
|
)
|
|
torch.cuda.empty_cache()
|
|
|
|
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,
|
|
correction_bias: Optional[torch.Tensor] = None,
|
|
activation: str = "silu",
|
|
inplace: bool = True,
|
|
no_combine: bool = False,
|
|
) -> torch.Tensor:
|
|
from sglang.srt.layers.moe.fused_moe_triton.fused_moe import fused_experts
|
|
from sglang.srt.layers.moe.topk import select_experts
|
|
|
|
# Expert selection
|
|
topk_weights, topk_ids = 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,
|
|
correction_bias=correction_bias,
|
|
)
|
|
|
|
if _is_hip and get_bool_env_var("USE_INT4_WEIGHT"):
|
|
# TODO: add triton kernel and add check get_bool_env_var("CK_MOE")
|
|
assert not no_combine, f"{no_combine=} is not supported."
|
|
return asm_moe(
|
|
x,
|
|
layer.w13_weight,
|
|
layer.w2_weight,
|
|
topk_weights,
|
|
topk_ids,
|
|
layer.w13_weight_scale1,
|
|
layer.w2_weight_scale1,
|
|
activation=activation,
|
|
)
|
|
if _is_hip and get_bool_env_var("CK_MOE"):
|
|
# TODO(CK_MOE): FP8 or FP8 block_quant only supports 'silu' for the time-being.
|
|
assert (
|
|
activation == "silu"
|
|
), f"CK_MOE: FP8 and/or FP8 bloack_quant {activation=} will be supported later, unset CK_MOE"
|
|
assert not no_combine, f"{no_combine=} is not supported."
|
|
if self.block_quant:
|
|
return asm_moe(
|
|
x,
|
|
layer.w13_weight,
|
|
layer.w2_weight,
|
|
topk_weights,
|
|
topk_ids,
|
|
layer.w13_weight_scale_inv,
|
|
layer.w2_weight_scale_inv,
|
|
block_shape=tuple(self.quant_config.weight_block_size),
|
|
expert_mask=None,
|
|
)
|
|
else:
|
|
return asm_moe(
|
|
x,
|
|
layer.w13_weight,
|
|
layer.w2_weight,
|
|
topk_weights,
|
|
topk_ids,
|
|
layer.w13_weight_scale1,
|
|
layer.w2_weight_scale1,
|
|
)
|
|
else:
|
|
# Expert fusion with FP8 quantization
|
|
return fused_experts(
|
|
x,
|
|
layer.w13_weight,
|
|
layer.w2_weight,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
inplace=inplace and not no_combine,
|
|
activation=activation,
|
|
use_fp8_w8a8=True,
|
|
w1_scale=(
|
|
layer.w13_weight_scale_inv
|
|
if self.block_quant
|
|
else layer.w13_weight_scale
|
|
),
|
|
w2_scale=(
|
|
layer.w2_weight_scale_inv
|
|
if self.block_quant
|
|
else layer.w2_weight_scale
|
|
),
|
|
a1_scale=layer.w13_input_scale,
|
|
a2_scale=layer.w2_input_scale,
|
|
block_shape=self.quant_config.weight_block_size,
|
|
no_combine=no_combine,
|
|
)
|
|
|
|
|
|
class Fp8KVCacheMethod(BaseKVCacheMethod):
|
|
"""
|
|
Supports loading kv-cache scaling factors from FP8 checkpoints.
|
|
"""
|
|
|
|
def __init__(self, quant_config: Fp8Config):
|
|
super().__init__(quant_config)
|