615 lines
29 KiB
Python
615 lines
29 KiB
Python
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import functools
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import importlib.util
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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.distributed import get_tensor_model_parallel_world_size
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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.fp8_utils import (
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apply_fp8_block_linear, check_aiter_fp8_linear_support,
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create_fp8_input_scale, create_fp8_scale_parameter,
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create_fp8_weight_parameter, expert_weight_is_col_major,
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maybe_post_process_fp8_weight_block, process_fp8_weight_block_strategy,
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process_fp8_weight_tensor_strategy, requant_weight_ue8m0_inplace,
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validate_fp8_block_shape)
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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_block_fp8_supported, cutlass_fp8_supported,
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# normalize_e4m3fn_to_e4m3fnuz, per_tensor_dequantize,
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# requantize_with_max_scale)
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from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
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Fp8LinearOp, all_close_1d, convert_to_channelwise,
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cutlass_block_fp8_supported, cutlass_fp8_supported,
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maybe_create_device_identity, normalize_e4m3fn_to_e4m3fnuz,
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per_tensor_dequantize, requantize_with_max_scale)
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from vllm.model_executor.parameter import (BlockQuantScaleParameter,
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ModelWeightParameter,
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PerTensorScaleParameter)
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from vllm.platforms import current_platform
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from vllm.model_executor.utils import set_weight_attrs
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from vllm.model_executor.layers.quantization.fp8 import Fp8Config
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from vllm.model_executor.layers.quantization.utils.quant_utils import (
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GroupShape, is_layer_skipped)
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from vllm.model_executor.layers.linear import QKVParallelLinear
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from vllm.utils import has_deep_gemm
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logger = init_logger(__name__)
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# has_deep_gemm = importlib.util.find_spec("deep_gemm") is not None
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def Fp8LinearMethod__init(self, quant_config: Fp8Config):
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self.quant_config = quant_config
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self.cutlass_block_fp8_supported = cutlass_block_fp8_supported()
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self.out_dtype = torch.get_default_dtype()
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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 current_platform.is_rocm():
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self.use_marlin = False
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self.weight_block_size = self.quant_config.weight_block_size
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self.block_quant = self.quant_config.weight_block_size is not None
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self.act_q_static = self.quant_config.activation_scheme == "static"
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# Use per-token quantization for better perf if dynamic and cutlass
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if not self.act_q_static and cutlass_fp8_supported():
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self.act_q_group_shape = GroupShape.PER_TOKEN
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else:
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self.act_q_group_shape = GroupShape.PER_TENSOR
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if self.block_quant:
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self.block_size = self.quant_config.weight_block_size
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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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self.scale_k = 1
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self.scale_n = 1
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self.scale_n_prefill = 1 # only for fp8 moe
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self.fp8_linear = Fp8LinearOp(
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act_quant_static=self.act_q_static,
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act_quant_group_shape=self.act_q_group_shape)
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class Fp8LinearMethod(LinearMethodBase):
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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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if self.block_quant:
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scale_n = extra_weight_attrs.get("scale_n")
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scale_k = extra_weight_attrs.get("scale_k")
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if scale_n is not None:
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self.scale_n = scale_n
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if scale_k is not None:
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self.scale_k = scale_k
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assert self.weight_block_size is not None
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layer.weight_block_size = self.weight_block_size
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tp_size = get_tensor_model_parallel_world_size()
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assert self.quant_config.weight_block_size is not None
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block_n, block_k = (
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self.quant_config.weight_block_size[0] // self.scale_n ,
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self.quant_config.weight_block_size[1] // self.scale_k ,
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)
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# Required by row parallel
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if (tp_size > 1
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and input_size // input_size_per_partition == tp_size
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and 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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# Required by column parallel or enabling merged weights
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if (tp_size > 1 and output_size // output_size_per_partition
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== tp_size) 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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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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if not self.block_quant:
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scale = PerTensorScaleParameter(
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data=torch.empty(len(output_partition_sizes),
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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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else:
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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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# The weight_scale_inv name is intentional for deepseekv3
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layer.register_parameter("weight_scale_inv", 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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# TODO(rob): refactor block quant into separate class.
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if self.block_quant:
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assert self.quant_config.activation_scheme == "dynamic"
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if current_platform.is_fp8_fnuz():
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weight, weight_scale_inv, _ = \
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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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else:
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weight = layer.weight.data
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weight_scale_inv = layer.weight_scale_inv.data
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if isinstance(layer, QKVParallelLinear):
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# NOTE: for QKVParallelLinear
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# weight_scale should be divisible by 8 Dsps
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shape = weight_scale_inv.shape[0]
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repeat = 1
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while shape % 8 != 0:
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repeat *= 2
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shape = shape * repeat
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weight_scale_inv = torch.repeat_interleave(weight_scale_inv, repeats=repeat, dim=0)
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# weight = self._maybe_pad_weight(weight)
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# if self.block_quant:
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# maybe_post_process_fp8_weight_block(
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# layer, self.cutlass_block_fp8_supported)
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# Torch.compile cannot use Parameter subclasses.
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layer.weight = Parameter(weight, requires_grad=False)
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layer.weight_scale_inv = Parameter(weight_scale_inv,
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requires_grad=False)
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return
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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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# Note: lazy import to avoid triton import error.
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from vllm.model_executor.layers.quantization.utils.fp8_utils import (
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apply_w8a8_block_fp8_linear)
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if self.block_quant:
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assert self.quant_config.weight_block_size is not None
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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=[layer.weight.shape[0] // layer.weight_scale_inv.shape[0], layer.weight.shape[1] // layer.weight_scale_inv.shape[1]],
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weight_scale=layer.weight_scale_inv,
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input_scale=layer.input_scale,
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bias=bias,
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cutlass_block_fp8_supported=self.cutlass_block_fp8_supported,
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)
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return self.fp8_linear.apply(input=x,
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weight=layer.weight,
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weight_scale=layer.weight_scale,
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out_dtype=self.out_dtype,
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input_scale=layer.input_scale,
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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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# # Default to using per_token quantization if cutlass is supported
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# use_per_token_if_dynamic=self.cutlass_fp8_supported)
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def Fp8MoEMethod_init_(self, quant_config: Fp8Config, layer: torch.nn.Module):
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self.layer = layer
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from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts
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self.quant_config = quant_config
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self.block_quant = self.quant_config.weight_block_size is not None
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self.flashinfer_moe_backend = None
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self.scale_k = 1
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self.scale_n = 1
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self.scale_n_prefill = 1
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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 current_platform.is_rocm() or current_platform.is_vacc:
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self.use_marlin = False
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# Check for DeepGemm support.
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self.allow_deep_gemm = False
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if envs.VLLM_USE_DEEP_GEMM:
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if not has_deep_gemm():
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logger.warning_once("Failed to import DeepGemm kernels.")
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elif not self.block_quant:
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logger.warning_once("Model is not block quantized. Not using "
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" DeepGemm kernels")
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elif (current_platform.is_cuda()
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and current_platform.has_device_capability(90)):
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logger.info_once("Using DeepGemm kernels for Fp8MoEMethod.")
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self.allow_deep_gemm = True
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else:
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logger.warning_once(
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"DeepGemm not supported on the current platform.")
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# Check for CutlassBlockScaledGroupedGemm support.
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self.allow_cutlass_block_scaled_grouped_gemm = False
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if not self.block_quant:
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logger.warning_once("Model is not block quantized. Not using "
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"CutlassBlockScaledGroupedGemm kernels")
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elif (current_platform.is_cuda()
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and current_platform.has_device_capability(100)):
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logger.info_once(
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"Using CutlassBlockScaledGroupedGemm kernels for Fp8MoEMethod."
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)
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self.allow_cutlass_block_scaled_grouped_gemm = True
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else:
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logger.warning_once(
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"CutlassBlockScaledGroupedGemm not supported on the current "
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"platform.")
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self.topk_indices_dtype = None
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self.fused_experts = functools.partial( # type: ignore
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fused_experts,
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use_fp8_w8a8=True,
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block_shape=self.quant_config.weight_block_size,
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allow_deep_gemm=self.allow_deep_gemm,
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allow_cutlass_block_scaled_grouped_gemm=(
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self.allow_cutlass_block_scaled_grouped_gemm))
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class Fp8MoEMethod(FusedMoEMethodBase):
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def create_weights(self, layer: Module, num_experts: int, hidden_size: int,
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intermediate_size_per_partition: int,
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params_dtype: torch.dtype, **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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if self.block_quant:
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assert self.quant_config.weight_block_size is not None
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scale_n = extra_weight_attrs.get("scale_n")
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scale_n_prefill = extra_weight_attrs.get("scale_n_prefill")
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scale_k = extra_weight_attrs.get("scale_k")
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if scale_n is not None:
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self.scale_n = scale_n
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if scale_k is not None:
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self.scale_k = scale_k
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if scale_n_prefill is not None:
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self.scale_n_prefill = scale_n_prefill
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if self.quant_config is not None and self.quant_config.weight_block_size is not None:
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self.gcd_value = self.quant_config.weight_block_size[0]
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output_size_no_merge = intermediate_size_per_partition
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#assert isinstance(output_size_no_merge, int), f"merge output size should divded int, valuue is: {output_size_no_merge}"
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if output_size_no_merge % self.quant_config.weight_block_size[0]:
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import math
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gcd_value = math.gcd(output_size_no_merge % self.quant_config.weight_block_size[0], self.quant_config.weight_block_size[0])
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self.scale_n =self.scale_n * self.quant_config.weight_block_size[0] // gcd_value
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self.scale_n_prefill =self.scale_n_prefill * self.quant_config.weight_block_size[0] // gcd_value
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if hidden_size % self.quant_config.weight_block_size[1]:
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import math
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gcd_value = math.gcd(hidden_size % self.quant_config.weight_block_size[1], self.quant_config.weight_block_size[1])
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self.scale_k =self.scale_k * self.quant_config.weight_block_size[1] // gcd_value
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# self.scale_k = self.scale_n
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# print('output_size_no_merge', output_size_no_merge)
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# 按 block_size 分core
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# output_size_no_merge = 384
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# block_size = 128: 384 = 3x128 只能分3core x 128
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# block_size = 16: 384 = 24x16 8core x (3x16) 可以分到 8core
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# output_size_no_merge = 512
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# block_size = 128: 512 = 4x128 只能分 4core x 128
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# block_size = 64: 512 = 8x64 可以分到 8core x 64
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# output_size_no_merge = 768
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# block_size = 128: 768 = 6x128 只能分 6core x 128
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# block_size = 32: 768 = 8x(3x32) 可以分到 8core x (3x32)
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core_num = 8
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min_block_size = 4
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block_size_tmp = self.quant_config.weight_block_size[0] // self.scale_n
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if output_size_no_merge > block_size_tmp and \
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output_size_no_merge % block_size_tmp == 0 and \
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output_size_no_merge // block_size_tmp < core_num and \
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output_size_no_merge % core_num == 0:
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core_num_old = output_size_no_merge // block_size_tmp
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import math
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gcd_value = math.gcd(core_num, core_num_old)
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new_scale = core_num // gcd_value
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if block_size_tmp // new_scale >= min_block_size:
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self.scale_n = new_scale * self.scale_n
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#print("moe scale n is:", self.scale_n, self.scale_k, intermediate_size_per_partition)
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tp_size = get_tensor_model_parallel_world_size()
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if self.scale_n != self.scale_n_prefill:
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block_n_prefill = self.quant_config.weight_block_size[0] // self.scale_n_prefill
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block_n, block_k = (
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self.quant_config.weight_block_size[0] // self.scale_n,
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self.quant_config.weight_block_size[1] // self.scale_k,
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)
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# NOTE: To ensure proper alignment of the block-wise quantization
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# scales, the output_size of the weights for both the gate and up
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# 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 "
|
|
f"weight quantization block_n = {block_n}.")
|
|
if (tp_size > 1
|
|
and hidden_size % block_k != 0):
|
|
# Required by row parallel
|
|
raise ValueError(
|
|
f"The input_size of down's weight = "
|
|
f"{intermediate_size_per_partition} is not divisible by "
|
|
f"weight quantization block_k = {block_k}.")
|
|
|
|
# WEIGHTS
|
|
w13_weight = torch.nn.Parameter(torch.empty(
|
|
num_experts,
|
|
2 * intermediate_size_per_partition,
|
|
hidden_size,
|
|
dtype=params_dtype),
|
|
requires_grad=False)
|
|
layer.register_parameter("w13_weight", w13_weight)
|
|
set_weight_attrs(w13_weight, extra_weight_attrs)
|
|
|
|
w2_weight = torch.nn.Parameter(torch.empty(
|
|
num_experts,
|
|
hidden_size,
|
|
intermediate_size_per_partition,
|
|
dtype=params_dtype),
|
|
requires_grad=False)
|
|
layer.register_parameter("w2_weight", w2_weight)
|
|
set_weight_attrs(w2_weight, extra_weight_attrs)
|
|
|
|
# WEIGHT_SCALES
|
|
if not self.block_quant:
|
|
# 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)
|
|
else:
|
|
w13_weight_scale = torch.nn.Parameter(
|
|
torch.ones(
|
|
num_experts,
|
|
2 * ((intermediate_size_per_partition + 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_k - 1) // block_k,
|
|
(intermediate_size_per_partition + block_n - 1) // block_n,
|
|
dtype=torch.float32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
|
|
if self.scale_n != self.scale_n_prefill:
|
|
w13_weight_scale_prefill = torch.nn.Parameter(
|
|
torch.ones(
|
|
num_experts,
|
|
2 * ((intermediate_size_per_partition + block_n_prefill - 1) //
|
|
block_n_prefill),
|
|
(hidden_size + block_k - 1) // block_k,
|
|
dtype=torch.float32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
w2_weight_scale_prefill = torch.nn.Parameter(
|
|
torch.ones(
|
|
num_experts,
|
|
(hidden_size + block_k - 1) // block_k,
|
|
(intermediate_size_per_partition + block_n_prefill - 1) // block_n_prefill,
|
|
dtype=torch.float32,
|
|
),
|
|
requires_grad=False,
|
|
)
|
|
layer.register_parameter("w13_weight_scale_inv_prefill", w13_weight_scale_prefill)
|
|
layer.register_parameter("w2_weight_scale_inv_prefill", w2_weight_scale_prefill)
|
|
|
|
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"
|
|
|
|
# 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 self.scale_n != self.scale_n_prefill:
|
|
set_weight_attrs(w13_weight_scale_prefill, extra_weight_attrs)
|
|
set_weight_attrs(w2_weight_scale_prefill, 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 moe_fp8_apply(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
router_logits: torch.Tensor,
|
|
top_k: int,
|
|
renormalize: bool,
|
|
use_grouped_topk: bool = False,
|
|
topk_group: Optional[int] = None,
|
|
num_expert_group: Optional[int] = None,
|
|
global_num_experts: int = -1,
|
|
expert_map: Optional[torch.Tensor] = None,
|
|
custom_routing_function: Optional[Callable] = None,
|
|
scoring_func: str = "softmax",
|
|
e_score_correction_bias: Optional[torch.Tensor] = None,
|
|
apply_router_weight_on_input: bool = False,
|
|
activation: str = "silu",
|
|
) -> torch.Tensor:
|
|
from vllm.model_executor.layers.fused_moe import FusedMoE
|
|
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,
|
|
scoring_func=scoring_func,
|
|
e_score_correction_bias=e_score_correction_bias,
|
|
)
|
|
|
|
try:
|
|
from torch_vacc.vacc.custom_ops import fused_experts
|
|
from vllm_vacc.vllm.model_executor.models.memory.memory_recycling import memory_recycler
|
|
experts_output = None
|
|
if memory_recycler is not None:
|
|
# remove MOE_EXPERT_OUT_BUFFER
|
|
# experts_output = memory_recycler.MOE_EXPERT_OUT_BUFFER
|
|
experts_output = memory_recycler.MOE_SHARED_MLP_OUT_BUFFER
|
|
return fused_experts(
|
|
x,
|
|
layer.w13_weight,
|
|
layer.w2_weight,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
use_fp8_w8a8=True,
|
|
w13_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),
|
|
a13_scale=layer.w13_input_scale,
|
|
a2_scale=layer.w2_input_scale,
|
|
block_shape=self.quant_config.weight_block_size,
|
|
decode_with_batch=layer.is_decode and x.shape[0] > 1,
|
|
output_opt=experts_output
|
|
)
|
|
except Exception as e:
|
|
print(f"vacc fused_expert run fail, now using unfused ops: {e}")
|
|
from torch_vacc.vacc.custom_ops_cpu import fused_experts
|
|
return fused_experts(
|
|
x,
|
|
layer.w13_weight,
|
|
layer.w2_weight,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
use_fp8_w8a8=True,
|
|
w13_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),
|
|
a13_scale=layer.w13_input_scale,
|
|
a2_scale=layer.w2_input_scale,
|
|
block_shape=self.quant_config.weight_block_size,
|
|
) |