ef6abf3dc7
Adds ALL files needed for Dockerfile build:
- qwen3_6_scripts/ (baseline patches + our optimizations)
- vllm/ (full vllm package)
- paged_attention_v2_pytorch.py (V2 with single-bmm optimization)
- Dockerfile + computility-run.yaml
Our optimizations vs baseline:
1. paged_attn.py: pre-gathered context KV (eliminates 194 gather calls),
Triton try/fallback, V2 heuristic, threshold 32K→64K
2. paged_attention_v2_pytorch.py: fills NotImplementedError,
single-bmm Phase 1 (195 launches → 3)
3. patch_enable_triton.py: HAS_TRITON=True with safety fallback
4. patch_triton_tuning.py: BLOCK=64, NUM_WARPS=4 for BI-V100
5. computility-run.yaml: gpu-memory-utilization 0.9→0.95,
max-num-batched-tokens 8192→16384
This repo can now be submitted to dev.modelhub.org.cn as-is.
84 lines
2.6 KiB
Python
84 lines
2.6 KiB
Python
from abc import ABC, abstractmethod
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from dataclasses import dataclass
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from typing import Callable, Optional, Tuple
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import torch
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from vllm.model_executor.layers.quantization.utils import replace_parameter
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from vllm.scalar_type import ScalarType
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@dataclass
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class MPLinearLayerConfig:
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full_weight_shape: Tuple[int, int] # [in, out]
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partition_weight_shape: Tuple[int, int]
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weight_type: ScalarType
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act_type: torch.dtype
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group_size: int
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zero_points: bool
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has_g_idx: bool
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class MPLinearKernel(ABC):
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@classmethod
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@abstractmethod
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def get_min_capability(cls) -> int:
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raise NotImplementedError
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@classmethod
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@abstractmethod
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def can_implement(cls,
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c: MPLinearLayerConfig) -> Tuple[bool, Optional[str]]:
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raise NotImplementedError
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def __init__(self,
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c: MPLinearLayerConfig,
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w_q_param_name: str,
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w_s_param_name: str,
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w_zp_param_name: Optional[str] = None,
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w_gidx_param_name: Optional[str] = None) -> None:
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assert self.can_implement(c)
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self.config = c
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self.w_q_name = w_q_param_name
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self.w_s_name = w_s_param_name
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self.w_zp_name = w_zp_param_name
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self.w_gidx_name = w_gidx_param_name
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@abstractmethod
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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raise NotImplementedError
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@abstractmethod
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def apply_weights(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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raise NotImplementedError
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def _transform_param(self, layer: torch.nn.Module, name: Optional[str],
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fn: Callable) -> None:
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if name is not None and getattr(layer, name, None) is not None:
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old_param = getattr(layer, name)
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new_param = fn(old_param)
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# replace the parameter with torch.nn.Parameter for TorchDynamo
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# compatibility
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replace_parameter(
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layer, name,
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torch.nn.Parameter(new_param.data, requires_grad=False))
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def _get_weight_params(
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self, layer: torch.nn.Module
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) -> Tuple[torch.Tensor, # w_q
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torch.Tensor, # w_s
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Optional[torch.Tensor], # w_zp,
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Optional[torch.Tensor] # w_gidx
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]:
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return (
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getattr(layer, self.w_q_name),
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getattr(layer, self.w_s_name),
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getattr(layer, self.w_zp_name or "", None),
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getattr(layer, self.w_gidx_name or "", None),
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)
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