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.
68 lines
2.4 KiB
Python
68 lines
2.4 KiB
Python
import os
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from importlib.util import find_spec
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from typing import Any, Dict, List, Optional
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from torch.nn import Module
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig)
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SUPPORTED_QUANT_DTYPE_LIST = ['s8', 'f8e4m3fn']
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class NeuronQuantConfig(QuantizationConfig):
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"""Int8 Quantization Config class for Neuron Backend."""
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def __init__(
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self,
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dequant_dtype: str = "f16",
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quantize_method: str = "vector_dynamic",
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) -> None:
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self.quant_dtype = os.getenv("NEURON_QUANT_DTYPE", "s8")
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if self.quant_dtype not in SUPPORTED_QUANT_DTYPE_LIST:
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raise ValueError(
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f"Neuron quantization datatype {self.quant_dtype} is not valid,"
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f"the quantization datatype should match one of the below types"
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f"{SUPPORTED_QUANT_DTYPE_LIST}")
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self.dequant_dtype = dequant_dtype
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self.quantize_method = quantize_method
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def get_name(self) -> str:
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return "neuron_quant"
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def get_supported_act_dtypes(self) -> List[str]:
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return SUPPORTED_QUANT_DTYPE_LIST
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@classmethod
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def get_min_capability(cls) -> int:
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raise NotImplementedError(
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"This function should not be called with Neuron Backend")
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@staticmethod
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def get_config_filenames() -> List[str]:
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return []
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@classmethod
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def from_config(cls, config: Dict[str, Any]) -> "NeuronQuantConfig":
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quantize_method = cls.get_from_keys(config, ["quantize_method"])
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dequant_dtype = cls.get_from_keys(config, ["dequant_dtype"])
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return cls(dequant_dtype=dequant_dtype,
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quantize_method=quantize_method)
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def get_quant_method(self, layer: Module, prefix: str) -> Optional[Any]:
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if find_spec("transformers_neuronx") is not None:
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return self.get_quantization_config()
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else:
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raise NotImplementedError(
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"Neuron Quantization is only supported through"
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" transformers_neuronx.")
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def get_scaled_act_names(self) -> List[str]:
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return []
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def get_quantization_config(self):
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from transformers_neuronx.config import QuantizationConfig
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return QuantizationConfig(quant_dtype=self.quant_dtype,
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dequant_dtype=self.dequant_dtype,
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quantize_method=self.quantize_method)
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