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.
58 lines
1.9 KiB
Python
58 lines
1.9 KiB
Python
from typing import Iterable, List, Optional, Tuple, Union
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import torch
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from torch import nn
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from vllm.attention import AttentionMetadata
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from vllm.model_executor.layers.pooler import Pooler, PoolingType
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from vllm.model_executor.pooling_metadata import PoolingMetadata
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from vllm.sequence import IntermediateTensors, PoolerOutput
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from .gemma2 import Gemma2Model
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from .interfaces import SupportsPP
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class Gemma2EmbeddingModel(nn.Module, SupportsPP):
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"""A model that uses Gemma2 with additional embedding functionalities.
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This class encapsulates the Gemma2Model and provides an interface for
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embedding operations and customized pooling functions.
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Attributes:
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model: An instance of Gemma2Model used for forward operations.
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_pooler: An instance of Pooler used for pooling operations.
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"""
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def __init__(
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self,
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**kwargs,
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) -> None:
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super().__init__()
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self.model = Gemma2Model(**kwargs)
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self._pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
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self.make_empty_intermediate_tensors = (
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self.model.make_empty_intermediate_tensors)
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def forward(
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self,
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input_ids: Optional[torch.Tensor],
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positions: torch.Tensor,
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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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return self.model(input_ids, positions, kv_caches, attn_metadata,
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intermediate_tensors, inputs_embeds)
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def pooler(
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self,
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hidden_states: torch.Tensor,
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pooling_metadata: PoolingMetadata,
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) -> Optional[PoolerOutput]:
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return self._pooler(hidden_states, pooling_metadata)
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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self.model.load_weights(weights)
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