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.
80 lines
2.6 KiB
Python
80 lines
2.6 KiB
Python
from dataclasses import dataclass
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from typing import Optional
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import torch
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from torch import nn
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from vllm.adapter_commons.layers import AdapterMapping
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from vllm.config import PromptAdapterConfig
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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VocabParallelEmbedding)
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@dataclass
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class PromptAdapterMapping(AdapterMapping):
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pass
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class VocabParallelEmbeddingWithPromptAdapter(nn.Module):
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def __init__(self, base_layer: VocabParallelEmbedding) -> None:
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super().__init__()
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self.base_layer = base_layer
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self.emb_layer = self.base_layer
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if 'LoRA' in base_layer.__class__.__name__:
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self.emb_layer = self.base_layer.base_layer
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def create_prompt_adapter_weights(
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self, prompt_adapter_config: PromptAdapterConfig):
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self.embeddings_tensors = torch.zeros(
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(
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prompt_adapter_config.max_prompt_adapters,
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prompt_adapter_config.max_prompt_adapter_token,
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self.emb_layer.embedding_dim,
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),
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dtype=self.emb_layer.weight.dtype,
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device=self.emb_layer.weight.device,
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)
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self.adapter_lengths = torch.zeros(
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prompt_adapter_config.max_prompt_adapters,
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dtype=torch.long,
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device=self.emb_layer.weight.device)
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self.indices_gpu: torch.Tensor
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self.embedding_indices_gpu: torch.Tensor
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def reset_prompt_adapter(self, index: int):
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self.embeddings_tensors[index] = 0
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def set_prompt_adapter(
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self,
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index: int,
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adapter_model: Optional[torch.Tensor],
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):
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self.reset_prompt_adapter(index)
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if adapter_model is not None:
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length = adapter_model.shape[0]
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self.embeddings_tensors[index, :length] = adapter_model
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self.adapter_lengths[index] = length
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def set_mapping(
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self,
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prompt_indices: torch.Tensor,
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prompt_embedding_indices: torch.Tensor,
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):
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self.indices_gpu = prompt_indices.to(
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device=self.emb_layer.weight.device)
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self.embedding_indices_gpu = prompt_embedding_indices.to(
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device=self.emb_layer.weight.device)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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hidden_states = self.base_layer(x)
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if self.embedding_indices_gpu.ndim > 1:
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valid_mask = self.indices_gpu != -1
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gathered_embeddings = self.embeddings_tensors[
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self.embedding_indices_gpu[:, 0],
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self.embedding_indices_gpu[:, 1]]
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# Update hidden states
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hidden_states[valid_mask] = gathered_embeddings
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return hidden_states |