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.
65 lines
2.6 KiB
Python
65 lines
2.6 KiB
Python
# adapted from https://huggingface.co/nvidia/NVLM-D-72B/blob/main/modeling_nvlm_d.py
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# --------------------------------------------------------
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# NVLM-D
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# Copyright (c) 2024 NVIDIA
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# Licensed under Apache 2.0 License [see LICENSE for details]
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# --------------------------------------------------------
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import torch.nn as nn
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from transformers import PretrainedConfig
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from vllm.inputs import INPUT_REGISTRY
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from .intern_vit import InternVisionModel
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from .internvl import (InternVLChatModel, InternVLInputPipeline,
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get_max_internvl_image_tokens)
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IMG_START = '<|vision_start|>'
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IMG_END = '<|vision_end|>'
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IMG_CONTEXT = '<|vision_pad|>'
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class NVLMInputPipeline(InternVLInputPipeline):
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def _create_image_prompt(self, feature_size: int, num_patches: int) -> str:
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tile_pos_identifiers = ([f"<tile_{i}>"
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for i in range(1, num_patches)] +
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["<tile_global_thumbnail>"])
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context_size = feature_size // num_patches
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return '<Image>' + ''.join(
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tile_pos_identifier + self.img_context_token * context_size
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for tile_pos_identifier in tile_pos_identifiers) + '</Image>'
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input_pipeline = NVLMInputPipeline(IMG_START, IMG_END, IMG_CONTEXT)
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@MULTIMODAL_REGISTRY.register_image_input_mapper(input_pipeline.input_mapper)
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@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_internvl_image_tokens)
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@INPUT_REGISTRY.register_dummy_data(input_pipeline.dummy_data)
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@INPUT_REGISTRY.register_input_processor(input_pipeline.input_processor)
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class NVLM_D_Model(InternVLChatModel):
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def _init_mlp1(self, config: PretrainedConfig) -> nn.Sequential:
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vit_hidden_size = config.vision_config.hidden_size
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llm_intermediate_size = config.text_config.intermediate_size
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llm_hidden_size = config.text_config.hidden_size
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return nn.Sequential(
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nn.LayerNorm(vit_hidden_size * int(1 / self.downsample_ratio)**2),
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nn.Linear(vit_hidden_size * int(1 / self.downsample_ratio)**2,
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llm_intermediate_size,
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bias=False),
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nn.GELU(),
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nn.Linear(llm_intermediate_size, llm_hidden_size, bias=False),
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)
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def _init_vision_model(self, config: PretrainedConfig,
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num_hidden_layers: int):
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# We added additional dummy heads to the original num of heads to make
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# the number of heads divisible by 8.
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return InternVisionModel(config.vision_config,
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num_hidden_layers_override=num_hidden_layers,
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num_dummy_heads=7)
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