b6a4baa659
Signed-off-by: Canlin Guo <canlinguosdu@gmail.com> Signed-off-by: lishunyang12 <lishunyang12@163.com> Signed-off-by: yuanwu <yuan.wu@intel.com> Co-authored-by: lishunyang12 <lishunyang12@163.com> Co-authored-by: yuanwu <yuan.wu@intel.com>
54 lines
2.0 KiB
Python
54 lines
2.0 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
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"""Two-stage MiniMax H3 topology."""
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from vllm_omni.config.stage_config import (
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PipelineConfig,
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StageExecutionType,
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StagePipelineConfig,
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)
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_PROCESSOR = "vllm_omni.model_executor.stage_input_processors.minimax_h3"
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_CHECKPOINT = "vllm_omni.model_executor.models.minimax_h3.checkpoint"
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_DIFFUSION_PIPELINE = "vllm_omni.diffusion.models.minimax_h3.pipeline_minimax_h3"
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MINIMAX_H3_PIPELINE = PipelineConfig(
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model_type="minimax_h3_disaggregated",
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default_deploy_config_name="minimax_h3_disaggregated.yaml",
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stage_cli_aliases={"text_encoder_tp_size": (0, "tensor_parallel_size")},
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model_arch="MiniMaxH3TextEncoder",
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stages=(
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StagePipelineConfig(
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stage_id=0,
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model_stage="text_encoder",
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execution_type=StageExecutionType.LLM_AR,
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input_sources=(),
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owns_tokenizer=True,
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requires_multimodal_data=True,
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model_arch="MiniMaxH3TextEncoder",
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engine_output_type="latent",
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prompt_transform_func=f"{_PROCESSOR}.prepare_text_encoder_prompt",
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sampling_constraints={
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"max_tokens": 1,
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"temperature": 0.0,
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"detokenize": False,
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},
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model_path_resolver=f"{_CHECKPOINT}.resolve_minimax_h3_model_root",
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),
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StagePipelineConfig(
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stage_id=1,
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model_stage="dit",
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execution_type=StageExecutionType.DIFFUSION,
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input_sources=(0,),
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final_output=True,
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final_output_type="video",
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requires_multimodal_data=True,
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model_arch="MiniMaxH3Pipeline",
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custom_process_input_func=f"{_PROCESSOR}.text_encoder2diffusion",
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omni_kv_config={"need_recv_cache": False},
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model_path_resolver=f"{_DIFFUSION_PIPELINE}.resolve_minimax_h3_diffusion_model_path",
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inline_diffusion=True,
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),
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),
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)
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