mindspeed-mm-weight-prep

name	mindspeed-mm-weight-prep
description	MindSpeed-MM weight conversion guide using mm-convert CLI tool. Covers HuggingFace to MindSpeed-MM format conversion, reverse conversion, and PP weight resplitting. Supports Qwen2VLConverter, Qwen2_5_VLConverter, InternVLConverter, WanConverter and more. Use when converting multimodal model weights on Ascend NPU.
keywords	["mindspeed-mm","weight","weight conversion","mm-convert","hf_to_mm","mm_to_hf","checkpoint","converter"]

MindSpeed-MM Weight Conversion

This Skill guides users through converting multimodal model weights between HuggingFace and MindSpeed-MM formats using the mm-convert CLI tool, including VLM (Vision-Language Models) and generative models (video generation, etc.).

mm-convert CLI Overview

mm-convert -h
# Available subcommands: Qwen2VLConverter, Qwen2_5_VLConverter, InternVLConverter, WanConverter, ...

Converters support different operations depending on the model. Common operations include:

Operation	Description
hf_to_mm	HuggingFace weights -> MindSpeed-MM format (Megatron models)
mm_to_hf	MindSpeed-MM weights -> HuggingFace format (export after training)
hf_to_dcp / dcp_to_hf	DCP format conversion (FSDP2 models, e.g., Qwen3VL, InternVL3.5)
source_to_mm	Source weights -> MM format (e.g., OpenSoraPlan v1.5)
resplit	PP weight resplitting (adjust parallelism strategy without re-converting)

Note: Not all converters support all operations. Check the Model Registry for the exact operations supported by each converter.

Converter Selection

Model Family	Converter Class	Key Parameters
Qwen2VL	Qwen2VLConverter	llm_pp_layers, vit_pp_layers, tp_size
Qwen2.5VL	Qwen2_5_VLConverter	llm_pp_layers, vit_pp_layers, tp_size
InternVL2.5, InternVL3	InternVLConverter	llm_pp_layers, vit_pp_layers, tp_size
Wan2.1, Wan2.2	WanConverter	source_path, target_path, target_parallel_config.pp_layers

Selection rule: Match the Converter directly based on the model name. Qwen2VL and Qwen2.5VL use different Converters and must not be mixed.

Quick Start

VLM: HuggingFace -> MindSpeed-MM

Using Qwen2.5-VL-3B-Instruct as an example (single device, no PP splitting):

mm-convert Qwen2_5_VLConverter hf_to_mm \
  --cfg.mm_dir "ckpt/mm_path/Qwen2.5-VL-3B-Instruct" \
  --cfg.hf_config.hf_dir "ckpt/hf_path/Qwen2.5-VL-3B-Instruct" \
  --cfg.parallel_config.llm_pp_layers [[36]] \
  --cfg.parallel_config.vit_pp_layers [[32]] \
  --cfg.parallel_config.tp_size 1

VLM: MindSpeed-MM -> HuggingFace

mm-convert Qwen2_5_VLConverter mm_to_hf \
  --cfg.save_hf_dir "ckpt/mm_to_hf/Qwen2.5-VL-3B-Instruct" \
  --cfg.mm_dir "ckpt/mm_path/Qwen2.5-VL-3B-Instruct" \
  --cfg.hf_config.hf_dir "ckpt/hf_path/Qwen2.5-VL-3B-Instruct" \
  --cfg.parallel_config.llm_pp_layers [36] \
  --cfg.parallel_config.vit_pp_layers [32] \
  --cfg.parallel_config.tp_size 1

Note: For mm_to_hf, llm_pp_layers and vit_pp_layers use a 1D list (e.g., [36]), not a nested list. --cfg.hf_config.hf_dir must point to the original HF weights directory (containing config.json).

Generative Model: Wan2.1 hf_to_mm

mm-convert WanConverter hf_to_mm \
  --cfg.source_path ./weights/Wan-AI/Wan2.1-T2V-1.3B-Diffusers/transformer/ \
  --cfg.target_path ./weights/Wan-AI/Wan2.1-T2V-1.3B-Diffusers/transformer/

Generative Model: Wan mm_to_hf

mm-convert WanConverter mm_to_hf \
  --cfg.source_path <saved_weight_path>/ \
  --cfg.target_path ./converted_weights/Wan-AI/Wan2.1-T2V-1.3B-Diffusers/transformer/ \
  --cfg.hf_dir <original_hf_weights>/transformer/

WanConverter uses a different parameter system from VLM Converters: it uses source_path / target_path instead of mm_dir / hf_config.hf_dir.

Parameter Passing Methods

mm-convert is based on jsonargparse and supports three parameter passing methods:

1. Command-line Arguments

Pass parameters directly on the command line with --cfg.xxx (as shown in the examples above).

2. YAML Configuration File

First generate an annotated template, then modify as needed:

# Generate configuration template
mm-convert Qwen2_5_VLConverter hf_to_mm --print_config=comments > config.yaml

# Use configuration file
mm-convert Qwen2_5_VLConverter hf_to_mm --config config.yaml

Configuration file example:

cfg:
  mm_dir: "ckpt/mm_path/Qwen2.5-VL-3B-Instruct"
  hf_config:
    hf_dir: "ckpt/hf_path/Qwen2.5-VL-3B-Instruct"
  parallel_config:
    llm_pp_layers: [[36]]
    vit_pp_layers: [[32]]
    tp_size: 1

3. Environment Variables

export JSONARGPARSE_DEFAULT_ENV=true
# Then set parameters via environment variables (refer to jsonargparse documentation for variable naming conventions)

PP Splitting Guide

PP Splitting for VLM Models

VLM models have two components that need splitting: LLM and ViT.

Parameter	Description	Example
llm_pp_layers	Number of layers per PP stage for LLM	[[1,10,10,7]] means 4 stages
vit_pp_layers	Number of layers per PP stage for ViT	[[32,0,0,0]] means all ViT in stage 0
tp_size	Tensor parallelism degree	Must match the TP value in the training script

Splitting Rules:

• Sum of elements in llm_pp_layers = total number of LLM layers
• Sum of elements in vit_pp_layers = total number of ViT layers
• Both lists must have the same length (= number of PP stages)
• Must be consistent with the pipeline_num_layers configuration in model.json
• ViT layers are typically concentrated in stage 0 (e.g., [[32,0,0,0]]), but can also be distributed

Example: 4-device PP (Qwen2.5-VL-7B, 28-layer LLM + 32-layer ViT):

--cfg.parallel_config.llm_pp_layers [[1,10,10,7]] \
--cfg.parallel_config.vit_pp_layers [[32,0,0,0]]

PP Splitting for Wan Models

Wan models use the target_parallel_config.pp_layers parameter:

--cfg.target_parallel_config.pp_layers [[6,6,6,6]]  # 24-layer model, evenly split across 4 stages

resplit: PP Weight Resplitting

When already-converted MM weights need a different PP strategy, use resplit instead of re-converting from HF:

mm-convert Qwen2_5_VLConverter resplit \
  --cfg.source_dir "ckpt/mm_path/old_pp/" \
  --cfg.target_dir "ckpt/mm_path/new_pp/" \
  --cfg.source_parallel_config.llm_pp_layers [[28]] \
  --cfg.source_parallel_config.vit_pp_layers [[32]] \
  --cfg.target_parallel_config.llm_pp_layers [[1,10,10,7]] \
  --cfg.target_parallel_config.vit_pp_layers [[32,0,0,0]] \
  --cfg.target_parallel_config.tp_size 1

FAQ

Q: Conversion fails due to transformers version mismatch

Error messages like KeyError or weight key mismatches. This is caused by the local transformers version not matching the version corresponding to the model weights. Solution: re-download the latest weights from HuggingFace, or install a matching transformers version.

Q: Weights saved from LayerZero training cannot be directly converted with mm_to_hf

Weights saved from LayerZero training have a different format from the standard MM format. They need post-processing (merging or reordering) before running mm_to_hf conversion.

Q: hf_to_mm and mm_to_hf use different pp_layers formats

hf_to_mm uses nested lists [[36]], while mm_to_hf uses 1D lists [36]. Be careful to distinguish between them.

Q: Loading fails due to tp_size mismatch

The tp_size during conversion must exactly match the TP value in the training script. If training uses TP=2, conversion must also set tp_size 2.

Q: llm and vit PP layer list lengths are inconsistent

llm_pp_layers and vit_pp_layers must have the same number of elements (i.e., the same number of PP stages).

Usage Order

After weight conversion is complete:

1. Start training -> Refer to the MindSpeed-MM Training Skill

References

• Detailed Conversion Guide - Complete parameter tables for each Converter, multi-model multi-scale examples
• MindSpeed-MM Repository