huawei-cloud-msmodelslim-model-analysis

Analyze candidate models before adapter implementation. Determine model implementation source (transformers or model-local), structural features, layer-by-layer loading requirements, and MoE fused weight risks. Use this skill when the user wants to: (1) assess model adaptation feasibility before creating msModelSlim adapters, (2) analyze model structure and type classification, (3) evaluate MoE compatibility for quantization. Trigger: user mentions "model analysis", "msModelSlim", "adapter", "transformers", "MoE", "layer-by-layer", "model assessment", "feasibility", "模型分析", "适配可行性", "模型评估", "MoE分析"

Overview

This skill analyzes candidate models before adapter implementation for msModelSlim.

Architecture: Implementation Source Detection → Model Type Classification → Structural Feature Analysis → Risk Assessment

Related Skills:

• huawei-cloud-msmodelslim-model-adapt - Adapter creation based on analysis results

Architecture Components

This skill involves the following cloud services and components:

• msModelSlim: Huawei Cloud's model quantization framework
• Transformers Library: Hugging Face Transformers for model loading
• ModelScope: Model download and management platform
• config.json: Model configuration file for analysis

Architecture Diagram:

text

┌─────────────────────────────────────────────────────────────┐
│           msModelSlim Model Analysis Skill                  │
├─────────────────────────────────────────────────────────────┤
│  ┌──────────────┐    ┌──────────────┐    ┌──────────────┐ │
│  │  Model       │───▶│  Source      │───▶│  Structure   │ │
│  │  Input       │    │  Detection   │    │  Analysis    │ │
│  │  (config)    │    │              │    │              │ │
│  └──────────────┘    └──────────────┘    └──────────────┘ │
│         │                   │                   │          │
│         ▼                   ▼                   ▼          │
│  ┌──────────────┐    ┌──────────────┐    ┌──────────────┐ │
│  │  Type        │    │  MoE         │    │  Risk        │ │
│  │  Classification│   │  Assessment │    │  Assessment │ │
│  └──────────────┘    └──────────────┘    └──────────────┘ │
└─────────────────────────────────────────────────────────────┘

Use Cases

Typical Problem Scenarios:

• Assessing model adaptation feasibility before creating msModelSlim adapters
• Analyzing model structure and type classification
• Evaluating MoE compatibility for quantization
• Determining if a model can be quantized with msModelSlim
• Identifying potential risks before adapter development

Typical User Phrases:

• "Analyze my model for msModelSlim compatibility"
• "Check if this model can be quantized"
• "Evaluate MoE fused weights risk"
• "Assess model adaptation feasibility"
• "Analyze model structure for quantization"
• "AnalysisModelmsModelSlim"
• "ModelQuantization"
• "CheckMoE"

Scope

Supported:

• Decoder-only LLM
• VLM text backbone analysis (LLM/text path only)

Not supported:

• Non-transformers implementations
• Multimodal generation models (image/video/audio generation)

Required Input

• Model path or model repository identifier

• config.json

• Optional: modeling_*.py, model.safetensors.index.json in the model directory

• If files are missing locally:

  • Download non-weight files using: modelscope download --model <org>/<model> --local_dir ./models/<name> --exclude '*.safetensors'
  • Read config.json and modeling_*.py from the download directory as input for analysis.

Hard Requirement: Parse Implementation Source First

Must complete before any structural analysis. Agent should manually parse following these steps:

1. Read config.json:

   • Get model_type
   • Get auto_map (if present)

2. Try parsing from transformers:

   • Check if transformers library supports the model_type.
   • Check if path exists: transformers/models/<model_type>/modeling_<model_type>.py.
   • If exists, record as transformers implementation.

3. If not parsed, try model-local implementation:

   • Check if files pointed by auto_map exist in the model directory.
   • Check if modeling_*.py files exist in the model directory.
   • If exists, record as model-local implementation.

4. If neither path available:

   • Stop analysis.
   • Request user to provide readable model implementation code.

Minimum Workflow

1. Parse implementation source (complete hard requirement above).

2. Determine model type, structural differences, and connections:

   • Type: Pure LLM / Multimodal understanding / Multimodal generation
   • Compare with common Qwen2-like LLMs, record special structural designs (e.g., MoE, non-standard attention, SSM/hybrid blocks, additional heads or parallel branches)
   • Check special structure connections (location, dependencies, serial/parallel/residual connections, impact on backbone traversal)

3. Identify structural features:

   • Decoder layer class, attention/MLP module naming, forward signature

4. Determine features affecting adaptation:

   • Layer traversal path and order
   • Whether layer-by-layer loading is needed
   • MoE fused expert weight risk
   • Quantized model dequantization script risk
   • MTP structure implementation availability and weight handling risk

5. Output structured analysis results (refer to template below).

6. Provide next steps:

   • Proceed to adapter creation workflow
   • Or block and explain what user needs to provide

Model Type, Structural Differences, and Connection Determination

(relative to common Qwen2)

• Pure LLM: Text token input only, backbone is decoder-only language model.
• Multimodal understanding: Contains vision/audio encoders, but generation path centers on text backbone; only text portion can be analyzed and adapted.
• Multimodal generation: Core goal is image/video/audio generation; current workflow does not support, should block and explain reason directly.
• Structural differences only need to record "existence + impact direction", no deep implementation details required.
• Connection relationships should record at minimum: which stage special structure is located in backbone, which modules it connects to, connection type (serial/parallel/residual), and impact on traversal/forward alignment.

MoE Layout Determination

• Non-fused MoE: Experts expanded by module/list (commonly each expert has its own gate/up/down linear layers).
• Fused MoE: Multiple expert weights packaged as tensor parameters, no longer independent linear layers.
• If any of gate/up/down stored in [..., num_experts, ...] or [num_experts, ...] form, treat as "fused".
• Three-dimensional expert weights (e.g., gate/up/down each fused into 3D parameters) uniformly classified as MoE fused, with "may need unpack" marked in report.

Required Output: Analysis Report

Agent should directly generate analysis report (Markdown format), must include following elements. Refer to template below:

markdown

# Analysis Report

## Model Identification
- Model Path/Repository: {model_path}
- `model_type`: {model_type}
- `architectures`: {architectures}

## Implementation Source Analysis
- Result: `transformers` | `model-local` | `unsupported`
- Basis:
  - Resolved file path: {path}
  - Related configuration fields (`model_type`, `auto_map`): {details}

## Model Features and Specifications
- Hidden size: {hidden_size}
- Number of layers: {num_layers}
- Attention heads / KV heads: {num_heads} / {num_kv_heads}
- Analyze only VLM text portion: Yes/No

## Model Type, Structural Differences and Connections
- Model type: Pure LLM | Multimodal understanding | Multimodal generation
- Special structures vs common Qwen2: {special_structures}
- Special structure connections: {special_structure_connections}
- Impact on adaptation workflow: {structure_impact}

## Layer-by-Layer Loading Assessment
- Need layer-by-layer loading: Yes/No
- Reason: {reason}
- Constraints (memory/runtime environment): {constraints}

## MoE Assessment
- Contains MoE: Yes/No
- Layout type: No MoE | Non-fused MoE | Fused MoE
- Suspected fused keys/modules: {keys}
- Expert weight form: Independent linear layers | Packaged tensors
- Needs unpack: Yes/No

## Adaptation Impact Points
- Decoder traversal path: {traversal_path}
- Attention module naming: {attn_module}
- MLP module naming: {mlp_module}
- `visit/forward` strict alignment points: {alignment_points}

## Quantization and MTP Risk Assessment
- Model already quantized: Yes/No
- Quantization determination basis: {quant_evidence}
- Dequantization script provided: Yes/No
- Dequantization script status: {dequant_status}
- MTP structure exists: Yes/No
- MTP implementation code accessibility: Accessible/Not accessible
- MTP risk description: {mtp_risk}

## Risks and Next Steps
- Risk level: Low | Medium | High
- Blockers: {blockers}
- Recommended next steps:
  - Proceed to adapter creation workflow
  - Or request user to provide implementation code

Risk Identification and User Communication Requirements (Mandatory)

• If identified as "model already quantized", must mark "missing dequantization script" as blocker, explicitly requiring user to actively provide dequantization script before continuing adaptation.

• If MTP structure identified but implementation code inaccessible, must explicitly inform:

  • Agent may not be able to fully implement MTP structure adaptation;
  • To continue, user needs to copy MTP-related weights themselves (map according to user-side implementation).

• When at least one of above two risk types hits, risk level must not be lower than "Medium".

Pass/Fail Criteria

• Pass: Implementation source is transformers or model-local, model type is pure LLM or multimodal understanding, and report is complete; if quantization/MTP risks hit, clear user action requirements given in report.
• Fail: Source not parsed, unsupported implementation type, determined as multimodal generation model, or hits "quantized model without dequantization script" blocking condition.

Enhanced Features

Automated Compatibility Checker

This skill includes an automated model compatibility checker that scans model architectures before migration:

Features:

• Migration Blocker Detection: Identifies unsupported operators, custom layers, and framework-specific features
• Early Warning System: Provides early warning for known issues with suggested workarounds
• Compatibility Score: Generates compatibility score with detailed breakdown
• Operator Coverage Analysis: Reports operator coverage rate for Ascend NPU support

Compatibility Check Categories:

Category	Check Items
Operator Support	Transformer layers, attention, normalization
Framework Features	Custom ops, dynamic shapes, control flow
Weight Formats	Safetensors, PyTorch, HF format compatibility
Special Structures	MoE, MTP, hybrid architectures

Output Format:

markdown

## Compatibility Check Result
- Overall Score: XX/100
- Passed: X/XX checks
- Warning: X items require attention
- Blockers: X items preventing migration

### Detailed Results
| Check Item        | Status     | Details                           |
|-------------------|------------|-----------------------------------|
| Operator coverage | ✓ Pass     | 95% of operators supported         |
| Custom layers     | ⚠️ Warning | 2 custom ops need AscendC impl    |
| Weight format     | ✓ Pass     | Standard Hugging Face format      |

Reference Documents

• Analysis Checklist - Analysis verification checklist
• Acceptance Criteria - Functional acceptance criteria
• Verification Method - Verification approach
• Troubleshooting - Common issues and solutions

Prerequisites

• transformers >= 4.40.0 installed
• Model code available for analysis
• Basic understanding of model structure

Analysis Workflow

The analysis workflow follows these steps:

1. Parse model configuration (config.json)
2. Determine implementation source (transformers or model-local)
3. Analyze model architecture and structural features
4. Assess MoE layout and fused weight risks
5. Generate structured analysis report
6. Provide adaptation recommendations

Parameter Reference

Parameter	Description	Required
model	Model name or path	Yes
output	Analysis report output path	No
detailed	Output detailed information	No