dependency-analysis

name	dependency-analysis
description	Analyze Python package dependencies for Ascend NPU compatibility. Use when examining requirements.txt, setup.py, environment files, and checking for CUDA-dependent packages.

Dependency Analysis for Ascend NPU

You are analyzing dependencies for Ascend NPU compatibility. This skill helps identify:

1. CUDA-dependent packages that need replacement
2. Version compatibility with torch_npu and CANN
3. Conflicts with Ascend software stack
4. NPU-compatible alternatives to CUDA packages

When to Use

Invoke this skill when:

• User asks about dependency compatibility
• Examining requirements files or environment configurations
• Checking for CUDA-specific package dependencies
• Planning environment setup for Ascend

Analysis Approach

1. Examine Dependency Files

Read these files from the repository:

• requirements.txt
• setup.py (check install_requires)
• pyproject.toml (check dependencies)
• environment.yml (conda environments)
• Pipfile

2. Key Compatibility Checks

PyTorch Version:

• torch_npu 2.1.0+ requires PyTorch 2.1.0+
• Check PyTorch version in dependencies
• Verify torch_npu compatibility

CUDA-Dependent Packages to Flag:

• cupy - CUDA NumPy replacement
• cudf - CUDA DataFrame library
• cuml - CUDA ML library
• spacy-cuda - CUDA-accelerated spaCy
• flash-attn - Flash Attention (has NPU equivalent)
• apex - NVIDIA APEX utilities
• xformers - Transformer optimizations
• triton - GPU programming language

Known Incompatibilities:

• Packages with hard-coded CUDA kernels
• Libraries requiring NVIDIA-specific cuDNN/cuBLAS
• Packages with no NPU support

3. Version Constraints

Ascend Stack Requirements:

• CANN: 8.0+ (typically 8.5.0 recommended)
• torch_npu: 2.1.0+ (match PyTorch minor version)
• Python: 3.8-3.10 (check torch_npu compatibility)
• Drivers: Ascend 910/310P driver versions

Output Format

Provide analysis in this structure:

Core Dependencies

• PyTorch version and torch_npu compatibility
• Key dependencies and their versions
• Critical version constraints

CUDA-Dependent Dependencies

List packages requiring replacement:

Package	Version	Issue	Suggested Alternative
flash-attn	2.x	CUDA-specific	torch_npu.npu_fusion_attention
cupy	12.x	CUDA-specific	numpy (or remove)

Version Constraints

• Specific version requirements for Ascend stack
• Pinning recommendations
• Dependency conflicts identified

Environment Requirements

• CANN version requirements
• Driver/firmware requirements
• torch_npu version requirements
• Installation order considerations

Recommended Replacements

Common CUDA → NPU Replacements:

flash-attn → torch_npu (built-in fusion attention)
torch.cuda.amp → torch.npu.amp
torch.distributed.nccl → torch.distributed.hccl
apex → torch_npu (AMP built-in)

Tools to Use

Documentation First:

• Read official Ascend documentation before analysis:
  • https://www.hiascend.com/doc_center/source/zh/Pytorch/730/ptmoddevg/trainingmigrguide/PT_LMTMOG_0002.html
  • https://www.hiascend.com/doc_center/source/zh/canncommercial/850/API/aolapi/operatorlist_00001.html

Dependency Analysis:

• Use Read to examine dependency files

Notes

• Not all CUDA dependencies need exact replacements
• Some packages work on CPU (performance impact)
• Prioritize critical dependencies first
• Consider transitive dependencies
• Suggest version pinning for reproducibility