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:

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