veomni-profile

name

veomni-profile

description

Use this skill for performance profiling and optimization. Two modes: (1) Analyze existing profile files (Chrome traces, memory snapshots) — write scripts to parse and summarize metrics per user requirements. (2) Generate profiles during development — configure ProfileConfig, run training, collect traces, analyze bottlenecks, and suggest optimizations. Trigger: 'profile', 'performance', 'slow', 'MFU', 'throughput', 'bottleneck', 'memory usage', 'trace', 'optimize training speed'.

VeOmni Profiling Infrastructure

Key components:

Component	Location	Purpose
ProfileConfig	veomni/arguments/arguments_types.py	Config fields: enable, start_step, end_step, trace_dir, profile_memory, with_stack, etc.
create_profiler()	veomni/utils/helper.py	Builds torch.profiler.profile (CUDA) or torch_npu.profiler (NPU) with schedule
ProfileTraceCallback	veomni/trainer/callbacks/trace_callback.py	Integrates profiler into the training loop via BaseTrainer
VeomniFlopsCounter	veomni/utils/count_flops.py	Analytical FLOPs/MFU computation per model family
EnvironMeter	veomni/utils/helper.py	Step-level throughput metrics (tokens/s, FLOPs, MFU)
merge_chrome_trace.py	scripts/profile/merge_chrome_trace.py	Merge multi-rank Chrome traces for unified viewing

Output formats:

• Chrome trace: veomni_rank{R}_{timestamp}.pt.trace.json.gz — viewable in chrome://tracing or Perfetto
• Memory snapshot: .pkl file via torch.cuda.memory._dump_snapshot — viewable with PyTorch Memory Viz

---

Mode 1: Analyze Existing Profile Files

User provides one or more profile files (Chrome traces, memory snapshots, logs). Write scripts to parse and analyze them.

Steps

1. Identify file types: .json.gz / .json (Chrome trace), .pkl (memory snapshot), .log / .txt (training logs with throughput metrics).

2. Understand the analysis goal — ask the user what they want to know:

   • Kernel-level breakdown (which CUDA kernels dominate wall time?)
   • Communication vs computation ratio (NCCL all-reduce, all-to-all, all-gather time)
   • Memory high-water mark and allocation timeline
   • Per-step time breakdown (forward, backward, optimizer, data loading)
   • MFU / hardware utilization
   • Comparison across multiple profiles (e.g. before/after optimization, different parallelism configs)

3. Write an analysis script using torch.profiler APIs or raw JSON parsing:

   import json, gzip
   from collections import defaultdict
   
   def load_chrome_trace(path):
       opener = gzip.open if path.endswith('.gz') else open
       with opener(path, 'rt') as f:
           return json.load(f)
   
   def analyze_kernel_time(trace):
       """Group events by kernel name, sum durations."""
       kernel_times = defaultdict(float)
       for event in trace.get('traceEvents', []):
           if event.get('cat') == 'kernel':
               kernel_times[event['name']] += event.get('dur', 0)
       return sorted(kernel_times.items(), key=lambda x: -x[1])
   

   Adapt the script to the user's specific analysis goal. Output tables, summaries, or CSV for further processing.

4. For multi-rank traces: use scripts/profile/merge_chrome_trace.py to merge before analysis, or analyze per-rank and compare.

5. For memory snapshots: load with pickle, analyze allocation records, identify peak usage and largest tensors.

6. Present findings: summarize top bottlenecks, compute/comm ratio, and actionable optimization suggestions.

---

Mode 2: Generate Profiles During Development

Actively profile a training run to identify performance bottlenecks or validate optimizations.

Step 1: Configure Profiling

Add or modify the profile section in the training YAML config:

train:
  profile:
    enable: true
    start_step: 5        # skip warmup steps
    end_step: 10         # capture 5 steps
    trace_dir: ./profile_output
    record_shapes: true
    profile_memory: true  # enable memory snapshot (CUDA only)
    with_stack: true      # capture Python call stacks
    with_modules: true    # annotate with nn.Module names
    rank0_only: true      # profile only rank 0 to reduce overhead

Or pass via CLI overrides: --train.profile.enable=true --train.profile.start_step=5 ...

Step 2: Run Training

source .venv/bin/activate
# Single GPU
python tasks/train_text.py --config configs/text/<model>.yaml

# Multi-GPU (profile will capture per-rank traces)
torchrun --nproc_per_node=8 tasks/train_text.py --config configs/text/<model>.yaml

Step 3: Collect and Analyze

1. Locate outputs in trace_dir:

   • veomni_rank*_.pt.trace.json.gz — Chrome trace
   • veomni_rank*_.pkl — memory snapshot (if profile_memory: true)

2. Write analysis scripts as in Mode 1 to extract the metrics the user needs.

3. Quick analysis shortcuts:

   • Kernel time breakdown: parse Chrome trace events with cat == 'kernel'
   • NCCL communication: filter events with names matching nccl (e.g. ncclAllReduceRingLLKernel)
   • Forward/backward split: use with_modules trace annotations to separate phases
   • Memory peak: load .pkl snapshot, find max allocated_bytes
   • MFU from logs: EnvironMeter already logs flops_achieved and flops_promised — grep training logs

4. For multi-rank comparison: merge traces with scripts/profile/merge_chrome_trace.py or analyze per-rank to find stragglers.

Step 4: Optimize

Based on findings, suggest and implement optimizations:

Bottleneck	Typical solutions
Attention kernels dominate	Switch to FlashAttention 3/4 (veomni/ops/kernels/attention/), check FA is actually active
NCCL communication > 30%	Increase compute/comm overlap, adjust FSDP reshard policy, try async SP
Memory OOM / high peak	Enable activation checkpointing, reduce micro-batch size, check for memory leaks
Data loading stalls	Increase num_workers, enable prefetch, check I/O throughput
Low MFU (< 40%)	Check dtype (bf16 vs fp32), verify tensor cores are used, check for host-device syncs
Uneven per-rank time	Check MoE load balancing, verify data distribution across ranks

Step 5: Validate

After optimization:

1. Re-profile with the same config to compare before/after.
2. Verify training correctness is preserved (loss matches baseline).
3. Document the optimization and results.

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NPU (Ascend) Profiling

On NPU, create_profiler() uses torch_npu.profiler instead of torch.profiler. Key differences:

• Output format includes AiC (Ascend insight Counters) metrics.
• Memory profiling uses NPU-specific APIs.
• Analysis tools differ — use Ascend Insight instead of Chrome tracing.
• Always guard NPU-specific analysis code with is_torch_npu_available().