Nvidia Megatron Bridge Parity Testing

Parity Testing for Megatron Bridge

This skill provides the decision framework for choosing the right verification tool and interpreting results. For the full model onboarding workflow (which includes parity testing as milestones 1 and 2), see the add-model-support skill.

Quick Decision: Which Tool to Run

All tools live under examples/conversion/.

3-Level Test Strategy

Level 1: State Dict Round-Trip (exact match)

The fastest and most fundamental check. If mappings can't perfectly round-trip weights, nothing else will work.

# Single-GPU round-trip
uv run python examples/conversion/hf_megatron_roundtrip.py \
    --hf-model-id <org>/<model>

# Multi-GPU with TP=2
uv run python -m torch.distributed.run --nproc_per_node=2 \
    examples/conversion/hf_megatron_roundtrip_multi_gpu.py \
    --hf-model-id <org>/<model> --tp 2

# Multi-GPU with PP=2
uv run python -m torch.distributed.run --nproc_per_node=2 \
    examples/conversion/hf_megatron_roundtrip_multi_gpu.py \
    --hf-model-id <org>/<model> --pp 2

Expected: Every weight shows "Matches Original: checkmark". Any "X" means the param mapping has an error.

Tolerance: Exact match (max_diff == 0.0). Round-trip conversions are pure tensor reshaping — no floating-point arithmetic is involved.

For programmatic verification inside scripts, use the built-in verifier:

from megatron.bridge.models.conversion.utils import weights_verification_table
weights_verification_table(bridge, hf_pretrained, megatron_model)

Level 2: Forward-Pass Parity (GPU / bfloat16)

After round-trip passes, verify that converted weights produce identical forward-pass output.

# Compare logits (loads both HF and Megatron models)
uv run python -m torch.distributed.run --nproc_per_node=2 \
    examples/conversion/compare_hf_and_megatron/compare.py \
    --hf_model_path <org>/<model> --tp 2 \
    --prompt "The capital of France is"

Expected: Cosine similarity > 99.99%, matching next-token predictions.

For large models that OOM compare.py (which loads both models), use text generation instead:

uv run python -m torch.distributed.run --nproc_per_node=2 \
    examples/conversion/hf_to_megatron_generate_text.py \
    --hf_model_path <org>/<model> --tp 2 \
    --prompt "The capital of France is" --max_new_tokens 50

Level 3: Training Parity (optional)

Verify that a few training steps produce decreasing loss. This catches gradient computation issues that forward-pass tests miss. Use a toy model with 2 layers and small dimensions. See the functional test pattern in the add-model-support skill (Milestone 3, Phase 6).

Tolerance Table

Comparison Utilities

These functions are useful when writing custom verification scripts or debugging failures. They are not part of the Bridge library — copy them into your script as needed.

import torch


def compare_tensors(a, b, name=""):
    """Compare two tensors and report similarity metrics."""
    max_diff = (a - b).abs().max().item()
    mean_diff = (a - b).abs().mean().item()
    cos_sim = torch.nn.functional.cosine_similarity(
        a.flatten().float(), b.flatten().float(), dim=0,
    ).item()
    print(f"{name}: max_diff={max_diff:.6e}, mean_diff={mean_diff:.6e}, cosine_sim={cos_sim:.8f}")
    return max_diff, mean_diff, cos_sim


def compare_state_dicts(sd_a, sd_b, prefix=""):
    """Compare two state dicts key-by-key, reporting per-parameter differences."""
    keys_a, keys_b = set(sd_a.keys()), set(sd_b.keys())
    missing, extra = keys_a - keys_b, keys_b - keys_a
    if missing:
        print(f"{prefix}Missing keys: {sorted(missing)}")
    if extra:
        print(f"{prefix}Extra keys: {sorted(extra)}")
    max_diffs = {}
    for key in sorted(keys_a & keys_b):
        diff = (sd_a[key].float() - sd_b[key].float()).abs().max().item()
        if diff > 0:
            max_diffs[key] = diff
            print(f"{prefix}{key}: max_diff={diff:.6e}")
    if not max_diffs and not missing and not extra:
        print(f"{prefix}All {len(keys_a & keys_b)} parameters match exactly.")
    return missing, extra, max_diffs

Debugging Workflow

When a parity test fails, follow this sequence:

1. Run single-GPU round-trip — if this fails, the mapping itself is wrong. Check the mapping_registry() in the bridge file.

2. If single-GPU passes but multi-GPU fails — the TP/PP scatter/gather is wrong. Compare the TP=1 result against each TP shard. See the nccl-contiguous-tensors skill for NCCL-specific issues.

3. If round-trip passes but forward pass fails — weights loaded correctly but the model architecture differs. Check provider_bridge() config mapping (normalization, activation, RoPE, etc.).

4. Use the debugging script template from the add-model-support skill to inspect runtime vs safetensors key naming and bridge config mapping.

For the full catalog of pitfalls (QKV interleaving, MoE fused exports, tied embeddings, FP8 dequantization, TE LayerNorm aliases, etc.), see the Pitfalls section of the add-model-support skill.

Code Anchors