Online Quantization Guide

ATOM can quantize or re-quantize model weights while loading them by passing --online_quant_config to the engine. The source checkpoint stays on disk unchanged; quantization happens in memory inside process_weights_after_loading right after the loader finishes copying tensors.

This guide covers when to use online quantization, the full configuration syntax, ready-to-run recipes for the most common model families, how to verify the result, and troubleshooting tips. For the dataclass-level field reference, see configuration_guide.md § 3.7.

---

1. When to use online quantization

Use online quantization when one of the following holds:

• The model only ships an unquantized (BF16/FP16) or FP8-block checkpoint, and you want to evaluate a different runtime format (e.g. MXFP4 experts) without rebuilding the checkpoint offline.

• You want to sweep mixed-precision recipes (different formats for attention vs. MoE experts vs. shared experts) on the same source weights.

• You need a quick A/B between FP8 and MXFP4 on the same model without downloading two separate Hugging Face repos.

Prefer an offline pre-quantized checkpoint (e.g. amd/DeepSeek-R1-0528-MXFP4) when one already exists for your target format — it has lower load time, deterministic per-layer assignment, and no online quantization overhead on every restart.

Supported source-checkpoint formats

Online quantization is only activated when the source model’s quant_method is one of:

Source quant_method	Behavior
(none, i.e. BF16/FP16 model)	Quantized directly from float weights.
fp8 (block FP8, QuantType.per_1x128)	FP8 block weights are dequantized to BF16 first, then re-quantized.
mxfp4	Not re-quantized. Source MXFP4 weights are currently passed through unchanged — there is no dequant path for per_1x32, so the requested target format does not take effect on these layers.

---

2. Configuration syntax

The flag accepts a single JSON object with three optional fields:

--online_quant_config '{
  "global_quant_config": "ptpc_fp8",
  "layer_quant_config": {"*expert*": "mxfp4"},
  "exclude_layer": ["lm_head", "*.gate.*"]
}'

Field	Type	Description
global_quant_config	str	Default target format applied to every Linear / MoE layer. Omit (or pass "") to leave non-matching layers at their source precision.
layer_quant_config	dict[str, str]	Per-layer target overrides. Keys are fnmatch-style globs such as "*expert*", "*.mlp.gate_proj". Matched layers override global_quant_config.
exclude_layer	str | list[str]	Layer name patterns to leave at source precision. Supports exact match and glob (*). Prefer a JSON list when excluding more than one pattern.

Resolution order for a given layer name:

1. If it matches exclude_layer → not quantized.

2. Otherwise, first matching layer_quant_config pattern (in dict order).

3. Otherwise, fall back to global_quant_config.

4. If global_quant_config is also empty, the layer keeps its source format.

2.1 Target formats

Only two target formats are currently supported. Any other string (for example ptpc_i8, mxi4, mxfp8) will either be rejected by the JSON parser or trigger an assertion in the loader when the layer’s weight is quantized.

Format string	Underlying QuantType	Weight dtype
ptpc_fp8	QuantType.per_Token	torch.float8_e4m3fn
mxfp4	QuantType.per_1x32	packed FP4 (torch.float4_e2m1fn_x2, group size 32)

2.2 Picking the right pattern

ATOM’s resolver runs against the fully-qualified layer name as reported by model.named_modules(). Useful patterns:

Pattern	Matches	Why
"*expert*"	MoE expert weights (e.g. model.layers.3.mlp.experts)	Substring match on the fused expert module.
"*.gate.*"	MoE router / gate Linear	Always exclude — quantizing the router destroys top-k accuracy.
"lm_head"	Output projection	Always exclude — kept at source precision avoids logit-distribution shift.
"*shared_expert*"	Shared experts in DeepSeek / Qwen3 MoE	Keep at higher precision if you see accuracy regressions.

---

3. Recipes

The four recipes below are the configurations validated in ROCm/ATOM#653. Each has been A/B tested against its offline-quantized equivalent on gsm8k accuracy and ISL=1024 / OSL=1024 / concurrency=128 throughput.

All commands assume you are inside the standard ATOM container (docker pull rocm/atom:latest).

3.1 Qwen3-30B-A3B-Thinking-2507 — full per-token FP8

BF16 source → every Linear and the fused expert module quantized to ptpc_fp8. The matching offline checkpoint is amd/Qwen3-30B-A3B-Thinking-2507-ptpc.

python -m atom.entrypoints.openai_server \
  --model Qwen/Qwen3-30B-A3B-Thinking-2507 \
  -tp 4 \
  --online_quant_config '{
    "global_quant_config": "ptpc_fp8",
    "exclude_layer": ["lm_head", "*.gate.*"]
  }'

3.2 Qwen3-235B-A22B-Instruct-2507 — full MXFP4

BF16 source → every Linear (including experts) quantized to mxfp4, served with expert parallel.

python -m atom.entrypoints.openai_server \
  --model Qwen/Qwen3-235B-A22B-Instruct-2507 \
  -tp 2 --enable-expert-parallel \
  --online_quant_config '{
    "global_quant_config": "mxfp4",
    "exclude_layer": ["lm_head", "*.gate.*"]
  }'

3.3 DeepSeek-R1-0528 — FP8 attention + MXFP4 experts

FP8 source → non-expert Linear stays at ptpc_fp8, fused MoE experts are downgraded to mxfp4. The matching offline checkpoint layout is amd/DeepSeek-R1-0528-MXFP4.

python -m atom.entrypoints.openai_server \
  --model deepseek-ai/DeepSeek-R1-0528 \
  --enforce-eager -tp 8 \
  --online_quant_config '{
    "global_quant_config": "ptpc_fp8",
    "layer_quant_config": {"*expert*": "mxfp4"},
    "exclude_layer": ["lm_head", "*.gate.*"]
  }'

--enforce-eager mirrors the configuration used by the PR’s accuracy reproduction. Drop it to get full CUDA-graph throughput; it does not affect the online quantization output.

3.4 DeepSeek-R1-0528 + MTP-3 — FP8 attention + MXFP4 experts

Same online quantization recipe as § 3.3, layered with MTP-3 speculative decoding for ~2.5× lower TPOT.

python -m atom.entrypoints.openai_server \
  --model deepseek-ai/DeepSeek-R1-0528 \
  --enforce-eager -tp 8 \
  --method mtp --num-speculative-tokens 3 \
  --online_quant_config '{
    "global_quant_config": "ptpc_fp8",
    "layer_quant_config": {"*expert*": "mxfp4"},
    "exclude_layer": ["lm_head", "*.gate.*"]
  }'

--method mtp --num-speculative-tokens 3 is independent of online quantization — it can be added to any of the recipes above without changing the --online_quant_config JSON.

---

4. Verifying the result

When online quantization runs, rank 0 writes online_quant_info_<timestamp>_<ns>.json to:

1. $ATOM_TORCH_PROFILER_DIR if the env var is set, otherwise

2. the current working directory.

A representative payload:

{
  "model": "Qwen/Qwen3-30B-A3B-Thinking-2507",
  "online_quant_config": {
    "global_quant_config": "ptpc_fp8",
    "exclude_layer": ["lm_head", "*.gate.*"]
  },
  "elapsed_seconds": 2.343,
  "num_layers": 144,
  "layers": [
    {
      "layer": "model.layers.0.self_attn.qkv_proj",
      "quant_type": "per_Token",
      "quant_dtype": "torch.float8_e4m3fn"
    },
    {
      "layer": "model.layers.0.mlp.experts",
      "quant_type": "per_Token",
      "quant_dtype": "torch.float8_e4m3fn"
    }
  ]
}

Things to check:

• num_layers matches your expectation. For a Qwen3 MoE with 48 transformer blocks you should see 48 × 3 = 144 entries (qkv_proj + o_proj + experts). A drastically smaller count usually means a typo in the pattern made everything fall into exclude_layer.

• Per-layer quant_type / quant_dtype reflect the format you intended for that pattern. The mapping is:

  Format string	quant_type	quant_dtype
  ptpc_fp8	per_Token	torch.float8_e4m3fn
  mxfp4	per_1x32	torch.uint8 (packed FP4x2)

• elapsed_seconds indicates the post-loading processing time on rank 0. A large jump from one restart to another with the same config usually points to a TP gather being triggered (see § 5.2).

The runtime also logs a one-line summary in the server log:

Weight post-processing done: 2.34 seconds, 144 layers online-quantized
Online quantization info saved to /root/online_quant_info_20260525_033839_112444436.json

---

5. Notes and gotchas

5.1 When online quantization activates

--online_quant_config is only applied when the source checkpoint’s quant_method is unquantized or per-block FP8 (see § 1).

5.2 Tensor-parallel behavior

Tensor-parallel weights are gathered onto a single rank before quantization only when local quantization would produce different scales than quantizing the full unpartitioned weight. Concretely:

• ptpc_fp8 (per_Token): scales are per output channel and the channel dimension is exactly what TP shards on, so quantization is done locally with no gather.

• mxfp4 (per_1x32): scales are within 32-element blocks along the input dimension; for RowParallelLinear this requires a gather on the input dim before quantization, then re-sharding. This is the most expensive case.

If load time grows linearly with TP size, your recipe is hitting the gather path.

5.3 Only Linear and fused MoE modules are quantized

Modules whose weights are not loaded through ATOM’s LinearMethodBase or FusedMoEMethodBase paths are skipped silently. In practice this means embeddings, layernorms, attention bias, and any custom op kept in BF16 will not appear in online_quant_info_*.json — that is expected.

5.4 Compile cache

The compile cache (/root/.cache/atom/*) is keyed on the full quantization config hash. Switching --online_quant_config between runs will trigger a recompile on first startup. If you are iterating rapidly:

rm -rf /root/.cache/atom/*

5.5 Always exclude the MoE gate

The MoE router (*.gate.*) is a tiny Linear that produces top-k routing logits. Quantizing it consistently produces large accuracy drops on every MoE model we have measured. Keep it in the exclude list unless you have a specific reason not to.

---