[DeepSeek-V4][fix] Detect MTP routed-expert layout separately - #16276
[DeepSeek-V4][fix] Detect MTP routed-expert layout separately#16276waynehacking8 wants to merge 3 commits into
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WalkthroughDeepSeek-V4 routed-expert quantization detection now supports independent dense and MTP layouts. The configuration uses shared MXFP4 and NVFP4 settings construction and warns when MTP detection falls back to dense settings. ChangesDeepSeek V4 MTP quantization
Estimated code review effort: 3 (Moderate) | ~25 minutes Mergeability Score: ⚪ Minimal · up to The change separates MTP expert-layout detection from dense experts to prevent incorrect quantization during speculative decoding; no actionable merge-blocking risk remains after normal checks and review. Sequence Diagram(s)sequenceDiagram
participant ModelConfig
participant SafetensorsIndex
participant LayoutDetector
participant MTPConfiguration
ModelConfig->>SafetensorsIndex: Probe dense routed-expert tensor
SafetensorsIndex-->>LayoutDetector: Return dense tensor metadata
LayoutDetector-->>ModelConfig: Return dense MXFP4 or NVFP4 layout
ModelConfig->>SafetensorsIndex: Probe MTP routed-expert tensor
SafetensorsIndex-->>LayoutDetector: Return MTP tensor metadata or missing probe
LayoutDetector-->>MTPConfiguration: Return MTP layout or fallback signal
MTPConfiguration-->>ModelConfig: Apply MTP quantization settings and warning when needed
Suggested reviewers: 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
✨ Finishing Touches🧪 Generate unit tests (beta)
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Addresses the CodeRabbit docstring-coverage warning on NVIDIA#16276. Signed-off-by: WEI CHENG CHIU <waynehacking8@gmail.com>
Addresses the CodeRabbit docstring-coverage warning on NVIDIA#16276. Signed-off-by: WEI CHENG CHIU <waynehacking8@gmail.com>
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@mikeiovine I rebased onto current main after #16433 and narrowed the PR to the remaining MTP layout commit; it is mergeable and DCO is green. When convenient, could you trigger |
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Approving — the comments below are optional touch-ups, not blockers.
The fix looks correct: MTP layers land at model.layers.{num_hidden_layers + i}, matching the key rewrite in modeling_deepseekv4.py:417, and probing only mtp.0 is consistent with the ckpt_nextn == 1 replication path in DeepseekV4ForCausalLM.__init__.
Main gap is test coverage. tests/unittest/_torch/modeling/test_modeling_deepseekv4.py:521 already has test_deepseek_v4_routed_moe_quant_config_covers_mtp_layers with exactly the fixture you need — extend it (or add a sibling) that writes a second header entry mtp.0.ffn.experts.0.w1.weight with dtype: U8 alongside the I8 dense entry, and assert the dense layers get W4A8_MXFP4_MXFP8/group 32 while model.layers.{num_hidden}..{num_hidden+n-1} get NVFP4/group 16. This is a pure-metadata code path, no GPU needed, and it's the only thing guarding a checkpoint layout nobody can reproduce in CI.
The PR description accurately matches the diff.
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Thanks for the careful review @brnguyen2 — and for confirming the MTP layer indexing against the key rewrite, that was the part I most wanted a second pair of eyes on. I've addressed all four points. @waynehacking8, the commit is (Full diff inlined at the bottom in case you'd rather apply it directly.) What changed
Tests — extending the existing fixture as you suggested. Three cases:
Metadata-only, no GPU, as you said. Two shards plus an index One deliberate deviation: the warning in case 3 is asserted by patching What I verified locally, and what I didn't. I ran the three cases against this exact Which brings me to the one thing still blocking: internal CI has never run on this PR (only DCO shows up in checks). @mikeiovine approved on 07-23 and @brnguyen2 today, so if either of you could kick off Full diff--- a/tensorrt_llm/_torch/model_config.py
+++ b/tensorrt_llm/_torch/model_config.py
@@ -60,6 +60,7 @@
_DEEPSEEK_V4_ARCHITECTURES = {"DeepseekV4ForCausalLM"}
_DEEPSEEK_V4_ROUTED_EXPERT_WEIGHT = "layers.0.ffn.experts.0.w1.weight"
+_DEEPSEEK_V4_MTP_ROUTED_EXPERT_WEIGHT = "mtp.0.ffn.experts.0.w1.weight"
_MINIMAX_M3_ARCHITECTURES = {
"MiniMaxM3SparseForCausalLM",
@@ -605,9 +606,11 @@
@staticmethod
def _detect_deepseek_v4_routed_moe_layout(
- checkpoint_dir: str) -> Optional[str]:
+ checkpoint_dir: str,
+ tensor_name: str = _DEEPSEEK_V4_ROUTED_EXPERT_WEIGHT,
+ ) -> Optional[str]:
tensor_info = ModelConfig._get_safetensors_header_for_tensor(
- checkpoint_dir, _DEEPSEEK_V4_ROUTED_EXPERT_WEIGHT)
+ checkpoint_dir, tensor_name)
if tensor_info is None:
return None
@@ -620,6 +623,24 @@
return None
@staticmethod
+ def _make_routed_experts_quant_config(layout: str,
+ moe_backend: str) -> QuantConfig:
+ """Build the routed-experts QuantConfig for a detected MoE layout."""
+ quant_config = QuantConfig()
+ if layout == "mxfp4":
+ quant_config.quant_algo = ModelConfig.get_mxfp4_quant_algo(
+ moe_backend)
+ quant_config.group_size = 32
+ else:
+ quant_config.quant_algo = QuantAlgo.NVFP4
+ quant_config.group_size = 16
+ quant_config.exclude_modules = [
+ 'block.*.attn.out', 'block.*.mlp.gate', 'block.*.attn.qkv',
+ 'embedding', 'unembedding'
+ ]
+ return quant_config
+
+ @staticmethod
def _is_deepseek_v4_base_checkpoint(checkpoint_dir: str) -> bool:
tensor_info = ModelConfig._get_safetensors_header_for_tensor(
checkpoint_dir, _DEEPSEEK_V4_ROUTED_EXPERT_WEIGHT)
@@ -658,18 +679,8 @@
"for MXFP4 or U8 for NVFP4.")
return layer_quant_config
- experts_quant_config = QuantConfig()
- if layout == "mxfp4":
- experts_quant_config.quant_algo = ModelConfig.get_mxfp4_quant_algo(
- moe_backend)
- experts_quant_config.group_size = 32
- else:
- experts_quant_config.quant_algo = QuantAlgo.NVFP4
- experts_quant_config.group_size = 16
- experts_quant_config.exclude_modules = [
- 'block.*.attn.out', 'block.*.mlp.gate', 'block.*.attn.qkv',
- 'embedding', 'unembedding'
- ]
+ experts_quant_config = ModelConfig._make_routed_experts_quant_config(
+ layout, moe_backend)
if layer_quant_config is None:
layer_quant_config = {}
@@ -692,21 +703,25 @@
and spec_config.spec_dec_mode.is_mtp_one_model()):
num_mtp = spec_config.num_nextn_predict_layers or 0
if num_mtp:
- mtp_info = ModelConfig._get_safetensors_header_for_tensor(
- checkpoint_dir, "mtp.0.ffn.experts.0.w1.weight")
- mtp_dtype = mtp_info.get("dtype") if mtp_info else None
- mtp_layout = {"I8": "mxfp4", "U8": "nvfp4"}.get(mtp_dtype)
- if mtp_layout is not None and mtp_layout != layout:
- mtp_experts_quant_config = QuantConfig()
- if mtp_layout == "mxfp4":
- mtp_experts_quant_config.quant_algo = (
- ModelConfig.get_mxfp4_quant_algo(moe_backend))
- mtp_experts_quant_config.group_size = 32
- else:
- mtp_experts_quant_config.quant_algo = QuantAlgo.NVFP4
- mtp_experts_quant_config.group_size = 16
- mtp_experts_quant_config.exclude_modules = (
- experts_quant_config.exclude_modules)
+ mtp_layout = ModelConfig._detect_deepseek_v4_routed_moe_layout(
+ checkpoint_dir, _DEEPSEEK_V4_MTP_ROUTED_EXPERT_WEIGHT)
+ if mtp_layout is None:
+ # Probe missed (unknown naming convention, MTP shard absent from
+ # the index, or an unexpected dtype/rank). Falling back to the
+ # dense config is exactly the failure this detection exists to
+ # prevent, so say so instead of failing later inside
+ # fused_moe load_quant_scales.
+ logger.warning(
+ "DeepSeek-V4 MTP routed-expert layout could not be detected "
+ "from %s; assuming the dense layout (%s). If this checkpoint "
+ "stores its MTP routed experts in a different format, "
+ "loading will fail in fused_moe load_quant_scales.",
+ _DEEPSEEK_V4_MTP_ROUTED_EXPERT_WEIGHT, layout)
+ mtp_experts_quant_config = experts_quant_config
+ elif mtp_layout != layout:
+ mtp_experts_quant_config = (
+ ModelConfig._make_routed_experts_quant_config(
+ mtp_layout, moe_backend))
logger.info(
"DeepSeek-V4 MTP routed experts use a different layout (%s) "
"than the dense experts (%s).", mtp_layout, layout)
--- a/tests/unittest/_torch/modeling/test_modeling_deepseekv4.py
+++ b/tests/unittest/_torch/modeling/test_modeling_deepseekv4.py
@@ -555,6 +555,110 @@
assert layer_quant_config[f"model.layers.{layer_idx}.mlp.experts"].quant_algo == quant_algo
+def _write_deepseek_v4_mtp_checkpoint(tmp_path, dense_dtype, mtp_dtype):
+ """Two-shard checkpoint whose dense and MTP routed experts may differ.
+
+ Mirrors the ModelOpt experts-only repacks (e.g. nvidia/DeepSeek-V4-Pro-NVFP4),
+ which re-quantize the dense experts but leave the MTP shard at the base
+ model's layout. Passing mtp_dtype=None omits the MTP entry entirely.
+ """
+ dense_tensor = "layers.0.ffn.experts.0.w1.weight"
+ mtp_tensor = "mtp.0.ffn.experts.0.w1.weight"
+ dense_shard = "model-00001-of-00002.safetensors"
+ mtp_shard = "model-00002-of-00002.safetensors"
+
+ _write_safetensors_header(tmp_path / dense_shard, dense_tensor, dense_dtype, [2, 2])
+ weight_map = {dense_tensor: dense_shard}
+ if mtp_dtype is not None:
+ _write_safetensors_header(tmp_path / mtp_shard, mtp_tensor, mtp_dtype, [2, 2])
+ weight_map[mtp_tensor] = mtp_shard
+
+ (tmp_path / "model.safetensors.index.json").write_text(json.dumps({"weight_map": weight_map}))
+
+
+def _deepseek_v4_mtp_spec_config(num_nextn_predict_layers):
+ class MTPMode:
+ @staticmethod
+ def is_mtp_one_model():
+ return True
+
+ class MTPConfig:
+ spec_dec_mode = MTPMode()
+
+ MTPConfig.num_nextn_predict_layers = num_nextn_predict_layers
+ return MTPConfig()
+
+
+@pytest.mark.parametrize(
+ "dense_dtype,mtp_dtype,dense_algo,dense_group,mtp_algo,mtp_group",
+ [
+ # ModelOpt experts-only NVFP4 repack: dense re-quantized to NVFP4 (U8),
+ # MTP left at the base model's MXFP4 (I8). This is the layout that
+ # crashed fused_moe load_quant_scales before the split detection.
+ ("U8", "I8", QuantAlgo.NVFP4, 16, QuantAlgo.W4A8_MXFP4_MXFP8, 32),
+ # Inverse direction, to pin that neither layout is hard-coded.
+ ("I8", "U8", QuantAlgo.W4A8_MXFP4_MXFP8, 32, QuantAlgo.NVFP4, 16),
+ ],
+)
+def test_deepseek_v4_mtp_routed_experts_detected_separately(
+ tmp_path, monkeypatch, dense_dtype, mtp_dtype, dense_algo, dense_group, mtp_algo, mtp_group
+):
+ monkeypatch.setattr("tensorrt_llm._torch.model_config.get_sm_version", lambda: 100)
+ _write_deepseek_v4_mtp_checkpoint(tmp_path, dense_dtype, mtp_dtype)
+
+ num_hidden_layers = 2
+ num_mtp = 3
+ layer_quant_config = ModelConfig._set_deepseek_v4_routed_moe_quant_config(
+ DeepseekV4Config(num_hidden_layers=num_hidden_layers),
+ str(tmp_path),
+ "TRTLLM",
+ None,
+ _deepseek_v4_mtp_spec_config(num_mtp),
+ )
+
+ for layer_idx in range(num_hidden_layers):
+ dense_config = layer_quant_config[f"model.layers.{layer_idx}.mlp.experts"]
+ assert dense_config.quant_algo == dense_algo
+ assert dense_config.group_size == dense_group
+
+ for i in range(num_mtp):
+ mtp_config = layer_quant_config[f"model.layers.{num_hidden_layers + i}.mlp.experts"]
+ assert mtp_config.quant_algo == mtp_algo
+ assert mtp_config.group_size == mtp_group
+
+
+def test_deepseek_v4_mtp_routed_experts_warn_when_probe_missing(tmp_path, monkeypatch):
+ """An undetectable MTP layout must fall back loudly, not silently.
+
+ The warning is captured by patching logger.warning rather than via caplog:
+ tensorrt_llm's logger sets propagate=False, so root-handler capture would
+ not see it.
+ """
+ monkeypatch.setattr("tensorrt_llm._torch.model_config.get_sm_version", lambda: 100)
+ _write_deepseek_v4_mtp_checkpoint(tmp_path, "U8", None)
+
+ warnings = []
+ monkeypatch.setattr(
+ "tensorrt_llm._torch.model_config.logger.warning",
+ lambda msg, *args: warnings.append(msg % args if args else msg),
+ )
+
+ num_hidden_layers = 2
+ num_mtp = 1
+ layer_quant_config = ModelConfig._set_deepseek_v4_routed_moe_quant_config(
+ DeepseekV4Config(num_hidden_layers=num_hidden_layers),
+ str(tmp_path),
+ "TRTLLM",
+ None,
+ _deepseek_v4_mtp_spec_config(num_mtp),
+ )
+
+ # Falls back to the dense layout, and says which probe missed.
+ mtp_config = layer_quant_config[f"model.layers.{num_hidden_layers}.mlp.experts"]
+ assert mtp_config.quant_algo == QuantAlgo.NVFP4
+ assert any("mtp.0.ffn.experts.0.w1.weight" in w for w in warnings)
+
+
def test_deepseek_v4_mtp_projection_uses_fp8_quant_config(monkeypatch):
def fake_decoder_layer_init(self, model_config, *_args, **_kwargs):
torch.nn.Module.__init__(self) |
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Gentle ping — this one is two clicks away from merging. @waynehacking8: the review follow-ups are still sitting in @mikeiovine @brnguyen2: after that lands, the only remaining gap is internal CI — checks still show DCO only, so No rush on my end; just flagging that the approvals are already in and nothing technical is outstanding. |
…MXFP4) The ModelOpt experts-only NVFP4 repacks (e.g. nvidia/DeepSeek-V4-Pro-NVFP4) re-quantize only the dense routed experts to NVFP4 (U8) and leave the MTP routed experts at the base model's MXFP4 (I8). _set_deepseek_v4_routed_moe_ quant_config detected a single layout from layers.0 and applied it to every MoE layer including the MTP layer, so the MTP experts got NVFP4 and crashed in fused_moe load_quant_scales. Detect the MTP expert dtype separately and assign the MTP layer indices the correct (MXFP4) config. Validated end-to-end on real weights: nvidia/DeepSeek-V4-Pro-NVFP4 on 4x B300, TP4, moe_backend=TRTLLM, rc15.post1 (equivalent change), with the construction fix from 3972f5e: loads 100% + serves + generates, MTP=1 and MTP=3 (mtp_eagle_one_model builds one shared MTP layer). MTP accept_len ~2.86. Signed-off-by: d3nb <wanxiren@gmail.com>
Signed-off-by: WEI CHENG CHIU <waynehacking8@gmail.com> Signed-off-by: d3nb <wanxiren@gmail.com>
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Inline comments:
In `@tensorrt_llm/_torch/model_config.py`:
- Around line 717-730: Preformat the warning and info messages in
_make_routed_experts_quant_config so each logger.warning and logger.info call
receives one fully interpolated string, preserving the probe path and layout
values. In tests/unittest/_torch/modeling/test_modeling_deepseekv4.py lines
640-644, update the warning mock to capture the already formatted message
without applying additional % interpolation.
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tensorrt_llm/_torch/model_config.pytests/unittest/_torch/modeling/test_modeling_deepseekv4.py
Signed-off-by: WEI CHENG CHIU <waynehacking8@gmail.com>
4 participants
Description
#16433 merged the base mixed-precision construction and loading fix for
nvidia/DeepSeek-V4-Pro-NVFP4, superseding the earlier construction commits in this PR. This PR now contains only the remaining MTP-specific fix, authored by @d3nb.The checkpoint stores dense routed experts as NVFP4 (
U8) but leaves MTP routed experts at the base model's MXFP4 layout (I8)._set_deepseek_v4_routed_moe_quant_config()previously detected one layout fromlayers.0and assigned it to every MoE layer, so MTP speculative decoding received NVFP4 configuration and crashed infused_moescale loading.The loader now probes the MTP expert header separately and assigns MXFP4 or NVFP4 configuration to the MTP layer range only when its layout differs from the dense experts. Matching or absent MTP headers retain the existing dense configuration.
Related: #16196, #16433.
Test Coverage
nvidia/DeepSeek-V4-Pro-NVFP4checkpoint using 4x B300, TP4, and the TRT-LLM MoE backend.main.PR Checklist
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