[TRTLLM-6746][feat]Enable two-model spec dec for MTP Eagle #7001
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Signed-off-by: qgai <[email protected]>
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📝 WalkthroughWalkthroughAdds MTP Eagle variants and one-model vs two-model speculative decoding, introduces MTP draft models and a dedicated DeepseekV3 weight loader, makes Eagle3 capture layers dynamic, extends decoding configs and speculative-mode predicates, and updates auto-model routing and executor draft overrides. Changes
Sequence Diagram(s)sequenceDiagram
autonumber
participant User
participant Quickstart
participant Config
participant AutoModel
participant DraftModel
participant TargetModel
Note over Quickstart,Config: build decoding config (Eagle/MTP + one-model flags)
User->>Quickstart: setup_llm(args)
Quickstart->>Config: construct Eagle/MTP config (is_mtp_eagle, mtp_eagle_one_model, layers_to_capture)
Quickstart->>AutoModel: create draft model (spec mode)
AutoModel->>AutoModel: remap arch (DeepseekV3 & max_draft_len==0 -> MTPDraftModelForCausalLM)
AutoModel-->>DraftModel: instantiate draft model
Quickstart->>TargetModel: instantiate target model
DraftModel->>DraftModel: load_weights (DeepseekV3WeightLoader.load_weights)
DraftModel->>TargetModel: optionally wire shared embeddings / lm_head
User->>TargetModel: generate(request with spec_metadata)
TargetModel->>DraftModel: request draft logits/hidden states
DraftModel-->>TargetModel: draft outputs
TargetModel-->>User: final tokens
Estimated code review effort🎯 5 (Critical) | ⏱️ ~120 minutes Possibly related PRs
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Actionable comments posted: 10
🔭 Outside diff range comments (8)
tensorrt_llm/_torch/speculative/mtp.py (3)
1-1: Add NVIDIA copyright header.Per coding guidelines, prepend the NVIDIA copyright header (current year) to all source files.
Apply at top of file:
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
70-71: Scalar copy_ misuse — use zero_/fill_ instead.copy_ expects a Tensor, not a Python scalar. This will error at runtime on some PyTorch versions.
Use zero_ or fill_ for in-place scalar writes:
- self.mtp_relaxed_delta_pool[slot_id].copy_( - 0, non_blocking=True) + self.mtp_relaxed_delta_pool[slot_id].zero_()- self.mtp_relaxed_delta_pool[free_slot_id].copy_(0, - non_blocking=True) + self.mtp_relaxed_delta_pool[free_slot_id].zero_()Also applies to: 79-81
1168-1196: Draft loop calls first MTP layer repeatedly (likely bug).Inside the loop over self.mtp_num_modules, you always invoke draft_model.mtp_layers[0] rather than indexing by i. This will ignore all but the first layer and produce incorrect tokens/hidden states when num_nextn_predict_layers > 1.
Use the ith layer:
- if i == 0: - hidden_states = draft_model.mtp_layers[0]( + if i == 0: + hidden_states = draft_model.mtp_layers[i]( embed_tokens=draft_model.embed_tokens, all_rank_num_tokens=spec_metadata.all_rank_num_tokens, all_rank_max_num_tokens=spec_metadata. all_rank_max_num_tokens, **inputs) ... - else: - hidden_states = draft_model.mtp_layers[0]( + else: + hidden_states = draft_model.mtp_layers[i]( embed_tokens=draft_model.embed_tokens, all_rank_num_tokens=spec_metadata. subseq_all_rank_num_tokens, all_rank_max_num_tokens=max( spec_metadata.subseq_all_rank_num_tokens) if spec_metadata.subseq_all_rank_num_tokens is not None else None, **inputs)examples/llm-api/quickstart_advanced.py (1)
1-1: Add NVIDIA copyright header.Per coding guidelines, prepend the NVIDIA copyright header (current year) to all source files.
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.tensorrt_llm/_torch/speculative/eagle3.py (1)
1-1: Add NVIDIA copyright header.Per coding guidelines, prepend the NVIDIA copyright header (current year) to all source files.
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.tensorrt_llm/_torch/speculative/utils.py (1)
1-1: Add NVIDIA copyright header.Per coding guidelines, prepend the NVIDIA copyright header (current year) to all source files.
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.tensorrt_llm/llmapi/llm_args.py (2)
1-1: Add NVIDIA copyright header.Per coding guidelines, prepend the NVIDIA copyright header (current year) to all source files.
+# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
1676-1691: EagleDecodingConfig validate: reinforces need for draft_model_dir in quickstart.Given speculative_model_dir is required for Eagle-based paths, the quickstart must pass --draft_model_dir in two-model MTP Eagle.
See related comment in quickstart_advanced.py with a concrete diff to fix this wiring mistake.
🧹 Nitpick comments (15)
tensorrt_llm/_torch/pyexecutor/py_executor_creator.py (1)
264-266: Avoid post-construction mutation of num_hidden_layers; guard against MTPDraftModel remapMutating pretrained_config.num_hidden_layers after the draft model is already instantiated risks mismatched assumptions (e.g., KV cache sizing, layer-indexed logic) and will also trigger even when AutoModel remapped to MTPDraftModelForCausalLM (architectures remains "DeepseekV3ForCausalLM"). Add guards so this only applies to an actual DeepseekV3ForCausalLM draft engine. Also handle missing/empty architectures gracefully.
Apply this minimal guard:
- if draft_model_engine.model.model_config.pretrained_config.architectures[ - 0] == "DeepseekV3ForCausalLM": - draft_model_engine.model.model_config.pretrained_config.num_hidden_layers = 1 + archs = getattr( + draft_model_engine.model.model_config.pretrained_config, + "architectures", None) + model_arch = archs[0] if archs and len(archs) > 0 else None + # Only force to 1 layer when the instantiated class is DeepseekV3ForCausalLM + if (model_arch == "DeepseekV3ForCausalLM" + and draft_model_engine.model.__class__.__name__ + == "DeepseekV3ForCausalLM"): + draft_model_engine.model.model_config.pretrained_config.num_hidden_layers = 1If the intent was to influence KV cache allocation only, consider moving this into a draft-engine construction-time knob (or pass through PyTorchModelEngine is_draft_model logic) rather than mutating config after instantiation.
Also, per repository guidelines, prepend the NVIDIA copyright header to this file.
# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.tensorrt_llm/_torch/speculative/interface.py (1)
99-109: Verify SM gating in extend_ctx; consider >=100 instead of ==100extend_ctx disables capture for TrtllmAttention only when get_sm_version() == 100. If future Blackwell revisions report >100, this may degrade unexpectedly. Consider using >= 100 unless there’s a known incompatibility.
Would you like me to adjust the predicate to >= 100?
tensorrt_llm/_torch/models/modeling_deepseekv3.py (4)
132-141: New DeepseekV3WeightLoader improves modularity; add a brief docstringThe separation of weight-loading concerns into a dedicated class is a solid move. Add a short docstring to clarify scope and expected inputs per guidelines.
Example:
class DeepseekV3WeightLoader: """TP-aware weight loader for DeepseekV3 variants. Args: model: The target model instance (e.g., DeepseekV3ForCausalLM or draft variant). is_draft_model: Whether weights are being loaded for the draft path. """Also, add the NVIDIA header to this file in addition to preserving upstream license:
# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
514-535: Wrap long line; comply with Ruff E501 (<=120 chars)predicted_tokens_per_seq assignment exceeds 120 chars.
- predicted_tokens_per_seq = model_config.spec_config.max_draft_len + 1 if model_config.spec_config is not None else 1 + spec_cfg = model_config.spec_config + predicted_tokens_per_seq = (spec_cfg.max_draft_len + 1) if spec_cfg is not None else 1
566-574: Prefer torch.sigmoid over torch.nn.functional.sigmoid (deprecation)torch.nn.functional.sigmoid is deprecated; use torch.sigmoid for clarity and future-proofing.
- scores = F.sigmoid(logits) + scores = torch.sigmoid(logits)
659-667: Ruff E501: wrap long call to DeepseekV3Gate constructorA few of the argument lines exceed 120 chars. Wrap to satisfy E501.
For example, break routed_scaling_factor line:
- routed_scaling_factor=config.routed_scaling_factor, + routed_scaling_factor=config.routed_scaling_factor,and ensure each argument is on its own line with hanging indentation (PEP8).
If you want, I can run through the file and submit a style-only patch for the flagged lines.
tensorrt_llm/_torch/speculative/model_drafter.py (1)
32-35: Add NVIDIA copyright header to filePer repo guidelines, prepend the header.
# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.tensorrt_llm/_torch/speculative/mtp.py (2)
495-497: None check order in boolean guard.Accessing attn_metadata.is_cuda_graph before verifying attn_metadata is not None risks AttributeError. Reverse the condition order.
- if attn_metadata.is_cuda_graph and attn_metadata is not None: + if attn_metadata is not None and attn_metadata.is_cuda_graph:
292-296: Minor performance nit: avoid clone() when not needed.Shifting input buffers uses clone() unnecessarily. copy_ achieves the same semantics with less overhead.
- input_ids[:-1] = input_ids[1:].clone() + input_ids[:-1].copy_(input_ids[1:]) ... - draft_hidden_states[:-1] = draft_hidden_states[1:].clone() + draft_hidden_states[:-1].copy_(draft_hidden_states[1:])tensorrt_llm/_torch/speculative/eagle3.py (1)
95-105: Default layer-capture pattern: “last-4” could be fragile.Using (1, mid-1, last-4) is a notable change. For shallow models (<=5 layers) you error out; for 6–7 layers, mid-1 and last-4 may collide or yield very early layers. Please confirm this is intentional and measured. Consider clamping and deduping, or fall back to capturing the last layer when spacing becomes too tight.
If intended, add a short inline comment explaining the rationale for last-4 vs last-1.
tensorrt_llm/_torch/models/modeling_speculative.py (5)
382-385: Use LongTensor for token indices in type hints.Token IDs and position IDs are conventionally
torch.LongTensor(int64). The current hints usetorch.IntTensorin some places.Apply this diff:
- input_ids: torch.IntTensor, - position_ids: torch.IntTensor, + input_ids: torch.LongTensor, + position_ids: torch.LongTensor,And:
- input_ids: torch.IntTensor = None, - position_ids: torch.IntTensor = None, + input_ids: torch.LongTensor = None, + position_ids: torch.LongTensor = None,Also applies to: 437-439
370-375: Align Embedding instantiation with TP mapping (optional).Elsewhere embeddings are usually constructed with mapping/tensor-parallel settings and gathered outputs. Consider matching that here to avoid surprises in TP runs.
Apply this diff:
- self.embed_tokens = Embedding( - self.config.vocab_size, - self.config.hidden_size, - dtype=self.config.torch_dtype, - ) + self.embed_tokens = Embedding( + self.config.vocab_size, + self.config.hidden_size, + dtype=self.config.torch_dtype, + mapping=model_config.mapping, + tensor_parallel_mode=TensorParallelMode.COLUMN, + gather_output=True, + )
431-433: Embedding sharing likely never happens; decide whether to always share or always own.
self.model.embed_tokensis created inMTPDraftModel.__init__, so thisif ... is Nonebranch will never run. If the intent is to share embeddings with the target model to save memory and keep weights in sync, set it unconditionally; otherwise, remove the dead branch.Proposed change to share:
- if self.model.embed_tokens is None: - self.model.embed_tokens = target_model.model.embed_tokens + # Share embeddings with target model to save memory and keep weights in sync. + self.model.embed_tokens = target_model.model.embed_tokensPlease confirm whether the draft model should own separate embeddings (loaded via weight loader) or share with the target model.
353-359: Optional: Add concise docstring to new public class.Add a short, Google-style docstring to clarify the per-layer draft behavior and expected inputs.
Example:
class MTPDraftModel(nn.Module): def __init__(self, model_config: ModelConfig[PretrainedConfig], layer_idx: int, aux_stream_dict: Dict[AuxStreamType, torch.cuda.Stream]): super().__init__() + """ + Per-layer MTP draft block used in the two-model MTP-Eagle flow. + + Args: + model_config: Global model configuration. + layer_idx: Index of the draft layer relative to the target stack. + aux_stream_dict: CUDA aux streams for attention/MoE overlap. + """
403-411: Optional: Add docstring to the public MTPDraftModelForCausalLM wrapper.Clarify the two-stream setup and how weights are loaded.
Example:
@register_auto_model("MTPDraftModelForCausalLM") class MTPDraftModelForCausalLM(DecoderModelForCausalLM[MTPDraftModel, PretrainedConfig]): def __init__(self, model_config: ModelConfig[PretrainedConfig]): - self.model_config = model_config + """ + Causal LM wrapper around a per-layer MTP draft model for two-model + MTP-Eagle speculative decoding. + + Sets up CUDA aux streams and delegates weight loading to the DeepseekV3 loader. + """ + self.model_config = model_config
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📒 Files selected for processing (11)
examples/llm-api/quickstart_advanced.py(1 hunks)tensorrt_llm/_torch/models/modeling_auto.py(1 hunks)tensorrt_llm/_torch/models/modeling_deepseekv3.py(9 hunks)tensorrt_llm/_torch/models/modeling_speculative.py(5 hunks)tensorrt_llm/_torch/pyexecutor/py_executor_creator.py(1 hunks)tensorrt_llm/_torch/speculative/eagle3.py(4 hunks)tensorrt_llm/_torch/speculative/interface.py(6 hunks)tensorrt_llm/_torch/speculative/model_drafter.py(1 hunks)tensorrt_llm/_torch/speculative/mtp.py(3 hunks)tensorrt_llm/_torch/speculative/utils.py(10 hunks)tensorrt_llm/llmapi/llm_args.py(6 hunks)
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🧠 Learnings (1)
📚 Learning: 2025-08-14T06:36:40.681Z
Learnt from: timlee0212
PR: NVIDIA/TensorRT-LLM#6886
File: tensorrt_llm/_torch/models/modeling_deepseekv3.py:0-0
Timestamp: 2025-08-14T06:36:40.681Z
Learning: In DeepSeek V3 model (tensorrt_llm/_torch/models/modeling_deepseekv3.py), the disagreement between AllReduce.__init__ guard and _compute_mlp_tp_size logic for MNNVL usage is expected by design. The AllReduce component and MLP TP-size computation intentionally use different criteria for MNNVL availability decisions.
Applied to files:
tensorrt_llm/_torch/models/modeling_deepseekv3.py
🪛 Ruff (0.12.2)
tensorrt_llm/_torch/models/modeling_deepseekv3.py
514-514: Line too long (124 > 120)
(E501)
659-659: Line too long (122 > 120)
(E501)
797-797: Line too long (125 > 120)
(E501)
803-803: Line too long (136 > 120)
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tensorrt_llm/_torch/models/modeling_speculative.py
463-463: Comparison to None should be cond is not None
Replace with cond is not None
(E711)
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🔇 Additional comments (24)
tensorrt_llm/_torch/speculative/interface.py (3)
24-29: Helper predicates for one-model MTP look goodis_mtp_one_model and is_mtp_eagle_one_model are clear and simplify downstream checks. No issues.
39-41: use_one_engine correctly covers MTP one-modelThe updated predicate now includes MTP one-model alongside EAGLE3 one-model, which is consistent with the intended architecture split.
196-202: Default SpecMetadata.is_layer_capture stub is fineProviding an overridable hook with a safe default is appropriate.
tensorrt_llm/_torch/models/modeling_deepseekv3.py (1)
1496-1499: Weight loader integration looks correctSwitching to DeepseekV3WeightLoader.load_weights simplifies the class and centralizes logic. LGTM.
tensorrt_llm/_torch/speculative/model_drafter.py (1)
32-35: Drop-first-token logic extended to MTP_EAGLE (two-model) — LGTMAligns with EAGLE3 behavior and the broader MTP Eagle flow. Safe for short prompts (returns empty tensor when len==1).
tensorrt_llm/_torch/speculative/mtp.py (3)
154-156: One-model Eagle gating: confirm behavior.Setting subseq_all_rank_num_tokens only for is_mtp_eagle_one_model looks intentional for the MTPEagle one-model path. Please confirm that two-model Eagle does not rely on subseq_all_rank_num_tokens here (it should be using Eagle3 metadata instead). If not, add the appropriate branch.
171-173: Double-check num_tokens adjustment gating.MTP vanilla uses total max_draft_len tokens, while MTP Eagle’s first step uses max_draft_len+1 and subsequent steps 1. The change to gate on not is_mtp_eagle_one_model() seems correct for the one-model Eagle path. Please verify that the two-model Eagle path won’t traverse this code (i.e., uses Eagle3 metadata exclusively).
181-202: Vanilla-only hidden-state updates gating looks correct.Updating hidden-state pointers only under is_mtp_one_model() aligns with excluding the MTP Eagle one-model path, which has its own flow.
examples/llm-api/quickstart_advanced.py (1)
186-193: One-model MTP path wiring looks consistent.Using MTPDecodingConfig with num_nextn_predict_layers and mtp_eagle_one_model mirrors the new spec_dec_mode predicates.
tensorrt_llm/_torch/speculative/eagle3.py (2)
39-41: Capacity of hidden_states now depends on config.num_capture_layers — verify alignment.Resource allocation uses EagleDecodingConfig.num_capture_layers, while metadata can derive layers_to_capture dynamically. Ensure config.num_capture_layers and metadata.layers_to_capture length always match, or document the assumption. Mismatch can cause OOB writes during capture.
187-219: Eagle3OneModelSpecMetadata mirrors capture logic — good parity.The mirrored lazy init and allocation for one-model Eagle3 aligns with the two-model path.
tensorrt_llm/_torch/speculative/utils.py (6)
22-30: One-model MTP metadata unification looks correct.Routing both vanilla MTP and MTP Eagle one-model through MTPSpecMetadata via is_mtp_one_model() simplifies the stack.
75-85: Resource manager for MTP Eagle one-model only when relaxed acceptance is enabled.This conditional avoids unnecessary buffers. Please confirm MTPEagleWorker never expects mtp_* pools when use_relaxed_acceptance_for_thinking=False. Otherwise, allocate conditionally per usage site.
92-101: Two-model Eagle and MTP Eagle share Eagle3ResourceManager — assert message updated.The assertion covers both modes. Good consistency.
111-118: Decoder selection via is_mtp_one_model() is fine if it includes Eagle one-model.Ensure SpeculativeDecodingMode.is_mtp_one_model() returns True for both vanilla MTP and MTP Eagle one-model. If not, add an explicit is_mtp_eagle_one_model() branch to return MTPSampler.
162-170: Worker selection split is clear and correct.MTP Vanilla → MTPWorker; MTP Eagle one-model → MTPEagleWorker; Eagle3 one-model → Eagle3OneModelWorker.
186-191: MTP one-model max_draft_len reconciliation is necessary — good.Ensures low-level APIs expecting max_draft_len receive a consistent value derived from num_nextn_predict_layers.
tensorrt_llm/llmapi/llm_args.py (4)
445-452: num_capture_layers logic: confirm alignment with eagle3.py defaults.If eagle3_layers_to_capture is unset, you return 1 for MTP Eagle and 3 otherwise. This should match eagle3.py’s computed layers_to_capture length. Please verify consistency to avoid allocation/usage mismatches.
457-461: Spec mode mapping for MTP Eagle is correct.EAGLE3_ONE_MODEL takes priority; otherwise MTP_EAGLE if flagged.
542-547: from_dict sets max_draft_len — good for downstream APIs.Keeps legacy code that reads max_draft_len working.
557-561: MTP_EAGLE vs MTP_EAGLE_ONE_MODEL selection looks right.Using num_nextn_predict_layers_from_model_config and mtp_eagle_one_model disambiguates both modes cleanly.
tensorrt_llm/_torch/models/modeling_speculative.py (3)
22-22: LGTM: AuxStreamType import is appropriate.Import looks correct and aligns with the new aux-stream wiring.
340-343: LGTM: MTP layer count computation is consistent with the new predicates.Using
is_mtp_one_model()andis_mtp_eagle_one_model()reads clearly, and the fallback tonum_nextn_predict_layersmakes sense.
468-474: LGTM: New MTP-Eagle branch in get_draft_model.Adding the
is_mtp_eagle()path to returnMTPDraftModelForCausalLMis consistent with enabling the two-model spec-dec for MTP Eagle.
| if not args.use_one_model: | ||
| print( | ||
| "MTP only supports one model style spec decode; ignoring default use_one_model=False" | ||
| print("Running MTP eagle with two model style.") | ||
| spec_config = EagleDecodingConfig( | ||
| max_draft_len=args.spec_decode_max_draft_len, | ||
| speculative_model_dir=args.model_dir, | ||
| eagle3_one_model=args.use_one_model, | ||
| is_mtp_eagle=True, | ||
| use_relaxed_acceptance_for_thinking=args. | ||
| use_relaxed_acceptance_for_thinking, | ||
| relaxed_topk=args.relaxed_topk, | ||
| relaxed_delta=args.relaxed_delta, | ||
| ) |
There was a problem hiding this comment.
Two-model MTP Eagle should use draft_model_dir, not model_dir.
EagleDecodingConfig.validate requires a draft model path. Using args.model_dir here is likely incorrect and will fail if EAGLE weights aren’t colocated with the main model.
Enforce draft_model_dir and wire it:
- if not args.use_one_model:
- print("Running MTP eagle with two model style.")
- spec_config = EagleDecodingConfig(
- max_draft_len=args.spec_decode_max_draft_len,
- speculative_model_dir=args.model_dir,
+ if not args.use_one_model:
+ print("Running MTP eagle with two model style.")
+ if args.draft_model_dir is None:
+ raise ValueError("For two-model MTP Eagle, --draft_model_dir must be provided.")
+ spec_config = EagleDecodingConfig(
+ max_draft_len=args.spec_decode_max_draft_len,
+ speculative_model_dir=args.draft_model_dir,
eagle3_one_model=args.use_one_model,
is_mtp_eagle=True,
use_relaxed_acceptance_for_thinking=args.
use_relaxed_acceptance_for_thinking,
relaxed_topk=args.relaxed_topk,
relaxed_delta=args.relaxed_delta,
)📝 Committable suggestion
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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| if not args.use_one_model: | |
| print( | |
| "MTP only supports one model style spec decode; ignoring default use_one_model=False" | |
| print("Running MTP eagle with two model style.") | |
| spec_config = EagleDecodingConfig( | |
| max_draft_len=args.spec_decode_max_draft_len, | |
| speculative_model_dir=args.model_dir, | |
| eagle3_one_model=args.use_one_model, | |
| is_mtp_eagle=True, | |
| use_relaxed_acceptance_for_thinking=args. | |
| use_relaxed_acceptance_for_thinking, | |
| relaxed_topk=args.relaxed_topk, | |
| relaxed_delta=args.relaxed_delta, | |
| ) | |
| if not args.use_one_model: | |
| print("Running MTP eagle with two model style.") | |
| if args.draft_model_dir is None: | |
| raise ValueError("For two-model MTP Eagle, --draft_model_dir must be provided.") | |
| spec_config = EagleDecodingConfig( | |
| max_draft_len=args.spec_decode_max_draft_len, | |
| speculative_model_dir=args.draft_model_dir, | |
| eagle3_one_model=args.use_one_model, | |
| is_mtp_eagle=True, | |
| use_relaxed_acceptance_for_thinking=args. | |
| use_relaxed_acceptance_for_thinking, | |
| relaxed_topk=args.relaxed_topk, | |
| relaxed_delta=args.relaxed_delta, | |
| ) |
🤖 Prompt for AI Agents
In examples/llm-api/quickstart_advanced.py around lines 173 to 184, the config
for two-model MTP Eagle incorrectly passes args.model_dir as the
draft/speculative model path; change it to use the draft model path
(args.draft_model_dir) and/or the proper EagleDecodingConfig parameter name
(draft_model_dir or speculative_model_dir as defined in the config API) so the
spec/draft weights are read from the draft model directory instead of the main
model_dir; update the argument passed to EagleDecodingConfig accordingly and
ensure the CLI/argparser exposes args.draft_model_dir.
| if model_arch == "DeepseekV3ForCausalLM" and config.spec_config.max_draft_len == 0: | ||
| model_arch = "MTPDraftModelForCausalLM" |
There was a problem hiding this comment.
Fix: guard config.spec_config None to avoid AttributeError
Accessing config.spec_config.max_draft_len without a None-check will raise if spec_config is None. Add a guard.
- if model_arch == "DeepseekV3ForCausalLM" and config.spec_config.max_draft_len == 0:
+ if (model_arch == "DeepseekV3ForCausalLM"
+ and config.spec_config is not None
+ and config.spec_config.max_draft_len == 0):
model_arch = "MTPDraftModelForCausalLM"Also, per repository guidelines, prepend the NVIDIA copyright header:
# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.🤖 Prompt for AI Agents
In tensorrt_llm/_torch/models/modeling_auto.py around lines 23-24, accessing
config.spec_config.max_draft_len can raise AttributeError when spec_config is
None; add a None-check so you only read max_draft_len if config.spec_config is
not None (e.g., guard with if config.spec_config and
config.spec_config.max_draft_len == 0) and update the branch accordingly; also
prepend the NVIDIA copyright header line "# Copyright (c) 2025, NVIDIA
CORPORATION. All rights reserved." at the top of the file.
| def _compute_shared_expert_tp_size(self, intermediate_size: int, | ||
| block_size: int) -> int: | ||
| """ | ||
| In the case of Deepseek-R1, the TP size of MLP is capped by intermediate_size // block_size. | ||
| For example, when the intermediate_size is 2048 and block scaling size is 128, | ||
| TP sizes are limited to {1, 2, 4, 8, 16} because of 2048/128 = 16. | ||
|
|
||
| Args: | ||
| intermediate_size (int): MLP intermediate size. | ||
| block_size (int): The quantization block scale size. In the case of Deepseek FP8 recipe, | ||
| it's 128. For NVFP4, it's 16. | ||
|
|
||
| Returns: | ||
| int: The computed tp_size. | ||
| """ | ||
|
|
||
| assert intermediate_size % block_size == 0, "intermediate_size must be divisible by block_size." | ||
|
|
||
| shared_output_scale = None | ||
| # The block scale size is 128, which requires shared_expert_intermediate_size to be divisible by 128. | ||
| if self.use_dp: | ||
| # If using attention DP, the shared experts also use DP instead of TP. | ||
| shared_tp_size = 1 | ||
| else: | ||
| # Due to the restriction of block scale size (i.e., 128), the supported TP sizes only include 1, 2, 4, 8, and 16. | ||
| # The math.gcd operation ensures that shared_tp_size falls in the supported TP sizes. | ||
| shared_tp_size = math.gcd( | ||
| intermediate_size // block_size, | ||
| self.mapping.tp_size, | ||
| ) | ||
| # If shared_tp_size has been overridden, the output of shared experts needs to be scaled down accordingly before all-reduce. | ||
| if shared_tp_size != self.mapping.tp_size: | ||
| shared_output_scale = shared_tp_size / self.mapping.tp_size | ||
|
|
||
| return shared_tp_size, shared_output_scale | ||
|
|
||
| def compute_routed_output(self, hidden_states, hidden_states_fp4, |
There was a problem hiding this comment.
Fix type annotation and docstring: function returns tuple, not int
_compute_shared_expert_tp_size returns a tuple (shared_tp_size, shared_output_scale), but the annotation and docstring say int. Align both.
- def _compute_shared_expert_tp_size(self, intermediate_size: int,
- block_size: int) -> int:
+ def _compute_shared_expert_tp_size(
+ self, intermediate_size: int, block_size: int
+ ) -> Tuple[int, Optional[float]]:
@@
- Returns:
- int: The computed tp_size.
+ Returns:
+ Tuple[int, Optional[float]]: (tp_size, shared_output_scale)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def _compute_shared_expert_tp_size(self, intermediate_size: int, | |
| block_size: int) -> int: | |
| """ | |
| In the case of Deepseek-R1, the TP size of MLP is capped by intermediate_size // block_size. | |
| For example, when the intermediate_size is 2048 and block scaling size is 128, | |
| TP sizes are limited to {1, 2, 4, 8, 16} because of 2048/128 = 16. | |
| Args: | |
| intermediate_size (int): MLP intermediate size. | |
| block_size (int): The quantization block scale size. In the case of Deepseek FP8 recipe, | |
| it's 128. For NVFP4, it's 16. | |
| Returns: | |
| int: The computed tp_size. | |
| """ | |
| assert intermediate_size % block_size == 0, "intermediate_size must be divisible by block_size." | |
| shared_output_scale = None | |
| # The block scale size is 128, which requires shared_expert_intermediate_size to be divisible by 128. | |
| if self.use_dp: | |
| # If using attention DP, the shared experts also use DP instead of TP. | |
| shared_tp_size = 1 | |
| else: | |
| # Due to the restriction of block scale size (i.e., 128), the supported TP sizes only include 1, 2, 4, 8, and 16. | |
| # The math.gcd operation ensures that shared_tp_size falls in the supported TP sizes. | |
| shared_tp_size = math.gcd( | |
| intermediate_size // block_size, | |
| self.mapping.tp_size, | |
| ) | |
| # If shared_tp_size has been overridden, the output of shared experts needs to be scaled down accordingly before all-reduce. | |
| if shared_tp_size != self.mapping.tp_size: | |
| shared_output_scale = shared_tp_size / self.mapping.tp_size | |
| return shared_tp_size, shared_output_scale | |
| def compute_routed_output(self, hidden_states, hidden_states_fp4, | |
| def _compute_shared_expert_tp_size( | |
| self, intermediate_size: int, block_size: int | |
| ) -> Tuple[int, Optional[float]]: | |
| """ | |
| In the case of Deepseek-R1, the TP size of MLP is capped by intermediate_size // block_size. | |
| For example, when the intermediate_size is 2048 and block scaling size is 128, | |
| TP sizes are limited to {1, 2, 4, 8, 16} because of 2048/128 = 16. | |
| Args: | |
| intermediate_size (int): MLP intermediate size. | |
| block_size (int): The quantization block scale size. In the case of Deepseek FP8 recipe, | |
| it's 128. For NVFP4, it's 16. | |
| Returns: | |
| Tuple[int, Optional[float]]: (tp_size, shared_output_scale) | |
| """ | |
| assert intermediate_size % block_size == 0, "intermediate_size must be divisible by block_size." | |
| shared_output_scale = None | |
| # The block scale size is 128, which requires shared_expert_intermediate_size to be divisible by 128. | |
| if self.use_dp: | |
| # If using attention DP, the shared experts also use DP instead of TP. | |
| shared_tp_size = 1 | |
| else: | |
| # Due to the restriction of block scale size (i.e., 128), the supported TP sizes only include 1, 2, 4, 8, and 16. | |
| # The math.gcd operation ensures that shared_tp_size falls in the supported TP sizes. | |
| shared_tp_size = math.gcd( | |
| intermediate_size // block_size, | |
| self.mapping.tp_size, | |
| ) | |
| # If shared_tp_size has been overridden, the output of shared experts needs to be scaled down accordingly before all-reduce. | |
| if shared_tp_size != self.mapping.tp_size: | |
| shared_output_scale = shared_tp_size / self.mapping.tp_size | |
| return shared_tp_size, shared_output_scale |
🧰 Tools
🪛 Ruff (0.12.2)
797-797: Line too long (125 > 120)
(E501)
803-803: Line too long (136 > 120)
(E501)
🤖 Prompt for AI Agents
In tensorrt_llm/_torch/models/modeling_deepseekv3.py around lines 773 to 809,
the _compute_shared_expert_tp_size function actually returns a tuple
(shared_tp_size, shared_output_scale) but is annotated and documented as
returning an int; update the function signature to return the correct typing
(e.g., Tuple[int, Optional[float]]), add necessary imports from typing (Tuple,
Optional) if not present, and modify the docstring Returns section to describe
the two-tuple (shared_tp_size: int, shared_output_scale: Optional[float]) and
their meanings.
svc-trtllm-gh-bot
added
the
Community want to contribute
| @@ -20,6 +20,8 @@ def from_config( | |||
| "") # Strip the appended EAGLE3 | |||
| if hasattr(config.pretrained_config, "draft_vocab_size"): | |||
| model_arch = "EAGLE3" + model_arch | |||
| if model_arch == "DeepseekV3ForCausalLM" and config.spec_config.max_draft_len == 0: | |||
There was a problem hiding this comment.
Could config.spec_config be None?
| else: | ||
| hidden_states = hidden_states[-1].unsqueeze(0) | ||
| return torch.chunk(v, tp_size, dim=dim)[idx].contiguous() |
There was a problem hiding this comment.
else is not necessary.
| else: | |
| hidden_states = hidden_states[-1].unsqueeze(0) | |
| return torch.chunk(v, tp_size, dim=dim)[idx].contiguous() | |
| return torch.chunk(v, tp_size, dim=dim)[idx].contiguous() |
|
|
||
| if not do_finalize: | ||
| return [shared_output, *routed_output] | ||
| else: |
There was a problem hiding this comment.
else is not necessary.
| if get_sm_version() == 100 and issubclass(attention_backend, | ||
| TrtllmAttention): | ||
| return False | ||
| else: | ||
| return True |
There was a problem hiding this comment.
Could be simplified to return not (get_sm_version() == ....
There was a problem hiding this comment.
Hi ziyixiong, I'll continue to modify it when #6210 is merged
There was a problem hiding this comment.
We should revert this to the way it is currently on trunk
There was a problem hiding this comment.
Have not reviewed fully. Will come back to it once #6210 is merged; looks like this one has a lot of overlap with it.
| print("Running MTP eagle with two model style.") | ||
| spec_config = EagleDecodingConfig( | ||
| max_draft_len=args.spec_decode_max_draft_len, | ||
| speculative_model_dir=args.model_dir, |
There was a problem hiding this comment.
Are the weights for MTP included in the target model checkpoint?
There was a problem hiding this comment.
Yes, it's included in the target model checkpoint but target model doesn't contain them anymore as the target model does't contain MTP layers anymore. There're 2 methods we can deal with it:
- Load it twice like what we did now.
- Save the
weightsastarget.weightsand load it in functionload_weights_from_target_model()withweight_loader.load_weights(target.weights)
The second method is much better, do you think so?
| print( | ||
| "MTP only supports one model style spec decode; ignoring default use_one_model=False" | ||
| print("Running MTP eagle with two model style.") | ||
| spec_config = EagleDecodingConfig( |
There was a problem hiding this comment.
Does it have to be EagleDecodingConfig? On the UX side, it's not great that you have to use different config objects for MTP_EAGLE 2-model vs 1-model.
There was a problem hiding this comment.
The pipeline of MTP_EAGLE 2-model is using Eagle3 pipeline, which is different from 1-model style, and I'm also using Eagle3ResourceManager and Eagle3SpecMetadata, so is this more reasonable to use EagleDecodingConfig?
There was a problem hiding this comment.
We should use MTPDecodingConfig for MTP even if it's using eagle stuff behind the scenes. It will be too confusing for end users otherwise.
There was a problem hiding this comment.
Thanks, will change it
sunnyqgg
changed the title
Signed-off-by: qgai <[email protected]>
There was a problem hiding this comment.
Actionable comments posted: 0
♻️ Duplicate comments (1)
examples/llm-api/quickstart_advanced.py (1)
173-179: Two-model MTP Eagle must use draft_model_dir (not model_dir) and guard when missingEagleDecodingConfig.validate requires a draft model path. Passing model_dir here will resolve to the target model path and likely break draft loading for two-model MTP Eagle.
Apply:
- if not args.use_one_model: - print("Running MTP eagle with two model style.") - spec_config = EagleDecodingConfig( - max_draft_len=args.spec_decode_max_draft_len, - speculative_model_dir=args.model_dir, - eagle3_one_model=args.use_one_model, - is_mtp_eagle=True) + if not args.use_one_model: + print("Running MTP eagle with two model style.") + if args.draft_model_dir is None: + raise ValueError("For two-model MTP Eagle, --draft_model_dir must be provided.") + spec_config = EagleDecodingConfig( + max_draft_len=args.spec_decode_max_draft_len, + speculative_model_dir=args.draft_model_dir, + eagle3_one_model=args.use_one_model, + is_mtp_eagle=True)
🧹 Nitpick comments (7)
tensorrt_llm/_torch/models/modeling_auto.py (2)
23-24: Good guard on spec_config; consider intent for EAGLE3 and wrap long line
- The None-check on spec_config prevents AttributeError. Looks good.
- Verify intent: this remap won’t trigger when draft_vocab_size is present (EAGLE3 path) since model_arch is prefixed to "EAGLE3...". If you also want the remap for EAGLE3 checkpoints, include "EAGLE3DeepseekV3ForCausalLM" in the condition or move the remap above the EAGLE3 prefix step.
Also fix Ruff E501 (126 > 120). Example:
- if model_arch == "DeepseekV3ForCausalLM" and config.spec_config is not None and config.spec_config.max_draft_len == 0: + if (model_arch == "DeepseekV3ForCausalLM" + and config.spec_config is not None + and config.spec_config.max_draft_len == 0): model_arch = "MTPDraftModelForCausalLM"
1-1: Prepend NVIDIA copyright headerPer repo guidelines, prepend the 2025 NVIDIA header.
+ # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. from typing import Genericexamples/llm-api/quickstart_advanced.py (2)
174-174: Prefer logger over print in library/examplesMinor: use the project logger to keep output consistent and suppressible.
- print("Running MTP eagle with two model style.") + from tensorrt_llm.logger import logger + logger.info("Running MTP eagle with two model style.")
1-1: Prepend NVIDIA copyright headerPer repo guidelines, add the 2025 header.
+ # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. import argparsetensorrt_llm/llmapi/llm_args.py (3)
428-447: Eagle capture layer controls and derived num_capture_layers are reasonable
- Tuple[int, ...] for eagle3_layers_to_capture is fine.
- num_capture_layers fallback to 1 for MTP Eagle and 3 otherwise makes sense.
Optional: add a validator to ensure elements are non-negative and strictly increasing when provided.
540-542: from_dict sets max_draft_len: OK; minor duplication elsewhereSetting max_draft_len = num_nextn_predict_layers here is fine. Note it’s also set again in BaseLlmArgs.validate_speculative_config (Lines 1701-1704), so you could deduplicate in one place later.
1-1: Prepend NVIDIA copyright headerPer repo guidelines, prepend the 2025 NVIDIA header.
+ # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. import copy
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🧬 Code Graph Analysis (3)
tensorrt_llm/llmapi/llm_args.py (1)
tensorrt_llm/_torch/speculative/interface.py (2)
SpeculativeDecodingMode(12-120)is_mtp_eagle(33-34)
examples/llm-api/quickstart_advanced.py (2)
tensorrt_llm/llmapi/llm_args.py (5)
EagleDecodingConfig(411-449)speculative_model_dir(1347-1348)model_dir(1104-1106)model_dir(1109-1113)MTPDecodingConfig(510-548)tensorrt_llm/_torch/speculative/interface.py (1)
is_mtp_eagle(33-34)
tensorrt_llm/_torch/models/modeling_auto.py (1)
tensorrt_llm/llmapi/llm_utils.py (1)
model_arch(92-94)
🪛 Ruff (0.12.2)
tensorrt_llm/_torch/models/modeling_auto.py
23-23: Line too long (126 > 120)
(E501)
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🔇 Additional comments (4)
tensorrt_llm/llmapi/llm_args.py (4)
12-13: Typing import updates look correctImporting Tuple/Type/TypeAlias is appropriate for new annotations.
454-456: Extended spec_dec_mode mapping for MTP EagleThe new branch returning MTP_EAGLE when is_mtp_eagle is set (and not one-model) aligns with the new SpeculativeDecodingMode predicates.
525-525: New flag for one-model MTP Eaglemtp_eagle_one_model adds the necessary distinction for one-model vs two-model handling. No concerns.
553-556: MTP spec_dec_mode branching aligns with new modesBranching on num_nextn_predict_layers_from_model_config and mtp_eagle_one_model to return MTP_EAGLE_ONE_MODEL / MTP_EAGLE looks consistent with interface predicates.
|
/bot run |
|
PR_Github #15924 [ run ] triggered by Bot |
|
PR_Github #15924 [ run ] completed with state |
| if get_sm_version() == 100 and issubclass(attention_backend, | ||
| TrtllmAttention): | ||
| return False | ||
| else: | ||
| return True |
There was a problem hiding this comment.
We should revert this to the way it is currently on trunk
| print( | ||
| "MTP only supports one model style spec decode; ignoring default use_one_model=False" | ||
| print("Running MTP eagle with two model style.") | ||
| spec_config = EagleDecodingConfig( |
There was a problem hiding this comment.
We should use MTPDecodingConfig for MTP even if it's using eagle stuff behind the scenes. It will be too confusing for end users otherwise.
| @@ -435,12 +438,22 @@ def validate(self) -> None: | |||
| if self.speculative_model_dir is None: | |||
| raise ValueError("Draft model must be provided for EAGLE") | |||
|
|
|||
| @functools.cached_property | |||
| def num_capture_layers(self): | |||
There was a problem hiding this comment.
Could you add a return type annotation for this function? It would improve readability for both humans and IDEs. The same applies to the other places as well.
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