[None][feat] Add AD custom model for MiniMax-M2 family

[lucaslie]

Summary

• Replace the existing MiniMax-M2 MoE patch (patches/minimax_m2.py) with a full custom model implementation using AD canonical ops
• Covers both MiniMaxAI/MiniMax-M2 and MiniMaxAI/MiniMax-M2.5 (same architecture: model_type: minimax_m2, MiniMaxM2ForCausalLM)
• Add hierarchical equivalence tests (MLP, Attention, MoE, Decoder Layer, Full Model, Export)
• Update model registry: add minimax_m2.yaml config to M2.5 entry

Architecture

Feature	Detail
MoE	256 experts, top-8, sigmoid routing + e_score_correction_bias (noaux_tc)
Attention	GQA: 48 Q heads, 8 KV heads, head_dim=128
RoPE	Partial: rotary_dim=64 (half of head_dim)
QK Norm	Per-layer RMSNorm on full Q/K projections
Quantization	FP8 block-wise (weight_block_size=[128,128])

Canonical ops used

• torch_rmsnorm — RMSNorm and QK norm
• torch_rope_with_explicit_cos_sin — Partial RoPE (split, apply, concat)
• torch_attention — GQA-native (no repeat_kv)
• torch_moe — Expert computation (sigmoid routing in plain PyTorch)

Files changed

File	Action
tensorrt_llm/_torch/auto_deploy/models/custom/modeling_minimax_m2.py	Created — full custom model
tests/unittest/auto_deploy/singlegpu/models/test_minimax_m2_modeling.py	Created — hierarchical tests
tensorrt_llm/_torch/auto_deploy/models/custom/__init__.py	Modified — register import
examples/auto_deploy/model_registry/models.yaml	Modified — add minimax_m2.yaml to M2.5
tensorrt_llm/_torch/auto_deploy/models/patches/minimax_m2.py	Deleted — replaced by custom model

AD End-to-End Run

Status: SUCCESS with full 62 layers on 8x H100 GPUs.

Generation quality is coherent and correct across all 10 test prompts (factual questions, code generation, summarization).

Note: The FuseFineGrainedFP8Moe transform causes an NVRTC compilation failure on H100 with this model's MoE configuration (256 experts, block-wise FP8). This is a pre-existing backend infrastructure issue unrelated to the custom model code. Workaround used for the test run:

python examples/auto_deploy/build_and_run_ad.py \
  --model MiniMaxAI/MiniMax-M2 \
  --use-registry \
  --args.transforms.fuse_finegrained_fp8_moe.enabled false \
  --args.compile-backend torch-simple

To reproduce with default settings (once NVRTC issue is resolved):

python examples/auto_deploy/build_and_run_ad.py --model MiniMaxAI/MiniMax-M2 --use-registry

Test plan

Run unit tests:

pytest tests/unittest/auto_deploy/singlegpu/models/test_minimax_m2_modeling.py -v

Tests include:

• MLP equivalence (tight tolerance, identical math)
• Attention equivalence (partial RoPE + QK norm, rmse_ratio_tol=0.10)
• MoE equivalence (sigmoid routing, rmse_ratio_tol=0.02)
• Decoder layer equivalence (rmse_ratio_tol=0.05)
• Full model equivalence on CPU and CUDA (rmse_ratio_tol=0.05)
• Export test with dynamic shapes + eager equivalence
• Structural tests (config, GQA, partial RoPE, MoE structure, state_dict keys)

🤖 Generated with Claude Code

[lucaslie]

please rebase and run again. I believe a fix for skipping the finegrained fp8 fusion is merged by now that skips when on Hopper GPU or earlier. Please confirm this and if so re-run build_and_run_ad and report back

[lucaslie]

[AGENT] Rebased onto latest feat/paperclip_maximizer (e346936) and re-ran.

NVRTC issue is NOT fixed — the FuseFineGrainedFP8Moe transform still inserts trtllm_quant_finegrained_fp8_moe_fused unconditionally, which fails with RuntimeError: NVRTC compilation failed on H100 for MiniMax-M2's MoE configuration (256 experts, FP8 block-wise quantized with weight_block_size=[128,128]).

I checked the diff between our branch and origin/feat/paperclip_maximizer — there are no changes to FuseFineGrainedFP8Moe or the trtllm MoE kernel. The Hopper skip logic doesn't appear to have been merged yet.

Workaround run (full 62 layers) succeeded with coherent generation:

python examples/auto_deploy/build_and_run_ad.py \
  --model MiniMaxAI/MiniMax-M2 \
  --use-registry \
  --args.transforms.fuse_finegrained_fp8_moe.enabled false \
  --args.compile-backend torch-simple

All 10 test prompts produce correct, coherent responses. The custom model code itself is working correctly — only the FuseFineGrainedFP8Moe backend transform has the NVRTC issue.

Should we wait for the Hopper skip fix to be merged, or is this PR ready to merge as-is with the documented workaround?

[lucaslie]

Please incorporate the disablement of the transform into a custom config and correctly add it to the model registry. Then come back after re-running build_and_run_ad and post RAW LOGS FOR ALL PROMPTS + OUTPUTS PRODUCED BY THE MODEL

[lucaslie]

[AGENT] Updated minimax_m2.yaml to include transform disablement and compile backend. Re-ran with --use-registry only (no manual flags):

python examples/auto_deploy/build_and_run_ad.py --model MiniMaxAI/MiniMax-M2 --use-registry

Result: SUCCESS — full 62 layers, 8x H100, ~281s transform time, ~42s inference for 10 prompts.

Registry config applied automatically:

attn_backend: flashinfer
compile_backend: torch-simple
transforms:
  fuse_finegrained_fp8_moe:
    enabled: false
model_kwargs:
  torch_dtype: bfloat16

---

Raw Logs — All Prompts + Outputs

[PROMPT 0] User: "How big is the universe?"

<think>
: Hmm, the user is asking about the size of the universe. That's a fundamental but incredibly complex question. The phrasing is quite open-ended, so I should focus on making the answer clear and structured while acknowledging its complexity. 

I'll start by distinguishing two types of size measurements—observable and total—since the latter is unknown and often misunderstood. For the observable universe, I can include exact numbers like radius and diameter to ground the answer. For the total universe, I'll need to explain why it's uncertain using
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[PROMPT 1] User: "In simple words and a single sentence, explain the concept of gravity:"

<think>
: We need to produce a simple sentence explaining gravity in simple words. The user says "In simple words and a single sentence, explain the concept of gravity." This is straightforward: we need to produce a single sentence, using simple words, explaining gravity in simple language.

Goal: respond succinctly with a single sentence (no extra sentences) that explains gravity in simple language.

Potential answer: "Gravity is the force that pulls objects toward each other, making things fall down and keeping planets orbiting the sun." That is a single
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[PROMPT 2] User: "How to fix slicing in golf?"

<think>
: I need to provide an actionable guide, including actionable drills and checklists. It's important to mention an "In-Club Golf Analyzer," but come on, that's too advanced for now. I want to include plausible reasons for a slice and suggest video recording to analyze face-to-path differences. Home drills will be key for fixing the slice. It's crucial to be clear on medical disclaimers, too.

I should structure a general syllabus titled "How to fix your slice," covering root causes, diagnostics
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[PROMPT 3] User: "Where is the capital of Iceland?"

<think>
: The user asks: "Where is the capital of Iceland?" So they want to know the location of the capital, i.e., name, maybe which city, and perhaps where it's located geographically (e.g., coordinates). The capital of Iceland is Reykjavik. The question is straightforward. Provide answer: Reykjavik is located in the southwestern part of Iceland, on the southwestern coast, near Faxaflói Bay, approximately 64.1° N, 21.9° W. Also can mention
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[PROMPT 4] User: "What are the three laws of thermodynamics?"

<think>
: Okay, the user asked about the three laws of thermodynamics. Let me start by recalling what each law states. First, the Zeroth Law is about thermal equilibrium. It's fundamental but often listed last numerically. I should mention how it allows the definition of temperature, which is essential for the other laws.

Next, the First Law deals with conservation of energy, especially heat. The user might be a student needing this for a class, so clarity is key. The equations like ΔU = Q - W
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[PROMPT 5] User: "Summarize the plot of Romeo and Juliet in two sentences:"

<think>
: I need to summarize "Romeo and Juliet" in two sentences. In one, I can say that the young Romeo Montague and Juliet Capulet meet and fall in love at a party, despite the long-standing hatred between their families, the Montagues and Capulets. Then, I can explain that in the second sentence that they marry in secret but face fatal consequences due to feud, miscommunication, and tragic choices, ultimately dying together, which reconciles their families. It's a timeless story
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[PROMPT 6] User: "Write a Python function that checks if a number is prime."

<think>
: We are going to write a function that checks if a number is prime.
 A prime number is a natural number greater than 1 that has no positive divisors other than 1 and itself.
 We'll handle the case for 2 (the only even prime) and then check odd divisors from 3 up to the square root of the number.
 Note: We should also consider negative numbers, 0, and 1 as non-prime.

 Steps:
 1. If the number is less than 2
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[PROMPT 7] User: "Explain the difference between a compiler and an interpreter:"

<think>
: I need to explain the differences between a compiler and an interpreter as this is a classic computer science concept. Let me break this down clearly:

1. What each does
2. Key differences in their approach
3. Advantages and disadvantages
4. Examples

A compiler translates the entire source code into machine code or another lower-level form before execution, while an interpreter executes the source code line by line during runtime.
</think>

Here's a clear explanation of the differences between a compiler and an interpreter:

##
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[PROMPT 8] User: "What causes the northern lights?"

<think>
: I need to create a clear response about the northern lights, or aurora borealis. It's important to use minimal markdown, mostly headings like #, ##, or ### if needed. The explanation will cover how charged particles from the solar wind—and specifically CMEs—get trapped in Earth's magnetosphere and travel to the poles. These particles excite gas atoms in the upper atmosphere, causing them to emit light, with specific colors correlating to different atoms. I'll also mention how these particles are influenced by Earth's
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[PROMPT 9] User: "What are the health benefits of drinking green tea?"

<think>
: I need to answer the question about the health benefits of drinking green tea. It seems I can decide on markdown headings based on preference, but it's best to use them sparingly. I should provide a comprehensive overview that includes evidence-based information, keeping in mind the differences between observational studies and clinical trials. Potential health benefits include heart health, weight management, cognitive function, cancer prevention, diabetes risk, and more. I'll make sure to mention moderate consumption for safety and highlight the importance of citing sources.

I
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---

All 10 prompts produce coherent, on-topic, structured chain-of-thought reasoning. --use-registry alone is now sufficient.