[None][fix] NVFP4 linear method's weight and weight_scale padding #10148
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Signed-off-by: jiant <[email protected]>
📝 WalkthroughWalkthroughModified the Changes
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~20 minutes
Pre-merge checks and finishing touches❌ Failed checks (1 warning, 1 inconclusive)
✅ Passed checks (1 passed)
✨ Finishing touches
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tensorrt_llm/_torch/modules/linear.py(1 hunks)
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Files:
tensorrt_llm/_torch/modules/linear.py
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Files:
tensorrt_llm/_torch/modules/linear.py
🧠 Learnings (5)
📚 Learning: 2025-08-19T12:45:11.997Z
Learnt from: amitz-nv
Repo: NVIDIA/TensorRT-LLM PR: 7033
File: tensorrt_llm/_torch/pyexecutor/model_engine.py:0-0
Timestamp: 2025-08-19T12:45:11.997Z
Learning: In tensorrt_llm/_torch/pyexecutor/model_engine.py, DoRA (Delta Orthogonal Rank Adaptation) functionality was removed from the PyTorch flow to eliminate issues with inverted DoRA detection logic. The original is_dora condition was checking if scaling_vec_pointer == 0, which was potentially incorrect.
Applied to files:
tensorrt_llm/_torch/modules/linear.py
📚 Learning: 2025-08-08T04:10:19.038Z
Learnt from: djns99
Repo: NVIDIA/TensorRT-LLM PR: 6728
File: cpp/tensorrt_llm/plugins/mixtureOfExperts/mixtureOfExpertsPlugin.cpp:966-966
Timestamp: 2025-08-08T04:10:19.038Z
Learning: TensorRT plugins currently don't support padding functionality, and TensorRT is not getting new features (in maintenance mode). This means that duplicating parameters like mExpertHiddenSize in function calls, even with TODO comments, can be acceptable as pragmatic solutions within these constraints.
Applied to files:
tensorrt_llm/_torch/modules/linear.py
📚 Learning: 2025-09-19T21:28:13.751Z
Learnt from: jhaotingc
Repo: NVIDIA/TensorRT-LLM PR: 7856
File: cpp/tensorrt_llm/thop/fp8BlockScaleMoe.cpp:159-166
Timestamp: 2025-09-19T21:28:13.751Z
Learning: In TensorRT-LLM blockScaleMoe routing (cpp/tensorrt_llm/kernels/trtllmGenKernels/blockScaleMoe/runner.cu), the DeepSeek routing method performs reinterpret_cast<float*>(routingLogits) at line 89, which could cause issues if routing_logits are BF16. However, Qwen3-FP8 models use RenormalizeNaive routing method and are not affected by this dtype casting issue.
Applied to files:
tensorrt_llm/_torch/modules/linear.py
📚 Learning: 2025-09-29T15:14:28.503Z
Learnt from: amitz-nv
Repo: NVIDIA/TensorRT-LLM PR: 8063
File: tensorrt_llm/lora_manager.py:1080-1112
Timestamp: 2025-09-29T15:14:28.503Z
Learning: In tensorrt_llm/lora_manager.py, when calculating part_sizes for attn_qkv fused LoRA modules, the sizes are correctly multiplied by tp_size because model_config.num_heads and model_config.num_kv_heads are already divided by tp_size (per-TP-rank values), so multiplication is needed to get the original full concatenated dimension size. The interleave_fused_lora_weights_for_tp function provides proper validation with asserts for total size and TP divisibility.
Applied to files:
tensorrt_llm/_torch/modules/linear.py
📚 Learning: 2025-09-29T15:14:28.503Z
Learnt from: amitz-nv
Repo: NVIDIA/TensorRT-LLM PR: 8063
File: tensorrt_llm/lora_manager.py:1080-1112
Timestamp: 2025-09-29T15:14:28.503Z
Learning: In tensorrt_llm/lora_manager.py, when calculating part_sizes for attn_qkv fused LoRA modules, the sizes are correctly multiplied by tp_size because model_config.num_heads and model_config.num_kv_heads are already divided by tp_size (per-TP-rank values), so multiplication is needed to get the original full concatenated dimension size. The interleave_fused_lora_weights_for_tp function provides proper validation.
Applied to files:
tensorrt_llm/_torch/modules/linear.py
🧬 Code graph analysis (1)
tensorrt_llm/_torch/modules/linear.py (2)
tensorrt_llm/_torch/utils.py (1)
unswizzle_sf(168-183)tensorrt_llm/functional.py (1)
pad(1327-1398)
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- GitHub Check: Pre-commit Check
| scale_pad_col = fp4_utils.pad_up( | ||
| (module.in_features + (col_pad_size * 2)) // | ||
| module.scaling_vector_size, 4) - scale_rows |
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Critical bug: Wrong variable used in scale_pad_col calculation.
Line 1154 subtracts scale_rows instead of scale_cols. Since scale_pad_col represents the additional column padding needed for the weight_scale tensor, it should subtract the original column dimension (scale_cols), not the row dimension (scale_rows).
This bug will cause incorrect padding calculations for the column dimension of weight_scale, potentially leading to shape mismatches or silent numerical errors.
🔎 Proposed fix
scale_pad_col = fp4_utils.pad_up(
(module.in_features + (col_pad_size * 2)) //
- module.scaling_vector_size, 4) - scale_rows
+ module.scaling_vector_size, 4) - scale_cols📝 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.
| scale_pad_col = fp4_utils.pad_up( | |
| (module.in_features + (col_pad_size * 2)) // | |
| module.scaling_vector_size, 4) - scale_rows | |
| scale_pad_col = fp4_utils.pad_up( | |
| (module.in_features + (col_pad_size * 2)) // | |
| module.scaling_vector_size, 4) - scale_cols |
🤖 Prompt for AI Agents
In tensorrt_llm/_torch/modules/linear.py around lines 1152 to 1154, the
calculation for scale_pad_col incorrectly subtracts scale_rows; change the
subtraction to scale_cols so the column padding is computed against the original
column dimension (scale_cols). Update the expression to subtract scale_cols
(ensure scale_cols is in scope) and run relevant shape/unit tests to validate
padding and downstream tensor shapes.
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PR_Github #29084 [ run ] triggered by Bot. Commit: |
Signed-off-by: jiant <[email protected]>
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PR_Github #29086 [ run ] triggered by Bot. Commit: |
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PR_Github #29084 [ run ] completed with state |
Wanli-Jiang
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Verified that it works well with nano and super v3. Thanks for your help!
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PR_Github #29086 [ run ] completed with state
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