[Quantization] Add compressed-tensors NVFP4 MoE Support #19990
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Summary of Changes
Hello @dsikka, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request significantly enhances vLLM's quantization capabilities by adding comprehensive support for NVFP4 quantization specifically for Mixture-of-Experts (MoE) models. It introduces a new quantization method that handles the unique structure of NVFP4 weights and integrates with optimized Marlin kernels to ensure high-performance inference for these quantized models.
Highlights
- NVFP4 MoE Support: Introduced
CompressedTensorsW4A4MoeMethodto enable support for NVFP4 (NVIDIA FP4) quantized Mixture-of-Experts (MoE) models, allowing for more efficient inference with these models. - Marlin Kernel Integration: Leveraged highly optimized Marlin kernels for the efficient execution of NVFP4 MoE operations, ensuring performance benefits for quantized models.
- Quantized Weight Handling: Implemented the loading and processing of NVFP4 quantized weights, including packed 4-bit weights (
torch.uint8), FP8 block scales (torch.float8_e4m3fn), and FP32 global scales, which are crucial for the new quantization scheme. - Dynamic Method Selection: Updated the quantization method selection logic to correctly identify and instantiate the new NVFP4 MoE method based on the detected quantization configuration.
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Code Review
The pull request adds support for MoE models with nvfp4 compressed tensors. The changes include modifications to the weight loader in fused_moe/layer.py and the addition of a new class CompressedTensorsW4A4MoeMethod in compressed_tensors_moe.py to handle the new quantization method. There are several blocks of code that are commented out, and a breakpoint that should be removed.
| @@ -1168,7 +1168,7 @@ def weight_loader(self, param: torch.nn.Parameter, | |||
| full_load = len(loaded_weight.shape) == 3 | |||
| if full_load: | |||
| shard_dim += 1 | |||
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| """ | |||
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This block of code is commented out. Consider removing it if it's no longer needed.
| if "ModelOpt" in quant_method_name: | ||
| if ('weight_scale_2' in weight_name | ||
| or 'input_scale' in weight_name): | ||
| """ |
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This block of code is commented out. Consider removing it if it's no longer needed.
| @@ -1220,7 +1220,7 @@ def weight_loader(self, param: torch.nn.Parameter, | |||
| expert_data=expert_data, | |||
| tp_rank=self.tp_rank) | |||
| return | |||
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| """ | |||
There was a problem hiding this comment.
This block of code is commented out. Consider removing it if it's no longer needed.
| @@ -1275,6 +1275,7 @@ def weight_loader(self, param: torch.nn.Parameter, | |||
| expert_data=expert_data, | |||
| tp_rank=self.tp_rank) | |||
| return | |||
| """ | |||
There was a problem hiding this comment.
This block of code is commented out. Consider removing it if it's no longer needed.
Signed-off-by: Dipika Sikka <[email protected]>
Signed-off-by: Dipika Sikka <[email protected]>
Signed-off-by: Dipika <[email protected]>
Purpose
Test Plan
Test Result
(Optional) Documentation Update