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Add: Support for Sparse24Bitmask Compressed Models #12097

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Add: Support for Sparse24Bitmask Compressed Models #12097

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rahul-tuli
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@rahul-tuli rahul-tuli commented Jan 15, 2025 •
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This PR adds support for models compressed using Sparse24BitMaskCompressor to use cutlass 2:4 Kernels

  • Adds support for compressed cases
  • Introduces a new BitmaskShapeParameter

This diff was manually tested on:

  • nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-chnl_wts_per_tok_dyn_act_fp8-BitM
  • nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-chnl_wts_per_tok_dyn_act_int8-BitM
  • nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-tensor_wts_tensor_act_fp8-BitM
  • nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-tensor_wts_tensor_act_int8-BitM
  • nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-tensor_wts_per_tok_dyn_act_fp8-BitM
  • nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-tensor_wts_per_tok_dyn_act_int8-BitM
  • nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-chnl_wts_tensor_act_fp8-BitM
  • nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-chnl_wts_tensor_act_int8-BitM

Also added unit tests for the compressed cases!!

Needs the following compressed-tensors PR to land:

Notion Doc: https://www.notion.so/SparseBitMask-24-work-15e863ebf65c80dcbc70e6317d552987

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@rahul-tuli rahul-tuli force-pushed the rahul-bitmask-additions branch from ab892d2 to 02ff821 Compare January 15, 2025 20:59
@rahul-tuli
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Add a test file with an 8B 2of4 compressed model for lm_eval_harness in buildkite
Add test cases for:

-> Sparse only
-> fp8 + sparse dynamic per token
-> fp8 scheme
-> int8 dynamic
-> int8 scheme

dsikka
dsikka reviewed Jan 16, 2025
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vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py
@@ -481,6 +495,19 @@ def supports_cutlass_24(

return weight_quant.num_bits == input_quant.num_bits == 8

def _get_model_compression_config(
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seems like an unnecessary function break out

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_24.py
assert all(
partition_size % 8 == 0
for partition_size in output_partition_sizes
), "All partitions must be divisible by 8 for 2:4 compressed models"
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maybe "for a 2:4 sparse compressed model"?

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_24.py
shape = BitMaskShapeParameter(data=torch.empty(
2 * len(output_partition_sizes), 1, dtype=torch.uint64),
weight_loader=weight_loader)
compressed = ModelWeightParameter(data=torch.empty(
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nit: parameter name

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_24.py
new_tensor = tensor.view(-1, 4)
zero_counts = (new_tensor == 0).sum(dim=1)
return (zero_counts >= 2).all().item()
def _decompress_bitmask_compressed_weight(
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docstring

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_24.py
for partition_size in output_partition_sizes
), "All partitions must be divisible by 8 for 2:4 compressed models"

shape = BitMaskShapeParameter(data=torch.empty(
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We dont need to shard the shape

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@rahul-tuli @dsikka