Add: Support for Sparse24Bitmask Compressed Models

.buildkite/lm-eval-harness/configs/SparseLlama3.1_2of4_fp8_compressed.yaml

@@ -0,0 +1,11 @@
+# bash ./run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-chnl_wts_per_tok_dyn_act_fp8-BitM -b "auto" -t 2
+model_name: "nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-chnl_wts_per_tok_dyn_act_fp8-BitM"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.6353
+  - name: "exact_match,flexible-extract"
+    value: 0.637
+limit: null
+num_fewshot: null 

tests/quantization/test_compressed_tensors.py

@@ -242,7 +242,10 @@ def test_compressed_tensors_kv_cache(vllm_runner):
 
 @pytest.mark.skipif(not sparse_cutlass_supported(),
                     reason="Sparse FP8 is not yet supported on this GPU type.")
-def _test_2of4_quant_models(qkv_proj, weight_strategy, input_strategy):
+def _test_2of4_quant_models(qkv_proj,
+                            weight_strategy,
+                            input_strategy,
+                            format="dense"):
     assert isinstance(qkv_proj.quant_method, CompressedTensorsLinearMethod)
     assert isinstance(qkv_proj.scheme, CompressedTensors24)
 
@@ -251,7 +254,7 @@ def _test_2of4_quant_models(qkv_proj, weight_strategy, input_strategy):
     assert qkv_proj.scheme.quantized
     assert qkv_proj.quant_method.quantization_config.sparsity_scheme_map
     sparsity_map = qkv_proj.quant_method.quantization_config.sparsity_scheme_map  # noqa: E501
-    assert sparsity_map.get("Linear").format == "dense"
+    assert sparsity_map.get("Linear").format == format
     assert sparsity_map.get("Linear").sparsity_structure == "2:4"
 
 
@@ -285,6 +288,72 @@ def check_model(model):
         assert output
 
 
+@pytest.mark.skipif(not current_platform.has_device_capability(90),
+                    reason="Sparse FP8 is not yet supported on this GPU type.")
+@pytest.mark.parametrize("args_2of4", [
+    ("nm-testing/TinyLlama-1.1B-Chat-v1.0-gsm8k-pruned.2of4-chnl_wts_per_tok_dyn_act_fp8-BitM",
+     "channel", "token"),
+    ("nm-testing/TinyLlama-1.1B-Chat-v1.0-gsm8k-pruned.2of4-chnl_wts_tensor_act_fp8-BitM",
+     "channel", "tensor"),
+    ("nm-testing/TinyLlama-1.1B-Chat-v1.0-gsm8k-pruned.2of4-tensor_wts_per_tok_dyn_act_fp8-BitM",
+     "tensor", "token"),
+    ("nm-testing/TinyLlama-1.1B-Chat-v1.0-gsm8k-pruned.2of4-tensor_wts_tensor_act_fp8-BitM",
+     "tensor", "tensor"),
+])
+def test_compressed_tensors_2of4_quant_fp8_compressed(vllm_runner, args_2of4):
+    model, weight_strategy, input_strategy = args_2of4
+    with vllm_runner(model) as llm:
+
+        def check_model(model):
+            layer = model.model.layers[0]
+
+            qkv_proj = layer.self_attn.qkv_proj
+            assert qkv_proj.scheme.weights_dtype == torch.float8_e4m3fn
+            _test_2of4_quant_models(qkv_proj,
+                                    weight_strategy,
+                                    input_strategy,
+                                    format="sparse-24-bitmask")
+
+        llm.apply_model(check_model)
+
+        output = llm.generate_greedy("Hello my name is", max_tokens=20)
+        print(output)
+        assert output
+
+
+@pytest.mark.skipif(not sparse_cutlass_supported(),
+                    reason="cutlass is not yet supported on this GPU type.")
+@pytest.mark.parametrize("args_2of4", [
+    ("nm-testing/TinyLlama-1.1B-Chat-v1.0-gsm8k-pruned.2of4-chnl_wts_per_tok_dyn_act_int8-BitM",
+     "channel", "token"),
+    ("nm-testing/TinyLlama-1.1B-Chat-v1.0-gsm8k-pruned.2of4-chnl_wts_tensor_act_int8-BitM",
+     "channel", "tensor"),
+    ("nm-testing/TinyLlama-1.1B-Chat-v1.0-gsm8k-pruned.2of4-tensor_wts_per_tok_dyn_act_int8-BitM",
+     "tensor", "token"),
+    ("nm-testing/TinyLlama-1.1B-Chat-v1.0-gsm8k-pruned.2of4-tensor_wts_tensor_act_int8-BitM",
+     "tensor", "tensor"),
+])
+def test_compressed_tensors_2of4_quant_int8_compressed(vllm_runner, args_2of4):
+    model, weight_strategy, input_strategy = args_2of4
+    with vllm_runner(model) as llm:
+
+        def check_model(model):
+            layer = model.model.layers[0]
+
+            qkv_proj = layer.self_attn.qkv_proj
+            assert qkv_proj.scheme.weights_dtype == torch.int8
+            _test_2of4_quant_models(qkv_proj,
+                                    weight_strategy,
+                                    input_strategy,
+                                    format="sparse-24-bitmask")
+
+        llm.apply_model(check_model)
+
+        output = llm.generate_greedy("Hello my name is", max_tokens=20)
+        print(output)
+        assert output
+
+
 @pytest.mark.skipif(not sparse_cutlass_supported(),
                     reason="Sparse FP8 is not yet supported on this GPU type.")
 @pytest.mark.parametrize("args_2of4", [
@@ -343,3 +412,35 @@ def check_model(model):
         output = llm.generate_greedy("Hello my name is", max_tokens=20)
         print(output)
         assert output
+
+
+@pytest.mark.skipif(not sparse_cutlass_supported(),
+                    reason="Cutlass is not yet supported on this GPU type.")
+@pytest.mark.parametrize(
+    "args_2of4",
+    [("nm-testing/llama2.c-stories42M-pruned2.4-compressed")])
+def test_compressed_tensors_2of4_sparse_compressed(vllm_runner, args_2of4):
+    model = args_2of4
+    with vllm_runner(model) as llm:
+
+        def check_model(model):
+            layer = model.model.layers[0]
+
+            qkv_proj = layer.self_attn.qkv_proj
+            assert isinstance(qkv_proj.quant_method,
+                              CompressedTensorsLinearMethod)
+            assert isinstance(qkv_proj.scheme, CompressedTensors24)
+
+            assert qkv_proj.scheme.weight_quant is None
+            assert qkv_proj.scheme.input_quant is None
+            assert not qkv_proj.scheme.quantized
+            assert qkv_proj.quant_method.quantization_config.sparsity_scheme_map
+            sparsity_map = qkv_proj.quant_method.quantization_config.sparsity_scheme_map  # noqa: E501
+            assert sparsity_map.get("Linear").format == "sparse-24-bitmask"
+            assert sparsity_map.get("Linear").sparsity_structure == "2:4"
+
+        llm.apply_model(check_model)
+
+        output = llm.generate_greedy("Hello my name is", max_tokens=20)
+        print(output)
+        assert output

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py

@@ -396,10 +396,16 @@ def get_scheme(
                                     sparsity_scheme=sparsity_scheme):
             # Have a valid sparsity scheme
             # Validate layer is supported by Cutlass 2:4 Kernel
-            scheme = CompressedTensors24(quantized=weight_quant is not None
-                                         or input_quant is not None,
-                                         weight_quant=weight_quant,
-                                         input_quant=input_quant)
+            model_compression_config = (None if sparsity_scheme is None
+                                        or sparsity_scheme.format == "dense"
+                                        else self.config)
+
+            scheme = CompressedTensors24(
+                quantized=weight_quant is not None or input_quant is not None,
+                weight_quant=weight_quant,
+                input_quant=input_quant,
+                model_compression_config=model_compression_config,
+            )
         else:
             # Find the quant_scheme
             scheme = self._get_scheme_from_parts(  # type: ignore
@@ -447,10 +453,21 @@ def supports_cutlass_24(
         :return: True if the layer is supported by the Cutlass 2:4 Kernel
             False otherwise
         """
-        is_valid_sparsity = (sparsity_scheme is not None
-                             and sparsity_scheme.sparsity_structure
-                             == SparsityStructure.TWO_FOUR.value
-                             and sparsity_scheme.format == "dense")
+        if sparsity_scheme is None:
+            return False
+
+        is_valid_sparsity_structure: bool = (
+            sparsity_scheme.sparsity_structure ==
+            SparsityStructure.TWO_FOUR.value)
+
+        valid_compressors = {
+            CompressionFormat.dense.value,
+            CompressionFormat.sparse_24_bitmask.value
+        }
+
+        is_valid_sparsity = (is_valid_sparsity_structure
+                             and sparsity_scheme.format in valid_compressors)
+
         if not is_valid_sparsity:
             return False
 

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_24.py

@@ -1,11 +1,15 @@
-from typing import Callable, List, Optional
+from typing import Any, Callable, Dict, List, Optional, Tuple
 
 import torch
+from compressed_tensors import CompressionFormat, ModelCompressor
 from compressed_tensors.quantization import (QuantizationArgs,
                                              QuantizationStrategy,
                                              QuantizationType)
+from compressed_tensors.utils import combine_shards
 
 from vllm import _custom_ops as ops
+from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
+                                               QKVParallelLinear)
 from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
     CompressedTensorsScheme)
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
@@ -20,14 +24,24 @@
 
 class CompressedTensors24(CompressedTensorsScheme):
 
-    def __init__(self,
-                 quantized: bool = False,
-                 weight_quant: Optional[QuantizationArgs] = None,
-                 input_quant: Optional[QuantizationArgs] = None):
+    def __init__(
+        self,
+        quantized: bool = False,
+        weight_quant: Optional[QuantizationArgs] = None,
+        input_quant: Optional[QuantizationArgs] = None,
+        model_compression_config: Optional[Dict[str, Any]] = None,
+    ):
 
         self.quantized = quantized
         self.weight_quant = weight_quant
         self.input_quant = input_quant
+        self.model_compressor = (
+            ModelCompressor.from_compression_config(model_compression_config)
+            if model_compression_config is not None else None)
+        self.do_sparse_decompress = (
+            self.model_compressor is not None
+            and self.model_compressor.sparsity_config.format
+            == CompressionFormat.sparse_24_bitmask.value)
 
     @classmethod
     def get_min_capability(cls) -> int:
@@ -47,6 +61,8 @@ def create_weights(self, layer: torch.nn.Module, input_size: int,
 
         self.output_dtype = params_dtype
         layer.logical_widths = output_partition_sizes
+        layer.input_size = input_size
+        layer.input_size_per_partition = input_size_per_partition
         self.weights_dtype: torch.dtype = self._get_params_dtype(params_dtype)
 
         # parameter to store uncompressed weight
@@ -57,6 +73,34 @@ def create_weights(self, layer: torch.nn.Module, input_size: int,
                                       input_dim=1,
                                       output_dim=0,
                                       weight_loader=weight_loader)
+        if self.do_sparse_decompress:
+            assert all(partition_size % 8 == 0
+                       for partition_size in output_partition_sizes
+                       ), "All partitions must be divisible by 8 for "
+            "2:4 sparse compressed models"
+
+            shape = BasevLLMParameter(data=torch.empty(2, 1,
+                                                       dtype=torch.int64),
+                                      weight_loader=weight_loader)
+            compressed_weight = ModelWeightParameter(
+                data=torch.empty(sum(output_partition_sizes),
+                                 input_size_per_partition // 2,
+                                 dtype=self.weights_dtype),
+                input_dim=1,
+                output_dim=0,
+                weight_loader=weight_loader)
+
+            bitmask = ModelWeightParameter(data=torch.empty(
+                sum(output_partition_sizes),
+                input_size_per_partition // 8,
+                dtype=torch.uint8),
+                                           input_dim=1,
+                                           output_dim=0,
+                                           weight_loader=weight_loader)
+
+            layer.register_parameter("shape", shape)
+            layer.register_parameter("compressed", compressed_weight)
+            layer.register_parameter("bitmask", bitmask)
 
         # Check if quantized, not just 2:4 Sparse
         if self.quantized:
@@ -112,6 +156,13 @@ def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         :param layer: The layer with the weights to be processed
         
         """
+        if self.do_sparse_decompress:
+            layer.weight.data = self._decompress_bitmask_compressed_weight(
+                compressed=layer.compressed,
+                bitmask=layer.bitmask,
+                layer=layer,
+            )
+
         # torch.compile workaround
         if hasattr(layer, "input_scale"):
             layer.input_scale = torch.nn.Parameter(layer.input_scale.data,
@@ -201,8 +252,55 @@ def _get_params_dtype(self, params_dtype: torch.dtype) -> torch.dtype:
 
         raise ValueError("Quantization type not supported by Cutlass")
 
+    def _decompress_bitmask_compressed_weight(
+            self, compressed: torch.Tensor, bitmask: torch.Tensor,
+            layer: torch.nn.Module) -> torch.Tensor:
+        """
+        Decompress a compressed 2:4 sparse weight tensor 
+        using the bitmask and return the result.
+        
+        This function also supports sharded decompression.
+
+        :param compressed: The 2:4 sparse weight tensor 
+            compressed using the sparse-24-bitmask compressor.
+        :param bitmask: The 2:4 bitmask associated with the compressed weights.
+        :param layer: The layer whose weights need to be processed 
+            after loading.
+        :return: The decompressed 2:4 sparse weight tensor.
+        """
 
-def check_24(tensor):
-    new_tensor = tensor.view(-1, 4)
-    zero_counts = (new_tensor == 0).sum(dim=1)
-    return (zero_counts >= 2).all().item()
+        sparsity_compressor = self.model_compressor.sparsity_compressor
+
+        def _process_split(bitmask_compressed_weight: torch.Tensor, shape,
+                           bitmask: torch.Tensor) -> torch.Tensor:
+            weight_data = dict(
+                compressed=bitmask_compressed_weight,
+                shape=shape,
+                bitmask=bitmask,
+            )
+            return sparsity_compressor.decompress_weight(weight_data)
+
+        split_weights: List[torch.Tensor] = []
+        split_bitmask: List[torch.Tensor] = []
+        split_shape: List[Tuple[int, int]] = []
+
+        if isinstance(layer, (QKVParallelLinear, MergedColumnParallelLinear)):
+            split_weights = torch.split(compressed, layer.logical_widths)
+            split_bitmask = torch.split(bitmask, layer.logical_widths)
+            split_shape = [(out, layer.input_size_per_partition)
+                           for out in layer.logical_widths]
+
+        if split_weights:
+            decompressed_shards = [
+                _process_split(compressed_weight, shape, bitmask)
+                for compressed_weight, shape, bitmask in zip(
+                    split_weights, split_shape, split_bitmask)
+            ]
+            decompressed = combine_shards(decompressed_shards)
+        else:
+            decompressed = sparsity_compressor.decompress_weight(
+                dict(compressed=compressed,
+                     shape=(layer.logical_widths[0],
+                            layer.input_size_per_partition),
+                     bitmask=bitmask))
+        return decompressed