Semi-structured 2:4 sparsity via SparseSemiStructuredTensor

examples/offline_inference_semi_structured_sparse.py

@@ -0,0 +1,14 @@
+from vllm import LLM, SamplingParams
+
+model = LLM(
+    "nm-testing/zephyr-50sparse-24", 
+    sparsity="semi_structured_sparse_w16a16",
+    enforce_eager=True,
+    dtype="float16",
+    tensor_parallel_size=1,
+    max_model_len=1024
+)
+
+sampling_params = SamplingParams(max_tokens=100, temperature=0)
+outputs = model.generate("Hello my name is", sampling_params=sampling_params)
+print(outputs[0].outputs[0].text)

vllm/config.py

@@ -148,7 +148,7 @@ def _verify_tokenizer_mode(self) -> None:
         self.tokenizer_mode = tokenizer_mode
 
     def _verify_sparsity(self) -> None:
-        supported_sparsity = ["sparse_w16a16"]
+        supported_sparsity = ["sparse_w16a16", "semi_structured_sparse_w16a16"]
 
         if self.quantization is not None:
             raise ValueError("Both sparsity and quantization detected. Only "

vllm/model_executor/layers/parameters/sparsity.py

@@ -1,6 +1,13 @@
 import torch
+from torch.sparse import SparseSemiStructuredTensor
 
-from magic_wand import SparseTensor, SparseBitmaskStorageFormat
+from typing import Type
+from magic_wand import (
+    SparseTensor, 
+    CompressedStorageFormat, 
+    SparseBitmaskStorageFormat, 
+    SparseSemiStructuredStorageFormat
+)
 
 
 class SparseParameter(SparseTensor):
@@ -10,20 +17,25 @@ def __new__(
         cls,
         shape: torch.Size,
         dtype: torch.dtype,
+        storage_format_cls: Type[CompressedStorageFormat] = SparseBitmaskStorageFormat
     ):
         assert torch.__version__ > (1,
                                     10), "SparseTensor requires PyTorch 1.11+"
+
         self = torch.Tensor._make_wrapper_subclass(cls,
                                                    size=shape,
                                                    dtype=dtype,
                                                    requires_grad=False)
-        self.storage_format_cls = SparseBitmaskStorageFormat
+        self.storage_format_cls = storage_format_cls
         self.compressed_data = None
         self.dense_data = None
         self._is_param = True
 
         return self
 
+    def has_compressed_data(self) -> bool:
+        return (self.compressed_data is not None)
+
     def get_dense_data(self) -> torch.Tensor:
         if self.dense_data is not None:
             raise ValueError(
@@ -39,8 +51,22 @@ def _unpack(self) -> torch.Tensor:
                                dtype=self.dtype,
                                device="cuda")
 
+    @classmethod
+    def _copy(cls, arg0, arg1):
+        assert arg0.shape == arg1.shape
+
+        if arg0.has_compressed_data():
+            arg0.compressed_data.copy_(arg1)
+        else:
+            arg0.compressed_data = arg0.storage_format_cls.compress(arg1)
+
+        return arg0
+
+    def copy_(self, src, non_blocking=False):
+        return SparseParameter._copy(self, src)
+
     def pack(self) -> None:
         if self.dense_data is None:
             raise ValueError("Called pack() but dense_data does not exist.")
         self.copy_(self.dense_data)
-        self.dense_data = None
+        self.dense_data = None

vllm/model_executor/layers/sparsity/__init__.py

@@ -2,9 +2,11 @@
 
 from vllm.model_executor.layers.sparsity.base_config import SparsityConfig
 from vllm.model_executor.layers.sparsity.sparse_w16a16 import SparseW16A16Config
+from vllm.model_executor.layers.sparsity.semi_structured_sparse_w16a16 import SemiStructuredSparseW16A16Config
 
 _SPARSITY_CONFIG_REGISTRY = {
     "sparse_w16a16": SparseW16A16Config,
+    "semi_structured_sparse_w16a16": SemiStructuredSparseW16A16Config,
 }
 
 

vllm/model_executor/layers/sparsity/base_config.py

@@ -2,13 +2,19 @@
 from typing import Any, Dict, List
 
 import torch
+from typing import Type
 
 from vllm.model_executor.layers.linear import LinearMethodBase
-
+from magic_wand import CompressedStorageFormat
 
 class SparsityConfig(ABC):
     """Base class for sparsity configs."""
 
+    @abstractmethod
+    def get_storage_format_cls(self) -> Type[CompressedStorageFormat]:
+        """Sparse representation format"""
+        raise NotImplementedError
+
     @abstractmethod
     def get_name(self) -> str:
         """Name of the sparse method."""

vllm/model_executor/layers/sparsity/semi_structured_sparse_w16a16.py

@@ -0,0 +1,47 @@
+import torch
+
+from typing import Any, Dict, List, Type
+from magic_wand import CompressedStorageFormat
+from vllm.model_executor.layers.sparsity.base_config import SparsityConfig
+from .sparse_w16a16_linear_method import SparseW16A16LinearMethod
+from magic_wand import (
+    CompressedStorageFormat,
+    SparseSemiStructuredStorageFormat
+)
+
+class SemiStructuredSparseW16A16Config(SparsityConfig):
+    """Config class for SemiStructuredSparseW16A16."""
+
+    def __init__(self) -> None:
+        pass
+
+    def __repr__(self) -> str:
+        return "SemiStructuredSparseW16A16Config()"
+
+    @classmethod
+    def get_storage_format_cls(cls) -> Type[CompressedStorageFormat]:
+        return SparseSemiStructuredStorageFormat
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "semi_structured_sparse_w16a16"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.float16, torch.bfloat16]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        # TODO: Update after checks on more GPUs
+        return 80
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        return ["sparsity_config.json"]
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "SemiStructuredSparseW16A16Config":
+        return cls()
+
+    def get_linear_method(self) -> "SparseW16A16LinearMethod":
+        return SparseW16A16LinearMethod(self,self.get_storage_format_cls())

vllm/model_executor/layers/sparsity/sparse_w16a16.py

@@ -1,12 +1,15 @@
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Type
 
 import torch
 import torch.nn.functional as F
 
-from vllm.model_executor.layers.linear import LinearMethodBase, set_weight_attrs
 from vllm.model_executor.layers.sparsity.base_config import SparsityConfig
-from vllm.model_executor.layers.parameters import SparseParameter
 
+from .sparse_w16a16_linear_method import SparseW16A16LinearMethod
+from magic_wand import (
+    CompressedStorageFormat,
+    SparseBitmaskStorageFormat
+)
 
 class SparseW16A16Config(SparsityConfig):
     """Config class for SparseW16A16.
@@ -21,6 +24,10 @@ def __init__(self) -> None:
     def __repr__(self) -> str:
         return "SparseW16A16Config()"
 
+    @classmethod
+    def get_storage_format_cls(cls) -> Type[CompressedStorageFormat]:
+        return SparseBitmaskStorageFormat
+
     @classmethod
     def get_name(cls) -> str:
         return "sparse_w16a16"
@@ -43,57 +50,4 @@ def from_config(cls, config: Dict[str, Any]) -> "SparseW16A16Config":
         return cls()
 
     def get_linear_method(self) -> "SparseW16A16LinearMethod":
-        return SparseW16A16LinearMethod(self)
-
-
-class SparseW16A16LinearMethod(LinearMethodBase):
-    """Linear method for Sparse W16A16.
-
-    Args:
-        sparsity_config: The sparse config.
-    """
-
-    def __init__(self, sparsity_config: SparseW16A16Config):
-        self.sparsity_config = sparsity_config
-
-    def create_weights(
-        self,
-        input_size_per_partition: int,
-        output_size_per_partition: int,
-        input_size: int,
-        output_size: int,
-        params_dtype: torch.dtype,
-    ) -> Dict[str, Any]:
-        weight = SparseParameter(
-            shape=torch.Size(
-                (output_size_per_partition, input_size_per_partition)),
-            dtype=params_dtype,
-        )
-
-        set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0})
-
-        return {"weight": weight}
-
-    def apply_weights(
-        self,
-        weights: Dict[str, Any],
-        x: torch.Tensor,
-        bias: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        sparse_weight = weights["weight"]
-
-        # Uncompress to dense
-        dense_weight = sparse_weight.to_dense()
-
-        # # Uncomment to verify sparsity
-        # density = torch.count_nonzero(
-        #     dense_weight).item() / dense_weight.numel()
-        # print(f"sparsity = {1.0 - density}")
-
-        # Standard matrix multiply
-        if bias is not None:
-            output = F.linear(x, dense_weight, bias)
-        else:
-            output = F.linear(x, dense_weight)
-
-        return output
+        return SparseW16A16LinearMethod(self,self.get_storage_format_cls())

vllm/model_executor/layers/sparsity/sparse_w16a16_linear_method.py

@@ -0,0 +1,61 @@
+from typing import Any, Dict, Optional, Type
+
+import torch
+import torch.nn.functional as F
+
+from vllm.model_executor.layers.linear import LinearMethodBase, set_weight_attrs
+from vllm.model_executor.layers.sparsity.base_config import SparsityConfig
+from vllm.model_executor.layers.parameters import SparseParameter
+from magic_wand import (
+    CompressedStorageFormat,
+    SparseSemiStructuredStorageFormat
+)
+
+class SparseW16A16LinearMethod(LinearMethodBase):
+    """Linear method for Sparse W16A16.
+
+    Args:
+        sparsity_config: The sparse config.
+    """
+    storage_format_cls: Type[CompressedStorageFormat] = None
+
+    def __init__(self, sparsity_config: SparsityConfig, storage_format_cls: Type[CompressedStorageFormat]):
+        self.sparsity_config = sparsity_config
+        self.storage_format_cls = storage_format_cls
+
+    def create_weights(
+        self,
+        input_size_per_partition: int,
+        output_size_per_partition: int,
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype
+    ) -> Dict[str, Any]:
+        weight = SparseParameter(
+            shape=torch.Size(
+                (output_size_per_partition, input_size_per_partition)),
+            dtype=params_dtype,
+            storage_format_cls=self.storage_format_cls
+        )
+
+        set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0})
+
+        return {"weight": weight}
+
+    def apply_weights(
+        self,
+        weights: Dict[str, Any],
+        x: torch.Tensor,
+        bias: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        sparse_weight = weights["weight"]
+
+        if self.storage_format_cls == SparseSemiStructuredStorageFormat:
+            output = F.linear(x, sparse_weight, bias)
+            return output
+        else:
+
+            # Standard matrix multiply
+            # Uncompress to dense
+            output = F.linear(x, sparse_weight.to_dense(), bias)
+            return output