Sparse fused gemm integration

vllm/model_executor/layers/linear.py

@@ -13,7 +13,7 @@
     divide, split_tensor_along_last_dim)
 from vllm.model_executor.utils import set_weight_attrs
 from vllm.logger import init_logger
-from vllm.model_executor.layers.parameters import SparseParameter, get_param_data
+from vllm.model_executor.layers.parameters import LazyCompressedParameter
 
 logger = init_logger(__name__)
 
@@ -196,7 +196,7 @@ def __init__(
     def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
         tp_rank = get_tensor_model_parallel_rank()
         output_dim = getattr(param, "output_dim", None)
-        param_data = get_param_data(param)
+        param_data = param.data
 
         if output_dim is not None:
             shard_size = param_data.shape[output_dim]
@@ -206,9 +206,8 @@ def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
         assert param_data.shape == loaded_weight.shape
         param_data.copy_(loaded_weight)
 
-        # If SparseParameter, repack dense data as sparse.
-        if isinstance(param, SparseParameter):
-            param.pack()
+        if isinstance(param, LazyCompressedParameter):
+            param.compress()
 
     def forward(self, input_):
         bias = self.bias if not self.skip_bias_add else None
@@ -257,6 +256,7 @@ def __init__(
         linear_method: Optional[LinearMethodBase] = None,
     ):
         self.output_sizes = output_sizes
+        self.loaded_shards = set()
         tp_size = get_tensor_model_parallel_world_size()
         assert all(output_size % tp_size == 0 for output_size in output_sizes)
         super().__init__(input_size, sum(output_sizes), bias, gather_output,
@@ -266,14 +266,9 @@ def weight_loader(self,
                       param: Parameter,
                       loaded_weight: torch.Tensor,
                       loaded_shard_id: Optional[int] = None):
-        param_data = get_param_data(param)
+        param_data = param.data
         output_dim = getattr(param, "output_dim", None)
         if loaded_shard_id is None:
-            if isinstance(param, SparseParameter):
-                raise NotImplementedError(
-                    "Passing loaded_shard_id=None not yet supported for SparseParameter"
-                )
-
             # Loaded weight is already packed.
             if output_dim is None:
                 assert param_data.shape == loaded_weight.shape
@@ -320,12 +315,17 @@ def weight_loader(self,
                     "Loading a weight without `output_dim` attribute in "
                     "MergedColumnParallelLinear, assume the weight is "
                     "the same for all partitions.")
+
+        self.loaded_shards.add(loaded_shard_id)
         assert param_data.shape == loaded_weight.shape
         param_data.copy_(loaded_weight)
 
-        # If Parameter, repack dense data as sparse.
-        if isinstance(param, SparseParameter):
-            param.pack()
+        # This is super hacky for now but we basically want to only compress once all
+        # of the shards are loaded, right now we just check if the number of shards
+        # loaded matches the number of outputs expected, assuming one shard per output
+        all_shards_loaded = (len(self.loaded_shards) == len(self.output_sizes))
+        if all_shards_loaded and isinstance(param, LazyCompressedParameter):
+            param.compress()
 
 
 class QKVParallelLinear(ColumnParallelLinear):
@@ -369,6 +369,7 @@ def __init__(
         if total_num_kv_heads is None:
             total_num_kv_heads = total_num_heads
         self.total_num_kv_heads = total_num_kv_heads
+        self.loaded_shards = set()
         # Divide the weight matrix along the last dimension.
         tp_size = get_tensor_model_parallel_world_size()
         self.num_heads = divide(self.total_num_heads, tp_size)
@@ -389,14 +390,9 @@ def weight_loader(self,
                       param: Parameter,
                       loaded_weight: torch.Tensor,
                       loaded_shard_id: Optional[str] = None):
-        param_data = get_param_data(param)
+        param_data = param.data
         output_dim = getattr(param, "output_dim", None)
         if loaded_shard_id is None:
-            if isinstance(param, SparseParameter):
-                raise NotImplementedError(
-                    "Passing loaded_shard_id=None not yet supported for SparseParameter"
-                )
-
             # Loaded weight is already packed.
             if output_dim is None:
                 assert param_data.shape == loaded_weight.shape
@@ -460,9 +456,14 @@ def weight_loader(self,
         assert param_data.shape == loaded_weight.shape
         param_data.copy_(loaded_weight)
 
-        # If SparseParameter, repack dense data as sparse.
-        if isinstance(param, SparseParameter):
-            param.pack()
+        self.loaded_shards.add(loaded_shard_id)
+
+        # This is super hacky for now but we basically want to only compress once
+        # all of the shards are loaded, for the QKV matrix this means
+        # loading shards "q", "k" and "v"
+        all_shards_loaded = (self.loaded_shards == set(["q", "k", "v"]))
+        if all_shards_loaded and isinstance(param, LazyCompressedParameter):
+            param.compress()
 
 
 class RowParallelLinear(torch.nn.Module):
@@ -546,7 +547,7 @@ def __init__(
     def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
         tp_rank = get_tensor_model_parallel_rank()
         input_dim = getattr(param, "input_dim", None)
-        param_data = get_param_data(param)
+        param_data = param.data
         if input_dim is not None:
             shard_size = param_data.shape[input_dim]
             start_idx = tp_rank * shard_size
@@ -555,9 +556,8 @@ def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
         assert param_data.shape == loaded_weight.shape
         param_data.copy_(loaded_weight)
 
-        # If SparseParameter, repack dense data as sparse.
-        if isinstance(param, SparseParameter):
-            param.pack()
+        if isinstance(param, LazyCompressedParameter):
+            param.compress()
 
     def forward(self, input_):
         # Set up backprop all-reduce.

vllm/model_executor/layers/parameters/__init__.py

@@ -1,10 +1,5 @@
-import torch
-from vllm.model_executor.layers.parameters.sparsity import SparseParameter
+from vllm.model_executor.layers.parameters.lazy_compressed import LazyCompressedParameter
 
-
-def get_param_data(param: torch.nn.Parameter) -> torch.Tensor:
-    """Gets parameter data in dense format."""
-    if isinstance(param, SparseParameter):
-        return param.get_dense_data()
-    else:
-        return param.data
+__all__ = [
+    "LazyCompressedParameter",
+]

vllm/model_executor/layers/parameters/lazy_compressed.py

@@ -0,0 +1,78 @@
+import numpy
+import torch
+from torch.utils._pytree import tree_map
+
+from typing import Type
+from magic_wand import (CompressedStorageFormat, SparseBitmaskStorageFormat)
+
+
+class LazyCompressedParameter(torch.Tensor):
+
+    @staticmethod
+    def __new__(cls,
+                uncompressed_data: torch.Tensor,
+                storage_format_cls: Type[
+                    CompressedStorageFormat] = SparseBitmaskStorageFormat,
+                compress_transposed: bool = False):
+        self = torch.Tensor._make_wrapper_subclass(
+            cls,
+            size=uncompressed_data.shape,
+            dtype=uncompressed_data.dtype,
+            requires_grad=False)
+        self.storage_format_cls = storage_format_cls
+        self.compressed_data = None
+        self.uncompressed_data = uncompressed_data
+        self.compress_transposed = compress_transposed
+        self._is_param = True
+
+        return self
+
+    @property
+    def has_compressed_data(self) -> bool:
+        return (self.compressed_data is not None)
+
+    @property
+    def has_uncompressed_data(self) -> bool:
+        return (self.uncompressed_data is not None)
+
+    @classmethod
+    def __torch_dispatch__(cls, func, types, args, kwargs):
+        ret_storage_format_cls = None
+
+        def unwrap(e):
+            nonlocal ret_storage_format_cls
+            if isinstance(e, LazyCompressedParameter):
+                assert ret_storage_format_cls is None or ret_storage_format_cls == e.storage_format_cls
+                ret_storage_format_cls = e.storage_format_cls
+            return e.uncompressed_data if isinstance(
+                e, LazyCompressedParameter) else e
+
+        rs = func(*tree_map(unwrap, args), **tree_map(unwrap, kwargs))
+
+        def wrap(e):
+            if isinstance(e,
+                          torch.Tensor) and ret_storage_format_cls is not None:
+                return LazyCompressedParameter(
+                    e, storage_format_cls=ret_storage_format_cls)
+            return e
+
+        rs = tree_map(wrap, rs)
+        return rs
+
+    def compress(self) -> None:
+        density = torch.count_nonzero(
+            self.uncompressed_data).item() / numpy.prod(self.shape)
+
+        # only compress if we have sufficient sparsity (>=45%), currently
+        # this applies globally across all formats including 2:4
+        if (1 - density) < 0.45:
+            return
+
+        if self.uncompressed_data is None:
+            raise ValueError(
+                "Called compress() but uncompressed_data does not exist.")
+        self.compressed_data = self.storage_format_cls.compress(
+            self.uncompressed_data.t(
+            ) if self.compress_transposed else self.uncompressed_data)
+        del self.uncompressed_data  # free memory
+        self.uncompressed_data = None

vllm/model_executor/layers/sparsity/sparse_w16a16.py

@@ -5,7 +5,7 @@
 from vllm.model_executor.layers.sparsity.base_config import SparsityConfig
 
 from .sparse_w16a16_linear_method import SparseW16A16LinearMethod
-from magic_wand import (CompressedStorageFormat, SparseBitmaskStorageFormat)
+from magic_wand import (CompressedStorageFormat, SparseBEGemmStorageFormat)
 
 
 class SparseW16A16Config(SparsityConfig):
@@ -23,7 +23,7 @@ def __repr__(self) -> str:
 
     @classmethod
     def get_storage_format_cls(cls) -> Type[CompressedStorageFormat]:
-        return SparseBitmaskStorageFormat
+        return SparseBEGemmStorageFormat
 
     @classmethod
     def get_name(cls) -> str:

vllm/model_executor/layers/sparsity/sparse_w16a16_linear_method.py

@@ -5,9 +5,9 @@
 
 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)
+from vllm.model_executor.layers.parameters import LazyCompressedParameter
+from magic_wand import (CompressedStorageFormat, SparseBEGemmStorageFormat)
+from magic_wand.ops import be_ds_gemm
 
 
 class SparseW16A16LinearMethod(LinearMethodBase):
@@ -27,10 +27,15 @@ 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)
+        supports_linear = (self.storage_format_cls !=
+                           SparseBEGemmStorageFormat)
+        weight = LazyCompressedParameter(
+            torch.empty((output_size_per_partition, input_size_per_partition),
+                        dtype=params_dtype),
+            storage_format_cls=self.storage_format_cls,
+            # if we don't support F.linear or something analogous,
+            # transpose when we compress so we can use a basic matmul
+            compress_transposed=not supports_linear)
 
         set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0})
 
@@ -42,14 +47,28 @@ def apply_weights(
         x: torch.Tensor,
         bias: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
-        sparse_weight = weights["weight"]
+        w: LazyCompressedParameter = weights["weight"]
 
-        if self.storage_format_cls == SparseSemiStructuredStorageFormat:
-            output = F.linear(x, sparse_weight, bias)
-            return output
+        # if we never compressed (likely due to insufficient sparsity),
+        # i.e. have uncompressed_data run normally
+        if w.has_uncompressed_data:
+            assert not w.has_compressed_data
+            output = F.linear(x, w.uncompressed_data, bias)
+        # The current 2:4 implementation was running dense so ignore it
+        #  for now and instead just explicitly decompress as usual
+        # elif self.storage_format_cls == SparseSemiStructuredStorageFormat:
+        #     assert bias is None
+        #     raise NotImplementedError
+        elif self.storage_format_cls == SparseBEGemmStorageFormat:
+            assert bias is None
+            assert w.compress_transposed
+            out_shape = (x.shape[:-1] + (w.shape[0], ))
+            reshaped_x = x.reshape(-1, x.shape[-1])
+            y = be_ds_gemm(reshaped_x, w.compressed_data)
+            return y.reshape(out_shape)
         else:
-
             # Standard matrix multiply
             # Uncompress to dense
-            output = F.linear(x, sparse_weight.to_dense(), bias)
-            return output
+            assert not w.compress_transposed
+            output = F.linear(x, w.compressed_data.decompress(), bias)
+        return output