Add BSR subclass +torch.compile and clean up superblock

torchao/sparsity/prototype/superblock/.gitignore

@@ -1,5 +1,8 @@
 */*.pyc
 
+# Model checkpoints
+*.pth
+
 # Editor temporaries
 *.swa
 *.swb

torchao/sparsity/prototype/superblock/benchmark.py

@@ -12,9 +12,12 @@
 import torch.utils.data
 import utils
 from torch import nn
+from torch.sparse._triton_ops_meta import optimize_bsr_dense_addmm
 
 sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
 from supermask import apply_supermask, SupermaskLinear
+from blocksparse import BlockSparseTensor
+from utils import benchmark_inference
 
 
 def apply_sparsity(model):
@@ -25,20 +28,12 @@ def apply_sparsity(model):
 
 def apply_bsr(model, blocksize):
     for name, module in model.named_modules():
-            if isinstance(module, torch.nn.Linear) and "mlp" in name:
-                try:
-                    module.weight = torch.nn.Parameter(to_bsr(module.weight.data, blocksize))
-                    print(f"Converted {name} to bsr format.")
-                except ValueError as e:
-                    print(f"Unable to convert weight of {name} to bsr format: {e}")
-
-
-def to_bsr(tensor, blocksize):
-    if tensor.ndim != 2:
-        raise ValueError("to_bsr expects 2D tensor")
-    if tensor.size(0) % blocksize or tensor.size(1) % blocksize:
-        raise ValueError("Tensor dimensions must be divisible by blocksize")
-    return tensor.to_sparse_bsr(blocksize)
+        if isinstance(module, torch.nn.Linear) and "mlp" in name:
+            try:
+                module.weight = torch.nn.Parameter(BlockSparseTensor.from_dense(module.weight.data, blocksize))
+                print(f"Converted {name} to bsr format.")
+            except ValueError as e:
+                print(f"Unable to convert weight of {name} to bsr format: {e}")
 
 
 def verify_sparsity(model):
@@ -49,23 +44,7 @@ def verify_sparsity(model):
             sparsity_percentage = (sparse_weights / total_weights) * 100
             print(f"Sparsity verified in layer {name}: {sparsity_percentage:.2f}%")
 
-
-def benchmark_in_ms(warmup, iters, f, *args, **kwargs):
-    for _ in range(warmup):
-        f(*args, **kwargs)
-    torch.cuda.synchronize()
-    start_event = torch.cuda.Event(enable_timing=True)
-    end_event = torch.cuda.Event(enable_timing=True)
-    start_event.record()
-
-    for _ in range(iters):
-        f(*args, **kwargs)
-
-    end_event.record()
-    torch.cuda.synchronize()
-    return start_event.elapsed_time(end_event) / float(iters)
-
-
+@torch.inference_mode
 def main(args):
     print(args)
     device = torch.device(args.device)
@@ -83,8 +62,11 @@ def main(args):
         print("Using float16")
         dtype = torch.float16
 
-    # Sample input
-    # input = torch.rand(32, 3, 224, 224, dtype=dtype).to(device)
+    if args.bsr and args.tune_kernel_params:
+        print("Tuning kernel params")
+        assert args.model == "vit_b_16", "--tune-kernel-params only supported for vit-b-16!"
+        optimize_bsr_dense_addmm(3072, 768, 50432, args.bsr, args.bsr, dtype=dtype, sparsity=args.sparsity_linear, verbose=True)
+        optimize_bsr_dense_addmm(768, 3072, 50432, args.bsr, args.bsr, dtype=dtype, sparsity=args.sparsity_linear, verbose=True)
 
     print("Creating model")
     model = torchvision.models.get_model(args.model, weights=args.weights, num_classes=num_classes)
@@ -112,7 +94,6 @@ def main(args):
             raise FileNotFoundError(f"No checkpoint found at {args.weights_path}.")
 
     model.to(device)
-    # output0 = model(input)
 
     if args.sparsify_weights:
         apply_sparsity(model)
@@ -134,9 +115,11 @@ def main(args):
         # output2 = model(input)
         # assert torch.allclose(output2, output1), "Output of model before and after changing format to BSR should be equal"
 
+    model = torch.compile(model, mode='max-autotune')
+
     image = torch.empty(args.batch_size, 3, args.val_crop_size, args.val_crop_size, dtype=dtype, device=device)
-    # model = torch.compile(model, mode='max-autotune')
-    return benchmark_in_ms(10, 100, model, image)
+
+    return benchmark_inference(10, 100, model, image)
 
 
 def get_args_parser(add_help=True):
@@ -169,11 +152,12 @@ def get_args_parser(add_help=True):
     parser.add_argument('--bsr', type=int, nargs='?', const=256, default=None, help='Convert sparsified weights to BSR format with optional block size (default: 256)')
     parser.add_argument("--bfloat16", action="store_true", help="Use bfloat16")
     parser.add_argument("--float16", action="store_true", help="Use float16")
+    parser.add_argument("--tune-kernel-params", action="store_true", help="Tune kernel params")
 
     return parser
 
 
 if __name__ == "__main__":
     args = get_args_parser().parse_args()
     result = main(args)
-    print(f"{result} ms", file=sys.stderr)
+    print(f"{result:.3f} ms", file=sys.stderr)

torchao/sparsity/prototype/superblock/blocksparse.py

@@ -0,0 +1,138 @@
+import torch
+from typing import Optional, Tuple, List, Dict, Any, Callable
+from torch.utils._python_dispatch import return_and_correct_aliasing
+from torchao.dtypes.utils import (
+    _implements,
+    _dispatch__torch_function__,
+    _dispatch__torch_dispatch__,
+)
+aten = torch.ops.aten
+
+# bsr wrapper custom op
+@torch.library.custom_op("blocksparse::linear", mutates_args=())
+def blocksparse_linear(A: torch.Tensor, crow_indices: torch.Tensor, col_indices: torch.Tensor, values: torch.Tensor, M: int, K: int, bias: torch.Tensor) -> torch.Tensor:
+    weight_bsr = torch.sparse_bsr_tensor(crow_indices, col_indices, values, size=(M, K))
+    return torch.nn.functional.linear(A, weight_bsr, bias)
+
+@torch.library.register_fake("blocksparse::linear")
+def blocksparse_linear_abstract(A: torch.Tensor, crow_indices: torch.Tensor, col_indices: torch.Tensor, values: torch.Tensor, M: int, K:int , bias: torch.Tensor) -> torch.Tensor:
+    new_shape = A.shape[:-1] + (bias.shape[0],)
+    return torch.empty(new_shape, dtype=A.dtype, device=A.device)
+
+# Subclass definition
+class BlockSparseTensor(torch.Tensor):
+    bsr_crow_indices: Optional[torch.Tensor]
+    bsr_col_indices: Optional[torch.Tensor]
+    bsr_values: Optional[torch.Tensor]
+
+    __slots__ = ["bsr_crow_indices", "bsr_col_indices", "bsr_values"] 
+
+    implements = classmethod(_implements)
+    __torch_dispatch__ = classmethod(_dispatch__torch_dispatch__)
+    __torch_function__ = classmethod(_dispatch__torch_function__)
+
+    @staticmethod
+    def __new__(  # noqa: PYI034
+        cls,
+        shape: torch.Size,
+        bsr_crow_indices: Optional[torch.Tensor],
+        bsr_col_indices: Optional[torch.Tensor],
+        bsr_values: Optional[torch.Tensor],
+        requires_grad: bool = False,
+    ):
+        if bsr_values is None:
+            raise ValueError("bsr values must be provided!")
+        else:
+            previous_tensor = bsr_values
+
+        kwargs = {
+            "device": previous_tensor.device,
+            "dtype": previous_tensor.dtype,
+            "layout": previous_tensor.layout,
+            "requires_grad": requires_grad,
+        }
+        tensor = torch.Tensor._make_wrapper_subclass(cls, shape, **kwargs)  # type: ignore[attr-defined]
+        tensor.bsr_crow_indices = bsr_crow_indices
+        tensor.bsr_col_indices = bsr_col_indices
+        tensor.bsr_values = bsr_values
+        return tensor
+
+    def __repr__(self) -> str:  # type: ignore[override]
+        assert hasattr(self, "shape")
+        return f"{self.__class__.__name__}(shape={self.shape})"
+
+    def __tensor_flatten__(self) -> Tuple[List[str], Tuple[torch.Size, bool]]:
+        inner_tensors = list(
+            filter(lambda x: getattr(self, x) is not None, self.__slots__)
+        )
+        tensor_meta = (self.shape, self.requires_grad)
+        return inner_tensors, tensor_meta
+
+    @classmethod
+    def __tensor_unflatten__(
+        cls,
+        inner_tensors,
+        tensor_meta: Tuple[torch.Size, bool],
+        outer_size,
+        outer_stride,
+    ) -> torch.Tensor:
+        shape,  requires_grad = tensor_meta
+        return cls(
+            shape=shape,
+            bsr_crow_indices=inner_tensors.get("bsr_crow_indices", None),
+            bsr_col_indices=inner_tensors.get("bsr_col_indices", None),
+            bsr_values=inner_tensors.get("bsr_values", None),
+            requires_grad=requires_grad,
+        )
+
+    @classmethod
+    def from_dense(cls, dense_tensor, blocksize):
+        bsr_tensor = dense_tensor.to_sparse_bsr(blocksize)
+        return cls(
+            shape=dense_tensor.shape,
+            bsr_crow_indices=bsr_tensor.crow_indices(),
+            bsr_col_indices=bsr_tensor.col_indices(),
+            bsr_values=bsr_tensor.values(),
+            requires_grad=False,
+        )
+
+    def apply_fn_to_shard(self, func):
+        return BlockSparseTensor(
+            shape = self.shape,
+            bsr_crow_indices=func(self.bsr_crow_indices),
+            bsr_col_indices=func(self.bsr_col_indices),
+            bsr_values=func(self.bsr_values),
+            requires_grad=self.requires_grad,
+        )
+
+# Subclass op dispatch registration 
+implements = BlockSparseTensor.implements
+
+@implements(aten.detach.default)
+def block_sparse_detach(func, types, args, kwargs):
+    return return_and_correct_aliasing(func, args, kwargs, args[0].apply_fn_to_shard(torch.detach))
+
+@implements(aten.values.default)
+def block_sparse_values(func, types, args, kwargs):
+    return args[0].bsr_values.detach()
+
+@implements(aten.crow_indices.default)
+def block_sparse_crow_indices(func, types, args, kwargs):
+    return args[0].bsr_crow_indices.detach()
+
+@implements(aten.col_indices.default)
+def block_sparse_col_indices(func, types, args, kwargs):
+    return args[0].bsr_col_indices.detach()
+
+@implements(aten._nnz.default)
+def block_sparse__nnz(func, types, args, kwargs):
+    return args[0].bsr_values.shape[0]
+
+@implements(torch.nn.functional.linear)
+def block_sparse_linear(func, types, args, kwargs):
+    x, w, bias = args
+    return torch.ops.blocksparse.linear(x,
+                                        w.crow_indices(),
+                                        w.col_indices(),
+                                        w.values(),
+                                        w.shape[0], w.shape[1], bias)

torchao/sparsity/prototype/superblock/evaluate.py

@@ -15,40 +15,7 @@
 
 sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
 from supermask import apply_supermask, SupermaskLinear
-
-
-def apply_sparsity(model):
-    for name, module in model.named_modules():
-        if isinstance(module, SupermaskLinear) and "mlp" in name:
-            module.sparsify_offline()
-
-
-def apply_bsr(model):
-    for name, module in model.named_modules():
-            if isinstance(module, torch.nn.Linear) and "mlp" in name:
-                try:
-                    module.weight = torch.nn.Parameter(to_bsr(module.weight.data, args.bsr))
-                    print(f"Converted {name} to bsr format.")
-                except ValueError as e:
-                    print(f"Unable to convert weight of {name} to bsr format: {e}")
-
-
-def to_bsr(tensor, blocksize):
-    if tensor.ndim != 2:
-        raise ValueError("to_bsr expects 2D tensor")
-    if tensor.size(0) % blocksize or tensor.size(1) % blocksize:
-        raise ValueError("Tensor dimensions must be divisible by blocksize")
-    return tensor.to_sparse_bsr(blocksize)
-
-
-def verify_sparsity(model):
-    for name, module in model.named_modules():
-        if isinstance(module, nn.Linear):
-            total_weights = module.weight.numel()
-            sparse_weights = (module.weight == 0).sum().item()
-            sparsity_percentage = (sparse_weights / total_weights) * 100
-            print(f"Sparsity verified in layer {name}: {sparsity_percentage:.2f}%")
-
+from benchmark import apply_sparsity, apply_bsr, verify_sparsity
 
 def _get_cache_path(filepath):
     h = hashlib.sha1(filepath.encode()).hexdigest()
@@ -82,16 +49,16 @@ def load_data(valdir, args):
             )
 
         # for META internal
-        dataset_test = torchvision.datasets.ImageFolder(
-            valdir,
-            preprocessing,
-        )
-        # for OSS
-        # dataset_test = torchvision.datasets.ImageNet(
+        # dataset_test = torchvision.datasets.ImageFolder(
         #     valdir,
-        #     split='val',
-        #     transform=preprocessing
+        #     preprocessing,
         # )
+        #for OSS
+        dataset_test = torchvision.datasets.ImageNet(
+            valdir,
+            split='val',
+            transform=preprocessing
+        )
         if args.cache_dataset:
             print(f"Saving dataset_test to {cache_path}")
             utils.mkdir(os.path.dirname(cache_path))

torchao/sparsity/prototype/superblock/supermask.py

@@ -7,7 +7,6 @@
 import torch.nn.functional as F
 import numpy as np
 
-
 # original supermask
 scores_min=None
 scores_max=9e9
@@ -21,34 +20,16 @@
 def percentile(t, q):
     """Return the value that is larger than q% of t"""
     k = 1 + round(.01 * float(q) * (t.numel() - 1))
-    return t.view(-1).kthvalue(k).values.item()
-
-
-def to_bsr(tensor, blocksize=256):
-    if tensor.ndim != 2:
-        print("Tensor is not 2D, skipping BSR conversion.")
-        return tensor  
-
-    if tensor.size(0) % blocksize or tensor.size(1) % blocksize:
-        print("Tensor dimensions are not divisible by blocksize, skipping BSR conversion.")
-        return tensor  
-
-    try:
-        converted_tensor = tensor.to_sparse_bsr(blocksize=blocksize)
-        print(f"Converted tensor to BSR format with blocksize: {blocksize}")
-        return converted_tensor 
-    except ValueError as e:
-        print(f"Unable to convert tensor to BSR format: {e}")
-        return tensor 
+    return t.view(-1).kthvalue(k).values
 
 
 class GetSubnet(torch.autograd.Function):
     """Supermask STE function"""
     @staticmethod
     def forward(ctx, scores, zeros, ones, sparsity):
-        scores.clamp_(min=scores_min,max=scores_max)
-        k_val = percentile(scores, sparsity*100)
-        return torch.where(scores < k_val, zeros.to(scores.device), ones.to(scores.device))
+        clamped_scores = scores.clamp(min=scores_min,max=scores_max)
+        k_val = percentile(clamped_scores, sparsity*100)
+        return torch.where(clamped_scores < k_val, zeros.to(scores.device), ones.to(scores.device))
     @staticmethod
     def backward(ctx, g):
         return g, None, None, None
@@ -130,7 +111,7 @@ def forward(self, x):
             subnet = self.get_mask()
             w = (self.weight*self.scale+self.shift) * subnet
         else:
-            w = self.weight.data
+            w = self.weight
         return F.linear(x, w, self.bias)