working now

torchao/sparsity/prototype/superblock/benchmark.py

@@ -25,7 +25,7 @@ def apply_sparsity(model):
             module.sparsify_offline()
 
 
-def apply_bsr(model, blocksize):
+def apply_bsr(model, blocksize=64):
     for name, module in model.named_modules():
             if isinstance(module, torch.nn.Linear) and "mlp" in name:
                 try:

torchao/sparsity/prototype/superblock/blocksparse_subclass.py

@@ -16,17 +16,24 @@
 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:
     shape = A.shape
     A_2d = A.view(-1, shape[-1])
-    bias = bias.unsqueeze(1).expand(-1, A_2d.shape[0])
-    weight_bsr = BlockSparseTensor(
-            shape = torch.Size([M, K]),
-            bsr_crow_indicies=crow_indices,
-            bsr_col_indicies=col_indices,
-            bsr_values=values,
-        )
-    res = bsr_dense_addmm(bias, weight_bsr, A_2d.t())
-    res = res.view(*shape[:-1], -1)
-    return res
-    # return bsr_dense_addmm(bias, weight, A_2d)
+    # custom_bias = bias.unsqueeze(1).expand(-1, A_2d.shape[0])
+    # print(bias.shape)
+    # print(bias)
+    # breakpoint()
+    # weight_bsr_subclass = BlockSparseTensor(
+    #         shape = torch.Size([M, K]),
+    #         bsr_crow_indicies=crow_indices,
+    #         bsr_col_indicies=col_indices,
+    #         bsr_values=values,
+    #     )
+    weight_bsr = torch.sparse_bsr_tensor(crow_indices, col_indices, values, size=(M, K))
+    res_good = torch.nn.functional.linear(A, weight_bsr, bias)
+    return res_good
+    # res = bsr_dense_addmm(custom_bias, weight_bsr_subclass, A_2d.t())
+    # res = res.view(*shape[:-1], -1)
+    # print(torch.allclose(res_good, res))
+    # breakpoint()
+    # return res
 
 # # Write the FakeTensor kernel
 @torch.library.register_fake("blocksparse::linear")

torchao/sparsity/prototype/superblock/evaluate.py

@@ -15,7 +15,7 @@
 
 sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
 from supermask import apply_supermask, SupermaskLinear
-
+# from benchmark import apply_sparsity, apply_bsr, verify_sparsity
 
 def apply_sparsity(model):
     for name, module in model.named_modules():
@@ -82,16 +82,16 @@ 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))