Add alg_id argument to SemiSparseWeightConfig (#4238) (#4238)

Summary:

As titled, by adding the alg_id, we now have chance to select most appropriate algorithm for optimal perf for specific gemm shapes ~

Fixes:
- Fixed typo "activiation" → "activation" in test
- Fixed formatting: line length violations and inconsistent indentation in `mm_search` call
- Moved `__post_init__` API usage logging into `__init__` in `SemiSparseWeightConfig`, since the class is not a `dataclass` and `__post_init__` was dead code
---
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Differential Revision: D99485146

Author: RandySheriff

test/sparsity/test_sparse_api.py

@@ -39,7 +39,7 @@ def test_sparse(self):
         apply_fake_sparsity(model)
         dense_result = model(input)
 
-        sparsify_(model, semi_sparse_weight())
+        sparsify_(model, semi_sparse_weight(alg_id=0))
         sparse_result = model(input)
 
         torch.testing.assert_close(dense_result, sparse_result, rtol=1e-3, atol=1e-3)

torchao/sparsity/sparse_api.py

@@ -8,7 +8,7 @@
 from typing import Callable, Optional
 
 import torch
-from torch.sparse import to_sparse_semi_structured
+from torch.sparse import SparseSemiStructuredTensor, to_sparse_semi_structured
 
 from torchao.core.config import AOBaseConfig
 from torchao.prototype.sparsity.sparsifier.weight_norm_sparsifier import (
@@ -75,6 +75,10 @@ class SemiSparseWeightConfig(AOBaseConfig):
     Configuration for converting the weight of linear modules to semi-structured (2:4) sparsity
     """
 
+    def __init__(self, alg_id: int = SparseSemiStructuredTensor._DEFAULT_ALG_ID):
+        super().__init__()
+        self.alg_id = alg_id
+
     def __post_init__(self):
         torch._C._log_api_usage_once("torchao.sparsity.SemiSparseWeightConfig")
 
@@ -88,7 +92,15 @@ def _semi_sparse_weight_transform(
     module: torch.nn.Module,
     config: SemiSparseWeightConfig,
 ) -> torch.nn.Module:
-    new_weight = to_sparse_semi_structured(module.weight)
+    is_nightly_or_source = "dev" in torch.__version__ or "git" in torch.__version__
+    if is_nightly_or_source:
+        new_weight = to_sparse_semi_structured(module.weight, alg_id=config.alg_id)
+    else:
+        if config.alg_id != SparseSemiStructuredTensor._DEFAULT_ALG_ID:
+            raise ValueError(
+                "SemiSparseWeightConfig.alg_id is only supported in nightly or source"
+            )
+        new_weight = to_sparse_semi_structured(module.weight)
     module.weight = torch.nn.Parameter(new_weight, requires_grad=False)
     module.extra_repr = types.MethodType(_linear_extra_repr, module)
     return module