Add cse for make_block_ptr in Triton codegen (#163399)

Summary: per title

Test Plan: added test cases

Differential Revision: D82648215

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163399
Approved by: https://github.com/jansel, https://github.com/njriasan

test/inductor/test_torchinductor_codegen_config_overrides.py

@@ -32,6 +32,8 @@ class CodegenInductorTest(InductorTestCase):
         *args,
         compile_kwargs: Optional[dict] = None,
         config_patches: Optional[dict] = None,
+        atol: float | None = 1e-05,
+        rtol: float | None = 1e-08,
     ):
         """
         Runs the module through Inductor, comparing to eager reference.
@@ -53,7 +55,7 @@ class CodegenInductorTest(InductorTestCase):
         ref_tensors = flatten_tensors(func(*args))
         actual_tensors = flatten_tensors(result)
         for ref, actual in zip(ref_tensors, actual_tensors):
-            self.assertTrue(torch.allclose(ref, actual))
+            self.assertTrue(torch.allclose(ref, actual, atol=atol, rtol=rtol))
 
         return result, code
 
@@ -89,6 +91,34 @@ class CodegenInductorTest(InductorTestCase):
         else:
             self.count_code(reinterpret_call, code, 2)
 
+    @requires_gpu()
+    @skipIf(GPU_TYPE == "mps", "Triton is not available for MPS")
+    def test_cse_make_block_ptr_reduction(self):
+        def func(a, b):
+            tmp0 = a * b
+            tmp1 = a + b
+            c = tmp0 + tmp1
+            return c.sum(dim=0)
+
+        config_patches = {
+            "triton.use_block_ptr": True,
+            "triton.tile_reductions": True,
+            "triton.prefer_nd_tiling": True,
+            "triton.max_tiles": 3,
+            "split_reductions": False,
+        }
+        a = torch.randn((512, 4096), device=torch.device(GPU_TYPE))
+        b = torch.randn((512, 4096), device=torch.device(GPU_TYPE))
+        _, code = self.run_and_compare(
+            func,
+            a,
+            b,
+            config_patches=config_patches,
+            atol=1e-4,
+        )
+        self.count_code("= tl.make_block_ptr(in_ptr", code, 2)
+        self.count_code("= tl.load(block_ptr", code, 2)
+
     @requires_gpu()
     @skipIf(GPU_TYPE == "mps", "Triton is not available for MPS")
     def test_kernel_fusion_thresholds(self):

test/inductor/test_torchinductor_strided_blocks.py

@@ -824,7 +824,7 @@ class CommonTemplate:
         [
             ((8, 8), 1, 1, True),  # Persistent Welford fallback
             subtest(
-                ((128, 128), 9, 2, False), decorators=[xfail_if_use_tensor_descriptor]
+                ((128, 128), 7, 2, False), decorators=[xfail_if_use_tensor_descriptor]
             ),  # Looped Welford reduction
         ],
     )
@@ -924,7 +924,7 @@ class CommonTemplate:
         result, (code,) = self._run_and_compare(
             foo,
             view,
-            expected_num_block_pointers=6,
+            expected_num_block_pointers=5,
             expected_num_triton_kernels=2,
             config_patches={
                 "triton.multi_kernel": True,

torch/_inductor/codegen/triton.py

@@ -2865,6 +2865,24 @@ class TritonKernel(SIMDKernel[TritonCSEVariable]):
         indexing: Union[BlockPtrOptions, TensorDescriptorOptions],
         other="",
     ) -> tuple[str, str]:
+        """Generate a block pointer or tensor descriptor for Triton kernel operations.
+
+        This method creates either a block pointer (for regular Triton operations) or
+        a tensor descriptor (for TMA operations) based on the indexing type. It handles
+        caching and reuse of descriptors for performance optimization.
+
+        Args:
+            name: The name of the buffer/tensor being accessed
+            var: The variable name for the pointer
+            indexing: Block pointer options or tensor descriptor options containing
+                     indexing information and boundary check settings
+            other: Additional parameters string (e.g., padding options)
+
+        Returns:
+            A tuple containing:
+            - block_descriptor: The generated block pointer or tensor descriptor variable name
+            - other: Modified additional parameters string with boundary check options
+        """
         check = indexing.boundary_check()
         if isinstance(indexing, TensorDescriptorOptions):
             if check and other:
@@ -2892,14 +2910,24 @@ class TritonKernel(SIMDKernel[TritonCSEVariable]):
                 # tensor descriptor.
                 block_descriptor = self.prologue_cache[var]
             else:
+                block_ptr_line = indexing.format(var, roffset=False)
+                block_var = self.cse.try_get(block_ptr_line)
+
+                # Early return if block descriptor already exists
+                if block_var:
+                    return str(block_var), other
+
                 block_descriptor_id = next(self.block_ptr_id)
                 if isinstance(indexing, BlockPtrOptions):
                     block_descriptor = f"block_ptr{block_descriptor_id}"
                 else:
                     block_descriptor = f"tma_descriptor{block_descriptor_id}"
-                line_body = DeferredLine(
-                    name, f"{block_descriptor} = {indexing.format(var, roffset=False)}"
+                named_var = self.cse.namedvar(
+                    block_descriptor, dtype=torch.uint64, shape=[]
                 )
+                self.cse.put(block_ptr_line, named_var)
+
+                line_body = DeferredLine(name, f"{block_descriptor} = {block_ptr_line}")
                 if indexing.can_lift:
                     self.prologue.writeline(line_body)
                     # Cache the descriptor for epilogue subtiling