Add warning to discourage use of acc += lhs @ rhs pattern

docs/api/exceptions.md

@@ -350,6 +350,10 @@ Warnings can be suppressed by including them in the `ignore_warnings` setting:
 
    Warns when operations return tensors on wrong device.
 
+.. autoclass:: TiledKMatmulAccumulationWarning
+
+   Warns when ``acc += lhs @ rhs`` pattern is used inside tiled device loops.
+
 ```
 
 ### Warning Suppression

helion/_compiler/type_propagation.py

@@ -1646,6 +1646,32 @@ def generic_visit(self, node: ast.AST) -> TypeInfo:
         super().generic_visit(node)
         raise exc.UnsupportedPythonType(f"ast.{node.__class__.__name__}")
 
+    @staticmethod
+    def _contains_matmul(node: ast.AST | None) -> bool:
+        if node is None:
+            return False
+
+        matmul_functions = ["torch.matmul", "torch.mm", "torch.bmm", "hl.dot"]
+
+        for sub_node in ast.walk(node):
+            # Check for @ operator
+            if isinstance(sub_node, ast.BinOp) and isinstance(sub_node.op, ast.MatMult):
+                return True
+
+            # Check for function calls
+            if not isinstance(sub_node, ast.Call):
+                continue
+
+            func = sub_node.func
+
+            # Check for matmul function calls
+            if isinstance(func, ast.Attribute) and isinstance(func.value, ast.Name):
+                qualified_name = f"{func.value.id}.{func.attr}"
+                if qualified_name in matmul_functions:
+                    return True
+
+        return False
+
     def _bool_op(self, op: ast.boolop, left: TypeInfo, right: TypeInfo) -> TypeInfo:
         try:
             val = left.truth_value()
@@ -2094,6 +2120,12 @@ def visit_AnnAssign(self, node: ast.AnnAssign) -> TypeInfo:
 
     def visit_AugAssign(self, node: ast.AugAssign) -> TypeInfo:
         assert isinstance(node.target, ExtendedAST)
+        if (
+            self.device_loop_depth > 0
+            and isinstance(node.op, ast.Add)
+            and self._contains_matmul(node.value)
+        ):
+            warning(exc.TiledKMatmulAccumulationWarning)
         try:
             type_info = self.visit(
                 create(

helion/exc.py

@@ -414,6 +414,22 @@ class BlockSizeIgnoredInInterpretMode(BaseWarning):
     message = "block_size is specified to be {0}, but in interpret mode, the full dimension size is always used."
 
 
+class TiledKMatmulAccumulationWarning(BaseWarning):
+    message = (
+        "Detected one of the following usage patterns inside a Helion device loop:\n"
+        "- `acc += lhs @ rhs`\n"
+        "- `acc += torch.matmul(lhs, rhs)`\n"
+        "- `acc += torch.mm(lhs, rhs)`\n"
+        "- `acc += torch.bmm(lhs, rhs)`\n"
+        "- `acc += hl.dot(lhs, rhs)`\n"
+        "For accurate numerics, please use one of:\n"
+        "- `torch.addmm(acc, ...)`\n"
+        "- `torch.baddbmm(acc, ...)`\n"
+        "- `hl.dot(acc=...)`\n"
+        "to accumulate across tiled-K iterations of a matmul operation."
+    )
+
+
 class AutotuningDisallowedInEnvironment(BaseError):
     message = "Autotuning is disabled {0}, please provide a config to @helion.kernel via the config= argument."
 

test/test_dot.py

@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+import contextlib
+import io
 import itertools
 from typing import Callable
 import unittest
@@ -288,6 +290,141 @@ def bmm(A: torch.Tensor, B: torch.Tensor) -> torch.Tensor:
         expected = torch.bmm(A, B).to(result.dtype) * 2
         torch.testing.assert_close(result, expected, atol=1e-2, rtol=1e-2)
 
+    def _assert_warning_in_stderr(
+        self, kernel, args, expected_result, warning_str, *, atol=1e-2, rtol=1e-2
+    ):
+        stderr_buffer = io.StringIO()
+        with contextlib.redirect_stderr(stderr_buffer):
+            _, out = code_and_output(kernel, args)
+
+        torch.testing.assert_close(out, expected_result, atol=atol, rtol=rtol)
+
+        warning_text = stderr_buffer.getvalue()
+        self.assertIn(warning_str, warning_text)
+
+    @skipIfRefEager("Warning emitted in compile mode only")
+    def test_augassign_at_operator_warning(self):
+        @helion.kernel(static_shapes=True)
+        def warn_kernel(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+            m, k = x.shape
+            k2, n = y.shape
+            assert k == k2
+            out = torch.empty([m, n], dtype=x.dtype, device=x.device)
+            for tile_m, tile_n in hl.tile([m, n]):
+                acc = hl.zeros([tile_m, tile_n], dtype=torch.float32)
+                for tile_k in hl.tile(k):
+                    lhs = x[tile_m, tile_k]
+                    rhs = y[tile_k, tile_n]
+                    acc += lhs @ rhs
+                out[tile_m, tile_n] = acc
+            return out
+
+        x = torch.randn(32, 16, device=DEVICE, dtype=torch.float32)
+        y = torch.randn(16, 32, device=DEVICE, dtype=torch.float32)
+
+        self._assert_warning_in_stderr(
+            warn_kernel, (x, y), x @ y, "WARNING[TiledKMatmulAccumulationWarning]"
+        )
+
+    @skipIfRefEager("Warning emitted in compile mode only")
+    def test_augassign_torch_matmul_warning(self):
+        @helion.kernel(static_shapes=True)
+        def warn_kernel(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+            m, k = x.shape
+            k2, n = y.shape
+            assert k == k2
+            out = torch.empty([m, n], dtype=x.dtype, device=x.device)
+            for tile_m, tile_n in hl.tile([m, n]):
+                acc = hl.zeros([tile_m, tile_n], dtype=torch.float32)
+                for tile_k in hl.tile(k):
+                    lhs = x[tile_m, tile_k]
+                    rhs = y[tile_k, tile_n]
+                    acc += torch.matmul(lhs, rhs)
+                out[tile_m, tile_n] = acc
+            return out
+
+        x = torch.randn(32, 16, device=DEVICE, dtype=torch.float32)
+        y = torch.randn(16, 32, device=DEVICE, dtype=torch.float32)
+
+        self._assert_warning_in_stderr(
+            warn_kernel, (x, y), x @ y, "WARNING[TiledKMatmulAccumulationWarning]"
+        )
+
+    @skipIfRefEager("Warning emitted in compile mode only")
+    def test_augassign_torch_mm_warning(self):
+        @helion.kernel(static_shapes=True)
+        def warn_kernel(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+            m, k = x.shape
+            k2, n = y.shape
+            assert k == k2
+            out = torch.empty([m, n], dtype=x.dtype, device=x.device)
+            for tile_m, tile_n in hl.tile([m, n]):
+                acc = hl.zeros([tile_m, tile_n], dtype=torch.float32)
+                for tile_k in hl.tile(k):
+                    lhs = x[tile_m, tile_k]
+                    rhs = y[tile_k, tile_n]
+                    acc += torch.mm(lhs, rhs)
+                out[tile_m, tile_n] = acc
+            return out
+
+        x = torch.randn(32, 16, device=DEVICE, dtype=torch.float32)
+        y = torch.randn(16, 32, device=DEVICE, dtype=torch.float32)
+
+        self._assert_warning_in_stderr(
+            warn_kernel, (x, y), x @ y, "WARNING[TiledKMatmulAccumulationWarning]"
+        )
+
+    @skipIfRefEager("Warning emitted in compile mode only")
+    def test_augassign_torch_bmm_warning(self):
+        @helion.kernel(static_shapes=True)
+        def warn_kernel(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+            b, m, k = x.shape
+            b2, k2, n = y.shape
+            assert b == b2 and k == k2
+            out = torch.empty([b, m, n], dtype=x.dtype, device=x.device)
+            for tile_b, tile_m, tile_n in hl.tile([b, m, n]):
+                acc = hl.zeros([tile_b, tile_m, tile_n], dtype=torch.float32)
+                for tile_k in hl.tile(k):
+                    lhs = x[tile_b, tile_m, tile_k]
+                    rhs = y[tile_b, tile_k, tile_n]
+                    acc += torch.bmm(lhs, rhs)
+                out[tile_b, tile_m, tile_n] = acc
+            return out
+
+        x = torch.randn(4, 32, 16, device=DEVICE, dtype=torch.float32)
+        y = torch.randn(4, 16, 32, device=DEVICE, dtype=torch.float32)
+
+        self._assert_warning_in_stderr(
+            warn_kernel,
+            (x, y),
+            torch.bmm(x, y),
+            "WARNING[TiledKMatmulAccumulationWarning]",
+        )
+
+    @skipIfRefEager("Warning emitted in compile mode only")
+    def test_augassign_hl_dot_warning(self):
+        @helion.kernel(static_shapes=True)
+        def no_warn_kernel(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+            m, k = x.shape
+            k2, n = y.shape
+            assert k == k2
+            out = torch.empty([m, n], dtype=x.dtype, device=x.device)
+            for tile_m, tile_n in hl.tile([m, n]):
+                acc = hl.zeros([tile_m, tile_n], dtype=torch.float32)
+                for tile_k in hl.tile(k):
+                    lhs = x[tile_m, tile_k]
+                    rhs = y[tile_k, tile_n]
+                    acc += hl.dot(lhs, rhs)
+                out[tile_m, tile_n] = acc
+            return out
+
+        x = torch.randn(32, 16, device=DEVICE, dtype=torch.float32)
+        y = torch.randn(16, 32, device=DEVICE, dtype=torch.float32)
+
+        self._assert_warning_in_stderr(
+            no_warn_kernel, (x, y), x @ y, "WARNING[TiledKMatmulAccumulationWarning]"
+        )
+
     # Note: numerical behavior for differing acc dtype is covered by existing dot tests; here we focus on codegen shape
 
     # torch.baddbmm codegen shape is covered indirectly by broader matmul tests; skipping a brittle code-inspection here