Expose tile.id

helion/language/__init__.py

@@ -13,6 +13,7 @@
 from .tile_ops import tile_begin as tile_begin
 from .tile_ops import tile_block_size as tile_block_size
 from .tile_ops import tile_end as tile_end
+from .tile_ops import tile_id as tile_id
 from .tile_ops import tile_index as tile_index
 from .tile_proxy import Tile as Tile
 from .tunable_ops import register_block_size as register_block_size

helion/language/tile_ops.py

@@ -129,3 +129,39 @@ def _(tile: torch.SymInt) -> torch.SymInt:
 
 # since we return tile above, no codegen is needed for this function.
 # codegen is handled in _get_symnode()
+
+
+@_decorators.api(tiles_as_sizes=True)
+def tile_id(tile: Tile) -> int:
+    """
+    Retrieve tile_id of a given tile or list of tiles.
+    This is equivalent to `tile.begin // tile.block_size`.
+    This can also be written as: `tile.id`.
+    """
+    raise exc.NotInsideKernel
+
+
+@_decorators.register_fake(tile_id)
+def _(tile: torch.SymInt) -> torch.SymInt:
+    assert isinstance(tile, torch.SymInt)
+    return CompileEnvironment.current().create_unbacked_symint()
+
+
+@_decorators.codegen(tile_id)
+def _(state: CodegenState) -> ast.AST:
+    t = state.proxy_arg(0)
+    env = CompileEnvironment.current()
+    assert isinstance(t, torch.SymInt)
+    index = env.get_block_id(t)
+    assert index is not None
+    # disable_flatten:
+    # The functions in this file can't be used in flattened loops.
+    env.config_spec.flatten_loops.disable_block_id(index)
+    offset = state.codegen.offset_var(index)
+
+    block_size = state.device_function.block_size_var(index)
+    if block_size is None:
+        expr_str = offset
+    else:
+        expr_str = f"{offset} // {block_size}"
+    return expr_from_string(expr_str)

helion/language/tile_proxy.py

@@ -34,7 +34,7 @@ class Tile(torch.Tensor):
     Tile's can be used as indices to tensors, e.g. `tensor[tile]`.  Tile's
     can also be use as sizes for allocations, e.g. `torch.empty([tile])`.
     There are also properties such as `tile.index`, `tile.begin`,
-    `tile.end`, and `tile.block_size` that can be used to retrieve various
+    `tile.end`, `tile.id` and `tile.block_size` that can be used to retrieve various
     information about the tile.
 
     Masking is implicit for tiles, so if the final tile is smaller than
@@ -133,6 +133,15 @@ def block_size(self) -> int:
 
         return tile_block_size(self)
 
+    @property
+    def id(self) -> int:
+        """
+        Alias for hl.tile_id, which retrieves the id of a tile.
+        """
+        from .tile_ops import tile_id
+
+        return tile_id(self)
+
 
 class _CheckForIndexCalls:
     """

test/test_indexing.py

@@ -294,6 +294,89 @@ def fn(x: torch.Tensor) -> torch.Tensor:
         )
         torch.testing.assert_close(result, expected)
 
+    def test_tile_id(self):
+        @helion.kernel
+        def test_tile_id_access(x: torch.Tensor) -> torch.Tensor:
+            out = torch.zeros_like(x, dtype=torch.int32)
+            for tile in hl.tile(x.size(0)):
+                out[tile] = tile.id
+            return out
+
+        x = torch.randn([64], device=DEVICE)
+        code, result = code_and_output(
+            test_tile_id_access,
+            (x,),
+            block_size=16,
+        )
+        expected = torch.arange(4, device=DEVICE, dtype=torch.int32).repeat_interleave(
+            repeats=16
+        )
+        torch.testing.assert_close(result, expected)
+        code, result = code_and_output(
+            test_tile_id_access,
+            (x,),
+            block_size=1,
+        )
+        expected = torch.arange(64, device=DEVICE, dtype=torch.int32)
+        torch.testing.assert_close(result, expected)
+
+    def test_tile_id_1d_indexing(self):
+        @helion.kernel
+        def test_tile_id_atomic_add(x: torch.Tensor) -> torch.Tensor:
+            out = torch.zeros_like(x, dtype=torch.int32)
+            for tile_m in hl.tile(x.size(0)):
+                hl.atomic_add(out, [tile_m.id], 1)
+            return out
+
+        x = torch.randn(64, device=DEVICE)
+        code, result = code_and_output(
+            test_tile_id_atomic_add,
+            (x,),
+            block_size=[
+                16,
+            ],
+        )
+
+        expected = torch.zeros(64, device=DEVICE, dtype=torch.int32)
+        expected[:4] = 1
+        torch.testing.assert_close(result, expected)
+        code, result = code_and_output(
+            test_tile_id_atomic_add,
+            (x,),
+            block_size=[
+                1,
+            ],
+        )
+        expected = torch.ones(64, device=DEVICE, dtype=torch.int32)
+        torch.testing.assert_close(result, expected)
+
+    @unittest.skip("flatten_loops config assert. issue#185")
+    def test_tile_id_2d_indexing(self):
+        @helion.kernel
+        def test_tile_id_index_st(x: torch.Tensor) -> torch.Tensor:
+            out = torch.zeros_like(x, dtype=torch.int32)
+            for tile_m, tile_n in hl.tile(x.size()):
+                out[tile_m.id, tile_n.id] = 1
+            return out
+
+        x = torch.randn(64, 64, device=DEVICE)
+        code, result = code_and_output(
+            test_tile_id_index_st,
+            (x,),
+            block_size=[16, 16],
+        )
+
+        expected = torch.zeros(64, 64, device=DEVICE, dtype=torch.int32)
+        expected[:4, :4] = 1
+        torch.testing.assert_close(result, expected)
+        code, result = code_and_output(
+            test_tile_id_index_st,
+            (x,),
+            block_size=[1, 1],
+        )
+        expected = torch.ones(64, 64, device=DEVICE, dtype=torch.int32)
+        torch.testing.assert_close(result, expected)
+
     def test_atomic_add_symint(self):
         @helion.kernel(config={"block_size": 32})
         def fn(x: torch.Tensor) -> torch.Tensor: