Refactor _decorators.codegen to allow multiple backends

helion/_compiler/compile_environment.py

@@ -79,6 +79,8 @@ def __init__(self, device: torch.device, settings: Settings) -> None:
         super().__init__()
         self.device = device
         self.settings = settings
+        # TODO(jansel): make backend configurable
+        self.backend = "triton"
         self.shape_env = ShapeEnv(
             specialize_zero_one=True,
             duck_shape=False,

helion/_compiler/generate_ast.py

@@ -272,7 +272,13 @@ def visit_For(self, node: ast.For) -> ast.AST | None:
                     assert fn_node._type_info is not None
                     fn = fn_node._type_info.proxy()
                     assert is_api_func(fn)
-                    assert fn._codegen is not None
+                    env = CompileEnvironment.current()
+                    codegen_fn = fn._codegen.get(env.backend)
+                    if codegen_fn is None:
+                        raise exc.BackendImplementationMissing(
+                            env.backend,
+                            f"codegen for API function {fn.__qualname__}",
+                        )
                     bound = fn._signature.bind(*args, **kwargs)
                     bound.apply_defaults()
 
@@ -285,7 +291,7 @@ def visit_For(self, node: ast.For) -> ast.AST | None:
                         ast_args=None,  # pyright: ignore[reportArgumentType]
                     )
 
-                    fn._codegen(state)
+                    codegen_fn(state)
                 assert node._root_id is not None
                 codegen_call_with_graph(
                     self,
@@ -376,11 +382,15 @@ def visit_Call(self, node: ast.Call) -> ast.AST:
                     [x.from_config(self.device_function.config) for x in block_infos]
                 )
             )
-        elif (
-            isinstance(fn_type_info := func_node._type_info, CallableType)
-            and is_api_func(api := fn_type_info.value)
-            and api._codegen is not None
+        elif isinstance(fn_type_info := func_node._type_info, CallableType) and (
+            is_api_func(api := fn_type_info.value)
         ):
+            codegen_fn = api._codegen.get(env.backend)
+            if codegen_fn is None:
+                raise exc.BackendImplementationMissing(
+                    env.backend,
+                    f"codegen for API function {api.__qualname__}",
+                )
             ast_args = []
             ast_kwargs = {}
             proxy_args = []
@@ -401,7 +411,7 @@ def visit_Call(self, node: ast.Call) -> ast.AST:
             proxy_params = api._signature.bind(*proxy_args, **proxy_kwargs)
             ast_params.apply_defaults()
             proxy_params.apply_defaults()
-            return api._codegen(  # pyright: ignore[reportReturnType]
+            return codegen_fn(  # pyright: ignore[reportReturnType]
                 CodegenState(
                     self,
                     None,

helion/_compiler/inductor_lowering.py

@@ -626,7 +626,6 @@ def codegen(self, ctx: GraphInterpreter, node: torch.fx.Node) -> object:
                 self.buffer.data.inner_fn(indices, reduction_indices)
             )
 
-        from .. import exc
         from .generate_ast import GenerateAST
 
         if not isinstance(ctx.cg, GenerateAST):
@@ -744,14 +743,19 @@ def codegen(self, ctx: GraphInterpreter, node: torch.fx.Node) -> object:
         ast_args = [*map_arg(node.args, lambda arg: ctx.env[arg])]
         proxy_args = [*map_arg(node.args, lambda arg: arg.meta["val"])]
 
-        assert self.api_func._codegen is not None
-        from .. import exc
+        env = CompileEnvironment.current()
+        codegen_fn = self.api_func._codegen.get(env.backend)
+        if codegen_fn is None:
+            raise exc.BackendImplementationMissing(
+                env.backend,
+                f"codegen for API function {self.api_func.__qualname__}",
+            )
         from .generate_ast import GenerateAST
 
         if not isinstance(ctx.cg, GenerateAST):
             raise exc.NotAllowedInHelperFunction
 
-        return self.api_func._codegen(
+        return codegen_fn(
             CodegenState(
                 ctx.cg,
                 fx_node=node,

helion/exc.py

@@ -52,6 +52,13 @@ class AutotuneError(BaseError):
     message = "{0}"
 
 
+class BackendImplementationMissing(BaseError):
+    message = "Backend '{backend}' is missing required implementation: {detail}"
+
+    def __init__(self, backend: str, detail: str) -> None:
+        super().__init__(backend=backend, detail=detail)
+
+
 class CacheAssertionError(BaseError):
     message = "Expected cache hit for kernel '{0}', but got cache miss. See stderr for diagnostic information."
 

helion/language/_decorators.py

@@ -55,7 +55,7 @@ class APIFunc(Protocol):
         _cache_type: Whether to cache the type information for repeated calls.
         _type_function: A callable that determines the return type of this function
             during type propagation phase.
-        _codegen: A callable that generates the device code for this function.
+        _codegen: Mapping of backend names to callables that generate device code.
         _fake_fn: A callable that provides a "fake" implementation used during
             tracing and compilation.
         _prepare_args: A callable that preprocesses the arguments before they're
@@ -72,7 +72,7 @@ class APIFunc(Protocol):
     _tiles_as_sizes: bool
     _cache_type: bool
     _type_function: Callable[..., TypeInfo] | None
-    _codegen: Callable[[CodegenState], object] | None
+    _codegen: dict[str, Callable[[CodegenState], object]]
     _fake_fn: Callable[..., object] | None
     _prepare_args: Callable[[tuple[object, ...]], tuple[object, ...]]
     _get_masked_value: Callable[[torch.fx.Node], float | bool | None] | None
@@ -189,7 +189,7 @@ def wrapper(*args: object, **kwargs: object) -> object:
             api._prepare_args = no_op_prepare_args
         api._cache_type = cache_type
         api._type_function = None
-        api._codegen = None
+        api._codegen = {}
         api._fake_fn = None
         api._get_masked_value = None
         api._to_device_ir = None
@@ -254,15 +254,16 @@ def _impl(
 
 def codegen(
     original_fn: Callable[..., object],
+    backend: str,
 ) -> _NoReturnDecorator[object]:
     def _impl(codegen_fn: Callable[[CodegenState], object]) -> Callable[..., Never]:
         assert is_api_func(original_fn), (
             f"{type_propagation.__qualname__} can only be used on API functions"
         )
-        assert original_fn._codegen is None, (
-            "codegen can only be used once per function"
+        assert backend not in original_fn._codegen, (
+            f"codegen already registered for backend {backend!r}"
         )
-        original_fn._codegen = codegen_fn
+        original_fn._codegen[backend] = codegen_fn
         return _no_call
 
     return _impl  # pyright: ignore[reportReturnType]

helion/language/_tracing_ops.py

@@ -40,7 +40,7 @@ def _get_symnode(debug_name: str) -> int:
     raise AssertionError("this should never be called")
 
 
-@_decorators.codegen(_get_symnode)
+@_decorators.codegen(_get_symnode, "triton")
 def _(state: CodegenState) -> ast.AST:
     val = state.fx_node.meta["val"]  # pyright: ignore[reportOptionalMemberAccess]
 
@@ -69,7 +69,7 @@ def _host_tensor(debug_name: str) -> torch.Tensor:
     raise AssertionError("this should never be called")
 
 
-@_decorators.codegen(_host_tensor)
+@_decorators.codegen(_host_tensor, "triton")
 def _(state: CodegenState) -> ast.AST:
     return expr_from_string("_host_tensor")  # should be unused
 
@@ -83,7 +83,7 @@ def _for_loop(
     raise AssertionError("this should never be called")
 
 
-@_decorators.codegen(_for_loop)
+@_decorators.codegen(_for_loop, "triton")
 def _(state: CodegenState) -> None:
     return HostFunction.current().device_ir.graphs[state.proxy_arg(0)].codegen(state)  # pyright: ignore[reportArgumentType,reportCallIssue]
 
@@ -100,7 +100,7 @@ def _while_loop(
     raise AssertionError("this should never be called")
 
 
-@_decorators.codegen(_while_loop)
+@_decorators.codegen(_while_loop, "triton")
 def _(state: CodegenState) -> None:
     return HostFunction.current().device_ir.graphs[state.proxy_arg(1)].codegen(state)  # pyright: ignore[reportArgumentType,reportCallIssue]
 
@@ -112,7 +112,7 @@ def _if(test: object, graph_id: int, args: list[object]) -> list[object]:
     raise AssertionError("this should never be called")
 
 
-@_decorators.codegen(_if)
+@_decorators.codegen(_if, "triton")
 def _(state: CodegenState) -> None:
     return HostFunction.current().device_ir.graphs[state.proxy_arg(1)].codegen(state)  # pyright: ignore[reportArgumentType,reportCallIssue]
 
@@ -139,7 +139,7 @@ def _(lhs: object, rhs: object) -> object:
     return torch.empty_like(lhs)
 
 
-@_decorators.codegen(_phi)
+@_decorators.codegen(_phi, "triton")
 def _(state: CodegenState) -> ast.Name:
     lhs = state.ast_arg(0)
     assert isinstance(lhs, ast.Name), lhs
@@ -180,7 +180,7 @@ def _and(left: object, right: object) -> object:
     raise NotInsideKernel
 
 
-@_decorators.codegen(_and)
+@_decorators.codegen(_and, "triton")
 def _(state: CodegenState) -> None:
     return expr_from_string(
         "{lhs} and {rhs}", lhs=state.ast_arg(0), rhs=state.ast_arg(1)
@@ -233,7 +233,7 @@ def _(left: object, right: object) -> object:
         return env.shape_env.create_unbacked_symbool()
 
 
-@_decorators.codegen(_or)
+@_decorators.codegen(_or, "triton")
 def _(state: CodegenState) -> None:
     return expr_from_string(
         "{lhs} or {rhs}", lhs=state.ast_arg(0), rhs=state.ast_arg(1)
@@ -258,7 +258,7 @@ def _(left: object) -> object:
         return env.shape_env.create_unbacked_symbool()
 
 
-@_decorators.codegen(_not)
+@_decorators.codegen(_not, "triton")
 def _(state: CodegenState) -> ast.AST:
     return expr_from_string(
         "not {lhs}",
@@ -289,7 +289,7 @@ def _(tensor: torch.Tensor, other: float) -> torch.Tensor:
     return torch.empty_like(tensor)
 
 
-@_decorators.codegen(_mask_to)
+@_decorators.codegen(_mask_to, "triton")
 def _(state: CodegenState) -> ast.AST:
     tensor = state.proxy_arg(0)
     assert isinstance(tensor, torch.Tensor)
@@ -351,7 +351,7 @@ def _(value: _T) -> _T:
     raise NotImplementedError(f"Unsupported type for _new_var: {type(value)}")
 
 
-@_decorators.codegen(_new_var)
+@_decorators.codegen(_new_var, "triton")
 def _(state: CodegenState) -> ast.AST:
     value = state.ast_arg(0)
     assert isinstance(value, ast.AST)

helion/language/atomic_ops.py

@@ -263,7 +263,7 @@ def apply(t: torch.Tensor, idx_tuple: tuple, v: object) -> None:
     return prev
 
 
-@_decorators.codegen(atomic_add)
+@_decorators.codegen(atomic_add, "triton")
 def _(state: CodegenState) -> ast.AST:
     value_expr = state.ast_args[2]
     return _codegen_common("atomic_add", state, _to_ast_values([value_expr]))
@@ -343,7 +343,7 @@ def _(
     return prev
 
 
-@_decorators.codegen(atomic_xchg)
+@_decorators.codegen(atomic_xchg, "triton")
 def _(state: CodegenState) -> ast.AST:
     value_expr = state.ast_args[2]
     return _codegen_common("atomic_xchg", state, _to_ast_values([value_expr]))
@@ -420,7 +420,7 @@ def _(
     return prev
 
 
-@_decorators.codegen(atomic_and)
+@_decorators.codegen(atomic_and, "triton")
 def _(state: CodegenState) -> ast.AST:
     value_expr = state.ast_args[2]
     return _codegen_common("atomic_and", state, _to_ast_values([value_expr]))
@@ -494,7 +494,7 @@ def _(
     return prev
 
 
-@_decorators.codegen(atomic_or)
+@_decorators.codegen(atomic_or, "triton")
 def _(state: CodegenState) -> ast.AST:
     value_expr = state.ast_args[2]
     return _codegen_common("atomic_or", state, _to_ast_values([value_expr]))
@@ -568,7 +568,7 @@ def _(
     return prev
 
 
-@_decorators.codegen(atomic_xor)
+@_decorators.codegen(atomic_xor, "triton")
 def _(state: CodegenState) -> ast.AST:
     value_expr = state.ast_args[2]
     return _codegen_common("atomic_xor", state, _to_ast_values([value_expr]))
@@ -634,7 +634,7 @@ def apply(t: torch.Tensor, idx: tuple, v: object) -> None:
     _ref_apply(target, index, apply, value)
 
 
-@_decorators.codegen(atomic_max)
+@_decorators.codegen(atomic_max, "triton")
 def _(state: CodegenState) -> ast.AST:
     value_expr = state.ast_args[2]
     return _codegen_common("atomic_max", state, _to_ast_values([value_expr]))
@@ -709,7 +709,7 @@ def _(
     return prev
 
 
-@_decorators.codegen(atomic_min)
+@_decorators.codegen(atomic_min, "triton")
 def _(state: CodegenState) -> ast.AST:
     value_expr = state.ast_args[2]
     return _codegen_common("atomic_min", state, _to_ast_values([value_expr]))
@@ -810,7 +810,7 @@ def _(
     return prev
 
 
-@_decorators.codegen(atomic_cas)
+@_decorators.codegen(atomic_cas, "triton")
 def _(state: CodegenState) -> ast.AST:
     exp_expr = state.ast_args[2]
     val_expr = state.ast_args[3]

helion/language/constexpr.py

@@ -94,7 +94,7 @@ def handle_symint(symint: torch.SymInt) -> int:
     return TypeInfo.from_example(specialized, origin=origin)
 
 
-@_decorators.codegen(specialize)
+@_decorators.codegen(specialize, "triton")
 def _(state: CodegenState) -> ast.AST:
     value = state.proxy_arg(0)
     specialized = _convert_specializable(value)

helion/language/creation_ops.py

@@ -128,7 +128,7 @@ def _full_fake(
     )
 
 
-@_decorators.codegen(full)
+@_decorators.codegen(full, "triton")
 def _full_codegen(state: CodegenState) -> ast.AST:
     fake_value = state.fake_value
     assert isinstance(fake_value, torch.Tensor)

helion/language/debug_ops.py

@@ -47,7 +47,7 @@ def _(*args: object, origin: Origin, **kwargs: object) -> TypeInfo:
     return LiteralType(origin, None)
 
 
-@_decorators.codegen(breakpoint)
+@_decorators.codegen(breakpoint, "triton")
 def _(state: CodegenState) -> None:
     state.add_statement("breakpoint()")
 

helion/language/device_print.py

@@ -67,7 +67,7 @@ def _(*args: object, origin: Origin, **kwargs: object) -> TypeInfo:
     return NoType(origin)
 
 
-@_decorators.codegen(device_print)
+@_decorators.codegen(device_print, "triton")
 def _(state: CodegenState) -> None:
     prefix = state.proxy_arg(0)
     call_args: list[ast.AST] = [create(ast.Constant, value=prefix)]

helion/language/inline_asm_ops.py

@@ -148,7 +148,7 @@ def _(
     return torch.empty(broadcast_shape, dtype=dtypes[0], device=env.device)
 
 
-@_decorators.codegen(inline_asm_elementwise)
+@_decorators.codegen(inline_asm_elementwise, "triton")
 def _(state: CodegenState) -> ast.AST | list[ast.AST]:
     # Get arguments
     asm_str = state.proxy_arg(0)

helion/language/inline_triton_ops.py

@@ -337,7 +337,7 @@ def _emit_output_assertions(
         )
 
 
-@_decorators.codegen(inline_triton)
+@_decorators.codegen(inline_triton, "triton")
 def _(state: CodegenState) -> ast.AST | list[ast.AST]:
     triton_source = state.proxy_arg(0)
     args_obj = state.proxy_arg(1)

helion/language/loops.py

@@ -460,7 +460,7 @@ def _allow_use_yz_grid(config_spec: ConfigSpec, block_ids: list[int]) -> bool:
     return hint < get_max_y_grid()
 
 
-@_decorators.codegen(tile)
+@_decorators.codegen(tile, "triton")
 def _(state: CodegenState) -> ast.AST:
     return _codegen_loop_helper(state)
 
@@ -753,7 +753,7 @@ def _(
     return IterType(origin, result)
 
 
-@_decorators.codegen(grid)
+@_decorators.codegen(grid, "triton")
 def _(state: CodegenState) -> ast.AST:
     return _codegen_loop_helper(state)