[HigherOrderOp] change signature of map_impl (#117161)

Summary:
X-link: https://github.com/pytorch/executorch/pull/1580

This PR changes the schema of map_impl from map_impl(f, num_mapped, *operands) to map_impl(f, mapped_args: Tuple, moperands: Tuple). This is to prepare for turning on dynamo for eager mode map, where we want to get rid of the num_mapped scalar.

Test Plan: Existing tests.

Differential Revision: D52495413

Pull Request resolved: https://github.com/pytorch/pytorch/pull/117161
Approved by: https://github.com/angelayi, https://github.com/tugsbayasgalan

test/dynamo/test_higher_order_ops.py

@@ -1121,7 +1121,7 @@ def forward(self, L_xs_ : torch.Tensor, L_y_ : torch.Tensor):
     l_xs_ = L_xs_
     l_y_ = L_y_
     map_body_1 = self.map_body_1
-    map_impl = torch.ops.higher_order.map_impl(map_body_1, 1, l_xs_, l_y_);  map_body_1 = l_xs_ = l_y_ = None
+    map_impl = torch.ops.higher_order.map_impl(map_body_1, [l_xs_], [l_y_]);  map_body_1 = l_xs_ = l_y_ = None
     getitem_1 = map_impl[0];  map_impl = None
     return (getitem_1,)""",
             )
@@ -1131,7 +1131,7 @@ def forward(self, L_xs_ : torch.Tensor, L_y_ : torch.Tensor):
 def forward(self, getitem, l_y_):
     getitem_1 = getitem[0]
     map_body_0 = self.map_body_0
-    map_impl = torch.ops.higher_order.map_impl(map_body_0, 1, getitem, l_y_);  map_body_0 = getitem = l_y_ = None
+    map_impl = torch.ops.higher_order.map_impl(map_body_0, [getitem], [l_y_]);  map_body_0 = getitem = l_y_ = None
     getitem_2 = map_impl[0];  map_impl = None
     return (getitem_2,)""",
             )
@@ -1152,7 +1152,7 @@ def forward(self, getitem, l_y_):
 def forward(self, L_x_ : torch.Tensor):
     l_x_ = L_x_
     map_body_0 = self.map_body_0
-    map_impl = torch.ops.higher_order.map_impl(map_body_0, 1, l_x_);  map_body_0 = l_x_ = None
+    map_impl = torch.ops.higher_order.map_impl(map_body_0, [l_x_], []);  map_body_0 = l_x_ = None
     getitem_1 = map_impl[0]
     getitem_2 = map_impl[1];  map_impl = None
     return (getitem_1, getitem_2)""",
@@ -1188,7 +1188,7 @@ def forward(self, getitem):
 def forward(self, L_x_ : torch.Tensor):
     l_x_ = L_x_
     map_body_0 = self.map_body_0
-    map_impl = torch.ops.higher_order.map_impl(map_body_0, 1, l_x_);  map_body_0 = l_x_ = None
+    map_impl = torch.ops.higher_order.map_impl(map_body_0, [l_x_], []);  map_body_0 = l_x_ = None
     getitem_1 = map_impl[0]
     getitem_2 = map_impl[1]
     getitem_3 = map_impl[2]
@@ -1237,7 +1237,7 @@ def forward(self, getitem):
 def forward(self, L_x_ : torch.Tensor):
     l_x_ = L_x_
     map_body_0 = self.map_body_0
-    map_impl = torch.ops.higher_order.map_impl(map_body_0, 1, l_x_, 3);  map_body_0 = l_x_ = None
+    map_impl = torch.ops.higher_order.map_impl(map_body_0, [l_x_], [3]);  map_body_0 = l_x_ = None
     getitem_1 = map_impl[0];  map_impl = None
     return (getitem_1,)""",
             )
@@ -1271,7 +1271,7 @@ def forward(self, getitem, const):
 def forward(self, L_x_ : torch.Tensor):
     l_x_ = L_x_
     map_body_0 = self.map_body_0
-    map_impl = torch.ops.higher_order.map_impl(map_body_0, 1, l_x_, 3);  map_body_0 = l_x_ = None
+    map_impl = torch.ops.higher_order.map_impl(map_body_0, [l_x_], [3]);  map_body_0 = l_x_ = None
     getitem_1 = map_impl[0];  map_impl = None
     return (getitem_1,)""",
             )

test/functorch/test_control_flow.py

@@ -1451,8 +1451,7 @@ def forward(self, arg0_1, arg1_1, arg2_1):
         self.assertExpectedInline(gm.code.strip(), """\
 def forward(self, pred_1, x_1):
     body_graph_0 = self.body_graph_0
-    map_impl = torch.ops.higher_order.map_impl(body_graph_0, 1, x_1, pred_1);\
-  body_graph_0 = x_1 = pred_1 = None
+    map_impl = torch.ops.higher_order.map_impl(body_graph_0, [x_1], [pred_1]);  body_graph_0 = x_1 = pred_1 = None
     getitem = map_impl[0];  map_impl = None
     return getitem""")
         self.assertExpectedInline(gm.body_graph_0.code.strip(), """\

torch/_dynamo/variables/higher_order_ops.py

@@ -792,8 +792,8 @@ class MapHigherOrderVariable(TorchHigherOrderOperatorVariable):
 
         p_args = (
             body_node,
-            1,  # right now we only supports num_mapped = 1
-            *([arg.as_proxy() for arg in args[1:]] + list(body_lifted_freevars.keys())),
+            [args[1].as_proxy()],
+            [arg.as_proxy() for arg in args[2:]] + list(body_lifted_freevars.keys()),
         )
         return _call_function_and_unflatten_output(
             tx, torch.ops.higher_order.map_impl, p_args, {}, body_r, body_spec

torch/_export/pass_base.py

@@ -200,8 +200,8 @@ class _ExportPassBaseDeprecatedDoNotUse(PassBase):
                 pred, true_fn, false_fn, inputs = args
                 return self.callback.call_cond(pred, true_fn, false_fn, inputs, meta)
             elif target == torch.ops.higher_order.map_impl:
-                f, num_args, *rest = args  # type: ignore[assignment]
-                return self.callback.call_map(f, num_args, list(rest), meta)
+                f, mapped_args, operands = args  # type: ignore[assignment]
+                return self.callback.call_map(f, mapped_args, operands, meta)
             # For other unregistered HigherOrderOps, just interpret them blindly
             elif isinstance(target, torch._ops.HigherOrderOperator):
                 return self.callback._fx(
@@ -357,18 +357,17 @@ class _ExportPassBaseDeprecatedDoNotUse(PassBase):
     def call_map(
         self,
         f: torch.fx.GraphModule,
-        num_args: int,
-        args: List[ProxyValue],
+        mapped_args: List[ProxyValue],
+        operands: List[ProxyValue],
         meta: NodeMetadata,
     ) -> ProxyValue:
-        xs = _unstack_pytree([arg.data for arg in args[:num_args]])[0]
-        pos_args = args[num_args:]
-        f_branch = self.call_submodule(f, tuple(xs + [arg.data for arg in pos_args]))
+        xs = _unstack_pytree([arg.data for arg in mapped_args])[0]
+        f_branch = self.call_submodule(f, tuple(xs + [arg.data for arg in operands]))
         assert f_branch is not None
         return self._fx(
             "call_function",
             torch.ops.higher_order.map_impl,
-            (f_branch.graph_module, num_args, *args),
+            (f_branch.graph_module, mapped_args, operands),
             {},
             meta,
         )

torch/_higher_order_ops/map.py

@@ -194,7 +194,7 @@ def map_wrapper(f, xs, *args):
         return flat_out
 
     return pytree.tree_unflatten(
-        map_impl(flat_fn, num_mapped_args, *flat_xs, *args), out_spec  # type: ignore[arg-type]
+        map_impl(flat_fn, flat_xs, args), out_spec  # type: ignore[arg-type]
     )
 
 
@@ -205,7 +205,11 @@ class MapAutogradOp(torch.autograd.Function):
         ctx._joint_graph = joint_graph
         ctx._num_mapped_args = num_mapped_args
         with torch._C._AutoDispatchBelowAutograd():
-            return (*map_impl(fw_graph, num_mapped_args, *flat_args),)
+            return (
+                *map_impl(
+                    fw_graph, flat_args[:num_mapped_args], flat_args[num_mapped_args:]
+                ),
+            )
 
     @staticmethod
     def backward(ctx, *flat_grads):
@@ -215,17 +219,13 @@ class MapAutogradOp(torch.autograd.Function):
 
         grads = map_impl(
             ctx._joint_graph,
-            ctx._num_mapped_args + len(flat_grads),
-            *fw_mapped_args,
-            *flat_grads,
-            *pos_args,
+            fw_mapped_args + flat_grads,
+            pos_args,
         )
         return None, None, None, *grads
 
 
-def trace_map(proxy_mode, func_overload, f, num_mapped, *args):
-    xs = list(args[:num_mapped])
-    pos_args = list(args[num_mapped:])
+def trace_map(proxy_mode, func_overload, f, xs, pos_args):
     leading_dim_size = xs[0].shape[0]
 
     example_input = _unstack_pytree(xs)[0]
@@ -254,7 +254,7 @@ def trace_map(proxy_mode, func_overload, f, num_mapped, *args):
 
         expanded_outs = pytree.tree_map(expand_tensor, example_outs)
 
-    node_args = (body_graph, num_mapped, *args)
+    node_args = (body_graph, list(xs), list(pos_args))
     proxy_args = pytree.tree_map(proxy_mode.tracer.unwrap_proxy, node_args)
     out_proxy = proxy_mode.tracer.create_proxy(
         "call_function", func_overload, proxy_args, {}, name="map_impl"
@@ -311,9 +311,7 @@ def _stack_pytree(pytrees):
 
 
 @map_impl.py_impl(DispatchKey.CompositeExplicitAutograd)
-def map_dense(f, num_mapped_args, *args):
-    xs = args[:num_mapped_args]
-    pos_args = args[num_mapped_args:]
+def map_dense(f, xs, pos_args):
     pytrees = []
     for inp in _unstack_pytree(xs):
         pytrees.append(f(*inp, *pos_args))
@@ -321,30 +319,29 @@ def map_dense(f, num_mapped_args, *args):
 
 
 @map_impl.py_impl(DispatchKey.Autograd)
-def map_autograd(f, num_mapped_args, *args):
-    fw_graph, bw_graph = create_fw_bw_graph(f, num_mapped_args, *args)
-    flat_out = MapAutogradOp.apply(fw_graph, bw_graph, num_mapped_args, *args)
+def map_autograd(f, xs, pos_args):
+    num_mapped_args = len(xs)
+    fw_graph, bw_graph = create_fw_bw_graph(f, num_mapped_args, *xs, *pos_args)
+    flat_out = MapAutogradOp.apply(fw_graph, bw_graph, num_mapped_args, *xs, *pos_args)
     return flat_out
 
 
 @map_impl.py_impl(ProxyTorchDispatchMode)
-def map_proxy_torch_dispatch_mode(mode, f, num_mapped, *args):
+def map_proxy_torch_dispatch_mode(mode, f, xs, args):
     if mode.enable_tracing:
-        return trace_map(mode, map_impl, f, num_mapped, *args)
+        return trace_map(mode, map_impl, f, xs, args)
     else:
-        return map_impl(f, num_mapped, *args)
+        return map_impl(f, xs, args)
 
 
 @map_impl.py_impl(FakeTensorMode)
-def map_fake_tensor_mode(mode, f, num_mapped, *args):
+def map_fake_tensor_mode(mode, f, xs, args):
     with mode:
-        return map_dense(f, num_mapped, *args)
+        return map_dense(f, xs, args)
 
 
 @map_impl.py_functionalize_impl
-def map_functionalize(ctx, f, num_mapped, *args):
-    xs = args[:num_mapped]
-    pos_args = args[num_mapped:]
+def map_functionalize(ctx, f, xs, pos_args):
     unwrapped_xs = ctx.unwrap_tensors(xs)
     unwrapped_args = ctx.unwrap_tensors(pos_args)
     wrapped_fn = ctx.functionalize(f)
@@ -358,5 +355,5 @@ def map_functionalize(ctx, f, num_mapped, *args):
         if _has_potential_branch_input_alias(f, example_inputs):
             raise UnsupportedAliasMutationException("torch.map is aliasing the input!")
 
-        map_return = map_impl(wrapped_fn, num_mapped, *unwrapped_xs, *unwrapped_args)
+        map_return = map_impl(wrapped_fn, unwrapped_xs, unwrapped_args)
         return ctx.wrap_tensors(map_return)

torch/export/_unlift.py

@@ -137,29 +137,41 @@ def _unlift(
                 buffers_to_mutate,
             )
         if node.op == "call_function" and node.target.__name__ == "map_impl":
-            body_graph, num_mapped, *operands = node.args
+            body_graph, mapped_args, operands = node.args
+            num_mapped = len(mapped_args)
             body_gm = getattr(gm, body_graph.name)
             inp_pos_to_buffer_name_for_submod = {}
-            real_operands = []
             # TODO Fix situation here to replace dot with underscore...
             state_dict_for_lookup = {
                 key.replace(".", "_"): value for key, value in state_dict.items()
             }
-            for ix, operand in enumerate(operands):
-                if operand.target in inp_pos_to_param_buffer_name.values():
-                    inp_pos_to_buffer_name_for_submod[ix] = operand.target
-                    if operand.target in state_dict_for_lookup:
-                        value = state_dict_for_lookup[operand.target]
-                    elif operand.target in tensor_constants:
-                        value = tensor_constants[operand.target]
-                    else:
-                        raise RuntimeError(f"Unable to find value for {operand.target}")
-                    body_gm.register_buffer(operand.target, value)
-                else:
-                    real_operands.append(operand)
-            node.args = (body_graph, num_mapped, *real_operands)
 
-            _, in_spec = pytree.tree_flatten(real_operands)
+            def _find_real_operands(operands, start_ix):
+                real_operands = []
+                for ix, operand in enumerate(operands):
+                    if operand.target in inp_pos_to_param_buffer_name.values():
+                        inp_pos_to_buffer_name_for_submod[
+                            ix + start_ix
+                        ] = operand.target
+                        if operand.target in state_dict_for_lookup:
+                            value = state_dict_for_lookup[operand.target]
+                        elif operand.target in tensor_constants:
+                            value = tensor_constants[operand.target]
+                        else:
+                            raise RuntimeError(
+                                f"Unable to find value for {operand.target}"
+                            )
+
+                        body_gm.register_buffer(operand.target, value)
+                    else:
+                        real_operands.append(operand)
+                return real_operands
+
+            real_mapped_args = _find_real_operands(mapped_args, 0)
+            real_mapped_operands = _find_real_operands(operands, num_mapped)
+            node.args = (body_graph, real_mapped_args, real_mapped_operands)
+
+            _, in_spec = pytree.tree_flatten(real_mapped_args + real_mapped_operands)
 
             _unlift(
                 body_gm,