[fx] change from #users to num_users in graph printout (pytorch#101140)

`#users` means stuff in various chat apps, which makes it annoying to copypasta graphs into them.

Pull Request resolved: pytorch#101140
Approved by: https://github.com/ezyang

Author: suo

docs/source/fx.rst

@@ -633,9 +633,9 @@ examine our traced module:
     # This print-out returns:
     """
     graph():
-        %x : [#users=1] = placeholder[target=x]
-        %y : [#users=1] = placeholder[target=y]
-        %add : [#users=1] = call_function[target=operator.add](args = (%x, %y), kwargs = {})
+        %x : [num_users=1] = placeholder[target=x]
+        %y : [num_users=1] = placeholder[target=y]
+        %add : [num_users=1] = call_function[target=operator.add](args = (%x, %y), kwargs = {})
         return add
     """
 

test/quantization/pt2e/test_quantize_pt2e.py

@@ -396,11 +396,11 @@ def test_qnnpack_quantizer_conv_linear(self):
         """
         This test fails because linear decompositon changes due to the presence of
         permute node. In the below linear 1 is decomposed as
-        %t_default : [#users=1] = call_function[target=torch.ops.aten.t.default](args = (%_param_constant2,), kwargs = {})
-        %clone_default : [#users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_default,), kwargs = {memory_format: torch.contiguous_format})  # noqa: B950
-        %_unsafe_view_default : [#users=1] = call_function[target=torch.ops.aten._unsafe_view.default](args = (%clone_default, [8, 16]), kwargs = {})  # noqa: B950
-        %mm_default : [#users=1] = call_function[target=torch.ops.aten.mm.default](args = (%_unsafe_view_default, %t_default), kwargs = {})  # noqa: B950
-        %view_default : [#users=1] = call_function[target=torch.ops.aten.view.default](args = (%mm_default, [2, 2, 2, 8]), kwargs = {})  # noqa: B950
+        %t_default : [num_users=1] = call_function[target=torch.ops.aten.t.default](args = (%_param_constant2,), kwargs = {})
+        %clone_default : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_default,), kwargs = {memory_format: torch.contiguous_format})  # noqa: B950
+        %_unsafe_view_default : [num_users=1] = call_function[target=torch.ops.aten._unsafe_view.default](args = (%clone_default, [8, 16]), kwargs = {})  # noqa: B950
+        %mm_default : [num_users=1] = call_function[target=torch.ops.aten.mm.default](args = (%_unsafe_view_default, %t_default), kwargs = {})  # noqa: B950
+        %view_default : [num_users=1] = call_function[target=torch.ops.aten.view.default](args = (%mm_default, [2, 2, 2, 8]), kwargs = {})  # noqa: B950
 
         Note the presence of cline and unsafe_view. This is due to permute
         """

test/test_fx.py

@@ -1273,7 +1273,7 @@ def forward(self, x):
         traced = symbolic_trace(st)
         traced.graph.lint()
         stringed = str(traced.graph)
-        for s in ['args', 'kwargs', '#users']:
+        for s in ['args', 'kwargs', 'num_users']:
             assert s in stringed
 
     def test_custom_proxy_type(self):

torch/fx/__init__.py

@@ -27,11 +27,11 @@ def forward(self, x):
     print(symbolic_traced.graph)
     """
     graph():
-        %x : [#users=1] = placeholder[target=x]
-        %param : [#users=1] = get_attr[target=param]
-        %add : [#users=1] = call_function[target=operator.add](args = (%x, %param), kwargs = {})
-        %linear : [#users=1] = call_module[target=linear](args = (%add,), kwargs = {})
-        %clamp : [#users=1] = call_method[target=clamp](args = (%linear,), kwargs = {min: 0.0, max: 1.0})
+        %x : [num_users=1] = placeholder[target=x]
+        %param : [num_users=1] = get_attr[target=param]
+        %add : [num_users=1] = call_function[target=operator.add](args = (%x, %param), kwargs = {})
+        %linear : [num_users=1] = call_module[target=linear](args = (%add,), kwargs = {})
+        %clamp : [num_users=1] = call_method[target=clamp](args = (%linear,), kwargs = {min: 0.0, max: 1.0})
         return clamp
     """
 

torch/fx/experimental/const_fold.py

@@ -229,13 +229,13 @@ def mod_partition(node: torch.fx.Node):
     # because we are fetching attributes directly from the root module, instead of
     # fetching them from const_gm. Example: The const_gm must have some format like:
     # graph():
-    #    %inp : [#users=1] = placeholder[target=const_inp]
-    #    %add : [#users=1] = call_function[target=operator.add](args = (%inp, %inp), kwargs = {})
+    #    %inp : [num_users=1] = placeholder[target=const_inp]
+    #    %add : [num_users=1] = call_function[target=operator.add](args = (%inp, %inp), kwargs = {})
     #    return add
     # We replace that with the following, which does not have any placeholders:
     # graph():
-    #    %inp_1 : [#users=1] = get_attr[target=const_inp]
-    #    %add : [#users=1] = call_function[target=operator.add](args = (%inp_1, %inp_1), kwargs = {})
+    #    %inp_1 : [num_users=1] = get_attr[target=const_inp]
+    #    %add : [num_users=1] = call_function[target=operator.add](args = (%inp_1, %inp_1), kwargs = {})
     #    return add
     root_const_gm = torch.fx.GraphModule(split, const_gm.graph)
     for node in root_const_gm.graph.nodes:

torch/fx/graph.py

@@ -700,12 +700,12 @@ def forward(self, x):
     .. code-block:: text
 
         graph(x):
-            %linear_weight : [#users=1] = self.linear.weight
-            %add_1 : [#users=1] = call_function[target=operator.add](args = (%x, %linear_weight), kwargs = {})
-            %linear_1 : [#users=1] = call_module[target=linear](args = (%add_1,), kwargs = {})
-            %relu_1 : [#users=1] = call_method[target=relu](args = (%linear_1,), kwargs = {})
-            %sum_1 : [#users=1] = call_function[target=torch.sum](args = (%relu_1,), kwargs = {dim: -1})
-            %topk_1 : [#users=1] = call_function[target=torch.topk](args = (%sum_1, 3), kwargs = {})
+            %linear_weight : [num_users=1] = self.linear.weight
+            %add_1 : [num_users=1] = call_function[target=operator.add](args = (%x, %linear_weight), kwargs = {})
+            %linear_1 : [num_users=1] = call_module[target=linear](args = (%add_1,), kwargs = {})
+            %relu_1 : [num_users=1] = call_method[target=relu](args = (%linear_1,), kwargs = {})
+            %sum_1 : [num_users=1] = call_function[target=torch.sum](args = (%relu_1,), kwargs = {dim: -1})
+            %topk_1 : [num_users=1] = call_function[target=torch.topk](args = (%sum_1, 3), kwargs = {})
             return topk_1
 
     For the semantics of operations represented in the ``Graph``, please see :class:`Node`.

torch/fx/node.py

@@ -472,18 +472,18 @@ def format_node(self,
                 return None
             maybe_typename = f'{_type_repr(self.type)} ' if self.type else ''
             default_val = '(default=' + str(self.args[0]) + ')' if self.args else ''
-            return f'%{self.name} : {maybe_typename}[#users={len(self.users)}] = {self.op}[target={self.target}]{default_val}'
+            return f'%{self.name} : {maybe_typename}[num_users={len(self.users)}] = {self.op}[target={self.target}]{default_val}'
         elif self.op == 'get_attr':
             maybe_typename = f'{_type_repr(self.type)} ' if self.type is not None else ''
-            return f'%{self.name} : {maybe_typename}[#users={len(self.users)}] = ' \
+            return f'%{self.name} : {maybe_typename}[num_users={len(self.users)}] = ' \
                    f'{self.op}[target={self._pretty_print_target(self.target)}]'
         elif self.op == 'output':
             if self.type and maybe_return_typename:
                 maybe_return_typename[0] = f' -> {_type_repr(self.type)}'
             return f'return {self.args[0]}'
         else:
             maybe_typename = f'{_type_repr(self.type)} ' if self.type is not None else ''
-            return f'%{self.name} : {maybe_typename}[#users={len(self.users)}] = ' \
+            return f'%{self.name} : {maybe_typename}[num_users={len(self.users)}] = ' \
                    f'{self.op}[target={self._pretty_print_target(self.target)}](' \
                    f'args = {_format_arg(self.args)}, kwargs = {_format_arg(self.kwargs)})'
 

torch/fx/passes/reinplace.py

@@ -81,9 +81,9 @@ def run_node(self, node: Node):
             # For multi-output views, we want to map each output view to the base,
             # but this mapping involves two separate nodes in FX IR.
             # e.g. "a, b = x_1.split(...)" becomes:
-            #    %split_tensor : [#users=2] = call_function[target=torch.ops.aten.split.Tensor](args = (%x_1, 2), kwargs = {})
-            #    %getitem : [#users=1] = call_function[target=operator.getitem](args = (%split_tensor, 0), kwargs = {})
-            #    %getitem_1 : [#users=1] = call_function[target=operator.getitem](args = (%split_tensor, 1), kwargs = {})
+            #    %split_tensor : [num_users=2] = call_function[target=torch.ops.aten.split.Tensor](args = (%x_1, 2), kwargs = {})
+            #    %getitem : [num_users=1] = call_function[target=operator.getitem](args = (%split_tensor, 0), kwargs = {})
+            #    %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%split_tensor, 1), kwargs = {})
             # And we'd like to set:
             #    getitem1.meta['view_of'] = x_1
             elif node.target is _operator.getitem: