var=torch.tensor(1)deffunction_with_global_access(y):returny+varx=torch.tensor([0,1,2])# _allow_non_fake_inputs=True is needed to capture the global variable# for demonstration purposes.gm=make_fx(function_with_global_access,tracing_mode="fake",_allow_non_fake_inputs=True)(x)# Non-strict Tracing captures the value of the global (1.)print("1. call function",function_with_global_access(x))print("1. call graph",gm(x))# However, after changing the global, the captured graph# produces a different result from the original functionvar=torch.tensor(2)print("2. call function",function_with_global_access(x))print("2. call graph",gm(x))# To capture a graph that can have a varying `var` tensor,# it must be an explicit input:deffunction_fixed(y,var):returny+varvar=torch.tensor(3)gm=make_fx(function_fixed,tracing_mode="fake")(x,var)print("3. call function",function_fixed(x,var))print("3. call graph",gm(x,var))var=torch.tensor(4)print("4. call function",function_fixed(x,var))print("4. call graph",gm(x,var))

1. call function tensor([1, 2, 3])1. call graph tensor([1, 2, 3])2. call function tensor([2, 3, 4])2. call graph tensor([1, 2, 3])3. call function tensor([3, 4, 5])3. call graph tensor([3, 4, 5])4. call function tensor([4, 5, 6])4. call graph tensor([4, 5, 6])

deffunction_with_side_effect(y):print(y)x=torch.tensor([0,1,2])_=function_with_side_effect(x)

tensor([0, 1, 2])

gm=make_fx(function_with_side_effect,tracing_mode="fake")(x)

FakeTensor(..., size=(3,), dtype=torch.int64)

_=gm(x)

lst=[]deffunction_with_input_list_mutation(lst):val=lst.pop()returnvalx=torch.tensor([0,1,2])y=torch.tensor([0,1,2])# Each time the function is executed, the list shrinks in sizelst=[x,y]function_with_input_list_mutation(lst)print("len(lst) after one call",len(lst))function_with_input_list_mutation(lst)print("len(lst) after two calls",len(lst))# With Non-strict Tracing, the length of the list shrinks during# the graph capture but not in invocations of the graph.lst=[x,y]gm=make_fx(function_with_input_list_mutation,tracing_mode="fake")(lst)print("len(lst) after graph capture",len(lst))gm(lst)print("len(lst) after one call to graph",len(lst))gm(lst)print("len(lst) after two calls to graph",len(lst))

len(lst) after one call 1len(lst) after two calls 0len(lst) after graph capture 2len(lst) after one call to graph 2len(lst) after two calls to graph 2

importctypes# Create a tensor with a single elementtensor=torch.tensor([42],dtype=torch.int32)# Using int32 for simplicitydeffunction_with_data_ptr(tensor):# Get the data pointerptr=tensor.data_ptr()# Cast the pointer to a ctypes pointerctypes_ptr=ctypes.cast(ptr,ctypes.POINTER(ctypes.c_int32))# Increment the value at the pointerctypes_ptr.contents.value+=1returntensortry:make_fx(function_with_data_ptr,tracing_mode="fake")(tensor)exceptExceptionase:print(e)

Cannot access data pointer of Tensor (e.g. FakeTensor, FunctionalTensor). If you're using torch.compile/export/fx, it is likely that we are erroneously tracing into a custom kernel. To fix this, please wrap the custom kernel into an opaque custom op. Please see the following for details: https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html