TorchScript ============ .. contents:: :local: .. automodule:: torch.jit .. currentmodule:: torch.jit TorchScript is a way to create serializable and optimizable models from PyTorch code. Any code written in TorchScript can be saved from a Python process and loaded in a process where there is no Python dependency. We provide tools to incrementally transition a model from a pure Python program to a TorchScript program that can be run independently from Python, for instance, in a standalone C++ program. This makes it possible to train models in PyTorch using familiar tools and then export the model via TorchScript to a production environment where it is not a good idea to run models as Python programs for performance and multi-threading reasons. Creating TorchScript Code -------------------------- .. autoclass:: ScriptModule :members: .. autofunction:: save .. autofunction:: load .. autofunction:: trace Mixing Tracing and Scripting ---------------------------- In many cases either tracing or scripting is an easier approach for converting a model to TorchScript. We allow you to compose tracing and scripting to suit the particular requirements of a part of a model. Scripted functions can call traced functions. This is particularly useful when you need to use control-flow around a simple feed-forward model. For instance the beam search of a sequence to sequence model will typically be written in script but can call an encoder module generated using tracing. Example:: import torch def foo(x, y): return 2 * x + y traced_foo = torch.jit.trace(foo, (torch.rand(3), torch.rand(3))) @torch.jit.script def bar(x): return traced_foo(x, x) Traced functions can call script functions. This is useful when a small part of a model requires some control-flow even though most of the model is just a feed-forward network. Control-flow inside of a script function called by a traced function is preserved correctly: Example:: import torch @torch.jit.script def foo(x, y): if x.max() > y.max(): r = x else: r = y return r def bar(x, y, z): return foo(x, y) + z traced_bar = torch.jit.trace(bar, (torch.rand(3), torch.rand(3), torch.rand(3))) This composition also works for ``ScriptModule``\s as well, where it can be used to generate a submodule using tracing that can be called from the methods of a script module: Example:: import torch import torchvision class MyScriptModule(torch.jit.ScriptModule): def __init__(self): super(MyScriptModule, self).__init__() self.means = torch.nn.Parameter(torch.tensor([103.939, 116.779, 123.68]) .resize_(1, 3, 1, 1)) self.resnet = torch.jit.trace(torchvision.models.resnet18(), torch.rand(1, 3, 224, 224)) @torch.jit.script_method def forward(self, input): return self.resnet(input - self.means) TorchScript Language Reference ------------------------------- TorchScript is a statically typed subset of Python that can either be written directly (using the ``@torch.jit.script`` decorator) or generated automatically from Python code via tracing. When using tracing, code is automatically converted into this subset of Python by recording only the actual operators on tensors and simply executing and discarding the other surrounding Python code. When writing TorchScript directly using ``@torch.jit.script`` decorator, the programmer must only use the subset of Python supported in TorchScript. This section documents what is supported in TorchScript as if it were a language reference for a stand alone language. Any features of Python not mentioned in this reference are not part of TorchScript. As a subset of Python any valid TorchScript function is also a valid Python function. This makes it possible to remove the ``@torch.jit.script`` decorator and debug the function using standard Python tools like ``pdb``. The reverse is not true: there are many valid python programs that are not valid TorchScript programs. Instead, TorchScript focuses specifically on the features of Python that are needed to represent neural network models in Torch. .. envvar:: PYTORCH_JIT=1 Setting the environment variable ``PYTORCH_JIT=0`` will disable all script and tracing annotations. If there is hard-to-debug error in one of your ScriptModules, you can use this flag to force everything to run using native Python. This allows the use of tools like ``pdb`` to debug code. Types ~~~~~ The largest difference between TorchScript and the full Python language is that TorchScript only supports a small set of types that are needed to express neural net models. In particular, TorchScript supports: .. csv-table:: :header: "Type", "Description" "``Tensor``", "A PyTorch tensor of any dtype, dimension, or backend" "``Tuple[T0, T1, ...]``", "A tuple containing subtypes ``T0``, ``T1``, etc. (e.g. ``Tuple[Tensor, Tensor]``)" "``bool``", "A boolean value" "``int``", "A scalar integer" "``float``", "A scalar floating point number" "``List[T]``", "A list of which all members are type ``T``" "``Optional[T]``", "A value which is either None or type ``T``" "``Dict[K, V]``", "A dict with key type ``K`` and value type ``V``. Only ``str``, ``int``, and ``float`` are allowed as key types." Unlike Python, each variable in TorchScript function must have a single static type. This makes it easier to optimize TorchScript functions. Example (a type mismatch):: @torch.jit.script def an_error(x): if x: r = torch.rand(1) else: r = 4 return r # Type mismatch: r is set to type Tensor in the true branch # and type int in the false branch Default Types ^^^^^^^^^^^^^ By default, all parameters to a TorchScript function are assumed to be Tensor. To specify that an argument to a TorchScript function is another type, it is possible to use MyPy-style type annotations using the types listed above: Example:: @torch.jit.script def foo(x, tup): # type: (int, Tuple[Tensor, Tensor]) -> Tensor t0, t1 = tup return t0 + t1 + x print(foo(3, (torch.rand(3), torch.rand(3)))) .. note:: It is also possible to annotate types with Python 3 type annotations. In our examples, we use comment-based annotations to ensure Python 2 compatibility as well. An empty list is assumed to be ``List[Tensor]`` and empty dicts ``Dict[str, Tensor]``. To instantiate an empty list or dict of other types, use ``torch.jit.annotate``. Example:: import torch from torch.jit import Tensor from typing import List, Tuple class EmptyDataStructures(torch.jit.ScriptModule): def __init__(self): super(EmptyDataStructures, self).__init__() @torch.jit.script_method def forward(self, x): # type: (Tensor) -> Tuple[List[Tuple[int, float]], Dict[str, int]] # This annotates the list to be a `List[Tuple[int, float]]` my_list = torch.jit.annotate(List[Tuple[int, float]], []) for i in range(10): my_list.append((x, x)) my_dict = torch.jit.annotate(Dict[str, int], {}) return my_list, my_dict Optional Type Refinement ^^^^^^^^^^^^^^^^^^^^^^^^ TorchScript will refine the type of a variable of type ``Optional[T]`` when a comparison to ``None`` is made inside the conditional of an if-statement. The compiler can reason about multiple ``None`` checks that are combined with ``and``, ``or``, and ``not``. Refinement will also occur for else blocks of if-statements that are not explicitly written. The expression must be emitted within the conditional; assigning a ``None`` check to a variable and using it in the conditional will not refine types. Example:: @torch.jit.script def optional_unwrap(x, y, z): # type: (Optional[int], Optional[int], Optional[int]) -> int if x is None: x = 1 x = x + 1 if y is not None and z is not None: x = y + z return x Classes ^^^^^^^^^^^^^^^^^^^^^^^^ Python classes can be used in TorchScript if they are annotated with ``@torch.jit.script``, similar to how you would declare a TorchScript function: :: @torch.jit.script class Foo: def __init__(self, x, y) self.x = x def aug_add_x(self, inc): self.x += inc This subset is restricted: * All functions must be valid TorchScript functions (including ``__init__()``) * Classes must be new-style classes, as we use ``__new__()`` to construct them with pybind11 * TorchScript classes are statically typed. Members are declared by assigning to self in the ``__init__()`` method For example, assigning outside of the ``__init__()`` method: :: @torch.jit.script class Foo: def assign_x(self): self.x = torch.rand(2, 3) Will result in: :: RuntimeError: Tried to set nonexistent attribute: x. Did you forget to initialize it in __init__()?: def assign_x(self): self.x = torch.rand(2, 3) ~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE * No expressions except method definitions are allowed in the body of the class * No support for inheritance or any other polymorphism strategy, except for inheriting from object to specify a new-style class After a class is defined, it can be used in both TorchScript and Python interchangeably like any other TorchScript type: :: @torch.jit.script class Pair: def __init__(self, first, second) self.first = first self.second = second @torch.jit.script def sum_pair(p): # type : (Pair) -> Tensor return p.first + p.second p = Pair(torch.rand(2, 3), torch.rand(2, 3) print(sum_pair(p)) Expressions ~~~~~~~~~~~ The following Python Expressions are supported Literals ^^^^^^^^ ``True``, ``False``, ``None``, ``'string literals'``, ``"string literals"``, number literals ``3`` (interpreted as int) ``3.4`` (interpreted as a float) List Construction """"""""""""""""" ``[3, 4]``, ``[]``, ``[torch.rand(3), torch.rand(4)]`` .. note:: An empty list is assumed have type ``List[Tensor]``. The types of other list literals are derived from the type of the members. To denote an empty list of another type, use ``torch.jit.annotate``. Tuple Construction """""""""""""""""" ``(3, 4)``, ``(3,)`` Dict Construction """"""""""""""""" ``{'hello': 3}``, ``{}``, ``{'a': torch.rand(3), 'b': torch.rand(4)}`` .. note:: An empty dict is assumed have type ``Dict[str, Tensor]``. The types of other dict literals are derived from the type of the members. To denote an empty dict of another type, use ``torch.jit.annotate``. Variables ^^^^^^^^^ ``my_variable_name`` .. note:: See `Variable Resolution`_ for how variables are resolved. Arithmetic Operators ^^^^^^^^^^^^^^^^^^^^ ``a + b`` ``a - b`` ``a * b`` ``a / b`` ``a ^ b`` ``a @ b`` Comparison Operators ^^^^^^^^^^^^^^^^^^^^ ``a == b`` ``a != b`` ``a < b`` ``a > b`` ``a <= b`` ``a >= b`` Logical Operators ^^^^^^^^^^^^^^^^^ ``a and b`` ``a or b`` ``not b`` Subscripts ^^^^^^^^^^ ``t[0]`` ``t[-1]`` ``t[0:2]`` ``t[1:]`` ``t[:1]`` ``t[:]`` ``t[0, 1]`` ``t[0, 1:2]`` ``t[0, :1]`` ``t[-1, 1:, 0]`` ``t[1:, -1, 0]`` ``t[i:j, i]`` Function Calls ^^^^^^^^^^^^^^ Calls to built-in functions: ``torch.rand(3, dtype=torch.int)`` Calls to other script functions: :: import torch @torch.jit.script def foo(x): return x + 1 @torch.jit.script def bar(x): return foo(x) Method Calls ^^^^^^^^^^^^ Calls to methods of builtin types like tensor: ``x.mm(y)`` When defining a Script method inside of a ScriptModule, the ``@script_method`` annotation is used. Inside of these methods it is possible to call other methods of this class or access methods on the submodules. Calling a submodule directly (e.g. ``self.resnet(input)``) is equivalent to calling its ``forward`` method (e.g. ``self.resnet.forward(input)``) :: import torch class MyScriptModule(torch.jit.ScriptModule): def __init__(self): super(MyScriptModule, self).__init__() self.means = torch.nn.Parameter(torch.tensor([103.939, 116.779, 123.68]) .resize_(1, 3, 1, 1)) self.resnet = torch.jit.trace(torchvision.models.resnet18(), torch.rand(1, 3, 224, 224)) @torch.jit.script_method def helper(self, input): return self.resnet(input - self.means) @torch.jit.script_method def forward(self, input): return self.helper(input) Ternary Expressions ^^^^^^^^^^^^^^^^^^^ ``x if x > y else y`` Casts ^^^^^ ``float(ten)`` ``int(3.5)`` ``bool(ten)`` Accessing Module Parameters ^^^^^^^^^^^^^^^^^^^^^^^^^^^ ``self.my_parameter`` ``self.my_submodule.my_parameter`` Statements ~~~~~~~~~~ TorchScript supports the following types of statements: Simple Assignments :: a = b a += b # short-hand for a = a + b, does not operate in-place on a a -= b Pattern Matching Assignments :: a, b = tuple_or_list a, b, *c = a_tuple Print Statements ``print("the result of an add:", a + b)`` If Statements :: if a < 4: r = -a elif a < 3: r = a + a else: r = 3 * a In addition to bools, floats, ints, and Tensors can be used in a conditional and will be implicitly casted to a boolean. While Loops :: a = 0 while a < 4: print(a) a += 1 For loops with ``range`` :: x = 0 for i in range(10): x *= i For loops over tuples: :: tup = (3, torch.rand(4)) for x in tup: print(x) .. note:: for loops over tuples will unroll the loop, generating a body for each member of the tuple. The body must type-check correctly for each member. For loops over constant ``torch.nn.ModuleList`` :: class SubModule(torch.jit.ScriptModule): def __init__(self): super(Sub, self).__init__() self.weight = nn.Parameter(torch.randn(2)) @torch.jit.script_method def forward(self, input): return self.weight + input class MyModule(torch.jit.ScriptModule): __constants__ = ['mods'] def __init__(self): super(MyModule, self).__init__() self.mods = torch.nn.ModuleList([SubModule() for i in range(10)]) @torch.jit.script_method def forward(self, v): for module in self.mods: v = m(v) return v .. note:: To use a ``nn.ModuleList`` inside a ``@script_method`` it must be marked constant by adding the name of the attribute to the ``__constants__`` list for the type. For loops over a ``nn.ModuleList`` will unroll the body of the loop at compile time, with each member of the constant module list. Return ``return a, b`` .. note:: TorchScript allows returns in the following circumstances: 1. At the end of a function 2. In an if-statement where and both return 3. In an if-statement where returns and is empty (an early return) Variable Resolution ~~~~~~~~~~~~~~~~~~~ TorchScript supports a subset of Python's variable resolution (i.e. scoping) rules. Local variables behave the same as in Python, except for the restriction that a variable must have the same type along all paths through a function. If a variable has a different type on different sides of an if statement, it is an error to use it after the end of the if statement. Similarly, a variable is not allowed to be used if it is only *defined* along some paths through the function. Example:: @torch.jit.script def foo(x): if x < 0: y = 4 print(y) # Error: undefined value y Non-local variables are resolved to Python values at compile time when the function is defined. These values are then converted into TorchScript values using the rules described in `Use of Python Values`_. Use of Python Values ~~~~~~~~~~~~~~~~~~~~ To make writing TorchScript more convenient, we allow script code to refer to Python values in the surrounding scope. For instance, any time there is a reference to ``torch``, the TorchScript compiler is actually resolving it to the ``torch`` Python module when the function is declared. These Python values are not a first class part of TorchScript. Instead they are de-sugared at compile-time into the primitive types that TorchScript supports. This depends on the dynamic type of the Python valued referenced when compilation occurs. This section describes the rules that are used when accessing Python values in TorchScript. Functions ^^^^^^^^^ TorchScript can call Python functions. This functionality is very useful when incrementally converting a model to TorchScript. The model can be moved function-by-function to TorchScript, leaving calls to Python functions in place. This way you can incrementally check the correctness of the model as you go. Example:: def foo(x): print("I am called with {}".format(x)) import pdb; pdb.set_trace() return x @torch.jit.script def bar(x) return foo(x + 1) Attempting to call ``save`` on a ScriptModule that contains calls to Python functions will fail. The intention is that this pathway is used for debugging and the calls removed or turned into script functions before saving. If you want to export a module with a Python function, add the ``@torch.jit.ignore`` decorator to the function which will replace these function calls with an exception when the model is saved: :: class M(torch.jit.ScriptModule): def __init__(self): super(M, self).__init__() @torch.jit.script_method def forward(self, x): self.ignored_code(x) return x + 2 @torch.jit.ignore def ignored_code(self, x): # non-TorchScript code import pdb; pdb.set_trace() m = M() # Runs, makes upcall to Python to run `ignored_code` m(torch.ones(2, 2)) # Replaces all calls to `ignored_code` with a `raise` m.save("m.pt") loaded = torch.jit.load("m.pt") # This runs `ignored_code` after saving which will raise an Exception! loaded(torch.ones(2, 2)) Attribute Lookup On Python Modules ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ TorchScript can lookup attributes on modules. Builtin functions like ``torch.add`` are accessed this way. This allows TorchScript to call functions defined in other modules. Python-defined Constants ^^^^^^^^^^^^^^^^^^^^^^^^ TorchScript also provides a way to use constants that are defined in Python. These can be used to hard-code hyper-parameters into the function, or to define universal constants. There are two ways of specifying that a Python value should be treated as a constant. 1. Values looked up as attributes of a module are assumed to be constant. Example: ``math.pi`` 2. Attributes of a ScriptModule can be marked constant by listing them as a member of the ``__constants__`` property of the class: Example:: class Foo(torch.jit.ScriptModule): __constants__ = ['a'] def __init__(self): super(Foo, self).__init__(False) self.a = 1 + 4 @torch.jit.script_method def forward(self, input): return self.a + input Supported constant Python Values are * ``int`` * ``float`` * ``bool`` * ``torch.device`` * ``torch.layout`` * ``torch.dtype`` * tuples containing supported types * ``torch.nn.ModuleList`` which can be used in a TorchScript for loop Module Attributes ^^^^^^^^^^^^^^^^^ The ``torch.nn.Parameter`` wrapper and ``register_buffer`` can be used to assign tensors to a ``ScriptModule``. In a similar vein, attributes of any type can be assign on a ``ScriptModule`` by wrapping them with ``torch.jit.Attribute`` and specifying the type. All types available in TorchScript are supported. These attributes are mutable and are saved in a separate archive in the serialized model binary. Tensor attributes are semantically the same as buffers. Example:: class Foo(torch.jit.ScriptModule): def __init__(self, a_dict): super(Foo, self).__init__(False) self.words = torch.jit.Attribute([], List[str]) self.some_dict = torch.jit.Attribute(a_dict, Dict[str, int]) @torch.jit.script_method def forward(self, input): # type: (str) -> int self.words.append(input) return self.some_dict[input] Debugging ~~~~~~~~~ Disable JIT for Debugging ^^^^^^^^^^^^^^^^^^^^^^^^^ If you want to disable all JIT modes (tracing and scripting) so you can debug your program in raw Python, you can use the ``PYTORCH_JIT`` environment variable. ``PYTORCH_JIT`` can be used to globally disable the JIT by setting its value to ``0``. Given an example script:: @torch.jit.script def scripted_fn(x : torch.Tensor): for i in range(12): x = x + x return x def fn(x): x = torch.neg(x) import pdb; pdb.set_trace() return scripted_fn(x) traced_fn = torch.jit.trace(fn, (torch.rand(4, 5),)) traced_fn(torch.rand(3, 4)) Debugging this script with PDB works except for when we invoke the ``@torch.jit.script`` function. We can globally disable JIT, so that we can call the ``@torch.jit.script`` function as a normal python function and not compile it. If the above script is called ``disable_jit_example.py``, we can invoke it like so:: $ PYTORCH_JIT=0 python disable_jit_example.py and we will be able to step into the ``@torch.jit.script`` function as a normal Python function. Inspecting Code ^^^^^^^^^^^^^^^ TorchScript provides a code pretty-printer for all ``ScriptModule`` instances. This pretty-printer gives an interpretation of the script method's code as valid Python syntax. For example:: @torch.jit.script def foo(len): # type: (int) -> torch.Tensor rv = torch.zeros(3, 4) for i in range(len): if i < 10: rv = rv - 1.0 else: rv = rv + 1.0 return rv print(foo.code) A ``ScriptModule`` with a single ``forward`` method will have an attribute ``code``, which you can use to inspect the ``ScriptModule``'s code. If the ``ScriptModule`` has more than one method, you will need to access ``.code`` on the method itself and not the module. We can inspect the code of a method named ``bar`` on a ScriptModule by accessing ``.bar.code``. The example script above produces the code:: def forward(self, len: int) -> Tensor: rv = torch.zeros([3, 4], dtype=None, layout=None, device=None) rv0 = rv for i in range(len): if torch.lt(i, 10): rv1 = torch.sub(rv0, 1., 1) else: rv1 = torch.add(rv0, 1., 1) rv0 = rv1 return rv0 This is TorchScript's compilation of the code for the ``forward`` method. You can use this to ensure TorchScript (tracing or scripting) has captured your model code correctly. Interpreting Graphs ^^^^^^^^^^^^^^^^^^^ TorchScript also has a representation at a lower level than the code pretty- printer, in the form of IR graphs. TorchScript uses a static single assignment (SSA) intermediate representation (IR) to represent computation. The instructions in this format consist of ATen (the C++ backend of PyTorch) operators and other primitive operators, including control flow operators for loops and conditionals. As an example:: @torch.jit.script def foo(len): # type: (int) -> torch.Tensor rv = torch.zeros(3, 4) for i in range(len): if i < 10: rv = rv - 1.0 else: rv = rv + 1.0 return rv print(foo.graph) ``.graph`` follows the same rules described in the `Inspecting Code`_ section with regard to ``forward`` method lookup. The example script above produces the graph:: graph(%len : int) { %15 : int = prim::Constant[value=1]() %9 : bool = prim::Constant[value=1]() %7 : Device = prim::Constant[value="cpu"]() %6 : int = prim::Constant[value=0]() %5 : int = prim::Constant[value=6]() %1 : int = prim::Constant[value=3]() %2 : int = prim::Constant[value=4]() %11 : int = prim::Constant[value=10]() %14 : float = prim::Constant[value=1]() %4 : int[] = prim::ListConstruct(%1, %2) %rv.1 : Tensor = aten::zeros(%4, %5, %6, %7) %rv : Tensor = prim::Loop(%len, %9, %rv.1) block0(%i : int, %13 : Tensor) { %12 : bool = aten::lt(%i, %11) %rv.4 : Tensor = prim::If(%12) block0() { %rv.2 : Tensor = aten::sub(%13, %14, %15) -> (%rv.2) } block1() { %rv.3 : Tensor = aten::add(%13, %14, %15) -> (%rv.3) } -> (%9, %rv.4) } return (%rv); } Take the instruction ``%rv.1 : Dynamic = aten::zeros(%3, %4, %5, %6)`` for example. ``%rv.1 : Dynamic`` means we assign the output to a (unique) value named ``rv.1``, and that value is of ``Dynamic`` type, i.e. we do not know its concrete shape. ``aten::zeros`` is the operator (equivalent to ``torch.zeros``) and the input list ``(%3, %4, %5, %6)`` specifies which values in scope should be passed as inputs. The schema for built-in functions like ``aten::zeros`` can be found at `Builtin Functions`_. Notice that operators can also have associated ``blocks``, namely the ``prim::Loop`` and ``prim::If`` operators. In the graph print-out, these operators are formatted to reflect their equivalent source code forms to facilitate easy debugging. Graphs can be inspected as shown to confirm that the computation described by a ``ScriptModule`` is correct, in both automated and manual fashion, as described below. Tracing Edge Cases ^^^^^^^^^^^^^^^^^^ There are some edge cases that exist where the trace of a given Python function/module will not be representative of the underlying code. These cases can include: * Tracing of control flow that is dependent on inputs (e.g. tensor shapes) * Tracing of in-place operations of tensor views (e.g. indexing on the left-hand side of an assignment) Note that these cases may in fact be traceable in the future. Automatic Trace Checking ^^^^^^^^^^^^^^^^^^^^^^^^ One way to automatically catch many errors in traces is by using ``check_inputs`` on the ``torch.jit.trace()`` API. ``check_inputs`` takes a list of tuples of inputs that will be used to re-trace the computation and verify the results. For example:: def loop_in_traced_fn(x): result = x[0] for i in range(x.size(0)): result = result * x[i] return result inputs = (torch.rand(3, 4, 5),) check_inputs = [(torch.rand(4, 5, 6),), (torch.rand(2, 3, 4),)] traced = torch.jit.trace(loop_in_traced_fn, inputs, check_inputs=check_inputs) Gives us the following diagnostic information:: ERROR: Graphs differed across invocations! Graph diff:: graph(%x : Tensor) { %1 : int = prim::Constant[value=0]() %2 : int = prim::Constant[value=0]() %result.1 : Tensor = aten::select(%x, %1, %2) %4 : int = prim::Constant[value=0]() %5 : int = prim::Constant[value=0]() %6 : Tensor = aten::select(%x, %4, %5) %result.2 : Tensor = aten::mul(%result.1, %6) %8 : int = prim::Constant[value=0]() %9 : int = prim::Constant[value=1]() %10 : Tensor = aten::select(%x, %8, %9) - %result : Tensor = aten::mul(%result.2, %10) + %result.3 : Tensor = aten::mul(%result.2, %10) ? ++ %12 : int = prim::Constant[value=0]() %13 : int = prim::Constant[value=2]() %14 : Tensor = aten::select(%x, %12, %13) + %result : Tensor = aten::mul(%result.3, %14) + %16 : int = prim::Constant[value=0]() + %17 : int = prim::Constant[value=3]() + %18 : Tensor = aten::select(%x, %16, %17) - %15 : Tensor = aten::mul(%result, %14) ? ^ ^ + %19 : Tensor = aten::mul(%result, %18) ? ^ ^ - return (%15); ? ^ + return (%19); ? ^ } This message indicates to us that the computation differed between when we first traced it and when we traced it with the ``check_inputs``. Indeed, the loop within the body of ``loop_in_traced_fn`` depends on the shape of the input ``x``, and thus when we try another ``x`` with a different shape, the trace differs. In this case, data-dependent control flow like this can be captured using script instead:: def fn(x): result = x[0] for i in range(x.size(0)): result = result * x[i] return result inputs = (torch.rand(3, 4, 5),) check_inputs = [(torch.rand(4, 5, 6),), (torch.rand(2, 3, 4),)] scripted_fn = torch.jit.script(fn) print(scripted_fn.graph) for input_tuple in [inputs] + check_inputs: torch.testing.assert_allclose(fn(*input_tuple), scripted_fn(*input_tuple)) Which produces:: graph(%x : Tensor) { %5 : bool = prim::Constant[value=1]() %1 : int = prim::Constant[value=0]() %result.1 : Tensor = aten::select(%x, %1, %1) %4 : int = aten::size(%x, %1) %result : Tensor = prim::Loop(%4, %5, %result.1) block0(%i : int, %7 : Tensor) { %10 : Tensor = aten::select(%x, %1, %i) %result.2 : Tensor = aten::mul(%7, %10) -> (%5, %result.2) } return (%result); } Tracer Warnings ^^^^^^^^^^^^^^^ The tracer produces warnings for several problematic patterns in traced computation. As an example, take a trace of a function that contains an in-place assignment on a slice (a view) of a Tensor:: def fill_row_zero(x): x[0] = torch.rand(*x.shape[1:2]) return x traced = torch.jit.trace(fill_row_zero, (torch.rand(3, 4),)) print(traced.graph) Produces several warnings and a graph which simply returns the input:: fill_row_zero.py:4: TracerWarning: There are 2 live references to the data region being modified when tracing in-place operator copy_ (possibly due to an assignment). This might cause the trace to be incorrect, because all other views that also reference this data will not reflect this change in the trace! On the other hand, if all other views use the same memory chunk, but are disjoint (e.g. are outputs of torch.split), this might still be safe. x[0] = torch.rand(*x.shape[1:2]) fill_row_zero.py:6: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error: Not within tolerance rtol=1e-05 atol=1e-05 at input[0, 1] (0.09115803241729736 vs. 0.6782537698745728) and 3 other locations (33.00%) traced = torch.jit.trace(fill_row_zero, (torch.rand(3, 4),)) graph(%0 : Float(3, 4)) { return (%0); } We can fix this by modifying the code to not use the in-place update, but rather build up the result tensor out-of-place with `torch.cat`:: def fill_row_zero(x): x = torch.cat((torch.rand(1, *x.shape[1:2]), x[1:2]), dim=0) return x traced = torch.jit.trace(fill_row_zero, (torch.rand(3, 4),)) print(traced.graph) Frequently Asked Questions -------------------------- Q: I would like to train a model on GPU and do inference on CPU. What are the best practices? First convert your model from GPU to CPU and then save it, like so: :: cpu_model = gpu_model.cpu() sample_input_cpu = sample_input_gpu.cpu() traced_cpu = torch.jit.trace(traced_cpu, sample_input_cpu) torch.jit.save(traced_cpu, "cpu.pth") traced_gpu = torch.jit.trace(traced_gpu, sample_input_gpu) torch.jit.save(traced_gpu, "gpu.pth") # ... later, when using the model: if use_gpu: model = torch.jit.load("gpu.pth") else: model = torch.jit.load("cpu.pth") model(input) This is recommended because the tracer may witness tensor creation on a specific device, so casting an already-loaded model may have unexpected effects. Casting the model *before* saving it ensures that the tracer has the correct device information. Q: How do I store attributes on a ``ScriptModule``? Say we have a model like: :: class Model(torch.jit.ScriptModule): def __init__(self): super(Model, self).__init__() self.x = 2 @torch.jit.script_method def forward(self): return self.x If ``Model`` is instantiated it will result in a compilation error since the compiler doesn't know about ``x``. There are 4 ways to inform the compiler of attributes on ``ScriptModule``: 1. ``nn.Parameter`` - values wrapped in ``nn.Parameter`` will work as they do on ``nn.Module``\s 2. ``register_buffer`` - values wrapped in ``register_buffer`` will work as they do on ``nn.Module``\s 3. ``__constants__`` - adding a list called ``__constants__`` at the class definition level will mark the contained names as constants. Constants are saved directly in the code of the model. See `Python-defined Constants`_. 4. ``torch.jit.Attribute`` - values wrapped in ``torch.jit.Attribute`` can be any ``TorchScript`` type, be mutated and are saved outside of the code of the model. See `Module Attributes`_. Builtin Functions ~~~~~~~~~~~~~~~~~ TorchScript supports a subset of the builtin tensor and neural network functions that PyTorch provides. Most methods on Tensor as well as functions in the ``torch`` namespace, all functions in ``torch.nn.functional`` and all modules from ``torch.nn`` are supported in TorchScript, excluding those in the table below. For unsupported modules, we suggest using :meth:`torch.jit.trace`. Unsupported ``torch.nn`` Modules :: torch.nn.modules.adaptive.AdaptiveLogSoftmaxWithLoss torch.nn.modules.normalization.CrossMapLRN2d torch.nn.modules.fold.Fold torch.nn.modules.fold.Unfold torch.nn.modules.rnn.GRU torch.nn.modules.rnn.RNN .. automodule:: torch.jit.supported_ops