trace(func, example_inputs, optimize=None, check_trace=True, check_inputs=None, check_tolerance=1e-05, strict=True, _force_outplace=False, _module_class=None, _compilation_unit=<torch.jit.CompilationUnit object>)¶
Trace a function and return an executable or
ScriptFunctionthat will be optimized using just-in-time compilation. Tracing is ideal for code that operates only on
Tensors and lists, dictionaries, and tuples of
Using torch.jit.trace and torch.jit.trace_module, you can turn an existing module or Python function into a TorchScript
ScriptModule. You must provide example inputs, and we run the function, recording the operations performed on all the tensors.
The resulting recording of a standalone function produces ScriptFunction.
The resulting recording of nn.Module.forward or nn.Module produces ScriptModule.
This module also contains any parameters that the original module had as well.
Tracing only correctly records functions and modules which are not data dependent (e.g., do not have conditionals on data in tensors) and do not have any untracked external dependencies (e.g., perform input/output or access global variables). Tracing only records operations done when the given function is run on the given tensors. Therefore, the returned ScriptModule will always run the same traced graph on any input. This has some important implications when your module is expected to run different sets of operations, depending on the input and/or the module state. For example,
Tracing will not record any control-flow like if-statements or loops. When this control-flow is constant across your module, this is fine and it often inlines the control-flow decisions. But sometimes the control-flow is actually part of the model itself. For instance, a recurrent network is a loop over the (possibly dynamic) length of an input sequence.
In the returned
ScriptModule, operations that have different behaviors in
evalmodes will always behave as if it is in the mode it was in during tracing, no matter which mode the ScriptModule is in.
In cases like these, tracing would not be appropriate and
scriptingis a better choice. If you trace such models, you may silently get incorrect results on subsequent invocations of the model. The tracer will try to emit warnings when doing something that may cause an incorrect trace to be produced.
func (callable or torch.nn.Module) – A Python function or torch.nn.Module that will be run with example_inputs. func arguments and return values must be tensors or (possibly nested) tuples that contain tensors. When a module is passed torch.jit.trace, only the
forwardmethod is run and traced (see
example_inputs (tuple or torch.Tensor) – A tuple of example inputs that will be passed to the function while tracing. The resulting trace can be run with inputs of different types and shapes assuming the traced operations support those types and shapes. example_inputs may also be a single Tensor in which case it is automatically wrapped in a tuple.
- Keyword Arguments
bool, optional) – Check if the same inputs run through traced code produce the same outputs. Default:
True. You might want to disable this if, for example, your network contains non- deterministic ops or if you are sure that the network is correct despite a checker failure.
check_inputs (list of tuples, optional) – A list of tuples of input arguments that should be used to check the trace against what is expected. Each tuple is equivalent to a set of input arguments that would be specified in
example_inputs. For best results, pass in a set of checking inputs representative of the space of shapes and types of inputs you expect the network to see. If not specified, the original
example_inputsare used for checking
check_tolerance (float, optional) – Floating-point comparison tolerance to use in the checker procedure. This can be used to relax the checker strictness in the event that results diverge numerically for a known reason, such as operator fusion.
bool, optional) – run the tracer in a strict mode or not (default:
True). Only turn this off when you want the tracer to record your mutable container types (currently
dict) and you are sure that the container you are using in your problem is a
constantstructure and does not get used as control flow (if, for) conditions.
If func is nn.Module or
forwardof nn.Module, trace returns a
ScriptModuleobject with a single
forwardmethod containing the traced code. The returned ScriptModule will have the same set of sub-modules and parameters as the original
funcis a standalone function,
Example (tracing a function):
import torch def foo(x, y): return 2 * x + y # Run `foo` with the provided inputs and record the tensor operations traced_foo = torch.jit.trace(foo, (torch.rand(3), torch.rand(3))) # `traced_foo` can now be run with the TorchScript interpreter or saved # and loaded in a Python-free environment
Example (tracing an existing module):
import torch import torch.nn as nn class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv = nn.Conv2d(1, 1, 3) def forward(self, x): return self.conv(x) n = Net() example_weight = torch.rand(1, 1, 3, 3) example_forward_input = torch.rand(1, 1, 3, 3) # Trace a specific method and construct `ScriptModule` with # a single `forward` method module = torch.jit.trace(n.forward, example_forward_input) # Trace a module (implicitly traces `forward`) and construct a # `ScriptModule` with a single `forward` method module = torch.jit.trace(n, example_forward_input)