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pytorch/torch/jit/__init__.py
Mike Ruberry e66445878d Adds dynamic versioning pattern (#40279) 
Summary:
BC NOTE:

This change makes it so modules saved with torch.jit.save in PyTorch 1.6 can be loaded by previous versions of PyTorch unless they use torch.div or (soon) torch.full. It also lets tensors saved using torch.save be loaded by previous versions. So this is the opposite of BC-breaking, but I'm using that label to highlight this issue since we don't have a "BC-improving" label.

PR NOTE:
When an operator's semantics change in PyTorch we want to do two things:

1) Preserve the semantics of older serialized Torchscript programs that use the operator
2) Ensure the new semantics are respected

Historically, this meant writing a Versioned Symbol that would remap older versions of the operator into current PyTorch code (1), and bumping the produced file format version (2). Unfortunately, bumping the produced file format version is a nuclear option for ensuring semantics are respected, since it also prevents older versions of PyTorch from loading anything (even tensors!) from newer versions.

Dynamic versioning addresses the nuclear consequences of bumping the produced file format version by only bumping it when necessary. That is, when an operator with changed semantics is detected in the serialized Torchscript. This will prevent Torchscript programs that use the changed operator from loading on earlier versions of PyTorch, as desired, but will have no impact on programs that don't use the changed operator.

Note that this change is only applicable when using torch.jit.save and torch.jit.load. torch.save pickles the given object using pickle (by default), which saves a function's Python directly.

No new tests for this behavior are added since the existing tests for versioned division in test_save_load already validate that models with div are loaded correctly at version 4.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40279

Reviewed By: dzhulgakov

Differential Revision: D22168291

Pulled By: mruberry

fbshipit-source-id: e71d6380e727e25123c7eedf6d80e5d7f1fe9f95
2020-06-24 12:52:50 -07:00

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import torch._C
import torch._jit_internal as _jit_internal
import torch.jit.annotations
import torch.testing
import torch.jit._recursive
from torch.jit._recursive import ScriptMethodStub, wrap_cpp_module
from torch.jit._builtins import _find_builtin, _get_builtin_table, _register_builtin # noqa
from torch._jit_internal import Future, _qualified_name
from torch.autograd import Variable, function
from torch.jit.frontend import get_jit_class_def, get_jit_def, get_default_args
from torch.nn import Module
from torch.serialization import validate_cuda_device
from torch._six import PY37, with_metaclass, string_classes, get_function_from_type
from torch.utils import set_module
from torch.autograd.grad_mode import _DecoratorContextManager
from typing import Optional, List
import collections
import contextlib
import copy
import functools
import inspect
import os
import pathlib
import pickle
import re
import sys
import textwrap
import warnings
import weakref
# These are imported so users can access them from the `torch.jit` module
from torch._jit_internal import Final, _overload, _overload_method
from torch._jit_internal import ignore, export, unused
def _parse_env(name, default, true_message, false_message):
value = os.environ.get(name)
if value is None:
return default
if value.lower() in {'1', 'true', 'yes'}:
return True
elif value.lower() in {'0', 'false', 'no'}:
return False
if value == '1v':
print(true_message)
return True
elif value == '0v':
print(false_message)
return False
raise ValueError('Unknown setting of {}. Try using 0 or 1.'.format(name))
_enabled = _parse_env('PYTORCH_JIT', True, "> Using PyTorch JIT", "> PyTorch JIT DISABLED")
_flatten = torch._C._jit_flatten
_unflatten = torch._C._jit_unflatten
_jit_script_class_compile = torch._C._jit_script_class_compile
# The Python CompilationUnit. All functions and modules defined in Python will
# live in here. It's defined in Python because doing in cpp creates static
# destruction order issues.
_python_cu = torch._C.CompilationUnit()
set_module(Future, "torch.jit")
_fork = torch._C.fork
_wait = torch._C.wait
if _enabled:
Attribute = collections.namedtuple('Attribute', ['value', 'type'])
else:
def Attribute(value, type):
return value
@contextlib.contextmanager
def optimized_execution(should_optimize):
"""
A context manager that controls whether the JIT's executor will run
optimizations before executing a function.
"""
stored_flag = torch._C._get_graph_executor_optimize()
torch._C._set_graph_executor_optimize(should_optimize)
try:
yield
finally:
torch._C._set_graph_executor_optimize(stored_flag)
@contextlib.contextmanager
def fuser(name):
"""
A context manager that facilitates switching between
backend fusers.
Valid names:
* ``fuser0`` - enables only legacy fuser
* ``fuser1`` - enables only NNC
* ``fuser2`` - enables only nvFuser
"""
old_cpu_fuse = torch._C._jit_can_fuse_on_cpu()
old_gpu_fuse = torch._C._jit_can_fuse_on_gpu()
old_texpr_fuser_state = torch._C._jit_texpr_fuser_enabled()
old_nvfuser_state = torch._C._jit_nvfuser_enabled()
if name == 'fuser0': # legacy fuser
torch._C._jit_override_can_fuse_on_cpu(True)
torch._C._jit_override_can_fuse_on_gpu(True)
torch._C._jit_set_texpr_fuser_enabled(False)
torch._C._jit_set_nvfuser_enabled(False)
elif name == 'fuser1': # NNC
old_profiling_executor = torch._C._jit_set_profiling_executor(True)
old_profiling_mode = torch._C._jit_set_profiling_mode(True)
torch._C._jit_override_can_fuse_on_cpu(False)
torch._C._jit_override_can_fuse_on_gpu(False)
torch._C._jit_set_texpr_fuser_enabled(True)
torch._C._jit_set_nvfuser_enabled(False)
elif name == 'fuser2': # nvFuser
torch._C._jit_override_can_fuse_on_cpu(False)
torch._C._jit_override_can_fuse_on_gpu(False)
torch._C._jit_set_texpr_fuser_enabled(False)
torch._C._jit_set_nvfuser_enabled(True)
else:
raise Exception("unrecognized fuser option")
try:
yield
finally:
if name == 'fuser1': # NNC
torch._C._jit_set_profiling_executor(old_profiling_executor)
torch._C._jit_set_profiling_mode(old_profiling_mode)
# recover the previous values
torch._C._jit_override_can_fuse_on_cpu(old_cpu_fuse)
torch._C._jit_override_can_fuse_on_gpu(old_gpu_fuse)
torch._C._jit_set_texpr_fuser_enabled(old_texpr_fuser_state)
torch._C._jit_set_nvfuser_enabled(old_nvfuser_state)
DEFAULT_EXTRA_FILES_MAP = torch._C.ExtraFilesMap()
def save(m, f, _extra_files=DEFAULT_EXTRA_FILES_MAP):
r"""
Save an offline version of this module for use in a separate process. The
saved module serializes all of the methods, submodules, parameters, and
attributes of this module. It can be loaded into the C++ API using
``torch::jit::load(filename)`` or into the Python API with
:func:`torch.jit.load <torch.jit.load>`.
To be able to save a module, it must not make any calls to native Python
functions. This means that all submodules must be subclasses of
:class:`ScriptModule` as well.
.. DANGER::
All modules, no matter their device, are always loaded onto the CPU
during loading. This is different from :func:`torch.load`'s semantics
and may change in the future.
Arguments:
m: A :class:`ScriptModule` to save.
f: A file-like object (has to implement write and flush) or a string
containing a file name.
_extra_files: Map from filename to contents which will be stored as part of `f`.
.. note::
torch.jit.save attempts to preserve the behavior of some operators
across versions. For example, dividing two integer tensors in
PyTorch 1.5 performed floor division, and if the module
containing that code is saved in PyTorch 1.5 and loaded in PyTorch 1.6
its division behavior will be preserved. The same module saved in
PyTorch 1.6 will fail to load in PyTorch 1.5, however, since the
behavior of division changed in 1.6, and 1.5 does not know how to
replicate the 1.6 behavior.
Example:
.. testcode::
import torch
import io
class MyModule(torch.nn.Module):
def forward(self, x):
return x + 10
m = torch.jit.script(MyModule())
# Save to file
torch.jit.save(m, 'scriptmodule.pt')
# This line is equivalent to the previous
m.save("scriptmodule.pt")
# Save to io.BytesIO buffer
buffer = io.BytesIO()
torch.jit.save(m, buffer)
# Save with extra files
extra_files = torch._C.ExtraFilesMap()
extra_files['foo.txt'] = 'bar'
torch.jit.save(m, 'scriptmodule.pt', _extra_files=extra_files)
"""
if isinstance(f, str) or isinstance(f, pathlib.Path):
m.save(f, _extra_files=_extra_files)
else:
ret = m.save_to_buffer(_extra_files=_extra_files)
f.write(ret)
def load(f, map_location=None, _extra_files=DEFAULT_EXTRA_FILES_MAP):
r"""
Load a :class:`ScriptModule` or :class:`ScriptFunction` previously
saved with :func:`torch.jit.save <torch.jit.save>`
All previously saved modules, no matter their device, are first loaded onto CPU,
and then are moved to the devices they were saved from. If this fails (e.g.
because the run time system doesn't have certain devices), an exception is
raised.
Arguments:
f: a file-like object (has to implement read, readline, tell, and seek),
or a string containing a file name
map_location (string or torch.device): A simplified version of
``map_location`` in `torch.jit.save` used to dynamically remap
storages to an alternative set of devices.
_extra_files (dictionary of filename to content): The extra
filenames given in the map would be loaded and their content
would be stored in the provided map.
Returns:
A :class:`ScriptModule` object.
Example:
.. testcode::
import torch
import io
torch.jit.load('scriptmodule.pt')
# Load ScriptModule from io.BytesIO object
with open('scriptmodule.pt', 'rb') as f:
buffer = io.BytesIO(f.read())
# Load all tensors to the original device
torch.jit.load(buffer)
# Load all tensors onto CPU, using a device
buffer.seek(0)
torch.jit.load(buffer, map_location=torch.device('cpu'))
# Load all tensors onto CPU, using a string
buffer.seek(0)
torch.jit.load(buffer, map_location='cpu')
# Load with extra files.
extra_files = torch._C.ExtraFilesMap()
extra_files['foo.txt'] = 'bar'
torch.jit.load('scriptmodule.pt', _extra_files=extra_files)
print(extra_files['foo.txt'])
.. testoutput::
:hide:
...
.. testcleanup::
import os
os.remove("scriptmodule.pt")
"""
if isinstance(f, string_classes):
if not os.path.exists(f):
raise ValueError("The provided filename {} does not exist".format(f))
if os.path.isdir(f):
raise ValueError("The provided filename {} is a directory".format(f))
map_location = validate_map_location(map_location)
cu = torch._C.CompilationUnit()
if isinstance(f, str) or isinstance(f, pathlib.Path):
cpp_module = torch._C.import_ir_module(cu, f, map_location, _extra_files)
else:
cpp_module = torch._C.import_ir_module_from_buffer(cu, f.read(), map_location, _extra_files)
# TODO: Pretty sure this approach loses ConstSequential status and such
return torch.jit._recursive.wrap_cpp_module(cpp_module)
def validate_map_location(map_location=None):
if isinstance(map_location, str):
map_location = torch.device(map_location)
elif not (map_location is None or
isinstance(map_location, torch.device)):
raise ValueError("map_location should be either None, string or torch.device, "
"but got type: " + str(type(map_location)))
if (str(map_location).startswith('cuda')):
validate_cuda_device(map_location)
return map_location
def export_opnames(m):
r"""
Returns a list of operator names of a script module and its submodules
"""
return torch._C._export_opnames(m._c)
def _get_trace_graph(f, args=(), kwargs=None, strict=True, _force_outplace=False,
return_inputs=False, _return_inputs_states=False):
"""
.. warning::
This function is internal-only and should only be used by the ONNX
exporter. If you are trying to get a graph through tracing, please go
through the public API instead::
trace = torch.jit.trace(nn.LSTMCell(), (input, hidden))
trace_graph = trace.graph
Trace a function or model, returning a tuple consisting of the both the
*trace* of an execution, as well as the original return value. If return_inputs,
also returns the trace inputs as part of the tuple
Tracing is guaranteed not to change the semantics of the function/module
that is traced.
Arguments:
f (torch.nn.Module or function): the function or module
to be traced.
args (tuple or Tensor): the positional arguments to pass to the
function/module to be traced. A non-tuple is assumed to
be a single positional argument to be passed to the model.
kwargs (dict): the keyword arguments to pass to the function/module
to be traced.
Example (trace a cell):
.. testcode::
trace = torch.jit.trace(nn.LSTMCell(), (input, hidden))
"""
if kwargs is None:
kwargs = {}
if not isinstance(args, tuple):
args = (args,)
outs = ONNXTracedModule(f, strict, _force_outplace, return_inputs, _return_inputs_states)(*args, **kwargs)
return outs
def _unique_state_dict(module, keep_vars=False):
# since Parameter.detach() always creates a new torch.Tensor instance,
# id(v) doesn't work with it. So we always get the Parameter or Buffer
# as values, and deduplicate the params using Parameters and Buffers
state_dict = module.state_dict(keep_vars=True)
filtered_dict = type(state_dict)()
seen_ids = set()
for k, v in state_dict.items():
if id(v) in seen_ids:
continue
seen_ids.add(id(v))
if keep_vars:
filtered_dict[k] = v
else:
filtered_dict[k] = v.detach()
return filtered_dict
def _create_interpreter_name_lookup_fn(frames_up=1):
def _get_interpreter_name_for_var(var):
frame = inspect.currentframe()
i = 0
while i < frames_up + 1:
frame = frame.f_back
i += 1
f_locals = frame.f_locals
f_globals = frame.f_globals
for k, v in f_locals.items():
if isinstance(v, torch.Tensor) and var is v:
return k if k != 'self' else ''
return ''
return _get_interpreter_name_for_var
class ConstMap:
def __init__(self, const_mapping):
self.const_mapping = const_mapping
def __getattr__(self, attr):
return self.const_mapping[attr]
class ONNXTracedModule(Module):
def __init__(self, inner, strict=True, force_outplace=False, return_inputs=False, return_inputs_states=False):
super(ONNXTracedModule, self).__init__()
# inner may be a Module, or it may be an arbitrary callable
# If it's a Module, we get its parameters automatically, which lets
# us avoid a special casing functions versus modules.
self.inner = inner
self.strict = strict
self._force_outplace = force_outplace
self._return_inputs = return_inputs
self._return_inputs_states = return_inputs_states
def forward(self, *args):
in_vars, in_desc = _flatten(args)
# NOTE: use full state, because we need it for BatchNorm export
# This differs from the compiler path, which doesn't support it at the moment.
module_state = list(_unique_state_dict(self, keep_vars=True).values())
ret_inputs = []
inputs_states = []
outs = []
def wrapper(*args):
trace_inputs = _unflatten(args[:len(in_vars)], in_desc)
ret_inputs.append(tuple(x.clone(memory_format=torch.preserve_format) for x in args))
if self._return_inputs_states:
inputs_states.append(_unflatten(args[:len(in_vars)], in_desc))
outs.append(self.inner(*trace_inputs))
if self._return_inputs_states:
inputs_states[0] = (inputs_states[0], trace_inputs)
out_vars, _ = _flatten(outs)
if len(out_vars) == 1:
return out_vars[0]
else:
return tuple(out_vars)
graph, out = torch._C._create_graph_by_tracing(
wrapper,
in_vars + module_state,
_create_interpreter_name_lookup_fn(),
self.strict,
self._force_outplace,
)
if self._return_inputs:
return graph, outs[0], ret_inputs[0]
if self._return_inputs_states:
return graph, outs[0], inputs_states[0]
else:
return graph, outs[0]
def _clone_inputs(args):
def clone_input(a):
if a is None:
return None
elif isinstance(a, torch.Tensor):
# TODO: figure out one liner to .clone() and set requires_grad
v = a.detach().clone(memory_format=torch.preserve_format).requires_grad_(a.requires_grad)
if a.grad is not None:
v.grad = clone_input(v.grad)
return v
else:
return a.clone(memory_format=torch.preserve_format)
return function._nested_map(lambda x: isinstance(x, torch.Tensor),
clone_input, condition_msg="tensors")(args)
# This is purely for developer debugging. We are not going to advertise it.
_JIT_TIME = os.environ.get('PYTORCH_JIT_TIME', False) # CUDA-only timing
_JIT_DISABLE = os.environ.get('PYTORCH_JIT_DISABLE', False)
_JIT_STATS = os.environ.get('PYTORCH_JIT_STATS', False)
@contextlib.contextmanager
def _time(trace_name, name, time=True):
if (not _JIT_TIME and not time) or not torch.cuda.is_available():
yield
return
stream = torch.cuda.current_stream()
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
stream.record_event(start)
try:
yield
finally:
stream.record_event(end)
end.synchronize()
print("{} {} time: {} ms".format(trace_name, name, start.elapsed_time(end)))
def verify(model, args, loss_fn=torch.sum, devices=None):
"""
Verify that a JIT compiled model has the same behavior as its uncompiled
version along with its backwards pass. If your model returns multiple
outputs, you must also specify a `loss_fn` to produce a loss for which
the backwards will be computed.
This function has side-effects (e.g., it executes your model / saves and loads
parameters), so don't expect the model to come out exactly the same as what
you passed in.
Arguments:
model (compiled torch.nn.Module or function): the module/function to be
verified. The module/function definition MUST have been decorated with
`@torch.jit.compile`.
args (tuple or Tensor): the positional arguments to pass to the
compiled function/module to be verified. A non-tuple is assumed to
be a single positional argument to be passed to the model.
loss_fn (function, optional): the loss function to be applied to
the output of the model, before backwards is invoked. By default,
we assume that a model returns a single result, and we :func:`torch.sum`
before calling backwards; if this is inappropriate, you can pass your
own loss function. Note that if a model returns a tuple of results,
these are passed as separate positional arguments to `loss_fn`.
devices (iterable of device IDs, optional): the GPU devices which the
compiled module will be run on. This determines the RNG state we
must save when running both compiled and uncompiled versions of the model.
"""
# TODO: In principle, we track device information in our trace, so it
# should be possible to check if our execution actually obeyed the 'devices'
# the user provided.
# TODO: Consider adding a utility function to torch.jit to test
# for this case
if not isinstance(model, torch._C.CompiledFunction):
raise TypeError("Cannot verify an uncompiled module. Add @torch.jit.compile to compile it")
is_module = isinstance(model, Module)
if not isinstance(args, tuple):
args = (args,)
saved_args = _clone_inputs(args)
if is_module:
saved_state = copy.deepcopy(model.state_dict())
def run_fwd_bwd(args, force_trace=False, assert_compiled=False):
params = list(model.parameters()) if is_module else []
in_vars, _ = _flatten((args, params))
# We use a special API to reset the trace and compile it from scratch.
compiled_fn = model
if force_trace:
compiled_fn.clear_cache()
if assert_compiled:
hits = compiled_fn.hits
out = model(*args)
if assert_compiled and compiled_fn.hits == hits:
raise RuntimeError("failed to use the compiled function")
if not isinstance(out, tuple):
out = (out, )
if loss_fn == torch.sum and len(out) != 1:
raise ValueError(("Model returns {} outputs, but default loss function "
"(torch.sum) can only handle a single output").format(len(out)))
out_vars, _ = _flatten(out)
saved_outs = [v.detach().clone(memory_format=torch.preserve_format) for v in out_vars]
loss = loss_fn(*out)
grads = torch.autograd.grad([loss], in_vars)
# TODO: I'm not sure if the clone here is necessary but it is safer
saved_grads = [v.detach().clone(memory_format=torch.preserve_format) for v in grads]
return (saved_outs, saved_grads)
with torch.random.fork_rng(devices, _caller="torch.jit.verify"):
uncompiled_outs, uncompiled_grads = run_fwd_bwd(args, force_trace=True)
assert model.has_trace_for(*args)
if is_module:
model.load_state_dict(saved_state)
compiled_outs, compiled_grads = run_fwd_bwd(args, assert_compiled=True)
_verify_equal(uncompiled_outs, compiled_outs)
_verify_equal(uncompiled_grads, compiled_grads)
def _verify_equal(xs, ys):
for x, y in zip(xs, ys):
if x.sub(y).abs().max() > 1e-6:
raise RuntimeError("JIT and real computation mismatch")
def indent(s):
return '\n'.join(['\t' + line for line in s.splitlines()])
class TracingCheckError(Exception):
def __init__(self, graph_diff_error, tensor_compare_error, extra_msg=None):
self.message = 'Tracing failed sanity checks!\n'
if extra_msg is not None:
self.message += extra_msg + '\n'
if graph_diff_error is not None:
self.message += 'ERROR: Graphs differed across invocations!\n'
self.message += indent(graph_diff_error) + '\n'
if tensor_compare_error is not None:
self.message += 'ERROR: Tensor-valued Constant nodes differed in value ' \
'across invocations. This often indicates that the tracer has' \
' encountered untraceable code.\n'
self.message += indent(tensor_compare_error) + '\n'
super(TracingCheckError, self).__init__(self.message)
# Check the traced module against a set of user-provided validation inputs
@torch.no_grad()
def _check_trace(check_inputs, func, traced_func, check_tolerance, strict,
force_outplace, is_trace_module, _module_class):
# Note: tracing is independent of optimizations, which consume the trace
for inputs in check_inputs:
if isinstance(inputs, torch.Tensor):
inputs = (inputs,)
if is_trace_module:
copied_dict = {}
for name, data in inputs.items():
copied_dict[name] = _clone_inputs(data)
check_mod = torch.jit.trace_module(
func.__self__ if hasattr(func, '__self__') else func,
copied_dict,
check_trace=False,
strict=strict,
_force_outplace=force_outplace,
_module_class=_module_class,
_compilation_unit=torch._C.CompilationUnit(),
)
check_mod_func = check_mod._c._get_method(traced_func.name)
inputs = inputs[traced_func.name]
if isinstance(inputs, (torch.Tensor, dict)):
inputs = (inputs,)
else:
check_mod = torch.jit.trace(
func,
_clone_inputs(inputs),
check_trace=False,
strict=strict,
_force_outplace=force_outplace,
_module_class=_module_class,
)
check_mod_func = check_mod
def graph_diagnostic_info():
mod_canonicalized = torch._C._jit_pass_canonicalize(traced_func.graph)
torch._C._jit_pass_inline(mod_canonicalized)
torch._C._jit_pass_erase_shape_information(mod_canonicalized)
mod_str = str(mod_canonicalized)
mod_str = re.sub(r'___torch_mangle_[0-9]+\.', '', mod_str)
check_canonicalized = torch._C._jit_pass_canonicalize(check_mod_func.graph)
torch._C._jit_pass_inline(check_canonicalized)
torch._C._jit_pass_erase_shape_information(check_canonicalized)
check_str = str(check_canonicalized)
check_str = re.sub(r'___torch_mangle_[0-9]+\.', '', check_str)
graph_diff_errors = None
if mod_str != check_str:
import difflib
graph_diff = difflib.ndiff(mod_str.splitlines(True),
check_str.splitlines(True))
graph_diff_errors = 'Graph diff:\n' + indent(''.join(graph_diff)) + '\n'
for n_mod, n_check in zip(mod_canonicalized.nodes(), check_canonicalized.nodes()):
if str(n_mod) != str(n_check):
graph_diff_errors += 'First diverging operator:\n'
node_diff = difflib.ndiff(str(n_mod).splitlines(True),
str(n_check).splitlines(True))
source_printout = 'Node diff:\n' + indent(''.join(node_diff)) + '\n'
mod_stack = n_mod.sourceRange()
if mod_stack:
source_printout += 'Trace source location:\n' + indent(mod_stack) + '\n'
check_stack = n_check.sourceRange()
if check_stack:
source_printout += 'Check source location:\n' + indent(check_stack) + '\n'
graph_diff_errors += source_printout
break # For now, only print out the first pair of nodes that diverges
tensor_compare_errors = None
# Check Tensor-valued constant nodes
for n_mod, n_check in zip(mod_canonicalized.nodes(), check_canonicalized.nodes()):
if n_mod.kind() != n_check.kind():
break # Graphs have already diverged
if n_mod.kind() == 'prim::Constant' and not (n_mod.mustBeNone() or n_check.mustBeNone()):
if not n_mod.hasAttribute('value'):
continue
if n_mod.kindOf('value') != 't' or n_check.kindOf('value') != 't':
continue
mod_tensor_val = n_mod.t('value')
check_tensor_val = n_check.t('value')
try:
torch.testing.assert_allclose(mod_tensor_val, check_tensor_val)
except (RuntimeError, AssertionError) as e:
if tensor_compare_errors is None:
tensor_compare_errors = ''
tensor_compare_errors += 'Node:\n' + indent(str(n_mod)) + '\n'
compare_stack = n_mod.sourceRange()
if compare_stack:
tensor_compare_errors += 'Source Location:\n' + indent(compare_stack) + '\n'
tensor_compare_errors += 'Comparison exception: ' + indent(str(e))
break # For now, only print the first diverging pair
return graph_diff_errors, tensor_compare_errors
def wrap_retval(x):
return x if isinstance(x, tuple) else (x,)
def run_mod_and_filter_tensor_outputs(mod, inputs, running_what):
try:
outs = wrap_retval(mod(*_clone_inputs(inputs)))
outs = [out for out in outs if isinstance(out, torch.Tensor)]
return outs
except Exception as e:
raise TracingCheckError(*graph_diagnostic_info(),
extra_msg='Encountered an exception while running the ' + running_what +
' with test inputs.\nException:\n' + indent(str(e)))
has_warned = [False]
def maybe_warn_nondeterministic():
if has_warned[0]:
return
has_warned[0] = True
nondeterm_ops = [op for op in traced_func.graph.nodes() if op.isNondeterministic()]
if len(nondeterm_ops) > 0:
nondeterministic_ops_warning = "Trace had nondeterministic nodes. "
nondeterministic_ops_warning += "Did you forget call .eval() on your model? Nodes:\n"
nondeterministic_ops_warning += "\n".join([indent(str(op)) for op in nondeterm_ops][:20])
nondeterministic_ops_warning += "\nThis may cause errors in trace checking. To disable trace checking,"\
" pass check_trace=False to torch.jit.trace()"
warnings.warn(nondeterministic_ops_warning, category=TracerWarning, stacklevel=5)
def compare_outputs(original, reference, match_what):
all_ok = True
for i, (orig, ref) in enumerate(zip(original, reference)):
try:
if orig.is_quantized:
orig = orig.dequantize()
if ref.is_quantized:
ref = ref.dequantize()
torch.testing.assert_allclose(orig.double(), ref.double(), rtol=check_tolerance,
atol=torch.testing._get_default_tolerance(orig, ref)[1])
except AssertionError as e:
maybe_warn_nondeterministic()
warnings.warn('Output nr ' + str(i + 1) + '. of the traced function does not match '
'the corresponding output of the ' + match_what + '. Detailed error:\n' + str(e),
category=TracerWarning, stacklevel=4)
all_ok = False
return all_ok
traced_outs = run_mod_and_filter_tensor_outputs(traced_func, inputs, 'trace')
fn_outs = run_mod_and_filter_tensor_outputs(func, inputs, 'Python function')
if compare_outputs(traced_outs, fn_outs, 'Python function'):
check_outs = run_mod_and_filter_tensor_outputs(check_mod_func, inputs, 'repeated trace')
compare_outputs(traced_outs, check_outs, 'repeated trace')
diag_info = graph_diagnostic_info()
if any(info is not None for info in diag_info):
raise TracingCheckError(*diag_info)
class TracerWarning(Warning):
@staticmethod
def ignore_lib_warnings():
# We ignore warnings from all submodules excluding the JIT, because we need them e.g. for _check_trace
warnings.filterwarnings('ignore', category=TracerWarning, module='torch.(?!jit)')
# We ignore the tracer warnings coming form inside the library, because all our shape
# checks in nn will trigger them.
TracerWarning.ignore_lib_warnings()
torch._C._tracer_warn_use_python()
def make_tuple(example_inputs):
if isinstance(example_inputs, (torch.Tensor, dict)):
return (example_inputs,)
# done primarily so that weird iterables fail here and not pybind11 code
if not isinstance(example_inputs, tuple):
return tuple(example_inputs)
return example_inputs
def make_module(mod, _module_class, _compilation_unit):
if isinstance(mod, ScriptModule):
return mod
elif torch._jit_internal.module_has_exports(mod):
def make_stubs_from_exported_methods(mod):
exported = []
for name in dir(mod):
item = getattr(mod, name, None)
if torch._jit_internal.get_torchscript_modifier(item) is _jit_internal.FunctionModifiers.EXPORT:
exported.append(name)
stubs = []
for method in exported:
stubs.append(torch.jit._recursive.make_stub_from_method(mod, method))
return stubs
return torch.jit._recursive.create_script_module(mod, make_stubs_from_exported_methods, share_types=False)
else:
if _module_class is None:
_module_class = TopLevelTracedModule
return _module_class(mod, _compilation_unit=_compilation_unit)
def wrap_check_inputs(check_inputs):
if check_inputs is None:
return None
return [{'forward' : c} for c in check_inputs]
def trace(func,
example_inputs,
optimize=None,
check_trace=True,
check_inputs=None,
check_tolerance=1e-5,
strict=True,
_force_outplace=False,
_module_class=None,
_compilation_unit=_python_cu):
"""
Trace a function and return an executable or :class:`ScriptFunction`
that will be optimized using just-in-time compilation. Tracing is ideal for
code that operates only on ``Tensor``\\s and lists, dictionaries, and
tuples of ``Tensor``\\s.
Using `torch.jit.trace` and `torch.jit.trace_module`, you can turn an
existing module or Python function into a TorchScript
:class:`ScriptFunction` or :class:`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.
Warning:
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 :class:`ScriptModule`, operations that have different
behaviors in ``training`` and ``eval`` modes 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
:func:`scripting <torch.jit.script>` is 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.
Arguments:
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
``forward`` method is run and traced (see :func:`torch.jit.trace
<torch.jit.trace_module>` for details).
example_inputs (tuple): 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:
check_trace (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_inputs`` are 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.
strict (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 ``list``/``dict``)
and you are sure that the containuer you are using in your
problem is a ``constant`` structure and does not get used as
control flow (if, for) conditions.
Returns:
If `func` is `nn.Module` or ``forward`` of `nn.Module`, `trace` returns
a :class:`ScriptModule` object with a single ``forward`` method
containing the traced code. The returned `ScriptModule` will
have the same set of sub-modules and parameters as the original
``nn.Module``. If ``func`` is a standalone function, ``trace``
returns `ScriptFunction`.
Example (tracing a function):
.. testcode::
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)
"""
if not _enabled:
return func
if optimize is not None:
warnings.warn("`optimize` is deprecated and has no effect. Use `with torch.jit.optimized_execution() instead")
if isinstance(func, torch.jit.ScriptModule):
# it is hard to trace it because the forward method on ScriptModule is already defined, so it
# would result in an error.
warnings.warn('The input to trace is already a ScriptModule, tracing it is a no-op. Returning the object as is.')
return func
if isinstance(func, torch.nn.Module):
return trace_module(func, {'forward': example_inputs}, None,
check_trace, wrap_check_inputs(check_inputs),
check_tolerance, strict, _force_outplace, _module_class)
if (hasattr(func, '__self__') and isinstance(func.__self__, torch.nn.Module) and
func.__name__ == 'forward'):
return trace_module(func.__self__, {'forward': example_inputs}, None,
check_trace, wrap_check_inputs(check_inputs),
check_tolerance, strict, _force_outplace, _module_class)
# Special case for common case of passing a single Tensor
if isinstance(example_inputs, (torch.Tensor, dict)):
example_inputs = (example_inputs,)
# done primarily so that weird iterables fail here and not pybind11 code
elif not isinstance(example_inputs, tuple):
example_inputs = tuple(example_inputs)
var_lookup_fn = _create_interpreter_name_lookup_fn(0)
if (hasattr(func, '__self__') and isinstance(func.__self__, torch.nn.Module)):
raise AttributeError("trace doesn't support compiling individual module's functions.\n"
"Please use trace_module")
name = _qualified_name(func)
traced = torch._C._create_function_from_trace(name, func, example_inputs,
var_lookup_fn,
strict,
_force_outplace)
# Check the trace against new traces created from user-specified inputs
if check_trace:
if check_inputs is not None:
_check_trace(check_inputs, func, traced, check_tolerance, strict, _force_outplace, False, _module_class)
else:
_check_trace([example_inputs], func, traced, check_tolerance, strict, _force_outplace, False, _module_class)
return traced
_trace_module_map = None
def trace_module(mod,
inputs,
optimize=None,
check_trace=True,
check_inputs=None,
check_tolerance=1e-5,
strict=True,
_force_outplace=False,
_module_class=None,
_compilation_unit=_python_cu):
"""
Trace a module and return an executable :class:`ScriptModule` that will be optimized
using just-in-time compilation. When a module is passed to :func:`torch.jit.trace <torch.jit.trace>`, only
the ``forward`` method is run and traced. With ``trace_module``, you can specify a dictionary of
method names to example inputs to trace (see the ``example_inputs``) argument below.
See :func:`torch.jit.trace <torch.jit.trace>` for more information on tracing.
Arguments:
mod (torch.nn.Module): A ``torch.nn.Module`` containing methods whose names are
specified in ``inputs``. The given methods will be compiled
as a part of a single `ScriptModule`.
inputs (dict): A dict containing sample inputs indexed by method names in ``mod``.
The inputs will be passed to methods whose names correspond to inputs'
keys while tracing.
``{ 'forward' : example_forward_input, 'method2': example_method2_input}``
Keyword arguments:
check_trace (``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 dicts, optional): A list of dicts 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_inputs`` are 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.
Returns:
A :class:`ScriptModule` object with a single ``forward`` method containing the traced code.
When ``func`` is a ``torch.nn.Module``, the returned :class:`ScriptModule` will have the same set of
sub-modules and parameters as ``func``.
Example (tracing a module with multiple methods)::
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)
def weighted_kernel_sum(self, weight):
return weight * self.conv.weight
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)
# Trace specific methods on a module (specified in `inputs`), constructs
# a `ScriptModule` with `forward` and `weighted_kernel_sum` methods
inputs = {'forward' : example_forward_input, 'weighted_kernel_sum' : example_weight}
module = torch.jit.trace_module(n, inputs)
"""
if not _enabled:
return mod
if optimize is not None:
warnings.warn("`optimize` is deprecated and has no effect. Use `with torch.jit.optimized_execution() instead")
var_lookup_fn = _create_interpreter_name_lookup_fn(0)
if not isinstance(mod, torch.nn.Module):
raise AttributeError("expected torch.nn.Module as the first argument")
if not isinstance(inputs, dict):
raise AttributeError("expected a dictionary of (method_name, input) pairs")
old_module_map = torch.jit._trace_module_map
try:
torch.jit._trace_module_map = {}
def register_submods(mod, prefix):
for name, child in mod.named_children():
submod_qualname = prefix + '.' + name
torch.jit._trace_module_map[child] = submod_qualname
register_submods(child, submod_qualname)
torch.jit._trace_module_map['__module'] = mod
register_submods(mod, '__module')
module = make_module(mod, _module_class, _compilation_unit)
for method_name, example_inputs in inputs.items():
# this is needed since Module.__call__ sets up some extra tracing
func = mod if method_name == "forward" else getattr(mod, method_name)
example_inputs = make_tuple(example_inputs)
module._c._create_method_from_trace(method_name, func, example_inputs, var_lookup_fn, strict, _force_outplace)
check_trace_method = module._c._get_method(method_name)
# Check the trace against new traces created from user-specified inputs
if check_trace:
if check_inputs is not None:
_check_trace(check_inputs, func, check_trace_method,
check_tolerance, strict, _force_outplace, True, _module_class)
else:
_check_trace([inputs], func, check_trace_method,
check_tolerance, strict, _force_outplace, True, _module_class)
finally:
torch.jit._trace_module_map = old_module_map
return module
def fork(func, *args, **kwargs):
"""
Creates an asynchronous task executing `func` and a reference to the value
of the result of this execution. `fork` will return immediately,
so the return value of `func` may not have been computed yet. To force completion
of the task and access the return value invoke `torch.jit.wait` on the Future. `fork` invoked
with a `func` which returns `T` is typed as `torch.jit.Future[T]`. `fork` calls can be arbitrarily
nested, and may be invoked with positional and keyword arguments.
Asynchronous execution will only occur when run in TorchScript. If run in pure python,
`fork` will not execute in parallel. `fork` will also not execute in parallel when invoked
while tracing, however the `fork` and `wait` calls will be captured in the exported IR Graph.
Warning:
`fork` tasks will execute non-deterministicly. We recommend only spawning
parallel fork tasks for pure functions that do not modify their inputs,
module attributes, or global state.
Arguments:
func (callable or torch.nn.Module): A Python function or `torch.nn.Module`
that will be invoked. If executed in TorchScript, it will execute asynchronously,
otherwise it will not. Traced invocations of fork will be captured in the IR.
*args, **kwargs: arguments to invoke `func` with.
Returns:
`torch.jit.Future[T]`: a reference to the execution of `func`. The value `T`
can only be accessed by forcing completion of `func` through `torch.jit.wait`.
Example (fork a free function):
.. testcode::
import torch
from torch import Tensor
def foo(a : Tensor, b : int) -> Tensor:
return a + b
def bar(a):
fut : torch.jit.Future[Tensor] = torch.jit.fork(foo, a, b=2)
return torch.jit.wait(fut)
script_bar = torch.jit.script(bar)
input = torch.tensor(2)
# only the scripted version executes asynchronously
assert script_bar(input) == bar(input)
# trace is not run asynchronously, but fork is captured in IR
graph = torch.jit.trace(bar, (input,)).graph
assert "fork" in str(graph)
Example (fork a module method):
.. testcode::
import torch
from torch import Tensor
class SubMod(torch.nn.Module):
def forward(self, a: Tensor, b : int):
return a + b
class Mod(torch.nn.Module):
def __init__(self):
super(self).__init__()
self.mod = SubMod()
def forward(self, input):
fut = torch.jit.fork(self.mod, a, b=2)
return torch.jit.wait(fut)
input = torch.tensor(2)
mod = Mod()
assert mod(input) == torch.jit.script(mod).forward(input)
"""
return torch._C.fork(func, *args, **kwargs)
def wait(future):
"""
Forces completion of a `torch.jit.Future[T]` asynchronous task, returning the
result of the task. See :func:`~fork` for docs and examples.
Arguments:
func (torch.jit.Future[T]): an asynchronous task reference, created through `torch.jit.fork`
Returns:
`T`: the return value of the the completed task
"""
return torch._C.wait(future)
def freeze(mod, preserved_attrs : Optional[List[str]] = None):
r"""
Freezing a :class:`ScriptModule` will clone it and attempt to inline the cloned
module's submodules, parameters, and attributes as constants in the TorchScript IR Graph.
By default, `forward` will be preserved, as well as attributes & methods specified in
`preserved_attrs`. Additionally, any attribute that is modified within a preserved
method will be preserved.
Freezing currently only accepts ScriptModules that are in eval mode.
Arguments:
mod (:class:`ScriptModule`): a module to be frozen
preserved_attrs (Optional[List[str]]): a list of attributes to preserve in addition to the forward method.
Attributes modified in preserved methods will also be preserved.
Returns:
Frozen :class:`ScriptModule`.
Example (Freezing a simple module with a Parameter):
.. testcode::
import torch
class MyModule(torch.nn.Module):
def __init__(self, N, M):
super(MyModule, self).__init__()
self.weight = torch.nn.Parameter(torch.rand(N, M))
self.linear = torch.nn.Linear(N, M)
def forward(self, input):
output = self.weight.mm(input)
output = self.linear(output)
return output
scripted_module = torch.jit.script(MyModule(2, 3).eval())
frozen_module = torch.jit.freeze(scripted_module)
# parameters have been removed and inlined into the Graph as constants
assert len(list(frozen_module.named_parameters())) == 0
# See the compiled graph as Python code
print(frozen_module.code)
Example (Freezing a module with preserved attributes)
.. testcode::
import torch
class MyModule2(torch.nn.Module):
def __init__(self):
super(MyModule2, self).__init__()
self.modified_tensor = torch.tensor(10.)
self.version = 1
def forward(self, input):
self.modified_tensor += 1
return input + self.modified_tensor
scripted_module = torch.jit.script(MyModule2().eval())
frozen_module = torch.jit.freeze(scripted_module, preserved_attrs=["version"])
# we've manually preserved `version`, so it still exists on the frozen module and can be modified
assert frozen_module.version == 1
frozen_module.version = 2
# `modified_tensor` is detected as being mutated in the forward, so freezing preserves
# it to retain model semantics
assert frozen_module(torch.tensor(1)) == torch.tensor(12)
# now that we've run it once, the next result will be incremented by one
assert frozen_module(torch.tensor(1)) == torch.tensor(13)
Note:
If you're not sure why an attribute is not being inlined as a constant, you can run
`dump_alias_db` on frozen_module.forward.graph to see if freezing has detected the
attribute is being modified.
"""
if not isinstance(mod, ScriptModule):
raise RuntimeError("Freezing expects a ScriptModule as input. "
"Please use torch.jit.script or torch.jit.trace to script your 'nn.Module'.")
if mod.training:
raise RuntimeError("Freezing is currently only implemented for modules in eval mode. "
"Please call .eval() on your module before freezing.")
preserved_attrs = preserved_attrs if preserved_attrs is not None else []
out = RecursiveScriptModule(torch._C._freeze_module(mod._c, preserved_attrs))
RecursiveScriptModule._finalize_scriptmodule(out)
return out
class CompilationUnit(object):
def __init__(self, lang=None, _frames_up=0):
self._c = torch._C.CompilationUnit()
if lang is not None:
self.define(lang, _frames_up=_frames_up + 1)
def define(self, lang, rcb=None, _frames_up=0):
if not rcb:
rcb = _jit_internal.createResolutionCallbackFromFrame(_frames_up + 1)
self._c.define(lang, rcb)
def __getattr__(self, attr):
r = self._c.find_function(attr)
if r is None:
raise AttributeError("'CompilationUnit' has no attribute '{}'".format(attr))
return r
def _try_get_dispatched_fn(fn):
if not callable(fn):
return None
return _jit_internal.boolean_dispatched.get(fn)
def _try_get_overloaded_fn(mod, field):
return mod._overloads.get(field, None) if isinstance(mod, ScriptModule) else None
class ScriptWarning(Warning):
pass
@contextlib.contextmanager
def _disable_emit_hooks():
hooks = torch._C._jit_get_emit_hooks()
torch._C._jit_set_emit_hooks(None, None)
yield
torch._C._jit_set_emit_hooks(hooks[0], hooks[1])
def _disable_emit_hooks_decorator(_DecoratorContextManager): # noqa: F811
def __enter__(self):
self.hooks = torch._C._jit_get_emit_hooks()
torch._C._jit_set_emit_hooks(None, None)
def __exit__(self, *args):
torch._C._jit_set_emit_hooks(self.hooks[0], self.hooks[1])
# ScriptClasses must be new-style classes because we construct them using their
# __new__ method.
def _is_new_style_class(cls):
if hasattr(cls, '__class__'):
return ('__dict__' in dir(cls) or hasattr(cls, '__slots__'))
def whichmodule(obj):
"""Find the module an object belong to."""
module_name = getattr(obj, '__module__', None)
# Protect the iteration by using a list copy of sys.modules against dynamic
# modules that trigger imports of other modules upon calls to getattr.
for name, module in list(sys.modules.items()):
if name == '__main__' or module is None:
continue
try:
if _getattribute(module, name)[0] is obj:
return module_name
except AttributeError:
pass
return '__main__'
def _recursive_compile_class(obj, loc):
_qual_name = _qualified_name(obj)
# We're starting a new compilation, so update the error call stack in
# case it fails
error_stack = torch._C.CallStack(_qual_name, loc)
rcb = _jit_internal.createResolutionCallbackForClassMethods(obj)
_compile_and_register_class(obj, rcb, _qual_name)
def _compile_and_register_class(obj, rcb, qualified_name):
ast = get_jit_class_def(obj, obj.__name__)
_jit_script_class_compile(qualified_name, ast, rcb)
_add_script_class(obj, qualified_name)
def script(obj, optimize=None, _frames_up=0, _rcb=None):
r"""
Scripting a function or ``nn.Module`` will inspect the source code, compile
it as TorchScript code using the TorchScript compiler, and return a :class:`ScriptModule` or
:class:`ScriptFunction`. TorchScript itself is a subset of the Python language, so not all
features in Python work, but we provide enough functionality to compute on
tensors and do control-dependent operations. For a complete guide, see the
:ref:`language-reference`.
``torch.jit.script`` can be used as a function for modules and functions, and as a decorator
``@torch.jit.script`` for :ref:`torchscript-classes` and functions.
Arguments:
obj (callable, class, or ``nn.Module``): The ``nn.Module``, function, or class type to
compile.
Returns:
If ``obj`` is ``nn.Module``, ``script`` returns
a :class:`ScriptModule` object. The returned :class:`ScriptModule` will
have the same set of sub-modules and parameters as the
original ``nn.Module``. If ``obj`` is a standalone function,
a :class:`ScriptFunction` will be returned.
**Scripting a function**
The ``@torch.jit.script`` decorator will construct a :class:`ScriptFunction`
by compiling the body of the function.
Example (scripting a function):
.. testcode::
import torch
@torch.jit.script
def foo(x, y):
if x.max() > y.max():
r = x
else:
r = y
return r
print(type(foo)) # torch.jit.ScriptFuncion
# See the compiled graph as Python code
print(foo.code)
# Call the function using the TorchScript interpreter
foo(torch.ones(2, 2), torch.ones(2, 2))
.. testoutput::
:hide:
...
**Scripting an nn.Module**
Scripting an ``nn.Module`` by default will compile the ``forward`` method and recursively
compile any methods, submodules, and functions called by ``forward``. If a ``nn.Module`` only uses
features supported in TorchScript, no changes to the original module code should be necessary. ``script``
will construct :class:`ScriptModule` that has copies of the attributes, parameters, and methods of
the original module.
Example (scripting a simple module with a Parameter):
.. testcode::
import torch
class MyModule(torch.nn.Module):
def __init__(self, N, M):
super(MyModule, self).__init__()
# This parameter will be copied to the new ScriptModule
self.weight = torch.nn.Parameter(torch.rand(N, M))
# When this submodule is used, it will be compiled
self.linear = torch.nn.Linear(N, M)
def forward(self, input):
output = self.weight.mv(input)
# This calls the `forward` method of the `nn.Linear` module, which will
# cause the `self.linear` submodule to be compiled to a `ScriptModule` here
output = self.linear(output)
return output
scripted_module = torch.jit.script(MyModule(2, 3))
Example (scripting a module with traced submodules):
.. testcode::
import torch
import torch.nn as nn
import torch.nn.functional as F
class MyModule(nn.Module):
def __init__(self):
super(MyModule, self).__init__()
# torch.jit.trace produces a ScriptModule's conv1 and conv2
self.conv1 = torch.jit.trace(nn.Conv2d(1, 20, 5), torch.rand(1, 1, 16, 16))
self.conv2 = torch.jit.trace(nn.Conv2d(20, 20, 5), torch.rand(1, 20, 16, 16))
def forward(self, input):
input = F.relu(self.conv1(input))
input = F.relu(self.conv2(input))
return input
scripted_module = torch.jit.script(MyModule())
To compile a method other than ``forward`` (and recursively compile anything it calls), add
the :func:`@torch.jit.export <torch.jit.export>` decorator to the method. To opt out of compilation
use :func:`@torch.jit.ignore <torch.jit.ignore>` or :func:`@torch.jit.unused <torch.jit.unused>`.
Example (an exported and ignored method in a module)::
import torch
import torch.nn as nn
class MyModule(nn.Module):
def __init__(self):
super(MyModule, self).__init__()
@torch.jit.export
def some_entry_point(self, input):
return input + 10
@torch.jit.ignore
def python_only_fn(self, input):
# This function won't be compiled, so any
# Python APIs can be used
import pdb
pdb.set_trace()
def forward(self, input):
if self.training:
self.python_only_fn(input)
return input * 99
scripted_module = torch.jit.script(MyModule())
print(scripted_module.some_entry_point(torch.randn(2, 2)))
print(scripted_module(torch.randn(2, 2)))
"""
if not _enabled:
return obj
if optimize is not None:
warnings.warn("`optimize` is deprecated and has no effect. Use `with torch.jit.optimized_execution() instead")
if isinstance(obj, ScriptModule):
return obj
if isinstance(obj, torch.nn.Module):
return torch.jit._recursive.create_script_module(obj, torch.jit._recursive.infer_methods_to_compile)
qualified_name = _qualified_name(obj)
if inspect.isclass(obj):
# If this type is a `nn.Module` subclass, they probably meant to pass
# an instance instead of a Module
if issubclass(obj, torch.nn.Module):
raise RuntimeError("Type '{}' cannot be compiled since it inherits"
" from nn.Module,"
" pass an instance instead".format(obj))
if not _is_new_style_class(obj):
raise RuntimeError("TorchScript classes must be new-style classes. "
"Please inherit from 'object'.")
if len(obj.mro()) > 2:
raise RuntimeError("TorchScript classes does not support inheritance yet. "
"Please directly inherit from 'object'.")
if _rcb is None:
_rcb = _jit_internal.createResolutionCallbackFromFrame(_frames_up + 1)
_compile_and_register_class(obj, _rcb, qualified_name)
return obj
else:
# this is a decorated fn, and we need to the underlying fn and its rcb
if hasattr(obj, "__script_if_tracing_wrapper"):
obj = obj.__original_fn
_rcb = _jit_internal.createResolutionCallbackFromClosure(obj)
_check_directly_compile_overloaded(obj)
maybe_already_compiled_fn = _try_get_jit_cached_function(obj)
if maybe_already_compiled_fn:
return maybe_already_compiled_fn
ast = get_jit_def(obj, obj.__name__)
if _rcb is None:
_rcb = _jit_internal.createResolutionCallbackFromClosure(obj)
fn = torch._C._jit_script_compile(qualified_name, ast, _rcb, get_default_args(obj))
# Forward docstrings
fn.__doc__ = obj.__doc__
_set_jit_function_cache(obj, fn)
return fn
def interface(obj):
if not inspect.isclass(obj):
raise RuntimeError("interface must be applied to a class")
if not _is_new_style_class(obj):
raise RuntimeError("TorchScript interfaces must inherit from 'object'")
# Expected MRO is:
# User module
# torch.nn.modules.module.Module
# object
is_module_interface = issubclass(obj, torch.nn.Module) and len(obj.mro()) == 3
if not is_module_interface and len(obj.mro()) > 2:
raise RuntimeError("TorchScript interface does not support inheritance yet. "
"Please directly inherit from 'object' or 'nn.Module'.")
qualified_name = _qualified_name(obj)
rcb = _jit_internal.createResolutionCallbackFromFrame(1)
# if this type is a `nn.Module` subclass, generate an module interface type
# instead of a class interface type, an module interface type only compile
# the user provided methods as part of the interface
ast = get_jit_class_def(obj, obj.__name__)
torch._C._jit_script_interface_compile(qualified_name, ast, rcb, is_module_interface)
obj.__torch_script_interface__ = True
return obj
def _script_if_tracing(fn):
"""
Compiles ``fn`` when it is first called during tracing. ``torch.jit.script``
has a non-negligible start up time when it is first called due to
lazy-initializations of many compiler builtins. Therefore you should not use
it in library code. However, you may want to have parts of your library work
in tracing even if they use control flow. In these cases, you should use
``@torch.jit._script_if_tracing`` to substitute for
``torch.jit.script``.
"""
@functools.wraps(fn)
def wrapper(*args, **kwargs):
if not is_tracing():
# Not tracing, don't do anything
return fn(*args, **kwargs)
compiled_fn = script(wrapper.__original_fn)
return compiled_fn(*args, **kwargs)
wrapper.__original_fn = fn
wrapper.__script_if_tracing_wrapper = True
return wrapper
def script_method(fn):
if not _enabled:
return fn
# NOTE: we need to traverse two frames here because the meta-class frame
# for ScriptModule will be present, as opposed to invoking @script on a
# a function or invoking define() on a CompilationUnit.
# The stack will look like:
#
# 0. createResolutionCallback()
# 1. script_method()
# 2. ScriptModule metaclass frame
# 3. Surrounding scope
#
# createResolutionCallback internally adds 1 to get us to the scope of this
# function (the calling function). Adding 2 gets us to the proper surrounding scope.
_rcb = _jit_internal.createResolutionCallbackFromFrame(frames_up=2)
ast = get_jit_def(fn, fn.__name__, self_name="ScriptModule")
return ScriptMethodStub(_rcb, ast, fn)
# These OrderedDictWrapper classes replace the actual OrderedDicts in
# module with versions that get/set properties inside of Module.
# This allows us to reuse most of nn.Module while still storing the
# data in C++.
# Each OrderedDict needs to support:
# x not in view
# x in view
# view[name] = ...
# view.values()
# del view[name]
# view.items()
# view.keys()
# len(view)
class OrderedDictWrapper(object):
def __init__(self, _c):
self._c = _c
def keys(self):
return [k for k, v in self.items()]
def values(self):
return [v for k, v in self.items()]
def __len__(self):
return len(self.values())
def __delitem__(self, k):
raise RuntimeError("cannot delete methods or parameters of a script module")
def items(self):
return self._c.items()
def __setitem__(self, k, v):
if k not in self:
raise RuntimeError("Can't add a new parameter after ScriptModule construction."
" Tried to add '{}".format(k))
self._c.setattr(k, v)
def __contains__(self, k):
return self._c.contains(k)
def __getitem__(self, k):
if k not in self:
raise KeyError(k)
return self._c.getattr(k)
class OrderedModuleDict(OrderedDictWrapper):
def __init__(self, module, python_dict):
super(OrderedModuleDict, self).__init__(torch._C.ModuleDict(module))
# contains _both_ script modules and non-script python-only modules
# because script modules are subclassed in python and the
# C++ Module class will not hold references to them,
# to ensure that you always get the same python value here
# we store it in the python dict as well
self._python_modules = python_dict
def items(self):
r = self._python_modules.items()
return r
def __contains__(self, k):
return k in self._python_modules
def __setitem__(self, k, v):
# Cases where sub-module can be re-assigned after ScriptModule construction
# 1. If the attr is an module interface type, it's guaranteed that the module is
# not inlined in the graph, so it's safe to swap a new ScriptModule in.
# 2. if the new value if a ScriptModule with the same JIT type, IR won't change
# and it's legit to swap a new module in.
# In these two cases we allow swapping a new scripted module and update the
# corresponding python module dict to keep sync.
# Note: the value to be swapped in has to be ScriptModule instead of nn.Module,
# otherwise it's illegal and we throw error.
if isinstance(v, ScriptModule):
self._c.setattr(k, v)
self._python_modules[k] = v
else:
raise RuntimeError("Cannot re-assign modules in a ScriptModule with non-scripted "
"module, tried to replace existing module '{}': {}".format(k, v))
def __getitem__(self, k):
return self._python_modules[k]
# For each user-defined class that subclasses ScriptModule, this meta-class:
# (1) finds all the methods annotated with @script_method in a ScriptModule and
# removes them from the class attributes
# (2) puts a wrapper around the class's __init__ method to recusively compile
# all of the script_methods with the module after the original __init__ has
# run. This has to occur after the user-defined __init__ so that submodules and
# parameters are initialized _before_ the script compiler resolve references to
# `self.param` or `self.module`.
class ScriptMeta(type):
def __init__(cls, name, bases, attrs): # noqa: B902
# Aggregate all the ScriptMethods and constants from superclasses
cls._methods = {}
cls._constants_set = set(getattr(cls, '__constants__', ()))
for base in reversed(bases):
for k, v in getattr(base, '_methods', {}).items():
cls._methods[k] = v
base_constants = getattr(base, '_constants_set', set())
cls._constants_set = cls._constants_set.union(base_constants)
# find all the script methods of the current class
for k, v in sorted(attrs.items()):
if isinstance(v, ScriptMethodStub):
delattr(cls, k)
cls._methods[v.original_method.__name__] = v
if getattr(cls, '_disable_script_meta', False):
# We leave built-in ScriptModule types alone, since this metaclass
# is only for compiling user classes that inherit from
# ScriptModule.
return super(ScriptMeta, cls).__init__(name, bases, attrs)
original_init = getattr(cls, '__init__', lambda self: None)
@functools.wraps(original_init)
def init_then_script(self, *args, **kwargs):
original_init(self, *args, **kwargs)
if type(self) == cls:
def make_stubs(module):
cls = type(module)
return [v for k, v in sorted(cls._methods.items())]
self.__dict__["_actual_script_module"] = torch.jit._recursive.create_script_module(self, make_stubs)
# Delete the Python attributes that now shadow the ScriptModule
# ones, so that __getattr__ and __setattr__ will properly find
# the scripted versions.
concrete_type = self._actual_script_module._concrete_type
for name in concrete_type.get_attributes():
delattr(self, name)
for name, _ in concrete_type.get_modules():
delattr(self, name)
for name in ("_parameters", "_buffers", "_modules"):
delattr(self, name)
cls.__init__ = init_then_script
return super(ScriptMeta, cls).__init__(name, bases, attrs)
if _enabled:
# this is a Python 'non-data descriptor' that causes the first access
# to ScriptModule's forward to lookup the forward method and stash
# it in the objects dict. Due to the standard rules for attribute lookup
# subsequent lookups will just directly return the previously looked up method.
# This is necessary because nn.Module defines forward as a method. If we
# did nothing __getattr__ would not be called. Instead we'd get nn.Module.forward
# which always throws an exception.
class _CachedForward(object):
def __get__(self, obj, cls):
return self.__getattr__('forward')
class ScriptModule(with_metaclass(ScriptMeta, Module)):
"""
``ScriptModule``s wrap a C++ ``torch::jit::Module``. ``ScriptModule``s
contain methods, attributes, parameters, and
constants. These can be accessed the same as on a normal ``nn.Module``.
"""
def __init__(self):
super(ScriptModule, self).__init__()
forward = _CachedForward()
def __getattr__(self, attr):
if "_actual_script_module" not in self.__dict__:
return super(ScriptModule, self).__getattr__(attr)
return getattr(self._actual_script_module, attr)
def __setattr__(self, attr, value):
if "_actual_script_module" not in self.__dict__:
# Unwrap torch.jit.Attribute into a regular setattr + recording
# the provided type in __annotations__.
#
# This ensures that if we use the attr again in `__init__`, it
# will look like the actual value, not an instance of Attribute.
if isinstance(value, Attribute):
# NB: Ensure that we set __annotations__ on the specific
# class in question, and not on a superclass (which would
# be wrong wrong wrong!).
# See also https://github.com/pytorch/pytorch/issues/39463
if "__annotations__" not in self.__class__.__dict__:
self.__class__.__annotations__ = {}
self.__annotations__[attr] = value.type
value = value.value
return super(ScriptModule, self).__setattr__(attr, value)
setattr(self._actual_script_module, attr, value)
def define(self, src):
if "_actual_script_module" in self.__dict__:
# If we have completed initialization, just defer to the
# backing RecursiveScriptModule to eagerly compile the provided
# source.
return self._actual_script_module.define(src)
# Otherwise, we are still in the object's __init__.
# In that case, add `src` as a stub to be compiled.
#
# We use frames_up=1 to get to the proper surrounding scope. The stack
# will look like:
# 0. createResolutionCallback
# 1. define()
# 2. surrounding scope.
#
# createResolutionCallback internally adds 1 to get us to our frame, then
# we add 1 to get to the proper surrounding scope.
rcb = _jit_internal.createResolutionCallbackFromFrame(frames_up=1)
ast = torch._C._parse_source_def(src)
self._methods[ast.name().name] = ScriptMethodStub(rcb, ast, None)
def _replicate_for_data_parallel(self):
return self._actual_script_module._replicate_for_data_parallel()
class RecursiveScriptModule(ScriptModule):
# XXX: RecursiveScriptModule inherits from ScriptModule for the sole
# reason that it retains the existing isinstance(ScriptModule)
# behavior.
r"""
The core data structure in TorchScript is the ``ScriptModule``. It is an
analogue of torch's ``nn.Module`` and represents an entire model as a tree of
submodules. Like normal modules, each individual module in a ``ScriptModule`` can
have submodules, parameters, and methods. In ``nn.Module``\s methods are implemented
as Python functions, but in ``ScriptModule``\s methods are implemented as
TorchScript functions, a statically-typed subset of Python that contains all
of PyTorch's built-in Tensor operations. This difference allows your
``ScriptModule``\s code to run without the need for a Python interpreter.
``ScriptModule``\s should not be created manually, instead use
either :func:`tracing <torch.jit.trace>` or :func:`scripting <torch.jit.script>`.
Tracing and scripting can be applied incrementally and :ref:`composed as necessary <Types>`.
* Tracing records the tensor operations as executed with a set of example inputs and uses these
operations to construct a computation graph. You can use the full dynamic behavior of Python with tracing,
but values other than Tensors and control flow aren't captured in the graph.
* Scripting inspects the Python code of the model
and compiles it to TorchScript. Scripting allows the use of many `types`_ of values and supports dynamic control flow.
Many, but not all features of Python are supported by the compiler, so changes to the source code may be necessary.
"""
_disable_script_meta = True
def __init__(self, cpp_module):
self.__dict__['_initializing'] = True
self._c = cpp_module
super(RecursiveScriptModule, self).__init__()
# Delete the 'training' attribute set up by `Module.__init__`. It
# will get set on the underlying cpp module, so we delete it here
# to avoid this version shadowing the cpp module version.
delattr(self, 'training')
@staticmethod
def _construct(cpp_module, init_fn):
"""
Construct a RecursiveScriptModule that's ready for use. PyTorch
code should use this to construct a RecursiveScriptModule instead
of instead of calling `__init__` directly, as it makes sure the
object is properly finalized (and in the future we may take
control of how the RecursiveScriptModule instance is created).
Arguments:
cpp_module: The C++ Module that will hold the actual state of
this RecursiveScriptModule instance.
init_fn: Lambda that initializes the RecursiveScriptModule passed to it.
"""
script_module = RecursiveScriptModule(cpp_module)
init_fn(script_module)
# Finalize the ScriptModule: replace the nn.Module state with our
# custom implementations and flip the _initializing bit.
RecursiveScriptModule._finalize_scriptmodule(script_module)
return script_module
@staticmethod
def _finalize_scriptmodule(script_module):
script_module._parameters = OrderedDictWrapper(torch._C.ParameterDict(script_module._c))
script_module._buffers = OrderedDictWrapper(torch._C.BufferDict(script_module._c))
script_module._modules = OrderedModuleDict(script_module._c, script_module._modules)
script_module._initializing = False
def _reconstruct(self, cpp_module):
"""
Re-construct an instance of RecursiveScriptModule using an instance of a C++ module.
Arguments:
cpp_module: The C++ module that this RecursiveScriptModule will be rebuilt around.
"""
self.__init__(cpp_module)
# Copy the concrete type from the C++ module to this ScriptModule.
self._concrete_type = torch._C.ConcreteModuleType.from_jit_type(self._c._type())
# Copy submodules from the C++ module to this ScriptModule.
modules = {}
for name, cpp_module in torch._C.ModuleDict(self._c).items():
modules[name] = wrap_cpp_module(cpp_module)
self._modules = OrderedModuleDict(self._c, modules)
# Copy parameters and buffers.
self._parameters = OrderedDictWrapper(torch._C.ParameterDict(self._c))
self._buffers = OrderedDictWrapper(torch._C.BufferDict(self._c))
# Get rid of the functions from the old C++ module.
self.__dict__ = {k: v for k, v in self.__dict__.items() if not isinstance(v, torch._C.ScriptMethod)}
self.__dict__['_initializing'] = False
@property
def graph(self):
r"""
Returns a string representation of the internal graph for the
``forward`` method. See `Interpreting Graphs`_ for details.
"""
return self.forward.graph
@property
def inlined_graph(self):
r"""
Returns a string representation of the internal graph for the
``forward`` method. This graph will be preprocessed to inline all function and method calls.
See `Interpreting Graphs`_ for details.
"""
return self.forward.inlined_graph
@property
def code(self):
r"""
Returns a pretty-printed representation (as valid Python syntax) of
the internal graph for the ``forward`` method. See `Inspecting Code`_
for details.
"""
return self.forward.code
@property
def code_with_constants(self):
r"""
Returns a tuple of:
[0] a pretty-printed representation (as valid Python syntax) of
the internal graph for the ``forward`` method. See `code`.
[1] a ConstMap following the CONSTANT.cN format of the output in [0].
The indices in the [0] output are keys to the underlying constant's values.
See `Inspecting Code`_ for details.
"""
r = self.forward.code_with_constants
return (r[0], ConstMap(r[1]))
def save(self, *args, **kwargs):
r"""
save(f, _extra_files=ExtraFilesMap{})
See :func:`torch.jit.save <torch.jit.save>` for details.
"""
return self._c.save(*args, **kwargs)
def _save_for_lite_interpreter(self, *args, **kwargs):
r"""
_save_for_lite_interpreter(f)
Add (or update) the bytecode session to the script model. The updated model is used
in lite interpreter for mobile applications.
Arguments:
f: a string containing a file name.
_extra_files: Map from filename to contents which will be stored as part of 'f'.
"""
return self._c._save_for_mobile(*args, **kwargs)
def _save_to_buffer_for_lite_interpreter(self, *args, **kwargs):
return self._c._save_to_buffer_for_mobile(*args, **kwargs)
def save_to_buffer(self, *args, **kwargs):
return self._c.save_to_buffer(*args, **kwargs)
def get_debug_state(self, *args, **kwargs):
return self._c.get_debug_state()
def extra_repr(self):
return 'original_name={}'.format(self.original_name)
def graph_for(self, *args, **kwargs):
return self.forward.graph_for(*args, **kwargs)
@property
def original_name(self):
if type(self) == str(self._c._type().name()):
return ''
return str(self._c._type().name())
def define(self, src):
# We use frames_up=1 to get to the proper surrounding scope. The stack
# will look like:
# 0. createResolutionCallback
# 1. define()
# 2. surrounding scope.
#
# createResolutionCallback internally adds 1 to get us to our frame, then
# we add 1 to get to the proper surrounding scope.
rcb = _jit_internal.createResolutionCallbackFromFrame(frames_up=1)
self._c._define(self._concrete_type, src, rcb)
def __getattr__(self, attr):
if '_initializing' not in self.__dict__:
raise RuntimeError("ScriptModule has not been initialized, did you forget to call super's init?")
if self._initializing:
return super(RecursiveScriptModule, self).__getattr__(attr)
# _modules check is before hasattr since modules are included as attributes in _c,
# but we want to get the python wrapper from _modules instead of the raw _c object.
if attr in self._modules:
return self._modules[attr]
elif self._c.hasattr(attr):
return self._c.getattr(attr)
elif self._c._has_method(attr):
script_method = self._c._get_method(attr)
# cache method so future calls do not go through __getattr__
# to improve invocation performance
self.__dict__[attr] = script_method
return script_method
return super(RecursiveScriptModule, self).__getattr__(attr)
def __setattr__(self, attr, value):
if self._initializing:
return super(RecursiveScriptModule, self).__setattr__(attr, value)
if attr in self._modules:
self._modules[attr] = value
elif self._c.hasattr(attr):
self._c.setattr(attr, value)
elif hasattr(self, "_concrete_type") and attr in self._concrete_type.get_constants().keys():
# TODO: we don't have _concrete_type set after load(), and in general we lose constant information.
# We should encode constants as class type attributes (or something) so it persists across save/load.
raise AttributeError("Cannot mutate TorchScript constant value: '{}'. Value: '{}'".format(attr, value))
else:
# We allow setting Python attributes on the ScriptModule, for
# when people want to stash some convenience info on it.
# TODO: it's possible that the following is confusing:
# s = torch.jit.script(...)
# s.python_attr = ...
# s.save() <--- this doesn't have `python_attr`
# It's fairly trivial to save enough info to warn in this case.
return super(RecursiveScriptModule, self).__setattr__(attr, value)
def __getstate__(self):
raise pickle.PickleError(
"ScriptModules cannot be deepcopied using copy.deepcopy or saved using torch.save. " +
"Mixed serialization of script and non-script modules is not supported. " +
"For purely script modules use my_script_module.save(<filename>) instead.")
def __copy__(self):
return torch.jit._recursive.wrap_cpp_module(copy.copy(self._c))
def __deepcopy__(self, memo):
return torch.jit._recursive.wrap_cpp_module(copy.deepcopy(self._c, memo))
# Python magic methods do method lookups on an object's class type, instead of looking up
# the method defines on the class instance. In order to continue to expose the magic methods
# of builtin-containers (ModuleList, Sequential, ModuleDict) to python we
# define magic methods here as a shim to the correct attribute.
def forward_magic_method(self, method_name, *args, **kwargs):
self_method = getattr(self, method_name)
if getattr(self_method, "__func__", None) == getattr(RecursiveScriptModule, method_name):
raise NotImplementedError()
return self_method(*args, **kwargs)
def __iter__(self):
return self.forward_magic_method("__iter__")
def __getitem__(self, idx):
return self.forward_magic_method("__getitem__", idx)
def __len__(self):
return self.forward_magic_method("__len__")
def __contains__(self, key):
return self.forward_magic_method("__contains__", key)
# dir is defined by the base nn.Module, so instead of throwing if
# it is not overriden, we call into the nn.Module __dir__ method
def __dir__(self):
self_method = self.__dir__
if self_method.__func__ == get_function_from_type(RecursiveScriptModule, "__dir__"):
return super(RecursiveScriptModule, self).__dir__()
return self_method()
# to resolve bool(value), python looks if __bool__ is defined then __iter__
# is defined then returns true for classes. because __iter__() on this
# class throws if it isn't overriden, we define __bool__ to preserve default behavior
def __bool__(self):
self_method = self.__bool__
if self_method.__func__ == get_function_from_type(RecursiveScriptModule, "__bool__"):
return True
return self_method()
def _replicate_for_data_parallel(self):
# we have to initialize ScriptModule properly so that
# it works with pybind11
def init_fn(script_module):
# Don't do anything here, we'll initialize the ScriptModule below
return
return RecursiveScriptModule._construct(self._c._replicate_for_data_parallel(), init_fn)
# Need to copy all RecursiveScriptModule methods to ScriptModule.
#
# This is because `super(MyScriptModule, self).foo()` does not use
# `__getattr__` to look up `foo`. So we need to make each method available on
# the ScriptModule manually.
for name, item in RecursiveScriptModule.__dict__.items():
if not callable(item) and not isinstance(item, property):
continue
if name.startswith('__') or hasattr(ScriptModule, name):
continue
# We can copy over the implementation wholesale because besides the
# `super()` thing above, ScriptModule behaves exactly like
# RecursiveScriptModule
setattr(ScriptModule, name, item)
def _get_methods(cls):
import inspect
# In Python 3 unbound methods are functions, but in Python 2 they are methods
return inspect.getmembers(cls, predicate=lambda x: inspect.isfunction(x) or inspect.ismethod(x))
_compiled_methods_whitelist = {
'forward', 'register_buffer', 'register_parameter', 'add_module',
'_apply', 'apply', 'cuda', 'cpu', 'to', 'type', 'float', 'double', 'half',
'state_dict', '_save_to_state_dict', 'load_state_dict',
'_load_from_state_dict', '_named_members', 'parameters', 'named_parameters',
'buffers', 'named_buffers', 'children', 'named_children', 'modules',
'named_modules', 'zero_grad', 'share_memory', '_get_name', 'extra_repr',
'_slow_forward', '_tracing_name', 'eval', 'train',
}
def _make_fail(name):
def fail(self, *args, **kwargs):
raise RuntimeError(name + " is not supported on ScriptModules")
return fail
for name, method in _get_methods(torch.nn.Module):
if name.startswith('__'):
continue
if name not in RecursiveScriptModule.__dict__ and name not in _compiled_methods_whitelist:
setattr(RecursiveScriptModule, method.__name__, _make_fail(name))
else:
# TODO MAKE SURE THAT DISABLING WORKS
class ScriptModule(torch.nn.Module):
def __init__(self):
super(ScriptModule, self).__init__()
class TracedModule(ScriptModule):
_disable_script_meta = True
def __init__(self, orig, id_set=None, _compilation_unit=None):
# XXX: orig can be a nn.Module or a function!
super(TracedModule, self).__init__()
assert(isinstance(orig, torch.nn.Module))
# Copy a subset of `orig` to a temporary nn.Module.
# This is a way to customize what will actually get compiled by create_script_module
id_set = set()
# This allows us to preserve the original module's qualified name by defining a new
# type with the attribute _jit_override_qualname. In torch._jit_internal._qualified_name
# we have a special case that will look up this attribute to override whatever qualname
# we would get from the python type system
class QualnameWrapper(torch.nn.Module):
pass
QualnameWrapper._jit_override_qualname = torch._jit_internal._qualified_name(type(orig))
tmp_module = QualnameWrapper()
def check_unique(param):
if param in id_set:
raise ValueError("TracedModules don't support parameter sharing between modules")
id_set.add(param)
tmp_module.training = orig.training
for name, param in orig._parameters.items():
if param is not None:
tmp_module._parameters[name] = param
check_unique(param)
for name, buf in orig._buffers.items():
if buf is not None:
tmp_module._buffers[name] = buf
check_unique(buf)
for name, val in orig.__dict__.items():
if torch._C._jit_is_script_object(val) and name not in orig._parameters and name not in orig._buffers:
setattr(tmp_module, name, val)
if orig._backward_hooks:
raise ValueError("Modules that have backward hooks assigned can't be compiled: " + str(orig))
for name, submodule in orig._modules.items():
tmp_module._modules[name] = make_module(submodule, TracedModule, _compilation_unit=None)
script_module = torch.jit._recursive.create_script_module(tmp_module, lambda module: (), share_types=False)
self.__dict__['_name'] = type(orig).__name__
self.__dict__['_actual_script_module'] = script_module
for name in ("_parameters", "_buffers", "_modules"):
delattr(self, name)
def forward(self, *args, **kwargs):
raise RuntimeError('Trace submodules cannot be called.')
def __getattr__(self, attr):
if "_actual_script_module" not in self.__dict__:
return super(TracedModule, self).__getattr__(attr)
return getattr(self._actual_script_module, attr)
def __setattr__(self, attr, value):
if "_actual_script_module" not in self.__dict__:
return super(TracedModule, self).__setattr__(attr, value)
setattr(self._actual_script_module, attr, value)
def _get_name(self):
return self._name
def extra_repr(self):
return 'original_name={}'.format(self._name)
if _enabled:
class TopLevelTracedModule(TracedModule):
forward = _CachedForward()
def _reconstruct(self, cpp_module):
"""
Re-construct an instance of TopLevelTracedModule using an instance of a C++ module.
Arguments:
cpp_module: The C++ module that this TopLevelTracedModule will be rebuilt around.
"""
self.__dict__['_actual_script_module']._reconstruct(cpp_module)
def is_scripting():
r"""
Function that returns True when in compilation and False otherwise. This
is useful especially with the @unused decorator to leave code in your
model that is not yet TorchScript compatible.
.. testcode::
import torch
@torch.jit.unused
def unsupported_linear_op(x):
return x
def linear(x):
if not torch.jit.is_scripting():
return torch.linear(x)
else:
return unsupported_linear_op(x)
"""
return False
def is_tracing():
"""
Returns ``True`` in tracing (if a function is called during the tracing of
code with ``torch.jit.trace``) and ``False`` otherwise.
"""
return torch._C._is_tracing
def _unwrap_optional(x):
assert x is not None, "Unwrapping null optional"
return x
_register_builtin(_unwrap_optional, 'aten::_unwrap_optional')
_register_builtin(_wait, 'aten::wait')
_register_builtin(wait, 'aten::wait')
_register_builtin(is_scripting, 'aten::is_scripting')
# Caching: we currently cache compilation of free functions and overloaded functions.
# To cache free functions we hold a weak ref to the function object and
# map to the compiled fn's qualified name.
# To cache overloaded functions we hold a weak ref to the function obj and
# map to all of its overloaded compiled fns.
# In the future we could consider caching more types of objects so that
# aliasing is preserved across separate compilations of the same object.
_jit_caching_layer = weakref.WeakKeyDictionary()
_jit_function_overload_caching = weakref.WeakKeyDictionary()
def _try_get_jit_cached_overloads(key):
qual_names = _jit_function_overload_caching.get(key, None)
if qual_names:
return [_python_cu.find_function(qual_name) for qual_name in qual_names]
else:
return None
def _set_jit_overload_cache(key, compiled_fns):
_jit_function_overload_caching[key] = [fn.qualified_name for fn in compiled_fns]
def _try_get_jit_cached_function(key):
if getattr(key, "__disable_jit_function_caching__", False) is True:
return None
qual_name = _jit_caching_layer.get(key, None)
if qual_name:
return _python_cu.find_function(qual_name)
else:
return None
def _set_jit_function_cache(key, value):
# only free functions currently supported
assert isinstance(value, torch.jit.ScriptFunction)
_jit_caching_layer[key] = value.qualified_name
# qualified_name => ScriptClass mapping
_script_classes = {}
def _add_script_class(cls, name):
cls.__torch_script_class__ = True
global _script_classes
_script_classes[name] = cls
def _get_script_class(name):
global _script_classes
if name not in _script_classes:
return None
return _script_classes[name]
# overloads are registered in _jit_internal and compiled here so that _overload
# can be used in nn/functional.py without an import cycle
def _check_overload_defaults(impl_defaults, overload_defaults, loc):
for name, overload_value in overload_defaults.items():
if name not in impl_defaults or impl_defaults[name] != overload_value:
raise torch.jit.frontend.FrontendError(
loc, "Default parameters on overloads do not affect the runtime so they "
"must equal to the default parameter on the implementation function. Found on "
"parameter {name}".format(name=name))
def _compile_function_with_overload(overload_fn, qual_name, impl_fn):
overload_decl = torch.jit.get_jit_def(overload_fn, overload_fn.__name__).decl()
overload_signature = torch.jit.annotations.get_signature(overload_fn, None, None, inspect.ismethod(overload_fn))
impl_ast = torch.jit.get_jit_def(impl_fn, impl_fn.__name__)
overload_defaults = get_default_args(overload_fn)
implementation_defaults = get_default_args(impl_fn)
_rcb = _jit_internal.createResolutionCallbackFromClosure(impl_fn)
_check_overload_defaults(implementation_defaults, overload_defaults, overload_decl.range())
fn = torch._C._jit_script_compile_overload(qual_name, overload_decl, impl_ast, _rcb,
implementation_defaults, overload_signature)
return fn
def _get_overloads(obj):
# check for cached compiled fns
existing_compiled_fns = _try_get_jit_cached_overloads(obj)
qual_name = _qualified_name(obj)
uncompiled_overloads = _jit_internal._get_fn_overloads(qual_name)
if uncompiled_overloads is None:
return existing_compiled_fns
compiled_fns = []
for overload_fn in uncompiled_overloads:
compiled_fns.append(_compile_function_with_overload(overload_fn, qual_name, obj))
if existing_compiled_fns:
compiled_fns = existing_compiled_fns + compiled_fns
# cache compilation, remove information stored to do compilation
_set_jit_overload_cache(obj, compiled_fns)
_jit_internal._clear_fn_overloads(qual_name)
return compiled_fns
def _check_directly_compile_overloaded(obj):
qual_name = _qualified_name(obj)
if _jit_internal._get_fn_overloads(qual_name) or _try_get_jit_cached_overloads(obj):
raise RuntimeError("Function {} cannot be directly compiled because it"
" is overloaded. It must be used in a context of a function"
" where its inputs can determine which overload to call.".format(qual_name))
# torch.jit.Error
Error = torch._C.JITException
set_module(Error, "torch.jit")
# This is not perfect but works in common cases
Error.__name__ = "Error"
Error.__qualname__ = "Error"
def _get_named_tuple_properties(obj):
assert issubclass(obj, tuple) and hasattr(obj, '_fields')
fields = list(obj._fields)
annotations = []
has_annotations = hasattr(obj, '__annotations__')
for field in fields:
if has_annotations and field in obj.__annotations__:
the_type = torch.jit.annotations.ann_to_type(obj.__annotations__[field], _jit_internal.fake_range())
annotations.append(the_type)
else:
annotations.append(torch._C.TensorType.get())
return type(obj).__name__, fields, annotations
def _create_named_tuple(t, unqual_name, field_names):
TupleType = collections.namedtuple(unqual_name, field_names)
return TupleType(*t)
class _disable_tracing(object):
def __enter__(self):
self.state = torch._C._get_tracing_state()
torch._C._set_tracing_state(None)
def __exit__(self, *args):
torch._C._set_tracing_state(self.state)
self.state = None
# for use in python if using annotate
def annotate(the_type, the_value):
# noop in python
return the_value
last_executed_optimized_graph = torch._C._last_executed_optimized_graph
def _graph_for(self, *args, **kwargs):
self(*args, **kwargs)
return last_executed_optimized_graph()
torch._C.ScriptMethod.graph_for = _graph_for
torch._C.ScriptFunction.graph_for = _graph_for
ScriptFunction = torch._C.ScriptFunction
ScriptFunction.__doc__ = """
Functionally equivalent to a :class:`ScriptModule`, but represents a single
function and does not have any attributes or Parameters.
"""
set_module(ScriptFunction, "torch.jit")
if not torch._C._jit_init():
raise RuntimeError("JIT initialization failed")
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