import torch._C
from torch.autograd import Variable, function
from torch.serialization import validate_cuda_device
from torch.nn import Module, ModuleList, Parameter, Sequential
from torch.jit.frontend import get_jit_class_def, get_jit_def, get_default_args
import torch.backends.cudnn as cudnn
import torch.jit.annotations
import torch._jit_internal as _jit_internal
from torch._six import with_metaclass, get_function_from_type, \
string_classes
from torch._jit_internal import ignore # noqa: F401
from ..nn.modules.utils import _single, _pair, _triple, _quadruple, \
_list_with_default
import torch.testing
import math
from collections import OrderedDict, namedtuple
import textwrap
import sys
import warnings
import weakref
import types
import contextlib
import os
import functools
import copy
import collections
import inspect
import pickle
if sys.version_info[0] > 2:
import pathlib
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
Future = torch._C.Future
_fork = torch._C.fork
_wait = torch._C.wait
@contextlib.contextmanager
def scope(scope_name):
tracing_state = torch._C._get_tracing_state()
if tracing_state:
tracing_state.push_scope(scope_name)
try:
yield
finally:
if tracing_state:
tracing_state.pop_scope()
DEFAULT_EXTRA_FILES_MAP = torch._C.ExtraFilesMap()
[docs]def load(f, map_location=None, _extra_files=DEFAULT_EXTRA_FILES_MAP):
r"""
Load a ``ScriptModule`` previously saved with :func:`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.
However, storages can be dynamically remapped to an alternative set of devices
using the `map_location` argument. Comparing to :func:`torch.load`, `map_location`
in this function is simplified, which only accepts a string (e.g., 'cpu', 'cuda:0'),
or torch.device (e.g., torch.device('cpu'))
Arguments:
f: a file-like object (has to implement read, readline, tell, and seek),
or a string containing a file name
map_location: can a string (e.g., 'cpu', 'cuda:0'), a device (e.g.,
torch.device('cpu'))
_extra_files: map from 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 ``ScriptModule`` object.
Example: ::
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
torch.jit.load(buffer, map_location=torch.device('cpu'))
# Load all tensors onto CPU, using a string
torch.jit.load(buffer, map_location='cpu')
# Load with extra files.
files = {'metadata.json' : ''}
torch.jit.load('scriptmodule.pt', _extra_files = files)
print (files['metadata.json'])
"""
m = ScriptModule()
def module_lookup(names):
curr = m
for name in names:
if not hasattr(curr, name):
setattr(curr, name, ScriptModule())
curr = getattr(curr, name)
return curr._c
if isinstance(f, string_classes):
if not os.path.exists(f):
raise ValueError("The provided filename {} does not exist".format(f))
if isinstance(map_location, string_classes):
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)
if isinstance(f, str) or \
(sys.version_info[0] == 2 and isinstance(f, unicode)) or \
(sys.version_info[0] == 3 and isinstance(f, pathlib.Path)):
torch._C.import_ir_module(module_lookup, f, map_location, _extra_files)
else:
torch._C.import_ir_module_from_buffer(module_lookup, f.read(), map_location, _extra_files)
return m
[docs]def save(m, f, _extra_files=DEFAULT_EXTRA_FILES_MAP):
"""
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 ``torch.jit.load(filename)``.
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 ``torch.jit.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 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'
.. warning::
If you are using Python 2, ``torch.save`` does NOT support ``StringIO.StringIO``
as a valid file-like object. This is because the write method should return
the number of bytes written; ``StringIO.write()`` does not do this.
Please use something like ``io.BytesIO`` instead.
Example: ::
m = torch.jit.ScriptModule()
# Save to file
torch.jit.save(m, '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 \
(sys.version_info[0] == 2 and isinstance(f, unicode)) or \
(sys.version_info[0] == 3 and 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 get_trace_graph(f, args=(), kwargs=None, _force_outplace=False, return_inputs=False):
"""
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.
>>> trace, out = jit.trace(nn.LSTMCell(), (input, hidden))
>>> print(trace)
"""
if kwargs is None:
kwargs = {}
if not isinstance(args, tuple):
args = (args,)
return LegacyTracedModule(f, _force_outplace, return_inputs)(*args, **kwargs)
def _unique_state_dict(module, keep_vars=False):
# since Parameter.data 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.data
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 ''
for k, v in f_globals.items():
if isinstance(v, torch.Tensor) and var is v:
return k if k != 'self' else ''
return ''
return _get_interpreter_name_for_var
class LegacyTracedModule(Module):
def __init__(self, inner, force_outplace=False, return_inputs=False):
super(LegacyTracedModule, 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._force_outplace = force_outplace
self._return_inputs = return_inputs
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())
trace, all_trace_inputs = torch._C._tracer_enter(*(in_vars + module_state))
ret_inputs = tuple(x.clone() for x in all_trace_inputs)
torch._C._tracer_set_force_outplace(self._force_outplace)
torch._C._tracer_set_get_unique_name_fn(_create_interpreter_name_lookup_fn())
try:
trace_inputs = _unflatten(all_trace_inputs[:len(in_vars)], in_desc)
out = self.inner(*trace_inputs)
out_vars, _ = _flatten(out)
torch._C._tracer_exit(tuple(out_vars))
except Exception:
torch._C._tracer_abandon()
raise
if self._return_inputs:
return trace, out, ret_inputs
else:
return trace, out
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 = Variable(a.data.clone(), requires_grad=a.requires_grad)
if a.grad is not None:
v.grad = clone_input(v.grad)
return v
else:
return a.clone()
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_DUMP = os.environ.get('PYTORCH_JIT_DUMP', False)
_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)
def _dump_trace(trace_name, pass_name, input_key, trace):
if not _JIT_DUMP:
return
import torch.contrib._graph_vis as graph_vis
filename = "{}_{}".format(trace_name, pass_name)
# TODO: Also paste out the backtrace when the trace was compiled
# (and maybe also when it was run?)
with open(filename + ".ir", "w") as f:
f.write("Input key: {}\n\n{}".format(input_key, str(trace)))
graph_vis.write(trace.graph(), filename + ".html")
@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.data.clone() 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.data.clone() 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, executor_options, module, check_tolerance, force_outplace):
# Note: tracing is independent of optimizations, which consume the trace
executor_options['optimize'] = False
for inputs in check_inputs:
if isinstance(inputs, torch.Tensor):
inputs = (inputs,)
check_mod = torch.jit.trace(
func,
_clone_inputs(inputs),
check_trace=False,
_force_outplace=force_outplace,
**executor_options)
def graph_diagnostic_info():
mod_canonicalized = torch._C._jit_pass_canonicalize(module.graph)
torch._C._jit_pass_erase_shape_information(mod_canonicalized)
check_canonicalized = torch._C._jit_pass_canonicalize(check_mod.graph)
torch._C._jit_pass_erase_shape_information(check_canonicalized)
graph_diff_errors = None
if str(mod_canonicalized) != str(check_canonicalized):
import difflib
graph_diff = difflib.ndiff(str(mod_canonicalized).splitlines(True),
str(check_canonicalized).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.getSourceLocation()
if mod_stack:
source_printout += 'Trace source location:\n' + indent(mod_stack) + '\n'
check_stack = n_check.getSourceLocation()
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 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.getSourceLocation()
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 module.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:
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(module, 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, 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()
[docs]def trace(func,
example_inputs,
optimize=True,
check_trace=True,
check_inputs=None,
check_tolerance=1e-5,
_force_outplace=False,
_module_class=None):
"""
Trace a function and return an executable ``ScriptModule`` that will be optimized
using just-in-time compilation.
.. 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). 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``.
arguments and returns to ``func`` must be tensors
or (possibly nested) tuples that
contain tensors.
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:
optimize (bool, optional): whether or not to apply optimizations. Default: ``True``.
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.
Returns:
A ``ScriptModule`` object with a single ``forward()`` method containing the traced code.
When ``func`` is a ``torch.nn.Module``, the returned ``ScriptModule`` will have the same set of
sub-modules and parameters as ``func``.
Example::
def f(x):
return x * 2
traced_f = torch.jit.trace(f, torch.rand(1))
"""
if not _enabled:
return func
executor_options = {'optimize': bool(optimize)}
# 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 isinstance(func, torch.nn.Module):
if _module_class is None:
_module_class = TopLevelTracedModule
traced = _module_class(func, **executor_options)
traced._c._create_method_from_trace('forward', func, example_inputs,
var_lookup_fn, _force_outplace)
else:
name = getattr(func, '__name__', 'forward')
if name == '<lambda>':
name = '_lambda' # make name a valid identifier
traced = torch._C._create_function_from_trace(name, func, example_inputs,
var_lookup_fn,
_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, executor_options, traced, check_tolerance, _force_outplace)
else:
_check_trace([example_inputs], func, executor_options, traced, check_tolerance, _force_outplace)
return traced
class CompilationUnit(object):
def __init__(self, lang=None, optimize=True, _frames_up=0):
self._c = torch._C.CompilationUnit()
self._c.set_optimized(optimize)
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.createResolutionCallback(_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 _import(self, src, constants):
""" test import logic for single function, use only for testing """
src = "op_version_set = 0\n{}".format(src)
torch._C._jit_import_functions(self._c, src, constants, None)
return self
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
def _try_compile_weak_script(fn):
entry = _jit_internal.compiled_weak_fns.get(fn)
if entry is None:
return None
if entry["status"] == _jit_internal.COMPILATION_PENDING:
compiled_fn = torch.jit.script(fn, True, 0, entry["rcb"])
del entry["rcb"]
_jit_internal.compiled_weak_fns[fn]["compiled_fn"] = compiled_fn
entry["status"] = _jit_internal.COMPILED
return compiled_fn
else:
return entry["compiled_fn"]
# 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__'
# Retrieves a fully-qualified name (module hierarchy + classname) for a given obj.
def _qualified_name(obj):
name = obj.__name__
module_name = obj.__module__
# The Python docs are very clear that `__module__` can be None, but I can't
# figure out when it actually would be.
if module_name is None:
raise RuntimeError("Could not get qualified name for class '{}': "
"__module__ can't be None.".format(name))
# if getattr(sys.modules[module_name], name) is not obj:
# raise RuntimeError("Could not get qualified name for class '{}': "
# "the attr {} on module {} is not the the class".format(name, name, module_name))
# __main__ is a builtin module, so rewrite it to "__torch__".
if module_name == "__main__":
module_name = "__torch__"
else:
# Everything else gets a "__torch__" prefix to avoid name collisions
# with the names of user values.
module_name = "__torch__." + module_name
if "." in name:
raise RuntimeError("Could not get qualified name for class '{}': "
"'{}' is not a valid identifier".format(name, name))
return module_name + "." + name
def script(obj, optimize=True, _frames_up=0, _rcb=None):
if not _enabled:
return obj
if _rcb is None:
_rcb = _jit_internal.createResolutionCallback(_frames_up + 1)
if inspect.isclass(obj):
if not _is_new_style_class(obj):
raise RuntimeError("TorchScript classes must be new-style classes. Please inherit from 'object'")
name = _qualified_name(obj)
ast = get_jit_class_def(obj, name)
_jit_script_class_compile(ast, _rcb)
_add_script_class(obj, name)
return obj
else:
ast = get_jit_def(obj)
fn = torch._C._jit_script_compile(ast, _rcb, get_default_args(obj))
# Forward docstrings
fn.__doc__ = obj.__doc__
return fn
ScriptMethodStub = namedtuple('ScriptMethodStub', ('resolution_callback', 'def_', 'original_method'))
def script_method(fn, _rcb=None):
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.
if _rcb is None:
_rcb = _jit_internal.createResolutionCallback(frames_up=2)
ast = get_jit_def(fn, self_name="ScriptModule")
return ScriptMethodStub(_rcb, ast, fn)
def _try_get_weak_module(mod):
"""
Get the WeakScriptModuleProxy corresponding to mod if it exists
"""
if not isinstance(mod, Module):
return None
return _jit_internal.weak_modules.get(mod)
def _try_get_ignored_op(fn):
if not callable(fn):
return False
if hasattr(fn, '__func__'):
fn = fn.__func__
return fn in _jit_internal.ignored_fns
def _is_weak_type(cls):
"""
Check if a type has been annotated with `weak_module`
"""
return cls in _jit_internal.weak_types
# These OrderedDictWrapper classes replace the actual OrderedDicts in
# module with versions that get/set properties inside of script::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, module):
self.module = module
def keys(self):
return [k for k, v in self.items()]
def values(self):
return [v for k, v in self.items()]
def __delitem__(self, k):
raise RuntimeError("cannot delete methods or parameters of a script module")
def items(self):
raise NotImplementedError
def __contains__(self, k):
raise NotImplementedError
def __getitem__(self, k):
raise NotImplementedError
def __setitem__(self, k, v):
raise NotImplementedError
class OrderedModuleDict(OrderedDictWrapper):
def __init__(self, module):
super(OrderedModuleDict, self).__init__(module)
# contains _both_ script modules and non-script python-only modules
# because script modules are subclassed in python and the
# C++ script::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 = OrderedDict()
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):
if k in self._python_modules:
raise RuntimeError("cannot re-assign modules in a ScriptModule")
if isinstance(v, ScriptModule):
self.module._register_module(k, v._c)
self._python_modules[k] = v
def __getitem__(self, k):
return self._python_modules[k]
class OrderedParameterDict(OrderedDictWrapper):
def __init__(self, module):
super(OrderedParameterDict, self).__init__(module)
def items(self):
return [(name, param) for name, param in self.module._get_parameters()]
def __setitem__(self, k, v):
self.module._register_parameter(k, v, False)
def __contains__(self, k):
return self.module._has_parameter(k)
def __getitem__(self, k):
if k not in self:
raise KeyError(k)
return self.module._get_parameter(k)
class OrderedBufferDict(OrderedDictWrapper):
def __init__(self, module):
super(OrderedBufferDict, self).__init__(module)
def items(self):
return [(name, param) for name, _, param in
self.module._get_attributes() if isinstance(param, torch.Tensor)]
def __setitem__(self, k, v):
self.module._register_buffer(k, v)
def __contains__(self, k):
return self.module._has_buffer(k)
def __getitem__(self, k):
if k not in self:
raise KeyError(k)
return self.module._get_buffer(k)
# base types that can be constants
# in addition, tuples and lists of these base types are also considered constants
# If you edit this list, then you also need to edit the handlers in
# ConstantValue in jit/script/init.cpp
_constant_types = (bool, float, int, str, type(None), types.FunctionType, torch.device, torch.layout, torch.dtype)
def _get_valid_constant(attr, v):
if isinstance(v, _constant_types):
return v
elif isinstance(v, tuple) or isinstance(v, list):
return tuple(_get_valid_constant(attr, x) for x in v)
constants = ", ".join(typ.__name__ for typ in _constant_types)
raise TypeError(textwrap.dedent("""
'{}' object for attribute '{}' is not a valid constant.
Valid constants are:
1. a nn.ModuleList
2. a value of type {{{}}}
3. a list or tuple of (2)
""".format(type(v).__name__, attr, constants)))
def _create_methods_from_stubs(self, stubs):
defs = [m.def_ for m in stubs]
rcbs = [m.resolution_callback for m in stubs]
defaults = [get_default_args(m.original_method) for m in stubs]
self._c._create_methods(self, defs, rcbs, defaults)
# 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, and
# (2) puts a wrapper around the class's __init__ method to register
# 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):
# this has to inherit from pybind11's metaclass otherwise we get
# issues because ScriptModule inherits from torch._C.ScriptModule,
# a pybind11 type
def __init__(cls, name, bases, attrs):
# find all the script methods
cls._original_methods = {}
methods = []
for k, v in sorted(attrs.items()):
if isinstance(v, ScriptMethodStub):
delattr(cls, k)
methods.append(v)
cls._original_methods[v.original_method.__name__] = v.original_method
# after the user's __init__ register all the script methods
# with the module
original_init = getattr(cls, '__init__', lambda self: None)
super_constants = getattr(super(cls), '_constants_set', set())
cls._constants_set = set(getattr(cls, '__constants__', ())).union(super_constants)
cls._overloads = dict(getattr(cls, '__overloads__', {}))
@functools.wraps(original_init)
def init_then_register(self, *args, **kwargs):
original_init(self, *args, **kwargs)
_create_methods_from_stubs(self, methods)
cls.__init__ = init_then_register
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)):
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
ScriptModules code to run without the need for a Python interpreter.
``ScriptModule``\s be created in two ways:
**Tracing:**
Using ``torch.jit.trace``, you can turn an existing module or Python
function into a TorchScript program. You must provide example inputs,
and we run the function, recording the operations performed on all the tensors. We turn the resulting recording
into a TorchScript method that is installed as the ``forward`` method of a
``ScriptModule``. This module also contains any parameters that the original
module had as well.
Example (tracing a function)::
import torch
def foo(x, y):
return 2 * x + y
traced_foo = torch.jit.trace(foo, (torch.rand(3), torch.rand(3)))
.. note::
Tracing a function will construct a ``ScriptModule`` with a single
``forward`` method that implements the function. The resulting
``ScriptModule`` has no parameters or attributes.
Example (tracing an existing module)::
import torch
import torchvision
traced_net = torch.jit.trace(torchvision.models.resnet18(),
torch.rand(1, 3, 224, 224))
.. note::
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 ``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 scripting is a better
choice.
**Scripting:**
You can write TorchScript code directly using Python syntax. You do this
using the ``@torch.jit.script`` decorator (for functions) or
``@torch.jit.script_method`` decorator (for methods) on subclasses of
``ScriptModule``. With this decorator the body of the annotated function is
directly translated into TorchScript. 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.
Example (scripting a function)::
import torch
@torch.jit.script
def foo(x, y):
if x.max() > y.max():
r = x
else:
r = y
return r
.. note::
A ``@torch.jit.script`` decorator will construct a ``ScriptModule`` with a single
``forward`` method that implements the function. The resulting
``ScriptModule`` has no parameters or attributes.
Example (scripting a simple module with a Parameter)::
import torch
class MyModule(torch.jit.ScriptModule):
def __init__(self, N, M):
super(MyModule, self).__init__()
self.weight = torch.nn.Parameter(torch.rand(N, M))
@torch.jit.script_method
def forward(self, input):
return self.weight.mv(input)
Example (scripting a module with traced submodules)::
import torch
import torch.nn as nn
import torch.nn.functional as F
class MyScriptModule(torch.jit.ScriptModule):
def __init__(self):
super(MyScriptModule, 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))
@torch.jit.script_method
def forward(self, input):
input = F.relu(self.conv1(input))
input = F.relu(self.conv2(input))
return input
"""
def __init__(self, optimize=True):
self.__dict__['_c'] = torch._C.ScriptModule()
Module.__init__(self)
self._c._set_optimized(optimize)
self._parameters = OrderedParameterDict(self._c)
self._buffers = OrderedBufferDict(self._c)
self._modules = OrderedModuleDict(self._c)
@property
def graph(self):
return self.forward.graph
@property
def code(self):
return self.forward.code
def save(self, *args, **kwargs):
return self._c.save(*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()
forward = _CachedForward()
def __getattr__(self, attr):
if '_c' not in self.__dict__:
raise RuntimeError("ScriptModule has not been initialized, did you forget to call super's init?")
if self._c._has_method(attr):
if attr in self.__class__._original_methods:
original_method = self.__class__._original_methods[attr]
script_method = self._c._get_method(attr)
script_method = functools.wraps(original_method)(script_method)
else:
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
if self._c._has_attribute(attr):
return self._c._get_attribute(attr)
return Module.__getattr__(self, attr)
def __setattr__(self, attr, value):
if attr not in self._constants_set:
if isinstance(value, Module) and _is_weak_type(type(value)):
# Compile weak script module
value = _make_strong(value)
if attr == 'training':
if self._c._has_buffer('training'):
self.__dict__['training'] = value
self._c._get_buffer('training').fill_(int(value))
return
if isinstance(value, Attribute):
the_type = torch.jit.annotations.ann_to_type(value.type)
try:
self._c._register_attribute(attr, the_type, value.value)
except RuntimeError:
raise RuntimeError("Could not register attribute '{}' of type '{}' for a value of type '{}'"
.format(attr, value.type, type(value.value)))
return
return super(ScriptModule, self).__setattr__(attr, value)
if hasattr(self, attr):
raise RuntimeError("attempting to re-assign constant '{}'".format(attr))
def conv_module_to_const(module_value):
if not isinstance(module_value, (ModuleList, Sequential)):
return module_value
for i in range(len(module_value)):
module_value[i] = conv_module_to_const(module_value[i])
if isinstance(module_value, Sequential):
return _ConstSequential(module_value)
else:
return _ConstModuleList(module_value)
if isinstance(value, (ModuleList, Sequential)):
# special case for list of modules. Modules need to be registered with their
# parent module. To do this, we create a ConstModuleList, which is itself a module, that
# contains each of these modules as submodules. The ConstModuleList then
# is set as an attribute of the parent module.
super(ScriptModule, self).__setattr__(attr, conv_module_to_const(value))
else:
super(ScriptModule, self).__setattr__(attr, _get_valid_constant(attr, value))
def __dir__(self):
return sorted(Module.__dir__(self) + self._method_names())
def define(self, lang):
# 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.createResolutionCallback(frames_up=1)
self._c._define(self, lang, rcb)
def copy(self):
m = ScriptModule()
def module_lookup(names):
curr = m
for name in names:
if not hasattr(curr, name):
setattr(curr, name, ScriptModule())
curr = getattr(curr, name)
return curr._c
self._c._copy_into(module_lookup, {}, [])
return m
def __getstate__(self):
raise pickle.PickleError(
"ScriptModules cannot be 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 graph_for(self, *args, **kwargs):
return self.forward.graph_for(*args, **kwargs)
class WeakScriptModuleProxy(ScriptModule):
def __init__(self, original, stubs):
# Guards behavior of __setattr__ and __getattr__ so ScriptModule
# __init__ can run correctly
self.__dict__['_initialized'] = False
super(WeakScriptModuleProxy, self).__init__()
self.__dict__["_original"] = weakref.ref(original)
# Copy Parameters / Modules / Buffers
for name in dir(original):
item = getattr(original, name)
if item is None and name in original._parameters:
# XXX: treat None value simply as module attributes instead of adding them to the parameter list
# TODO: need to handle this more generally when non-tensor attributes added to module
object.__setattr__(self, name, item)
elif isinstance(item, Parameter) or (isinstance(item, Module) and item is not self):
ScriptModule.__setattr__(self, name, item)
for name in original._buffers:
if original._buffers[name] is None:
object.__setattr__(self, name, None)
else:
self.register_buffer(name, original._buffers[name])
# Copy constants
self.__dict__["_constants_set"] = set(getattr(original, "__constants__", []))
# Copy overloads
self.__dict__["_overloads"] = dict(getattr(original, "__overloads__", {}))
self.__dict__["_initialized"] = True
_create_methods_from_stubs(self, stubs)
def __getattr__(self, attr):
# Try to get the attribute directly, if that fails, fall back to the
# weak module itself
try:
return ScriptModule.__getattr__(self, attr)
except AttributeError:
if self.__dict__["_initialized"]:
return getattr(self.__dict__["_original"](), attr)
else:
# Only fall back to original once __init__() is done
raise AttributeError("Weak module has no attribute '{}'"
.format(attr))
def __setattr__(self, attr, value):
# Once constructed, no new properties can be set
if not self.__dict__["_initialized"]:
# If constructing, don't fall back to original module
return ScriptModule.__setattr__(self, attr, value)
if hasattr(self, attr):
return ScriptModule.__setattr__(self, attr, value)
else:
raise AttributeError("Cannot set new attribute '{}' on "
"weak script module once it has been "
"created".format(attr))
else:
[docs] class ScriptModule(torch.nn.Module):
def __init__(self, optimize=True):
super(ScriptModule, self).__init__()
def _get_weak_stubs(cls):
"""
Calls script_method for each method on the type of the object passed in and
returns the generated ScriptMethodStubs
"""
stubs = []
for name in dir(cls):
func = get_function_from_type(cls, name)
if func in _jit_internal.weak_script_methods:
entry = _jit_internal.weak_script_methods[func]
stub = script_method(entry["original_method"], entry["rcb"])
stubs.append(stub)
return stubs
def _make_strong(mod):
"""
Converts a weak module into a subclass of ScriptModule
"""
if mod in _jit_internal.weak_modules:
return _jit_internal.weak_modules[mod]
stubs = _jit_internal.weak_types.get(type(mod))["method_stubs"]
if stubs is None:
# Generate stubs and and store on weak_types in case this type is
# used again
stubs = _get_weak_stubs(type(mod))
_jit_internal.weak_types[type(mod)]["method_stubs"] = stubs
# Create proxy with stubs
proxy = WeakScriptModuleProxy(mod, stubs)
_jit_internal.weak_modules[mod] = proxy
return proxy
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', '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 ScriptModule.__dict__ and name not in _compiled_methods_whitelist:
setattr(ScriptModule, method.__name__, _make_fail(name))
class TracedModule(ScriptModule):
__frozen = False
def __init__(self, orig, id_set=None, optimize=True):
# XXX: orig can be a nn.Module or a function!
super(TracedModule, self).__init__(optimize=optimize)
if id_set is None:
id_set = set()
assert(isinstance(orig, torch.nn.Module))
self._name = 'TracedModule[' + type(orig).__name__ + ']'
def check_unique(param):
if param in id_set:
raise ValueError("TracedModules don't support parameter sharing between modules")
id_set.add(param)
self.training = orig.training
for name, param in orig._parameters.items():
if param is not None:
self._parameters[name] = param
check_unique(param)
for name, buf in orig._buffers.items():
if buf is not None:
self._buffers[name] = buf
check_unique(buf)
if orig._backward_hooks or orig._forward_hooks or orig._forward_pre_hooks:
raise ValueError("Modules that have hooks assigned can't be compiled")
for name, submodule in orig._modules.items():
if isinstance(submodule, ScriptModule) and not isinstance(submodule, TracedModule):
self._modules[name] = submodule.copy()
else:
self._modules[name] = TracedModule(submodule, id_set, optimize=optimize)
self._freeze()
def forward(self, *args, **kwargs):
raise RuntimeError('Trace submodules cannot be called.')
def _freeze(self):
self.__frozen = True
def _get_name(self):
return self._name
def __setattr__(self, attr, value):
if not self.__frozen or hasattr(self, attr):
return super(TracedModule, self).__setattr__(attr, value)
raise RuntimeError("Cannot set new properties on a traced module.")
class TopLevelTracedModule(TracedModule):
forward = _CachedForward()
class _ConstModuleList(ScriptModule):
def __init__(self, modules):
super(_ConstModuleList, self).__init__()
for i, module in enumerate(modules):
if _is_weak_type(type(module)):
module = _make_strong(module)
self.add_module(str(i), module)
def __getitem__(self, idx):
if isinstance(idx, slice):
return _ConstModuleList(list(self._modules.values())[idx])
else:
if not (-len(self) <= idx < len(self)):
raise IndexError('index {} is out of range'.format(idx))
if idx < 0:
idx += len(self)
return self._modules[str(idx)]
def __len__(self):
return len(self._modules)
def __iter__(self):
return iter(self._modules.values())
def __dir__(self):
keys = super(_ConstModuleList, self).__dir__()
keys = [key for key in keys if not key.isdigit()]
return keys
class _ConstSequential(_ConstModuleList):
__constants__ = ['mods']
def __init__(self, mods):
super(_ConstSequential, self).__init__(mods._modules.values())
# we define the forward method via self.define rather than
# making it a direct class member (with a @script) annotation
# because, in optimized runtime environments where only .pyc files
# are shipped, we cant retrieve the source code.
# TODO: find a workaround for this and remove this hack
self.define("""
def forward(self, input):
for m in self:
input = m(input)
return input
""")
_builtin_table = None
_modules_containing_builtins = (torch, torch._C._nn)
def _unwrap_optional(x):
assert x is not None, "Unwrapping null optional"
return x
# lazily built to ensure the correct initialization order
def _get_builtin_table():
global _builtin_table
if _builtin_table is not None:
return _builtin_table
_builtin_table = {}
def register_all(mod):
for name in dir(mod):
v = getattr(mod, name)
if callable(v):
_builtin_table[id(v)] = "aten::" + name
for mod in _modules_containing_builtins:
register_all(mod)
_builtin_table[id(warnings.warn)] = "aten::warn"
_builtin_table[id(_single)] = "aten::_single"
_builtin_table[id(_pair)] = "aten::_pair"
_builtin_table[id(_triple)] = "aten::_triple"
_builtin_table[id(_quadruple)] = "aten::_quadruple"
_builtin_table[id(_list_with_default)] = "aten::list_with_default"
_builtin_table[id(_unwrap_optional)] = "aten::_unwrap_optional"
_builtin_table[id(cudnn.is_acceptable)] = "aten::cudnn_is_acceptable"
_builtin_table[id(torch._C._infer_size)] = "aten::_infer_size"
_builtin_table[id(torch.nn.functional._no_grad_embedding_renorm_)] = "aten::_no_grad_embedding_renorm_"
_builtin_table[id(math.floor)] = "aten::floor"
_builtin_table[id(math.ceil)] = "aten::ceil"
_builtin_table[id(math.log)] = "aten::log"
_builtin_table[id(math.log1p)] = "aten::log1p"
_builtin_table[id(math.log10)] = "aten::log10"
_builtin_table[id(math.exp)] = "aten::exp"
_builtin_table[id(math.sqrt)] = "aten::sqrt"
_builtin_table[id(math.pow)] = "aten::pow"
_builtin_table[id(torch.nn.functional.interpolate)] = "aten::__interpolate"
_builtin_table[id(torch.nn.functional.upsample_nearest)] = "aten::__upsample_nearest"
_builtin_table[id(torch.nn.functional.upsample)] = "aten::__upsample"
_builtin_table[id(torch.nn.functional.upsample_bilinear)] = "aten::__upsample_bilinear"
_builtin_table[id(torch.nn.functional.assert_int_or_pair)] = "aten::_assert_int_or_pair"
_builtin_table[id(torch.nn.utils.rnn.get_packed_sequence)] = "aten::_pack_sequence"
_builtin_table[id(torch.nn.init._no_grad_fill_)] = "aten::_no_grad_fill_"
_builtin_table[id(torch.nn.init._no_grad_normal_)] = "aten::_no_grad_normal_"
_builtin_table[id(torch.nn.init._no_grad_uniform_)] = "aten::_no_grad_uniform_"
_builtin_table[id(torch.nn.init._no_grad_zero_)] = "aten::_no_grad_zero_"
return _builtin_table
def _register_builtin(fn, op):
_get_builtin_table()[id(fn)] = op
def _find_builtin(fn):
return _get_builtin_table().get(id(fn))
_register_builtin(len, 'aten::len')
_register_builtin(_wait, 'aten::wait')
# qualified_name => ScriptClass mapping
_script_classes = {}
def _add_script_class(cls, name):
global _script_classes
_script_classes[name] = cls
def _get_script_class(name):
global _script_classes
if name not in _script_classes:
raise RuntimeError("Unknown reference to ScriptClass '{}'. "
"Did you forget to import it?".format(name))
return _script_classes[name]
# torch.jit.Error
Error = torch._C.JITException
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
Attribute = collections.namedtuple('Attribute', ['value', 'type'])
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.Function.graph_for = _graph_for
Function = torch._C.Function
if not torch._C._jit_init():
raise RuntimeError("JIT initialization failed")
