megengine.jit.tracing 源代码

# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
# Copyright (c) 2014-2020 Megvii Inc. All rights reserved.
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
import collections
import contextlib
import functools
import itertools
import json
import typing
import warnings
import weakref

import numpy as np

from ..core._imperative_rt import GraphProfiler
from ..core._imperative_rt.ops import OprAttr
from ..core._trace_option import set_tensor_shape
from ..core.ops.special import Const
from ..core.tensor import megbrain_graph as G
from ..core.tensor.core import OpBase, TensorBase, TensorWrapperBase, apply
from ..core.tensor.raw_tensor import OpDef, RawTensor, as_raw_tensor
from ..core.tensor.tensor import Tensor
from .sublinear_memory_config import SublinearMemoryConfig

[文档]class TraceMismatchError(RuntimeError): pass
active_trace = None skip_tracing = False
[文档]def is_tracing(): if active_trace is None: return False else: return not skip_tracing
[文档]@contextlib.contextmanager def exclude_from_trace(): global skip_tracing if skip_tracing: yield return try: skip_tracing = True if active_trace is not None: active_trace._begin_excluded_region() yield finally: skip_tracing = False
[文档]class TensorInfo: __slots__ = ( # collected attributes "external", "exported", "data_read", "shape_read", "value_read", "device", "dtype", "shape", "bound_data", # resources for execution "varnode", "data_setter", "shape_reader", "value_reader", "data_reader", ) def __init__(self): self.exported = None self.data_read = None self.shape_read = None self.value_read = None self.bound_data = None self.data_setter = None self.shape_reader = None self.value_reader = None self.data_reader = None
[文档]class trace: """ Wraps a callable and provide: * tracing via :meth:`.trace` and :meth:`.dump` * accelerated evalutaion via :meth:`.__call__` :param function: the function will be traced. :param symbolic: whether to apply symbolic execution for tracing. Default: False :param capture_as_const: capture global vars or closures as const value. Default: False :param sublinear_memory_config: configuration for sublinear memory optimization. If not None, it enables sublinear memory optimization with given setting. :param profiling: whether to profile compiled trace. Default: False :param opt_level: optimization level for compiling trace. :param symbolic_shape: whether to use symbolic shape for tracing. Default: True """ def __new__(cls, *args, **kwargs): if not args: return functools.partial(cls, **kwargs) return super().__new__(cls) def __init__( self, function, symbolic=False, capture_as_const=False, sublinear_memory_config: SublinearMemoryConfig = None, profiling: bool = False, opt_level: int = None, tensor_shape: bool = True, ): self.__wrapped__ = function self._symbolic = symbolic self._capture_as_const = capture_as_const self._sublinear_memory_config = sublinear_memory_config self._profiling = profiling self._profiler = None self._graph_opt_level = opt_level self._tensor_shape = tensor_shape self._reset() def _reset(self): self._untraced = True self._tinfo = [] # handle -> TensorInfo self._seq = [] self._pc = 0 self._graph = None self._need_reset_nodes = None self._lazy_eval_graph = None self._lazy_eval_tensors = [] self._lazy_eval_tensor_count = 0 self._active_tensors = weakref.WeakSet() self._tensor_remaps = None self._inputs_to_restore = None self._arg_bindings = None self._kwarg_bindings = None self._output_bindings = None self._output_names = None set_tensor_shape(self._tensor_shape) def _new_handle(self): handle = len(self._tinfo) info = TensorInfo() self._tinfo.append(info) return handle, info def _apply_op(self, op, args): assert not self._untraced # check against trace if self._pc >= len(self._seq): raise TraceMismatchError("trace should end here, but more op observed") record = self._seq[self._pc] op_, ihandles, ohandles = record if op != op_: # FIXME: will be removed once better rng implementation is done if isinstance(op, OprAttr) and ( op.type in ("UniformRNG", "GaussianRNG") and op.type == op_.type ): if op.param[8:] != op_.param[8:]: raise TraceMismatchError("op different from last time") else: raise TraceMismatchError("op different from last time") if len(ihandles) != len(args): raise TraceMismatchError("op input size different from last time") for h, x in zip(ihandles, args): info = self._tinfo[h] if info.external: if ( x.__class__ is CompiledTensorProxy and not self._tinfo[x._CompiledTensorProxy__handle].exported ): raise TraceMismatchError( "failed to capture: input was an external tensor " "last time, got an internal tensor this time" ) if info.bound_data: if x.__class__ is CompiledTensorProxy: raise TraceMismatchError( "const capture violated: was an external tensor " "last time, got an internal tensor this time" ) if x._handle != info.bound_data._handle: if not np.array_equal(x.numpy(), info.bound_data.numpy()): raise TraceMismatchError( "const capture violated: got " "a different tensor this time" ) else: if info.dtype != x.dtype: raise TraceMismatchError( "failed to capture: different dtype from last time" ) if info.device != x.device: raise TraceMismatchError( "failed to capture: different device from last time" ) info.data_setter.set_value(x._dev_tensor()) else: if x.__class__ is not CompiledTensorProxy: if x not in self._tensor_remaps: raise TraceMismatchError( "unexpected capture: trying to use an external tensor as " "input, but that input was an internal tensor last time" ) else: x = self._tensor_remaps[x] if x._CompiledTensorProxy__handle != h: raise TraceMismatchError( "mis-wiring: input edge to an data flow " "graph node is different from last time" ) self._pc += 1 outputs = tuple([CompiledTensorProxy(h) for h in ohandles]) self._active_tensors.update(outputs) return outputs def _record_op(self, op, inputs, outputs): if skip_tracing: for x in inputs: h = getattr(x, "_TraceMixin__handle", None) if h is not None: self._tinfo[h].data_read = True return ihandles = [] for x in inputs: h = getattr(x, "_TraceMixin__handle", None) if h is None or (not self._capture_as_const and self._tinfo[h].exported): h, info = self._new_handle() info.external = True info.device = x.device info.dtype = x.dtype info.shape = x.shape if self._capture_as_const: info.bound_data = x ihandles.append(h) ohandles = [] for x in outputs: h, info = self._new_handle() ohandles.append(h) info.external = False TraceMixin._TraceMixin__inject(x, h) self._seq.append((op, tuple(ihandles), tuple(ohandles))) self._active_tensors.update(outputs) def _record_const(self, op, outputs): pass def _set_active(self, active: bool): global active_trace if active: if active_trace: raise NotImplementedError("sorry, not implemented: nested trace") active_trace = self else: assert active_trace is self active_trace = None def _init_trace(self, symbolic: bool): apply.enable(apply_with_tracing) apply.enable(apply_const_with_tracing) if symbolic: apply.enable(apply_symbolic_mode) apply.enable(apply_const_symbolic_mode) self._lazy_eval_graph = G.Graph() def _take_escaped_tensors(self): escaped_tensors = tuple(self._active_tensors) self._active_tensors.clear() return escaped_tensors def _lazy_eval(self, lazy_eval_graph, lazy_eval_tensors): active_lazy_eval_tensors = [] visited = set() readers = [] for x in lazy_eval_tensors: x = x() if x is None or x in visited: continue reader = G.OutputNode(x._LazyEvalTensor__varnode).outputs[0] readers.append(reader) active_lazy_eval_tensors.append(x) visited.add(x) self._apply_graph_options(lazy_eval_graph) lazy_eval_graph.compile(*readers) lazy_eval_graph() for r, x in zip(readers, active_lazy_eval_tensors): assign_raw_tensor(x, as_raw_tensor(r.op.get_value())) @contextlib.contextmanager def _setup(self): interrupted = False def do_enter(): self._set_active(True) if self._untraced: self._init_trace(self._symbolic) else: apply.enable(apply_compiled_mode) if self._graph is None: self._compile() self._graph.execute() def do_finalize(): escaped_tensors = self._take_escaped_tensors() if self._untraced: for x in escaped_tensors: info = self._tinfo[x._TraceMixin__handle] info.data_read = True x._TraceMixin__restore() if self._inputs_to_restore: for x in self._inputs_to_restore: x._TraceMixin__restore() if self._symbolic and self._lazy_eval_tensors: # eval lazy eval tensors self._lazy_eval(self._lazy_eval_graph, self._lazy_eval_tensors) self._lazy_eval_graph = None self._lazy_eval_tensors = None self._untraced = False else: # compiled_tensor leaks if self._pc == len(self._seq): for x in escaped_tensors: try: assign_raw_tensor(x, as_raw_tensor(x._dev_tensor())) except TraceMismatchError: # TraceMismatchError thrown in do_exit pass self._graph.wait() self._reset_exec_env() # reset status self._pc = 0 self._tensor_remaps = None apply.disable(apply_with_tracing) apply.disable(apply_const_with_tracing) apply.disable(apply_symbolic_mode) apply.disable(apply_const_symbolic_mode) apply.disable(apply_compiled_mode) self._set_active(False) def do_exit(): if not self._untraced and self._pc != len(self._seq): raise TraceMismatchError("premature end") if not self._symbolic or not self._untraced: for x in self._active_tensors: x._dev_tensor() try: do_enter() yield do_exit() except: interrupted = True raise finally: do_finalize() if interrupted: self._reset() def _begin_excluded_region(self): if self._capture_as_const: raise RuntimeError( "exclude_from_trace cannot be used with capture_as_const" ) if self._untraced: # conditionally reading a compiled tensor in excluded region # is permitted, so we have to assume every tensor might be read for x in self._active_tensors: info = self._tinfo[x._TraceMixin__handle] info.exported = True info.data_read = True def _apply_graph_options(self, graph): graph.options.seq_opt.enable_seq_comp_node_opt = False # graph opt level if self._graph_opt_level is not None: graph.options.graph_opt_level = self._graph_opt_level # sublinear if self._sublinear_memory_config is not None: graph.options.enable_sublinear_memory_opt = True sublinear_config = graph.options.sublinear_mem_config sublinear_config.lb_memory = self._sublinear_memory_config.lb_memory sublinear_config.genetic_nr_iter = ( self._sublinear_memory_config.genetic_nr_iter ) sublinear_config.genetic_pool_size = ( self._sublinear_memory_config.genetic_pool_size ) sublinear_config.thresh_nr_try = self._sublinear_memory_config.thresh_nr_try sublinear_config.num_worker = self._sublinear_memory_config.num_worker # profile if self._profiling: self._profiler = GraphProfiler(graph) def _compile(self): graph = self._graph = G.Graph() graph.options.no_force_inplace = True graph.options.async_exec_level = 0b100 self._apply_graph_options(graph) # graph.options.graph_opt_level = 0 need_reset_nodes = self._need_reset_nodes = [] # links enforce ordering of I/O nodes links = () readers = [] if self._capture_as_const: for h in itertools.chain(self._arg_bindings, self._kwarg_bindings.values()): info = self._tinfo[h] opnode = info.data_setter = G.InputNode( device=info.device, dtype=info.dtype, shape=info.shape, graph=graph ) need_reset_nodes.append(opnode) info.varnode = opnode.outputs[0] links += opnode.outputs[1:] for op, ihandles, ohandles in self._seq: ivars = [] for h in ihandles: info = self._tinfo[h] if not hasattr(info, "varnode"): assert info.external if info.bound_data: info.varnode = graph.make_const(info.bound_data._dev_tensor()) else: opnode = info.data_setter = G.InputNode( *links, device=info.device, dtype=info.dtype, shape=info.shape, graph=graph, ) need_reset_nodes.append(opnode) info.varnode, *links = opnode.outputs ivars.append(info.varnode) ovars = apply(op, *ivars) assert len(ovars) == len(ohandles) for h, v in zip(ohandles, ovars): info = self._tinfo[h] info.varnode = v def add_reader(opnode): nonlocal links need_reset_nodes.append(opnode) readers.append(opnode.outputs[0]) links = opnode.outputs if info.data_read: # Shape can be obtained from data so doesn't need its own # output node. On the other hand, value is read separately # to leverage eager h2d copy info.shape_read = False opnode = info.data_reader = G.OutputNode(v, *links) add_reader(opnode) if info.value_read: opnode = info.value_reader = G.ValueOutputNode(v, *links) add_reader(opnode) if info.shape_read: opnode = info.shape_reader = G.AttrOutputNode(v, *links) add_reader(opnode) graph.compile(*readers) def _reset_exec_env(self): for opnode in self._need_reset_nodes: opnode.reset() def _require_shape(self, handle): info = self._tinfo[handle] info.shape_read = True def _require_value(self, handle): info = self._tinfo[handle] info.value_read = True def _require_data(self, handle): info = self._tinfo[handle] info.data_read = True def __call__(self, *args, **kwargs): with self._setup(): if self._capture_as_const: self._process_inputs(*args, **kwargs) outputs = self.__wrapped__(*args, **kwargs) if self._capture_as_const: self._process_outputs(outputs) return outputs
[文档] def dump( self, file, *, arg_names=None, output_names=None, append=False, optimize_for_inference=True, **kwargs ): r"""Serializes trace to file system. :param file: output file, could be file object or filename. :param arg_names: names of the input tensors in the traced function. :param output_names: names of the output tensors in the traced function, use the default name if not specified. :param append: whether output is appended to ``file``. Only works when ``file`` is str. :param optimize_for_inference: enbale optmizations, will skip all optimize options if this is False. Default: True :Keyword Arguments: * enable_io16xc32 -- whether to use float16 for I/O between oprs and use float32 as internal computation precision. Note the output var would be changed to float16. * enable_ioc16 -- whether to use float16 for both I/O and computation precision. * enable_hwcd4 -- whether to use NHWCD4 data layout. This is faster on some OpenCL backend. * enable_nchw88 -- whether to use NCHW88 data layout, currently used in X86 AVX backend. * enable_nchw44 -- whether to use NCHW44 data layout, currently used in arm backend. * enable_nchw44_dot -- whether to use NCHW44_dot data layout, currently used in armv8.2+dotprod backend. * enable_nchw4 -- whether to use NCHW4 data layout, currently used in nvidia backend(based on cudnn). * enable_nchw32 -- whether to use NCHW32 data layout, currently used in nvidia backend with tensorcore(based on cudnn). * enable_chwn4 -- whether to use CHWN4 data layout, currently used in nvidia backend with tensorcore. * enable_fuse_conv_bias_nonlinearity: whether to fuse conv+bias+nonlinearty into one opr. * enable_fuse_conv_bias_with_z: whether to fuse conv_bias with z input for inference on nvidia backend(this optimization pass will result in mismatch of the precision of output of training and inference) """ if not self._capture_as_const: raise ValueError( "you must specify capture_as_const=True at __init__ to use dump" ) if self._untraced: raise RuntimeError("should run at least once before calling dump") if self._output_names and output_names: raise TypeError( "cannot specify output_names when output is already in dict format" ) if output_names and not isinstance(output_names, output_names = (output_names,) if output_names and len(output_names) != len(self._output_bindings): raise ValueError( "wrong number of output_names, should be {} values".format( len(self._output_bindings) ) ) if arg_names and not isinstance(arg_names, arg_names = (arg_names,) if arg_names and len(arg_names) != len(self._arg_bindings): raise ValueError( "wrong number of arg_names, should be {} values".format( len(self._arg_bindings) ) ) output_names = output_names or self._output_names h2v = {} graph = G.Graph() for i, h in enumerate(self._arg_bindings): info = self._tinfo[h] h2v[h] = graph.make_h2d( dtype=info.dtype, device=info.device, shape=info.shape, name=arg_names[i] if arg_names else None, ) for k, h in self._kwarg_bindings.items(): info = self._tinfo[h] h2v[h] = graph.make_h2d( dtype=info.dtype, device=info.device, shape=info.shape, name=k ) for op, ihandles, ohandles in self._seq: ivars = [] for h in ihandles: info = self._tinfo[h] if h not in h2v: assert info.external assert info.bound_data h2v[h] = graph.make_const( info.bound_data.numpy(), dtype=info.dtype, device=info.device ) ivars.append(h2v[h]) ovars = apply(op, *ivars) assert len(ovars) == len(ohandles) h2v.update(zip(ohandles, ovars)) dest_vars = [] for i, h in enumerate(self._output_bindings): v = h2v[h] if output_names: = output_names[i] dest_vars.append(v) if optimize_for_inference: dest_vars = G.optimize_for_inference(dest_vars, **kwargs) if isinstance(file, str): permission = "wb" if append == False else "ab" file = open(file, permission) dump_content, dump_info = G.dump_graph(dest_vars) file.write(dump_content) return dump_info
def _process_inputs(self, *args, **kwargs): if self._untraced: self._inputs_to_restore = [] def record_input(x): if x is None: return h, info = self._new_handle() info.external = False info.device = x.device info.dtype = x.dtype info.shape = x.shape TraceMixin._TraceMixin__inject(x, h) self._inputs_to_restore.append(x) return h self._arg_bindings = [] for i, x in enumerate(args): x = find_raw_tensor(x) if x is None: raise TypeError( "positional arguments should all be tensor " "but args[%d] cannot be recognized as one" % i ) self._arg_bindings.append(record_input(x)) self._kwarg_bindings = {} for k, x in kwargs.items(): x = find_raw_tensor(x) if x is not None: self._kwarg_bindings[k] = record_input(x) else: if len(args) != len(self._arg_bindings): raise TraceMismatchError("positional argument length mismatch") self._tensor_remaps = {} for i, (h, x) in enumerate(zip(self._arg_bindings, args)): x = find_raw_tensor(x) if x is None: raise TypeError( "positional arguments should all be tensor " "but args[%d] cannot be recognized as one" % i ) info = self._tinfo[h] if x.dtype != info.dtype: raise TypeError("args[%d].dtype different from last time" % i) if x.device != info.device: raise TypeError("args[%d].device different from last time" % i) info.data_setter.set_value(x._dev_tensor()) self._tensor_remaps[x] = CompiledTensorProxy(h) kwargs_tensors = {} for k, x in kwargs.items(): x = find_raw_tensor(x) if x is not None: kwargs_tensors[k] = x if set(kwargs_tensors) != set(self._kwarg_bindings): too_many = set(kwargs_tensors) - set(self._kwarg_bindings) too_few = set(self._kwarg_bindings) - set(kwargs_tensors) if too_many: raise TraceMismatchError( "keyword arguments found to be tensor this time " "but were non-tensor previously: %s" % " ".join(too_many) ) if too_few: raise TraceMismatchError( "keyword arguments found to be non-tensor this time " "but were tensor previously: %s" % " ".join(too_few) ) for k, h in self._kwarg_bindings.items(): x = kwargs_tensors[k] info = self._tinfo[h] if x.dtype != info.dtype: raise TypeError("kwargs[%s].dtype different from last time" % k) if x.device != info.device: raise TypeError("kwargs[%s].device different from last time" % k) info.data_setter.set_value(x._dev_tensor()) self._tensor_remaps[x] = CompiledTensorProxy(h) def _process_outputs(self, outputs): output_names = None if isinstance(outputs, output_names, outputs = zip(*sorted(outputs.items())) elif not isinstance(outputs, outputs = (outputs,) if not self._untraced: if output_names != self._output_names: too_many = set(output_names) - set(self._output_names) too_few = set(self._output_names) - set(output_names) if too_many: raise TraceMismatchError( "output has more keys than last time: %s" % " ".join(too_many) ) if too_few: raise TraceMismatchError( "output has less keys than last time: %s" % " ".join(too_few) ) if len(outputs) != len(self._output_bindings): raise TraceMismatchError("output size differs from last time") else: self._output_names = output_names self._output_bindings = [] for i, x in enumerate(outputs): x = find_raw_tensor(x) if x is None: raise TypeError("every item of return value should be tensor") if self._untraced: if not isinstance(x, TraceMixin): raise RuntimeError("output is not computed from inputs") h = x._TraceMixin__handle self._output_bindings.append(h) else: if not isinstance(x, CompiledTensorProxy): raise RuntimeError("output is not computed from inputs") h = x._CompiledTensorProxy__handle if h != self._output_bindings[i]: raise TraceMismatchError( "retval[%s] is a different tensor than last time" % (output_names and output_names[i] or i) )
[文档] def get_profile(self): """ Get profiling result for compiled trace. :return: a json compatible object. """ if not self._profiler: raise RuntimeError("trace is not set with profiling=True") return json.loads(self._profiler.get())
[文档] def trace(self, *args, **kwargs): raise NotImplementedError( "trace is deemed unbeneficial with the new " "tracing mechanism. You should alwasy use __call__." )
[文档]class CompiledTensorProxy(RawTensor): """ Duck-typed RawTensor """ def __init__(self, handle): self.__handle = handle self.__info = active_trace._tinfo[handle] self.__shape = None self.__data = None self.__value = None @property def dtype(self): return self.__info.varnode.dtype @property def device(self): return self.__info.varnode.device @property def shape(self): if self.__shape is None: if self.__info.shape_read: self.__shape = self.__info.shape_reader.get_value().shape elif self.__info.data_read: self.__shape = self._dev_tensor().shape else: raise TraceMismatchError("shape of this tensor is not read in trace") return self.__shape
[文档] def numpy(self): if self.__value is None: if self.__info.value_read: self.__value = self.__info.value_reader.get_value() elif self.__info.data_read: self.__value = self._dev_tensor().numpy() else: raise TraceMismatchError("value of this tensor is not read in trace") return self.__value
def _dev_tensor(self): if self.__data is None: if not self.__info.data_read: raise TraceMismatchError("raw data of this tensor is not read in trace") self.__data = self.__info.data_reader.get_value() return self.__data def __del__(self): if self.__info.shape_read and self.__shape is not None: self.__info.shape_reader.drop_value() if self.__info.value_read and self.__value is not None: self.__info.value_reader.drop_value() if self.__info.data_read and self.__data is not None: self.__info.data_reader.drop_value()
[文档]class LazyEvalTensor(RawTensor): def __init__(self, varnode): self.__varnode = varnode @property def dtype(self): return self.__varnode.dtype @property def device(self): return self.__varnode.device @property def shape(self): return self.__varnode.shape
[文档] def numpy(self): return self.__varnode.value
def _dev_tensor(self): raise RuntimeError("cannot access data during symbolic tracing")
[文档]class TraceMixin: __subclass_cache = {} def __inject(self, handle): cache = __class__.__subclass_cache cls = self.__class__ subcls = cache.get(cls) if subcls is None: subcls = cache[cls] = type("Traced" + cls.__name__, (__class__, cls), {}) self.__class__ = subcls self.__handle = handle self.__cls = cls return self def __restore(self): cls = self.__cls del self.__handle del self.__cls self.__class__ = cls return self @property def shape(self): if not skip_tracing: active_trace._require_shape(self.__handle) return super().shape
[文档] def numpy(self): if not skip_tracing: active_trace._require_value(self.__handle) return super().numpy()
def _dev_tensor(self): if not skip_tracing: active_trace._require_data(self.__handle) return super()._dev_tensor()
[文档]class TracedRawTensor(TraceMixin, RawTensor): pass
[文档]class TracedLazyTensor(TraceMixin, LazyEvalTensor): pass
[文档]def assign_raw_tensor(lhs, rhs): handle = rhs._handle rhs.__dict__.clear() lhs.__dict__.clear() lhs.__class__ = RawTensor lhs.__init__(handle)
# this hook turns RawTensor into LazyEvalTensor
[文档]@apply.register() def apply_symbolic_mode(op: OpDef, *args: RawTensor): graph = active_trace._lazy_eval_graph ivars = [ getattr(x, "_LazyEvalTensor__varnode", None) or graph.make_const(x._dev_tensor()) for x in args ] ovars = apply(op, *ivars) outputs = [LazyEvalTensor(v) for v in ovars] active_trace._lazy_eval_tensors.extend(weakref.ref(oup) for oup in outputs) return outputs
[文档]@apply.register() def apply_const_symbolic_mode(op: Const, *args: RawTensor): graph = active_trace._lazy_eval_graph ret = LazyEvalTensor(graph.make_const(op.value, dtype=op.dtype, device=op.device)) active_trace._lazy_eval_tensors.append(weakref.ref(ret)) return (ret,)
[文档]@apply.register() def apply_compiled_mode(op: OpDef, *args: RawTensor): if skip_tracing: args = [ as_raw_tensor(x._dev_tensor()) if x.__class__ is CompiledTensorProxy else x for x in args ] return apply.super(op, *args) return active_trace._apply_op(op, args)
apply.disable(apply_compiled_mode) # this hook injects TraceMixin
[文档]@apply.register() def apply_with_tracing(op: OpDef, *args: RawTensor): outputs = apply.super(op, *args) active_trace._record_op(op, args, outputs) return outputs
[文档]@apply.register() def apply_const_with_tracing(op: Const, *args: RawTensor): outputs = apply.super(op, *args) active_trace._record_const(op, outputs) return outputs
[文档]class BrokenRawTensor(RawTensor): def __getattribute__(self, _): raise RuntimeError("broken due to misuse of tracing") def __setattr__(self, *_): raise RuntimeError("broken due to misuse of tracing")
[文档]@functools.singledispatch def find_raw_tensor(x): return None
@find_raw_tensor.register(RawTensor) def _(x): return x @find_raw_tensor.register(TensorWrapperBase) def _(x): x = getattr(x, "__wrapped__", None) if x is not None: return find_raw_tensor(x) @find_raw_tensor.register(Tensor) def _(x): x = getattr(x, "_data", None) if x is not None: return find_raw_tensor(x)