# -*- coding: utf-8 -*-
import collections
import contextlib
import functools
import itertools
import json
import os
import pickle
import re
import struct
import sys
from collections import OrderedDict, defaultdict
from typing import Any, Sequence
import cv2
import numpy as np
from .. import tensor
from ..core import _imperative_rt as rt
from ..core._imperative_rt import (
CompNode,
GraphProfiler,
GraphProfiler2,
SerializationMetadata,
)
from ..core._imperative_rt.core2 import Tensor as RawTensor
from ..core._imperative_rt.core2 import Trace, TraceError # skip_tracing,
from ..core._imperative_rt.core2 import add_backward_callback as _add_backward_callback
from ..core._imperative_rt.core2 import (
get_marked_input_tensor,
get_marked_output_tensor,
get_marked_tensor,
marked_input_tensor,
name_tensor,
)
from ..core._imperative_rt.graph import _set_priority_to_id
from ..core._imperative_rt.ops import (
AssertEqual,
CollectiveComm,
ExternOpr,
RemoteRecv,
RemoteSend,
set_jit_enabled,
)
from ..core._trace_option import set_symbolic_shape
from ..core.tensor import megbrain_graph as G
from ..logger import get_logger
from ..tensor import Tensor
from ..utils import comp_graph_tools as cgtools
from ..utils.naming import AutoNaming
from ..utils.profiler import is_profiling
from .dtr_config import DTRConfig
from .graph_opt_config import GraphOptimizationConfig
from .sublinear_memory_config import SublinearMemoryConfig
logger = get_logger(__name__)
def _input_node_use_static_shape():
return os.environ.get("MEGENGINE_INPUT_NODE_USE_STATIC_SHAPE") is not None
active_trace = None
skip_tracing = False
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 or (active_trace is None):
yield
return
try:
skip_tracing = True
if active_trace is not None:
active_trace._begin_excluded_region()
yield
if active_trace is not None:
active_trace._end_excluded_region()
finally:
skip_tracing = False
def array_comparator(lhs, rhs):
return np.all(lhs == rhs)
[文档]class trace:
"""Wraps a callable and provide:
* tracing via :meth:`.trace` and :meth:`.dump`
* accelerated evalutaion via :meth:`.__call__`
Args:
function(Callable): the function will be traced.
symbolic(bool): whether to apply symbolic execution for tracing. Default: False
capture_as_const(bool): capture global vars or closures as const value. Default: False
record_only: if True, won't run even if call the function. Default: False
sublinear_memory_config(SublinearMemoryConfig): configuration for sublinear memory optimization.
If not None, it enables sublinear memory optimization with given setting.
dtr_config(DTRConfig): configuration for DTR sublinear memory optimization.
If not None, it enables DTR optimization with given setting.
profiling(bool): whether to profile compiled trace. Default: False
opt_level(int): optimization level for compiling trace. Default: 2
graph_opt_config(GraphOptimizationConfig): configuration for graph optimization. Default: None
symbolic_shape(bool): whether to use symbolic shape for tracing. Default: True
without_host(bool): if True, will run python code of wrapped function on the first call,
and run the compiled graph/function on subsequent calls. if False, will run python code every time.
Default: False
imperative(bool): if True, will use imperative runtime to execute captured op seq. Default: False
"""
third_party_backend = False
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,
record_only=False,
sublinear_memory_config: SublinearMemoryConfig = None,
dtr_config: DTRConfig = None,
profiling: bool = False,
opt_level: int = 2,
graph_opt_config: GraphOptimizationConfig = None,
symbolic_shape: bool = True,
without_host: bool = False,
imperative: bool = False,
):
self.__wrapped__ = function
self._capture_as_const = capture_as_const or record_only
self._arg_bindings = None
self._kwarg_bindings = None
self._output_bindings = None
self._symbolic_shape = symbolic_shape
self._graph_options = {
"no_force_inplace": True,
"graph_opt_level": opt_level,
"seq_opt.enable_seq_comp_node_opt": False,
}
# prevent cyclic reference
graph_options = self._graph_options
if dtr_config is not None:
graph_options["enable_dtr_memory_opt"] = True
graph_options[
"dtr_config.eviction_threshold"
] = dtr_config.eviction_threshold
graph_options[
"dtr_config.evictee_minimum_size"
] = dtr_config.evictee_minimum_size
graph_options[
"dtr_config.recomp_memory_factor"
] = dtr_config.recomp_memory_factor
graph_options[
"dtr_config.recomp_time_factor"
] = dtr_config.recomp_time_factor
if graph_opt_config is not None:
mapping = {None: 0, False: 1, True: 2}
graph_options["graph_opt.jit_config.fuse_dimshuffle"] = mapping[
graph_opt_config.jit_fuse_dimshuffle
]
graph_options["graph_opt.jit_config.fuse_reduce"] = mapping[
graph_opt_config.jit_fuse_reduce
]
if sublinear_memory_config is not None:
graph_options["enable_sublinear_memory_opt"] = True
graph_options[
"sublinear_mem_config.lb_memory_mb"
] = sublinear_memory_config.lb_memory_mb
graph_options[
"sublinear_mem_config.genetic_nr_iter"
] = sublinear_memory_config.genetic_nr_iter
graph_options[
"sublinear_mem_config.genetic_pool_size"
] = sublinear_memory_config.genetic_pool_size
graph_options[
"sublinear_mem_config.thresh_nr_try"
] = sublinear_memory_config.thresh_nr_try
graph_options[
"sublinear_mem_config.num_worker"
] = sublinear_memory_config.num_worker
if int(os.getenv("MEGENGINE_INPLACE_UPDATE", "0")):
graph_options["var_sanity_check_first_run"] = False
def apply_options(options):
for k, v in graph_options.items():
words = k.split(".")
suboptions = options
for word in words[:-1]:
suboptions = getattr(suboptions, word)
setattr(suboptions, words[-1], v)
self._trace = Trace()
self._trace.symbolic = symbolic or record_only
self._trace.capture_as_const = capture_as_const or record_only
self._trace.no_exec = record_only
self._trace.imperative = imperative
self._trace.options_visitor = apply_options
self._trace.profile = profiling
self._trace.array_comparator = array_comparator
self._trace.record_input_shapes = _input_node_use_static_shape()
self._trace.without_host = without_host
self.check_external = True
self.traced = False
self.input_num = 0
self.output_num = 0
self.arg_list = []
self.out_list = []
self.overall = True
# forward keeped activation
self.keeped_activation = []
self.third_party_backend_compiled = False
@property
def check_external(self):
return self._trace.check_external
@check_external.setter
def check_external(self, flag):
self._trace.check_external = flag
@property
def without_host(self):
return self._trace.without_host
def flatten_inputs(self, *args, **kwargs):
from ..traced_module.pytree import tree_flatten, SUPPORTED_LEAF_CLS
from ..module import Module
from ..optimizer import Optimizer
if Optimizer not in SUPPORTED_LEAF_CLS:
SUPPORTED_LEAF_CLS.append(Optimizer)
tensor_args = []
modules = []
fargs, _ = tree_flatten((args, kwargs))
for a in fargs:
if isinstance(a, RawTensor):
tensor_args.append(a)
elif isinstance(a, Module):
modules.append(a)
elif isinstance(a, Optimizer):
states = a._state.values()
for s in states:
assert isinstance(s, dict)
tensor_args.extend(list(s.values()))
for m in modules:
tensor_args.extend(list(m.parameters()))
grads = []
for t in tensor_args:
if t.grad is not None:
grads.append(t.grad)
for m in modules:
tensor_args.extend(list(m.buffers()))
tensor_args.extend(grads)
return tensor_args
def compile(self):
raise NotImplementedError
def execute(self, *args, **kwargs):
raise NotImplementedError
def setup_env(self):
pass
def unset_env(self):
pass
def compile_and_exec(self, *args, **kwargs):
if not self.third_party_backend_compiled:
self.compile()
self.third_party_backend_compiled = True
return self.execute(*args, **kwargs)
def convert_optimizer_state_to_tensor(self, *args, **kwargs):
from ..traced_module.pytree import tree_flatten, SUPPORTED_LEAF_CLS
from ..optimizer import Optimizer
from ..tensor import Tensor
if Optimizer not in SUPPORTED_LEAF_CLS:
SUPPORTED_LEAF_CLS.append(Optimizer)
args, _ = tree_flatten((args, kwargs))
for arg in args:
if isinstance(arg, Optimizer):
for param_group in arg.param_groups:
for k, v in param_group.items():
if k == "params":
continue
if not isinstance(v, (Tensor, Sequence)):
param_group[k] = Tensor(v)
elif isinstance(v, Sequence) and not isinstance(v[0], Tensor):
new_v = []
for i in range(len(v)):
new_v.append(Tensor(v[i]))
param_group[k] = new_v
def setup_io_without_trace(self, inputs, outputs):
self.traced = True
self.arg_list = [i for i in inputs if i != -1]
self.out_list = outputs
self.input_num = len(self.arg_list)
self.output_num = len([i for i in outputs if i != -1])
def setup_without_host(self):
self.inp_modules = set()
self.module_tensors = set()
self.tensor_to_attr = dict()
self.attr_to_key = dict()
self.update_param_dict = dict()
self.update_opt_param_dict = dict()
self.capture_optimizer_state = set()
self.capture_optimizer_hyper_param = []
self.opt_param_dict = dict()
def __call__(self, *args, **kwargs):
if not self.without_host:
return self.trace_normal(*args, **kwargs)
elif self.overall:
return self.trace_without_host_overall(*args, **kwargs)
else:
return self.trace_without_host(*args, **kwargs)
def trace_normal(self, *args, **kwargs):
global active_trace
symbolic_shape = None
outputs = None
try:
active_trace = self
self._trace.enter()
if self._capture_as_const:
self._process_inputs(*args, **kwargs)
symbolic_shape = set_symbolic_shape(self._symbolic_shape)
outputs = self.__wrapped__(*args, **kwargs)
finally:
handling_exc = sys.exc_info() != (None,) * 3
active_trace = None
if symbolic_shape is not None:
symbolic_shape = set_symbolic_shape(symbolic_shape)
assert symbolic_shape == self._symbolic_shape
if self._capture_as_const and (outputs is not None):
self._process_outputs(outputs)
try:
# may raise TraceError
self._trace.exit()
except TraceError:
if not handling_exc:
raise
return outputs
def trace_without_host(self, *args, **kwargs):
from ..traced_module.pytree import tree_flatten, SUPPORTED_LEAF_CLS
from ..module import Module
from ..utils.module_utils import get_expand_structure
from ..tensor import Tensor
from ..optimizer import Optimizer
assert self.without_host and not self.overall
global active_trace
symbolic_shape = None
outputs = None
if self.traced and self.third_party_backend:
return self.compile_and_exec(*args, **kwargs)
try:
active_trace = self
self._trace.enter()
if self._trace.compiled():
arglist = self.flatten_inputs(*args, **kwargs)
idx = 0
inp_dict = {}
for a in arglist:
if isinstance(a, RawTensor):
inp_dict[self.arg_list[idx]] = a
idx += 1
self._trace.put_datas(inp_dict)
outlist = []
for i in self.out_list:
if i == -1:
if not hasattr(self, "outdef"):
outlist.append(None)
else:
outlist.append(self._trace.get_data(i))
keep_vars = []
for i in self.keeped_activation:
keep_vars.append(self._trace.get_data(i))
outputs = (
self.outdef.unflatten(outlist)
if hasattr(self, "outdef")
else outlist
)
if keep_vars:
return outputs, keep_vars
else:
return outputs
arg_list = self.flatten_inputs(*args, **kwargs)
for i, arg in enumerate(arg_list):
arg_list[i]._reset(get_marked_input_tensor(self.input_num, arg))
self.arg_list.append(self.input_num)
self.input_num += 1
arg_ids = [id(i) for i in arg_list]
del arg_list
self.input_need_update_dict = {}
origin_reset = Tensor._reset
def tensor_reset_hook(obj, other):
if id(obj) in arg_ids:
other = get_marked_output_tensor(self.output_num, other)
self.input_need_update_dict[
arg_ids.index(id(obj))
] = self.output_num
self.output_num += 1
origin_reset(obj, other)
symbolic_shape = set_symbolic_shape(self._symbolic_shape)
Tensor._reset = tensor_reset_hook
if self.third_party_backend:
self.setup_env()
outputs = self.__wrapped__(*args, **kwargs)
Tensor._reset = origin_reset
finally:
handling_exc = sys.exc_info() != (None,) * 3
active_trace = None
if symbolic_shape is not None:
symbolic_shape = set_symbolic_shape(symbolic_shape)
assert symbolic_shape == self._symbolic_shape
if self.third_party_backend:
self.unset_env()
if (
self._capture_as_const
and (outputs is not None)
and not self.without_host
):
self._process_outputs(outputs)
if not self._trace.compiled():
outlist, self.outdef = tree_flatten(outputs)
if outputs is not None:
for i, out in enumerate(outlist):
assert isinstance(out, RawTensor), type(out)
outlist[i] = get_marked_output_tensor(self.output_num, out)
del out
self.out_list.append(self.output_num)
self.output_num += 1
outputs = self.outdef.unflatten(outlist)
try:
# may raise TraceError
self._trace.exit()
except Exception as e:
if isinstance(e, TraceError):
if not handling_exc:
raise
else:
self._trace.set_execption(str(e))
raise
self.traced = True
return outputs
def trace_without_host_overall(self, *args, **kwargs):
# record overall train step include forward, backward, param update in a single trace object
from ..traced_module.pytree import tree_flatten, SUPPORTED_LEAF_CLS
from ..module import Module
from ..utils.module_utils import get_expand_structure
from ..tensor import Tensor
from ..optimizer import Optimizer
assert self.without_host
global active_trace
symbolic_shape = None
outputs = None
if self.traced and self.third_party_backend:
return self.compile_and_exec(*args, **kwargs)
try:
active_trace = self
if Optimizer not in SUPPORTED_LEAF_CLS:
SUPPORTED_LEAF_CLS.append(Optimizer)
if not self.traced:
self.convert_optimizer_state_to_tensor(*args, **kwargs)
self._trace.enter()
if self._trace.compiled():
arglist, _ = tree_flatten((args, kwargs))
idx = 0
inp_dict = {}
opt_hyper_inps = []
for a in arglist:
if isinstance(a, RawTensor):
inp_dict[self.arg_list[idx]] = a
idx += 1
if isinstance(a, Optimizer):
opt_hyper_inps.extend(
[Tensor(pg["lr"]) for pg in a.add_param_groups]
)
for t, key in zip(opt_hyper_inps, self.capture_optimizer_hyper_param):
inp_dict[key] = t
for t, key in self.opt_param_dict.items():
inp_dict[key] = t
self._trace.put_datas(inp_dict)
for attr, key in self.attr_to_key.items():
param = get_expand_structure(attr[0], attr[1])
self._trace.put_data(key, param)
outlist = []
for i in self.out_list:
if i == -1:
if not hasattr(self, "outdef"):
outlist.append(None)
else:
outlist.append(self._trace.get_data(i))
for attr, key in self.update_param_dict.items():
param = get_expand_structure(attr[0], attr[1])
param._reset(self._trace.get_data(key))
for state, key in self.update_opt_param_dict.items():
state._reset(self._trace.get_data(key))
keep_vars = []
for i in self.keeped_activation:
keep_vars.append(self._trace.get_data(i))
outputs = (
self.outdef.unflatten(outlist)
if hasattr(self, "outdef")
else outlist
)
if keep_vars:
return outputs, keep_vars
else:
return outputs
self.setup_without_host()
def get_attr_hook(obj, attr):
rst = object.__getattribute__(obj, attr)
if isinstance(rst, RawTensor):
assert rst in self.tensor_to_attr
attr = self.tensor_to_attr[rst]
if attr not in self.attr_to_key:
self.attr_to_key[attr] = self.input_num
self.input_num += 1
marked_input_tensor(self.attr_to_key[attr], rst)
return rst
origin_reset = Tensor._reset
self.update_param_num = 0
def tensor_wrapper_resethook(obj, other):
if obj in self.tensor_to_attr:
attr = self.tensor_to_attr[obj]
other = get_marked_output_tensor(self.output_num, other)
self.update_param_num += 1
self.update_param_dict[attr] = self.output_num
self.output_num += 1
elif obj in self.capture_optimizer_state:
other = get_marked_output_tensor(self.output_num, other)
self.update_opt_param_dict[obj] = self.output_num
self.output_num += 1
origin_reset(obj, other)
arg_list, self.argdef = tree_flatten((args, kwargs))
for i, arg in enumerate(arg_list):
if isinstance(arg, Module):
for k, v in arg.named_tensors():
if v not in self.tensor_to_attr:
self.tensor_to_attr[v] = (arg, k)
self.inp_modules.add(arg)
elif isinstance(arg, RawTensor):
arg_list[i] = get_marked_input_tensor(self.input_num, arg)
self.arg_list.append(self.input_num)
self.input_num += 1
elif isinstance(arg, Optimizer):
opt_state_dict = arg.state_dict(keep_var=True)
for state in opt_state_dict["param_groups"]:
state.pop("lr")
opt_params, _ = tree_flatten(opt_state_dict)
for p in opt_params:
if isinstance(p, Tensor):
self.capture_optimizer_state.add(p)
for pg in arg.param_groups:
pg["lr"] = get_marked_input_tensor(
self.input_num, Tensor(pg["lr"])
)
self.capture_optimizer_hyper_param.append(self.input_num)
self.input_num += 1
self.opt_param_dict = {}
for t in self.capture_optimizer_state:
if t not in self.tensor_to_attr: # not module parameter
mark_param = get_marked_input_tensor(self.input_num, t)
self.opt_param_dict[t] = self.input_num
t[...] = mark_param
self.input_num += 1
args, kwargs = self.argdef.unflatten(arg_list)
Module.__getattribute__ = get_attr_hook
Tensor._reset = tensor_wrapper_resethook
symbolic_shape = set_symbolic_shape(self._symbolic_shape)
if self.third_party_backend:
self.setup_env()
outputs = self.__wrapped__(*args, **kwargs)
del arg_list
del args
del kwargs
Module.__getattribute__ = object.__getattribute__
Tensor._reset = origin_reset
for attr, key in self.attr_to_key.items():
param = get_expand_structure(attr[0], attr[1])
finally:
handling_exc = sys.exc_info() != (None,) * 3
active_trace = None
if symbolic_shape is not None:
symbolic_shape = set_symbolic_shape(symbolic_shape)
assert symbolic_shape == self._symbolic_shape
if self.third_party_backend:
self.unset_env()
if (
self._capture_as_const
and (outputs is not None)
and not self.without_host
):
self._process_outputs(outputs)
if not self._trace.compiled():
outlist, self.outdef = tree_flatten(outputs)
for i, out in enumerate(outlist):
assert isinstance(out, RawTensor), f"get out of type {type(out)}"
outlist[i] = get_marked_output_tensor(self.output_num, out)
del out
self.out_list.append(self.output_num)
self.output_num += 1
outputs = self.outdef.unflatten(outlist)
try:
# may raise TraceError
self._trace.exit()
except Exception as e:
if isinstance(e, TraceError):
if not handling_exc:
raise
else:
self._trace.set_execption(str(e))
raise
self.traced = True
return outputs
@property
def ops(self):
return self._trace.ops
@property
def vars(self):
return self._trace.vars
def _process_inputs(self, *args, **kwargs):
for i, arg in enumerate(args):
assert isinstance(
arg, RawTensor
), "Only support tensor type args when capture_as_const is enabled"
name_tensor("arg_{}".format(i), arg)
# TODO: mark kwargs in order
for k, kwarg in kwargs.items():
if isinstance(kwarg, RawTensor):
name_tensor("kwarg_{}".format(k), kwarg)
if self._arg_bindings is None:
self._arg_bindings = [
("arg_{}".format(i), arg._tuple_shape) for i, arg in enumerate(args)
]
if self._kwarg_bindings is None:
self._kwarg_bindings = {
"kwarg_{}".format(k): (k, kwarg._tuple_shape)
for k, kwarg in kwargs.items()
if isinstance(kwarg, RawTensor)
}
def _process_outputs(self, outputs):
assert (
isinstance(outputs, RawTensor)
or (
isinstance(outputs, Sequence) and not (isinstance(outputs[0], Sequence))
)
or isinstance(outputs, collections.abc.Mapping)
), "Unsupport outputs type, should be Tensor, List[Tensor] or Dict[tensor_name, Tensor]"
if isinstance(outputs, RawTensor):
outputs = [outputs]
if not isinstance(outputs, Sequence):
outputs = [outputs]
if isinstance(outputs, collections.abc.Mapping):
output_names, outputs = zip(*sorted(outputs.items()))
else:
# output_names = ["output_{}".format(i) for i in range(len(outputs))]
output_names = None
self._output_names = output_names
for i, output in enumerate(outputs):
assert isinstance(
output, RawTensor
), "Only support return tensors when capture_as_const is enabled"
name_tensor("output_{}".format(i), output)
if self._output_bindings is None:
self._output_bindings = ["output_{}".format(i) for i in range(len(outputs))]
def _begin_excluded_region(self):
self._trace.begin_excluded_region()
def _end_excluded_region(self):
self._trace.end_excluded_region()
def _make_feed(
self,
graph,
outputs,
input_data,
repeat,
silent,
no_assert,
maxerr,
resize_input,
input_transform,
):
def auto_reformat_image(path, data, dst_shape):
"""reformat image to target shape
:param data: image data as numpy array
:param dst_shape: target shape
"""
dim3_format = False # required input format does not contain batch
hwc_format = False # required input format is NHWC
if not dst_shape: # input tensor shape is not predefined
if len(data.shape) == 2:
chl = 1
h = data.shape[0]
w = data.shape[1]
else:
assert (
len(data.shape) == 3
), "Input image must be of dimension 2 or 3"
h, w, chl = data.shape
dst_shape = (1, chl, h, w)
if len(dst_shape) == 3:
dst_shape = (1,) + dst_shape
dim3_format = True
assert len(dst_shape) == 4, "bad dst_shape: {}".format(dst_shape)
chl = dst_shape[1]
if chl in [1, 3]:
n, c, h, w = dst_shape
dst_shape = (n, h, w, c)
else:
chl = dst_shape[3]
assert chl in [
1,
3,
], "can not infer input format from shape: {}".format(dst_shape)
hwc_format = True
# dst_shape has now been normalized to NHWC format
if resize_input:
h, w = dst_shape[1:3]
data = cv2.resize(data, (w, h))
logger.info("input {} resized to {}".format(path, data.shape))
if chl == 1:
data = cv2.cvtColor(data, cv2.COLOR_BGR2GRAY)
data = data[:, :, np.newaxis]
assert data.ndim == 3
data = data[np.newaxis]
# data normalized to NHWC format
if not hwc_format:
data = np.transpose(data, (0, 3, 1, 2))
if dim3_format:
data = np.squeeze(data, 0)
return data
def read_input_data(dst_shape, dtype, path):
def check_shape_equal(dst_shape, data_shape):
if len(dst_shape):
assert len(data_shape) == len(
dst_shape
), "input/data shapes mismatch: {} vs {}".format(
dst_shape, data_shape
)
if data_shape[1:] != dst_shape[1:]:
logger.warning(
"dst_shape is {}; data_shape is {}".format(
dst_shape, data_shape
)
)
if path.startswith("#"):
assert not resize_input
assert not input_transform
spec = path
m = re.match(
r"^#rand\(([-0-9.]*)\s*,\s*([-0-9.]*)\s*(,[^\)]+)?\)$", spec
)
assert m, "bad spec {}".format(spec)
rng_min = float(m.group(1))
rng_max = float(m.group(2))
if m.group(3):
shape_str = m.group(3)
try:
shape = shape_str[1:].split(",")
if shape[-1].strip() == "...":
shape = shape[:-1]
shape.extend(list(dst_shape[len(shape) :]))
data_shape = tuple(map(int, shape))
except ValueError as e:
raise ValueError("bad spec {}: {}".format(spec, e.args))
else:
data_shape = dst_shape
check_shape_equal(dst_shape, data_shape)
return np.random.uniform(rng_min, rng_max, data_shape).astype(dtype)
# try to load image
data = cv2.imread(path, cv2.IMREAD_COLOR)
if data is None:
assert not resize_input
data = np.load(path)
assert isinstance(data, np.ndarray)
else:
# load image succeeds, so we expect input format is image format
data = auto_reformat_image(path, data, dst_shape)
data = np.repeat(data, repeat, axis=0)
if repeat > 1:
logger.info(
"repeat input for {} times, data shape is {}".format(
repeat, data.shape
)
)
check_shape_equal(dst_shape, data.shape)
if input_transform:
data = eval(input_transform, {"data": data, "np": np})
return data
def gen_one_testcase(inputs, spec):
paths = spec.split(";")
if len(paths) != len(inputs):
if len(paths) == 1 and paths[0].startswith("#"):
paths = ["{}:{}".format(name, paths[0]) for name in inputs.keys()]
assert len(paths) == len(
inputs
), "required inputs: {}; data paths: {}".format(inputs.keys(), paths)
if len(paths) == 1 and ":" not in paths[0]:
paths[0] = next(iter(inputs.keys())) + ":" + paths[0]
ret = {}
for path in paths:
var, path = path.split(":")
ret[var] = read_input_data(inputs[var].shape, inputs[var].dtype, path)
return ret
inputs = cgtools.get_dep_vars(outputs, "Host2DeviceCopy")
inputs = {i.name: i for i in inputs}
if not no_assert:
replace_varmap = {}
inp_map = {}
# replace var use InputNode
for name, var in inputs.items():
inp = G.InputNode(
device="xpux", dtype=var.dtype, shape=var.shape, graph=graph
)
replace_varmap[var] = inp.outputs[0]._node
inp_map[name] = inp
new = cgtools.replace_vars(outputs, replace_varmap)
if isinstance(new, rt.VarNode):
new = list(new)
output_nodes = [G.OutputNode(var) for var in new]
func = graph.compile(*[node.outputs[0]._node for node in output_nodes])
def make_dev_tensor(value, dtype=None, device=None):
return tensor(value, dtype=dtype, device=device)._dev_tensor()
def calculate(*args, **kwargs):
output_val = []
# set inputs value
for name, var in inputs.items():
val = kwargs.pop(name, None)
assert val is not None, "miss input name{}".format(name)
dev_tensor = make_dev_tensor(val, dtype=var.dtype, device="xpux")
inp_map[name].set_value(dev_tensor)
func.execute()
for res in output_nodes:
output_val.append(res.get_value().numpy())
return output_val
def expect_name(var):
return "{}:expect".format(var.name)
testcases = []
np.set_printoptions(precision=2, threshold=4, suppress=True)
data_list = []
for item in input_data:
if item.startswith("@"):
with open(item[1:], "r") as f:
data_list.extend(
[line.rstrip() for line in f if line.rstrip() != ""]
)
else:
data_list.append(item)
for inp_spec in data_list:
cur_testcase = gen_one_testcase(inputs, inp_spec)
assert len(cur_testcase) == len(
inputs
), "required inputs: {}; given data: {}".format(
inputs.keys(), cur_testcase.keys()
)
if not no_assert:
outputs_get = calculate(**cur_testcase)
for var, val in zip(outputs, outputs_get):
cur_testcase[expect_name(var)] = val
logger.info(
"generate test groundtruth: var={} shape={} range=({}, {})"
" mean={} var={}".format(
var,
val.shape,
val.min(),
val.max(),
np.mean(val),
np.var(val),
)
)
testcases.append(cur_testcase)
logger.info(
"add testcase: \n {}".format(
"\n ".join(
"{}: shape={} dtype={} range=({:.2f},{:.2f}) "
"mean={:.2f} sd={:.2f}".format(
k, v.shape, v.dtype, v.min(), v.max(), np.mean(v), np.std(v)
)
for k, v in sorted(cur_testcase.items())
)
)
)
if not no_assert:
def expect_shp(var):
ret = var.shape
if ret:
return ret
return testcases[0][expect_name(var)].shape
def assert_equal(expect, real, **kwargs):
op = AssertEqual(**kwargs)
(res,) = G.apply_normal_varnode(op, expect, real)
return res._node
verbose = not silent
outputs_new = []
for i in outputs:
device = rt.CompNode("xpux")
dtype = i.dtype
name = expect_name(i)
shape = expect_shp(i)
# make expect output as one input of model.
expect_get = rt.make_h2d(graph, device, dtype, shape, name)
# insert assert opr to check expect and real.
outputs_new.append(
assert_equal(expect_get, i, verbose=verbose, maxerr=maxerr,)
)
inputs[expect_name(i)] = expect_get
outputs = outputs_new
return {"outputs": outputs, "testcases": testcases}
[文档] def dump(
self,
file,
*,
arg_names=None,
output_names=None,
append=False,
keep_var_name: int = 1,
keep_opr_name: bool = False,
keep_param_name: bool = False,
keep_opr_priority: bool = False,
no_change_graph: bool = False,
strip_info_file=None,
append_json=False,
optimize_for_inference=True,
user_info: Any = None,
enable_metadata: bool = True,
input_data=None,
repeat=1,
silent=False,
no_assert=False,
maxerr=1e-4,
resize_input=False,
input_transform=None,
dump_format: str = None,
model_version: int = 2,
compat_older_version: str = None,
**kwargs
):
r"""Serializes trace to file system.
Args:
file: output file, could be file object or filename.
arg_names: names of the input tensors in the traced function.
output_names: names of the output tensors in the traced function,
use the default name if not specified.
append: whether output is appended to ``file``.
Only works when ``file`` is str.
keep_var_name: level for keeping variable names:
* 0: none of the names are kept
* 1: (default)keep names of output vars
* 2: keep names of all (output and internal) vars
keep_opr_name: whether to keep operator names.
keep_param_name: whether to keep param names, so param values can be
easily manipulated after loading model
keep_opr_priority: whether to keep priority setting for operators
no_change_graph: whether to change the compute graph when dump, for
model compatibility, some operators will convert to its compatible
format in this version.
* if set False, some operators maybe convert to other operator for
compatibility, all operators will ensure compatibility.
* if set True, no operator will change in the graph when dump.
strip_info_file: a string for path or a file handler. if is not None,
then the dump information for code strip would be written to ``strip_info_file``
append_json: will be check when `strip_info_file` is not None. if set
true, the information for code strip will be append to strip_info_file.
if set false, will rewrite strip_info_file
optimize_for_inference: enbale optmizations,
will skip all optimize options if this is False. Default: True
user_info: any type object, which will be pickled to bytes.
enable_metadata: whether to save metadata into output file.
input_data: input test data and current network output would be used as groundtruth.
The format is "var0:file0;var1:file1..." to specify data files for input vars.
It can also be "#rand(min,max,shape...)" for generating random input data, for
example, "#rand(0,255)", "#rand(0,255,1,3,224,224)" or "#rand(0, 255, 1, ...)"
where `...` means the remaining part of the original shape. If the shape is not
specified, the shape of corresponding input tensors in the network will be used.
If there is only one input var, its name can be omitted. Each data file can either
be an image which can be loaded by opencv, or a pickled numpy.ndarray. This option
can be given multiple times to add multiple testcases. If you start the data
with the letter @, the rest should be a filename, and each line in the file should
be a single datum in the format described above. *NOTE* If `input_data` is not None,
you can only use load-and-run to run the output file.
repeat: how many times the input image is repeated. Useful when running benchmark for
batch size other than one. Have no effect on randomly generated input data.
silent: whether set verbose to False in assert_equal opr.
no_assert: whether insert assert_equal opr to check result; this option is useful for
benchmarking.
maxerr: max error for assert_equal check during runtime.
resize_input: whether resize input image to fit input var shape.
input_transform: a python expression to transform the input data.
Example: data / np.std(data)
dump_format: using different dump formats. the open source MegEngine
defaults to the FBS_V2 format, there are two format FBS_V2 and FBS to choose,
internal MegEngine have an other choice of internal proprietary formats
model_version: the model version of FBS_V2, begin with version 2, this
works only when dump format is FBS_V2.
compat_older_version: the specified megbrain version which is less than 8.16 for model forward compatibility, only support "8.14" currently. Default: None.
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_nchw64 --
whether to use NCHW64 data layout, used for fast int4
support on Nvidia GPU.
* 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)
* enable_fuse_preprocess: whether to fuse astype\pad_channel\dimshuffle and
etc opr
"""
if compat_older_version:
compat_older_version = compat_older_version.strip()
assert (
compat_older_version == "8.14"
), "Forward compatibility for older version only support 8.14 currently."
assert (
not no_change_graph
), "forward compatibility for mgb8.14 will change the graph."
assert (
dump_format == "FBS"
), "forward compatibility for older version only works when dump_format is FBS"
if not self._capture_as_const:
raise ValueError(
"you must specify capture_as_const=True at __init__ to use dump"
)
if not hasattr(self, "_output_names"):
raise ValueError(
"the traced function without return values cannot be dumped, the traced function should return List[Tensor] or Dict[str, Tensor]"
)
if self._output_names and output_names:
raise TypeError(
"cannot specify output_names when output is already in dict format"
)
if output_names and isinstance(output_names, str):
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)
)
)
prefer_input_names = arg_names is not None
if arg_names is None:
arg_names = ["arg_%d" % i for i in range(len(self._arg_bindings))]
if isinstance(arg_names, str):
arg_names = (arg_names,)
arg_names = [arg_name if arg_name is not None else "" for arg_name in 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
if output_names is None:
output_names = [""] * len(self._output_bindings)
# output_names = ["output_{}".format(i) for i in range(len(self._output_bindings))]
input_bindings = []
def normalize_shape(shape):
return (1,) if shape == () else shape
for arg_name, (arg_id, arg_shape) in zip(arg_names, self._arg_bindings):
input_bindings.append((arg_id, arg_name, normalize_shape(arg_shape)))
for kwarg_id, (kwarg_name, kwarg_shape) in self._kwarg_bindings.items():
input_bindings.append((kwarg_id, kwarg_name, normalize_shape(kwarg_shape)))
graph = G.Graph()
jit_enabled = set_jit_enabled(False)
dest_vars = self._trace.dump(
graph,
input_bindings,
[*zip(self._output_bindings, output_names)],
prefer_input_names,
)
set_jit_enabled(jit_enabled)
# dest_vars = [i._node for i in dest_vars]
if input_data is not None:
feeds = self._make_feed(
graph,
dest_vars,
input_data,
repeat,
silent,
no_assert,
maxerr,
resize_input,
input_transform,
)
assert (
isinstance(feeds, dict) and feeds["testcases"]
), "testcases can not be empty"
dest_vars = feeds["outputs"]
if optimize_for_inference:
dest_vars, optimize_options = G.optimize_for_inference(dest_vars, **kwargs)
dest_vars = [i._node for i in dest_vars]
metadata = SerializationMetadata()
if enable_metadata:
metadata.user_info = pickle.dumps(user_info)
metadata.is_valid = True
metadata.graph_modified = False
if optimize_for_inference:
metadata.optimize_options = optimize_options
if isinstance(file, str):
permission = "wb" if append == False else "ab"
file = open(file, permission)
if keep_opr_priority:
_set_priority_to_id(dest_vars)
if input_data is not None:
file.write(b"mgbtest0")
file.write(struct.pack("I", len(feeds["testcases"])))
dump_content, dump_info = G.dump_graph(
dest_vars,
keep_var_name=keep_var_name,
keep_opr_name=keep_opr_name,
keep_param_name=keep_param_name,
keep_opr_priority=keep_opr_priority,
no_change_graph=no_change_graph,
strip_info_file=strip_info_file,
append_json=append_json,
metadata=metadata,
dump_format=dump_format,
model_version=model_version,
compat_older_version=compat_older_version,
)
file.write(dump_content)
if input_data is not None:
inputs = cgtools.get_dep_vars(dest_vars, "Host2DeviceCopy")
inputs = sorted((i.name, i.dtype) for i in inputs)
def make_dev_tensor(value, dtype=None, device=None):
return tensor(value, dtype=dtype, device=device)._dev_tensor()
for testcase in feeds["testcases"]:
assert isinstance(testcase, dict)
cg = G.Graph()
output_mgbvars = []
for name, dtype in inputs:
output_mgbvars.append(
cg.make_const(
make_dev_tensor(
testcase.pop(name), dtype=dtype, device="cpux"
)
)
)
assert not testcase, "extra inputs provided in testcase: {}".format(
testcase.keys()
)
dump_content, _ = G.dump_graph(
output_mgbvars, strip_info_file=strip_info_file, append_json=True,
)
file.write(dump_content)
return dump_info
def get_profile(self):
return json.loads(self._trace.get_profile())