megengine.jit.tracing 源代码

# -*- coding: utf-8 -*-
import collections
import contextlib
import functools
import itertools
import json
import os
import pickle
import re
import struct
import sys
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 GraphProfiler, GraphProfiler2, SerializationMetadata
from ..core._imperative_rt.core2 import Tensor as RawTensor
from ..core._imperative_rt.core2 import Trace, TraceError, name_tensor  # skip_tracing,
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 ..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: the function will be traced. symbolic: whether to apply symbolic execution for tracing. Default: False capture_as_const: 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: configuration for sublinear memory optimization. If not None, it enables sublinear memory optimization with given setting. profiling: whether to profile compiled trace. Default: False opt_level: optimization level for compiling trace. Default: 2 graph_opt_config: configuration for graph optimization. Default: None 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, 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, ): 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.options_visitor = apply_options self._trace.profile = profiling self._trace.array_comparator = array_comparator self._trace.record_input_shapes = _input_node_use_static_shape() def __call__(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 _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, **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. 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 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, ) 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())