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MegEngine
MegEngine
提交 657db8dc 编写于 作者: M Megvii Engine Team
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chore(tools): remove dump_with_testcase_mge, user should use jit.dump instead 

GitOrigin-RevId: a88db6e47552a6e83ade847de92e782e63d00a3f
上级 4d22e85b
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imperative/python/megengine/tools/README.md
浏览文件 @ 657db8dc
......@@ -75,16 +75,6 @@ python3 -m megengine.tools.draw_graph -i dump.json -o dump.dot
dot -Tpng dump.dot -o dump.png
```
### dump_with_testcase_mge
将待测数据提前注入模型文件，并在本地运行得到期望结果，可与实际运行的结果进行比对以检查是否出错。
输入: 一个 MegEngine 模型文件，可选一些 npy 文件作为模型输入(也可以随机生成输入，如下面的命令示例)
输出: 一个带输入的 MegEngine 模型文件
执行命令: `python3 -m megengine.tools.dump_with_testcase_mge model.mge -d "#rand(0,255,14,2)"`
### graph_info_analyze
将图和内存信息的 json 文件的文件夹 logs 转换为 TensorBoard 的输入文件夹 logs_p。以便 TensorBoard 对图结构以及内存信息进行可视化。
......
imperative/python/megengine/tools/dump_with_testcase_mge.py 已删除 100755 → 0
浏览文件 @ 4d22e85b
# -*- coding: utf-8 -*-
import argparse
import os
import re
import struct
import cv2
import numpy as np
import megengine as mge
import megengine.core._imperative_rt as rt
import megengine.core.tensor.megbrain_graph as G
from megengine import tensor
from megengine.core.ops import builtin
from megengine.utils import comp_graph_tools as cgtools
logger = mge.get_logger(__name__)
def auto_reformat_image(args, 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 args.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(args, dst_shape, dtype, path, repeat):
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 args.resize_input
assert not args.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 args.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(args, 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 args.input_transform:
data = eval(args.input_transform, {"data": data, "np": np})
return data
def gen_one_testcase(args, 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(":")
if args.repeat:
repeat = args.repeat
else:
repeat = 1
ret[var] = read_input_data(
args, inputs[var].shape, inputs[var].dtype, path, repeat
)
return ret
def make_feeds(args):
ret = G.load_graph(args.input)
cg_rt, outputs = ret.graph, ret.output_vars_list
inputs = cgtools.get_dep_vars(outputs, "Host2DeviceCopy")
inputs = {i.name: i for i in inputs}
if not args.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=cg_rt
)
replace_varmap[var] = inp.outputs[0]
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 = cg_rt.compile([node.outputs[0] 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 args.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(args, inputs, inp_spec)
assert len(cur_testcase) == len(
inputs
), "required inputs: {}; given data: {}".format(
inputs.keys(), cur_testcase.keys()
)
if not args.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 args.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 = builtin.AssertEqual(**kwargs)
(res,) = G.apply_normal_varnode(op, expect, real)
return res
verbose = not args.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(cg_rt, device, dtype, shape, name)
# insert assert opr to check expect and real.
outputs_new.append(
assert_equal(expect_get, i, verbose=verbose, maxerr=args.maxerr,)
)
inputs[expect_name(i)] = expect_get
outputs = outputs_new
return {"outputs": outputs, "testcases": testcases}
def optimize_for_inference(args, outputs):
args_list = [
"enable_io16xc32",
"enable_ioc16",
"enable_hwcd4",
"enable_nchw4",
"enable_nchw88",
"enable_nchw44",
"enable_nchw44_dot",
"enable_nchw32",
"enable_chwn4",
"enable_fuse_conv_bias_nonlinearity",
"enable_fuse_conv_bias_with_z",
"enable_fuse_preprocess",
]
kwargs = {}
for k in args_list:
if getattr(args, k):
kwargs[k] = True
if args.optimize_for_inference:
outputs = G.optimize_for_inference(outputs, **kwargs)
return outputs
def main():
parser = argparse.ArgumentParser(
description="Pack computing graph, input values and expected output "
"values into one file for checking correctness. README.md gives more "
"details on the usage",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument("input", help="MegEngine dumped model file")
parser.add_argument("-o", "--output", help="output file", required=True)
parser.add_argument(
"-d",
"--data",
default=[],
action="append",
required=True,
help="Given input test data when input file is a network, "
"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. "
" *NOTE* "
"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. ",
)
parser.add_argument(
"--repeat",
type=int,
default=1,
help="Specify 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.",
)
parser.add_argument(
"--silent",
action="store_true",
help="set verbose to False in asserti_equal opr",
)
parser.add_argument(
"--optimize-for-inference",
action="store_true",
help="enable optimization for inference",
)
parser.add_argument(
"--no-assert",
action="store_true",
help="do not insert assert_equal opr to check result; "
"this option is useful for benchmarking",
)
parser.add_argument(
"--maxerr",
type=float,
default=1e-4,
help="max error for assert_equal check during runtime",
)
parser.add_argument(
"--resize-input",
action="store_true",
help="resize input image to fit input var shape",
)
parser.add_argument(
"--input-transform",
help="a python expression to transform the input data. "
"Example: data / np.std(data)",
)
parser.add_argument(
"--discard-var-name",
action="store_true",
help="discard variable and param names in the " "generated output",
)
parser.add_argument(
"--output-strip-info", action="store_true", help="output code strip information"
)
parser.add_argument(
"--enable-io16xc32",
action="store_true",
help="transform the mode to float16 io float32 compute",
)
parser.add_argument(
"--enable-ioc16",
action="store_true",
help="transform the dtype of the model to float16 io " "and compute",
)
parser.add_argument(
"--enable-fuse-conv-bias-nonlinearity",
action="store_true",
help="fuse convolution bias and nonlinearity opr to a "
"conv_bias opr and compute",
)
parser.add_argument(
"--enable-hwcd4",
action="store_true",
help="transform the model format from NCHW to NHWCD4 "
"for inference; you may need to disable CUDA and set "
"MGB_USE_MEGDNN_DBG=2",
)
parser.add_argument(
"--enable-nchw4",
action="store_true",
help="transform the model format from NCHW to NCHW4 " "for inference",
)
parser.add_argument(
"--enable-nchw88",
action="store_true",
help="transform the model format from NCHW to NCHW88 " "for inference",
)
parser.add_argument(
"--enable-nchw44",
action="store_true",
help="transform the model format from NCHW to NCHW44 " "for inference",
)
parser.add_argument(
"--enable-nchw44-dot",
action="store_true",
help="transform the model format from NCHW to NCHW44_DOT "
"for optimizing armv8.2 dot in inference",
)
parser.add_argument(
"--enable-nchw32",
action="store_true",
help="transform the model format from NCHW4 to NCHW32 "
"for inference on nvidia TensoCore",
)
parser.add_argument(
"--enable-chwn4",
action="store_true",
help="transform the model format to CHWN4 "
"for inference, mainly used for nvidia tensorcore",
)
parser.add_argument(
"--enable-fuse-conv-bias-with-z",
action="store_true",
help="fuse conv_bias with z input for inference on "
"nvidia GPU (this optimization pass will result in mismatch "
"of the precision of output of training and inference)",
)
parser.add_argument(
"--enable-fuse-preprocess",
action="store_true",
help="fuse astype\pad_channel\dimshuffle and etc opr " "from h2d opr",
)
args = parser.parse_args()
feeds = make_feeds(args)
assert isinstance(feeds, dict) and feeds["testcases"], "testcases can not be empty"
output_mgbvars = feeds["outputs"]
output_mgbvars = optimize_for_inference(args, output_mgbvars)
inputs = cgtools.get_dep_vars(output_mgbvars, "Host2DeviceCopy")
inputs = sorted((i.name, i.dtype) for i in inputs)
if args.discard_var_name:
sereg_kwargs = dict(keep_var_name=0, keep_param_name=False)
else:
sereg_kwargs = dict(keep_var_name=2, keep_param_name=True)
strip_info_file = args.output + ".json" if args.output_strip_info else None
with open(args.output, "wb") as fout:
fout.write(b"mgbtest0")
fout.write(struct.pack("I", len(feeds["testcases"])))
dump_content, stat = G.dump_graph(
output_mgbvars,
append_json=True,
strip_info_file=strip_info_file,
**sereg_kwargs,
)
fout.write(dump_content)
logger.info(
"graph dump sizes: tot_size={:.3f}KiB overhead={:.3f}KiB".format(
stat.tot_bytes / 1024, (stat.tot_bytes - stat.tensor_value_bytes) / 1024
)
)
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()
)
with open(args.output, "ab") as fout:
dump_content, _ = G.dump_graph(
output_mgbvars, strip_info_file=strip_info_file, append_json=True
)
fout.write(dump_content)
if __name__ == "__main__":
main()
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