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fix(lite/load_and_run): fix cuda options build error for load and run 

GitOrigin-RevId: b84b51d341
revert-411-add-tools
Megvii Engine Team 3 years ago
parent
9646f1aac4
commit
be84d13f48
4 changed files with 10 additions and 413 deletions
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  1. +0
    -404
      lite/load_and_run/dump_with_testcase.py
  2. +7
    -5
      lite/load_and_run/src/options/device_options.cpp
  3. +2
    -3
      lite/load_and_run/src/options/device_options.h
  4. +1
    -1
      lite/load_and_run/src/options/layout_trans_options.cpp

+ 0
- 404
lite/load_and_run/dump_with_testcase.py View File

@@ -1,404 +0,0 @@
#!/usr/bin/env mdl
# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

from megskull.graph import NodeFilter, FpropEnv
from megskull.opr.all import AssertEqual, DataProvider, BatchNormalization
from megskull.utils.logconf import get_logger
from meghair.utils import io
import megbrain as mgb

import argparse
import struct
import re
import os

import numpy as np
import cv2

logger = 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 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):
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 = io.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].imm_shape,
inputs[var].dtype, path, repeat)
return ret


def make_feeds(args):
outputs = io.load_network(args.input).outputs
if not args.no_assert:
env = FpropEnv(verbose_fprop=False)
# set flag so ExternCOprPlaceholder produce expected output
env.flags.user['extern_c_opr_eval'] = True
func = env.comp_graph.compile(None, [mgb.copy_output(env.get_mgbvar(i))
for i in outputs])

def expect_name(var): return 'expect:{}'.format(var.name)

nf = NodeFilter.make_all_deps(*outputs)
inputs = {i.name: i for i in nf.data_provider()}
if args.init_bn:
for i in nf:
if isinstance(i, BatchNormalization):
if i._iter.get_value() == 0:
i._iter.set_value(1)
i._variance.set_value(np.ones(i._variance.shape))

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 = func(**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.partial_shape.determined_shape
if ret:
return ret
return testcases[0][expect_name(var)].shape

verbose = not args.silent
outputs = [AssertEqual(DataProvider(expect_name(i), expect_shp(i),
dtype=i.dtype,
comp_node=i.comp_node),
i, verbose=verbose, maxerr=args.maxerr)
for i in outputs]
return {'outputs': outputs, 'testcases': testcases}

def optimize_for_inference(args, outputs):
args_map = {
'enable_io16xc32': 'f16_io_f32_comp',
'enable_ioc16': 'f16_io_comp',
'enable_hwcd4': 'use_nhwcd4',
'enable_nchw4': 'use_nchw4',
'enable_nchw88': 'use_nchw88',
'enable_nchw44': 'use_nchw44',
'enable_nchw44_dot': 'use_nchw44_dot',
'enable_nchw32': 'use_nchw32',
'enable_chwn4': 'use_chwn4',
'enable_fuse_conv_bias_nonlinearity': 'fuse_conv_bias_nonlinearity',
'enable_fuse_conv_bias_with_z': 'fuse_conv_bias_with_z',
'enable_nchw64': 'use_nchw64',
'enable_fuse_preprocess': 'fuse_preprocess',
}

kwargs = {}
for k, v in args_map.items():
if getattr(args, k):
assert args.optimize_for_inference, (
'optimize_for_inference should be set when {} is given'.format(
k))
kwargs[v] = True

if args.optimize_for_inference:
return mgb.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='input file; see README for details')
parser.add_argument('-o', '--output', help='output file', required=True)
parser.add_argument('--init-bn', action='store_true',
help='initialize untrained batch-normalization, to '
'avoid NaN or Inf results')
parser.add_argument(
'-d', '--data', default=[], action='append',
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 DataProvider 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 AssertEqual opr')
parser.add_argument('--optimize-for-inference', action='store_true',
help='enbale optimization for inference')
parser.add_argument('--no-assert', action='store_true',
help='do not insert AssertEqual opr to check result; '
'this option is useful for benchmarking')
parser.add_argument('--maxerr', type=float, default=AssertEqual.maxerr,
help='max error for AssertEqual 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-chwn4', action='store_true',
help='transform the model format to CHWN4 '
'for inference, mainly used for nvidia tensorcore')
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-nchw64', action='store_true',
help='transform the model format from NCHW to NCHW64 '
'for inference on Nvidia GPU')
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 op')
args = parser.parse_args()
if args.data:
feeds = make_feeds(args)
else:
feeds = io.load(args.input)

assert isinstance(feeds, dict) and feeds['testcases'], (
'testcases can not be empty')

env = FpropEnv(verbose_fprop=False)

outputs = feeds['outputs']
output_mgbvars = list(map(env.get_mgbvar, outputs))

output_mgbvars = optimize_for_inference(args, output_mgbvars)

inputs = sorted(((i.name, i.dtype) for i in
NodeFilter.make_all_deps(*outputs).data_provider()))
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)

with open(args.output, 'wb') as fout:
fout.write(b'mgbtest0')
fout.write(struct.pack('I', len(feeds['testcases'])))
stat = mgb.serialize_comp_graph_to_file(
args.output, output_mgbvars, append=True,
output_strip_info=args.output_strip_info,
**sereg_kwargs)
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))

for testcase in feeds['testcases']:
assert isinstance(testcase, dict)
cg = mgb.comp_graph()
cn = mgb.comp_node('cpux')
output_mgbvars = []
for name, dtype in inputs:
output_mgbvars.append(cg.make_shared(cn, value=testcase.pop(name),
dtype=dtype))
assert not testcase, 'extra inputs provided in testcase: {}'.format(
testcase.keys())

mgb.serialize_comp_graph_to_file(
args.output,
output_mgbvars,
append=True,
output_strip_info=args.output_strip_info,
append_json=True)

if __name__ == '__main__':
main()

+ 7
- 5
lite/load_and_run/src/options/device_options.cpp View File

@@ -31,8 +31,9 @@ void XPUDeviceOption::config_model_internel<ModelLite>(
LITE_WARN("using cpu device\n");
model->get_config().device_type = LiteDeviceType::LITE_CPU;
}
#if MGE_WITH_CUDA
#if LITE_WITH_CUDA
if (enable_cuda) {
LITE_WARN("using cuda device\n");
model->get_config().device_type = LiteDeviceType::LITE_CUDA;
}
#endif
@@ -75,11 +76,12 @@ void XPUDeviceOption::config_model_internel<ModelMdl>(
loc.type = mgb::CompNode::DeviceType::CPU;
};
}
#if MGE_WITH_CUDA
#if MGB_CUDA
if (enable_cuda) {
mgb_log_warn("using cuda device\n");
model->get_mdl_config().comp_node_mapper = [](mgb::CompNode::Locator& loc) {
loc.type = mgb::CompNode::DeviceType::CUDA;
loc.device = 0;
};
}
#endif
@@ -130,7 +132,7 @@ void XPUDeviceOption::config_model_internel<ModelMdl>(
XPUDeviceOption::XPUDeviceOption() {
m_option_name = "xpu_device";
enable_cpu = FLAGS_cpu;
#if MGE_WITH_CUDA
#if MGB_CUDA
enable_cuda = FLAGS_cuda;
#endif
enable_cpu_default = FLAGS_cpu_default;
@@ -163,7 +165,7 @@ XPUDeviceOption::XPUDeviceOption() {

bool XPUDeviceOption::is_valid() {
bool ret = FLAGS_cpu || FLAGS_cpu_default;
#if MGE_WITH_CUDA
#if MGB_CUDA
ret = ret || FLAGS_cuda;
#endif
ret = ret || FLAGS_multithread >= 0;
@@ -188,7 +190,7 @@ void XPUDeviceOption::config_model(
}
///////////////////////// xpu gflags ////////////////////////////
DEFINE_bool(cpu, false, "set CPU device as running device");
#if MGE_WITH_CUDA
#if MGB_CUDA || LITE_WITH_CUDA
DEFINE_bool(cuda, false, "set CUDA device as running device ");
#endif
DEFINE_bool(cpu_default, false, "set running device as CPU device with inplace mode");


+ 2
- 3
lite/load_and_run/src/options/device_options.h View File

@@ -6,14 +6,13 @@
*
* \copyright Copyright (c) 2020-2021 Megvii Inc. All rights reserved.
*/

#pragma once
#include <gflags/gflags.h>
#include "models/model.h"
#include "option_base.h"

DECLARE_bool(cpu);
#if MGE_WITH_CUDA
#if MGB_CUDA || LITE_WITH_CUDA
DECLARE_bool(cuda);
#endif
DECLARE_bool(cpu_default);
@@ -35,7 +34,7 @@ private:
template <typename ModelImpl>
void config_model_internel(RuntimeParam&, std::shared_ptr<ModelImpl>){};
bool enable_cpu;
#if MGE_WITH_CUDA
#if MGB_CUDA || LITE_WITH_CUDA
bool enable_cuda;
#endif
bool enable_cpu_default;


+ 1
- 1
lite/load_and_run/src/options/layout_trans_options.cpp View File

@@ -113,7 +113,7 @@ bool GoptLayoutOption::is_valid() {
ret = true;
}
}
ret = ret || FLAGS_layout_transform_dump.empty();
ret = ret || !FLAGS_layout_transform_dump.empty();
return ret;
}



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