def dispatch_func(func, *args, **kwargs):
"""The wrapped dispatch function"""
tgt = _target.current_target()
context = DispatchContext.current
if context is None:
raise RuntimeError("DispatchContext is not initialized")
workload = func(*args, **kwargs)
cfg = context.query(tgt, workload)
return dispatch_dict[cfg.template_key](cfg, *args, **kwargs)
def _get_schedule(wkl, target=None):
""" Get the platform specific schedule. """
if target is None:
target = _target.current_target()
else:
target = _target.Target(target)
assert target in _CONV_SCHEDULE, "no schedule for such target: {}".format(
target)
return _CONV_SCHEDULE[target](wkl)
def dispatch_func(func, *args, **kwargs):
"""The wrapped dispatch function"""
tgt = _target.current_target()
context = DispatchContext.current
if context is None:
raise RuntimeError("DispatchContext is not initialized")
workload = func(*args, **kwargs)
cfg = context.query(tgt, workload)
if cfg.template_key:
return dispatch_dict[cfg.template_key](cfg, *args, **kwargs)
else:
assert dispatch_dict, "No func registered for this dispatcher"
for v in dispatch_dict.values():
return v(cfg, *args, **kwargs)
def dispatch_func(func, *args, **kwargs):
"""The wrapped dispatch function"""
tgt = _target.current_target()
workload = func(*args, **kwargs)
cfg = DispatchContext.current.query(tgt, workload)
if cfg.is_fallback and not cfg.template_key:
# first try 'direct' template
if 'direct' in dispatch_dict:
return dispatch_dict['direct'](cfg, *args, **kwargs)
# otherwise pick a random template
for v in dispatch_dict.values():
return v(cfg, *args, **kwargs)
else:
return dispatch_dict[cfg.template_key](cfg, *args, **kwargs)
def dispatch_func(func, *args, **kwargs):
"""The wrapped dispatch function"""
tgt = _target.current_target()
workload = func(*args, **kwargs)
cfg = DispatchContext.current.query(tgt, workload)
if cfg.is_fallback and not cfg.template_key:
# first try 'direct' template
if 'direct' in dispatch_dict:
return dispatch_dict['direct'](cfg, *args, **kwargs)
# otherwise pick a random template
for v in dispatch_dict.values():
return v(cfg, *args, **kwargs)
else:
return dispatch_dict[cfg.template_key](cfg, *args, **kwargs)
def dispatch_func(func, *args, **kwargs):
#The wrapped dispatch function
tgt = _target.current_target()
workload = func(*args, **kwargs)
cfg = DispatchContext.current.query(tgt, workload)
if cfg.is_fallback and not cfg.template_key:
# first try 'direct' template
if 'direct' in dispatch_dict:
if tgt.device_type == 14:
with open(Dp.DPUAutoTuningLibraryPath, 'r') as f:
result = f.read()
if len(result) > 10:
f.seek(0)
DPUAutoTuningData = json.load(f)
inputStr = transferIntoStr(workload)
if inputStr in DPUAutoTuningData:
cfg = DPUAutoTuningData[inputStr]
else:
cfg = None
else:
cfg = None
f.close()
if cfg == None:
#TODO:wjq
cfg = searchDPUOptSch(workload)
with open(Dp.DPUAutoTuningLibraryPath, 'r') as f:
result = f.read()
if len(result) > 10:
f.seek(0)
DPUAutoTuningData = json.load(f)
inputStr = transferIntoStr(workload)
if inputStr in DPUAutoTuningData:
cfg = DPUAutoTuningData[inputStr]
else:
f.close()
raise ValueError("add cfg fail")
else:
f.close()
raise ValueError("add cfg fail")
f.close()
return dispatch_dict['direct'](cfg, *args, **kwargs)
else:
return dispatch_dict['direct'](cfg, *args, **kwargs)
# otherwise pick a random template
for v in dispatch_dict.values():
return v(cfg, *args, **kwargs)
else:
return dispatch_dict[cfg.template_key](cfg, *args, **kwargs)
def conv2d(data, kernel, stride, padding, layout='NCHW'):
"""Conv2D operator.
Parameters
----------
input : tvm.Tensor
4-D with shape [batch, in_channel, in_height, in_width]
filter : tvm.Tensor
4-D with shape [num_filter, in_channel, filter_height, filter_width]
stride : int or a list/tuple of two ints
stride size, or [stride_height, stride_width]
padding : int or a list/tuple of two ints
padding size, or [pad_height, pad_width]
layout : str
layout of data
Returns
-------
output : tvm.Tensor
4-D with shape [batch, out_channel, out_height, out_width]
"""
# search platform specific declaration first
target = _target.current_target()
if target in _CONV_DECLARATION:
return _CONV_DECLARATION[target](data, kernel, stride, padding, layout)
# default declaration
if layout == 'NCHW':
return conv2d_nchw(data, kernel, stride, padding)
elif layout == 'HWCN':
return conv2d_hwcn(data, kernel, stride, padding)
else:
raise ValueError("not support this layout {} yet".format(layout))