def __topi_nn_conv2d_NCHWc(*args, **kwargs):
assert not kwargs, "Do not support kwargs in template function call"
data, kernel, strides, padding, dilation, layout, dtype = deserialize_args(args)
raw_data_shape = get_const_tuple(data.shape)
raw_kernel_shape = get_const_tuple(kernel.shape)
# get config here
cfg = get_config()
_create_schedule_template(cfg, data, kernel, strides, padding, dilation, layout)
cfg.add_flop(1)
# change shape with the value in config
ic_bn = cfg["tile_ic"].val if hasattr(cfg["tile_ic"], "val") else cfg["tile_ic"].size[-1]
oc_bn = cfg["tile_oc"].val if hasattr(cfg["tile_oc"], "val") else cfg["tile_oc"].size[-1]
new_data_shape = (raw_data_shape[0], raw_data_shape[1] // ic_bn,
raw_data_shape[2], raw_data_shape[3], ic_bn)
new_kernel_shape = (raw_kernel_shape[0] // oc_bn, raw_kernel_shape[1] // ic_bn,
raw_kernel_shape[2], raw_kernel_shape[3], ic_bn, oc_bn)
new_data = tvm.placeholder(new_data_shape, data.dtype)
new_kernel = tvm.placeholder(new_kernel_shape, kernel.dtype)
C = _decl_cl_spatialpack_NCHWc(cfg, new_data, new_kernel, strides, padding, dilation, dtype)
s = _schedule_conv2d_NCHWc(cfg, [C])
return s, [new_data, new_kernel, C]
def _topi_nn_conv2d_NCHWc(*args, **kwargs):
assert not kwargs, "Do not support kwargs in template function call"
args = deserialize_args(args)
if len(args) == 7:
data, kernel, strides, padding, dilation, origin_layout, dtype = args
else:
assert len(args) == 8
data, kernel, strides, padding, dilation, origin_layout, out_layout, dtype = args
raw_data_shape = get_const_tuple(data.shape)
raw_kernel_shape = get_const_tuple(kernel.shape)
# get config here
cfg = get_config()
_create_tuning_space(cfg, data, kernel, strides, padding, dilation,
origin_layout)
# change shape with the value in config
ic_bn, oc_bn, ow_bn = (cfg["tile_ic"].size[-1], cfg["tile_oc"].size[-1],
cfg["tile_ow"].size[-1])
new_data_shape = (raw_data_shape[0], raw_data_shape[1] // ic_bn,
raw_data_shape[2], raw_data_shape[3], ic_bn)
data_layout = "NCHW%dc" % ic_bn
out_layout = "NCHW%dc" % oc_bn
new_kernel_shape = (raw_kernel_shape[0] // oc_bn,
raw_kernel_shape[1] // ic_bn, raw_kernel_shape[2],
raw_kernel_shape[3], ic_bn, oc_bn)
new_data = tvm.placeholder(new_data_shape, data.dtype)
new_kernel = tvm.placeholder(new_kernel_shape, kernel.dtype)
C = _declaration_conv_NCHWc(cfg, new_data, new_kernel, strides, padding,
dilation, data_layout, out_layout, dtype)
s = _schedule_conv2d_NCHWc(cfg, [C])
return s, [new_data, new_kernel, C]
def _topi_nn_depthwise_conv2d_NCHWc(*args, **kwargs):
assert not kwargs, "Do not support kwargs in template function call"
data, kernel, strides, padding, dilation, dtype = deserialize_args(args)
batch, in_channel, height, width = get_const_tuple(data.shape)
filter_channel, channel_multiplier, kh, kw = get_const_tuple(kernel.shape)
ph, pw = padding if isinstance(padding, (tuple, list)) else (padding, padding)
sh, sw = strides if isinstance(strides, (tuple, list)) else (strides, strides)
out_height = (height - kh + 2 * ph) // sh + 1
out_width = (width - kw + 2 * pw) // sw + 1
out_channel = filter_channel * channel_multiplier
# get config here
cfg = get_config()
cfg.define_split("tile_ic", in_channel, num_outputs=2)
cfg.define_split("tile_oc", out_channel, num_outputs=2)
cfg.define_split("tile_ow", out_width, num_outputs=2, filter=lambda y: y.size[-1] <= 64)
# change shape with the value in config
ic_bn, oc_bn = cfg["tile_ic"].size[-1], cfg["tile_oc"].size[-1]
new_data_shape = (batch, in_channel // ic_bn, height, width, ic_bn)
new_kernel_shape = (out_channel // oc_bn, kh, kw, oc_bn)
new_data = tvm.placeholder(new_data_shape, data.dtype)
new_kernel = tvm.placeholder(new_kernel_shape, kernel.dtype)
data_layout = "NCHW%dc" % ic_bn
out_layout = "NCHW%dc" % oc_bn
C = _depthwise_conv2d_NCHWc_cpu(cfg, new_data, new_kernel, strides, padding, dilation,
data_layout, out_layout, dtype)
s = schedule_depthwise_conv2d_NCHWc(cfg, [C])
return s, [new_data, new_kernel, C]
def _topi_nn_conv2d_NCHWc(*args, **kwargs):
assert not kwargs, "Do not support kwargs in template function call"
args = deserialize_args(args)
if len(args) == 7:
data, kernel, strides, padding, dilation, origin_layout, dtype = args
else:
assert len(args) == 8
data, kernel, strides, padding, dilation, origin_layout, out_layout, dtype = args
raw_data_shape = get_const_tuple(data.shape)
raw_kernel_shape = get_const_tuple(kernel.shape)
# get config here
cfg = get_config()
_create_tuning_space(cfg, data, kernel, strides, padding, dilation, origin_layout)
# change shape with the value in config
ic_bn, oc_bn, ow_bn = (cfg["tile_ic"].size[-1], cfg["tile_oc"].size[-1],
cfg["tile_ow"].size[-1])
new_data_shape = (raw_data_shape[0], raw_data_shape[1] // ic_bn,
raw_data_shape[2], raw_data_shape[3], ic_bn)
data_layout = "NCHW%dc" % ic_bn
out_layout = "NCHW%dc" % oc_bn
new_kernel_shape = (raw_kernel_shape[0] // oc_bn, raw_kernel_shape[1] // ic_bn,
raw_kernel_shape[2], raw_kernel_shape[3], ic_bn, oc_bn)
new_data = tvm.placeholder(new_data_shape, data.dtype)
new_kernel = tvm.placeholder(new_kernel_shape, kernel.dtype)
C = _declaration_conv_NCHWc(cfg, new_data, new_kernel, strides, padding, dilation,
data_layout, out_layout, dtype)
s = _schedule_conv2d_NCHWc(cfg, [C])
return s, [new_data, new_kernel, C]
def layout_transform(*args):
"""Autotvm layout transform template."""
args = deserialize_args(args)
cfg = get_config()
cfg.add_flop(-1)
data = args[0]
out = topi.layout_transform(*args)
sch = topi.generic.schedule_injective([out])
return sch, [data, out]
def _topi_nn_conv2d(*args, **kwargs):
assert not kwargs, "Do not support kwargs in template function call"
args = deserialize_args(args)
A, W = args[:2]
with tvm.target.vta():
res = topi.nn.conv2d(*args, **kwargs)
res = topi.right_shift(res, 8)
res = my_clip(res, 0, 127)
res = topi.cast(res, "int8")
if tvm.target.current_target().device_name == 'vta':
s = topi.generic.schedule_conv2d_nchw([res])
else:
s = tvm.create_schedule([res.op])
return s, [A, W, res]
def _topi_nn_depthwise_conv2d_NCHWc(*args, **kwargs):
assert not kwargs, "Do not support kwargs in template function call"
data, kernel, strides, padding, dilation, dtype = deserialize_args(args)
batch, in_channel, height, width = get_const_tuple(data.shape)
filter_channel, channel_multiplier, kh, kw = get_const_tuple(kernel.shape)
ph, pw = padding if isinstance(padding,
(tuple, list)) else (padding, padding)
sh, sw = strides if isinstance(strides,
(tuple, list)) else (strides, strides)
out_height = (height - kh + 2 * ph) // sh + 1
out_width = (width - kw + 2 * pw) // sw + 1
out_channel = filter_channel * channel_multiplier
# get config here
cfg = get_config()
cfg.define_split("tile_ic", in_channel, num_outputs=2)
cfg.define_split("tile_oc", out_channel, num_outputs=2)
cfg.define_split("tile_ow",
out_width,
num_outputs=2,
filter=lambda y: y.size[-1] <= 64)
# change shape with the value in config
ic_bn, oc_bn = cfg["tile_ic"].size[-1], cfg["tile_oc"].size[-1]
new_data_shape = (batch, in_channel // ic_bn, height, width, ic_bn)
new_kernel_shape = (out_channel // oc_bn, 1, kh, kw, 1, oc_bn)
new_data = tvm.placeholder(new_data_shape, data.dtype)
new_kernel = tvm.placeholder(new_kernel_shape, kernel.dtype)
data_layout = "NCHW%dc" % ic_bn
out_layout = "NCHW%dc" % oc_bn
C = _depthwise_conv2d_NCHWc_cpu(cfg, new_data, new_kernel, strides,
padding, dilation, data_layout, out_layout,
dtype)
s = schedule_depthwise_conv2d_NCHWc(cfg, [C])
return s, [new_data, new_kernel, C]
