def _create_cc(self, x, gy, hint, e=Engine()):
if x.ndim == 2:
fmt = m.memory.nc
else:
fmt = m.memory.nchw
x = array(x, fmt, e)
gy = array(gy, fmt, e)
diff_pd = gy.memory.get_primitive_desc()
outputs = CC.reorder_if_must(x, diff_pd, e, self.dag)
if len(outputs) == 2:
x, self.itm_arr = outputs[:2]
else:
x = outputs[0]
mem_pd = x.memory.get_primitive_desc()
cc_d = eltwise_backward.desc(eltwise_relu, diff_pd.desc(),
mem_pd.desc(), 0.0, 0.0)
cc_pd = eltwise_backward.primitive_desc(cc_d, e, hint)
# gx = mdarray(cc_pd.diff_src_primitive_desc())
# print("gx.format=", m.get_fmt(cc_pd.diff_src_primitive_desc()))
gx = gy
self.dag.push_back(eltwise_backward.eltwise_backward(cc_pd,
at(x.memory), at(gy.memory), gx.memory))
self.x = x
self.gy = gy
self._hint = hint
self.outputs = gx,
def _create_cc(self, x, gy, hint, y, ws, ksize, stride, pad, cover_all, e):
self.ksize = ksize
self.stride = stride
self.pad = pad
self.cover_all = cover_all
self.x = array(x, m.memory.nchw, e)
gy = array(gy, m.memory.nchw, e)
if self.alg_kind is pooling_max:
gy_md = y.memory.get_primitive_desc().desc()
else:
gy_md = gy.memory.get_primitive_desc().desc()
gx_md = m.desc(x.shape, m.memory.f32, m.memory.any)
# x_md = self.x.memory.get_primitive_desc().desc()
n, c, h, w = x.shape
sy, sx = _pair(stride)
kh, kw = _pair(ksize)
p_upper, p_left = _pair(pad)
yh = conv.get_conv_outsize(h, kh, sy, p_upper, cover_all=cover_all)
assert yh > 0, 'Height in the output should be positive.'
yw = conv.get_conv_outsize(w, kw, sx, p_left, cover_all=cover_all)
assert yw > 0, 'Width in the output should be positive.'
p_down = sy * (yh - 1) + kh - h - p_upper
p_right = sx * (yw - 1) + kw - w - p_left
cc_d = pooling_backward.desc(self.alg_kind, gx_md, gy_md, stride,
ksize, (p_upper, p_left),
(p_down, p_right), zero)
cc_pd = pooling_backward.primitive_desc(cc_d, e, hint)
gx = mdarray(cc_pd.diff_src_primitive_desc())
if self.alg_kind is pooling_max:
# For max pooling reorder y if needed
outputs = reorder_if_must(gy, y.memory.get_primitive_desc(), e,
self.dag_)
if len(outputs) == 2:
self.reordered_gy, self.itm_arr = outputs[:2]
else:
self.reordered_gy = outputs[0]
self.dag_.push_back(
pooling_backward.pooling_backward(
cc_pd, at(self.reordered_gy.memory), at(ws.memory),
gx.memory))
else:
# There is no workspace for average pooling
self.dag_.push_back(
pooling_backward.pooling_backward(cc_pd, at(gy.memory),
gx.memory))
self._hint = hint
self.gy = gy
self.outputs = gx,
def mkl_sum(xs, func=None):
e = Engine()
xarrays = () # prevent the obj from gc
xs_arrays = () # prevent the obj from gc
itm_arr = None # prvent the obj from gc
xs_mpdl = m.mpd_list()
xs_pl = ()
scales = m.vectord()
pl = primitive_list()
for i in range(len(xs)):
xarray = array(xs[i], _x_format(xs[i].ndim), e)
xmpd = xarray.memory.get_primitive_desc()
if i == 0:
xmpd_best = xmpd
else:
if m.get_fmt(xmpd) > m.get_fmt(xmpd_best):
xmpd_best = xmpd
xs_arrays += (xarray,)
for x in xs_arrays:
outputs = reorder_if_must(x, xmpd_best, e, pl)
if len(outputs) == 2:
xarray, itm_arr = outputs[:2]
else:
xarray = outputs[0]
xarrays += (xarray,)
scales.push_back(1.0)
xs_mpdl.push_back(xarray.memory.get_primitive_desc())
xs_pl += (at(xarray.memory), )
cc_pd = sum.primitive_desc(scales, xs_mpdl)
if func is not None and hasattr(func, 'hint'): # this is only used for grad accumulate currently
cc = ComputeComplex.get_bd_cc(func.hint, pos=(func.rank, func.fanout))
if cc is not None:
y = cc.gy
else:
y = mdarray(cc_pd.dst_primitive_desc())
else:
y = mdarray(cc_pd.dst_primitive_desc())
pl.push_back(sum.sum(cc_pd, xs_pl, y.memory))
s = Stream()
s.submit(pl)
s.wait()
return y
def _create_cc(self, x, W, b, e=Engine()):
y_d = m.desc((x.shape[0], W.shape[0]), m.memory.f32, m.memory.any)
# Create primitive_desc from any
cc_d = create_forward_desc(ip_forward.desc, y_d, x, W, b)
cc_pd = ip_forward.primitive_desc(cc_d, e)
# Transform inputs
self.x = array(x, _x_format(x.ndim), e)
w_mpd = cc_pd.weights_primitive_desc()
self.usr_w = array(W, _W_format(W.ndim), e)
outputs = CC.reorder_if_must(self.usr_w, w_mpd, e, self.dag)
if len(outputs) == 2:
self.W, self.itm_arr = outputs[:2]
else:
self.W = outputs[0]
if b is not None:
self.b = array(b, m.memory.x, e)
y = linear_f_op(cc_pd, self.x, self.W, self.b, self.dag)
else:
y = linear_f_op(cc_pd, self.x, self.W, self.dag)
# Prepare output
# y = mdarray(cc_pd.dst_primitive_desc())
# dag = self.dag_
# # Reorder if must
# x_m = reorder_if_must(self.x.memory,
# cc_pd.src_primitive_desc(), dag)
# W_m = reorder_if_must(self.W.memory,
# cc_pd.weights_primitive_desc(), dag)
# if b is None:
# dag.push_back(ip_forward.inner_product_forward(cc_pd,
# at(x_m), at(W_m), y.memory))
# else:
# dag.push_back(ip_forward.inner_product_forward(cc_pd,
# at(x_m), at(W_m), at(self.b.memory), y.memory))
# self.x_m = x_m
# self.W_m = W_m
self._hint = cc_pd
self.outputs = y,
def _create_cc(self, inputs, fwd_x, gy, hint, flags, eps, mean, var, e):
self.train = configuration.config.train
self.flags = flags
self.eps = eps
x, gamma, beta = inputs[:3]
# self.x = array(x, m.memory.nchw, e)
self.x = fwd_x
x_mpd = self.x.memory.get_primitive_desc()
x_md = x_mpd.desc()
gy = array(gy, m.memory.nchw, e)
outputs = reorder_if_must(gy, x_mpd, e, self.dag_)
if len(outputs) == 2:
self.gy_src = gy
gy, self.itm_arr = outputs[:2]
else:
self.gy_src = gy
gy = outputs[0]
gy_md = gy.memory.get_primitive_desc().desc()
cc_d = bn_backward.desc(backward, gy_md, x_md, eps, flags)
cc_pd = bn_backward.primitive_desc(cc_d, e, hint)
gx = mdarray(self.x.memory.get_primitive_desc(), gy.memory)
if flags & use_scale_shift:
w = numpy.concatenate((gamma, beta), axis=0).reshape((2, -1))
self.w = array(w, m.memory.nc, e)
self.mean = array(mean, m.memory.x, e)
self.var = array(var, m.memory.x, e)
self.gw = mdarray(cc_pd.diff_weights_primitive_desc())
bwd_p = bn_backward.batch_normalization_backward(
cc_pd, at(self.x.memory), at(self.mean.memory),
at(self.var.memory), at(gy.memory), at(self.w.memory),
gx.memory, self.gw.memory)
else:
bwd_p = bn_backward.batch_normalization_backward(
cc_pd, at(self.x.memory), at(self.mean.memory),
at(self.var.memory), at(gy.memory), gx.memory)
self.dag_.push_back(bwd_p)
self._hint = hint
self.gy = gy
self.outputs = gx, self.gw
def _create_cc(self, inputs, e):
x0, x1 = inputs[:2]
xs_mpdl = m.mpd_list()
xs_pl = ()
scales = m.vectord()
self.x0 = x0
self.x1 = x1
self.x1_reordered = reorder_if_must(x1, x0.memory.get_primitive_desc(),
e, self.dag_)[0]
scales.push_back(1.0)
scales.push_back(1.0)
xs_mpdl.push_back(x0.memory.get_primitive_desc())
xs_mpdl.push_back(self.x1_reordered.memory.get_primitive_desc())
cc_pd = sum.primitive_desc(scales, xs_mpdl)
xs_pl = (at(x0.memory), at(self.x1_reordered.memory))
y = mdarray(cc_pd.dst_primitive_desc())
self.dag_.push_back(sum.sum(cc_pd, xs_pl, y.memory))
self.outputs = y,
def _create_cc(self, x, W, b, stride, pad, cover_all, e):
super(ConvolutionForward, self).__init__()
g = conv.conv_geometry(x.shape, W.shape, stride, pad, cover_all)
y_d = m.desc(g.out_shape, m.memory.f32, m.memory.any)
# Create primitive_desc from any
cc_d = create_forward_desc(conv_forward.desc, y_d, (x, W, b),
g.geometry)
cc_pd = conv_forward.primitive_desc(cc_d, e)
w_mpd = cc_pd.weights_primitive_desc()
self.usr_w = array(W, m.memory.oihw, e)
outputs = CC.reorder_if_must(self.usr_w, w_mpd, e, self.dag)
if len(outputs) == 2:
self.W, self.itm_arr = outputs[:2]
else:
self.W = outputs[0]
# Record weight reorder primitive hint
if self.usr_w is not self.W:
wro = WeightReorderOptimization()
wro.reorder = self.dag.size() - 1
wro.optimized = False
self.weight_reorder_opt = wro
else:
self.weight_reorder_opt = None
self.x = array(x, m.memory.nchw, e)
if b is not None:
self.b = array(b, m.memory.x, e)
if b is None:
y = conv_f_op(cc_pd, self.x, self.W, self.dag)
else:
y = conv_f_op(cc_pd, self.x, self.W, self.b, self.dag)
self._hint = cc_pd
self.outputs = y,
def _create_cc(self, inputs, eps, mean, var, e):
self.eps = eps
self.mean = None
self.var = None
self.w = None
self.train = configuration.config.train
x, gamma, beta = inputs[:3]
fmt_desired = m.get_desired_format(x.shape[1])
x = array(x, m.memory.nchw, e)
# x = array(x, fmt_desired, e)
assert x.dtype == numpy.dtype('float32')
x_desired_md = m.desc(x.shape, m.memory.f32, fmt_desired)
x_desired_mpd = m.primitive_desc(x_desired_md, e)
outputs = reorder_if_must(x, x_desired_mpd, e, self.dag_)
if len(outputs) == 2:
self.x, self.itm_arr = outputs[:2]
self.x_src = x
else:
self.x = outputs[0]
self.x_src = x
w = numpy.concatenate((gamma, beta), axis=0).reshape((2, -1))
self.numpy_w = w
self.w = array(w, m.memory.nc, e)
scale_shift = True
self.flags = use_scale_shift
if mean is None:
fwd_prop_kind = forward_training
global_stats = False
else:
fwd_prop_kind = forward_scoring
self.flags |= use_global_stats
global_stats = True
self.mean = array(mean, m.memory.x, e)
self.var = array(var, m.memory.x, e)
x_md = self.x.memory.get_primitive_desc().desc()
cc_d = bn_forward.desc(fwd_prop_kind, x_md, eps, self.flags)
cc_pd = bn_forward.primitive_desc(cc_d, e)
y = mdarray(cc_pd.dst_primitive_desc())
# TODO reorder weight
# if scale_shift is True:
# w = mdarray(cc_pd.weights_primitive_desc())
if scale_shift is True and global_stats is False:
self.mean = mdarray(cc_pd.mean_primitive_desc())
self.var = mdarray(cc_pd.variance_primitive_desc())
if (not configuration.config.train) and (not global_stats):
if scale_shift is True:
bnf = bn_forward.batch_normalization_forward(
cc_pd, at(self.x.memory), at(self.w.memory), y.memory)
else:
bnf = bn_forward.batch_normalization_forward(
cc_pd, at(self.x.memory), y.memory)
elif global_stats is True:
if scale_shift is True:
bnf = bn_forward.batch_normalization_forward(
cc_pd, at(self.x.memory), at(self.mean.memory),
at(self.var.memory), at(self.w.memory), y.memory)
else:
bnf = bn_forward.batch_normalization_forward(
cc_pd, at(self.x.memory), self.mean.memory,
self.var.memory, y.memory)
else:
if scale_shift is True:
bnf = bn_forward.batch_normalization_forward(
cc_pd, at(self.x.memory), at(self.w.memory), y.memory,
self.mean.memory, self.var.memory)
else:
bnf = bn_forward.batch_normalization_forward(
cc_pd, at(self.x.memory), y.memory, self.mean.memory,
self.var.memory)
self.dag_.push_back(bnf)
self._hint = cc_pd
self.outputs = y, self.flags, self.mean, self.var
