def local_ConvGradWeights_mkl(node):
if not mkl_available():
return
if not isinstance(node.op, AbstractConv2d_gradWeights):
return
if node.inputs[1].type.ndim != 4 and node.inputs[1].type.ndim != 5:
return
if node.op.filter_dilation != (1, 1):
return
if None in node.op.kshp:
return
if None in node.op.imshp:
return
try:
image, gz, zshp = node.inputs
weight = node.inputs[2].owner.inputs[0].owner.inputs[0]
image_internal = U2IConv(imshp=node.op.imshp,
kshp=node.op.kshp,
subsample=node.op.subsample,
border_mode=node.op.border_mode,
filter_dilation=node.op.filter_dilation)(image)
convOut = mkl_conv.Conv2D(imshp=node.op.imshp,
kshp=node.op.kshp,
border_mode=node.op.border_mode,
subsample=node.op.subsample,
filter_flip=node.op.filter_flip,
filter_dilation=node.op.filter_dilation)(image_internal, weight)
gz_internal = I2UGrad()(convOut, gz)
gradWeight = mkl_conv.ConvGradWeights(border_mode=node.op.border_mode,
subsample=node.op.subsample,
imshp=node.op.imshp,
kshp=node.op.kshp)(image_internal, weight, gz_internal)
rval = gradWeight
return [rval]
except Exception as e:
msg = ('Failed to apply local opt to Op %s. '
'Exception message: %s\n') % (node.op, str(e))
_logger.warning(msg)
return
def local_ConvGroup_mkl(node):
if not mkl_available():
return
if not isinstance(node.op, mkl_conv.AbstractConvGroup):
return
# image
if node.inputs[0].type.ndim != 4:
return
# weight
if node.inputs[1].type.ndim not in [4, 5]:
return
try:
assert len(node.inputs) in [2, 3]
if len(node.inputs) == 2:
image, weight, = node.inputs
bias = None
else:
image, weight, bias, = node.inputs
image_internal = U2IConv(imshp=node.op.imshp,
kshp=node.op.kshp,
subsample=node.op.subsample,
border_mode=node.op.border_mode,
filter_dilation=node.op.filter_dilation)(image)
conv_out = mkl_conv.Conv2D(imshp=node.op.imshp,
kshp=node.op.kshp,
subsample=node.op.subsample,
border_mode=node.op.border_mode,
filter_flip=node.op.filter_flip,
filter_dilation=node.op.filter_dilation)(image_internal, weight, bias)
conv_out = I2U()(conv_out)
rval = conv_out
return [rval]
except Exception as e:
msg = ('Failed to apply local opt to Op %s. '
'Exception message: %s\n') % (node.op, str(e))
_logger.warning(msg)
return
def local_Conv2D_mkl(node):
if not mkl_available():
return
if not isinstance(node.op, AbstractConv2d):
return
if node.op.filter_dilation != (1, 1):
return
if node.inputs[1].type.ndim != 4 and node.inputs[1].type.ndim != 5:
return
if None in node.op.kshp:
return
if None in node.op.imshp:
return
try:
image, weight = node.inputs
image_internal = U2IConv(imshp=node.op.imshp,
kshp=node.op.kshp,
subsample=node.op.subsample,
border_mode=node.op.border_mode,
filter_dilation=node.op.filter_dilation)(image)
convOut = mkl_conv.Conv2D(imshp=node.op.imshp,
kshp=node.op.kshp,
border_mode=node.op.border_mode,
subsample=node.op.subsample,
filter_flip=node.op.filter_flip,
filter_dilation=node.op.filter_dilation)(image_internal, weight)
z_user = I2U()(convOut)
reval = z_user
return [reval]
except Exception as e:
msg = ('Failed to apply local opt to Op %s. '
'Exception message: %s\n') % (node.op, str(e))
_logger.warning(msg)
return
def local_ConvGroupGrad_mkl(node):
if not mkl_available():
return
if not isinstance(node.op, mkl_conv.AbstractConvGroupGrad):
return
# image
if node.inputs[0].type.ndim != 4:
return
# weight
if node.inputs[2].type.ndim not in [4, 5]:
return
try:
assert len(node.inputs) in [3, 4]
if len(node.inputs) == 3:
image, gz, weight = node.inputs
bias = None
else:
image, gz, weight, bias = node.inputs
image_internal = U2IConv(imshp=node.op.imshp,
kshp=node.op.kshp,
subsample=node.op.subsample,
border_mode=node.op.border_mode,
filter_dilation=node.op.filter_dilation)(image)
conv_out = mkl_conv.Conv2D(imshp=node.op.imshp,
kshp=node.op.kshp,
subsample=node.op.subsample,
border_mode=node.op.border_mode,
filter_flip=node.op.filter_flip,
filter_dilation=node.op.filter_dilation)(image_internal, weight, bias)
gz_internal = I2UGrad()(conv_out, gz)
grad_image = mkl_conv.ConvGradInputs(imshp=node.op.imshp,
kshp=node.op.kshp,
subsample=node.op.subsample,
border_mode=node.op.border_mode,
filter_flip=node.op.filter_flip,
filter_dilation=node.op.filter_dilation)(image_internal, weight, gz_internal)
grad_image = U2IGrad()(image, grad_image)
grad_out = mkl_conv.ConvGradWeights(imshp=node.op.imshp,
kshp=node.op.kshp,
subsample=node.op.subsample,
border_mode=node.op.border_mode,
filter_flip=node.op.filter_flip,
filter_dilation=node.op.filter_dilation)(image_internal, weight, gz_internal, bias)
if isinstance(grad_out, (list, tuple)):
grad_weight, grad_bias, = grad_out
else:
grad_weight = grad_out
if len(node.outputs) == 3:
assert len(grad_out) == 2
rval = [grad_image, grad_weight, grad_bias]
else:
rval = [grad_image, grad_weight]
return rval
except Exception as e:
msg = ('Failed to apply local opt to Op %s. '
'Exception message: %s\n') % (node.op, str(e))
_logger.warning(msg)
return
def apply(self, fgraph):
if not mkl_available():
return
did_something = True
while did_something:
did_something = False
topo = fgraph.toposort()
for node in topo:
if (node in fgraph.apply_nodes) and isinstance(node.op, AbstractConv2d):
inp = node.inputs
out = node.outputs
imshp = getattr(node.op, 'imshp', None)
kshp = getattr(node.op, 'kshp', None)
border_mode = getattr(node.op, 'border_mode', 'valid')
subsample = getattr(node.op, 'subsample', (1, 1))
filter_flip = getattr(node.op, 'filter_flip', False)
filter_dilation = getattr(node.op, 'filter_dilation', (1, 1))
# Get Elemwise node
if (len(out) == 1 and (not out[0] in fgraph.outputs) and
isinstance(out[0].clients[0][0].op, tensor.Elemwise) and
isinstance(out[0].clients[0][0].op.scalar_op, scalar.Add)):
if len(out[0].clients[0][0].inputs) == 2:
if out[0].clients[0][0].inputs[0] is out[0]:
bias = out[0].clients[0][0].inputs[1]
else:
bias = out[0].clients[0][0].inputs[0]
# Get DimShuffle node
bias_owner = bias.owner
if (bias_owner is None):
try:
inp_0 = U2IConv(imshp=imshp, kshp=kshp, border_mode=border_mode, subsample=subsample,
filter_dilation=filter_dilation)(inp[0])
out_0 = mkl_conv.Conv2D(imshp=imshp,
kshp=kshp,
border_mode=border_mode,
subsample=subsample,
filter_flip=filter_flip,
filter_dilation=filter_dilation)(image=inp_0, weight=inp[1], bias=bias)
fgraph.repalce_validate(out[0].clients[0][0].outputs[0],
out_0,
'ReplaceConvBias')
did_something = True
except Exception as e:
raise
elif isinstance(bias_owner.op, tensor.DimShuffle) and (bias_owner.inputs[0].owner is None):
try:
inp_0 = U2IConv(imshp=imshp, kshp=kshp, border_mode=border_mode, subsample=subsample,
filter_dilation=filter_dilation)(inp[0])
out_0 = mkl_conv.Conv2D(imshp=imshp,
kshp=kshp,
border_mode=border_mode,
subsample=subsample,
filter_flip=filter_flip,
filter_dilation=filter_dilation)(image=inp_0, weight=inp[1], bias=bias_owner.inputs[0])
out_1 = I2U()(out_0)
fgraph.replace_validate(out[0].clients[0][0].outputs[0],
out_1,
'ReplaceConvBias')
did_something = True
except Exception as e:
raise
else:
pass
elif (node in fgraph.apply_nodes) and isinstance(node.op, AbstractConv2d_gradWeights):
inp = node.inputs # 0-image, 1-gz, 2-shape
out = node.outputs
imshp = getattr(node.op, 'imshp', None)
kshp = getattr(node.op, 'kshp', None)
border_mode = getattr(node.op, 'border_mode', 'valid')
subsample = getattr(node.op, 'subsample', (1, 1))
filter_flip = getattr(node.op, 'filter_flip', False)
filter_dilation = getattr(node.op, 'filter_dilation', (1, 1))
assert len(inp) == 3 and len(out) == 1
for i, c in enumerate(inp[0].clients):
if hasattr(c[0], 'op') and isinstance(c[0].op, U2IConv) and self._check_attributes_(c[0], node):
for cc in c[0].outputs[0].clients:
if isinstance(cc[0].op, mkl_conv.Conv2D) and len(cc[0].inputs) == 3:
weight, bias = cc[0].inputs[1:3]
try:
inp_0 = U2IConv(imshp=imshp, kshp=kshp, border_mode=border_mode, subsample=subsample,
filter_dilation=filter_dilation)(inp[0])
conv_fw = mkl_conv.Conv2D(imshp=imshp,
kshp=kshp,
border_mode=border_mode,
subsample=subsample,
filter_flip=filter_flip,
filter_dilation=filter_dilation)(inp_0, weight, bias)
gz = I2UGrad()(conv_fw, inp[1])
out_0, out_1 = mkl_conv.ConvGradWeights(imshp=imshp,
kshp=kshp,
border_mode=border_mode,
subsample=subsample,
filter_flip=filter_flip,
filter_dilation=filter_dilation)(image=inp_0, weight=weight, gradz=gz, bias=bias)
# Get BiasGrad
oriBiasGrad = None # BiasGrad in original function graph
gz_node = inp[1].owner
for i, o in enumerate(gz_node.outputs):
if inp[1] is o and len(o.clients) >= 2:
oriBiasGrad = self._check_grad_bias_(gz_node, i)
fgraph.replace_validate(out[0], out_0, 'ReplaceConvBias')
if oriBiasGrad:
fgraph.replace_validate(oriBiasGrad, out_1, 'ReplaceConvBias')
did_something = True
except Exception as e:
raise
elif (node in fgraph.apply_nodes) and isinstance(node.op, AbstractConv2d_gradInputs):
inp = node.inputs # 0-weight, 1-gz, 2-shape
out = node.outputs
imshp = getattr(node.op, 'imshp', None)
kshp = getattr(node.op, 'kshp', None)
border_mode = getattr(node.op, 'border_mode', 'valid')
subsample = getattr(node.op, 'subsample', (1, 1))
filter_flip = getattr(node.op, 'filter_flip', False)
filter_dilation = getattr(node.op, 'filter_dilation', (1, 1))
assert len(inp) == 3 and len(out) == 1
list_Conv2D = [c[0] for c in inp[0].clients if (hasattr(c[0], 'op') and
isinstance(c[0].op, mkl_conv.Conv2D) and
len(c[0].inputs) == 3 and
self._check_attributes_(c[0], node))]
if 3 > len(list_Conv2D) > 0:
x = list_Conv2D[0].inputs[0].owner.inputs[0]
bias = list_Conv2D[0].inputs[2]
inp_0 = list_Conv2D[0].inputs[0]
try:
inp_0 = U2IConv(imshp=imshp, kshp=kshp, border_mode=border_mode, subsample=subsample,
filter_dilation=filter_dilation)(x)
conv_fw = mkl_conv.Conv2D(imshp=imshp,
kshp=kshp,
border_mode=border_mode,
subsample=subsample,
filter_flip=filter_flip,
filter_dilation=filter_dilation)(inp_0, inp[0], bias)
gz = I2UGrad()(conv_fw, inp[1])
out_0 = mkl_conv.ConvGradInputs(imshp=imshp,
kshp=kshp,
border_mode=border_mode,
subsample=subsample,
filter_flip=filter_flip,
filter_dilation=filter_dilation)(inp_0, inp[0], gz)
inp_grad = U2IGrad()(x, out_0)
fgraph.replace_validate(out[0], inp_grad, 'ReplaceConvBias')
did_something = True
except Exception as e:
raise e
else:
pass