def clip_v11_gpu(node: Node, alloc_map, config: Config) -> Callable[[], None]:
input_io = node.inputs["input"]
min_io = node.get_input("min")
max_io = node.get_input("max")
output_io = node.outputs["output"]
inp = input_io.get_data(alloc_map)
min_data = min_io.get_data(alloc_map)
if min_data is None:
min_data = cupy.array([float("-inf")])
max_data = max_io.get_data(alloc_map)
if max_data is None:
max_data = cupy.array([float("inf")])
output = output_io.get_data(alloc_map)
def fn():
with cupy.cuda.Device(node.device_id):
cupy.copyto(
output,
chainer.functions.clip(inp, min_data[0], max_data[0]).array)
return fn
def conv_gpu(node: Node, alloc_map, config: Config) -> Callable[[], None]:
"""
GPU Function:
Y = X CONV W (Using padding, stride and dilaton attribute
"""
x_io = node.inputs["X"]
w_io = node.inputs["W"]
b_io = node.get_input("B")
y_io = node.outputs["Y"]
x = x_io.get_data(alloc_map)
w = w_io.get_data(alloc_map)
b = b_io.get_data(alloc_map)
y = y_io.get_data(alloc_map)
stride = node.get_attr("strides")[
0] # Assuming same stride in all directions
padding = node.get_attr("pads")[
0] # Assuming same padding in all directions
dilations = node.get_attr("dilations")[
0] # Assuming same padding in all directions
groups = node.get_attr("group", 1)
stride = (stride, stride)
padding = (padding, padding)
dilations = (dilations, dilations)
def fn():
# time_st = datetime.datetime.now()
# logging.log(logging.INFO, f"CONVOP got --> {x[-1]} CONVOP")
with cupy.cuda.Device(node.device_id):
cupy.cudnn.convolution_forward(x,
w,
b,
y,
padding,
stride,
dilations,
groups,
auto_tune=False,
tensor_core='auto')
# time_end = datetime.datetime.now()
# logging.log(logging.INFO, f"TIMER: <{node.operator},{node.node_id}> {time_st} -> {time_end}")
# logging.log(logging.INFO, f"CONV sent --> {y[-1]} CONV")
return fn
def conv_cpu(node: Node, alloc_map, config: Config) -> Callable[[], None]:
"""
Function:
Y = X CONV W (Using padding, stride and dilaton attribute
"""
x_io = node.inputs["X"]
w_io = node.inputs["W"]
b_io = node.get_input("B")
y_io = node.outputs["Y"]
x = x_io.get_data(alloc_map)
w = w_io.get_data(alloc_map)
b = b_io.get_data(alloc_map)
y = y_io.get_data(alloc_map)
# Assuming same stride in all directions
stride = node.get_attr("strides", [1])[0]
# Assuming same padding in all directions
padding = node.get_attr("pads", [0])[0]
dilations = node.get_attr(
"dilations", [1])[0] # Assuming same padding in all directions
groups = node.get_attr("group", 1)
def fn():
np.copyto(
y,
(chainer.functions.convolution_2d(
x,
w,
b=b,
stride=stride,
pad=padding,
dilate=dilations,
groups=groups,
)).array,
)
return fn
def clip_v11_cpu(node: Node, alloc_map, config: Config) -> Callable[[], None]:
input_io = node.inputs["input"]
min_io = node.get_input("min")
max_io = node.get_input("max")
output_io = node.outputs["output"]
inp = input_io.get_data(alloc_map)
min_data = min_io.get_data(alloc_map)
if min_data is None:
min_data = [-np.inf]
max_data = max_io.get_data(alloc_map)
if max_data is None:
max_data = [np.inf]
output = output_io.get_data(alloc_map)
def fn():
np.copyto(output,
chainer.functions.clip(inp, min_data[0], max_data[0]).array)
return fn
