def expm1_handler(converter: TensorFlowConverter, tf_op: "tf.Operation"):
console.warning(
"[TensorFlowConverter] In WebDNN, \"Expm1(x)\" is converted into \"Exp(x)-1\", which is not enough accurate as Expm1 when"
"x is so small that \"Exp(x) == 1\" in floating point accuracy.")
x = converter.get_variable(tf_op.inputs[0])
y = Exp(None)(x)[0] - 1
converter.set_variable(tf_op.outputs[0], y)
def _convert_logsumexp(converter: ChainerConverter,
c_op: "chainer.functions.LogSumExp"):
x = converter.get_variable(c_op.inputs[0])
if c_op.axis is None:
axes = list(x.order.axes)
else:
axes = [x.order.axes[i] for i in c_op.axis]
# TODO: Conversion result is wrong in case x.shape[category_axis] is placeholder.
if any(not Placeholder.check_resolved(x.shape_dict[axis])
for axis in axes):
raise NotImplementedError(
"[ChainerConverter] \"LogSumExp\" for dynamic number of categories is not supported"
)
max_x = x
for axis in axes:
max_x, = Max(None, axis=axis)(max_x)
exp_delta_x, = Exp(None)(x - max_x)
sum_exp_delta_x = exp_delta_x
for axis in axes:
sum_exp_delta_x, = Sum(None, axis=axis)(sum_exp_delta_x)
y = Log(None)(sum_exp_delta_x)[0] + max_x
converter.set_variable(c_op.outputs[0](), y)
def _convert_expm1(converter: ChainerConverter,
c_op: "chainer.functions.Expm1"):
console.warning(
"[ChainerConverter] In WebDNN, \"Expm1(x)\" is converted into \"Exp(x)-1\", which is not enough accurate as Expm1 when"
"x is so small that \"Exp(x) == 1\" in floating point accuracy.")
x = converter.get_variable(c_op.inputs[0])
y = Exp(None)(x)[0] - 1
converter.set_variable(c_op.outputs[0](), y)
def _convert_selu(converter: ONNXConverter, onnx_op: INodeProto):
x = converter.get_variable(onnx_op.input[0])
attrs = attribute_dict(onnx_op)
alpha = attrs["alpha"].f if "alpha" in attrs else 1.6732
gamma = attrs["gamma"].f if "gamma" in attrs else 1.0507
y, = Select(None)(x > 0, gamma * x,
gamma * (alpha * Exp(None)(x)[0] - alpha))
converter.set_variable(onnx_op.output[0], y)
def template(x_order=OrderNHWC, y_order=OrderNHWC, description: str = ""):
vx = np.random.rand(2, 3, 4, 5) - 0.5
vy = np.exp(vx)
x = Variable(vx.shape, order=OrderNHWC)
y, = Exp(None)(x)
x.change_order(x_order)
y.change_order(y_order)
generate_kernel_test_case(
description=f"Exp {description}",
graph=Graph([x], [y]),
inputs={
x: np.transpose(vx, [OrderNHWC.axes_dict[a] for a in x.order.axes])
},
expected={
y: np.transpose(vy, [OrderNHWC.axes_dict[a] for a in y.order.axes])
},
)
def _convert_log_softmax(converter: ChainerConverter,
c_op: "chainer.functions.LogSoftmax"):
x = converter.get_variable(c_op.inputs[0])
axis = x.order.axes[1]
max_x, = Max(None, axis=axis)(x)
exp_delta_x, = Exp(None)(x - max_x)
sum_exp_delta_x, = Sum(None, axis=axis)(exp_delta_x)
log_sum_delta_exp, = Log(None)(sum_exp_delta_x)
y = x - (log_sum_delta_exp + max_x)
converter.set_variable(c_op.outputs[0](), y)
def _convert_log_softmax(converter: ChainerConverter,
c_op: "chainer.functions.LogSoftmax"):
x = converter.get_variable(c_op.inputs[0])
axis = x.order.axes[1]
# TODO: Conversion result is wrong in case x.shape[1] is placeholder.
if not Placeholder.check_resolved(x.shape[1]):
raise NotImplementedError(
"[ChainerConverter] \"LogSoftMax\" for dynamic number of cateogries is not supported"
)
max_x, = Max(None, axis=axis)(x)
exp_delta_x, = Exp(None)(x - max_x)
sum_exp_delta_x, = Sum(None, axis=axis)(exp_delta_x)
log_sum_delta_exp, = Log(None)(sum_exp_delta_x)
y = x - (log_sum_delta_exp + max_x)
converter.set_variable(c_op.outputs[0](), y)
def optimize(self, graph: Graph) -> Tuple[Graph, bool]:
flag_changed = False
for softmax in traverse.filter_nodes(traverse.listup_operators(graph),
Softmax):
x = softmax.inputs["x"]
y = softmax.outputs["y"]
axis = softmax.parameters["axis"]
softmax.remove_all()
flag_changed = True
max_x, = Max(None, axis=axis)(x)
delta_x = x - max_x
exp_delta_x, = Exp(None)(delta_x)
sum_exp_delta_x, = Sum(None, axis=axis)(exp_delta_x)
new_y = exp_delta_x / sum_exp_delta_x
new_y.change_order(y.order)
OptimizeRule.replace_variable(graph, new_y, y)
return graph, flag_changed
def _convert_logsumexp(converter: ChainerConverter,
c_op: "chainer.functions.LogSumExp"):
x = converter.get_variable(c_op.inputs[0])
if c_op.axis is None:
axes = list(x.order.axes)
else:
axes = [x.order.axes[i] for i in c_op.axis]
max_x = x
for axis in axes:
max_x, = Max(None, axis=axis)(max_x)
exp_delta_x, = Exp(None)(x - max_x)
sum_exp_delta_x = exp_delta_x
for axis in axes:
sum_exp_delta_x, = Sum(None, axis=axis)(sum_exp_delta_x)
y = Log(None)(sum_exp_delta_x)[0] + max_x
converter.set_variable(c_op.outputs[0](), y)
def _convert_reduce_logsumexp(converter: ONNXConverter, onnx_op: INodeProto):
x = converter.get_variable(onnx_op.input[0])
attrs = attribute_dict(onnx_op)
axes = [x.order.axes[i] for i in attrs["axes"].ints]
keepdims = (attrs["keepdims"].i if "keepdims" in attrs else 1) == 1
max_x = x
for axis in axes:
max_x, = Max(None, axis=axis)(max_x)
exp_delta_x, = Exp(None)(x - max_x)
sum_exp_delta_x = exp_delta_x
for axis in axes:
sum_exp_delta_x, = Sum(None, axis=axis)(sum_exp_delta_x)
y = Log(None)(sum_exp_delta_x)[0] + max_x
if not keepdims:
y = y.squeeze(axis=axes)
converter.set_variable(onnx_op.output[0], y)
def _convert_exp(converter: ONNXConverter, onnx_op: INodeProto):
x0 = converter.get_variable(onnx_op.input[0])
y, = Exp(None)(x0)
converter.set_variable(onnx_op.output[0], y)
def expm1_handler(converter: TensorFlowConverter, tf_op: "tf.Operation"):
x = converter.get_variable(tf_op.inputs[0])
y, = Exp(None)(x)
y = y - 1
converter.set_variable(tf_op.outputs[0], y)
def _convert_exp(converter: ChainerConverter, c_op: "chainer.functions.Exp"):
x = converter.get_variable(c_op.inputs[0])
y, = Exp(None)(x)
converter.set_variable(c_op.outputs[0](), y)
