def _sparse_mm(context, node):
inputs = _get_inputs(context, node, expected=2)
x = mb.custom_torch_sparse_matmul(x=inputs[0],
y=inputs[1],
x_is_sparse=True,
y_is_sparse=True,
name=node.name)
context.add(x)
def constant_pad_nd(context, node):
inputs = _get_inputs(context, node, expected=3)
new_pad = inputs[1].val.reshape((-1, 2))[::-1].reshape(-1).tolist()
new_pad = [0] * (2 * len(inputs[0].shape) - len(new_pad)) + new_pad
padded = mb.pad(x=inputs[0],
pad=np.array(new_pad),
mode="constant",
constant_val=float(0),
name=node.name)
context.add(padded)
def cosine_similarity(context, node):
inputs = _get_inputs(context, node, expected=4)
dim = inputs[-2].val
eps = inputs[-1].val
xy = mb.mul(x=inputs[0], y=inputs[1])
sum_xy = mb.reduce_sum(x=xy, axes=[dim])
xx = mb.mul(x=inputs[0], y=inputs[0])
sum_xx = mb.reduce_sum(x=xx, axes=[dim])
yy = mb.mul(x=inputs[1], y=inputs[1])
sum_yy = mb.reduce_sum(x=yy, axes=[dim])
mul_sum_xy = mb.mul(x=sum_xx, y=sum_yy)
div_12 = mb.maximum(x=mul_sum_xy, y=eps * eps)
div_sqrt = mb.sqrt(x=div_12)
cs = mb.real_div(x=sum_xy, y=div_sqrt, name=node.name)
context.add(cs)
def silu(context, node):
inputs = _get_inputs(context, node, expected=1)
x = inputs[0]
y = mb.sigmoid(x=x)
z = mb.mul(x=x, y=y, name=node.name)
context.add(z)
def type_as(context, node):
inputs = _get_inputs(context, node)
context.add(mb.cast(x=inputs[0], dtype='fp32'), node.name)
