def trt_transposed_matmul_converter(network, target, args, kwargs, name):
lhs, rhs, lhs_transposed, rhs_transposed = args
layer = network.add_matrix_multiply(
lhs,
trt.MatrixOperation.TRANSPOSE if lhs_transposed else trt.MatrixOperation.NONE,
rhs,
trt.MatrixOperation.TRANSPOSE if rhs_transposed else trt.MatrixOperation.NONE,
)
set_layer_name(layer, target, name)
return layer.get_output(0)
def trt_transposed_linear_converter(network, target, args, kwargs, name):
input, weight, bias = args
weight = get_trt_tensor(network, weight.t(), f"{name}_weight")
bias = get_trt_tensor(network, bias.reshape(1, -1), f"{name}_bias")
input, weight = broadcast(network, input, weight, f"{input.name}_broadcast", f"{weight.name}_broadcast")
layer = network.add_matrix_multiply(
input,
trt.MatrixOperation.TRANSPOSE,
weight,
trt.MatrixOperation.NONE,
)
set_layer_name(layer, target, f"{name}_mm")
return add_binary_elementwise_layer(
network, layer.get_output(0), bias, trt.ElementWiseOperation.SUM, target, f"{name}_add"
)