ParakeetJoint.mlmodelc/model.mil

program(1.3)
[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3405.2.1"}, {"coremlc-version", "3404.23.1"}, {"coremltools-component-torch", "2.5.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})]
{
    func main<ios18>(tensor<fp16, [1, 1, 1024]> enc, tensor<fp16, [1, 1, 640]> pred) {
            tensor<fp16, [640, 1024]> enc_weight_to_fp16 = const()[name = string("enc_weight_to_fp16"), val = tensor<fp16, [640, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
            tensor<fp16, [640]> enc_bias_to_fp16 = const()[name = string("enc_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848)))];
            tensor<fp16, [1, 1, 640]> linear_0_cast_fp16 = linear(bias = enc_bias_to_fp16, weight = enc_weight_to_fp16, x = enc)[name = string("linear_0_cast_fp16")];
            tensor<fp16, [640, 640]> pred_weight_to_fp16 = const()[name = string("pred_weight_to_fp16"), val = tensor<fp16, [640, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1312192)))];
            tensor<fp16, [640]> pred_bias_to_fp16 = const()[name = string("pred_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2131456)))];
            tensor<fp16, [1, 1, 640]> linear_1_cast_fp16 = linear(bias = pred_bias_to_fp16, weight = pred_weight_to_fp16, x = pred)[name = string("linear_1_cast_fp16")];
            tensor<int32, [1]> var_13_axes_0 = const()[name = string("op_13_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<fp16, [1, 1, 1, 640]> var_13_cast_fp16 = expand_dims(axes = var_13_axes_0, x = linear_0_cast_fp16)[name = string("op_13_cast_fp16")];
            tensor<int32, [1]> var_15_axes_0 = const()[name = string("op_15_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 1, 640]> var_15_cast_fp16 = expand_dims(axes = var_15_axes_0, x = linear_1_cast_fp16)[name = string("op_15_cast_fp16")];
            tensor<fp16, [1, 1, 1, 640]> input_1_cast_fp16 = add(x = var_13_cast_fp16, y = var_15_cast_fp16)[name = string("input_1_cast_fp16")];
            tensor<fp16, [1, 1, 1, 640]> input_cast_fp16 = relu(x = input_1_cast_fp16)[name = string("input_cast_fp16")];
            tensor<fp16, [8198, 640]> joint_net_2_weight_to_fp16 = const()[name = string("joint_net_2_weight_to_fp16"), val = tensor<fp16, [8198, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2132800)))];
            tensor<fp16, [8198]> joint_net_2_bias_to_fp16 = const()[name = string("joint_net_2_bias_to_fp16"), val = tensor<fp16, [8198]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12626304)))];
            tensor<fp16, [1, 1, 1, 8198]> output = linear(bias = joint_net_2_bias_to_fp16, weight = joint_net_2_weight_to_fp16, x = input_cast_fp16)[name = string("linear_2_cast_fp16")];
        } -> (output);
}