gemma3_embeddings.mlmodelc/model.mil

program(1.3)
[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.5.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
{
    func main<ios18>(tensor<int32, [1, ?]> input_ids) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, dict<string, tensor<int32, [?]>>>>>((("DefaultShapes", {{"input_ids", [1, 1]}}), ("EnumeratedShapes", {{"79ae981e", {{"input_ids", [1, 1]}}}, {"ed9b58c8", {{"input_ids", [1, 64]}}}})))] {
            tensor<fp16, [262144, 640]> embed_tokens_weight = const()[name = string("embed_tokens_weight"), val = tensor<fp16, [262144, 640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
            int32 hidden_states_1_batch_dims_0 = const()[name = string("hidden_states_1_batch_dims_0"), val = int32(0)];
            bool hidden_states_1_validate_indices_0 = const()[name = string("hidden_states_1_validate_indices_0"), val = bool(false)];
            int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)];
            tensor<bool, [1, ?]> greater_equal_0 = greater_equal(x = input_ids, y = greater_equal_0_y_0)[name = string("greater_equal_0")];
            int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(262144)];
            tensor<int32, [1, ?]> add_0 = add(x = input_ids, y = slice_by_index_0)[name = string("add_0")];
            tensor<int32, [1, ?]> select_0 = select(a = input_ids, b = add_0, cond = greater_equal_0)[name = string("select_0")];
            int32 hidden_states_1_axis_1 = const()[name = string("hidden_states_1_axis_1"), val = int32(0)];
            tensor<fp16, [1, ?, 640]> hidden_states_1 = gather(axis = hidden_states_1_axis_1, batch_dims = hidden_states_1_batch_dims_0, indices = select_0, validate_indices = hidden_states_1_validate_indices_0, x = embed_tokens_weight)[name = string("hidden_states_1")];
            fp16 var_7_to_fp16 = const()[name = string("op_7_to_fp16"), val = fp16(0x1.94cp+4)];
            tensor<fp16, [1, ?, 640]> hidden_states = mul(x = hidden_states_1, y = var_7_to_fp16)[name = string("hidden_states_cast_fp16")];
        } -> (hidden_states);
}