| program(1.3) |
| [buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}})] |
| { |
| func main<ios18>(tensor<int32, [1, 16, 125]> audio_codes) { |
| tensor<int32, [3]> var_401_begin_0 = const()[name = string("op_401_begin_0"), val = tensor<int32, [3]>([0, 0, 0])]; |
| tensor<int32, [3]> var_401_end_0 = const()[name = string("op_401_end_0"), val = tensor<int32, [3]>([1, 1, 125])]; |
| tensor<bool, [3]> var_401_end_mask_0 = const()[name = string("op_401_end_mask_0"), val = tensor<bool, [3]>([true, false, true])]; |
| tensor<int32, [1, 1, 125]> var_401 = slice_by_index(begin = var_401_begin_0, end = var_401_end_0, end_mask = var_401_end_mask_0, x = audio_codes)[name = string("op_401")]; |
| tensor<int32, [3]> var_414_begin_0 = const()[name = string("op_414_begin_0"), val = tensor<int32, [3]>([0, 0, 0])]; |
| tensor<int32, [3]> var_414_end_0 = const()[name = string("op_414_end_0"), val = tensor<int32, [3]>([1, 1, 125])]; |
| tensor<bool, [3]> var_414_end_mask_0 = const()[name = string("op_414_end_mask_0"), val = tensor<bool, [3]>([true, false, true])]; |
| tensor<bool, [3]> var_414_squeeze_mask_0 = const()[name = string("op_414_squeeze_mask_0"), val = tensor<bool, [3]>([false, true, false])]; |
| tensor<int32, [1, 125]> var_414 = slice_by_index(begin = var_414_begin_0, end = var_414_end_0, end_mask = var_414_end_mask_0, squeeze_mask = var_414_squeeze_mask_0, x = var_401)[name = string("op_414")]; |
| int32 q_1_batch_dims_0 = const()[name = string("q_1_batch_dims_0"), val = int32(0)]; |
| bool q_1_validate_indices_0 = const()[name = string("q_1_validate_indices_0"), val = bool(false)]; |
| tensor<fp16, [2048, 256]> first_vq_0_embedding_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [2048, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524416))))[name = string("first_vq_0_embedding_to_fp16_palettized")]; |
| string var_414_to_int16_dtype_0 = const()[name = string("op_414_to_int16_dtype_0"), val = string("int16")]; |
| string cast_49_dtype_0 = const()[name = string("cast_49_dtype_0"), val = string("int32")]; |
| int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; |
| tensor<int16, [1, 125]> var_414_to_int16 = cast(dtype = var_414_to_int16_dtype_0, x = var_414)[name = string("cast_19")]; |
| tensor<int32, [1, 125]> cast_49 = cast(dtype = cast_49_dtype_0, x = var_414_to_int16)[name = string("cast_18")]; |
| tensor<bool, [1, 125]> greater_equal_0 = greater_equal(x = cast_49, 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(2048)]; |
| tensor<int32, [1, 125]> add_0 = add(x = cast_49, y = slice_by_index_0)[name = string("add_0")]; |
| tensor<int32, [1, 125]> select_0 = select(a = cast_49, b = add_0, cond = greater_equal_0)[name = string("select_0")]; |
| string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; |
| string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; |
| int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; |
| tensor<int16, [1, 125]> select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_17")]; |
| tensor<int32, [1, 125]> cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_16")]; |
| tensor<bool, [1, 125]> greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; |
| int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(2048)]; |
| tensor<int32, [1, 125]> add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; |
| tensor<int32, [1, 125]> select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; |
| int32 q_1_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("q_1_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; |
| tensor<fp16, [1, 125, 256]> q_1_cast_fp16_cast_uint16_cast_uint16 = gather(axis = q_1_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = q_1_batch_dims_0, indices = select_0_1, validate_indices = q_1_validate_indices_0, x = first_vq_0_embedding_to_fp16_palettized)[name = string("q_1_cast_fp16_cast_uint16_cast_uint16")]; |
| tensor<int32, [3]> input_3_perm_0 = const()[name = string("input_3_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| string q1_pad_type_0 = const()[name = string("q1_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> q1_strides_0 = const()[name = string("q1_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> q1_pad_0 = const()[name = string("q1_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> q1_dilations_0 = const()[name = string("q1_dilations_0"), val = tensor<int32, [1]>([1])]; |
| int32 q1_groups_0 = const()[name = string("q1_groups_0"), val = int32(1)]; |
| tensor<fp16, [512, 256, 1]> first_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [512, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524992))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(656128))))[name = string("first_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 256, 125]> input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = q_1_cast_fp16_cast_uint16_cast_uint16)[name = string("transpose_39")]; |
| tensor<fp16, [1, 512, 125]> q1_cast_fp16 = conv(dilations = q1_dilations_0, groups = q1_groups_0, pad = q1_pad_0, pad_type = q1_pad_type_0, strides = q1_strides_0, weight = first_proj_weight_to_fp16_palettized, x = input_3_cast_fp16)[name = string("q1_cast_fp16")]; |
| tensor<int32, [3]> var_447_begin_0 = const()[name = string("op_447_begin_0"), val = tensor<int32, [3]>([0, 1, 0])]; |
| tensor<int32, [3]> var_447_end_0 = const()[name = string("op_447_end_0"), val = tensor<int32, [3]>([1, 16, 125])]; |
| tensor<bool, [3]> var_447_end_mask_0 = const()[name = string("op_447_end_mask_0"), val = tensor<bool, [3]>([true, true, true])]; |
| tensor<int32, [1, 15, 125]> var_447 = slice_by_index(begin = var_447_begin_0, end = var_447_end_0, end_mask = var_447_end_mask_0, x = audio_codes)[name = string("op_447")]; |
| tensor<int32, [3]> var_460_begin_0 = const()[name = string("op_460_begin_0"), val = tensor<int32, [3]>([0, 0, 0])]; |
| tensor<int32, [3]> var_460_end_0 = const()[name = string("op_460_end_0"), val = tensor<int32, [3]>([1, 1, 125])]; |
| tensor<bool, [3]> var_460_end_mask_0 = const()[name = string("op_460_end_mask_0"), val = tensor<bool, [3]>([true, false, true])]; |
| tensor<bool, [3]> var_460_squeeze_mask_0 = const()[name = string("op_460_squeeze_mask_0"), val = tensor<bool, [3]>([false, true, false])]; |
| tensor<int32, [1, 125]> var_460 = slice_by_index(begin = var_460_begin_0, end = var_460_end_0, end_mask = var_460_end_mask_0, squeeze_mask = var_460_squeeze_mask_0, x = var_447)[name = string("op_460")]; |
| int32 q_3_axis_0 = const()[name = string("q_3_axis_0"), val = int32(0)]; |
| int32 q_3_batch_dims_0 = const()[name = string("q_3_batch_dims_0"), val = int32(0)]; |
| bool q_3_validate_indices_0 = const()[name = string("q_3_validate_indices_0"), val = bool(false)]; |
| tensor<fp16, [2048, 256]> rest_vq_0_embedding_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [2048, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(656704))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1181056))))[name = string("rest_vq_0_embedding_to_fp16_palettized")]; |
| string var_460_to_uint16_dtype_0 = const()[name = string("op_460_to_uint16_dtype_0"), val = string("uint16")]; |
| tensor<uint16, [1, 125]> var_460_to_uint16 = cast(dtype = var_460_to_uint16_dtype_0, x = var_460)[name = string("cast_15")]; |
| tensor<fp16, [1, 125, 256]> q_3_cast_fp16_cast_uint16 = gather(axis = q_3_axis_0, batch_dims = q_3_batch_dims_0, indices = var_460_to_uint16, validate_indices = q_3_validate_indices_0, x = rest_vq_0_embedding_to_fp16_palettized)[name = string("q_3_cast_fp16_cast_uint16")]; |
| tensor<int32, [3]> var_480_begin_0 = const()[name = string("op_480_begin_0"), val = tensor<int32, [3]>([0, 1, 0])]; |
| tensor<int32, [3]> var_480_end_0 = const()[name = string("op_480_end_0"), val = tensor<int32, [3]>([1, 2, 125])]; |
| tensor<bool, [3]> var_480_end_mask_0 = const()[name = string("op_480_end_mask_0"), val = tensor<bool, [3]>([true, false, true])]; |
| tensor<bool, [3]> var_480_squeeze_mask_0 = const()[name = string("op_480_squeeze_mask_0"), val = tensor<bool, [3]>([false, true, false])]; |
| tensor<int32, [1, 125]> var_480 = slice_by_index(begin = var_480_begin_0, end = var_480_end_0, end_mask = var_480_end_mask_0, squeeze_mask = var_480_squeeze_mask_0, x = var_447)[name = string("op_480")]; |
| int32 q_5_axis_0 = const()[name = string("q_5_axis_0"), val = int32(0)]; |
| int32 q_5_batch_dims_0 = const()[name = string("q_5_batch_dims_0"), val = int32(0)]; |
| bool q_5_validate_indices_0 = const()[name = string("q_5_validate_indices_0"), val = bool(false)]; |
| tensor<fp16, [2048, 256]> rest_vq_1_embedding_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [2048, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1181632))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1705984))))[name = string("rest_vq_1_embedding_to_fp16_palettized")]; |
| string var_480_to_uint16_dtype_0 = const()[name = string("op_480_to_uint16_dtype_0"), val = string("uint16")]; |
| tensor<uint16, [1, 125]> var_480_to_uint16 = cast(dtype = var_480_to_uint16_dtype_0, x = var_480)[name = string("cast_14")]; |
| tensor<fp16, [1, 125, 256]> q_5_cast_fp16_cast_uint16 = gather(axis = q_5_axis_0, batch_dims = q_5_batch_dims_0, indices = var_480_to_uint16, validate_indices = q_5_validate_indices_0, x = rest_vq_1_embedding_to_fp16_palettized)[name = string("q_5_cast_fp16_cast_uint16")]; |
| tensor<fp16, [1, 125, 256]> qr_3_cast_fp16 = add(x = q_3_cast_fp16_cast_uint16, y = q_5_cast_fp16_cast_uint16)[name = string("qr_3_cast_fp16")]; |
| tensor<int32, [3]> var_502_begin_0 = const()[name = string("op_502_begin_0"), val = tensor<int32, [3]>([0, 2, 0])]; |
| tensor<int32, [3]> var_502_end_0 = const()[name = string("op_502_end_0"), val = tensor<int32, [3]>([1, 3, 125])]; |
| tensor<bool, [3]> var_502_end_mask_0 = const()[name = string("op_502_end_mask_0"), val = tensor<bool, [3]>([true, false, true])]; |
| tensor<bool, [3]> var_502_squeeze_mask_0 = const()[name = string("op_502_squeeze_mask_0"), val = tensor<bool, [3]>([false, true, false])]; |
| tensor<int32, [1, 125]> var_502 = slice_by_index(begin = var_502_begin_0, end = var_502_end_0, end_mask = var_502_end_mask_0, squeeze_mask = var_502_squeeze_mask_0, x = var_447)[name = string("op_502")]; |
| int32 q_7_axis_0 = const()[name = string("q_7_axis_0"), val = int32(0)]; |
| int32 q_7_batch_dims_0 = const()[name = string("q_7_batch_dims_0"), val = int32(0)]; |
| bool q_7_validate_indices_0 = const()[name = string("q_7_validate_indices_0"), val = bool(false)]; |
| tensor<fp16, [2048, 256]> rest_vq_2_embedding_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [2048, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1706560))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2230912))))[name = string("rest_vq_2_embedding_to_fp16_palettized")]; |
| string var_502_to_uint16_dtype_0 = const()[name = string("op_502_to_uint16_dtype_0"), val = string("uint16")]; |
| tensor<uint16, [1, 125]> var_502_to_uint16 = cast(dtype = var_502_to_uint16_dtype_0, x = var_502)[name = string("cast_13")]; |
| tensor<fp16, [1, 125, 256]> q_7_cast_fp16_cast_uint16 = gather(axis = q_7_axis_0, batch_dims = q_7_batch_dims_0, indices = var_502_to_uint16, validate_indices = q_7_validate_indices_0, x = rest_vq_2_embedding_to_fp16_palettized)[name = string("q_7_cast_fp16_cast_uint16")]; |
| tensor<fp16, [1, 125, 256]> qr_5_cast_fp16 = add(x = qr_3_cast_fp16, y = q_7_cast_fp16_cast_uint16)[name = string("qr_5_cast_fp16")]; |
| tensor<int32, [3]> var_524_begin_0 = const()[name = string("op_524_begin_0"), val = tensor<int32, [3]>([0, 3, 0])]; |
| tensor<int32, [3]> var_524_end_0 = const()[name = string("op_524_end_0"), val = tensor<int32, [3]>([1, 4, 125])]; |
| tensor<bool, [3]> var_524_end_mask_0 = const()[name = string("op_524_end_mask_0"), val = tensor<bool, [3]>([true, false, true])]; |
| tensor<bool, [3]> var_524_squeeze_mask_0 = const()[name = string("op_524_squeeze_mask_0"), val = tensor<bool, [3]>([false, true, false])]; |
| tensor<int32, [1, 125]> var_524 = slice_by_index(begin = var_524_begin_0, end = var_524_end_0, end_mask = var_524_end_mask_0, squeeze_mask = var_524_squeeze_mask_0, x = var_447)[name = string("op_524")]; |
| int32 q_9_axis_0 = const()[name = string("q_9_axis_0"), val = int32(0)]; |
| int32 q_9_batch_dims_0 = const()[name = string("q_9_batch_dims_0"), val = int32(0)]; |
| bool q_9_validate_indices_0 = const()[name = string("q_9_validate_indices_0"), val = bool(false)]; |
| tensor<fp16, [2048, 256]> rest_vq_3_embedding_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [2048, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2231488))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2755840))))[name = string("rest_vq_3_embedding_to_fp16_palettized")]; |
| string var_524_to_uint16_dtype_0 = const()[name = string("op_524_to_uint16_dtype_0"), val = string("uint16")]; |
| tensor<uint16, [1, 125]> var_524_to_uint16 = cast(dtype = var_524_to_uint16_dtype_0, x = var_524)[name = string("cast_12")]; |
| tensor<fp16, [1, 125, 256]> q_9_cast_fp16_cast_uint16 = gather(axis = q_9_axis_0, batch_dims = q_9_batch_dims_0, indices = var_524_to_uint16, validate_indices = q_9_validate_indices_0, x = rest_vq_3_embedding_to_fp16_palettized)[name = string("q_9_cast_fp16_cast_uint16")]; |
| tensor<fp16, [1, 125, 256]> qr_7_cast_fp16 = add(x = qr_5_cast_fp16, y = q_9_cast_fp16_cast_uint16)[name = string("qr_7_cast_fp16")]; |
| tensor<int32, [3]> var_546_begin_0 = const()[name = string("op_546_begin_0"), val = tensor<int32, [3]>([0, 4, 0])]; |
| tensor<int32, [3]> var_546_end_0 = const()[name = string("op_546_end_0"), val = tensor<int32, [3]>([1, 5, 125])]; |
| tensor<bool, [3]> var_546_end_mask_0 = const()[name = string("op_546_end_mask_0"), val = tensor<bool, [3]>([true, false, true])]; |
| tensor<bool, [3]> var_546_squeeze_mask_0 = const()[name = string("op_546_squeeze_mask_0"), val = tensor<bool, [3]>([false, true, false])]; |
| tensor<int32, [1, 125]> var_546 = slice_by_index(begin = var_546_begin_0, end = var_546_end_0, end_mask = var_546_end_mask_0, squeeze_mask = var_546_squeeze_mask_0, x = var_447)[name = string("op_546")]; |
| int32 q_11_axis_0 = const()[name = string("q_11_axis_0"), val = int32(0)]; |
| int32 q_11_batch_dims_0 = const()[name = string("q_11_batch_dims_0"), val = int32(0)]; |
| bool q_11_validate_indices_0 = const()[name = string("q_11_validate_indices_0"), val = bool(false)]; |
| tensor<fp16, [2048, 256]> rest_vq_4_embedding_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [2048, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2756416))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3280768))))[name = string("rest_vq_4_embedding_to_fp16_palettized")]; |
| string var_546_to_uint16_dtype_0 = const()[name = string("op_546_to_uint16_dtype_0"), val = string("uint16")]; |
| tensor<uint16, [1, 125]> var_546_to_uint16 = cast(dtype = var_546_to_uint16_dtype_0, x = var_546)[name = string("cast_11")]; |
| tensor<fp16, [1, 125, 256]> q_11_cast_fp16_cast_uint16 = gather(axis = q_11_axis_0, batch_dims = q_11_batch_dims_0, indices = var_546_to_uint16, validate_indices = q_11_validate_indices_0, x = rest_vq_4_embedding_to_fp16_palettized)[name = string("q_11_cast_fp16_cast_uint16")]; |
| tensor<fp16, [1, 125, 256]> qr_9_cast_fp16 = add(x = qr_7_cast_fp16, y = q_11_cast_fp16_cast_uint16)[name = string("qr_9_cast_fp16")]; |
| tensor<int32, [3]> var_568_begin_0 = const()[name = string("op_568_begin_0"), val = tensor<int32, [3]>([0, 5, 0])]; |
| tensor<int32, [3]> var_568_end_0 = const()[name = string("op_568_end_0"), val = tensor<int32, [3]>([1, 6, 125])]; |
| tensor<bool, [3]> var_568_end_mask_0 = const()[name = string("op_568_end_mask_0"), val = tensor<bool, [3]>([true, false, true])]; |
| tensor<bool, [3]> var_568_squeeze_mask_0 = const()[name = string("op_568_squeeze_mask_0"), val = tensor<bool, [3]>([false, true, false])]; |
| tensor<int32, [1, 125]> var_568 = slice_by_index(begin = var_568_begin_0, end = var_568_end_0, end_mask = var_568_end_mask_0, squeeze_mask = var_568_squeeze_mask_0, x = var_447)[name = string("op_568")]; |
| int32 q_13_axis_0 = const()[name = string("q_13_axis_0"), val = int32(0)]; |
| int32 q_13_batch_dims_0 = const()[name = string("q_13_batch_dims_0"), val = int32(0)]; |
| bool q_13_validate_indices_0 = const()[name = string("q_13_validate_indices_0"), val = bool(false)]; |
| tensor<fp16, [2048, 256]> rest_vq_5_embedding_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [2048, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3281344))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3805696))))[name = string("rest_vq_5_embedding_to_fp16_palettized")]; |
| string var_568_to_uint16_dtype_0 = const()[name = string("op_568_to_uint16_dtype_0"), val = string("uint16")]; |
| tensor<uint16, [1, 125]> var_568_to_uint16 = cast(dtype = var_568_to_uint16_dtype_0, x = var_568)[name = string("cast_10")]; |
| tensor<fp16, [1, 125, 256]> q_13_cast_fp16_cast_uint16 = gather(axis = q_13_axis_0, batch_dims = q_13_batch_dims_0, indices = var_568_to_uint16, validate_indices = q_13_validate_indices_0, x = rest_vq_5_embedding_to_fp16_palettized)[name = string("q_13_cast_fp16_cast_uint16")]; |
| tensor<fp16, [1, 125, 256]> qr_11_cast_fp16 = add(x = qr_9_cast_fp16, y = q_13_cast_fp16_cast_uint16)[name = string("qr_11_cast_fp16")]; |
| tensor<int32, [3]> var_590_begin_0 = const()[name = string("op_590_begin_0"), val = tensor<int32, [3]>([0, 6, 0])]; |
| tensor<int32, [3]> var_590_end_0 = const()[name = string("op_590_end_0"), val = tensor<int32, [3]>([1, 7, 125])]; |
| tensor<bool, [3]> var_590_end_mask_0 = const()[name = string("op_590_end_mask_0"), val = tensor<bool, [3]>([true, false, true])]; |
| tensor<bool, [3]> var_590_squeeze_mask_0 = const()[name = string("op_590_squeeze_mask_0"), val = tensor<bool, [3]>([false, true, false])]; |
| tensor<int32, [1, 125]> var_590 = slice_by_index(begin = var_590_begin_0, end = var_590_end_0, end_mask = var_590_end_mask_0, squeeze_mask = var_590_squeeze_mask_0, x = var_447)[name = string("op_590")]; |
| int32 q_15_axis_0 = const()[name = string("q_15_axis_0"), val = int32(0)]; |
| int32 q_15_batch_dims_0 = const()[name = string("q_15_batch_dims_0"), val = int32(0)]; |
| bool q_15_validate_indices_0 = const()[name = string("q_15_validate_indices_0"), val = bool(false)]; |
| tensor<fp16, [2048, 256]> rest_vq_6_embedding_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [2048, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3806272))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4330624))))[name = string("rest_vq_6_embedding_to_fp16_palettized")]; |
| string var_590_to_uint16_dtype_0 = const()[name = string("op_590_to_uint16_dtype_0"), val = string("uint16")]; |
| tensor<uint16, [1, 125]> var_590_to_uint16 = cast(dtype = var_590_to_uint16_dtype_0, x = var_590)[name = string("cast_9")]; |
| tensor<fp16, [1, 125, 256]> q_15_cast_fp16_cast_uint16 = gather(axis = q_15_axis_0, batch_dims = q_15_batch_dims_0, indices = var_590_to_uint16, validate_indices = q_15_validate_indices_0, x = rest_vq_6_embedding_to_fp16_palettized)[name = string("q_15_cast_fp16_cast_uint16")]; |
| tensor<fp16, [1, 125, 256]> qr_13_cast_fp16 = add(x = qr_11_cast_fp16, y = q_15_cast_fp16_cast_uint16)[name = string("qr_13_cast_fp16")]; |
| tensor<int32, [3]> var_612_begin_0 = const()[name = string("op_612_begin_0"), val = tensor<int32, [3]>([0, 7, 0])]; |
| tensor<int32, [3]> var_612_end_0 = const()[name = string("op_612_end_0"), val = tensor<int32, [3]>([1, 8, 125])]; |
| tensor<bool, [3]> var_612_end_mask_0 = const()[name = string("op_612_end_mask_0"), val = tensor<bool, [3]>([true, false, true])]; |
| tensor<bool, [3]> var_612_squeeze_mask_0 = const()[name = string("op_612_squeeze_mask_0"), val = tensor<bool, [3]>([false, true, false])]; |
| tensor<int32, [1, 125]> var_612 = slice_by_index(begin = var_612_begin_0, end = var_612_end_0, end_mask = var_612_end_mask_0, squeeze_mask = var_612_squeeze_mask_0, x = var_447)[name = string("op_612")]; |
| int32 q_17_axis_0 = const()[name = string("q_17_axis_0"), val = int32(0)]; |
| int32 q_17_batch_dims_0 = const()[name = string("q_17_batch_dims_0"), val = int32(0)]; |
| bool q_17_validate_indices_0 = const()[name = string("q_17_validate_indices_0"), val = bool(false)]; |
| tensor<fp16, [2048, 256]> rest_vq_7_embedding_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [2048, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4331200))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4855552))))[name = string("rest_vq_7_embedding_to_fp16_palettized")]; |
| string var_612_to_uint16_dtype_0 = const()[name = string("op_612_to_uint16_dtype_0"), val = string("uint16")]; |
| tensor<uint16, [1, 125]> var_612_to_uint16 = cast(dtype = var_612_to_uint16_dtype_0, x = var_612)[name = string("cast_8")]; |
| tensor<fp16, [1, 125, 256]> q_17_cast_fp16_cast_uint16 = gather(axis = q_17_axis_0, batch_dims = q_17_batch_dims_0, indices = var_612_to_uint16, validate_indices = q_17_validate_indices_0, x = rest_vq_7_embedding_to_fp16_palettized)[name = string("q_17_cast_fp16_cast_uint16")]; |
| tensor<fp16, [1, 125, 256]> qr_15_cast_fp16 = add(x = qr_13_cast_fp16, y = q_17_cast_fp16_cast_uint16)[name = string("qr_15_cast_fp16")]; |
| tensor<int32, [3]> var_634_begin_0 = const()[name = string("op_634_begin_0"), val = tensor<int32, [3]>([0, 8, 0])]; |
| tensor<int32, [3]> var_634_end_0 = const()[name = string("op_634_end_0"), val = tensor<int32, [3]>([1, 9, 125])]; |
| tensor<bool, [3]> var_634_end_mask_0 = const()[name = string("op_634_end_mask_0"), val = tensor<bool, [3]>([true, false, true])]; |
| tensor<bool, [3]> var_634_squeeze_mask_0 = const()[name = string("op_634_squeeze_mask_0"), val = tensor<bool, [3]>([false, true, false])]; |
| tensor<int32, [1, 125]> var_634 = slice_by_index(begin = var_634_begin_0, end = var_634_end_0, end_mask = var_634_end_mask_0, squeeze_mask = var_634_squeeze_mask_0, x = var_447)[name = string("op_634")]; |
| int32 q_19_axis_0 = const()[name = string("q_19_axis_0"), val = int32(0)]; |
| int32 q_19_batch_dims_0 = const()[name = string("q_19_batch_dims_0"), val = int32(0)]; |
| bool q_19_validate_indices_0 = const()[name = string("q_19_validate_indices_0"), val = bool(false)]; |
| tensor<fp16, [2048, 256]> rest_vq_8_embedding_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [2048, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4856128))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5380480))))[name = string("rest_vq_8_embedding_to_fp16_palettized")]; |
| string var_634_to_uint16_dtype_0 = const()[name = string("op_634_to_uint16_dtype_0"), val = string("uint16")]; |
| tensor<uint16, [1, 125]> var_634_to_uint16 = cast(dtype = var_634_to_uint16_dtype_0, x = var_634)[name = string("cast_7")]; |
| tensor<fp16, [1, 125, 256]> q_19_cast_fp16_cast_uint16 = gather(axis = q_19_axis_0, batch_dims = q_19_batch_dims_0, indices = var_634_to_uint16, validate_indices = q_19_validate_indices_0, x = rest_vq_8_embedding_to_fp16_palettized)[name = string("q_19_cast_fp16_cast_uint16")]; |
| tensor<fp16, [1, 125, 256]> qr_17_cast_fp16 = add(x = qr_15_cast_fp16, y = q_19_cast_fp16_cast_uint16)[name = string("qr_17_cast_fp16")]; |
| tensor<int32, [3]> var_656_begin_0 = const()[name = string("op_656_begin_0"), val = tensor<int32, [3]>([0, 9, 0])]; |
| tensor<int32, [3]> var_656_end_0 = const()[name = string("op_656_end_0"), val = tensor<int32, [3]>([1, 10, 125])]; |
| tensor<bool, [3]> var_656_end_mask_0 = const()[name = string("op_656_end_mask_0"), val = tensor<bool, [3]>([true, false, true])]; |
| tensor<bool, [3]> var_656_squeeze_mask_0 = const()[name = string("op_656_squeeze_mask_0"), val = tensor<bool, [3]>([false, true, false])]; |
| tensor<int32, [1, 125]> var_656 = slice_by_index(begin = var_656_begin_0, end = var_656_end_0, end_mask = var_656_end_mask_0, squeeze_mask = var_656_squeeze_mask_0, x = var_447)[name = string("op_656")]; |
| int32 q_21_axis_0 = const()[name = string("q_21_axis_0"), val = int32(0)]; |
| int32 q_21_batch_dims_0 = const()[name = string("q_21_batch_dims_0"), val = int32(0)]; |
| bool q_21_validate_indices_0 = const()[name = string("q_21_validate_indices_0"), val = bool(false)]; |
| tensor<fp16, [2048, 256]> rest_vq_9_embedding_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [2048, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5381056))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5905408))))[name = string("rest_vq_9_embedding_to_fp16_palettized")]; |
| string var_656_to_uint16_dtype_0 = const()[name = string("op_656_to_uint16_dtype_0"), val = string("uint16")]; |
| tensor<uint16, [1, 125]> var_656_to_uint16 = cast(dtype = var_656_to_uint16_dtype_0, x = var_656)[name = string("cast_6")]; |
| tensor<fp16, [1, 125, 256]> q_21_cast_fp16_cast_uint16 = gather(axis = q_21_axis_0, batch_dims = q_21_batch_dims_0, indices = var_656_to_uint16, validate_indices = q_21_validate_indices_0, x = rest_vq_9_embedding_to_fp16_palettized)[name = string("q_21_cast_fp16_cast_uint16")]; |
| tensor<fp16, [1, 125, 256]> qr_19_cast_fp16 = add(x = qr_17_cast_fp16, y = q_21_cast_fp16_cast_uint16)[name = string("qr_19_cast_fp16")]; |
| tensor<int32, [3]> var_678_begin_0 = const()[name = string("op_678_begin_0"), val = tensor<int32, [3]>([0, 10, 0])]; |
| tensor<int32, [3]> var_678_end_0 = const()[name = string("op_678_end_0"), val = tensor<int32, [3]>([1, 11, 125])]; |
| tensor<bool, [3]> var_678_end_mask_0 = const()[name = string("op_678_end_mask_0"), val = tensor<bool, [3]>([true, false, true])]; |
| tensor<bool, [3]> var_678_squeeze_mask_0 = const()[name = string("op_678_squeeze_mask_0"), val = tensor<bool, [3]>([false, true, false])]; |
| tensor<int32, [1, 125]> var_678 = slice_by_index(begin = var_678_begin_0, end = var_678_end_0, end_mask = var_678_end_mask_0, squeeze_mask = var_678_squeeze_mask_0, x = var_447)[name = string("op_678")]; |
| int32 q_23_axis_0 = const()[name = string("q_23_axis_0"), val = int32(0)]; |
| int32 q_23_batch_dims_0 = const()[name = string("q_23_batch_dims_0"), val = int32(0)]; |
| bool q_23_validate_indices_0 = const()[name = string("q_23_validate_indices_0"), val = bool(false)]; |
| tensor<fp16, [2048, 256]> rest_vq_10_embedding_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [2048, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5905984))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6430336))))[name = string("rest_vq_10_embedding_to_fp16_palettized")]; |
| string var_678_to_uint16_dtype_0 = const()[name = string("op_678_to_uint16_dtype_0"), val = string("uint16")]; |
| tensor<uint16, [1, 125]> var_678_to_uint16 = cast(dtype = var_678_to_uint16_dtype_0, x = var_678)[name = string("cast_5")]; |
| tensor<fp16, [1, 125, 256]> q_23_cast_fp16_cast_uint16 = gather(axis = q_23_axis_0, batch_dims = q_23_batch_dims_0, indices = var_678_to_uint16, validate_indices = q_23_validate_indices_0, x = rest_vq_10_embedding_to_fp16_palettized)[name = string("q_23_cast_fp16_cast_uint16")]; |
| tensor<fp16, [1, 125, 256]> qr_21_cast_fp16 = add(x = qr_19_cast_fp16, y = q_23_cast_fp16_cast_uint16)[name = string("qr_21_cast_fp16")]; |
| tensor<int32, [3]> var_700_begin_0 = const()[name = string("op_700_begin_0"), val = tensor<int32, [3]>([0, 11, 0])]; |
| tensor<int32, [3]> var_700_end_0 = const()[name = string("op_700_end_0"), val = tensor<int32, [3]>([1, 12, 125])]; |
| tensor<bool, [3]> var_700_end_mask_0 = const()[name = string("op_700_end_mask_0"), val = tensor<bool, [3]>([true, false, true])]; |
| tensor<bool, [3]> var_700_squeeze_mask_0 = const()[name = string("op_700_squeeze_mask_0"), val = tensor<bool, [3]>([false, true, false])]; |
| tensor<int32, [1, 125]> var_700 = slice_by_index(begin = var_700_begin_0, end = var_700_end_0, end_mask = var_700_end_mask_0, squeeze_mask = var_700_squeeze_mask_0, x = var_447)[name = string("op_700")]; |
| int32 q_25_axis_0 = const()[name = string("q_25_axis_0"), val = int32(0)]; |
| int32 q_25_batch_dims_0 = const()[name = string("q_25_batch_dims_0"), val = int32(0)]; |
| bool q_25_validate_indices_0 = const()[name = string("q_25_validate_indices_0"), val = bool(false)]; |
| tensor<fp16, [2048, 256]> rest_vq_11_embedding_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [2048, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6430912))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6955264))))[name = string("rest_vq_11_embedding_to_fp16_palettized")]; |
| string var_700_to_uint16_dtype_0 = const()[name = string("op_700_to_uint16_dtype_0"), val = string("uint16")]; |
| tensor<uint16, [1, 125]> var_700_to_uint16 = cast(dtype = var_700_to_uint16_dtype_0, x = var_700)[name = string("cast_4")]; |
| tensor<fp16, [1, 125, 256]> q_25_cast_fp16_cast_uint16 = gather(axis = q_25_axis_0, batch_dims = q_25_batch_dims_0, indices = var_700_to_uint16, validate_indices = q_25_validate_indices_0, x = rest_vq_11_embedding_to_fp16_palettized)[name = string("q_25_cast_fp16_cast_uint16")]; |
| tensor<fp16, [1, 125, 256]> qr_23_cast_fp16 = add(x = qr_21_cast_fp16, y = q_25_cast_fp16_cast_uint16)[name = string("qr_23_cast_fp16")]; |
| tensor<int32, [3]> var_722_begin_0 = const()[name = string("op_722_begin_0"), val = tensor<int32, [3]>([0, 12, 0])]; |
| tensor<int32, [3]> var_722_end_0 = const()[name = string("op_722_end_0"), val = tensor<int32, [3]>([1, 13, 125])]; |
| tensor<bool, [3]> var_722_end_mask_0 = const()[name = string("op_722_end_mask_0"), val = tensor<bool, [3]>([true, false, true])]; |
| tensor<bool, [3]> var_722_squeeze_mask_0 = const()[name = string("op_722_squeeze_mask_0"), val = tensor<bool, [3]>([false, true, false])]; |
| tensor<int32, [1, 125]> var_722 = slice_by_index(begin = var_722_begin_0, end = var_722_end_0, end_mask = var_722_end_mask_0, squeeze_mask = var_722_squeeze_mask_0, x = var_447)[name = string("op_722")]; |
| int32 q_27_axis_0 = const()[name = string("q_27_axis_0"), val = int32(0)]; |
| int32 q_27_batch_dims_0 = const()[name = string("q_27_batch_dims_0"), val = int32(0)]; |
| bool q_27_validate_indices_0 = const()[name = string("q_27_validate_indices_0"), val = bool(false)]; |
| tensor<fp16, [2048, 256]> rest_vq_12_embedding_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [2048, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6955840))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7480192))))[name = string("rest_vq_12_embedding_to_fp16_palettized")]; |
| string var_722_to_uint16_dtype_0 = const()[name = string("op_722_to_uint16_dtype_0"), val = string("uint16")]; |
| tensor<uint16, [1, 125]> var_722_to_uint16 = cast(dtype = var_722_to_uint16_dtype_0, x = var_722)[name = string("cast_3")]; |
| tensor<fp16, [1, 125, 256]> q_27_cast_fp16_cast_uint16 = gather(axis = q_27_axis_0, batch_dims = q_27_batch_dims_0, indices = var_722_to_uint16, validate_indices = q_27_validate_indices_0, x = rest_vq_12_embedding_to_fp16_palettized)[name = string("q_27_cast_fp16_cast_uint16")]; |
| tensor<fp16, [1, 125, 256]> qr_25_cast_fp16 = add(x = qr_23_cast_fp16, y = q_27_cast_fp16_cast_uint16)[name = string("qr_25_cast_fp16")]; |
| tensor<int32, [3]> var_744_begin_0 = const()[name = string("op_744_begin_0"), val = tensor<int32, [3]>([0, 13, 0])]; |
| tensor<int32, [3]> var_744_end_0 = const()[name = string("op_744_end_0"), val = tensor<int32, [3]>([1, 14, 125])]; |
| tensor<bool, [3]> var_744_end_mask_0 = const()[name = string("op_744_end_mask_0"), val = tensor<bool, [3]>([true, false, true])]; |
| tensor<bool, [3]> var_744_squeeze_mask_0 = const()[name = string("op_744_squeeze_mask_0"), val = tensor<bool, [3]>([false, true, false])]; |
| tensor<int32, [1, 125]> var_744 = slice_by_index(begin = var_744_begin_0, end = var_744_end_0, end_mask = var_744_end_mask_0, squeeze_mask = var_744_squeeze_mask_0, x = var_447)[name = string("op_744")]; |
| int32 q_29_axis_0 = const()[name = string("q_29_axis_0"), val = int32(0)]; |
| int32 q_29_batch_dims_0 = const()[name = string("q_29_batch_dims_0"), val = int32(0)]; |
| bool q_29_validate_indices_0 = const()[name = string("q_29_validate_indices_0"), val = bool(false)]; |
| tensor<fp16, [2048, 256]> rest_vq_13_embedding_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [2048, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7480768))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8005120))))[name = string("rest_vq_13_embedding_to_fp16_palettized")]; |
| string var_744_to_uint16_dtype_0 = const()[name = string("op_744_to_uint16_dtype_0"), val = string("uint16")]; |
| tensor<uint16, [1, 125]> var_744_to_uint16 = cast(dtype = var_744_to_uint16_dtype_0, x = var_744)[name = string("cast_2")]; |
| tensor<fp16, [1, 125, 256]> q_29_cast_fp16_cast_uint16 = gather(axis = q_29_axis_0, batch_dims = q_29_batch_dims_0, indices = var_744_to_uint16, validate_indices = q_29_validate_indices_0, x = rest_vq_13_embedding_to_fp16_palettized)[name = string("q_29_cast_fp16_cast_uint16")]; |
| tensor<fp16, [1, 125, 256]> qr_cast_fp16 = add(x = qr_25_cast_fp16, y = q_29_cast_fp16_cast_uint16)[name = string("qr_cast_fp16")]; |
| tensor<int32, [3]> var_766_begin_0 = const()[name = string("op_766_begin_0"), val = tensor<int32, [3]>([0, 14, 0])]; |
| tensor<int32, [3]> var_766_end_0 = const()[name = string("op_766_end_0"), val = tensor<int32, [3]>([1, 15, 125])]; |
| tensor<bool, [3]> var_766_end_mask_0 = const()[name = string("op_766_end_mask_0"), val = tensor<bool, [3]>([true, false, true])]; |
| tensor<bool, [3]> var_766_squeeze_mask_0 = const()[name = string("op_766_squeeze_mask_0"), val = tensor<bool, [3]>([false, true, false])]; |
| tensor<int32, [1, 125]> var_766 = slice_by_index(begin = var_766_begin_0, end = var_766_end_0, end_mask = var_766_end_mask_0, squeeze_mask = var_766_squeeze_mask_0, x = var_447)[name = string("op_766")]; |
| int32 q_31_axis_0 = const()[name = string("q_31_axis_0"), val = int32(0)]; |
| int32 q_31_batch_dims_0 = const()[name = string("q_31_batch_dims_0"), val = int32(0)]; |
| bool q_31_validate_indices_0 = const()[name = string("q_31_validate_indices_0"), val = bool(false)]; |
| tensor<fp16, [2048, 256]> rest_vq_14_embedding_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [2048, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8005696))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8530048))))[name = string("rest_vq_14_embedding_to_fp16_palettized")]; |
| string var_766_to_uint16_dtype_0 = const()[name = string("op_766_to_uint16_dtype_0"), val = string("uint16")]; |
| tensor<uint16, [1, 125]> var_766_to_uint16 = cast(dtype = var_766_to_uint16_dtype_0, x = var_766)[name = string("cast_1")]; |
| tensor<fp16, [1, 125, 256]> q_31_cast_fp16_cast_uint16 = gather(axis = q_31_axis_0, batch_dims = q_31_batch_dims_0, indices = var_766_to_uint16, validate_indices = q_31_validate_indices_0, x = rest_vq_14_embedding_to_fp16_palettized)[name = string("q_31_cast_fp16_cast_uint16")]; |
| tensor<fp16, [1, 125, 256]> input_35_cast_fp16 = add(x = qr_cast_fp16, y = q_31_cast_fp16_cast_uint16)[name = string("input_35_cast_fp16")]; |
| string var_791_pad_type_0 = const()[name = string("op_791_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> var_791_strides_0 = const()[name = string("op_791_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_791_pad_0 = const()[name = string("op_791_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> var_791_dilations_0 = const()[name = string("op_791_dilations_0"), val = tensor<int32, [1]>([1])]; |
| int32 var_791_groups_0 = const()[name = string("op_791_groups_0"), val = int32(1)]; |
| tensor<int32, [3]> transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<fp16, [512, 256, 1]> rest_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [512, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8530624))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8661760))))[name = string("rest_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 256, 125]> transpose_0_cast_fp16 = transpose(perm = transpose_0_perm_0, x = input_35_cast_fp16)[name = string("transpose_38")]; |
| tensor<fp16, [1, 512, 125]> var_791_cast_fp16 = conv(dilations = var_791_dilations_0, groups = var_791_groups_0, pad = var_791_pad_0, pad_type = var_791_pad_type_0, strides = var_791_strides_0, weight = rest_proj_weight_to_fp16_palettized, x = transpose_0_cast_fp16)[name = string("op_791_cast_fp16")]; |
| tensor<fp16, [1, 512, 125]> hidden_state_1_cast_fp16 = add(x = q1_cast_fp16, y = var_791_cast_fp16)[name = string("hidden_state_1_cast_fp16")]; |
| tensor<int32, [6]> input_37_pad_0 = const()[name = string("input_37_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 0])]; |
| string input_37_mode_0 = const()[name = string("input_37_mode_0"), val = string("constant")]; |
| fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)]; |
| tensor<fp16, [1, 512, 127]> input_37_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = input_37_mode_0, pad = input_37_pad_0, x = hidden_state_1_cast_fp16)[name = string("input_37_cast_fp16")]; |
| string var_815_pad_type_0 = const()[name = string("op_815_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> var_815_strides_0 = const()[name = string("op_815_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_815_pad_0 = const()[name = string("op_815_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> var_815_dilations_0 = const()[name = string("op_815_dilations_0"), val = tensor<int32, [1]>([1])]; |
| int32 var_815_groups_0 = const()[name = string("op_815_groups_0"), val = int32(1)]; |
| tensor<fp16, [1024, 512, 3]> pre_conv_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8662336))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10235264))))[name = string("pre_conv_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [1024]> pre_conv_conv_bias_to_fp16 = const()[name = string("pre_conv_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10235840)))]; |
| tensor<fp16, [1, 1024, 125]> var_815_cast_fp16 = conv(bias = pre_conv_conv_bias_to_fp16, dilations = var_815_dilations_0, groups = var_815_groups_0, pad = var_815_pad_0, pad_type = var_815_pad_type_0, strides = var_815_strides_0, weight = pre_conv_conv_weight_to_fp16_palettized, x = input_37_cast_fp16)[name = string("op_815_cast_fp16")]; |
| tensor<int32, [3]> input_39_perm_0 = const()[name = string("input_39_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<fp16, [512, 1024]> pre_transformer_input_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [512, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10237952))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10762304))))[name = string("pre_transformer_input_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [512]> pre_transformer_input_proj_bias_to_fp16 = const()[name = string("pre_transformer_input_proj_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10762880)))]; |
| tensor<fp16, [1, 125, 1024]> input_39_cast_fp16 = transpose(perm = input_39_perm_0, x = var_815_cast_fp16)[name = string("transpose_37")]; |
| tensor<fp16, [1, 125, 512]> linear_0_cast_fp16 = linear(bias = pre_transformer_input_proj_bias_to_fp16, weight = pre_transformer_input_proj_weight_to_fp16_palettized, x = input_39_cast_fp16)[name = string("linear_0_cast_fp16")]; |
| tensor<fp16, [512]> const_10_to_fp16 = const()[name = string("const_10_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10763968)))]; |
| tensor<fp16, [1, 125, 512]> var_923_cast_fp16 = mul(x = const_10_to_fp16, y = linear_0_cast_fp16)[name = string("op_923_cast_fp16")]; |
| fp16 var_916_promoted_to_fp16 = const()[name = string("op_916_promoted_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 125, 512]> var_924_cast_fp16 = pow(x = linear_0_cast_fp16, y = var_916_promoted_to_fp16)[name = string("op_924_cast_fp16")]; |
| tensor<int32, [1]> var_926_axes_0 = const()[name = string("op_926_axes_0"), val = tensor<int32, [1]>([-1])]; |
| bool var_926_keep_dims_0 = const()[name = string("op_926_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 125, 1]> var_926_cast_fp16 = reduce_mean(axes = var_926_axes_0, keep_dims = var_926_keep_dims_0, x = var_924_cast_fp16)[name = string("op_926_cast_fp16")]; |
| fp16 var_927_to_fp16 = const()[name = string("op_927_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 125, 1]> var_928_cast_fp16 = add(x = var_926_cast_fp16, y = var_927_to_fp16)[name = string("op_928_cast_fp16")]; |
| fp32 var_929_epsilon_0 = const()[name = string("op_929_epsilon_0"), val = fp32(0x1.197998p-40)]; |
| tensor<fp16, [1, 125, 1]> var_929_cast_fp16 = rsqrt(epsilon = var_929_epsilon_0, x = var_928_cast_fp16)[name = string("op_929_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> hn_1_cast_fp16 = mul(x = var_923_cast_fp16, y = var_929_cast_fp16)[name = string("hn_1_cast_fp16")]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_0_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10765056))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11289408))))[name = string("pre_transformer_layers_0_self_attn_q_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1024]> linear_1_bias_0_to_fp16 = const()[name = string("linear_1_bias_0_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11289984)))]; |
| tensor<fp16, [1, 125, 1024]> linear_1_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_0_self_attn_q_proj_weight_to_fp16_palettized, x = hn_1_cast_fp16)[name = string("linear_1_cast_fp16")]; |
| tensor<int32, [4]> var_951 = const()[name = string("op_951"), val = tensor<int32, [4]>([1, 125, -1, 64])]; |
| tensor<fp16, [1, 125, 16, 64]> var_952_cast_fp16 = reshape(shape = var_951, x = linear_1_cast_fp16)[name = string("op_952_cast_fp16")]; |
| tensor<int32, [4]> q_33_perm_0 = const()[name = string("q_33_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_0_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11292096))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11816448))))[name = string("pre_transformer_layers_0_self_attn_k_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_2_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_0_self_attn_k_proj_weight_to_fp16_palettized, x = hn_1_cast_fp16)[name = string("linear_2_cast_fp16")]; |
| tensor<int32, [4]> var_961 = const()[name = string("op_961"), val = tensor<int32, [4]>([1, 125, -1, 64])]; |
| tensor<fp16, [1, 125, 16, 64]> var_962_cast_fp16 = reshape(shape = var_961, x = linear_2_cast_fp16)[name = string("op_962_cast_fp16")]; |
| tensor<int32, [4]> k_1_perm_0 = const()[name = string("k_1_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_0_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11817024))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12341376))))[name = string("pre_transformer_layers_0_self_attn_v_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_3_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_0_self_attn_v_proj_weight_to_fp16_palettized, x = hn_1_cast_fp16)[name = string("linear_3_cast_fp16")]; |
| tensor<int32, [4]> var_971 = const()[name = string("op_971"), val = tensor<int32, [4]>([1, 125, -1, 64])]; |
| tensor<fp16, [1, 125, 16, 64]> var_972_cast_fp16 = reshape(shape = var_971, x = linear_3_cast_fp16)[name = string("op_972_cast_fp16")]; |
| tensor<int32, [4]> v_1_perm_0 = const()[name = string("v_1_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> var_988_begin_0 = const()[name = string("op_988_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 32])]; |
| tensor<int32, [4]> var_988_end_0 = const()[name = string("op_988_end_0"), val = tensor<int32, [4]>([1, 16, 125, 64])]; |
| tensor<bool, [4]> var_988_end_mask_0 = const()[name = string("op_988_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])]; |
| tensor<fp16, [1, 16, 125, 64]> q_33_cast_fp16 = transpose(perm = q_33_perm_0, x = var_952_cast_fp16)[name = string("transpose_36")]; |
| tensor<fp16, [1, 16, 125, 32]> var_988_cast_fp16 = slice_by_index(begin = var_988_begin_0, end = var_988_end_0, end_mask = var_988_end_mask_0, x = q_33_cast_fp16)[name = string("op_988_cast_fp16")]; |
| fp16 const_14_promoted_to_fp16 = const()[name = string("const_14_promoted_to_fp16"), val = fp16(-0x1p+0)]; |
| tensor<fp16, [1, 16, 125, 32]> var_989_cast_fp16 = mul(x = var_988_cast_fp16, y = const_14_promoted_to_fp16)[name = string("op_989_cast_fp16")]; |
| tensor<int32, [4]> var_999_begin_0 = const()[name = string("op_999_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [4]> var_999_end_0 = const()[name = string("op_999_end_0"), val = tensor<int32, [4]>([1, 16, 125, 32])]; |
| tensor<bool, [4]> var_999_end_mask_0 = const()[name = string("op_999_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])]; |
| tensor<fp16, [1, 16, 125, 32]> var_999_cast_fp16 = slice_by_index(begin = var_999_begin_0, end = var_999_end_0, end_mask = var_999_end_mask_0, x = q_33_cast_fp16)[name = string("op_999_cast_fp16")]; |
| int32 var_1001 = const()[name = string("op_1001"), val = int32(-1)]; |
| bool var_1002_interleave_0 = const()[name = string("op_1002_interleave_0"), val = bool(false)]; |
| tensor<fp16, [1, 16, 125, 64]> var_1002_cast_fp16 = concat(axis = var_1001, interleave = var_1002_interleave_0, values = (var_989_cast_fp16, var_999_cast_fp16))[name = string("op_1002_cast_fp16")]; |
| tensor<fp16, [1, 1, 125, 64]> op_1004_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1, 1, 125, 64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12341952))), lut = tensor<fp16, [1, 1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12350016))))[name = string("op_1004_to_fp16_palettized")]; |
| tensor<fp16, [1, 16, 125, 64]> var_1005_cast_fp16 = mul(x = var_1002_cast_fp16, y = op_1004_to_fp16_palettized)[name = string("op_1005_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> q_35_cast_fp16 = add(x = q_33_cast_fp16, y = var_1005_cast_fp16)[name = string("q_35_cast_fp16")]; |
| tensor<int32, [4]> var_1020_begin_0 = const()[name = string("op_1020_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 32])]; |
| tensor<int32, [4]> var_1020_end_0 = const()[name = string("op_1020_end_0"), val = tensor<int32, [4]>([1, 16, 125, 64])]; |
| tensor<bool, [4]> var_1020_end_mask_0 = const()[name = string("op_1020_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])]; |
| tensor<fp16, [1, 16, 125, 64]> k_1_cast_fp16 = transpose(perm = k_1_perm_0, x = var_962_cast_fp16)[name = string("transpose_35")]; |
| tensor<fp16, [1, 16, 125, 32]> var_1020_cast_fp16 = slice_by_index(begin = var_1020_begin_0, end = var_1020_end_0, end_mask = var_1020_end_mask_0, x = k_1_cast_fp16)[name = string("op_1020_cast_fp16")]; |
| fp16 const_17_promoted_to_fp16 = const()[name = string("const_17_promoted_to_fp16"), val = fp16(-0x1p+0)]; |
| tensor<fp16, [1, 16, 125, 32]> var_1021_cast_fp16 = mul(x = var_1020_cast_fp16, y = const_17_promoted_to_fp16)[name = string("op_1021_cast_fp16")]; |
| tensor<int32, [4]> var_1031_begin_0 = const()[name = string("op_1031_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [4]> var_1031_end_0 = const()[name = string("op_1031_end_0"), val = tensor<int32, [4]>([1, 16, 125, 32])]; |
| tensor<bool, [4]> var_1031_end_mask_0 = const()[name = string("op_1031_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])]; |
| tensor<fp16, [1, 16, 125, 32]> var_1031_cast_fp16 = slice_by_index(begin = var_1031_begin_0, end = var_1031_end_0, end_mask = var_1031_end_mask_0, x = k_1_cast_fp16)[name = string("op_1031_cast_fp16")]; |
| int32 var_1033 = const()[name = string("op_1033"), val = int32(-1)]; |
| bool var_1034_interleave_0 = const()[name = string("op_1034_interleave_0"), val = bool(false)]; |
| tensor<fp16, [1, 16, 125, 64]> var_1034_cast_fp16 = concat(axis = var_1033, interleave = var_1034_interleave_0, values = (var_1021_cast_fp16, var_1031_cast_fp16))[name = string("op_1034_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> var_1037_cast_fp16 = mul(x = var_1034_cast_fp16, y = op_1004_to_fp16_palettized)[name = string("op_1037_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> k_3_cast_fp16 = add(x = k_1_cast_fp16, y = var_1037_cast_fp16)[name = string("k_3_cast_fp16")]; |
| bool var_1043_transpose_x_1 = const()[name = string("op_1043_transpose_x_1"), val = bool(false)]; |
| bool var_1043_transpose_y_1 = const()[name = string("op_1043_transpose_y_1"), val = bool(true)]; |
| tensor<fp16, [1, 16, 125, 125]> var_1043_cast_fp16 = matmul(transpose_x = var_1043_transpose_x_1, transpose_y = var_1043_transpose_y_1, x = q_35_cast_fp16, y = k_3_cast_fp16)[name = string("op_1043_cast_fp16")]; |
| fp16 var_1044_to_fp16 = const()[name = string("op_1044_to_fp16"), val = fp16(0x1p-3)]; |
| tensor<fp16, [1, 16, 125, 125]> aw_1_cast_fp16 = mul(x = var_1043_cast_fp16, y = var_1044_to_fp16)[name = string("aw_1_cast_fp16")]; |
| tensor<fp16, [1, 1, 125, 125]> op_1058_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1, 1, 125, 125]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12350592))), lut = tensor<fp16, [1, 1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12366336))))[name = string("op_1058_to_fp16_palettized")]; |
| tensor<fp16, [1, 16, 125, 125]> var_1060_cast_fp16 = add(x = aw_1_cast_fp16, y = op_1058_to_fp16_palettized)[name = string("op_1060_cast_fp16")]; |
| int32 var_1061 = const()[name = string("op_1061"), val = int32(-1)]; |
| tensor<fp16, [1, 16, 125, 125]> var_1063_cast_fp16 = softmax(axis = var_1061, x = var_1060_cast_fp16)[name = string("op_1063_cast_fp16")]; |
| bool var_1069_transpose_x_0 = const()[name = string("op_1069_transpose_x_0"), val = bool(false)]; |
| bool var_1069_transpose_y_0 = const()[name = string("op_1069_transpose_y_0"), val = bool(false)]; |
| tensor<fp16, [1, 16, 125, 64]> v_1_cast_fp16 = transpose(perm = v_1_perm_0, x = var_972_cast_fp16)[name = string("transpose_34")]; |
| tensor<fp16, [1, 16, 125, 64]> var_1069_cast_fp16 = matmul(transpose_x = var_1069_transpose_x_0, transpose_y = var_1069_transpose_y_0, x = var_1063_cast_fp16, y = v_1_cast_fp16)[name = string("op_1069_cast_fp16")]; |
| tensor<int32, [4]> var_1072_perm_0 = const()[name = string("op_1072_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_1082 = const()[name = string("op_1082"), val = tensor<int32, [3]>([1, 125, -1])]; |
| tensor<fp16, [1, 125, 16, 64]> var_1072_cast_fp16 = transpose(perm = var_1072_perm_0, x = var_1069_cast_fp16)[name = string("transpose_33")]; |
| tensor<fp16, [1, 125, 1024]> var_1083_cast_fp16 = reshape(shape = var_1082, x = var_1072_cast_fp16)[name = string("op_1083_cast_fp16")]; |
| tensor<fp16, [512, 1024]> pre_transformer_layers_0_self_attn_o_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [512, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12366912))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12891264))))[name = string("pre_transformer_layers_0_self_attn_o_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [512]> linear_4_bias_0_to_fp16 = const()[name = string("linear_4_bias_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12891840)))]; |
| tensor<fp16, [1, 125, 512]> linear_4_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = pre_transformer_layers_0_self_attn_o_proj_weight_to_fp16_palettized, x = var_1083_cast_fp16)[name = string("linear_4_cast_fp16")]; |
| tensor<fp16, [512]> pre_transformer_layers_0_self_attn_layer_scale_scale_to_fp16 = const()[name = string("pre_transformer_layers_0_self_attn_layer_scale_scale_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12892928)))]; |
| tensor<fp16, [1, 125, 512]> var_1090_cast_fp16 = mul(x = pre_transformer_layers_0_self_attn_layer_scale_scale_to_fp16, y = linear_4_cast_fp16)[name = string("op_1090_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> h_5_cast_fp16 = add(x = linear_0_cast_fp16, y = var_1090_cast_fp16)[name = string("h_5_cast_fp16")]; |
| tensor<fp16, [512]> const_21_to_fp16 = const()[name = string("const_21_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12894016)))]; |
| tensor<fp16, [1, 125, 512]> var_1103_cast_fp16 = mul(x = const_21_to_fp16, y = h_5_cast_fp16)[name = string("op_1103_cast_fp16")]; |
| fp16 var_1096_promoted_to_fp16 = const()[name = string("op_1096_promoted_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 125, 512]> var_1104_cast_fp16 = pow(x = h_5_cast_fp16, y = var_1096_promoted_to_fp16)[name = string("op_1104_cast_fp16")]; |
| tensor<int32, [1]> var_1106_axes_0 = const()[name = string("op_1106_axes_0"), val = tensor<int32, [1]>([-1])]; |
| bool var_1106_keep_dims_0 = const()[name = string("op_1106_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 125, 1]> var_1106_cast_fp16 = reduce_mean(axes = var_1106_axes_0, keep_dims = var_1106_keep_dims_0, x = var_1104_cast_fp16)[name = string("op_1106_cast_fp16")]; |
| fp16 var_1107_to_fp16 = const()[name = string("op_1107_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 125, 1]> var_1108_cast_fp16 = add(x = var_1106_cast_fp16, y = var_1107_to_fp16)[name = string("op_1108_cast_fp16")]; |
| fp32 var_1109_epsilon_0 = const()[name = string("op_1109_epsilon_0"), val = fp32(0x1.197998p-40)]; |
| tensor<fp16, [1, 125, 1]> var_1109_cast_fp16 = rsqrt(epsilon = var_1109_epsilon_0, x = var_1108_cast_fp16)[name = string("op_1109_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> input_43_cast_fp16 = mul(x = var_1103_cast_fp16, y = var_1109_cast_fp16)[name = string("input_43_cast_fp16")]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_0_mlp_gate_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12895104))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13419456))))[name = string("pre_transformer_layers_0_mlp_gate_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_5_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_0_mlp_gate_proj_weight_to_fp16_palettized, x = input_43_cast_fp16)[name = string("linear_5_cast_fp16")]; |
| tensor<fp16, [1, 125, 1024]> var_1120_cast_fp16 = silu(x = linear_5_cast_fp16)[name = string("op_1120_cast_fp16")]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_0_mlp_up_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13420032))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13944384))))[name = string("pre_transformer_layers_0_mlp_up_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_6_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_0_mlp_up_proj_weight_to_fp16_palettized, x = input_43_cast_fp16)[name = string("linear_6_cast_fp16")]; |
| tensor<fp16, [1, 125, 1024]> input_47_cast_fp16 = mul(x = var_1120_cast_fp16, y = linear_6_cast_fp16)[name = string("input_47_cast_fp16")]; |
| tensor<fp16, [512, 1024]> pre_transformer_layers_0_mlp_down_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [512, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13944960))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14469312))))[name = string("pre_transformer_layers_0_mlp_down_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 512]> linear_7_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = pre_transformer_layers_0_mlp_down_proj_weight_to_fp16_palettized, x = input_47_cast_fp16)[name = string("linear_7_cast_fp16")]; |
| tensor<fp16, [512]> pre_transformer_layers_0_mlp_layer_scale_scale_to_fp16 = const()[name = string("pre_transformer_layers_0_mlp_layer_scale_scale_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14469888)))]; |
| tensor<fp16, [1, 125, 512]> var_1127_cast_fp16 = mul(x = pre_transformer_layers_0_mlp_layer_scale_scale_to_fp16, y = linear_7_cast_fp16)[name = string("op_1127_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> h_9_cast_fp16 = add(x = h_5_cast_fp16, y = var_1127_cast_fp16)[name = string("h_9_cast_fp16")]; |
| tensor<fp16, [512]> const_22_to_fp16 = const()[name = string("const_22_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14470976)))]; |
| tensor<fp16, [1, 125, 512]> var_1140_cast_fp16 = mul(x = const_22_to_fp16, y = h_9_cast_fp16)[name = string("op_1140_cast_fp16")]; |
| fp16 var_1133_promoted_to_fp16 = const()[name = string("op_1133_promoted_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 125, 512]> var_1141_cast_fp16 = pow(x = h_9_cast_fp16, y = var_1133_promoted_to_fp16)[name = string("op_1141_cast_fp16")]; |
| tensor<int32, [1]> var_1143_axes_0 = const()[name = string("op_1143_axes_0"), val = tensor<int32, [1]>([-1])]; |
| bool var_1143_keep_dims_0 = const()[name = string("op_1143_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 125, 1]> var_1143_cast_fp16 = reduce_mean(axes = var_1143_axes_0, keep_dims = var_1143_keep_dims_0, x = var_1141_cast_fp16)[name = string("op_1143_cast_fp16")]; |
| fp16 var_1144_to_fp16 = const()[name = string("op_1144_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 125, 1]> var_1145_cast_fp16 = add(x = var_1143_cast_fp16, y = var_1144_to_fp16)[name = string("op_1145_cast_fp16")]; |
| fp32 var_1146_epsilon_0 = const()[name = string("op_1146_epsilon_0"), val = fp32(0x1.197998p-40)]; |
| tensor<fp16, [1, 125, 1]> var_1146_cast_fp16 = rsqrt(epsilon = var_1146_epsilon_0, x = var_1145_cast_fp16)[name = string("op_1146_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> hn_3_cast_fp16 = mul(x = var_1140_cast_fp16, y = var_1146_cast_fp16)[name = string("hn_3_cast_fp16")]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_1_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14472064))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14996416))))[name = string("pre_transformer_layers_1_self_attn_q_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_8_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_1_self_attn_q_proj_weight_to_fp16_palettized, x = hn_3_cast_fp16)[name = string("linear_8_cast_fp16")]; |
| tensor<int32, [4]> var_1168 = const()[name = string("op_1168"), val = tensor<int32, [4]>([1, 125, -1, 64])]; |
| tensor<fp16, [1, 125, 16, 64]> var_1169_cast_fp16 = reshape(shape = var_1168, x = linear_8_cast_fp16)[name = string("op_1169_cast_fp16")]; |
| tensor<int32, [4]> q_37_perm_0 = const()[name = string("q_37_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_1_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14996992))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15521344))))[name = string("pre_transformer_layers_1_self_attn_k_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_9_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_1_self_attn_k_proj_weight_to_fp16_palettized, x = hn_3_cast_fp16)[name = string("linear_9_cast_fp16")]; |
| tensor<int32, [4]> var_1178 = const()[name = string("op_1178"), val = tensor<int32, [4]>([1, 125, -1, 64])]; |
| tensor<fp16, [1, 125, 16, 64]> var_1179_cast_fp16 = reshape(shape = var_1178, x = linear_9_cast_fp16)[name = string("op_1179_cast_fp16")]; |
| tensor<int32, [4]> k_5_perm_0 = const()[name = string("k_5_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_1_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15521920))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16046272))))[name = string("pre_transformer_layers_1_self_attn_v_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_10_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_1_self_attn_v_proj_weight_to_fp16_palettized, x = hn_3_cast_fp16)[name = string("linear_10_cast_fp16")]; |
| tensor<int32, [4]> var_1188 = const()[name = string("op_1188"), val = tensor<int32, [4]>([1, 125, -1, 64])]; |
| tensor<fp16, [1, 125, 16, 64]> var_1189_cast_fp16 = reshape(shape = var_1188, x = linear_10_cast_fp16)[name = string("op_1189_cast_fp16")]; |
| tensor<int32, [4]> v_3_perm_0 = const()[name = string("v_3_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> var_1205_begin_0 = const()[name = string("op_1205_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 32])]; |
| tensor<int32, [4]> var_1205_end_0 = const()[name = string("op_1205_end_0"), val = tensor<int32, [4]>([1, 16, 125, 64])]; |
| tensor<bool, [4]> var_1205_end_mask_0 = const()[name = string("op_1205_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])]; |
| tensor<fp16, [1, 16, 125, 64]> q_37_cast_fp16 = transpose(perm = q_37_perm_0, x = var_1169_cast_fp16)[name = string("transpose_32")]; |
| tensor<fp16, [1, 16, 125, 32]> var_1205_cast_fp16 = slice_by_index(begin = var_1205_begin_0, end = var_1205_end_0, end_mask = var_1205_end_mask_0, x = q_37_cast_fp16)[name = string("op_1205_cast_fp16")]; |
| fp16 const_26_promoted_to_fp16 = const()[name = string("const_26_promoted_to_fp16"), val = fp16(-0x1p+0)]; |
| tensor<fp16, [1, 16, 125, 32]> var_1206_cast_fp16 = mul(x = var_1205_cast_fp16, y = const_26_promoted_to_fp16)[name = string("op_1206_cast_fp16")]; |
| tensor<int32, [4]> var_1216_begin_0 = const()[name = string("op_1216_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [4]> var_1216_end_0 = const()[name = string("op_1216_end_0"), val = tensor<int32, [4]>([1, 16, 125, 32])]; |
| tensor<bool, [4]> var_1216_end_mask_0 = const()[name = string("op_1216_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])]; |
| tensor<fp16, [1, 16, 125, 32]> var_1216_cast_fp16 = slice_by_index(begin = var_1216_begin_0, end = var_1216_end_0, end_mask = var_1216_end_mask_0, x = q_37_cast_fp16)[name = string("op_1216_cast_fp16")]; |
| int32 var_1218 = const()[name = string("op_1218"), val = int32(-1)]; |
| bool var_1219_interleave_0 = const()[name = string("op_1219_interleave_0"), val = bool(false)]; |
| tensor<fp16, [1, 16, 125, 64]> var_1219_cast_fp16 = concat(axis = var_1218, interleave = var_1219_interleave_0, values = (var_1206_cast_fp16, var_1216_cast_fp16))[name = string("op_1219_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> var_1222_cast_fp16 = mul(x = var_1219_cast_fp16, y = op_1004_to_fp16_palettized)[name = string("op_1222_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> q_39_cast_fp16 = add(x = q_37_cast_fp16, y = var_1222_cast_fp16)[name = string("q_39_cast_fp16")]; |
| tensor<int32, [4]> var_1237_begin_0 = const()[name = string("op_1237_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 32])]; |
| tensor<int32, [4]> var_1237_end_0 = const()[name = string("op_1237_end_0"), val = tensor<int32, [4]>([1, 16, 125, 64])]; |
| tensor<bool, [4]> var_1237_end_mask_0 = const()[name = string("op_1237_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])]; |
| tensor<fp16, [1, 16, 125, 64]> k_5_cast_fp16 = transpose(perm = k_5_perm_0, x = var_1179_cast_fp16)[name = string("transpose_31")]; |
| tensor<fp16, [1, 16, 125, 32]> var_1237_cast_fp16 = slice_by_index(begin = var_1237_begin_0, end = var_1237_end_0, end_mask = var_1237_end_mask_0, x = k_5_cast_fp16)[name = string("op_1237_cast_fp16")]; |
| fp16 const_29_promoted_to_fp16 = const()[name = string("const_29_promoted_to_fp16"), val = fp16(-0x1p+0)]; |
| tensor<fp16, [1, 16, 125, 32]> var_1238_cast_fp16 = mul(x = var_1237_cast_fp16, y = const_29_promoted_to_fp16)[name = string("op_1238_cast_fp16")]; |
| tensor<int32, [4]> var_1248_begin_0 = const()[name = string("op_1248_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [4]> var_1248_end_0 = const()[name = string("op_1248_end_0"), val = tensor<int32, [4]>([1, 16, 125, 32])]; |
| tensor<bool, [4]> var_1248_end_mask_0 = const()[name = string("op_1248_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])]; |
| tensor<fp16, [1, 16, 125, 32]> var_1248_cast_fp16 = slice_by_index(begin = var_1248_begin_0, end = var_1248_end_0, end_mask = var_1248_end_mask_0, x = k_5_cast_fp16)[name = string("op_1248_cast_fp16")]; |
| int32 var_1250 = const()[name = string("op_1250"), val = int32(-1)]; |
| bool var_1251_interleave_0 = const()[name = string("op_1251_interleave_0"), val = bool(false)]; |
| tensor<fp16, [1, 16, 125, 64]> var_1251_cast_fp16 = concat(axis = var_1250, interleave = var_1251_interleave_0, values = (var_1238_cast_fp16, var_1248_cast_fp16))[name = string("op_1251_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> var_1254_cast_fp16 = mul(x = var_1251_cast_fp16, y = op_1004_to_fp16_palettized)[name = string("op_1254_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> k_7_cast_fp16 = add(x = k_5_cast_fp16, y = var_1254_cast_fp16)[name = string("k_7_cast_fp16")]; |
| bool var_1260_transpose_x_1 = const()[name = string("op_1260_transpose_x_1"), val = bool(false)]; |
| bool var_1260_transpose_y_1 = const()[name = string("op_1260_transpose_y_1"), val = bool(true)]; |
| tensor<fp16, [1, 16, 125, 125]> var_1260_cast_fp16 = matmul(transpose_x = var_1260_transpose_x_1, transpose_y = var_1260_transpose_y_1, x = q_39_cast_fp16, y = k_7_cast_fp16)[name = string("op_1260_cast_fp16")]; |
| fp16 var_1261_to_fp16 = const()[name = string("op_1261_to_fp16"), val = fp16(0x1p-3)]; |
| tensor<fp16, [1, 16, 125, 125]> aw_5_cast_fp16 = mul(x = var_1260_cast_fp16, y = var_1261_to_fp16)[name = string("aw_5_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 125]> var_1277_cast_fp16 = add(x = aw_5_cast_fp16, y = op_1058_to_fp16_palettized)[name = string("op_1277_cast_fp16")]; |
| int32 var_1278 = const()[name = string("op_1278"), val = int32(-1)]; |
| tensor<fp16, [1, 16, 125, 125]> var_1280_cast_fp16 = softmax(axis = var_1278, x = var_1277_cast_fp16)[name = string("op_1280_cast_fp16")]; |
| bool var_1286_transpose_x_0 = const()[name = string("op_1286_transpose_x_0"), val = bool(false)]; |
| bool var_1286_transpose_y_0 = const()[name = string("op_1286_transpose_y_0"), val = bool(false)]; |
| tensor<fp16, [1, 16, 125, 64]> v_3_cast_fp16 = transpose(perm = v_3_perm_0, x = var_1189_cast_fp16)[name = string("transpose_30")]; |
| tensor<fp16, [1, 16, 125, 64]> var_1286_cast_fp16 = matmul(transpose_x = var_1286_transpose_x_0, transpose_y = var_1286_transpose_y_0, x = var_1280_cast_fp16, y = v_3_cast_fp16)[name = string("op_1286_cast_fp16")]; |
| tensor<int32, [4]> var_1289_perm_0 = const()[name = string("op_1289_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_1299 = const()[name = string("op_1299"), val = tensor<int32, [3]>([1, 125, -1])]; |
| tensor<fp16, [1, 125, 16, 64]> var_1289_cast_fp16 = transpose(perm = var_1289_perm_0, x = var_1286_cast_fp16)[name = string("transpose_29")]; |
| tensor<fp16, [1, 125, 1024]> var_1300_cast_fp16 = reshape(shape = var_1299, x = var_1289_cast_fp16)[name = string("op_1300_cast_fp16")]; |
| tensor<fp16, [512, 1024]> pre_transformer_layers_1_self_attn_o_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [512, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16046848))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16571200))))[name = string("pre_transformer_layers_1_self_attn_o_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 512]> linear_11_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = pre_transformer_layers_1_self_attn_o_proj_weight_to_fp16_palettized, x = var_1300_cast_fp16)[name = string("linear_11_cast_fp16")]; |
| tensor<fp16, [512]> pre_transformer_layers_1_self_attn_layer_scale_scale_to_fp16 = const()[name = string("pre_transformer_layers_1_self_attn_layer_scale_scale_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16571776)))]; |
| tensor<fp16, [1, 125, 512]> var_1307_cast_fp16 = mul(x = pre_transformer_layers_1_self_attn_layer_scale_scale_to_fp16, y = linear_11_cast_fp16)[name = string("op_1307_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> h_13_cast_fp16 = add(x = h_9_cast_fp16, y = var_1307_cast_fp16)[name = string("h_13_cast_fp16")]; |
| tensor<fp16, [512]> const_33_to_fp16 = const()[name = string("const_33_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16572864)))]; |
| tensor<fp16, [1, 125, 512]> var_1320_cast_fp16 = mul(x = const_33_to_fp16, y = h_13_cast_fp16)[name = string("op_1320_cast_fp16")]; |
| fp16 var_1313_promoted_to_fp16 = const()[name = string("op_1313_promoted_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 125, 512]> var_1321_cast_fp16 = pow(x = h_13_cast_fp16, y = var_1313_promoted_to_fp16)[name = string("op_1321_cast_fp16")]; |
| tensor<int32, [1]> var_1323_axes_0 = const()[name = string("op_1323_axes_0"), val = tensor<int32, [1]>([-1])]; |
| bool var_1323_keep_dims_0 = const()[name = string("op_1323_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 125, 1]> var_1323_cast_fp16 = reduce_mean(axes = var_1323_axes_0, keep_dims = var_1323_keep_dims_0, x = var_1321_cast_fp16)[name = string("op_1323_cast_fp16")]; |
| fp16 var_1324_to_fp16 = const()[name = string("op_1324_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 125, 1]> var_1325_cast_fp16 = add(x = var_1323_cast_fp16, y = var_1324_to_fp16)[name = string("op_1325_cast_fp16")]; |
| fp32 var_1326_epsilon_0 = const()[name = string("op_1326_epsilon_0"), val = fp32(0x1.197998p-40)]; |
| tensor<fp16, [1, 125, 1]> var_1326_cast_fp16 = rsqrt(epsilon = var_1326_epsilon_0, x = var_1325_cast_fp16)[name = string("op_1326_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> input_51_cast_fp16 = mul(x = var_1320_cast_fp16, y = var_1326_cast_fp16)[name = string("input_51_cast_fp16")]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_1_mlp_gate_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16573952))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17098304))))[name = string("pre_transformer_layers_1_mlp_gate_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_12_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_1_mlp_gate_proj_weight_to_fp16_palettized, x = input_51_cast_fp16)[name = string("linear_12_cast_fp16")]; |
| tensor<fp16, [1, 125, 1024]> var_1337_cast_fp16 = silu(x = linear_12_cast_fp16)[name = string("op_1337_cast_fp16")]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_1_mlp_up_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17098880))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17623232))))[name = string("pre_transformer_layers_1_mlp_up_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_13_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_1_mlp_up_proj_weight_to_fp16_palettized, x = input_51_cast_fp16)[name = string("linear_13_cast_fp16")]; |
| tensor<fp16, [1, 125, 1024]> input_55_cast_fp16 = mul(x = var_1337_cast_fp16, y = linear_13_cast_fp16)[name = string("input_55_cast_fp16")]; |
| tensor<fp16, [512, 1024]> pre_transformer_layers_1_mlp_down_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [512, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17623808))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18148160))))[name = string("pre_transformer_layers_1_mlp_down_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 512]> linear_14_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = pre_transformer_layers_1_mlp_down_proj_weight_to_fp16_palettized, x = input_55_cast_fp16)[name = string("linear_14_cast_fp16")]; |
| tensor<fp16, [512]> pre_transformer_layers_1_mlp_layer_scale_scale_to_fp16 = const()[name = string("pre_transformer_layers_1_mlp_layer_scale_scale_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18148736)))]; |
| tensor<fp16, [1, 125, 512]> var_1344_cast_fp16 = mul(x = pre_transformer_layers_1_mlp_layer_scale_scale_to_fp16, y = linear_14_cast_fp16)[name = string("op_1344_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> h_17_cast_fp16 = add(x = h_13_cast_fp16, y = var_1344_cast_fp16)[name = string("h_17_cast_fp16")]; |
| tensor<fp16, [512]> const_34_to_fp16 = const()[name = string("const_34_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18149824)))]; |
| tensor<fp16, [1, 125, 512]> var_1357_cast_fp16 = mul(x = const_34_to_fp16, y = h_17_cast_fp16)[name = string("op_1357_cast_fp16")]; |
| fp16 var_1350_promoted_to_fp16 = const()[name = string("op_1350_promoted_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 125, 512]> var_1358_cast_fp16 = pow(x = h_17_cast_fp16, y = var_1350_promoted_to_fp16)[name = string("op_1358_cast_fp16")]; |
| tensor<int32, [1]> var_1360_axes_0 = const()[name = string("op_1360_axes_0"), val = tensor<int32, [1]>([-1])]; |
| bool var_1360_keep_dims_0 = const()[name = string("op_1360_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 125, 1]> var_1360_cast_fp16 = reduce_mean(axes = var_1360_axes_0, keep_dims = var_1360_keep_dims_0, x = var_1358_cast_fp16)[name = string("op_1360_cast_fp16")]; |
| fp16 var_1361_to_fp16 = const()[name = string("op_1361_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 125, 1]> var_1362_cast_fp16 = add(x = var_1360_cast_fp16, y = var_1361_to_fp16)[name = string("op_1362_cast_fp16")]; |
| fp32 var_1363_epsilon_0 = const()[name = string("op_1363_epsilon_0"), val = fp32(0x1.197998p-40)]; |
| tensor<fp16, [1, 125, 1]> var_1363_cast_fp16 = rsqrt(epsilon = var_1363_epsilon_0, x = var_1362_cast_fp16)[name = string("op_1363_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> hn_5_cast_fp16 = mul(x = var_1357_cast_fp16, y = var_1363_cast_fp16)[name = string("hn_5_cast_fp16")]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_2_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18150912))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18675264))))[name = string("pre_transformer_layers_2_self_attn_q_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_15_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_2_self_attn_q_proj_weight_to_fp16_palettized, x = hn_5_cast_fp16)[name = string("linear_15_cast_fp16")]; |
| tensor<int32, [4]> var_1385 = const()[name = string("op_1385"), val = tensor<int32, [4]>([1, 125, -1, 64])]; |
| tensor<fp16, [1, 125, 16, 64]> var_1386_cast_fp16 = reshape(shape = var_1385, x = linear_15_cast_fp16)[name = string("op_1386_cast_fp16")]; |
| tensor<int32, [4]> q_41_perm_0 = const()[name = string("q_41_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_2_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18675840))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19200192))))[name = string("pre_transformer_layers_2_self_attn_k_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_16_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_2_self_attn_k_proj_weight_to_fp16_palettized, x = hn_5_cast_fp16)[name = string("linear_16_cast_fp16")]; |
| tensor<int32, [4]> var_1395 = const()[name = string("op_1395"), val = tensor<int32, [4]>([1, 125, -1, 64])]; |
| tensor<fp16, [1, 125, 16, 64]> var_1396_cast_fp16 = reshape(shape = var_1395, x = linear_16_cast_fp16)[name = string("op_1396_cast_fp16")]; |
| tensor<int32, [4]> k_9_perm_0 = const()[name = string("k_9_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_2_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19200768))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19725120))))[name = string("pre_transformer_layers_2_self_attn_v_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_17_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_2_self_attn_v_proj_weight_to_fp16_palettized, x = hn_5_cast_fp16)[name = string("linear_17_cast_fp16")]; |
| tensor<int32, [4]> var_1405 = const()[name = string("op_1405"), val = tensor<int32, [4]>([1, 125, -1, 64])]; |
| tensor<fp16, [1, 125, 16, 64]> var_1406_cast_fp16 = reshape(shape = var_1405, x = linear_17_cast_fp16)[name = string("op_1406_cast_fp16")]; |
| tensor<int32, [4]> v_5_perm_0 = const()[name = string("v_5_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> var_1422_begin_0 = const()[name = string("op_1422_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 32])]; |
| tensor<int32, [4]> var_1422_end_0 = const()[name = string("op_1422_end_0"), val = tensor<int32, [4]>([1, 16, 125, 64])]; |
| tensor<bool, [4]> var_1422_end_mask_0 = const()[name = string("op_1422_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])]; |
| tensor<fp16, [1, 16, 125, 64]> q_41_cast_fp16 = transpose(perm = q_41_perm_0, x = var_1386_cast_fp16)[name = string("transpose_28")]; |
| tensor<fp16, [1, 16, 125, 32]> var_1422_cast_fp16 = slice_by_index(begin = var_1422_begin_0, end = var_1422_end_0, end_mask = var_1422_end_mask_0, x = q_41_cast_fp16)[name = string("op_1422_cast_fp16")]; |
| fp16 const_38_promoted_to_fp16 = const()[name = string("const_38_promoted_to_fp16"), val = fp16(-0x1p+0)]; |
| tensor<fp16, [1, 16, 125, 32]> var_1423_cast_fp16 = mul(x = var_1422_cast_fp16, y = const_38_promoted_to_fp16)[name = string("op_1423_cast_fp16")]; |
| tensor<int32, [4]> var_1433_begin_0 = const()[name = string("op_1433_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [4]> var_1433_end_0 = const()[name = string("op_1433_end_0"), val = tensor<int32, [4]>([1, 16, 125, 32])]; |
| tensor<bool, [4]> var_1433_end_mask_0 = const()[name = string("op_1433_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])]; |
| tensor<fp16, [1, 16, 125, 32]> var_1433_cast_fp16 = slice_by_index(begin = var_1433_begin_0, end = var_1433_end_0, end_mask = var_1433_end_mask_0, x = q_41_cast_fp16)[name = string("op_1433_cast_fp16")]; |
| int32 var_1435 = const()[name = string("op_1435"), val = int32(-1)]; |
| bool var_1436_interleave_0 = const()[name = string("op_1436_interleave_0"), val = bool(false)]; |
| tensor<fp16, [1, 16, 125, 64]> var_1436_cast_fp16 = concat(axis = var_1435, interleave = var_1436_interleave_0, values = (var_1423_cast_fp16, var_1433_cast_fp16))[name = string("op_1436_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> var_1439_cast_fp16 = mul(x = var_1436_cast_fp16, y = op_1004_to_fp16_palettized)[name = string("op_1439_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> q_43_cast_fp16 = add(x = q_41_cast_fp16, y = var_1439_cast_fp16)[name = string("q_43_cast_fp16")]; |
| tensor<int32, [4]> var_1454_begin_0 = const()[name = string("op_1454_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 32])]; |
| tensor<int32, [4]> var_1454_end_0 = const()[name = string("op_1454_end_0"), val = tensor<int32, [4]>([1, 16, 125, 64])]; |
| tensor<bool, [4]> var_1454_end_mask_0 = const()[name = string("op_1454_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])]; |
| tensor<fp16, [1, 16, 125, 64]> k_9_cast_fp16 = transpose(perm = k_9_perm_0, x = var_1396_cast_fp16)[name = string("transpose_27")]; |
| tensor<fp16, [1, 16, 125, 32]> var_1454_cast_fp16 = slice_by_index(begin = var_1454_begin_0, end = var_1454_end_0, end_mask = var_1454_end_mask_0, x = k_9_cast_fp16)[name = string("op_1454_cast_fp16")]; |
| fp16 const_41_promoted_to_fp16 = const()[name = string("const_41_promoted_to_fp16"), val = fp16(-0x1p+0)]; |
| tensor<fp16, [1, 16, 125, 32]> var_1455_cast_fp16 = mul(x = var_1454_cast_fp16, y = const_41_promoted_to_fp16)[name = string("op_1455_cast_fp16")]; |
| tensor<int32, [4]> var_1465_begin_0 = const()[name = string("op_1465_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [4]> var_1465_end_0 = const()[name = string("op_1465_end_0"), val = tensor<int32, [4]>([1, 16, 125, 32])]; |
| tensor<bool, [4]> var_1465_end_mask_0 = const()[name = string("op_1465_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])]; |
| tensor<fp16, [1, 16, 125, 32]> var_1465_cast_fp16 = slice_by_index(begin = var_1465_begin_0, end = var_1465_end_0, end_mask = var_1465_end_mask_0, x = k_9_cast_fp16)[name = string("op_1465_cast_fp16")]; |
| int32 var_1467 = const()[name = string("op_1467"), val = int32(-1)]; |
| bool var_1468_interleave_0 = const()[name = string("op_1468_interleave_0"), val = bool(false)]; |
| tensor<fp16, [1, 16, 125, 64]> var_1468_cast_fp16 = concat(axis = var_1467, interleave = var_1468_interleave_0, values = (var_1455_cast_fp16, var_1465_cast_fp16))[name = string("op_1468_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> var_1471_cast_fp16 = mul(x = var_1468_cast_fp16, y = op_1004_to_fp16_palettized)[name = string("op_1471_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> k_11_cast_fp16 = add(x = k_9_cast_fp16, y = var_1471_cast_fp16)[name = string("k_11_cast_fp16")]; |
| bool var_1477_transpose_x_1 = const()[name = string("op_1477_transpose_x_1"), val = bool(false)]; |
| bool var_1477_transpose_y_1 = const()[name = string("op_1477_transpose_y_1"), val = bool(true)]; |
| tensor<fp16, [1, 16, 125, 125]> var_1477_cast_fp16 = matmul(transpose_x = var_1477_transpose_x_1, transpose_y = var_1477_transpose_y_1, x = q_43_cast_fp16, y = k_11_cast_fp16)[name = string("op_1477_cast_fp16")]; |
| fp16 var_1478_to_fp16 = const()[name = string("op_1478_to_fp16"), val = fp16(0x1p-3)]; |
| tensor<fp16, [1, 16, 125, 125]> aw_9_cast_fp16 = mul(x = var_1477_cast_fp16, y = var_1478_to_fp16)[name = string("aw_9_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 125]> var_1494_cast_fp16 = add(x = aw_9_cast_fp16, y = op_1058_to_fp16_palettized)[name = string("op_1494_cast_fp16")]; |
| int32 var_1495 = const()[name = string("op_1495"), val = int32(-1)]; |
| tensor<fp16, [1, 16, 125, 125]> var_1497_cast_fp16 = softmax(axis = var_1495, x = var_1494_cast_fp16)[name = string("op_1497_cast_fp16")]; |
| bool var_1503_transpose_x_0 = const()[name = string("op_1503_transpose_x_0"), val = bool(false)]; |
| bool var_1503_transpose_y_0 = const()[name = string("op_1503_transpose_y_0"), val = bool(false)]; |
| tensor<fp16, [1, 16, 125, 64]> v_5_cast_fp16 = transpose(perm = v_5_perm_0, x = var_1406_cast_fp16)[name = string("transpose_26")]; |
| tensor<fp16, [1, 16, 125, 64]> var_1503_cast_fp16 = matmul(transpose_x = var_1503_transpose_x_0, transpose_y = var_1503_transpose_y_0, x = var_1497_cast_fp16, y = v_5_cast_fp16)[name = string("op_1503_cast_fp16")]; |
| tensor<int32, [4]> var_1506_perm_0 = const()[name = string("op_1506_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_1516 = const()[name = string("op_1516"), val = tensor<int32, [3]>([1, 125, -1])]; |
| tensor<fp16, [1, 125, 16, 64]> var_1506_cast_fp16 = transpose(perm = var_1506_perm_0, x = var_1503_cast_fp16)[name = string("transpose_25")]; |
| tensor<fp16, [1, 125, 1024]> var_1517_cast_fp16 = reshape(shape = var_1516, x = var_1506_cast_fp16)[name = string("op_1517_cast_fp16")]; |
| tensor<fp16, [512, 1024]> pre_transformer_layers_2_self_attn_o_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [512, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19725696))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20250048))))[name = string("pre_transformer_layers_2_self_attn_o_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 512]> linear_18_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = pre_transformer_layers_2_self_attn_o_proj_weight_to_fp16_palettized, x = var_1517_cast_fp16)[name = string("linear_18_cast_fp16")]; |
| tensor<fp16, [512]> pre_transformer_layers_2_self_attn_layer_scale_scale_to_fp16 = const()[name = string("pre_transformer_layers_2_self_attn_layer_scale_scale_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20250624)))]; |
| tensor<fp16, [1, 125, 512]> var_1524_cast_fp16 = mul(x = pre_transformer_layers_2_self_attn_layer_scale_scale_to_fp16, y = linear_18_cast_fp16)[name = string("op_1524_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> h_21_cast_fp16 = add(x = h_17_cast_fp16, y = var_1524_cast_fp16)[name = string("h_21_cast_fp16")]; |
| tensor<fp16, [512]> const_45_to_fp16 = const()[name = string("const_45_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20251712)))]; |
| tensor<fp16, [1, 125, 512]> var_1537_cast_fp16 = mul(x = const_45_to_fp16, y = h_21_cast_fp16)[name = string("op_1537_cast_fp16")]; |
| fp16 var_1530_promoted_to_fp16 = const()[name = string("op_1530_promoted_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 125, 512]> var_1538_cast_fp16 = pow(x = h_21_cast_fp16, y = var_1530_promoted_to_fp16)[name = string("op_1538_cast_fp16")]; |
| tensor<int32, [1]> var_1540_axes_0 = const()[name = string("op_1540_axes_0"), val = tensor<int32, [1]>([-1])]; |
| bool var_1540_keep_dims_0 = const()[name = string("op_1540_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 125, 1]> var_1540_cast_fp16 = reduce_mean(axes = var_1540_axes_0, keep_dims = var_1540_keep_dims_0, x = var_1538_cast_fp16)[name = string("op_1540_cast_fp16")]; |
| fp16 var_1541_to_fp16 = const()[name = string("op_1541_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 125, 1]> var_1542_cast_fp16 = add(x = var_1540_cast_fp16, y = var_1541_to_fp16)[name = string("op_1542_cast_fp16")]; |
| fp32 var_1543_epsilon_0 = const()[name = string("op_1543_epsilon_0"), val = fp32(0x1.197998p-40)]; |
| tensor<fp16, [1, 125, 1]> var_1543_cast_fp16 = rsqrt(epsilon = var_1543_epsilon_0, x = var_1542_cast_fp16)[name = string("op_1543_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> input_59_cast_fp16 = mul(x = var_1537_cast_fp16, y = var_1543_cast_fp16)[name = string("input_59_cast_fp16")]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_2_mlp_gate_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20252800))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20777152))))[name = string("pre_transformer_layers_2_mlp_gate_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_19_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_2_mlp_gate_proj_weight_to_fp16_palettized, x = input_59_cast_fp16)[name = string("linear_19_cast_fp16")]; |
| tensor<fp16, [1, 125, 1024]> var_1554_cast_fp16 = silu(x = linear_19_cast_fp16)[name = string("op_1554_cast_fp16")]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_2_mlp_up_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20777728))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21302080))))[name = string("pre_transformer_layers_2_mlp_up_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_20_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_2_mlp_up_proj_weight_to_fp16_palettized, x = input_59_cast_fp16)[name = string("linear_20_cast_fp16")]; |
| tensor<fp16, [1, 125, 1024]> input_63_cast_fp16 = mul(x = var_1554_cast_fp16, y = linear_20_cast_fp16)[name = string("input_63_cast_fp16")]; |
| tensor<fp16, [512, 1024]> pre_transformer_layers_2_mlp_down_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [512, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21302656))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21827008))))[name = string("pre_transformer_layers_2_mlp_down_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 512]> linear_21_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = pre_transformer_layers_2_mlp_down_proj_weight_to_fp16_palettized, x = input_63_cast_fp16)[name = string("linear_21_cast_fp16")]; |
| tensor<fp16, [512]> pre_transformer_layers_2_mlp_layer_scale_scale_to_fp16 = const()[name = string("pre_transformer_layers_2_mlp_layer_scale_scale_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21827584)))]; |
| tensor<fp16, [1, 125, 512]> var_1561_cast_fp16 = mul(x = pre_transformer_layers_2_mlp_layer_scale_scale_to_fp16, y = linear_21_cast_fp16)[name = string("op_1561_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> h_25_cast_fp16 = add(x = h_21_cast_fp16, y = var_1561_cast_fp16)[name = string("h_25_cast_fp16")]; |
| tensor<fp16, [512]> const_46_to_fp16 = const()[name = string("const_46_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21828672)))]; |
| tensor<fp16, [1, 125, 512]> var_1574_cast_fp16 = mul(x = const_46_to_fp16, y = h_25_cast_fp16)[name = string("op_1574_cast_fp16")]; |
| fp16 var_1567_promoted_to_fp16 = const()[name = string("op_1567_promoted_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 125, 512]> var_1575_cast_fp16 = pow(x = h_25_cast_fp16, y = var_1567_promoted_to_fp16)[name = string("op_1575_cast_fp16")]; |
| tensor<int32, [1]> var_1577_axes_0 = const()[name = string("op_1577_axes_0"), val = tensor<int32, [1]>([-1])]; |
| bool var_1577_keep_dims_0 = const()[name = string("op_1577_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 125, 1]> var_1577_cast_fp16 = reduce_mean(axes = var_1577_axes_0, keep_dims = var_1577_keep_dims_0, x = var_1575_cast_fp16)[name = string("op_1577_cast_fp16")]; |
| fp16 var_1578_to_fp16 = const()[name = string("op_1578_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 125, 1]> var_1579_cast_fp16 = add(x = var_1577_cast_fp16, y = var_1578_to_fp16)[name = string("op_1579_cast_fp16")]; |
| fp32 var_1580_epsilon_0 = const()[name = string("op_1580_epsilon_0"), val = fp32(0x1.197998p-40)]; |
| tensor<fp16, [1, 125, 1]> var_1580_cast_fp16 = rsqrt(epsilon = var_1580_epsilon_0, x = var_1579_cast_fp16)[name = string("op_1580_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> hn_7_cast_fp16 = mul(x = var_1574_cast_fp16, y = var_1580_cast_fp16)[name = string("hn_7_cast_fp16")]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_3_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21829760))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22354112))))[name = string("pre_transformer_layers_3_self_attn_q_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_22_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_3_self_attn_q_proj_weight_to_fp16_palettized, x = hn_7_cast_fp16)[name = string("linear_22_cast_fp16")]; |
| tensor<int32, [4]> var_1602 = const()[name = string("op_1602"), val = tensor<int32, [4]>([1, 125, -1, 64])]; |
| tensor<fp16, [1, 125, 16, 64]> var_1603_cast_fp16 = reshape(shape = var_1602, x = linear_22_cast_fp16)[name = string("op_1603_cast_fp16")]; |
| tensor<int32, [4]> q_45_perm_0 = const()[name = string("q_45_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_3_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22354688))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22879040))))[name = string("pre_transformer_layers_3_self_attn_k_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_23_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_3_self_attn_k_proj_weight_to_fp16_palettized, x = hn_7_cast_fp16)[name = string("linear_23_cast_fp16")]; |
| tensor<int32, [4]> var_1612 = const()[name = string("op_1612"), val = tensor<int32, [4]>([1, 125, -1, 64])]; |
| tensor<fp16, [1, 125, 16, 64]> var_1613_cast_fp16 = reshape(shape = var_1612, x = linear_23_cast_fp16)[name = string("op_1613_cast_fp16")]; |
| tensor<int32, [4]> k_13_perm_0 = const()[name = string("k_13_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_3_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22879616))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23403968))))[name = string("pre_transformer_layers_3_self_attn_v_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_24_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_3_self_attn_v_proj_weight_to_fp16_palettized, x = hn_7_cast_fp16)[name = string("linear_24_cast_fp16")]; |
| tensor<int32, [4]> var_1622 = const()[name = string("op_1622"), val = tensor<int32, [4]>([1, 125, -1, 64])]; |
| tensor<fp16, [1, 125, 16, 64]> var_1623_cast_fp16 = reshape(shape = var_1622, x = linear_24_cast_fp16)[name = string("op_1623_cast_fp16")]; |
| tensor<int32, [4]> v_7_perm_0 = const()[name = string("v_7_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> var_1639_begin_0 = const()[name = string("op_1639_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 32])]; |
| tensor<int32, [4]> var_1639_end_0 = const()[name = string("op_1639_end_0"), val = tensor<int32, [4]>([1, 16, 125, 64])]; |
| tensor<bool, [4]> var_1639_end_mask_0 = const()[name = string("op_1639_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])]; |
| tensor<fp16, [1, 16, 125, 64]> q_45_cast_fp16 = transpose(perm = q_45_perm_0, x = var_1603_cast_fp16)[name = string("transpose_24")]; |
| tensor<fp16, [1, 16, 125, 32]> var_1639_cast_fp16 = slice_by_index(begin = var_1639_begin_0, end = var_1639_end_0, end_mask = var_1639_end_mask_0, x = q_45_cast_fp16)[name = string("op_1639_cast_fp16")]; |
| fp16 const_50_promoted_to_fp16 = const()[name = string("const_50_promoted_to_fp16"), val = fp16(-0x1p+0)]; |
| tensor<fp16, [1, 16, 125, 32]> var_1640_cast_fp16 = mul(x = var_1639_cast_fp16, y = const_50_promoted_to_fp16)[name = string("op_1640_cast_fp16")]; |
| tensor<int32, [4]> var_1650_begin_0 = const()[name = string("op_1650_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [4]> var_1650_end_0 = const()[name = string("op_1650_end_0"), val = tensor<int32, [4]>([1, 16, 125, 32])]; |
| tensor<bool, [4]> var_1650_end_mask_0 = const()[name = string("op_1650_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])]; |
| tensor<fp16, [1, 16, 125, 32]> var_1650_cast_fp16 = slice_by_index(begin = var_1650_begin_0, end = var_1650_end_0, end_mask = var_1650_end_mask_0, x = q_45_cast_fp16)[name = string("op_1650_cast_fp16")]; |
| int32 var_1652 = const()[name = string("op_1652"), val = int32(-1)]; |
| bool var_1653_interleave_0 = const()[name = string("op_1653_interleave_0"), val = bool(false)]; |
| tensor<fp16, [1, 16, 125, 64]> var_1653_cast_fp16 = concat(axis = var_1652, interleave = var_1653_interleave_0, values = (var_1640_cast_fp16, var_1650_cast_fp16))[name = string("op_1653_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> var_1656_cast_fp16 = mul(x = var_1653_cast_fp16, y = op_1004_to_fp16_palettized)[name = string("op_1656_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> q_47_cast_fp16 = add(x = q_45_cast_fp16, y = var_1656_cast_fp16)[name = string("q_47_cast_fp16")]; |
| tensor<int32, [4]> var_1671_begin_0 = const()[name = string("op_1671_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 32])]; |
| tensor<int32, [4]> var_1671_end_0 = const()[name = string("op_1671_end_0"), val = tensor<int32, [4]>([1, 16, 125, 64])]; |
| tensor<bool, [4]> var_1671_end_mask_0 = const()[name = string("op_1671_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])]; |
| tensor<fp16, [1, 16, 125, 64]> k_13_cast_fp16 = transpose(perm = k_13_perm_0, x = var_1613_cast_fp16)[name = string("transpose_23")]; |
| tensor<fp16, [1, 16, 125, 32]> var_1671_cast_fp16 = slice_by_index(begin = var_1671_begin_0, end = var_1671_end_0, end_mask = var_1671_end_mask_0, x = k_13_cast_fp16)[name = string("op_1671_cast_fp16")]; |
| fp16 const_53_promoted_to_fp16 = const()[name = string("const_53_promoted_to_fp16"), val = fp16(-0x1p+0)]; |
| tensor<fp16, [1, 16, 125, 32]> var_1672_cast_fp16 = mul(x = var_1671_cast_fp16, y = const_53_promoted_to_fp16)[name = string("op_1672_cast_fp16")]; |
| tensor<int32, [4]> var_1682_begin_0 = const()[name = string("op_1682_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [4]> var_1682_end_0 = const()[name = string("op_1682_end_0"), val = tensor<int32, [4]>([1, 16, 125, 32])]; |
| tensor<bool, [4]> var_1682_end_mask_0 = const()[name = string("op_1682_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])]; |
| tensor<fp16, [1, 16, 125, 32]> var_1682_cast_fp16 = slice_by_index(begin = var_1682_begin_0, end = var_1682_end_0, end_mask = var_1682_end_mask_0, x = k_13_cast_fp16)[name = string("op_1682_cast_fp16")]; |
| int32 var_1684 = const()[name = string("op_1684"), val = int32(-1)]; |
| bool var_1685_interleave_0 = const()[name = string("op_1685_interleave_0"), val = bool(false)]; |
| tensor<fp16, [1, 16, 125, 64]> var_1685_cast_fp16 = concat(axis = var_1684, interleave = var_1685_interleave_0, values = (var_1672_cast_fp16, var_1682_cast_fp16))[name = string("op_1685_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> var_1688_cast_fp16 = mul(x = var_1685_cast_fp16, y = op_1004_to_fp16_palettized)[name = string("op_1688_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> k_15_cast_fp16 = add(x = k_13_cast_fp16, y = var_1688_cast_fp16)[name = string("k_15_cast_fp16")]; |
| bool var_1694_transpose_x_1 = const()[name = string("op_1694_transpose_x_1"), val = bool(false)]; |
| bool var_1694_transpose_y_1 = const()[name = string("op_1694_transpose_y_1"), val = bool(true)]; |
| tensor<fp16, [1, 16, 125, 125]> var_1694_cast_fp16 = matmul(transpose_x = var_1694_transpose_x_1, transpose_y = var_1694_transpose_y_1, x = q_47_cast_fp16, y = k_15_cast_fp16)[name = string("op_1694_cast_fp16")]; |
| fp16 var_1695_to_fp16 = const()[name = string("op_1695_to_fp16"), val = fp16(0x1p-3)]; |
| tensor<fp16, [1, 16, 125, 125]> aw_13_cast_fp16 = mul(x = var_1694_cast_fp16, y = var_1695_to_fp16)[name = string("aw_13_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 125]> var_1711_cast_fp16 = add(x = aw_13_cast_fp16, y = op_1058_to_fp16_palettized)[name = string("op_1711_cast_fp16")]; |
| int32 var_1712 = const()[name = string("op_1712"), val = int32(-1)]; |
| tensor<fp16, [1, 16, 125, 125]> var_1714_cast_fp16 = softmax(axis = var_1712, x = var_1711_cast_fp16)[name = string("op_1714_cast_fp16")]; |
| bool var_1720_transpose_x_0 = const()[name = string("op_1720_transpose_x_0"), val = bool(false)]; |
| bool var_1720_transpose_y_0 = const()[name = string("op_1720_transpose_y_0"), val = bool(false)]; |
| tensor<fp16, [1, 16, 125, 64]> v_7_cast_fp16 = transpose(perm = v_7_perm_0, x = var_1623_cast_fp16)[name = string("transpose_22")]; |
| tensor<fp16, [1, 16, 125, 64]> var_1720_cast_fp16 = matmul(transpose_x = var_1720_transpose_x_0, transpose_y = var_1720_transpose_y_0, x = var_1714_cast_fp16, y = v_7_cast_fp16)[name = string("op_1720_cast_fp16")]; |
| tensor<int32, [4]> var_1723_perm_0 = const()[name = string("op_1723_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_1733 = const()[name = string("op_1733"), val = tensor<int32, [3]>([1, 125, -1])]; |
| tensor<fp16, [1, 125, 16, 64]> var_1723_cast_fp16 = transpose(perm = var_1723_perm_0, x = var_1720_cast_fp16)[name = string("transpose_21")]; |
| tensor<fp16, [1, 125, 1024]> var_1734_cast_fp16 = reshape(shape = var_1733, x = var_1723_cast_fp16)[name = string("op_1734_cast_fp16")]; |
| tensor<fp16, [512, 1024]> pre_transformer_layers_3_self_attn_o_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [512, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23404544))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23928896))))[name = string("pre_transformer_layers_3_self_attn_o_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 512]> linear_25_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = pre_transformer_layers_3_self_attn_o_proj_weight_to_fp16_palettized, x = var_1734_cast_fp16)[name = string("linear_25_cast_fp16")]; |
| tensor<fp16, [512]> pre_transformer_layers_3_self_attn_layer_scale_scale_to_fp16 = const()[name = string("pre_transformer_layers_3_self_attn_layer_scale_scale_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23929472)))]; |
| tensor<fp16, [1, 125, 512]> var_1741_cast_fp16 = mul(x = pre_transformer_layers_3_self_attn_layer_scale_scale_to_fp16, y = linear_25_cast_fp16)[name = string("op_1741_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> h_29_cast_fp16 = add(x = h_25_cast_fp16, y = var_1741_cast_fp16)[name = string("h_29_cast_fp16")]; |
| tensor<fp16, [512]> const_57_to_fp16 = const()[name = string("const_57_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23930560)))]; |
| tensor<fp16, [1, 125, 512]> var_1754_cast_fp16 = mul(x = const_57_to_fp16, y = h_29_cast_fp16)[name = string("op_1754_cast_fp16")]; |
| fp16 var_1747_promoted_to_fp16 = const()[name = string("op_1747_promoted_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 125, 512]> var_1755_cast_fp16 = pow(x = h_29_cast_fp16, y = var_1747_promoted_to_fp16)[name = string("op_1755_cast_fp16")]; |
| tensor<int32, [1]> var_1757_axes_0 = const()[name = string("op_1757_axes_0"), val = tensor<int32, [1]>([-1])]; |
| bool var_1757_keep_dims_0 = const()[name = string("op_1757_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 125, 1]> var_1757_cast_fp16 = reduce_mean(axes = var_1757_axes_0, keep_dims = var_1757_keep_dims_0, x = var_1755_cast_fp16)[name = string("op_1757_cast_fp16")]; |
| fp16 var_1758_to_fp16 = const()[name = string("op_1758_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 125, 1]> var_1759_cast_fp16 = add(x = var_1757_cast_fp16, y = var_1758_to_fp16)[name = string("op_1759_cast_fp16")]; |
| fp32 var_1760_epsilon_0 = const()[name = string("op_1760_epsilon_0"), val = fp32(0x1.197998p-40)]; |
| tensor<fp16, [1, 125, 1]> var_1760_cast_fp16 = rsqrt(epsilon = var_1760_epsilon_0, x = var_1759_cast_fp16)[name = string("op_1760_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> input_67_cast_fp16 = mul(x = var_1754_cast_fp16, y = var_1760_cast_fp16)[name = string("input_67_cast_fp16")]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_3_mlp_gate_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23931648))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24456000))))[name = string("pre_transformer_layers_3_mlp_gate_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_26_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_3_mlp_gate_proj_weight_to_fp16_palettized, x = input_67_cast_fp16)[name = string("linear_26_cast_fp16")]; |
| tensor<fp16, [1, 125, 1024]> var_1771_cast_fp16 = silu(x = linear_26_cast_fp16)[name = string("op_1771_cast_fp16")]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_3_mlp_up_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24456576))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24980928))))[name = string("pre_transformer_layers_3_mlp_up_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_27_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_3_mlp_up_proj_weight_to_fp16_palettized, x = input_67_cast_fp16)[name = string("linear_27_cast_fp16")]; |
| tensor<fp16, [1, 125, 1024]> input_71_cast_fp16 = mul(x = var_1771_cast_fp16, y = linear_27_cast_fp16)[name = string("input_71_cast_fp16")]; |
| tensor<fp16, [512, 1024]> pre_transformer_layers_3_mlp_down_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [512, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24981504))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25505856))))[name = string("pre_transformer_layers_3_mlp_down_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 512]> linear_28_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = pre_transformer_layers_3_mlp_down_proj_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = string("linear_28_cast_fp16")]; |
| tensor<fp16, [512]> pre_transformer_layers_3_mlp_layer_scale_scale_to_fp16 = const()[name = string("pre_transformer_layers_3_mlp_layer_scale_scale_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25506432)))]; |
| tensor<fp16, [1, 125, 512]> var_1778_cast_fp16 = mul(x = pre_transformer_layers_3_mlp_layer_scale_scale_to_fp16, y = linear_28_cast_fp16)[name = string("op_1778_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> h_33_cast_fp16 = add(x = h_29_cast_fp16, y = var_1778_cast_fp16)[name = string("h_33_cast_fp16")]; |
| tensor<fp16, [512]> const_58_to_fp16 = const()[name = string("const_58_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25507520)))]; |
| tensor<fp16, [1, 125, 512]> var_1791_cast_fp16 = mul(x = const_58_to_fp16, y = h_33_cast_fp16)[name = string("op_1791_cast_fp16")]; |
| fp16 var_1784_promoted_to_fp16 = const()[name = string("op_1784_promoted_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 125, 512]> var_1792_cast_fp16 = pow(x = h_33_cast_fp16, y = var_1784_promoted_to_fp16)[name = string("op_1792_cast_fp16")]; |
| tensor<int32, [1]> var_1794_axes_0 = const()[name = string("op_1794_axes_0"), val = tensor<int32, [1]>([-1])]; |
| bool var_1794_keep_dims_0 = const()[name = string("op_1794_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 125, 1]> var_1794_cast_fp16 = reduce_mean(axes = var_1794_axes_0, keep_dims = var_1794_keep_dims_0, x = var_1792_cast_fp16)[name = string("op_1794_cast_fp16")]; |
| fp16 var_1795_to_fp16 = const()[name = string("op_1795_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 125, 1]> var_1796_cast_fp16 = add(x = var_1794_cast_fp16, y = var_1795_to_fp16)[name = string("op_1796_cast_fp16")]; |
| fp32 var_1797_epsilon_0 = const()[name = string("op_1797_epsilon_0"), val = fp32(0x1.197998p-40)]; |
| tensor<fp16, [1, 125, 1]> var_1797_cast_fp16 = rsqrt(epsilon = var_1797_epsilon_0, x = var_1796_cast_fp16)[name = string("op_1797_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> hn_9_cast_fp16 = mul(x = var_1791_cast_fp16, y = var_1797_cast_fp16)[name = string("hn_9_cast_fp16")]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_4_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25508608))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26032960))))[name = string("pre_transformer_layers_4_self_attn_q_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_29_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_4_self_attn_q_proj_weight_to_fp16_palettized, x = hn_9_cast_fp16)[name = string("linear_29_cast_fp16")]; |
| tensor<int32, [4]> var_1819 = const()[name = string("op_1819"), val = tensor<int32, [4]>([1, 125, -1, 64])]; |
| tensor<fp16, [1, 125, 16, 64]> var_1820_cast_fp16 = reshape(shape = var_1819, x = linear_29_cast_fp16)[name = string("op_1820_cast_fp16")]; |
| tensor<int32, [4]> q_49_perm_0 = const()[name = string("q_49_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_4_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26033536))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26557888))))[name = string("pre_transformer_layers_4_self_attn_k_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_30_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_4_self_attn_k_proj_weight_to_fp16_palettized, x = hn_9_cast_fp16)[name = string("linear_30_cast_fp16")]; |
| tensor<int32, [4]> var_1829 = const()[name = string("op_1829"), val = tensor<int32, [4]>([1, 125, -1, 64])]; |
| tensor<fp16, [1, 125, 16, 64]> var_1830_cast_fp16 = reshape(shape = var_1829, x = linear_30_cast_fp16)[name = string("op_1830_cast_fp16")]; |
| tensor<int32, [4]> k_17_perm_0 = const()[name = string("k_17_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_4_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26558464))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27082816))))[name = string("pre_transformer_layers_4_self_attn_v_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_31_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_4_self_attn_v_proj_weight_to_fp16_palettized, x = hn_9_cast_fp16)[name = string("linear_31_cast_fp16")]; |
| tensor<int32, [4]> var_1839 = const()[name = string("op_1839"), val = tensor<int32, [4]>([1, 125, -1, 64])]; |
| tensor<fp16, [1, 125, 16, 64]> var_1840_cast_fp16 = reshape(shape = var_1839, x = linear_31_cast_fp16)[name = string("op_1840_cast_fp16")]; |
| tensor<int32, [4]> v_9_perm_0 = const()[name = string("v_9_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> var_1856_begin_0 = const()[name = string("op_1856_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 32])]; |
| tensor<int32, [4]> var_1856_end_0 = const()[name = string("op_1856_end_0"), val = tensor<int32, [4]>([1, 16, 125, 64])]; |
| tensor<bool, [4]> var_1856_end_mask_0 = const()[name = string("op_1856_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])]; |
| tensor<fp16, [1, 16, 125, 64]> q_49_cast_fp16 = transpose(perm = q_49_perm_0, x = var_1820_cast_fp16)[name = string("transpose_20")]; |
| tensor<fp16, [1, 16, 125, 32]> var_1856_cast_fp16 = slice_by_index(begin = var_1856_begin_0, end = var_1856_end_0, end_mask = var_1856_end_mask_0, x = q_49_cast_fp16)[name = string("op_1856_cast_fp16")]; |
| fp16 const_62_promoted_to_fp16 = const()[name = string("const_62_promoted_to_fp16"), val = fp16(-0x1p+0)]; |
| tensor<fp16, [1, 16, 125, 32]> var_1857_cast_fp16 = mul(x = var_1856_cast_fp16, y = const_62_promoted_to_fp16)[name = string("op_1857_cast_fp16")]; |
| tensor<int32, [4]> var_1867_begin_0 = const()[name = string("op_1867_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [4]> var_1867_end_0 = const()[name = string("op_1867_end_0"), val = tensor<int32, [4]>([1, 16, 125, 32])]; |
| tensor<bool, [4]> var_1867_end_mask_0 = const()[name = string("op_1867_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])]; |
| tensor<fp16, [1, 16, 125, 32]> var_1867_cast_fp16 = slice_by_index(begin = var_1867_begin_0, end = var_1867_end_0, end_mask = var_1867_end_mask_0, x = q_49_cast_fp16)[name = string("op_1867_cast_fp16")]; |
| int32 var_1869 = const()[name = string("op_1869"), val = int32(-1)]; |
| bool var_1870_interleave_0 = const()[name = string("op_1870_interleave_0"), val = bool(false)]; |
| tensor<fp16, [1, 16, 125, 64]> var_1870_cast_fp16 = concat(axis = var_1869, interleave = var_1870_interleave_0, values = (var_1857_cast_fp16, var_1867_cast_fp16))[name = string("op_1870_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> var_1873_cast_fp16 = mul(x = var_1870_cast_fp16, y = op_1004_to_fp16_palettized)[name = string("op_1873_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> q_51_cast_fp16 = add(x = q_49_cast_fp16, y = var_1873_cast_fp16)[name = string("q_51_cast_fp16")]; |
| tensor<int32, [4]> var_1888_begin_0 = const()[name = string("op_1888_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 32])]; |
| tensor<int32, [4]> var_1888_end_0 = const()[name = string("op_1888_end_0"), val = tensor<int32, [4]>([1, 16, 125, 64])]; |
| tensor<bool, [4]> var_1888_end_mask_0 = const()[name = string("op_1888_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])]; |
| tensor<fp16, [1, 16, 125, 64]> k_17_cast_fp16 = transpose(perm = k_17_perm_0, x = var_1830_cast_fp16)[name = string("transpose_19")]; |
| tensor<fp16, [1, 16, 125, 32]> var_1888_cast_fp16 = slice_by_index(begin = var_1888_begin_0, end = var_1888_end_0, end_mask = var_1888_end_mask_0, x = k_17_cast_fp16)[name = string("op_1888_cast_fp16")]; |
| fp16 const_65_promoted_to_fp16 = const()[name = string("const_65_promoted_to_fp16"), val = fp16(-0x1p+0)]; |
| tensor<fp16, [1, 16, 125, 32]> var_1889_cast_fp16 = mul(x = var_1888_cast_fp16, y = const_65_promoted_to_fp16)[name = string("op_1889_cast_fp16")]; |
| tensor<int32, [4]> var_1899_begin_0 = const()[name = string("op_1899_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [4]> var_1899_end_0 = const()[name = string("op_1899_end_0"), val = tensor<int32, [4]>([1, 16, 125, 32])]; |
| tensor<bool, [4]> var_1899_end_mask_0 = const()[name = string("op_1899_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])]; |
| tensor<fp16, [1, 16, 125, 32]> var_1899_cast_fp16 = slice_by_index(begin = var_1899_begin_0, end = var_1899_end_0, end_mask = var_1899_end_mask_0, x = k_17_cast_fp16)[name = string("op_1899_cast_fp16")]; |
| int32 var_1901 = const()[name = string("op_1901"), val = int32(-1)]; |
| bool var_1902_interleave_0 = const()[name = string("op_1902_interleave_0"), val = bool(false)]; |
| tensor<fp16, [1, 16, 125, 64]> var_1902_cast_fp16 = concat(axis = var_1901, interleave = var_1902_interleave_0, values = (var_1889_cast_fp16, var_1899_cast_fp16))[name = string("op_1902_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> var_1905_cast_fp16 = mul(x = var_1902_cast_fp16, y = op_1004_to_fp16_palettized)[name = string("op_1905_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> k_19_cast_fp16 = add(x = k_17_cast_fp16, y = var_1905_cast_fp16)[name = string("k_19_cast_fp16")]; |
| bool var_1911_transpose_x_1 = const()[name = string("op_1911_transpose_x_1"), val = bool(false)]; |
| bool var_1911_transpose_y_1 = const()[name = string("op_1911_transpose_y_1"), val = bool(true)]; |
| tensor<fp16, [1, 16, 125, 125]> var_1911_cast_fp16 = matmul(transpose_x = var_1911_transpose_x_1, transpose_y = var_1911_transpose_y_1, x = q_51_cast_fp16, y = k_19_cast_fp16)[name = string("op_1911_cast_fp16")]; |
| fp16 var_1912_to_fp16 = const()[name = string("op_1912_to_fp16"), val = fp16(0x1p-3)]; |
| tensor<fp16, [1, 16, 125, 125]> aw_17_cast_fp16 = mul(x = var_1911_cast_fp16, y = var_1912_to_fp16)[name = string("aw_17_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 125]> var_1928_cast_fp16 = add(x = aw_17_cast_fp16, y = op_1058_to_fp16_palettized)[name = string("op_1928_cast_fp16")]; |
| int32 var_1929 = const()[name = string("op_1929"), val = int32(-1)]; |
| tensor<fp16, [1, 16, 125, 125]> var_1931_cast_fp16 = softmax(axis = var_1929, x = var_1928_cast_fp16)[name = string("op_1931_cast_fp16")]; |
| bool var_1937_transpose_x_0 = const()[name = string("op_1937_transpose_x_0"), val = bool(false)]; |
| bool var_1937_transpose_y_0 = const()[name = string("op_1937_transpose_y_0"), val = bool(false)]; |
| tensor<fp16, [1, 16, 125, 64]> v_9_cast_fp16 = transpose(perm = v_9_perm_0, x = var_1840_cast_fp16)[name = string("transpose_18")]; |
| tensor<fp16, [1, 16, 125, 64]> var_1937_cast_fp16 = matmul(transpose_x = var_1937_transpose_x_0, transpose_y = var_1937_transpose_y_0, x = var_1931_cast_fp16, y = v_9_cast_fp16)[name = string("op_1937_cast_fp16")]; |
| tensor<int32, [4]> var_1940_perm_0 = const()[name = string("op_1940_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_1950 = const()[name = string("op_1950"), val = tensor<int32, [3]>([1, 125, -1])]; |
| tensor<fp16, [1, 125, 16, 64]> var_1940_cast_fp16 = transpose(perm = var_1940_perm_0, x = var_1937_cast_fp16)[name = string("transpose_17")]; |
| tensor<fp16, [1, 125, 1024]> var_1951_cast_fp16 = reshape(shape = var_1950, x = var_1940_cast_fp16)[name = string("op_1951_cast_fp16")]; |
| tensor<fp16, [512, 1024]> pre_transformer_layers_4_self_attn_o_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [512, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27083392))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27607744))))[name = string("pre_transformer_layers_4_self_attn_o_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 512]> linear_32_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = pre_transformer_layers_4_self_attn_o_proj_weight_to_fp16_palettized, x = var_1951_cast_fp16)[name = string("linear_32_cast_fp16")]; |
| tensor<fp16, [512]> pre_transformer_layers_4_self_attn_layer_scale_scale_to_fp16 = const()[name = string("pre_transformer_layers_4_self_attn_layer_scale_scale_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27608320)))]; |
| tensor<fp16, [1, 125, 512]> var_1958_cast_fp16 = mul(x = pre_transformer_layers_4_self_attn_layer_scale_scale_to_fp16, y = linear_32_cast_fp16)[name = string("op_1958_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> h_37_cast_fp16 = add(x = h_33_cast_fp16, y = var_1958_cast_fp16)[name = string("h_37_cast_fp16")]; |
| tensor<fp16, [512]> const_69_to_fp16 = const()[name = string("const_69_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27609408)))]; |
| tensor<fp16, [1, 125, 512]> var_1971_cast_fp16 = mul(x = const_69_to_fp16, y = h_37_cast_fp16)[name = string("op_1971_cast_fp16")]; |
| fp16 var_1964_promoted_to_fp16 = const()[name = string("op_1964_promoted_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 125, 512]> var_1972_cast_fp16 = pow(x = h_37_cast_fp16, y = var_1964_promoted_to_fp16)[name = string("op_1972_cast_fp16")]; |
| tensor<int32, [1]> var_1974_axes_0 = const()[name = string("op_1974_axes_0"), val = tensor<int32, [1]>([-1])]; |
| bool var_1974_keep_dims_0 = const()[name = string("op_1974_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 125, 1]> var_1974_cast_fp16 = reduce_mean(axes = var_1974_axes_0, keep_dims = var_1974_keep_dims_0, x = var_1972_cast_fp16)[name = string("op_1974_cast_fp16")]; |
| fp16 var_1975_to_fp16 = const()[name = string("op_1975_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 125, 1]> var_1976_cast_fp16 = add(x = var_1974_cast_fp16, y = var_1975_to_fp16)[name = string("op_1976_cast_fp16")]; |
| fp32 var_1977_epsilon_0 = const()[name = string("op_1977_epsilon_0"), val = fp32(0x1.197998p-40)]; |
| tensor<fp16, [1, 125, 1]> var_1977_cast_fp16 = rsqrt(epsilon = var_1977_epsilon_0, x = var_1976_cast_fp16)[name = string("op_1977_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> input_75_cast_fp16 = mul(x = var_1971_cast_fp16, y = var_1977_cast_fp16)[name = string("input_75_cast_fp16")]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_4_mlp_gate_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27610496))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28134848))))[name = string("pre_transformer_layers_4_mlp_gate_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_33_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_4_mlp_gate_proj_weight_to_fp16_palettized, x = input_75_cast_fp16)[name = string("linear_33_cast_fp16")]; |
| tensor<fp16, [1, 125, 1024]> var_1988_cast_fp16 = silu(x = linear_33_cast_fp16)[name = string("op_1988_cast_fp16")]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_4_mlp_up_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28135424))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28659776))))[name = string("pre_transformer_layers_4_mlp_up_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_34_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_4_mlp_up_proj_weight_to_fp16_palettized, x = input_75_cast_fp16)[name = string("linear_34_cast_fp16")]; |
| tensor<fp16, [1, 125, 1024]> input_79_cast_fp16 = mul(x = var_1988_cast_fp16, y = linear_34_cast_fp16)[name = string("input_79_cast_fp16")]; |
| tensor<fp16, [512, 1024]> pre_transformer_layers_4_mlp_down_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [512, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28660352))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29184704))))[name = string("pre_transformer_layers_4_mlp_down_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 512]> linear_35_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = pre_transformer_layers_4_mlp_down_proj_weight_to_fp16_palettized, x = input_79_cast_fp16)[name = string("linear_35_cast_fp16")]; |
| tensor<fp16, [512]> pre_transformer_layers_4_mlp_layer_scale_scale_to_fp16 = const()[name = string("pre_transformer_layers_4_mlp_layer_scale_scale_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29185280)))]; |
| tensor<fp16, [1, 125, 512]> var_1995_cast_fp16 = mul(x = pre_transformer_layers_4_mlp_layer_scale_scale_to_fp16, y = linear_35_cast_fp16)[name = string("op_1995_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> h_41_cast_fp16 = add(x = h_37_cast_fp16, y = var_1995_cast_fp16)[name = string("h_41_cast_fp16")]; |
| tensor<fp16, [512]> const_70_to_fp16 = const()[name = string("const_70_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29186368)))]; |
| tensor<fp16, [1, 125, 512]> var_2008_cast_fp16 = mul(x = const_70_to_fp16, y = h_41_cast_fp16)[name = string("op_2008_cast_fp16")]; |
| fp16 var_2001_promoted_to_fp16 = const()[name = string("op_2001_promoted_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 125, 512]> var_2009_cast_fp16 = pow(x = h_41_cast_fp16, y = var_2001_promoted_to_fp16)[name = string("op_2009_cast_fp16")]; |
| tensor<int32, [1]> var_2011_axes_0 = const()[name = string("op_2011_axes_0"), val = tensor<int32, [1]>([-1])]; |
| bool var_2011_keep_dims_0 = const()[name = string("op_2011_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 125, 1]> var_2011_cast_fp16 = reduce_mean(axes = var_2011_axes_0, keep_dims = var_2011_keep_dims_0, x = var_2009_cast_fp16)[name = string("op_2011_cast_fp16")]; |
| fp16 var_2012_to_fp16 = const()[name = string("op_2012_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 125, 1]> var_2013_cast_fp16 = add(x = var_2011_cast_fp16, y = var_2012_to_fp16)[name = string("op_2013_cast_fp16")]; |
| fp32 var_2014_epsilon_0 = const()[name = string("op_2014_epsilon_0"), val = fp32(0x1.197998p-40)]; |
| tensor<fp16, [1, 125, 1]> var_2014_cast_fp16 = rsqrt(epsilon = var_2014_epsilon_0, x = var_2013_cast_fp16)[name = string("op_2014_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> hn_11_cast_fp16 = mul(x = var_2008_cast_fp16, y = var_2014_cast_fp16)[name = string("hn_11_cast_fp16")]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_5_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29187456))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29711808))))[name = string("pre_transformer_layers_5_self_attn_q_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_36_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_5_self_attn_q_proj_weight_to_fp16_palettized, x = hn_11_cast_fp16)[name = string("linear_36_cast_fp16")]; |
| tensor<int32, [4]> var_2036 = const()[name = string("op_2036"), val = tensor<int32, [4]>([1, 125, -1, 64])]; |
| tensor<fp16, [1, 125, 16, 64]> var_2037_cast_fp16 = reshape(shape = var_2036, x = linear_36_cast_fp16)[name = string("op_2037_cast_fp16")]; |
| tensor<int32, [4]> q_53_perm_0 = const()[name = string("q_53_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_5_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29712384))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30236736))))[name = string("pre_transformer_layers_5_self_attn_k_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_37_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_5_self_attn_k_proj_weight_to_fp16_palettized, x = hn_11_cast_fp16)[name = string("linear_37_cast_fp16")]; |
| tensor<int32, [4]> var_2046 = const()[name = string("op_2046"), val = tensor<int32, [4]>([1, 125, -1, 64])]; |
| tensor<fp16, [1, 125, 16, 64]> var_2047_cast_fp16 = reshape(shape = var_2046, x = linear_37_cast_fp16)[name = string("op_2047_cast_fp16")]; |
| tensor<int32, [4]> k_21_perm_0 = const()[name = string("k_21_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_5_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30237312))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30761664))))[name = string("pre_transformer_layers_5_self_attn_v_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_38_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_5_self_attn_v_proj_weight_to_fp16_palettized, x = hn_11_cast_fp16)[name = string("linear_38_cast_fp16")]; |
| tensor<int32, [4]> var_2056 = const()[name = string("op_2056"), val = tensor<int32, [4]>([1, 125, -1, 64])]; |
| tensor<fp16, [1, 125, 16, 64]> var_2057_cast_fp16 = reshape(shape = var_2056, x = linear_38_cast_fp16)[name = string("op_2057_cast_fp16")]; |
| tensor<int32, [4]> v_11_perm_0 = const()[name = string("v_11_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> var_2073_begin_0 = const()[name = string("op_2073_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 32])]; |
| tensor<int32, [4]> var_2073_end_0 = const()[name = string("op_2073_end_0"), val = tensor<int32, [4]>([1, 16, 125, 64])]; |
| tensor<bool, [4]> var_2073_end_mask_0 = const()[name = string("op_2073_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])]; |
| tensor<fp16, [1, 16, 125, 64]> q_53_cast_fp16 = transpose(perm = q_53_perm_0, x = var_2037_cast_fp16)[name = string("transpose_16")]; |
| tensor<fp16, [1, 16, 125, 32]> var_2073_cast_fp16 = slice_by_index(begin = var_2073_begin_0, end = var_2073_end_0, end_mask = var_2073_end_mask_0, x = q_53_cast_fp16)[name = string("op_2073_cast_fp16")]; |
| fp16 const_74_promoted_to_fp16 = const()[name = string("const_74_promoted_to_fp16"), val = fp16(-0x1p+0)]; |
| tensor<fp16, [1, 16, 125, 32]> var_2074_cast_fp16 = mul(x = var_2073_cast_fp16, y = const_74_promoted_to_fp16)[name = string("op_2074_cast_fp16")]; |
| tensor<int32, [4]> var_2084_begin_0 = const()[name = string("op_2084_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [4]> var_2084_end_0 = const()[name = string("op_2084_end_0"), val = tensor<int32, [4]>([1, 16, 125, 32])]; |
| tensor<bool, [4]> var_2084_end_mask_0 = const()[name = string("op_2084_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])]; |
| tensor<fp16, [1, 16, 125, 32]> var_2084_cast_fp16 = slice_by_index(begin = var_2084_begin_0, end = var_2084_end_0, end_mask = var_2084_end_mask_0, x = q_53_cast_fp16)[name = string("op_2084_cast_fp16")]; |
| int32 var_2086 = const()[name = string("op_2086"), val = int32(-1)]; |
| bool var_2087_interleave_0 = const()[name = string("op_2087_interleave_0"), val = bool(false)]; |
| tensor<fp16, [1, 16, 125, 64]> var_2087_cast_fp16 = concat(axis = var_2086, interleave = var_2087_interleave_0, values = (var_2074_cast_fp16, var_2084_cast_fp16))[name = string("op_2087_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> var_2090_cast_fp16 = mul(x = var_2087_cast_fp16, y = op_1004_to_fp16_palettized)[name = string("op_2090_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> q_55_cast_fp16 = add(x = q_53_cast_fp16, y = var_2090_cast_fp16)[name = string("q_55_cast_fp16")]; |
| tensor<int32, [4]> var_2105_begin_0 = const()[name = string("op_2105_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 32])]; |
| tensor<int32, [4]> var_2105_end_0 = const()[name = string("op_2105_end_0"), val = tensor<int32, [4]>([1, 16, 125, 64])]; |
| tensor<bool, [4]> var_2105_end_mask_0 = const()[name = string("op_2105_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])]; |
| tensor<fp16, [1, 16, 125, 64]> k_21_cast_fp16 = transpose(perm = k_21_perm_0, x = var_2047_cast_fp16)[name = string("transpose_15")]; |
| tensor<fp16, [1, 16, 125, 32]> var_2105_cast_fp16 = slice_by_index(begin = var_2105_begin_0, end = var_2105_end_0, end_mask = var_2105_end_mask_0, x = k_21_cast_fp16)[name = string("op_2105_cast_fp16")]; |
| fp16 const_77_promoted_to_fp16 = const()[name = string("const_77_promoted_to_fp16"), val = fp16(-0x1p+0)]; |
| tensor<fp16, [1, 16, 125, 32]> var_2106_cast_fp16 = mul(x = var_2105_cast_fp16, y = const_77_promoted_to_fp16)[name = string("op_2106_cast_fp16")]; |
| tensor<int32, [4]> var_2116_begin_0 = const()[name = string("op_2116_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [4]> var_2116_end_0 = const()[name = string("op_2116_end_0"), val = tensor<int32, [4]>([1, 16, 125, 32])]; |
| tensor<bool, [4]> var_2116_end_mask_0 = const()[name = string("op_2116_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])]; |
| tensor<fp16, [1, 16, 125, 32]> var_2116_cast_fp16 = slice_by_index(begin = var_2116_begin_0, end = var_2116_end_0, end_mask = var_2116_end_mask_0, x = k_21_cast_fp16)[name = string("op_2116_cast_fp16")]; |
| int32 var_2118 = const()[name = string("op_2118"), val = int32(-1)]; |
| bool var_2119_interleave_0 = const()[name = string("op_2119_interleave_0"), val = bool(false)]; |
| tensor<fp16, [1, 16, 125, 64]> var_2119_cast_fp16 = concat(axis = var_2118, interleave = var_2119_interleave_0, values = (var_2106_cast_fp16, var_2116_cast_fp16))[name = string("op_2119_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> var_2122_cast_fp16 = mul(x = var_2119_cast_fp16, y = op_1004_to_fp16_palettized)[name = string("op_2122_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> k_23_cast_fp16 = add(x = k_21_cast_fp16, y = var_2122_cast_fp16)[name = string("k_23_cast_fp16")]; |
| bool var_2128_transpose_x_1 = const()[name = string("op_2128_transpose_x_1"), val = bool(false)]; |
| bool var_2128_transpose_y_1 = const()[name = string("op_2128_transpose_y_1"), val = bool(true)]; |
| tensor<fp16, [1, 16, 125, 125]> var_2128_cast_fp16 = matmul(transpose_x = var_2128_transpose_x_1, transpose_y = var_2128_transpose_y_1, x = q_55_cast_fp16, y = k_23_cast_fp16)[name = string("op_2128_cast_fp16")]; |
| fp16 var_2129_to_fp16 = const()[name = string("op_2129_to_fp16"), val = fp16(0x1p-3)]; |
| tensor<fp16, [1, 16, 125, 125]> aw_21_cast_fp16 = mul(x = var_2128_cast_fp16, y = var_2129_to_fp16)[name = string("aw_21_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 125]> var_2145_cast_fp16 = add(x = aw_21_cast_fp16, y = op_1058_to_fp16_palettized)[name = string("op_2145_cast_fp16")]; |
| int32 var_2146 = const()[name = string("op_2146"), val = int32(-1)]; |
| tensor<fp16, [1, 16, 125, 125]> var_2148_cast_fp16 = softmax(axis = var_2146, x = var_2145_cast_fp16)[name = string("op_2148_cast_fp16")]; |
| bool var_2154_transpose_x_0 = const()[name = string("op_2154_transpose_x_0"), val = bool(false)]; |
| bool var_2154_transpose_y_0 = const()[name = string("op_2154_transpose_y_0"), val = bool(false)]; |
| tensor<fp16, [1, 16, 125, 64]> v_11_cast_fp16 = transpose(perm = v_11_perm_0, x = var_2057_cast_fp16)[name = string("transpose_14")]; |
| tensor<fp16, [1, 16, 125, 64]> var_2154_cast_fp16 = matmul(transpose_x = var_2154_transpose_x_0, transpose_y = var_2154_transpose_y_0, x = var_2148_cast_fp16, y = v_11_cast_fp16)[name = string("op_2154_cast_fp16")]; |
| tensor<int32, [4]> var_2157_perm_0 = const()[name = string("op_2157_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_2167 = const()[name = string("op_2167"), val = tensor<int32, [3]>([1, 125, -1])]; |
| tensor<fp16, [1, 125, 16, 64]> var_2157_cast_fp16 = transpose(perm = var_2157_perm_0, x = var_2154_cast_fp16)[name = string("transpose_13")]; |
| tensor<fp16, [1, 125, 1024]> var_2168_cast_fp16 = reshape(shape = var_2167, x = var_2157_cast_fp16)[name = string("op_2168_cast_fp16")]; |
| tensor<fp16, [512, 1024]> pre_transformer_layers_5_self_attn_o_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [512, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30762240))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31286592))))[name = string("pre_transformer_layers_5_self_attn_o_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 512]> linear_39_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = pre_transformer_layers_5_self_attn_o_proj_weight_to_fp16_palettized, x = var_2168_cast_fp16)[name = string("linear_39_cast_fp16")]; |
| tensor<fp16, [512]> pre_transformer_layers_5_self_attn_layer_scale_scale_to_fp16 = const()[name = string("pre_transformer_layers_5_self_attn_layer_scale_scale_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31287168)))]; |
| tensor<fp16, [1, 125, 512]> var_2175_cast_fp16 = mul(x = pre_transformer_layers_5_self_attn_layer_scale_scale_to_fp16, y = linear_39_cast_fp16)[name = string("op_2175_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> h_45_cast_fp16 = add(x = h_41_cast_fp16, y = var_2175_cast_fp16)[name = string("h_45_cast_fp16")]; |
| tensor<fp16, [512]> const_81_to_fp16 = const()[name = string("const_81_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31288256)))]; |
| tensor<fp16, [1, 125, 512]> var_2188_cast_fp16 = mul(x = const_81_to_fp16, y = h_45_cast_fp16)[name = string("op_2188_cast_fp16")]; |
| fp16 var_2181_promoted_to_fp16 = const()[name = string("op_2181_promoted_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 125, 512]> var_2189_cast_fp16 = pow(x = h_45_cast_fp16, y = var_2181_promoted_to_fp16)[name = string("op_2189_cast_fp16")]; |
| tensor<int32, [1]> var_2191_axes_0 = const()[name = string("op_2191_axes_0"), val = tensor<int32, [1]>([-1])]; |
| bool var_2191_keep_dims_0 = const()[name = string("op_2191_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 125, 1]> var_2191_cast_fp16 = reduce_mean(axes = var_2191_axes_0, keep_dims = var_2191_keep_dims_0, x = var_2189_cast_fp16)[name = string("op_2191_cast_fp16")]; |
| fp16 var_2192_to_fp16 = const()[name = string("op_2192_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 125, 1]> var_2193_cast_fp16 = add(x = var_2191_cast_fp16, y = var_2192_to_fp16)[name = string("op_2193_cast_fp16")]; |
| fp32 var_2194_epsilon_0 = const()[name = string("op_2194_epsilon_0"), val = fp32(0x1.197998p-40)]; |
| tensor<fp16, [1, 125, 1]> var_2194_cast_fp16 = rsqrt(epsilon = var_2194_epsilon_0, x = var_2193_cast_fp16)[name = string("op_2194_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> input_83_cast_fp16 = mul(x = var_2188_cast_fp16, y = var_2194_cast_fp16)[name = string("input_83_cast_fp16")]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_5_mlp_gate_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31289344))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31813696))))[name = string("pre_transformer_layers_5_mlp_gate_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_40_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_5_mlp_gate_proj_weight_to_fp16_palettized, x = input_83_cast_fp16)[name = string("linear_40_cast_fp16")]; |
| tensor<fp16, [1, 125, 1024]> var_2205_cast_fp16 = silu(x = linear_40_cast_fp16)[name = string("op_2205_cast_fp16")]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_5_mlp_up_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31814272))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32338624))))[name = string("pre_transformer_layers_5_mlp_up_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_41_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_5_mlp_up_proj_weight_to_fp16_palettized, x = input_83_cast_fp16)[name = string("linear_41_cast_fp16")]; |
| tensor<fp16, [1, 125, 1024]> input_87_cast_fp16 = mul(x = var_2205_cast_fp16, y = linear_41_cast_fp16)[name = string("input_87_cast_fp16")]; |
| tensor<fp16, [512, 1024]> pre_transformer_layers_5_mlp_down_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [512, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32339200))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32863552))))[name = string("pre_transformer_layers_5_mlp_down_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 512]> linear_42_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = pre_transformer_layers_5_mlp_down_proj_weight_to_fp16_palettized, x = input_87_cast_fp16)[name = string("linear_42_cast_fp16")]; |
| tensor<fp16, [512]> pre_transformer_layers_5_mlp_layer_scale_scale_to_fp16 = const()[name = string("pre_transformer_layers_5_mlp_layer_scale_scale_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32864128)))]; |
| tensor<fp16, [1, 125, 512]> var_2212_cast_fp16 = mul(x = pre_transformer_layers_5_mlp_layer_scale_scale_to_fp16, y = linear_42_cast_fp16)[name = string("op_2212_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> h_49_cast_fp16 = add(x = h_45_cast_fp16, y = var_2212_cast_fp16)[name = string("h_49_cast_fp16")]; |
| tensor<fp16, [512]> const_82_to_fp16 = const()[name = string("const_82_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32865216)))]; |
| tensor<fp16, [1, 125, 512]> var_2225_cast_fp16 = mul(x = const_82_to_fp16, y = h_49_cast_fp16)[name = string("op_2225_cast_fp16")]; |
| fp16 var_2218_promoted_to_fp16 = const()[name = string("op_2218_promoted_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 125, 512]> var_2226_cast_fp16 = pow(x = h_49_cast_fp16, y = var_2218_promoted_to_fp16)[name = string("op_2226_cast_fp16")]; |
| tensor<int32, [1]> var_2228_axes_0 = const()[name = string("op_2228_axes_0"), val = tensor<int32, [1]>([-1])]; |
| bool var_2228_keep_dims_0 = const()[name = string("op_2228_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 125, 1]> var_2228_cast_fp16 = reduce_mean(axes = var_2228_axes_0, keep_dims = var_2228_keep_dims_0, x = var_2226_cast_fp16)[name = string("op_2228_cast_fp16")]; |
| fp16 var_2229_to_fp16 = const()[name = string("op_2229_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 125, 1]> var_2230_cast_fp16 = add(x = var_2228_cast_fp16, y = var_2229_to_fp16)[name = string("op_2230_cast_fp16")]; |
| fp32 var_2231_epsilon_0 = const()[name = string("op_2231_epsilon_0"), val = fp32(0x1.197998p-40)]; |
| tensor<fp16, [1, 125, 1]> var_2231_cast_fp16 = rsqrt(epsilon = var_2231_epsilon_0, x = var_2230_cast_fp16)[name = string("op_2231_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> hn_13_cast_fp16 = mul(x = var_2225_cast_fp16, y = var_2231_cast_fp16)[name = string("hn_13_cast_fp16")]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_6_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32866304))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33390656))))[name = string("pre_transformer_layers_6_self_attn_q_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_43_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_6_self_attn_q_proj_weight_to_fp16_palettized, x = hn_13_cast_fp16)[name = string("linear_43_cast_fp16")]; |
| tensor<int32, [4]> var_2253 = const()[name = string("op_2253"), val = tensor<int32, [4]>([1, 125, -1, 64])]; |
| tensor<fp16, [1, 125, 16, 64]> var_2254_cast_fp16 = reshape(shape = var_2253, x = linear_43_cast_fp16)[name = string("op_2254_cast_fp16")]; |
| tensor<int32, [4]> q_57_perm_0 = const()[name = string("q_57_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_6_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33391232))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33915584))))[name = string("pre_transformer_layers_6_self_attn_k_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_44_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_6_self_attn_k_proj_weight_to_fp16_palettized, x = hn_13_cast_fp16)[name = string("linear_44_cast_fp16")]; |
| tensor<int32, [4]> var_2263 = const()[name = string("op_2263"), val = tensor<int32, [4]>([1, 125, -1, 64])]; |
| tensor<fp16, [1, 125, 16, 64]> var_2264_cast_fp16 = reshape(shape = var_2263, x = linear_44_cast_fp16)[name = string("op_2264_cast_fp16")]; |
| tensor<int32, [4]> k_25_perm_0 = const()[name = string("k_25_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_6_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33916160))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34440512))))[name = string("pre_transformer_layers_6_self_attn_v_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_45_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_6_self_attn_v_proj_weight_to_fp16_palettized, x = hn_13_cast_fp16)[name = string("linear_45_cast_fp16")]; |
| tensor<int32, [4]> var_2273 = const()[name = string("op_2273"), val = tensor<int32, [4]>([1, 125, -1, 64])]; |
| tensor<fp16, [1, 125, 16, 64]> var_2274_cast_fp16 = reshape(shape = var_2273, x = linear_45_cast_fp16)[name = string("op_2274_cast_fp16")]; |
| tensor<int32, [4]> v_13_perm_0 = const()[name = string("v_13_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> var_2290_begin_0 = const()[name = string("op_2290_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 32])]; |
| tensor<int32, [4]> var_2290_end_0 = const()[name = string("op_2290_end_0"), val = tensor<int32, [4]>([1, 16, 125, 64])]; |
| tensor<bool, [4]> var_2290_end_mask_0 = const()[name = string("op_2290_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])]; |
| tensor<fp16, [1, 16, 125, 64]> q_57_cast_fp16 = transpose(perm = q_57_perm_0, x = var_2254_cast_fp16)[name = string("transpose_12")]; |
| tensor<fp16, [1, 16, 125, 32]> var_2290_cast_fp16 = slice_by_index(begin = var_2290_begin_0, end = var_2290_end_0, end_mask = var_2290_end_mask_0, x = q_57_cast_fp16)[name = string("op_2290_cast_fp16")]; |
| fp16 const_86_promoted_to_fp16 = const()[name = string("const_86_promoted_to_fp16"), val = fp16(-0x1p+0)]; |
| tensor<fp16, [1, 16, 125, 32]> var_2291_cast_fp16 = mul(x = var_2290_cast_fp16, y = const_86_promoted_to_fp16)[name = string("op_2291_cast_fp16")]; |
| tensor<int32, [4]> var_2301_begin_0 = const()[name = string("op_2301_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [4]> var_2301_end_0 = const()[name = string("op_2301_end_0"), val = tensor<int32, [4]>([1, 16, 125, 32])]; |
| tensor<bool, [4]> var_2301_end_mask_0 = const()[name = string("op_2301_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])]; |
| tensor<fp16, [1, 16, 125, 32]> var_2301_cast_fp16 = slice_by_index(begin = var_2301_begin_0, end = var_2301_end_0, end_mask = var_2301_end_mask_0, x = q_57_cast_fp16)[name = string("op_2301_cast_fp16")]; |
| int32 var_2303 = const()[name = string("op_2303"), val = int32(-1)]; |
| bool var_2304_interleave_0 = const()[name = string("op_2304_interleave_0"), val = bool(false)]; |
| tensor<fp16, [1, 16, 125, 64]> var_2304_cast_fp16 = concat(axis = var_2303, interleave = var_2304_interleave_0, values = (var_2291_cast_fp16, var_2301_cast_fp16))[name = string("op_2304_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> var_2307_cast_fp16 = mul(x = var_2304_cast_fp16, y = op_1004_to_fp16_palettized)[name = string("op_2307_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> q_59_cast_fp16 = add(x = q_57_cast_fp16, y = var_2307_cast_fp16)[name = string("q_59_cast_fp16")]; |
| tensor<int32, [4]> var_2322_begin_0 = const()[name = string("op_2322_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 32])]; |
| tensor<int32, [4]> var_2322_end_0 = const()[name = string("op_2322_end_0"), val = tensor<int32, [4]>([1, 16, 125, 64])]; |
| tensor<bool, [4]> var_2322_end_mask_0 = const()[name = string("op_2322_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])]; |
| tensor<fp16, [1, 16, 125, 64]> k_25_cast_fp16 = transpose(perm = k_25_perm_0, x = var_2264_cast_fp16)[name = string("transpose_11")]; |
| tensor<fp16, [1, 16, 125, 32]> var_2322_cast_fp16 = slice_by_index(begin = var_2322_begin_0, end = var_2322_end_0, end_mask = var_2322_end_mask_0, x = k_25_cast_fp16)[name = string("op_2322_cast_fp16")]; |
| fp16 const_89_promoted_to_fp16 = const()[name = string("const_89_promoted_to_fp16"), val = fp16(-0x1p+0)]; |
| tensor<fp16, [1, 16, 125, 32]> var_2323_cast_fp16 = mul(x = var_2322_cast_fp16, y = const_89_promoted_to_fp16)[name = string("op_2323_cast_fp16")]; |
| tensor<int32, [4]> var_2333_begin_0 = const()[name = string("op_2333_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [4]> var_2333_end_0 = const()[name = string("op_2333_end_0"), val = tensor<int32, [4]>([1, 16, 125, 32])]; |
| tensor<bool, [4]> var_2333_end_mask_0 = const()[name = string("op_2333_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])]; |
| tensor<fp16, [1, 16, 125, 32]> var_2333_cast_fp16 = slice_by_index(begin = var_2333_begin_0, end = var_2333_end_0, end_mask = var_2333_end_mask_0, x = k_25_cast_fp16)[name = string("op_2333_cast_fp16")]; |
| int32 var_2335 = const()[name = string("op_2335"), val = int32(-1)]; |
| bool var_2336_interleave_0 = const()[name = string("op_2336_interleave_0"), val = bool(false)]; |
| tensor<fp16, [1, 16, 125, 64]> var_2336_cast_fp16 = concat(axis = var_2335, interleave = var_2336_interleave_0, values = (var_2323_cast_fp16, var_2333_cast_fp16))[name = string("op_2336_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> var_2339_cast_fp16 = mul(x = var_2336_cast_fp16, y = op_1004_to_fp16_palettized)[name = string("op_2339_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> k_27_cast_fp16 = add(x = k_25_cast_fp16, y = var_2339_cast_fp16)[name = string("k_27_cast_fp16")]; |
| bool var_2345_transpose_x_1 = const()[name = string("op_2345_transpose_x_1"), val = bool(false)]; |
| bool var_2345_transpose_y_1 = const()[name = string("op_2345_transpose_y_1"), val = bool(true)]; |
| tensor<fp16, [1, 16, 125, 125]> var_2345_cast_fp16 = matmul(transpose_x = var_2345_transpose_x_1, transpose_y = var_2345_transpose_y_1, x = q_59_cast_fp16, y = k_27_cast_fp16)[name = string("op_2345_cast_fp16")]; |
| fp16 var_2346_to_fp16 = const()[name = string("op_2346_to_fp16"), val = fp16(0x1p-3)]; |
| tensor<fp16, [1, 16, 125, 125]> aw_25_cast_fp16 = mul(x = var_2345_cast_fp16, y = var_2346_to_fp16)[name = string("aw_25_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 125]> var_2362_cast_fp16 = add(x = aw_25_cast_fp16, y = op_1058_to_fp16_palettized)[name = string("op_2362_cast_fp16")]; |
| int32 var_2363 = const()[name = string("op_2363"), val = int32(-1)]; |
| tensor<fp16, [1, 16, 125, 125]> var_2365_cast_fp16 = softmax(axis = var_2363, x = var_2362_cast_fp16)[name = string("op_2365_cast_fp16")]; |
| bool var_2371_transpose_x_0 = const()[name = string("op_2371_transpose_x_0"), val = bool(false)]; |
| bool var_2371_transpose_y_0 = const()[name = string("op_2371_transpose_y_0"), val = bool(false)]; |
| tensor<fp16, [1, 16, 125, 64]> v_13_cast_fp16 = transpose(perm = v_13_perm_0, x = var_2274_cast_fp16)[name = string("transpose_10")]; |
| tensor<fp16, [1, 16, 125, 64]> var_2371_cast_fp16 = matmul(transpose_x = var_2371_transpose_x_0, transpose_y = var_2371_transpose_y_0, x = var_2365_cast_fp16, y = v_13_cast_fp16)[name = string("op_2371_cast_fp16")]; |
| tensor<int32, [4]> var_2374_perm_0 = const()[name = string("op_2374_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_2384 = const()[name = string("op_2384"), val = tensor<int32, [3]>([1, 125, -1])]; |
| tensor<fp16, [1, 125, 16, 64]> var_2374_cast_fp16 = transpose(perm = var_2374_perm_0, x = var_2371_cast_fp16)[name = string("transpose_9")]; |
| tensor<fp16, [1, 125, 1024]> var_2385_cast_fp16 = reshape(shape = var_2384, x = var_2374_cast_fp16)[name = string("op_2385_cast_fp16")]; |
| tensor<fp16, [512, 1024]> pre_transformer_layers_6_self_attn_o_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [512, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34441088))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34965440))))[name = string("pre_transformer_layers_6_self_attn_o_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 512]> linear_46_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = pre_transformer_layers_6_self_attn_o_proj_weight_to_fp16_palettized, x = var_2385_cast_fp16)[name = string("linear_46_cast_fp16")]; |
| tensor<fp16, [512]> pre_transformer_layers_6_self_attn_layer_scale_scale_to_fp16 = const()[name = string("pre_transformer_layers_6_self_attn_layer_scale_scale_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34966016)))]; |
| tensor<fp16, [1, 125, 512]> var_2392_cast_fp16 = mul(x = pre_transformer_layers_6_self_attn_layer_scale_scale_to_fp16, y = linear_46_cast_fp16)[name = string("op_2392_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> h_53_cast_fp16 = add(x = h_49_cast_fp16, y = var_2392_cast_fp16)[name = string("h_53_cast_fp16")]; |
| tensor<fp16, [512]> const_93_to_fp16 = const()[name = string("const_93_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34967104)))]; |
| tensor<fp16, [1, 125, 512]> var_2405_cast_fp16 = mul(x = const_93_to_fp16, y = h_53_cast_fp16)[name = string("op_2405_cast_fp16")]; |
| fp16 var_2398_promoted_to_fp16 = const()[name = string("op_2398_promoted_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 125, 512]> var_2406_cast_fp16 = pow(x = h_53_cast_fp16, y = var_2398_promoted_to_fp16)[name = string("op_2406_cast_fp16")]; |
| tensor<int32, [1]> var_2408_axes_0 = const()[name = string("op_2408_axes_0"), val = tensor<int32, [1]>([-1])]; |
| bool var_2408_keep_dims_0 = const()[name = string("op_2408_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 125, 1]> var_2408_cast_fp16 = reduce_mean(axes = var_2408_axes_0, keep_dims = var_2408_keep_dims_0, x = var_2406_cast_fp16)[name = string("op_2408_cast_fp16")]; |
| fp16 var_2409_to_fp16 = const()[name = string("op_2409_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 125, 1]> var_2410_cast_fp16 = add(x = var_2408_cast_fp16, y = var_2409_to_fp16)[name = string("op_2410_cast_fp16")]; |
| fp32 var_2411_epsilon_0 = const()[name = string("op_2411_epsilon_0"), val = fp32(0x1.197998p-40)]; |
| tensor<fp16, [1, 125, 1]> var_2411_cast_fp16 = rsqrt(epsilon = var_2411_epsilon_0, x = var_2410_cast_fp16)[name = string("op_2411_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> input_91_cast_fp16 = mul(x = var_2405_cast_fp16, y = var_2411_cast_fp16)[name = string("input_91_cast_fp16")]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_6_mlp_gate_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34968192))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35492544))))[name = string("pre_transformer_layers_6_mlp_gate_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_47_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_6_mlp_gate_proj_weight_to_fp16_palettized, x = input_91_cast_fp16)[name = string("linear_47_cast_fp16")]; |
| tensor<fp16, [1, 125, 1024]> var_2422_cast_fp16 = silu(x = linear_47_cast_fp16)[name = string("op_2422_cast_fp16")]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_6_mlp_up_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35493120))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36017472))))[name = string("pre_transformer_layers_6_mlp_up_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_48_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_6_mlp_up_proj_weight_to_fp16_palettized, x = input_91_cast_fp16)[name = string("linear_48_cast_fp16")]; |
| tensor<fp16, [1, 125, 1024]> input_95_cast_fp16 = mul(x = var_2422_cast_fp16, y = linear_48_cast_fp16)[name = string("input_95_cast_fp16")]; |
| tensor<fp16, [512, 1024]> pre_transformer_layers_6_mlp_down_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [512, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36018048))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36542400))))[name = string("pre_transformer_layers_6_mlp_down_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 512]> linear_49_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = pre_transformer_layers_6_mlp_down_proj_weight_to_fp16_palettized, x = input_95_cast_fp16)[name = string("linear_49_cast_fp16")]; |
| tensor<fp16, [512]> pre_transformer_layers_6_mlp_layer_scale_scale_to_fp16 = const()[name = string("pre_transformer_layers_6_mlp_layer_scale_scale_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36542976)))]; |
| tensor<fp16, [1, 125, 512]> var_2429_cast_fp16 = mul(x = pre_transformer_layers_6_mlp_layer_scale_scale_to_fp16, y = linear_49_cast_fp16)[name = string("op_2429_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> h_57_cast_fp16 = add(x = h_53_cast_fp16, y = var_2429_cast_fp16)[name = string("h_57_cast_fp16")]; |
| tensor<fp16, [512]> const_94_to_fp16 = const()[name = string("const_94_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36544064)))]; |
| tensor<fp16, [1, 125, 512]> var_2442_cast_fp16 = mul(x = const_94_to_fp16, y = h_57_cast_fp16)[name = string("op_2442_cast_fp16")]; |
| fp16 var_2435_promoted_to_fp16 = const()[name = string("op_2435_promoted_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 125, 512]> var_2443_cast_fp16 = pow(x = h_57_cast_fp16, y = var_2435_promoted_to_fp16)[name = string("op_2443_cast_fp16")]; |
| tensor<int32, [1]> var_2445_axes_0 = const()[name = string("op_2445_axes_0"), val = tensor<int32, [1]>([-1])]; |
| bool var_2445_keep_dims_0 = const()[name = string("op_2445_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 125, 1]> var_2445_cast_fp16 = reduce_mean(axes = var_2445_axes_0, keep_dims = var_2445_keep_dims_0, x = var_2443_cast_fp16)[name = string("op_2445_cast_fp16")]; |
| fp16 var_2446_to_fp16 = const()[name = string("op_2446_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 125, 1]> var_2447_cast_fp16 = add(x = var_2445_cast_fp16, y = var_2446_to_fp16)[name = string("op_2447_cast_fp16")]; |
| fp32 var_2448_epsilon_0 = const()[name = string("op_2448_epsilon_0"), val = fp32(0x1.197998p-40)]; |
| tensor<fp16, [1, 125, 1]> var_2448_cast_fp16 = rsqrt(epsilon = var_2448_epsilon_0, x = var_2447_cast_fp16)[name = string("op_2448_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> hn_cast_fp16 = mul(x = var_2442_cast_fp16, y = var_2448_cast_fp16)[name = string("hn_cast_fp16")]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_7_self_attn_q_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36545152))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37069504))))[name = string("pre_transformer_layers_7_self_attn_q_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_50_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_7_self_attn_q_proj_weight_to_fp16_palettized, x = hn_cast_fp16)[name = string("linear_50_cast_fp16")]; |
| tensor<int32, [4]> var_2470 = const()[name = string("op_2470"), val = tensor<int32, [4]>([1, 125, -1, 64])]; |
| tensor<fp16, [1, 125, 16, 64]> var_2471_cast_fp16 = reshape(shape = var_2470, x = linear_50_cast_fp16)[name = string("op_2471_cast_fp16")]; |
| tensor<int32, [4]> q_61_perm_0 = const()[name = string("q_61_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_7_self_attn_k_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37070080))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37594432))))[name = string("pre_transformer_layers_7_self_attn_k_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_51_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_7_self_attn_k_proj_weight_to_fp16_palettized, x = hn_cast_fp16)[name = string("linear_51_cast_fp16")]; |
| tensor<int32, [4]> var_2480 = const()[name = string("op_2480"), val = tensor<int32, [4]>([1, 125, -1, 64])]; |
| tensor<fp16, [1, 125, 16, 64]> var_2481_cast_fp16 = reshape(shape = var_2480, x = linear_51_cast_fp16)[name = string("op_2481_cast_fp16")]; |
| tensor<int32, [4]> k_29_perm_0 = const()[name = string("k_29_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_7_self_attn_v_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37595008))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38119360))))[name = string("pre_transformer_layers_7_self_attn_v_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_52_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_7_self_attn_v_proj_weight_to_fp16_palettized, x = hn_cast_fp16)[name = string("linear_52_cast_fp16")]; |
| tensor<int32, [4]> var_2490 = const()[name = string("op_2490"), val = tensor<int32, [4]>([1, 125, -1, 64])]; |
| tensor<fp16, [1, 125, 16, 64]> var_2491_cast_fp16 = reshape(shape = var_2490, x = linear_52_cast_fp16)[name = string("op_2491_cast_fp16")]; |
| tensor<int32, [4]> v_perm_0 = const()[name = string("v_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> var_2507_begin_0 = const()[name = string("op_2507_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 32])]; |
| tensor<int32, [4]> var_2507_end_0 = const()[name = string("op_2507_end_0"), val = tensor<int32, [4]>([1, 16, 125, 64])]; |
| tensor<bool, [4]> var_2507_end_mask_0 = const()[name = string("op_2507_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])]; |
| tensor<fp16, [1, 16, 125, 64]> q_61_cast_fp16 = transpose(perm = q_61_perm_0, x = var_2471_cast_fp16)[name = string("transpose_8")]; |
| tensor<fp16, [1, 16, 125, 32]> var_2507_cast_fp16 = slice_by_index(begin = var_2507_begin_0, end = var_2507_end_0, end_mask = var_2507_end_mask_0, x = q_61_cast_fp16)[name = string("op_2507_cast_fp16")]; |
| fp16 const_98_promoted_to_fp16 = const()[name = string("const_98_promoted_to_fp16"), val = fp16(-0x1p+0)]; |
| tensor<fp16, [1, 16, 125, 32]> var_2508_cast_fp16 = mul(x = var_2507_cast_fp16, y = const_98_promoted_to_fp16)[name = string("op_2508_cast_fp16")]; |
| tensor<int32, [4]> var_2518_begin_0 = const()[name = string("op_2518_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [4]> var_2518_end_0 = const()[name = string("op_2518_end_0"), val = tensor<int32, [4]>([1, 16, 125, 32])]; |
| tensor<bool, [4]> var_2518_end_mask_0 = const()[name = string("op_2518_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])]; |
| tensor<fp16, [1, 16, 125, 32]> var_2518_cast_fp16 = slice_by_index(begin = var_2518_begin_0, end = var_2518_end_0, end_mask = var_2518_end_mask_0, x = q_61_cast_fp16)[name = string("op_2518_cast_fp16")]; |
| int32 var_2520 = const()[name = string("op_2520"), val = int32(-1)]; |
| bool var_2521_interleave_0 = const()[name = string("op_2521_interleave_0"), val = bool(false)]; |
| tensor<fp16, [1, 16, 125, 64]> var_2521_cast_fp16 = concat(axis = var_2520, interleave = var_2521_interleave_0, values = (var_2508_cast_fp16, var_2518_cast_fp16))[name = string("op_2521_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> var_2524_cast_fp16 = mul(x = var_2521_cast_fp16, y = op_1004_to_fp16_palettized)[name = string("op_2524_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> q_cast_fp16 = add(x = q_61_cast_fp16, y = var_2524_cast_fp16)[name = string("q_cast_fp16")]; |
| tensor<int32, [4]> var_2539_begin_0 = const()[name = string("op_2539_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 32])]; |
| tensor<int32, [4]> var_2539_end_0 = const()[name = string("op_2539_end_0"), val = tensor<int32, [4]>([1, 16, 125, 64])]; |
| tensor<bool, [4]> var_2539_end_mask_0 = const()[name = string("op_2539_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])]; |
| tensor<fp16, [1, 16, 125, 64]> k_29_cast_fp16 = transpose(perm = k_29_perm_0, x = var_2481_cast_fp16)[name = string("transpose_7")]; |
| tensor<fp16, [1, 16, 125, 32]> var_2539_cast_fp16 = slice_by_index(begin = var_2539_begin_0, end = var_2539_end_0, end_mask = var_2539_end_mask_0, x = k_29_cast_fp16)[name = string("op_2539_cast_fp16")]; |
| fp16 const_101_promoted_to_fp16 = const()[name = string("const_101_promoted_to_fp16"), val = fp16(-0x1p+0)]; |
| tensor<fp16, [1, 16, 125, 32]> var_2540_cast_fp16 = mul(x = var_2539_cast_fp16, y = const_101_promoted_to_fp16)[name = string("op_2540_cast_fp16")]; |
| tensor<int32, [4]> var_2550_begin_0 = const()[name = string("op_2550_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [4]> var_2550_end_0 = const()[name = string("op_2550_end_0"), val = tensor<int32, [4]>([1, 16, 125, 32])]; |
| tensor<bool, [4]> var_2550_end_mask_0 = const()[name = string("op_2550_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])]; |
| tensor<fp16, [1, 16, 125, 32]> var_2550_cast_fp16 = slice_by_index(begin = var_2550_begin_0, end = var_2550_end_0, end_mask = var_2550_end_mask_0, x = k_29_cast_fp16)[name = string("op_2550_cast_fp16")]; |
| int32 var_2552 = const()[name = string("op_2552"), val = int32(-1)]; |
| bool var_2553_interleave_0 = const()[name = string("op_2553_interleave_0"), val = bool(false)]; |
| tensor<fp16, [1, 16, 125, 64]> var_2553_cast_fp16 = concat(axis = var_2552, interleave = var_2553_interleave_0, values = (var_2540_cast_fp16, var_2550_cast_fp16))[name = string("op_2553_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> var_2556_cast_fp16 = mul(x = var_2553_cast_fp16, y = op_1004_to_fp16_palettized)[name = string("op_2556_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 64]> k_cast_fp16 = add(x = k_29_cast_fp16, y = var_2556_cast_fp16)[name = string("k_cast_fp16")]; |
| bool var_2562_transpose_x_1 = const()[name = string("op_2562_transpose_x_1"), val = bool(false)]; |
| bool var_2562_transpose_y_1 = const()[name = string("op_2562_transpose_y_1"), val = bool(true)]; |
| tensor<fp16, [1, 16, 125, 125]> var_2562_cast_fp16 = matmul(transpose_x = var_2562_transpose_x_1, transpose_y = var_2562_transpose_y_1, x = q_cast_fp16, y = k_cast_fp16)[name = string("op_2562_cast_fp16")]; |
| fp16 var_2563_to_fp16 = const()[name = string("op_2563_to_fp16"), val = fp16(0x1p-3)]; |
| tensor<fp16, [1, 16, 125, 125]> aw_29_cast_fp16 = mul(x = var_2562_cast_fp16, y = var_2563_to_fp16)[name = string("aw_29_cast_fp16")]; |
| tensor<fp16, [1, 16, 125, 125]> var_2579_cast_fp16 = add(x = aw_29_cast_fp16, y = op_1058_to_fp16_palettized)[name = string("op_2579_cast_fp16")]; |
| int32 var_2580 = const()[name = string("op_2580"), val = int32(-1)]; |
| tensor<fp16, [1, 16, 125, 125]> var_2582_cast_fp16 = softmax(axis = var_2580, x = var_2579_cast_fp16)[name = string("op_2582_cast_fp16")]; |
| bool var_2588_transpose_x_0 = const()[name = string("op_2588_transpose_x_0"), val = bool(false)]; |
| bool var_2588_transpose_y_0 = const()[name = string("op_2588_transpose_y_0"), val = bool(false)]; |
| tensor<fp16, [1, 16, 125, 64]> v_cast_fp16 = transpose(perm = v_perm_0, x = var_2491_cast_fp16)[name = string("transpose_6")]; |
| tensor<fp16, [1, 16, 125, 64]> var_2588_cast_fp16 = matmul(transpose_x = var_2588_transpose_x_0, transpose_y = var_2588_transpose_y_0, x = var_2582_cast_fp16, y = v_cast_fp16)[name = string("op_2588_cast_fp16")]; |
| tensor<int32, [4]> var_2591_perm_0 = const()[name = string("op_2591_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_2601 = const()[name = string("op_2601"), val = tensor<int32, [3]>([1, 125, -1])]; |
| tensor<fp16, [1, 125, 16, 64]> var_2591_cast_fp16 = transpose(perm = var_2591_perm_0, x = var_2588_cast_fp16)[name = string("transpose_5")]; |
| tensor<fp16, [1, 125, 1024]> var_2602_cast_fp16 = reshape(shape = var_2601, x = var_2591_cast_fp16)[name = string("op_2602_cast_fp16")]; |
| tensor<fp16, [512, 1024]> pre_transformer_layers_7_self_attn_o_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [512, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38119936))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38644288))))[name = string("pre_transformer_layers_7_self_attn_o_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 512]> linear_53_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = pre_transformer_layers_7_self_attn_o_proj_weight_to_fp16_palettized, x = var_2602_cast_fp16)[name = string("linear_53_cast_fp16")]; |
| tensor<fp16, [512]> pre_transformer_layers_7_self_attn_layer_scale_scale_to_fp16 = const()[name = string("pre_transformer_layers_7_self_attn_layer_scale_scale_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38644864)))]; |
| tensor<fp16, [1, 125, 512]> var_2609_cast_fp16 = mul(x = pre_transformer_layers_7_self_attn_layer_scale_scale_to_fp16, y = linear_53_cast_fp16)[name = string("op_2609_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> h_61_cast_fp16 = add(x = h_57_cast_fp16, y = var_2609_cast_fp16)[name = string("h_61_cast_fp16")]; |
| tensor<fp16, [512]> const_105_to_fp16 = const()[name = string("const_105_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38645952)))]; |
| tensor<fp16, [1, 125, 512]> var_2622_cast_fp16 = mul(x = const_105_to_fp16, y = h_61_cast_fp16)[name = string("op_2622_cast_fp16")]; |
| fp16 var_2615_promoted_to_fp16 = const()[name = string("op_2615_promoted_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 125, 512]> var_2623_cast_fp16 = pow(x = h_61_cast_fp16, y = var_2615_promoted_to_fp16)[name = string("op_2623_cast_fp16")]; |
| tensor<int32, [1]> var_2625_axes_0 = const()[name = string("op_2625_axes_0"), val = tensor<int32, [1]>([-1])]; |
| bool var_2625_keep_dims_0 = const()[name = string("op_2625_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 125, 1]> var_2625_cast_fp16 = reduce_mean(axes = var_2625_axes_0, keep_dims = var_2625_keep_dims_0, x = var_2623_cast_fp16)[name = string("op_2625_cast_fp16")]; |
| fp16 var_2626_to_fp16 = const()[name = string("op_2626_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 125, 1]> var_2627_cast_fp16 = add(x = var_2625_cast_fp16, y = var_2626_to_fp16)[name = string("op_2627_cast_fp16")]; |
| fp32 var_2628_epsilon_0 = const()[name = string("op_2628_epsilon_0"), val = fp32(0x1.197998p-40)]; |
| tensor<fp16, [1, 125, 1]> var_2628_cast_fp16 = rsqrt(epsilon = var_2628_epsilon_0, x = var_2627_cast_fp16)[name = string("op_2628_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> input_99_cast_fp16 = mul(x = var_2622_cast_fp16, y = var_2628_cast_fp16)[name = string("input_99_cast_fp16")]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_7_mlp_gate_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38647040))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39171392))))[name = string("pre_transformer_layers_7_mlp_gate_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_54_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_7_mlp_gate_proj_weight_to_fp16_palettized, x = input_99_cast_fp16)[name = string("linear_54_cast_fp16")]; |
| tensor<fp16, [1, 125, 1024]> var_2639_cast_fp16 = silu(x = linear_54_cast_fp16)[name = string("op_2639_cast_fp16")]; |
| tensor<fp16, [1024, 512]> pre_transformer_layers_7_mlp_up_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39171968))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39696320))))[name = string("pre_transformer_layers_7_mlp_up_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 1024]> linear_55_cast_fp16 = linear(bias = linear_1_bias_0_to_fp16, weight = pre_transformer_layers_7_mlp_up_proj_weight_to_fp16_palettized, x = input_99_cast_fp16)[name = string("linear_55_cast_fp16")]; |
| tensor<fp16, [1, 125, 1024]> input_103_cast_fp16 = mul(x = var_2639_cast_fp16, y = linear_55_cast_fp16)[name = string("input_103_cast_fp16")]; |
| tensor<fp16, [512, 1024]> pre_transformer_layers_7_mlp_down_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [512, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39696896))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40221248))))[name = string("pre_transformer_layers_7_mlp_down_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1, 125, 512]> linear_56_cast_fp16 = linear(bias = linear_4_bias_0_to_fp16, weight = pre_transformer_layers_7_mlp_down_proj_weight_to_fp16_palettized, x = input_103_cast_fp16)[name = string("linear_56_cast_fp16")]; |
| tensor<fp16, [512]> pre_transformer_layers_7_mlp_layer_scale_scale_to_fp16 = const()[name = string("pre_transformer_layers_7_mlp_layer_scale_scale_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40221824)))]; |
| tensor<fp16, [1, 125, 512]> var_2646_cast_fp16 = mul(x = pre_transformer_layers_7_mlp_layer_scale_scale_to_fp16, y = linear_56_cast_fp16)[name = string("op_2646_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> h_65_cast_fp16 = add(x = h_61_cast_fp16, y = var_2646_cast_fp16)[name = string("h_65_cast_fp16")]; |
| tensor<fp16, [512]> const_106_to_fp16 = const()[name = string("const_106_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40222912)))]; |
| tensor<fp16, [1, 125, 512]> var_2659_cast_fp16 = mul(x = const_106_to_fp16, y = h_65_cast_fp16)[name = string("op_2659_cast_fp16")]; |
| fp16 var_2652_promoted_to_fp16 = const()[name = string("op_2652_promoted_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 125, 512]> var_2660_cast_fp16 = pow(x = h_65_cast_fp16, y = var_2652_promoted_to_fp16)[name = string("op_2660_cast_fp16")]; |
| tensor<int32, [1]> var_2662_axes_0 = const()[name = string("op_2662_axes_0"), val = tensor<int32, [1]>([-1])]; |
| bool var_2662_keep_dims_0 = const()[name = string("op_2662_keep_dims_0"), val = bool(true)]; |
| tensor<fp16, [1, 125, 1]> var_2662_cast_fp16 = reduce_mean(axes = var_2662_axes_0, keep_dims = var_2662_keep_dims_0, x = var_2660_cast_fp16)[name = string("op_2662_cast_fp16")]; |
| fp16 var_2663_to_fp16 = const()[name = string("op_2663_to_fp16"), val = fp16(0x1.5p-17)]; |
| tensor<fp16, [1, 125, 1]> var_2664_cast_fp16 = add(x = var_2662_cast_fp16, y = var_2663_to_fp16)[name = string("op_2664_cast_fp16")]; |
| fp32 var_2665_epsilon_0 = const()[name = string("op_2665_epsilon_0"), val = fp32(0x1.197998p-40)]; |
| tensor<fp16, [1, 125, 1]> var_2665_cast_fp16 = rsqrt(epsilon = var_2665_epsilon_0, x = var_2664_cast_fp16)[name = string("op_2665_cast_fp16")]; |
| tensor<fp16, [1, 125, 512]> input_105_cast_fp16 = mul(x = var_2659_cast_fp16, y = var_2665_cast_fp16)[name = string("input_105_cast_fp16")]; |
| tensor<fp16, [1024, 512]> pre_transformer_output_proj_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40224000))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40748352))))[name = string("pre_transformer_output_proj_weight_to_fp16_palettized")]; |
| tensor<fp16, [1024]> pre_transformer_output_proj_bias_to_fp16 = const()[name = string("pre_transformer_output_proj_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40748928)))]; |
| tensor<fp16, [1, 125, 1024]> linear_57_cast_fp16 = linear(bias = pre_transformer_output_proj_bias_to_fp16, weight = pre_transformer_output_proj_weight_to_fp16_palettized, x = input_105_cast_fp16)[name = string("linear_57_cast_fp16")]; |
| tensor<int32, [3]> var_2673 = const()[name = string("op_2673"), val = tensor<int32, [3]>([0, 2, 1])]; |
| string hidden_state_3_pad_type_0 = const()[name = string("hidden_state_3_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> hidden_state_3_strides_0 = const()[name = string("hidden_state_3_strides_0"), val = tensor<int32, [1]>([2])]; |
| tensor<int32, [2]> hidden_state_3_pad_0 = const()[name = string("hidden_state_3_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> hidden_state_3_dilations_0 = const()[name = string("hidden_state_3_dilations_0"), val = tensor<int32, [1]>([1])]; |
| int32 hidden_state_3_groups_0 = const()[name = string("hidden_state_3_groups_0"), val = int32(1)]; |
| tensor<int32, [3]> hidden_state_3_has_output_shape_output_shape_0 = const()[name = string("hidden_state_3_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 1024, 250])]; |
| tensor<fp16, [1024, 1024, 2]> upsample_0_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 1024, 2]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40751040))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42848256))))[name = string("upsample_0_0_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [1024]> upsample_0_0_conv_bias_to_fp16 = const()[name = string("upsample_0_0_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42848832)))]; |
| tensor<fp16, [1, 1024, 125]> input_107_cast_fp16 = transpose(perm = var_2673, x = linear_57_cast_fp16)[name = string("transpose_4")]; |
| tensor<fp16, [1, 1024, 250]> hidden_state_3_has_output_shape_cast_fp16 = conv_transpose(bias = upsample_0_0_conv_bias_to_fp16, dilations = hidden_state_3_dilations_0, groups = hidden_state_3_groups_0, output_shape = hidden_state_3_has_output_shape_output_shape_0, pad = hidden_state_3_pad_0, pad_type = hidden_state_3_pad_type_0, strides = hidden_state_3_strides_0, weight = upsample_0_0_conv_weight_to_fp16_palettized, x = input_107_cast_fp16)[name = string("hidden_state_3_has_output_shape_cast_fp16")]; |
| tensor<int32, [6]> input_109_pad_0 = const()[name = string("input_109_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 6, 0])]; |
| string input_109_mode_0 = const()[name = string("input_109_mode_0"), val = string("constant")]; |
| fp16 const_108_to_fp16 = const()[name = string("const_108_to_fp16"), val = fp16(0x0p+0)]; |
| tensor<fp16, [1, 1024, 256]> input_109_cast_fp16 = pad(constant_val = const_108_to_fp16, mode = input_109_mode_0, pad = input_109_pad_0, x = hidden_state_3_has_output_shape_cast_fp16)[name = string("input_109_cast_fp16")]; |
| string var_2719_pad_type_0 = const()[name = string("op_2719_pad_type_0"), val = string("valid")]; |
| int32 var_2719_groups_0 = const()[name = string("op_2719_groups_0"), val = int32(1024)]; |
| tensor<int32, [1]> var_2719_strides_0 = const()[name = string("op_2719_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_2719_pad_0 = const()[name = string("op_2719_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> var_2719_dilations_0 = const()[name = string("op_2719_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<fp16, [1024, 1, 7]> upsample_0_1_dwconv_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42850944))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42858176))))[name = string("upsample_0_1_dwconv_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [1024]> upsample_0_1_dwconv_conv_bias_to_fp16 = const()[name = string("upsample_0_1_dwconv_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42858752)))]; |
| tensor<fp16, [1, 1024, 250]> var_2719_cast_fp16 = conv(bias = upsample_0_1_dwconv_conv_bias_to_fp16, dilations = var_2719_dilations_0, groups = var_2719_groups_0, pad = var_2719_pad_0, pad_type = var_2719_pad_type_0, strides = var_2719_strides_0, weight = upsample_0_1_dwconv_conv_weight_to_fp16_palettized, x = input_109_cast_fp16)[name = string("op_2719_cast_fp16")]; |
| tensor<int32, [3]> var_2721 = const()[name = string("op_2721"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<int32, [1]> input_113_axes_0 = const()[name = string("input_113_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1024]> upsample_0_1_norm_weight_to_fp16 = const()[name = string("upsample_0_1_norm_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42860864)))]; |
| tensor<fp16, [1024]> upsample_0_1_norm_bias_to_fp16 = const()[name = string("upsample_0_1_norm_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42862976)))]; |
| fp16 var_2690_to_fp16 = const()[name = string("op_2690_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 250, 1024]> input_111_cast_fp16 = transpose(perm = var_2721, x = var_2719_cast_fp16)[name = string("transpose_3")]; |
| tensor<fp16, [1, 250, 1024]> input_113_cast_fp16 = layer_norm(axes = input_113_axes_0, beta = upsample_0_1_norm_bias_to_fp16, epsilon = var_2690_to_fp16, gamma = upsample_0_1_norm_weight_to_fp16, x = input_111_cast_fp16)[name = string("input_113_cast_fp16")]; |
| tensor<fp16, [4096, 1024]> upsample_0_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42865088))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47059456))))[name = string("upsample_0_1_pwconv1_weight_to_fp16_palettized")]; |
| tensor<fp16, [4096]> upsample_0_1_pwconv1_bias_to_fp16 = const()[name = string("upsample_0_1_pwconv1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47060032)))]; |
| tensor<fp16, [1, 250, 4096]> linear_58_cast_fp16 = linear(bias = upsample_0_1_pwconv1_bias_to_fp16, weight = upsample_0_1_pwconv1_weight_to_fp16_palettized, x = input_113_cast_fp16)[name = string("linear_58_cast_fp16")]; |
| string input_117_mode_0 = const()[name = string("input_117_mode_0"), val = string("EXACT")]; |
| tensor<fp16, [1, 250, 4096]> input_117_cast_fp16 = gelu(mode = input_117_mode_0, x = linear_58_cast_fp16)[name = string("input_117_cast_fp16")]; |
| tensor<fp16, [1024, 4096]> upsample_0_1_pwconv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47068288))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51262656))))[name = string("upsample_0_1_pwconv2_weight_to_fp16_palettized")]; |
| tensor<fp16, [1024]> upsample_0_1_pwconv2_bias_to_fp16 = const()[name = string("upsample_0_1_pwconv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51263232)))]; |
| tensor<fp16, [1, 250, 1024]> linear_59_cast_fp16 = linear(bias = upsample_0_1_pwconv2_bias_to_fp16, weight = upsample_0_1_pwconv2_weight_to_fp16_palettized, x = input_117_cast_fp16)[name = string("linear_59_cast_fp16")]; |
| tensor<fp16, [1024]> upsample_0_1_gamma_to_fp16 = const()[name = string("upsample_0_1_gamma_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51265344)))]; |
| tensor<fp16, [1, 250, 1024]> hidden_states_5_cast_fp16 = mul(x = upsample_0_1_gamma_to_fp16, y = linear_59_cast_fp16)[name = string("hidden_states_5_cast_fp16")]; |
| tensor<int32, [3]> var_2735 = const()[name = string("op_2735"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<fp16, [1, 1024, 250]> hidden_states_7_cast_fp16 = transpose(perm = var_2735, x = hidden_states_5_cast_fp16)[name = string("transpose_2")]; |
| tensor<fp16, [1, 1024, 250]> input_119_cast_fp16 = add(x = hidden_state_3_has_output_shape_cast_fp16, y = hidden_states_7_cast_fp16)[name = string("input_119_cast_fp16")]; |
| string hidden_state_7_pad_type_0 = const()[name = string("hidden_state_7_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> hidden_state_7_strides_0 = const()[name = string("hidden_state_7_strides_0"), val = tensor<int32, [1]>([2])]; |
| tensor<int32, [2]> hidden_state_7_pad_0 = const()[name = string("hidden_state_7_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> hidden_state_7_dilations_0 = const()[name = string("hidden_state_7_dilations_0"), val = tensor<int32, [1]>([1])]; |
| int32 hidden_state_7_groups_0 = const()[name = string("hidden_state_7_groups_0"), val = int32(1)]; |
| tensor<int32, [3]> hidden_state_7_has_output_shape_output_shape_0 = const()[name = string("hidden_state_7_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 1024, 500])]; |
| tensor<fp16, [1024, 1024, 2]> upsample_1_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 1024, 2]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51267456))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53364672))))[name = string("upsample_1_0_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [1024]> upsample_1_0_conv_bias_to_fp16 = const()[name = string("upsample_1_0_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53365248)))]; |
| tensor<fp16, [1, 1024, 500]> hidden_state_7_has_output_shape_cast_fp16 = conv_transpose(bias = upsample_1_0_conv_bias_to_fp16, dilations = hidden_state_7_dilations_0, groups = hidden_state_7_groups_0, output_shape = hidden_state_7_has_output_shape_output_shape_0, pad = hidden_state_7_pad_0, pad_type = hidden_state_7_pad_type_0, strides = hidden_state_7_strides_0, weight = upsample_1_0_conv_weight_to_fp16_palettized, x = input_119_cast_fp16)[name = string("hidden_state_7_has_output_shape_cast_fp16")]; |
| tensor<int32, [6]> input_121_pad_0 = const()[name = string("input_121_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 6, 0])]; |
| string input_121_mode_0 = const()[name = string("input_121_mode_0"), val = string("constant")]; |
| fp16 const_110_to_fp16 = const()[name = string("const_110_to_fp16"), val = fp16(0x0p+0)]; |
| tensor<fp16, [1, 1024, 506]> input_121_cast_fp16 = pad(constant_val = const_110_to_fp16, mode = input_121_mode_0, pad = input_121_pad_0, x = hidden_state_7_has_output_shape_cast_fp16)[name = string("input_121_cast_fp16")]; |
| string var_2782_pad_type_0 = const()[name = string("op_2782_pad_type_0"), val = string("valid")]; |
| int32 var_2782_groups_0 = const()[name = string("op_2782_groups_0"), val = int32(1024)]; |
| tensor<int32, [1]> var_2782_strides_0 = const()[name = string("op_2782_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_2782_pad_0 = const()[name = string("op_2782_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> var_2782_dilations_0 = const()[name = string("op_2782_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<fp16, [1024, 1, 7]> upsample_1_1_dwconv_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53367360))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53374592))))[name = string("upsample_1_1_dwconv_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [1024]> upsample_1_1_dwconv_conv_bias_to_fp16 = const()[name = string("upsample_1_1_dwconv_conv_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53375168)))]; |
| tensor<fp16, [1, 1024, 500]> var_2782_cast_fp16 = conv(bias = upsample_1_1_dwconv_conv_bias_to_fp16, dilations = var_2782_dilations_0, groups = var_2782_groups_0, pad = var_2782_pad_0, pad_type = var_2782_pad_type_0, strides = var_2782_strides_0, weight = upsample_1_1_dwconv_conv_weight_to_fp16_palettized, x = input_121_cast_fp16)[name = string("op_2782_cast_fp16")]; |
| tensor<int32, [3]> var_2784 = const()[name = string("op_2784"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<int32, [1]> input_125_axes_0 = const()[name = string("input_125_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp16, [1024]> upsample_1_1_norm_weight_to_fp16 = const()[name = string("upsample_1_1_norm_weight_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53377280)))]; |
| tensor<fp16, [1024]> upsample_1_1_norm_bias_to_fp16 = const()[name = string("upsample_1_1_norm_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53379392)))]; |
| fp16 var_2753_to_fp16 = const()[name = string("op_2753_to_fp16"), val = fp16(0x1.1p-20)]; |
| tensor<fp16, [1, 500, 1024]> input_123_cast_fp16 = transpose(perm = var_2784, x = var_2782_cast_fp16)[name = string("transpose_1")]; |
| tensor<fp16, [1, 500, 1024]> input_125_cast_fp16 = layer_norm(axes = input_125_axes_0, beta = upsample_1_1_norm_bias_to_fp16, epsilon = var_2753_to_fp16, gamma = upsample_1_1_norm_weight_to_fp16, x = input_123_cast_fp16)[name = string("input_125_cast_fp16")]; |
| tensor<fp16, [4096, 1024]> upsample_1_1_pwconv1_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [4096, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53381504))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57575872))))[name = string("upsample_1_1_pwconv1_weight_to_fp16_palettized")]; |
| tensor<fp16, [4096]> upsample_1_1_pwconv1_bias_to_fp16 = const()[name = string("upsample_1_1_pwconv1_bias_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57576448)))]; |
| tensor<fp16, [1, 500, 4096]> linear_60_cast_fp16 = linear(bias = upsample_1_1_pwconv1_bias_to_fp16, weight = upsample_1_1_pwconv1_weight_to_fp16_palettized, x = input_125_cast_fp16)[name = string("linear_60_cast_fp16")]; |
| string input_129_mode_0 = const()[name = string("input_129_mode_0"), val = string("EXACT")]; |
| tensor<fp16, [1, 500, 4096]> input_129_cast_fp16 = gelu(mode = input_129_mode_0, x = linear_60_cast_fp16)[name = string("input_129_cast_fp16")]; |
| tensor<fp16, [1024, 4096]> upsample_1_1_pwconv2_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1024, 4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57584704))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61779072))))[name = string("upsample_1_1_pwconv2_weight_to_fp16_palettized")]; |
| tensor<fp16, [1024]> upsample_1_1_pwconv2_bias_to_fp16 = const()[name = string("upsample_1_1_pwconv2_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61779648)))]; |
| tensor<fp16, [1, 500, 1024]> linear_61_cast_fp16 = linear(bias = upsample_1_1_pwconv2_bias_to_fp16, weight = upsample_1_1_pwconv2_weight_to_fp16_palettized, x = input_129_cast_fp16)[name = string("linear_61_cast_fp16")]; |
| tensor<fp16, [1024]> upsample_1_1_gamma_to_fp16 = const()[name = string("upsample_1_1_gamma_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61781760)))]; |
| tensor<fp16, [1, 500, 1024]> hidden_states_13_cast_fp16 = mul(x = upsample_1_1_gamma_to_fp16, y = linear_61_cast_fp16)[name = string("hidden_states_13_cast_fp16")]; |
| tensor<int32, [3]> var_2798 = const()[name = string("op_2798"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<fp16, [1, 1024, 500]> hidden_states_15_cast_fp16 = transpose(perm = var_2798, x = hidden_states_13_cast_fp16)[name = string("transpose_0")]; |
| tensor<fp16, [1, 1024, 500]> hidden_state_11_cast_fp16 = add(x = hidden_state_7_has_output_shape_cast_fp16, y = hidden_states_15_cast_fp16)[name = string("hidden_state_11_cast_fp16")]; |
| tensor<int32, [6]> input_131_pad_0 = const()[name = string("input_131_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 6, 0])]; |
| string input_131_mode_0 = const()[name = string("input_131_mode_0"), val = string("constant")]; |
| fp16 const_112_to_fp16 = const()[name = string("const_112_to_fp16"), val = fp16(0x0p+0)]; |
| tensor<fp16, [1, 1024, 506]> input_131_cast_fp16 = pad(constant_val = const_112_to_fp16, mode = input_131_mode_0, pad = input_131_pad_0, x = hidden_state_11_cast_fp16)[name = string("input_131_cast_fp16")]; |
| string var_2823_pad_type_0 = const()[name = string("op_2823_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> var_2823_strides_0 = const()[name = string("op_2823_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_2823_pad_0 = const()[name = string("op_2823_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> var_2823_dilations_0 = const()[name = string("op_2823_dilations_0"), val = tensor<int32, [1]>([1])]; |
| int32 var_2823_groups_0 = const()[name = string("op_2823_groups_0"), val = int32(1)]; |
| tensor<fp16, [1536, 1024, 7]> audio_decoder_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1536, 1024, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61783872))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72793984))))[name = string("audio_decoder_0_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [1536]> audio_decoder_0_conv_bias_to_fp16 = const()[name = string("audio_decoder_0_conv_bias_to_fp16"), val = tensor<fp16, [1536]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72794560)))]; |
| tensor<fp16, [1, 1536, 500]> var_2823_cast_fp16 = conv(bias = audio_decoder_0_conv_bias_to_fp16, dilations = var_2823_dilations_0, groups = var_2823_groups_0, pad = var_2823_pad_0, pad_type = var_2823_pad_type_0, strides = var_2823_strides_0, weight = audio_decoder_0_conv_weight_to_fp16_palettized, x = input_131_cast_fp16)[name = string("op_2823_cast_fp16")]; |
| tensor<fp16, [1, 1536, 1]> alpha_3_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1, 1536, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72797696))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72799296))))[name = string("alpha_3_to_fp16_palettized")]; |
| tensor<fp16, [1, 1536, 500]> var_2863_cast_fp16 = mul(x = var_2823_cast_fp16, y = alpha_3_to_fp16_palettized)[name = string("op_2863_cast_fp16")]; |
| tensor<fp16, [1, 1536, 500]> var_2864_cast_fp16 = sin(x = var_2863_cast_fp16)[name = string("op_2864_cast_fp16")]; |
| fp16 var_2839_promoted_to_fp16 = const()[name = string("op_2839_promoted_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 1536, 500]> var_2865_cast_fp16 = pow(x = var_2864_cast_fp16, y = var_2839_promoted_to_fp16)[name = string("op_2865_cast_fp16")]; |
| tensor<fp16, [1, 1536, 1]> op_2860_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1, 1536, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72799872))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72801472))))[name = string("op_2860_to_fp16_palettized")]; |
| tensor<fp16, [1, 1536, 500]> var_2866_cast_fp16 = mul(x = op_2860_to_fp16_palettized, y = var_2865_cast_fp16)[name = string("op_2866_cast_fp16")]; |
| tensor<fp16, [1, 1536, 500]> input_133_cast_fp16 = add(x = var_2823_cast_fp16, y = var_2866_cast_fp16)[name = string("input_133_cast_fp16")]; |
| string hidden_state_13_pad_type_0 = const()[name = string("hidden_state_13_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> hidden_state_13_strides_0 = const()[name = string("hidden_state_13_strides_0"), val = tensor<int32, [1]>([8])]; |
| tensor<int32, [2]> hidden_state_13_pad_0 = const()[name = string("hidden_state_13_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> hidden_state_13_dilations_0 = const()[name = string("hidden_state_13_dilations_0"), val = tensor<int32, [1]>([1])]; |
| int32 hidden_state_13_groups_0 = const()[name = string("hidden_state_13_groups_0"), val = int32(1)]; |
| tensor<int32, [3]> hidden_state_13_has_output_shape_output_shape_0 = const()[name = string("hidden_state_13_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 768, 4008])]; |
| tensor<fp16, [1536, 768, 16]> audio_decoder_1_block_1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1536, 768, 16]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72802048))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91676480))))[name = string("audio_decoder_1_block_1_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [768]> audio_decoder_1_block_1_conv_bias_to_fp16 = const()[name = string("audio_decoder_1_block_1_conv_bias_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91677056)))]; |
| tensor<fp16, [1, 768, 4008]> hidden_state_13_has_output_shape_cast_fp16 = conv_transpose(bias = audio_decoder_1_block_1_conv_bias_to_fp16, dilations = hidden_state_13_dilations_0, groups = hidden_state_13_groups_0, output_shape = hidden_state_13_has_output_shape_output_shape_0, pad = hidden_state_13_pad_0, pad_type = hidden_state_13_pad_type_0, strides = hidden_state_13_strides_0, weight = audio_decoder_1_block_1_conv_weight_to_fp16_palettized, x = input_133_cast_fp16)[name = string("hidden_state_13_has_output_shape_cast_fp16")]; |
| tensor<int32, [3]> hidden_state_15_begin_0 = const()[name = string("hidden_state_15_begin_0"), val = tensor<int32, [3]>([0, 0, 0])]; |
| tensor<int32, [3]> hidden_state_15_end_0 = const()[name = string("hidden_state_15_end_0"), val = tensor<int32, [3]>([1, 768, 4000])]; |
| tensor<bool, [3]> hidden_state_15_end_mask_0 = const()[name = string("hidden_state_15_end_mask_0"), val = tensor<bool, [3]>([true, true, false])]; |
| tensor<fp16, [1, 768, 4000]> hidden_state_15_cast_fp16 = slice_by_index(begin = hidden_state_15_begin_0, end = hidden_state_15_end_0, end_mask = hidden_state_15_end_mask_0, x = hidden_state_13_has_output_shape_cast_fp16)[name = string("hidden_state_15_cast_fp16")]; |
| tensor<fp16, [1, 768, 1]> alpha_7_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1, 768, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91678656))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91679488))))[name = string("alpha_7_to_fp16_palettized")]; |
| tensor<fp16, [1, 768, 4000]> var_2900_cast_fp16 = mul(x = hidden_state_15_cast_fp16, y = alpha_7_to_fp16_palettized)[name = string("op_2900_cast_fp16")]; |
| tensor<fp16, [1, 768, 4000]> var_2901_cast_fp16 = sin(x = var_2900_cast_fp16)[name = string("op_2901_cast_fp16")]; |
| fp16 var_2839_promoted_1_to_fp16 = const()[name = string("op_2839_promoted_1_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 768, 4000]> var_2902_cast_fp16 = pow(x = var_2901_cast_fp16, y = var_2839_promoted_1_to_fp16)[name = string("op_2902_cast_fp16")]; |
| tensor<fp16, [1, 768, 1]> op_2897_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1, 768, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91680064))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91680896))))[name = string("op_2897_to_fp16_palettized")]; |
| tensor<fp16, [1, 768, 4000]> var_2903_cast_fp16 = mul(x = op_2897_to_fp16_palettized, y = var_2902_cast_fp16)[name = string("op_2903_cast_fp16")]; |
| tensor<fp16, [1, 768, 4000]> hidden_state_17_cast_fp16 = add(x = hidden_state_15_cast_fp16, y = var_2903_cast_fp16)[name = string("hidden_state_17_cast_fp16")]; |
| tensor<int32, [6]> input_135_pad_0 = const()[name = string("input_135_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 6, 0])]; |
| string input_135_mode_0 = const()[name = string("input_135_mode_0"), val = string("constant")]; |
| fp16 const_115_to_fp16 = const()[name = string("const_115_to_fp16"), val = fp16(0x0p+0)]; |
| tensor<fp16, [1, 768, 4006]> input_135_cast_fp16 = pad(constant_val = const_115_to_fp16, mode = input_135_mode_0, pad = input_135_pad_0, x = hidden_state_17_cast_fp16)[name = string("input_135_cast_fp16")]; |
| string var_2918_pad_type_0 = const()[name = string("op_2918_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> var_2918_strides_0 = const()[name = string("op_2918_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_2918_pad_0 = const()[name = string("op_2918_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> var_2918_dilations_0 = const()[name = string("op_2918_dilations_0"), val = tensor<int32, [1]>([1])]; |
| int32 var_2918_groups_0 = const()[name = string("op_2918_groups_0"), val = int32(1)]; |
| tensor<fp16, [768, 768, 7]> audio_decoder_1_block_2_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [768, 768, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91681472))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95810304))))[name = string("audio_decoder_1_block_2_conv1_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [768]> audio_decoder_1_block_2_conv1_conv_bias_to_fp16 = const()[name = string("audio_decoder_1_block_2_conv1_conv_bias_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95810880)))]; |
| tensor<fp16, [1, 768, 4000]> var_2918_cast_fp16 = conv(bias = audio_decoder_1_block_2_conv1_conv_bias_to_fp16, dilations = var_2918_dilations_0, groups = var_2918_groups_0, pad = var_2918_pad_0, pad_type = var_2918_pad_type_0, strides = var_2918_strides_0, weight = audio_decoder_1_block_2_conv1_conv_weight_to_fp16_palettized, x = input_135_cast_fp16)[name = string("op_2918_cast_fp16")]; |
| tensor<fp16, [1, 768, 1]> alpha_11_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1, 768, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95812480))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95813312))))[name = string("alpha_11_to_fp16_palettized")]; |
| tensor<fp16, [1, 768, 4000]> var_2933_cast_fp16 = mul(x = var_2918_cast_fp16, y = alpha_11_to_fp16_palettized)[name = string("op_2933_cast_fp16")]; |
| tensor<fp16, [1, 768, 4000]> var_2934_cast_fp16 = sin(x = var_2933_cast_fp16)[name = string("op_2934_cast_fp16")]; |
| fp16 var_2839_promoted_2_to_fp16 = const()[name = string("op_2839_promoted_2_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 768, 4000]> var_2935_cast_fp16 = pow(x = var_2934_cast_fp16, y = var_2839_promoted_2_to_fp16)[name = string("op_2935_cast_fp16")]; |
| tensor<fp16, [1, 768, 1]> op_2930_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1, 768, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95813888))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95814720))))[name = string("op_2930_to_fp16_palettized")]; |
| tensor<fp16, [1, 768, 4000]> var_2936_cast_fp16 = mul(x = op_2930_to_fp16_palettized, y = var_2935_cast_fp16)[name = string("op_2936_cast_fp16")]; |
| tensor<fp16, [1, 768, 4000]> hidden_state_19_cast_fp16 = add(x = var_2918_cast_fp16, y = var_2936_cast_fp16)[name = string("hidden_state_19_cast_fp16")]; |
| string var_2951_pad_type_0 = const()[name = string("op_2951_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> var_2951_strides_0 = const()[name = string("op_2951_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_2951_pad_0 = const()[name = string("op_2951_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> var_2951_dilations_0 = const()[name = string("op_2951_dilations_0"), val = tensor<int32, [1]>([1])]; |
| int32 var_2951_groups_0 = const()[name = string("op_2951_groups_0"), val = int32(1)]; |
| tensor<fp16, [768, 768, 1]> audio_decoder_1_block_2_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [768, 768, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95815296))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96405184))))[name = string("audio_decoder_1_block_2_conv2_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [768]> audio_decoder_1_block_2_conv2_conv_bias_to_fp16 = const()[name = string("audio_decoder_1_block_2_conv2_conv_bias_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96405760)))]; |
| tensor<fp16, [1, 768, 4000]> var_2951_cast_fp16 = conv(bias = audio_decoder_1_block_2_conv2_conv_bias_to_fp16, dilations = var_2951_dilations_0, groups = var_2951_groups_0, pad = var_2951_pad_0, pad_type = var_2951_pad_type_0, strides = var_2951_strides_0, weight = audio_decoder_1_block_2_conv2_conv_weight_to_fp16_palettized, x = hidden_state_19_cast_fp16)[name = string("op_2951_cast_fp16")]; |
| tensor<fp16, [1, 768, 4000]> hidden_states_23_cast_fp16 = add(x = var_2951_cast_fp16, y = hidden_state_15_cast_fp16)[name = string("hidden_states_23_cast_fp16")]; |
| tensor<fp16, [1, 768, 1]> alpha_15_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1, 768, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96407360))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96408192))))[name = string("alpha_15_to_fp16_palettized")]; |
| tensor<fp16, [1, 768, 4000]> var_2971_cast_fp16 = mul(x = hidden_states_23_cast_fp16, y = alpha_15_to_fp16_palettized)[name = string("op_2971_cast_fp16")]; |
| tensor<fp16, [1, 768, 4000]> var_2972_cast_fp16 = sin(x = var_2971_cast_fp16)[name = string("op_2972_cast_fp16")]; |
| fp16 var_2839_promoted_3_to_fp16 = const()[name = string("op_2839_promoted_3_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 768, 4000]> var_2973_cast_fp16 = pow(x = var_2972_cast_fp16, y = var_2839_promoted_3_to_fp16)[name = string("op_2973_cast_fp16")]; |
| tensor<fp16, [1, 768, 1]> op_2968_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1, 768, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96408768))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96409600))))[name = string("op_2968_to_fp16_palettized")]; |
| tensor<fp16, [1, 768, 4000]> var_2974_cast_fp16 = mul(x = op_2968_to_fp16_palettized, y = var_2973_cast_fp16)[name = string("op_2974_cast_fp16")]; |
| tensor<fp16, [1, 768, 4000]> hidden_state_23_cast_fp16 = add(x = hidden_states_23_cast_fp16, y = var_2974_cast_fp16)[name = string("hidden_state_23_cast_fp16")]; |
| tensor<int32, [6]> input_139_pad_0 = const()[name = string("input_139_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 18, 0])]; |
| string input_139_mode_0 = const()[name = string("input_139_mode_0"), val = string("constant")]; |
| fp16 const_119_to_fp16 = const()[name = string("const_119_to_fp16"), val = fp16(0x0p+0)]; |
| tensor<fp16, [1, 768, 4018]> input_139_cast_fp16 = pad(constant_val = const_119_to_fp16, mode = input_139_mode_0, pad = input_139_pad_0, x = hidden_state_23_cast_fp16)[name = string("input_139_cast_fp16")]; |
| string var_2989_pad_type_0 = const()[name = string("op_2989_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> var_2989_dilations_0 = const()[name = string("op_2989_dilations_0"), val = tensor<int32, [1]>([3])]; |
| tensor<int32, [1]> var_2989_strides_0 = const()[name = string("op_2989_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_2989_pad_0 = const()[name = string("op_2989_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| int32 var_2989_groups_0 = const()[name = string("op_2989_groups_0"), val = int32(1)]; |
| tensor<fp16, [768, 768, 7]> audio_decoder_1_block_3_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [768, 768, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96410176))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100539008))))[name = string("audio_decoder_1_block_3_conv1_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [768]> audio_decoder_1_block_3_conv1_conv_bias_to_fp16 = const()[name = string("audio_decoder_1_block_3_conv1_conv_bias_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100539584)))]; |
| tensor<fp16, [1, 768, 4000]> var_2989_cast_fp16 = conv(bias = audio_decoder_1_block_3_conv1_conv_bias_to_fp16, dilations = var_2989_dilations_0, groups = var_2989_groups_0, pad = var_2989_pad_0, pad_type = var_2989_pad_type_0, strides = var_2989_strides_0, weight = audio_decoder_1_block_3_conv1_conv_weight_to_fp16_palettized, x = input_139_cast_fp16)[name = string("op_2989_cast_fp16")]; |
| tensor<fp16, [1, 768, 1]> alpha_19_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1, 768, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100541184))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100542016))))[name = string("alpha_19_to_fp16_palettized")]; |
| tensor<fp16, [1, 768, 4000]> var_3004_cast_fp16 = mul(x = var_2989_cast_fp16, y = alpha_19_to_fp16_palettized)[name = string("op_3004_cast_fp16")]; |
| tensor<fp16, [1, 768, 4000]> var_3005_cast_fp16 = sin(x = var_3004_cast_fp16)[name = string("op_3005_cast_fp16")]; |
| fp16 var_2839_promoted_4_to_fp16 = const()[name = string("op_2839_promoted_4_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 768, 4000]> var_3006_cast_fp16 = pow(x = var_3005_cast_fp16, y = var_2839_promoted_4_to_fp16)[name = string("op_3006_cast_fp16")]; |
| tensor<fp16, [1, 768, 1]> op_3001_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1, 768, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100542592))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100543424))))[name = string("op_3001_to_fp16_palettized")]; |
| tensor<fp16, [1, 768, 4000]> var_3007_cast_fp16 = mul(x = op_3001_to_fp16_palettized, y = var_3006_cast_fp16)[name = string("op_3007_cast_fp16")]; |
| tensor<fp16, [1, 768, 4000]> hidden_state_25_cast_fp16 = add(x = var_2989_cast_fp16, y = var_3007_cast_fp16)[name = string("hidden_state_25_cast_fp16")]; |
| string var_3022_pad_type_0 = const()[name = string("op_3022_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> var_3022_strides_0 = const()[name = string("op_3022_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_3022_pad_0 = const()[name = string("op_3022_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> var_3022_dilations_0 = const()[name = string("op_3022_dilations_0"), val = tensor<int32, [1]>([1])]; |
| int32 var_3022_groups_0 = const()[name = string("op_3022_groups_0"), val = int32(1)]; |
| tensor<fp16, [768, 768, 1]> audio_decoder_1_block_3_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [768, 768, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100544000))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101133888))))[name = string("audio_decoder_1_block_3_conv2_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [768]> audio_decoder_1_block_3_conv2_conv_bias_to_fp16 = const()[name = string("audio_decoder_1_block_3_conv2_conv_bias_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101134464)))]; |
| tensor<fp16, [1, 768, 4000]> var_3022_cast_fp16 = conv(bias = audio_decoder_1_block_3_conv2_conv_bias_to_fp16, dilations = var_3022_dilations_0, groups = var_3022_groups_0, pad = var_3022_pad_0, pad_type = var_3022_pad_type_0, strides = var_3022_strides_0, weight = audio_decoder_1_block_3_conv2_conv_weight_to_fp16_palettized, x = hidden_state_25_cast_fp16)[name = string("op_3022_cast_fp16")]; |
| tensor<fp16, [1, 768, 4000]> hidden_states_27_cast_fp16 = add(x = var_3022_cast_fp16, y = hidden_states_23_cast_fp16)[name = string("hidden_states_27_cast_fp16")]; |
| tensor<fp16, [1, 768, 1]> alpha_23_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1, 768, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101136064))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101136896))))[name = string("alpha_23_to_fp16_palettized")]; |
| tensor<fp16, [1, 768, 4000]> var_3042_cast_fp16 = mul(x = hidden_states_27_cast_fp16, y = alpha_23_to_fp16_palettized)[name = string("op_3042_cast_fp16")]; |
| tensor<fp16, [1, 768, 4000]> var_3043_cast_fp16 = sin(x = var_3042_cast_fp16)[name = string("op_3043_cast_fp16")]; |
| fp16 var_2839_promoted_5_to_fp16 = const()[name = string("op_2839_promoted_5_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 768, 4000]> var_3044_cast_fp16 = pow(x = var_3043_cast_fp16, y = var_2839_promoted_5_to_fp16)[name = string("op_3044_cast_fp16")]; |
| tensor<fp16, [1, 768, 1]> op_3039_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1, 768, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101137472))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101138304))))[name = string("op_3039_to_fp16_palettized")]; |
| tensor<fp16, [1, 768, 4000]> var_3045_cast_fp16 = mul(x = op_3039_to_fp16_palettized, y = var_3044_cast_fp16)[name = string("op_3045_cast_fp16")]; |
| tensor<fp16, [1, 768, 4000]> hidden_state_29_cast_fp16 = add(x = hidden_states_27_cast_fp16, y = var_3045_cast_fp16)[name = string("hidden_state_29_cast_fp16")]; |
| tensor<int32, [6]> input_143_pad_0 = const()[name = string("input_143_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 54, 0])]; |
| string input_143_mode_0 = const()[name = string("input_143_mode_0"), val = string("constant")]; |
| fp16 const_123_to_fp16 = const()[name = string("const_123_to_fp16"), val = fp16(0x0p+0)]; |
| tensor<fp16, [1, 768, 4054]> input_143_cast_fp16 = pad(constant_val = const_123_to_fp16, mode = input_143_mode_0, pad = input_143_pad_0, x = hidden_state_29_cast_fp16)[name = string("input_143_cast_fp16")]; |
| string var_3060_pad_type_0 = const()[name = string("op_3060_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> var_3060_dilations_0 = const()[name = string("op_3060_dilations_0"), val = tensor<int32, [1]>([9])]; |
| tensor<int32, [1]> var_3060_strides_0 = const()[name = string("op_3060_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_3060_pad_0 = const()[name = string("op_3060_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| int32 var_3060_groups_0 = const()[name = string("op_3060_groups_0"), val = int32(1)]; |
| tensor<fp16, [768, 768, 7]> audio_decoder_1_block_4_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [768, 768, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(101138880))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105267712))))[name = string("audio_decoder_1_block_4_conv1_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [768]> audio_decoder_1_block_4_conv1_conv_bias_to_fp16 = const()[name = string("audio_decoder_1_block_4_conv1_conv_bias_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105268288)))]; |
| tensor<fp16, [1, 768, 4000]> var_3060_cast_fp16 = conv(bias = audio_decoder_1_block_4_conv1_conv_bias_to_fp16, dilations = var_3060_dilations_0, groups = var_3060_groups_0, pad = var_3060_pad_0, pad_type = var_3060_pad_type_0, strides = var_3060_strides_0, weight = audio_decoder_1_block_4_conv1_conv_weight_to_fp16_palettized, x = input_143_cast_fp16)[name = string("op_3060_cast_fp16")]; |
| tensor<fp16, [1, 768, 1]> alpha_27_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1, 768, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105269888))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105270720))))[name = string("alpha_27_to_fp16_palettized")]; |
| tensor<fp16, [1, 768, 4000]> var_3075_cast_fp16 = mul(x = var_3060_cast_fp16, y = alpha_27_to_fp16_palettized)[name = string("op_3075_cast_fp16")]; |
| tensor<fp16, [1, 768, 4000]> var_3076_cast_fp16 = sin(x = var_3075_cast_fp16)[name = string("op_3076_cast_fp16")]; |
| fp16 var_2839_promoted_6_to_fp16 = const()[name = string("op_2839_promoted_6_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 768, 4000]> var_3077_cast_fp16 = pow(x = var_3076_cast_fp16, y = var_2839_promoted_6_to_fp16)[name = string("op_3077_cast_fp16")]; |
| tensor<fp16, [1, 768, 1]> op_3072_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1, 768, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105271296))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105272128))))[name = string("op_3072_to_fp16_palettized")]; |
| tensor<fp16, [1, 768, 4000]> var_3078_cast_fp16 = mul(x = op_3072_to_fp16_palettized, y = var_3077_cast_fp16)[name = string("op_3078_cast_fp16")]; |
| tensor<fp16, [1, 768, 4000]> hidden_state_31_cast_fp16 = add(x = var_3060_cast_fp16, y = var_3078_cast_fp16)[name = string("hidden_state_31_cast_fp16")]; |
| string var_3093_pad_type_0 = const()[name = string("op_3093_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> var_3093_strides_0 = const()[name = string("op_3093_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_3093_pad_0 = const()[name = string("op_3093_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> var_3093_dilations_0 = const()[name = string("op_3093_dilations_0"), val = tensor<int32, [1]>([1])]; |
| int32 var_3093_groups_0 = const()[name = string("op_3093_groups_0"), val = int32(1)]; |
| tensor<fp16, [768, 768, 1]> audio_decoder_1_block_4_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [768, 768, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105272704))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105862592))))[name = string("audio_decoder_1_block_4_conv2_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [768]> audio_decoder_1_block_4_conv2_conv_bias_to_fp16 = const()[name = string("audio_decoder_1_block_4_conv2_conv_bias_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105863168)))]; |
| tensor<fp16, [1, 768, 4000]> var_3093_cast_fp16 = conv(bias = audio_decoder_1_block_4_conv2_conv_bias_to_fp16, dilations = var_3093_dilations_0, groups = var_3093_groups_0, pad = var_3093_pad_0, pad_type = var_3093_pad_type_0, strides = var_3093_strides_0, weight = audio_decoder_1_block_4_conv2_conv_weight_to_fp16_palettized, x = hidden_state_31_cast_fp16)[name = string("op_3093_cast_fp16")]; |
| tensor<fp16, [1, 768, 4000]> hidden_states_31_cast_fp16 = add(x = var_3093_cast_fp16, y = hidden_states_27_cast_fp16)[name = string("hidden_states_31_cast_fp16")]; |
| tensor<fp16, [1, 768, 1]> alpha_31_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1, 768, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105864768))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105865600))))[name = string("alpha_31_to_fp16_palettized")]; |
| tensor<fp16, [1, 768, 4000]> var_3134_cast_fp16 = mul(x = hidden_states_31_cast_fp16, y = alpha_31_to_fp16_palettized)[name = string("op_3134_cast_fp16")]; |
| tensor<fp16, [1, 768, 4000]> var_3135_cast_fp16 = sin(x = var_3134_cast_fp16)[name = string("op_3135_cast_fp16")]; |
| fp16 var_3110_promoted_to_fp16 = const()[name = string("op_3110_promoted_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 768, 4000]> var_3136_cast_fp16 = pow(x = var_3135_cast_fp16, y = var_3110_promoted_to_fp16)[name = string("op_3136_cast_fp16")]; |
| tensor<fp16, [1, 768, 1]> op_3131_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1, 768, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105866176))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105867008))))[name = string("op_3131_to_fp16_palettized")]; |
| tensor<fp16, [1, 768, 4000]> var_3137_cast_fp16 = mul(x = op_3131_to_fp16_palettized, y = var_3136_cast_fp16)[name = string("op_3137_cast_fp16")]; |
| tensor<fp16, [1, 768, 4000]> input_147_cast_fp16 = add(x = hidden_states_31_cast_fp16, y = var_3137_cast_fp16)[name = string("input_147_cast_fp16")]; |
| string hidden_state_35_pad_type_0 = const()[name = string("hidden_state_35_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> hidden_state_35_strides_0 = const()[name = string("hidden_state_35_strides_0"), val = tensor<int32, [1]>([5])]; |
| tensor<int32, [2]> hidden_state_35_pad_0 = const()[name = string("hidden_state_35_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> hidden_state_35_dilations_0 = const()[name = string("hidden_state_35_dilations_0"), val = tensor<int32, [1]>([1])]; |
| int32 hidden_state_35_groups_0 = const()[name = string("hidden_state_35_groups_0"), val = int32(1)]; |
| tensor<int32, [3]> hidden_state_35_has_output_shape_output_shape_0 = const()[name = string("hidden_state_35_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 384, 20005])]; |
| tensor<fp16, [768, 384, 10]> audio_decoder_2_block_1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [768, 384, 10]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105867584))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(108816768))))[name = string("audio_decoder_2_block_1_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [384]> audio_decoder_2_block_1_conv_bias_to_fp16 = const()[name = string("audio_decoder_2_block_1_conv_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(108817344)))]; |
| tensor<fp16, [1, 384, 20005]> hidden_state_35_has_output_shape_cast_fp16 = conv_transpose(bias = audio_decoder_2_block_1_conv_bias_to_fp16, dilations = hidden_state_35_dilations_0, groups = hidden_state_35_groups_0, output_shape = hidden_state_35_has_output_shape_output_shape_0, pad = hidden_state_35_pad_0, pad_type = hidden_state_35_pad_type_0, strides = hidden_state_35_strides_0, weight = audio_decoder_2_block_1_conv_weight_to_fp16_palettized, x = input_147_cast_fp16)[name = string("hidden_state_35_has_output_shape_cast_fp16")]; |
| tensor<int32, [3]> hidden_state_37_begin_0 = const()[name = string("hidden_state_37_begin_0"), val = tensor<int32, [3]>([0, 0, 0])]; |
| tensor<int32, [3]> hidden_state_37_end_0 = const()[name = string("hidden_state_37_end_0"), val = tensor<int32, [3]>([1, 384, 20000])]; |
| tensor<bool, [3]> hidden_state_37_end_mask_0 = const()[name = string("hidden_state_37_end_mask_0"), val = tensor<bool, [3]>([true, true, false])]; |
| tensor<fp16, [1, 384, 20000]> hidden_state_37_cast_fp16 = slice_by_index(begin = hidden_state_37_begin_0, end = hidden_state_37_end_0, end_mask = hidden_state_37_end_mask_0, x = hidden_state_35_has_output_shape_cast_fp16)[name = string("hidden_state_37_cast_fp16")]; |
| tensor<fp16, [1, 384, 1]> alpha_35_to_fp16 = const()[name = string("alpha_35_to_fp16"), val = tensor<fp16, [1, 384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(108818176)))]; |
| tensor<fp16, [1, 384, 20000]> var_3171_cast_fp16 = mul(x = hidden_state_37_cast_fp16, y = alpha_35_to_fp16)[name = string("op_3171_cast_fp16")]; |
| tensor<fp16, [1, 384, 20000]> var_3172_cast_fp16 = sin(x = var_3171_cast_fp16)[name = string("op_3172_cast_fp16")]; |
| fp16 var_3110_promoted_1_to_fp16 = const()[name = string("op_3110_promoted_1_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 384, 20000]> var_3173_cast_fp16 = pow(x = var_3172_cast_fp16, y = var_3110_promoted_1_to_fp16)[name = string("op_3173_cast_fp16")]; |
| tensor<fp16, [1, 384, 1]> var_3168_to_fp16 = const()[name = string("op_3168_to_fp16"), val = tensor<fp16, [1, 384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(108819008)))]; |
| tensor<fp16, [1, 384, 20000]> var_3174_cast_fp16 = mul(x = var_3168_to_fp16, y = var_3173_cast_fp16)[name = string("op_3174_cast_fp16")]; |
| tensor<fp16, [1, 384, 20000]> hidden_state_39_cast_fp16 = add(x = hidden_state_37_cast_fp16, y = var_3174_cast_fp16)[name = string("hidden_state_39_cast_fp16")]; |
| tensor<int32, [6]> input_149_pad_0 = const()[name = string("input_149_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 6, 0])]; |
| string input_149_mode_0 = const()[name = string("input_149_mode_0"), val = string("constant")]; |
| fp16 const_128_to_fp16 = const()[name = string("const_128_to_fp16"), val = fp16(0x0p+0)]; |
| tensor<fp16, [1, 384, 20006]> input_149_cast_fp16 = pad(constant_val = const_128_to_fp16, mode = input_149_mode_0, pad = input_149_pad_0, x = hidden_state_39_cast_fp16)[name = string("input_149_cast_fp16")]; |
| string var_3189_pad_type_0 = const()[name = string("op_3189_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> var_3189_strides_0 = const()[name = string("op_3189_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_3189_pad_0 = const()[name = string("op_3189_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> var_3189_dilations_0 = const()[name = string("op_3189_dilations_0"), val = tensor<int32, [1]>([1])]; |
| int32 var_3189_groups_0 = const()[name = string("op_3189_groups_0"), val = int32(1)]; |
| tensor<fp16, [384, 384, 7]> audio_decoder_2_block_2_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [384, 384, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(108819840))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109852096))))[name = string("audio_decoder_2_block_2_conv1_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [384]> audio_decoder_2_block_2_conv1_conv_bias_to_fp16 = const()[name = string("audio_decoder_2_block_2_conv1_conv_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109852672)))]; |
| tensor<fp16, [1, 384, 20000]> var_3189_cast_fp16 = conv(bias = audio_decoder_2_block_2_conv1_conv_bias_to_fp16, dilations = var_3189_dilations_0, groups = var_3189_groups_0, pad = var_3189_pad_0, pad_type = var_3189_pad_type_0, strides = var_3189_strides_0, weight = audio_decoder_2_block_2_conv1_conv_weight_to_fp16_palettized, x = input_149_cast_fp16)[name = string("op_3189_cast_fp16")]; |
| tensor<fp16, [1, 384, 1]> alpha_39_to_fp16 = const()[name = string("alpha_39_to_fp16"), val = tensor<fp16, [1, 384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109853504)))]; |
| tensor<fp16, [1, 384, 20000]> var_3204_cast_fp16 = mul(x = var_3189_cast_fp16, y = alpha_39_to_fp16)[name = string("op_3204_cast_fp16")]; |
| tensor<fp16, [1, 384, 20000]> var_3205_cast_fp16 = sin(x = var_3204_cast_fp16)[name = string("op_3205_cast_fp16")]; |
| fp16 var_3110_promoted_2_to_fp16 = const()[name = string("op_3110_promoted_2_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 384, 20000]> var_3206_cast_fp16 = pow(x = var_3205_cast_fp16, y = var_3110_promoted_2_to_fp16)[name = string("op_3206_cast_fp16")]; |
| tensor<fp16, [1, 384, 1]> var_3201_to_fp16 = const()[name = string("op_3201_to_fp16"), val = tensor<fp16, [1, 384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109854336)))]; |
| tensor<fp16, [1, 384, 20000]> var_3207_cast_fp16 = mul(x = var_3201_to_fp16, y = var_3206_cast_fp16)[name = string("op_3207_cast_fp16")]; |
| tensor<fp16, [1, 384, 20000]> hidden_state_41_cast_fp16 = add(x = var_3189_cast_fp16, y = var_3207_cast_fp16)[name = string("hidden_state_41_cast_fp16")]; |
| string var_3222_pad_type_0 = const()[name = string("op_3222_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> var_3222_strides_0 = const()[name = string("op_3222_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_3222_pad_0 = const()[name = string("op_3222_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> var_3222_dilations_0 = const()[name = string("op_3222_dilations_0"), val = tensor<int32, [1]>([1])]; |
| int32 var_3222_groups_0 = const()[name = string("op_3222_groups_0"), val = int32(1)]; |
| tensor<fp16, [384, 384, 1]> audio_decoder_2_block_2_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [384, 384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109855168))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110002688))))[name = string("audio_decoder_2_block_2_conv2_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [384]> audio_decoder_2_block_2_conv2_conv_bias_to_fp16 = const()[name = string("audio_decoder_2_block_2_conv2_conv_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110003264)))]; |
| tensor<fp16, [1, 384, 20000]> var_3222_cast_fp16 = conv(bias = audio_decoder_2_block_2_conv2_conv_bias_to_fp16, dilations = var_3222_dilations_0, groups = var_3222_groups_0, pad = var_3222_pad_0, pad_type = var_3222_pad_type_0, strides = var_3222_strides_0, weight = audio_decoder_2_block_2_conv2_conv_weight_to_fp16_palettized, x = hidden_state_41_cast_fp16)[name = string("op_3222_cast_fp16")]; |
| tensor<fp16, [1, 384, 20000]> hidden_states_37_cast_fp16 = add(x = var_3222_cast_fp16, y = hidden_state_37_cast_fp16)[name = string("hidden_states_37_cast_fp16")]; |
| tensor<fp16, [1, 384, 1]> alpha_43_to_fp16 = const()[name = string("alpha_43_to_fp16"), val = tensor<fp16, [1, 384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110004096)))]; |
| tensor<fp16, [1, 384, 20000]> var_3242_cast_fp16 = mul(x = hidden_states_37_cast_fp16, y = alpha_43_to_fp16)[name = string("op_3242_cast_fp16")]; |
| tensor<fp16, [1, 384, 20000]> var_3243_cast_fp16 = sin(x = var_3242_cast_fp16)[name = string("op_3243_cast_fp16")]; |
| fp16 var_3110_promoted_3_to_fp16 = const()[name = string("op_3110_promoted_3_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 384, 20000]> var_3244_cast_fp16 = pow(x = var_3243_cast_fp16, y = var_3110_promoted_3_to_fp16)[name = string("op_3244_cast_fp16")]; |
| tensor<fp16, [1, 384, 1]> var_3239_to_fp16 = const()[name = string("op_3239_to_fp16"), val = tensor<fp16, [1, 384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110004928)))]; |
| tensor<fp16, [1, 384, 20000]> var_3245_cast_fp16 = mul(x = var_3239_to_fp16, y = var_3244_cast_fp16)[name = string("op_3245_cast_fp16")]; |
| tensor<fp16, [1, 384, 20000]> hidden_state_45_cast_fp16 = add(x = hidden_states_37_cast_fp16, y = var_3245_cast_fp16)[name = string("hidden_state_45_cast_fp16")]; |
| tensor<int32, [6]> input_153_pad_0 = const()[name = string("input_153_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 18, 0])]; |
| string input_153_mode_0 = const()[name = string("input_153_mode_0"), val = string("constant")]; |
| fp16 const_132_to_fp16 = const()[name = string("const_132_to_fp16"), val = fp16(0x0p+0)]; |
| tensor<fp16, [1, 384, 20018]> input_153_cast_fp16 = pad(constant_val = const_132_to_fp16, mode = input_153_mode_0, pad = input_153_pad_0, x = hidden_state_45_cast_fp16)[name = string("input_153_cast_fp16")]; |
| string var_3260_pad_type_0 = const()[name = string("op_3260_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> var_3260_dilations_0 = const()[name = string("op_3260_dilations_0"), val = tensor<int32, [1]>([3])]; |
| tensor<int32, [1]> var_3260_strides_0 = const()[name = string("op_3260_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_3260_pad_0 = const()[name = string("op_3260_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| int32 var_3260_groups_0 = const()[name = string("op_3260_groups_0"), val = int32(1)]; |
| tensor<fp16, [384, 384, 7]> audio_decoder_2_block_3_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [384, 384, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110005760))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111038016))))[name = string("audio_decoder_2_block_3_conv1_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [384]> audio_decoder_2_block_3_conv1_conv_bias_to_fp16 = const()[name = string("audio_decoder_2_block_3_conv1_conv_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111038592)))]; |
| tensor<fp16, [1, 384, 20000]> var_3260_cast_fp16 = conv(bias = audio_decoder_2_block_3_conv1_conv_bias_to_fp16, dilations = var_3260_dilations_0, groups = var_3260_groups_0, pad = var_3260_pad_0, pad_type = var_3260_pad_type_0, strides = var_3260_strides_0, weight = audio_decoder_2_block_3_conv1_conv_weight_to_fp16_palettized, x = input_153_cast_fp16)[name = string("op_3260_cast_fp16")]; |
| tensor<fp16, [1, 384, 1]> alpha_47_to_fp16 = const()[name = string("alpha_47_to_fp16"), val = tensor<fp16, [1, 384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111039424)))]; |
| tensor<fp16, [1, 384, 20000]> var_3275_cast_fp16 = mul(x = var_3260_cast_fp16, y = alpha_47_to_fp16)[name = string("op_3275_cast_fp16")]; |
| tensor<fp16, [1, 384, 20000]> var_3276_cast_fp16 = sin(x = var_3275_cast_fp16)[name = string("op_3276_cast_fp16")]; |
| fp16 var_3110_promoted_4_to_fp16 = const()[name = string("op_3110_promoted_4_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 384, 20000]> var_3277_cast_fp16 = pow(x = var_3276_cast_fp16, y = var_3110_promoted_4_to_fp16)[name = string("op_3277_cast_fp16")]; |
| tensor<fp16, [1, 384, 1]> var_3272_to_fp16 = const()[name = string("op_3272_to_fp16"), val = tensor<fp16, [1, 384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111040256)))]; |
| tensor<fp16, [1, 384, 20000]> var_3278_cast_fp16 = mul(x = var_3272_to_fp16, y = var_3277_cast_fp16)[name = string("op_3278_cast_fp16")]; |
| tensor<fp16, [1, 384, 20000]> hidden_state_47_cast_fp16 = add(x = var_3260_cast_fp16, y = var_3278_cast_fp16)[name = string("hidden_state_47_cast_fp16")]; |
| string var_3293_pad_type_0 = const()[name = string("op_3293_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> var_3293_strides_0 = const()[name = string("op_3293_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_3293_pad_0 = const()[name = string("op_3293_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> var_3293_dilations_0 = const()[name = string("op_3293_dilations_0"), val = tensor<int32, [1]>([1])]; |
| int32 var_3293_groups_0 = const()[name = string("op_3293_groups_0"), val = int32(1)]; |
| tensor<fp16, [384, 384, 1]> audio_decoder_2_block_3_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [384, 384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111041088))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111188608))))[name = string("audio_decoder_2_block_3_conv2_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [384]> audio_decoder_2_block_3_conv2_conv_bias_to_fp16 = const()[name = string("audio_decoder_2_block_3_conv2_conv_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111189184)))]; |
| tensor<fp16, [1, 384, 20000]> var_3293_cast_fp16 = conv(bias = audio_decoder_2_block_3_conv2_conv_bias_to_fp16, dilations = var_3293_dilations_0, groups = var_3293_groups_0, pad = var_3293_pad_0, pad_type = var_3293_pad_type_0, strides = var_3293_strides_0, weight = audio_decoder_2_block_3_conv2_conv_weight_to_fp16_palettized, x = hidden_state_47_cast_fp16)[name = string("op_3293_cast_fp16")]; |
| tensor<fp16, [1, 384, 20000]> hidden_states_41_cast_fp16 = add(x = var_3293_cast_fp16, y = hidden_states_37_cast_fp16)[name = string("hidden_states_41_cast_fp16")]; |
| tensor<fp16, [1, 384, 1]> alpha_51_to_fp16 = const()[name = string("alpha_51_to_fp16"), val = tensor<fp16, [1, 384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111190016)))]; |
| tensor<fp16, [1, 384, 20000]> var_3313_cast_fp16 = mul(x = hidden_states_41_cast_fp16, y = alpha_51_to_fp16)[name = string("op_3313_cast_fp16")]; |
| tensor<fp16, [1, 384, 20000]> var_3314_cast_fp16 = sin(x = var_3313_cast_fp16)[name = string("op_3314_cast_fp16")]; |
| fp16 var_3110_promoted_5_to_fp16 = const()[name = string("op_3110_promoted_5_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 384, 20000]> var_3315_cast_fp16 = pow(x = var_3314_cast_fp16, y = var_3110_promoted_5_to_fp16)[name = string("op_3315_cast_fp16")]; |
| tensor<fp16, [1, 384, 1]> var_3310_to_fp16 = const()[name = string("op_3310_to_fp16"), val = tensor<fp16, [1, 384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111190848)))]; |
| tensor<fp16, [1, 384, 20000]> var_3316_cast_fp16 = mul(x = var_3310_to_fp16, y = var_3315_cast_fp16)[name = string("op_3316_cast_fp16")]; |
| tensor<fp16, [1, 384, 20000]> hidden_state_51_cast_fp16 = add(x = hidden_states_41_cast_fp16, y = var_3316_cast_fp16)[name = string("hidden_state_51_cast_fp16")]; |
| tensor<int32, [6]> input_157_pad_0 = const()[name = string("input_157_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 54, 0])]; |
| string input_157_mode_0 = const()[name = string("input_157_mode_0"), val = string("constant")]; |
| fp16 const_136_to_fp16 = const()[name = string("const_136_to_fp16"), val = fp16(0x0p+0)]; |
| tensor<fp16, [1, 384, 20054]> input_157_cast_fp16 = pad(constant_val = const_136_to_fp16, mode = input_157_mode_0, pad = input_157_pad_0, x = hidden_state_51_cast_fp16)[name = string("input_157_cast_fp16")]; |
| string var_3331_pad_type_0 = const()[name = string("op_3331_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> var_3331_dilations_0 = const()[name = string("op_3331_dilations_0"), val = tensor<int32, [1]>([9])]; |
| tensor<int32, [1]> var_3331_strides_0 = const()[name = string("op_3331_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_3331_pad_0 = const()[name = string("op_3331_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| int32 var_3331_groups_0 = const()[name = string("op_3331_groups_0"), val = int32(1)]; |
| tensor<fp16, [384, 384, 7]> audio_decoder_2_block_4_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [384, 384, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111191680))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112223936))))[name = string("audio_decoder_2_block_4_conv1_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [384]> audio_decoder_2_block_4_conv1_conv_bias_to_fp16 = const()[name = string("audio_decoder_2_block_4_conv1_conv_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112224512)))]; |
| tensor<fp16, [1, 384, 20000]> var_3331_cast_fp16 = conv(bias = audio_decoder_2_block_4_conv1_conv_bias_to_fp16, dilations = var_3331_dilations_0, groups = var_3331_groups_0, pad = var_3331_pad_0, pad_type = var_3331_pad_type_0, strides = var_3331_strides_0, weight = audio_decoder_2_block_4_conv1_conv_weight_to_fp16_palettized, x = input_157_cast_fp16)[name = string("op_3331_cast_fp16")]; |
| tensor<fp16, [1, 384, 1]> alpha_55_to_fp16 = const()[name = string("alpha_55_to_fp16"), val = tensor<fp16, [1, 384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112225344)))]; |
| tensor<fp16, [1, 384, 20000]> var_3346_cast_fp16 = mul(x = var_3331_cast_fp16, y = alpha_55_to_fp16)[name = string("op_3346_cast_fp16")]; |
| tensor<fp16, [1, 384, 20000]> var_3347_cast_fp16 = sin(x = var_3346_cast_fp16)[name = string("op_3347_cast_fp16")]; |
| fp16 var_3110_promoted_6_to_fp16 = const()[name = string("op_3110_promoted_6_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 384, 20000]> var_3348_cast_fp16 = pow(x = var_3347_cast_fp16, y = var_3110_promoted_6_to_fp16)[name = string("op_3348_cast_fp16")]; |
| tensor<fp16, [1, 384, 1]> var_3343_to_fp16 = const()[name = string("op_3343_to_fp16"), val = tensor<fp16, [1, 384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112226176)))]; |
| tensor<fp16, [1, 384, 20000]> var_3349_cast_fp16 = mul(x = var_3343_to_fp16, y = var_3348_cast_fp16)[name = string("op_3349_cast_fp16")]; |
| tensor<fp16, [1, 384, 20000]> hidden_state_53_cast_fp16 = add(x = var_3331_cast_fp16, y = var_3349_cast_fp16)[name = string("hidden_state_53_cast_fp16")]; |
| string var_3364_pad_type_0 = const()[name = string("op_3364_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> var_3364_strides_0 = const()[name = string("op_3364_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_3364_pad_0 = const()[name = string("op_3364_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> var_3364_dilations_0 = const()[name = string("op_3364_dilations_0"), val = tensor<int32, [1]>([1])]; |
| int32 var_3364_groups_0 = const()[name = string("op_3364_groups_0"), val = int32(1)]; |
| tensor<fp16, [384, 384, 1]> audio_decoder_2_block_4_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [384, 384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112227008))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112374528))))[name = string("audio_decoder_2_block_4_conv2_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [384]> audio_decoder_2_block_4_conv2_conv_bias_to_fp16 = const()[name = string("audio_decoder_2_block_4_conv2_conv_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112375104)))]; |
| tensor<fp16, [1, 384, 20000]> var_3364_cast_fp16 = conv(bias = audio_decoder_2_block_4_conv2_conv_bias_to_fp16, dilations = var_3364_dilations_0, groups = var_3364_groups_0, pad = var_3364_pad_0, pad_type = var_3364_pad_type_0, strides = var_3364_strides_0, weight = audio_decoder_2_block_4_conv2_conv_weight_to_fp16_palettized, x = hidden_state_53_cast_fp16)[name = string("op_3364_cast_fp16")]; |
| tensor<fp16, [1, 384, 20000]> hidden_states_45_cast_fp16 = add(x = var_3364_cast_fp16, y = hidden_states_41_cast_fp16)[name = string("hidden_states_45_cast_fp16")]; |
| tensor<fp16, [1, 384, 1]> alpha_59_to_fp16 = const()[name = string("alpha_59_to_fp16"), val = tensor<fp16, [1, 384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112375936)))]; |
| tensor<fp16, [1, 384, 20000]> var_3405_cast_fp16 = mul(x = hidden_states_45_cast_fp16, y = alpha_59_to_fp16)[name = string("op_3405_cast_fp16")]; |
| tensor<fp16, [1, 384, 20000]> var_3406_cast_fp16 = sin(x = var_3405_cast_fp16)[name = string("op_3406_cast_fp16")]; |
| fp16 var_3381_promoted_to_fp16 = const()[name = string("op_3381_promoted_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 384, 20000]> var_3407_cast_fp16 = pow(x = var_3406_cast_fp16, y = var_3381_promoted_to_fp16)[name = string("op_3407_cast_fp16")]; |
| tensor<fp16, [1, 384, 1]> var_3402_to_fp16 = const()[name = string("op_3402_to_fp16"), val = tensor<fp16, [1, 384, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112376768)))]; |
| tensor<fp16, [1, 384, 20000]> var_3408_cast_fp16 = mul(x = var_3402_to_fp16, y = var_3407_cast_fp16)[name = string("op_3408_cast_fp16")]; |
| tensor<fp16, [1, 384, 20000]> input_161_cast_fp16 = add(x = hidden_states_45_cast_fp16, y = var_3408_cast_fp16)[name = string("input_161_cast_fp16")]; |
| string hidden_state_57_pad_type_0 = const()[name = string("hidden_state_57_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> hidden_state_57_strides_0 = const()[name = string("hidden_state_57_strides_0"), val = tensor<int32, [1]>([4])]; |
| tensor<int32, [2]> hidden_state_57_pad_0 = const()[name = string("hidden_state_57_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> hidden_state_57_dilations_0 = const()[name = string("hidden_state_57_dilations_0"), val = tensor<int32, [1]>([1])]; |
| int32 hidden_state_57_groups_0 = const()[name = string("hidden_state_57_groups_0"), val = int32(1)]; |
| tensor<int32, [3]> hidden_state_57_has_output_shape_output_shape_0 = const()[name = string("hidden_state_57_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 192, 80004])]; |
| tensor<fp16, [384, 192, 8]> audio_decoder_3_block_1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [384, 192, 8]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112377600))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112967488))))[name = string("audio_decoder_3_block_1_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [192]> audio_decoder_3_block_1_conv_bias_to_fp16 = const()[name = string("audio_decoder_3_block_1_conv_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112968064)))]; |
| tensor<fp16, [1, 192, 80004]> hidden_state_57_has_output_shape_cast_fp16 = conv_transpose(bias = audio_decoder_3_block_1_conv_bias_to_fp16, dilations = hidden_state_57_dilations_0, groups = hidden_state_57_groups_0, output_shape = hidden_state_57_has_output_shape_output_shape_0, pad = hidden_state_57_pad_0, pad_type = hidden_state_57_pad_type_0, strides = hidden_state_57_strides_0, weight = audio_decoder_3_block_1_conv_weight_to_fp16_palettized, x = input_161_cast_fp16)[name = string("hidden_state_57_has_output_shape_cast_fp16")]; |
| tensor<int32, [3]> hidden_state_59_begin_0 = const()[name = string("hidden_state_59_begin_0"), val = tensor<int32, [3]>([0, 0, 0])]; |
| tensor<int32, [3]> hidden_state_59_end_0 = const()[name = string("hidden_state_59_end_0"), val = tensor<int32, [3]>([1, 192, 80000])]; |
| tensor<bool, [3]> hidden_state_59_end_mask_0 = const()[name = string("hidden_state_59_end_mask_0"), val = tensor<bool, [3]>([true, true, false])]; |
| tensor<fp16, [1, 192, 80000]> hidden_state_59_cast_fp16 = slice_by_index(begin = hidden_state_59_begin_0, end = hidden_state_59_end_0, end_mask = hidden_state_59_end_mask_0, x = hidden_state_57_has_output_shape_cast_fp16)[name = string("hidden_state_59_cast_fp16")]; |
| tensor<fp16, [1, 192, 1]> alpha_63_to_fp16 = const()[name = string("alpha_63_to_fp16"), val = tensor<fp16, [1, 192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112968512)))]; |
| tensor<fp16, [1, 192, 80000]> var_3442_cast_fp16 = mul(x = hidden_state_59_cast_fp16, y = alpha_63_to_fp16)[name = string("op_3442_cast_fp16")]; |
| tensor<fp16, [1, 192, 80000]> var_3443_cast_fp16 = sin(x = var_3442_cast_fp16)[name = string("op_3443_cast_fp16")]; |
| fp16 var_3381_promoted_1_to_fp16 = const()[name = string("op_3381_promoted_1_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 192, 80000]> var_3444_cast_fp16 = pow(x = var_3443_cast_fp16, y = var_3381_promoted_1_to_fp16)[name = string("op_3444_cast_fp16")]; |
| tensor<fp16, [1, 192, 1]> var_3439_to_fp16 = const()[name = string("op_3439_to_fp16"), val = tensor<fp16, [1, 192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112968960)))]; |
| tensor<fp16, [1, 192, 80000]> var_3445_cast_fp16 = mul(x = var_3439_to_fp16, y = var_3444_cast_fp16)[name = string("op_3445_cast_fp16")]; |
| tensor<fp16, [1, 192, 80000]> hidden_state_61_cast_fp16 = add(x = hidden_state_59_cast_fp16, y = var_3445_cast_fp16)[name = string("hidden_state_61_cast_fp16")]; |
| tensor<int32, [6]> input_163_pad_0 = const()[name = string("input_163_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 6, 0])]; |
| string input_163_mode_0 = const()[name = string("input_163_mode_0"), val = string("constant")]; |
| fp16 const_141_to_fp16 = const()[name = string("const_141_to_fp16"), val = fp16(0x0p+0)]; |
| tensor<fp16, [1, 192, 80006]> input_163_cast_fp16 = pad(constant_val = const_141_to_fp16, mode = input_163_mode_0, pad = input_163_pad_0, x = hidden_state_61_cast_fp16)[name = string("input_163_cast_fp16")]; |
| string var_3460_pad_type_0 = const()[name = string("op_3460_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> var_3460_strides_0 = const()[name = string("op_3460_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_3460_pad_0 = const()[name = string("op_3460_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> var_3460_dilations_0 = const()[name = string("op_3460_dilations_0"), val = tensor<int32, [1]>([1])]; |
| int32 var_3460_groups_0 = const()[name = string("op_3460_groups_0"), val = int32(1)]; |
| tensor<fp16, [192, 192, 7]> audio_decoder_3_block_2_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [192, 192, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112969408))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113227520))))[name = string("audio_decoder_3_block_2_conv1_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [192]> audio_decoder_3_block_2_conv1_conv_bias_to_fp16 = const()[name = string("audio_decoder_3_block_2_conv1_conv_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113228096)))]; |
| tensor<fp16, [1, 192, 80000]> var_3460_cast_fp16 = conv(bias = audio_decoder_3_block_2_conv1_conv_bias_to_fp16, dilations = var_3460_dilations_0, groups = var_3460_groups_0, pad = var_3460_pad_0, pad_type = var_3460_pad_type_0, strides = var_3460_strides_0, weight = audio_decoder_3_block_2_conv1_conv_weight_to_fp16_palettized, x = input_163_cast_fp16)[name = string("op_3460_cast_fp16")]; |
| tensor<fp16, [1, 192, 1]> alpha_67_to_fp16 = const()[name = string("alpha_67_to_fp16"), val = tensor<fp16, [1, 192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113228544)))]; |
| tensor<fp16, [1, 192, 80000]> var_3475_cast_fp16 = mul(x = var_3460_cast_fp16, y = alpha_67_to_fp16)[name = string("op_3475_cast_fp16")]; |
| tensor<fp16, [1, 192, 80000]> var_3476_cast_fp16 = sin(x = var_3475_cast_fp16)[name = string("op_3476_cast_fp16")]; |
| fp16 var_3381_promoted_2_to_fp16 = const()[name = string("op_3381_promoted_2_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 192, 80000]> var_3477_cast_fp16 = pow(x = var_3476_cast_fp16, y = var_3381_promoted_2_to_fp16)[name = string("op_3477_cast_fp16")]; |
| tensor<fp16, [1, 192, 1]> var_3472_to_fp16 = const()[name = string("op_3472_to_fp16"), val = tensor<fp16, [1, 192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113228992)))]; |
| tensor<fp16, [1, 192, 80000]> var_3478_cast_fp16 = mul(x = var_3472_to_fp16, y = var_3477_cast_fp16)[name = string("op_3478_cast_fp16")]; |
| tensor<fp16, [1, 192, 80000]> hidden_state_63_cast_fp16 = add(x = var_3460_cast_fp16, y = var_3478_cast_fp16)[name = string("hidden_state_63_cast_fp16")]; |
| string var_3493_pad_type_0 = const()[name = string("op_3493_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> var_3493_strides_0 = const()[name = string("op_3493_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_3493_pad_0 = const()[name = string("op_3493_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> var_3493_dilations_0 = const()[name = string("op_3493_dilations_0"), val = tensor<int32, [1]>([1])]; |
| int32 var_3493_groups_0 = const()[name = string("op_3493_groups_0"), val = int32(1)]; |
| tensor<fp16, [192, 192, 1]> audio_decoder_3_block_2_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [192, 192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113229440))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113266368))))[name = string("audio_decoder_3_block_2_conv2_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [192]> audio_decoder_3_block_2_conv2_conv_bias_to_fp16 = const()[name = string("audio_decoder_3_block_2_conv2_conv_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113266944)))]; |
| tensor<fp16, [1, 192, 80000]> var_3493_cast_fp16 = conv(bias = audio_decoder_3_block_2_conv2_conv_bias_to_fp16, dilations = var_3493_dilations_0, groups = var_3493_groups_0, pad = var_3493_pad_0, pad_type = var_3493_pad_type_0, strides = var_3493_strides_0, weight = audio_decoder_3_block_2_conv2_conv_weight_to_fp16_palettized, x = hidden_state_63_cast_fp16)[name = string("op_3493_cast_fp16")]; |
| tensor<fp16, [1, 192, 80000]> hidden_states_51_cast_fp16 = add(x = var_3493_cast_fp16, y = hidden_state_59_cast_fp16)[name = string("hidden_states_51_cast_fp16")]; |
| tensor<fp16, [1, 192, 1]> alpha_71_to_fp16 = const()[name = string("alpha_71_to_fp16"), val = tensor<fp16, [1, 192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113267392)))]; |
| tensor<fp16, [1, 192, 80000]> var_3513_cast_fp16 = mul(x = hidden_states_51_cast_fp16, y = alpha_71_to_fp16)[name = string("op_3513_cast_fp16")]; |
| tensor<fp16, [1, 192, 80000]> var_3514_cast_fp16 = sin(x = var_3513_cast_fp16)[name = string("op_3514_cast_fp16")]; |
| fp16 var_3381_promoted_3_to_fp16 = const()[name = string("op_3381_promoted_3_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 192, 80000]> var_3515_cast_fp16 = pow(x = var_3514_cast_fp16, y = var_3381_promoted_3_to_fp16)[name = string("op_3515_cast_fp16")]; |
| tensor<fp16, [1, 192, 1]> var_3510_to_fp16 = const()[name = string("op_3510_to_fp16"), val = tensor<fp16, [1, 192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113267840)))]; |
| tensor<fp16, [1, 192, 80000]> var_3516_cast_fp16 = mul(x = var_3510_to_fp16, y = var_3515_cast_fp16)[name = string("op_3516_cast_fp16")]; |
| tensor<fp16, [1, 192, 80000]> hidden_state_67_cast_fp16 = add(x = hidden_states_51_cast_fp16, y = var_3516_cast_fp16)[name = string("hidden_state_67_cast_fp16")]; |
| tensor<int32, [6]> input_167_pad_0 = const()[name = string("input_167_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 18, 0])]; |
| string input_167_mode_0 = const()[name = string("input_167_mode_0"), val = string("constant")]; |
| fp16 const_145_to_fp16 = const()[name = string("const_145_to_fp16"), val = fp16(0x0p+0)]; |
| tensor<fp16, [1, 192, 80018]> input_167_cast_fp16 = pad(constant_val = const_145_to_fp16, mode = input_167_mode_0, pad = input_167_pad_0, x = hidden_state_67_cast_fp16)[name = string("input_167_cast_fp16")]; |
| string var_3531_pad_type_0 = const()[name = string("op_3531_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> var_3531_dilations_0 = const()[name = string("op_3531_dilations_0"), val = tensor<int32, [1]>([3])]; |
| tensor<int32, [1]> var_3531_strides_0 = const()[name = string("op_3531_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_3531_pad_0 = const()[name = string("op_3531_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| int32 var_3531_groups_0 = const()[name = string("op_3531_groups_0"), val = int32(1)]; |
| tensor<fp16, [192, 192, 7]> audio_decoder_3_block_3_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [192, 192, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113268288))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113526400))))[name = string("audio_decoder_3_block_3_conv1_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [192]> audio_decoder_3_block_3_conv1_conv_bias_to_fp16 = const()[name = string("audio_decoder_3_block_3_conv1_conv_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113526976)))]; |
| tensor<fp16, [1, 192, 80000]> var_3531_cast_fp16 = conv(bias = audio_decoder_3_block_3_conv1_conv_bias_to_fp16, dilations = var_3531_dilations_0, groups = var_3531_groups_0, pad = var_3531_pad_0, pad_type = var_3531_pad_type_0, strides = var_3531_strides_0, weight = audio_decoder_3_block_3_conv1_conv_weight_to_fp16_palettized, x = input_167_cast_fp16)[name = string("op_3531_cast_fp16")]; |
| tensor<fp16, [1, 192, 1]> alpha_75_to_fp16 = const()[name = string("alpha_75_to_fp16"), val = tensor<fp16, [1, 192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113527424)))]; |
| tensor<fp16, [1, 192, 80000]> var_3546_cast_fp16 = mul(x = var_3531_cast_fp16, y = alpha_75_to_fp16)[name = string("op_3546_cast_fp16")]; |
| tensor<fp16, [1, 192, 80000]> var_3547_cast_fp16 = sin(x = var_3546_cast_fp16)[name = string("op_3547_cast_fp16")]; |
| fp16 var_3381_promoted_4_to_fp16 = const()[name = string("op_3381_promoted_4_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 192, 80000]> var_3548_cast_fp16 = pow(x = var_3547_cast_fp16, y = var_3381_promoted_4_to_fp16)[name = string("op_3548_cast_fp16")]; |
| tensor<fp16, [1, 192, 1]> var_3543_to_fp16 = const()[name = string("op_3543_to_fp16"), val = tensor<fp16, [1, 192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113527872)))]; |
| tensor<fp16, [1, 192, 80000]> var_3549_cast_fp16 = mul(x = var_3543_to_fp16, y = var_3548_cast_fp16)[name = string("op_3549_cast_fp16")]; |
| tensor<fp16, [1, 192, 80000]> hidden_state_69_cast_fp16 = add(x = var_3531_cast_fp16, y = var_3549_cast_fp16)[name = string("hidden_state_69_cast_fp16")]; |
| string var_3564_pad_type_0 = const()[name = string("op_3564_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> var_3564_strides_0 = const()[name = string("op_3564_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_3564_pad_0 = const()[name = string("op_3564_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> var_3564_dilations_0 = const()[name = string("op_3564_dilations_0"), val = tensor<int32, [1]>([1])]; |
| int32 var_3564_groups_0 = const()[name = string("op_3564_groups_0"), val = int32(1)]; |
| tensor<fp16, [192, 192, 1]> audio_decoder_3_block_3_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [192, 192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113528320))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113565248))))[name = string("audio_decoder_3_block_3_conv2_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [192]> audio_decoder_3_block_3_conv2_conv_bias_to_fp16 = const()[name = string("audio_decoder_3_block_3_conv2_conv_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113565824)))]; |
| tensor<fp16, [1, 192, 80000]> var_3564_cast_fp16 = conv(bias = audio_decoder_3_block_3_conv2_conv_bias_to_fp16, dilations = var_3564_dilations_0, groups = var_3564_groups_0, pad = var_3564_pad_0, pad_type = var_3564_pad_type_0, strides = var_3564_strides_0, weight = audio_decoder_3_block_3_conv2_conv_weight_to_fp16_palettized, x = hidden_state_69_cast_fp16)[name = string("op_3564_cast_fp16")]; |
| tensor<fp16, [1, 192, 80000]> hidden_states_55_cast_fp16 = add(x = var_3564_cast_fp16, y = hidden_states_51_cast_fp16)[name = string("hidden_states_55_cast_fp16")]; |
| tensor<fp16, [1, 192, 1]> alpha_79_to_fp16 = const()[name = string("alpha_79_to_fp16"), val = tensor<fp16, [1, 192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113566272)))]; |
| tensor<fp16, [1, 192, 80000]> var_3584_cast_fp16 = mul(x = hidden_states_55_cast_fp16, y = alpha_79_to_fp16)[name = string("op_3584_cast_fp16")]; |
| tensor<fp16, [1, 192, 80000]> var_3585_cast_fp16 = sin(x = var_3584_cast_fp16)[name = string("op_3585_cast_fp16")]; |
| fp16 var_3381_promoted_5_to_fp16 = const()[name = string("op_3381_promoted_5_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 192, 80000]> var_3586_cast_fp16 = pow(x = var_3585_cast_fp16, y = var_3381_promoted_5_to_fp16)[name = string("op_3586_cast_fp16")]; |
| tensor<fp16, [1, 192, 1]> var_3581_to_fp16 = const()[name = string("op_3581_to_fp16"), val = tensor<fp16, [1, 192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113566720)))]; |
| tensor<fp16, [1, 192, 80000]> var_3587_cast_fp16 = mul(x = var_3581_to_fp16, y = var_3586_cast_fp16)[name = string("op_3587_cast_fp16")]; |
| tensor<fp16, [1, 192, 80000]> hidden_state_73_cast_fp16 = add(x = hidden_states_55_cast_fp16, y = var_3587_cast_fp16)[name = string("hidden_state_73_cast_fp16")]; |
| tensor<int32, [6]> input_171_pad_0 = const()[name = string("input_171_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 54, 0])]; |
| string input_171_mode_0 = const()[name = string("input_171_mode_0"), val = string("constant")]; |
| fp16 const_149_to_fp16 = const()[name = string("const_149_to_fp16"), val = fp16(0x0p+0)]; |
| tensor<fp16, [1, 192, 80054]> input_171_cast_fp16 = pad(constant_val = const_149_to_fp16, mode = input_171_mode_0, pad = input_171_pad_0, x = hidden_state_73_cast_fp16)[name = string("input_171_cast_fp16")]; |
| string var_3602_pad_type_0 = const()[name = string("op_3602_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> var_3602_dilations_0 = const()[name = string("op_3602_dilations_0"), val = tensor<int32, [1]>([9])]; |
| tensor<int32, [1]> var_3602_strides_0 = const()[name = string("op_3602_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_3602_pad_0 = const()[name = string("op_3602_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| int32 var_3602_groups_0 = const()[name = string("op_3602_groups_0"), val = int32(1)]; |
| tensor<fp16, [192, 192, 7]> audio_decoder_3_block_4_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [192, 192, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113567168))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113825280))))[name = string("audio_decoder_3_block_4_conv1_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [192]> audio_decoder_3_block_4_conv1_conv_bias_to_fp16 = const()[name = string("audio_decoder_3_block_4_conv1_conv_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113825856)))]; |
| tensor<fp16, [1, 192, 80000]> var_3602_cast_fp16 = conv(bias = audio_decoder_3_block_4_conv1_conv_bias_to_fp16, dilations = var_3602_dilations_0, groups = var_3602_groups_0, pad = var_3602_pad_0, pad_type = var_3602_pad_type_0, strides = var_3602_strides_0, weight = audio_decoder_3_block_4_conv1_conv_weight_to_fp16_palettized, x = input_171_cast_fp16)[name = string("op_3602_cast_fp16")]; |
| tensor<fp16, [1, 192, 1]> alpha_83_to_fp16 = const()[name = string("alpha_83_to_fp16"), val = tensor<fp16, [1, 192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113826304)))]; |
| tensor<fp16, [1, 192, 80000]> var_3617_cast_fp16 = mul(x = var_3602_cast_fp16, y = alpha_83_to_fp16)[name = string("op_3617_cast_fp16")]; |
| tensor<fp16, [1, 192, 80000]> var_3618_cast_fp16 = sin(x = var_3617_cast_fp16)[name = string("op_3618_cast_fp16")]; |
| fp16 var_3381_promoted_6_to_fp16 = const()[name = string("op_3381_promoted_6_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 192, 80000]> var_3619_cast_fp16 = pow(x = var_3618_cast_fp16, y = var_3381_promoted_6_to_fp16)[name = string("op_3619_cast_fp16")]; |
| tensor<fp16, [1, 192, 1]> var_3614_to_fp16 = const()[name = string("op_3614_to_fp16"), val = tensor<fp16, [1, 192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113826752)))]; |
| tensor<fp16, [1, 192, 80000]> var_3620_cast_fp16 = mul(x = var_3614_to_fp16, y = var_3619_cast_fp16)[name = string("op_3620_cast_fp16")]; |
| tensor<fp16, [1, 192, 80000]> hidden_state_75_cast_fp16 = add(x = var_3602_cast_fp16, y = var_3620_cast_fp16)[name = string("hidden_state_75_cast_fp16")]; |
| string var_3635_pad_type_0 = const()[name = string("op_3635_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> var_3635_strides_0 = const()[name = string("op_3635_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_3635_pad_0 = const()[name = string("op_3635_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> var_3635_dilations_0 = const()[name = string("op_3635_dilations_0"), val = tensor<int32, [1]>([1])]; |
| int32 var_3635_groups_0 = const()[name = string("op_3635_groups_0"), val = int32(1)]; |
| tensor<fp16, [192, 192, 1]> audio_decoder_3_block_4_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [192, 192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113827200))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113864128))))[name = string("audio_decoder_3_block_4_conv2_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [192]> audio_decoder_3_block_4_conv2_conv_bias_to_fp16 = const()[name = string("audio_decoder_3_block_4_conv2_conv_bias_to_fp16"), val = tensor<fp16, [192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113864704)))]; |
| tensor<fp16, [1, 192, 80000]> var_3635_cast_fp16 = conv(bias = audio_decoder_3_block_4_conv2_conv_bias_to_fp16, dilations = var_3635_dilations_0, groups = var_3635_groups_0, pad = var_3635_pad_0, pad_type = var_3635_pad_type_0, strides = var_3635_strides_0, weight = audio_decoder_3_block_4_conv2_conv_weight_to_fp16_palettized, x = hidden_state_75_cast_fp16)[name = string("op_3635_cast_fp16")]; |
| tensor<fp16, [1, 192, 80000]> hidden_states_59_cast_fp16 = add(x = var_3635_cast_fp16, y = hidden_states_55_cast_fp16)[name = string("hidden_states_59_cast_fp16")]; |
| tensor<fp16, [1, 192, 1]> alpha_87_to_fp16 = const()[name = string("alpha_87_to_fp16"), val = tensor<fp16, [1, 192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113865152)))]; |
| tensor<fp16, [1, 192, 80000]> var_3675_cast_fp16 = mul(x = hidden_states_59_cast_fp16, y = alpha_87_to_fp16)[name = string("op_3675_cast_fp16")]; |
| tensor<fp16, [1, 192, 80000]> var_3676_cast_fp16 = sin(x = var_3675_cast_fp16)[name = string("op_3676_cast_fp16")]; |
| fp16 var_3651_promoted_to_fp16 = const()[name = string("op_3651_promoted_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 192, 80000]> var_3677_cast_fp16 = pow(x = var_3676_cast_fp16, y = var_3651_promoted_to_fp16)[name = string("op_3677_cast_fp16")]; |
| tensor<fp16, [1, 192, 1]> var_3672_to_fp16 = const()[name = string("op_3672_to_fp16"), val = tensor<fp16, [1, 192, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113865600)))]; |
| tensor<fp16, [1, 192, 80000]> var_3678_cast_fp16 = mul(x = var_3672_to_fp16, y = var_3677_cast_fp16)[name = string("op_3678_cast_fp16")]; |
| tensor<fp16, [1, 192, 80000]> input_175_cast_fp16 = add(x = hidden_states_59_cast_fp16, y = var_3678_cast_fp16)[name = string("input_175_cast_fp16")]; |
| string hidden_state_79_pad_type_0 = const()[name = string("hidden_state_79_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> hidden_state_79_strides_0 = const()[name = string("hidden_state_79_strides_0"), val = tensor<int32, [1]>([3])]; |
| tensor<int32, [2]> hidden_state_79_pad_0 = const()[name = string("hidden_state_79_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> hidden_state_79_dilations_0 = const()[name = string("hidden_state_79_dilations_0"), val = tensor<int32, [1]>([1])]; |
| int32 hidden_state_79_groups_0 = const()[name = string("hidden_state_79_groups_0"), val = int32(1)]; |
| tensor<int32, [3]> hidden_state_79_has_output_shape_output_shape_0 = const()[name = string("hidden_state_79_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 96, 240003])]; |
| tensor<fp16, [192, 96, 6]> audio_decoder_4_block_1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [192, 96, 6]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113866048))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113976704))))[name = string("audio_decoder_4_block_1_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [96]> audio_decoder_4_block_1_conv_bias_to_fp16 = const()[name = string("audio_decoder_4_block_1_conv_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113977280)))]; |
| tensor<fp16, [1, 96, 240003]> hidden_state_79_has_output_shape_cast_fp16 = conv_transpose(bias = audio_decoder_4_block_1_conv_bias_to_fp16, dilations = hidden_state_79_dilations_0, groups = hidden_state_79_groups_0, output_shape = hidden_state_79_has_output_shape_output_shape_0, pad = hidden_state_79_pad_0, pad_type = hidden_state_79_pad_type_0, strides = hidden_state_79_strides_0, weight = audio_decoder_4_block_1_conv_weight_to_fp16_palettized, x = input_175_cast_fp16)[name = string("hidden_state_79_has_output_shape_cast_fp16")]; |
| tensor<int32, [3]> hidden_state_81_begin_0 = const()[name = string("hidden_state_81_begin_0"), val = tensor<int32, [3]>([0, 0, 0])]; |
| tensor<int32, [3]> hidden_state_81_end_0 = const()[name = string("hidden_state_81_end_0"), val = tensor<int32, [3]>([1, 96, 240000])]; |
| tensor<bool, [3]> hidden_state_81_end_mask_0 = const()[name = string("hidden_state_81_end_mask_0"), val = tensor<bool, [3]>([true, true, false])]; |
| tensor<fp16, [1, 96, 240000]> hidden_state_81_cast_fp16 = slice_by_index(begin = hidden_state_81_begin_0, end = hidden_state_81_end_0, end_mask = hidden_state_81_end_mask_0, x = hidden_state_79_has_output_shape_cast_fp16)[name = string("hidden_state_81_cast_fp16")]; |
| tensor<fp16, [1, 96, 1]> alpha_91_to_fp16 = const()[name = string("alpha_91_to_fp16"), val = tensor<fp16, [1, 96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113977536)))]; |
| tensor<fp16, [1, 96, 240000]> var_3712_cast_fp16 = mul(x = hidden_state_81_cast_fp16, y = alpha_91_to_fp16)[name = string("op_3712_cast_fp16")]; |
| tensor<fp16, [1, 96, 240000]> var_3713_cast_fp16 = sin(x = var_3712_cast_fp16)[name = string("op_3713_cast_fp16")]; |
| fp16 var_3651_promoted_1_to_fp16 = const()[name = string("op_3651_promoted_1_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 96, 240000]> var_3714_cast_fp16 = pow(x = var_3713_cast_fp16, y = var_3651_promoted_1_to_fp16)[name = string("op_3714_cast_fp16")]; |
| tensor<fp16, [1, 96, 1]> var_3709_to_fp16 = const()[name = string("op_3709_to_fp16"), val = tensor<fp16, [1, 96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113977792)))]; |
| tensor<fp16, [1, 96, 240000]> var_3715_cast_fp16 = mul(x = var_3709_to_fp16, y = var_3714_cast_fp16)[name = string("op_3715_cast_fp16")]; |
| tensor<fp16, [1, 96, 240000]> hidden_state_83_cast_fp16 = add(x = hidden_state_81_cast_fp16, y = var_3715_cast_fp16)[name = string("hidden_state_83_cast_fp16")]; |
| tensor<int32, [6]> input_177_pad_0 = const()[name = string("input_177_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 6, 0])]; |
| string input_177_mode_0 = const()[name = string("input_177_mode_0"), val = string("constant")]; |
| fp16 const_154_to_fp16 = const()[name = string("const_154_to_fp16"), val = fp16(0x0p+0)]; |
| tensor<fp16, [1, 96, 240006]> input_177_cast_fp16 = pad(constant_val = const_154_to_fp16, mode = input_177_mode_0, pad = input_177_pad_0, x = hidden_state_83_cast_fp16)[name = string("input_177_cast_fp16")]; |
| string var_3730_pad_type_0 = const()[name = string("op_3730_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> var_3730_strides_0 = const()[name = string("op_3730_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_3730_pad_0 = const()[name = string("op_3730_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> var_3730_dilations_0 = const()[name = string("op_3730_dilations_0"), val = tensor<int32, [1]>([1])]; |
| int32 var_3730_groups_0 = const()[name = string("op_3730_groups_0"), val = int32(1)]; |
| tensor<fp16, [96, 96, 7]> audio_decoder_4_block_2_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [96, 96, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113978048))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114042624))))[name = string("audio_decoder_4_block_2_conv1_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [96]> audio_decoder_4_block_2_conv1_conv_bias_to_fp16 = const()[name = string("audio_decoder_4_block_2_conv1_conv_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114043200)))]; |
| tensor<fp16, [1, 96, 240000]> var_3730_cast_fp16 = conv(bias = audio_decoder_4_block_2_conv1_conv_bias_to_fp16, dilations = var_3730_dilations_0, groups = var_3730_groups_0, pad = var_3730_pad_0, pad_type = var_3730_pad_type_0, strides = var_3730_strides_0, weight = audio_decoder_4_block_2_conv1_conv_weight_to_fp16_palettized, x = input_177_cast_fp16)[name = string("op_3730_cast_fp16")]; |
| tensor<fp16, [1, 96, 1]> alpha_95_to_fp16 = const()[name = string("alpha_95_to_fp16"), val = tensor<fp16, [1, 96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114043456)))]; |
| tensor<fp16, [1, 96, 240000]> var_3745_cast_fp16 = mul(x = var_3730_cast_fp16, y = alpha_95_to_fp16)[name = string("op_3745_cast_fp16")]; |
| tensor<fp16, [1, 96, 240000]> var_3746_cast_fp16 = sin(x = var_3745_cast_fp16)[name = string("op_3746_cast_fp16")]; |
| fp16 var_3651_promoted_2_to_fp16 = const()[name = string("op_3651_promoted_2_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 96, 240000]> var_3747_cast_fp16 = pow(x = var_3746_cast_fp16, y = var_3651_promoted_2_to_fp16)[name = string("op_3747_cast_fp16")]; |
| tensor<fp16, [1, 96, 1]> var_3742_to_fp16 = const()[name = string("op_3742_to_fp16"), val = tensor<fp16, [1, 96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114043712)))]; |
| tensor<fp16, [1, 96, 240000]> var_3748_cast_fp16 = mul(x = var_3742_to_fp16, y = var_3747_cast_fp16)[name = string("op_3748_cast_fp16")]; |
| tensor<fp16, [1, 96, 240000]> hidden_state_85_cast_fp16 = add(x = var_3730_cast_fp16, y = var_3748_cast_fp16)[name = string("hidden_state_85_cast_fp16")]; |
| string var_3763_pad_type_0 = const()[name = string("op_3763_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> var_3763_strides_0 = const()[name = string("op_3763_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_3763_pad_0 = const()[name = string("op_3763_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> var_3763_dilations_0 = const()[name = string("op_3763_dilations_0"), val = tensor<int32, [1]>([1])]; |
| int32 var_3763_groups_0 = const()[name = string("op_3763_groups_0"), val = int32(1)]; |
| tensor<fp16, [96, 96, 1]> audio_decoder_4_block_2_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [96, 96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114043968))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114053248))))[name = string("audio_decoder_4_block_2_conv2_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [96]> audio_decoder_4_block_2_conv2_conv_bias_to_fp16 = const()[name = string("audio_decoder_4_block_2_conv2_conv_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114053824)))]; |
| tensor<fp16, [1, 96, 240000]> var_3763_cast_fp16 = conv(bias = audio_decoder_4_block_2_conv2_conv_bias_to_fp16, dilations = var_3763_dilations_0, groups = var_3763_groups_0, pad = var_3763_pad_0, pad_type = var_3763_pad_type_0, strides = var_3763_strides_0, weight = audio_decoder_4_block_2_conv2_conv_weight_to_fp16_palettized, x = hidden_state_85_cast_fp16)[name = string("op_3763_cast_fp16")]; |
| tensor<fp16, [1, 96, 240000]> hidden_states_65_cast_fp16 = add(x = var_3763_cast_fp16, y = hidden_state_81_cast_fp16)[name = string("hidden_states_65_cast_fp16")]; |
| tensor<fp16, [1, 96, 1]> alpha_99_to_fp16 = const()[name = string("alpha_99_to_fp16"), val = tensor<fp16, [1, 96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114054080)))]; |
| tensor<fp16, [1, 96, 240000]> var_3783_cast_fp16 = mul(x = hidden_states_65_cast_fp16, y = alpha_99_to_fp16)[name = string("op_3783_cast_fp16")]; |
| tensor<fp16, [1, 96, 240000]> var_3784_cast_fp16 = sin(x = var_3783_cast_fp16)[name = string("op_3784_cast_fp16")]; |
| fp16 var_3651_promoted_3_to_fp16 = const()[name = string("op_3651_promoted_3_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 96, 240000]> var_3785_cast_fp16 = pow(x = var_3784_cast_fp16, y = var_3651_promoted_3_to_fp16)[name = string("op_3785_cast_fp16")]; |
| tensor<fp16, [1, 96, 1]> var_3780_to_fp16 = const()[name = string("op_3780_to_fp16"), val = tensor<fp16, [1, 96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114054336)))]; |
| tensor<fp16, [1, 96, 240000]> var_3786_cast_fp16 = mul(x = var_3780_to_fp16, y = var_3785_cast_fp16)[name = string("op_3786_cast_fp16")]; |
| tensor<fp16, [1, 96, 240000]> hidden_state_89_cast_fp16 = add(x = hidden_states_65_cast_fp16, y = var_3786_cast_fp16)[name = string("hidden_state_89_cast_fp16")]; |
| tensor<int32, [6]> input_181_pad_0 = const()[name = string("input_181_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 18, 0])]; |
| string input_181_mode_0 = const()[name = string("input_181_mode_0"), val = string("constant")]; |
| fp16 const_158_to_fp16 = const()[name = string("const_158_to_fp16"), val = fp16(0x0p+0)]; |
| tensor<fp16, [1, 96, 240018]> input_181_cast_fp16 = pad(constant_val = const_158_to_fp16, mode = input_181_mode_0, pad = input_181_pad_0, x = hidden_state_89_cast_fp16)[name = string("input_181_cast_fp16")]; |
| string var_3801_pad_type_0 = const()[name = string("op_3801_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> var_3801_dilations_0 = const()[name = string("op_3801_dilations_0"), val = tensor<int32, [1]>([3])]; |
| tensor<int32, [1]> var_3801_strides_0 = const()[name = string("op_3801_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_3801_pad_0 = const()[name = string("op_3801_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| int32 var_3801_groups_0 = const()[name = string("op_3801_groups_0"), val = int32(1)]; |
| tensor<fp16, [96, 96, 7]> audio_decoder_4_block_3_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [96, 96, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114054592))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114119168))))[name = string("audio_decoder_4_block_3_conv1_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [96]> audio_decoder_4_block_3_conv1_conv_bias_to_fp16 = const()[name = string("audio_decoder_4_block_3_conv1_conv_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114119744)))]; |
| tensor<fp16, [1, 96, 240000]> var_3801_cast_fp16 = conv(bias = audio_decoder_4_block_3_conv1_conv_bias_to_fp16, dilations = var_3801_dilations_0, groups = var_3801_groups_0, pad = var_3801_pad_0, pad_type = var_3801_pad_type_0, strides = var_3801_strides_0, weight = audio_decoder_4_block_3_conv1_conv_weight_to_fp16_palettized, x = input_181_cast_fp16)[name = string("op_3801_cast_fp16")]; |
| tensor<fp16, [1, 96, 1]> alpha_103_to_fp16 = const()[name = string("alpha_103_to_fp16"), val = tensor<fp16, [1, 96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114120000)))]; |
| tensor<fp16, [1, 96, 240000]> var_3816_cast_fp16 = mul(x = var_3801_cast_fp16, y = alpha_103_to_fp16)[name = string("op_3816_cast_fp16")]; |
| tensor<fp16, [1, 96, 240000]> var_3817_cast_fp16 = sin(x = var_3816_cast_fp16)[name = string("op_3817_cast_fp16")]; |
| fp16 var_3651_promoted_4_to_fp16 = const()[name = string("op_3651_promoted_4_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 96, 240000]> var_3818_cast_fp16 = pow(x = var_3817_cast_fp16, y = var_3651_promoted_4_to_fp16)[name = string("op_3818_cast_fp16")]; |
| tensor<fp16, [1, 96, 1]> var_3813_to_fp16 = const()[name = string("op_3813_to_fp16"), val = tensor<fp16, [1, 96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114120256)))]; |
| tensor<fp16, [1, 96, 240000]> var_3819_cast_fp16 = mul(x = var_3813_to_fp16, y = var_3818_cast_fp16)[name = string("op_3819_cast_fp16")]; |
| tensor<fp16, [1, 96, 240000]> hidden_state_91_cast_fp16 = add(x = var_3801_cast_fp16, y = var_3819_cast_fp16)[name = string("hidden_state_91_cast_fp16")]; |
| string var_3834_pad_type_0 = const()[name = string("op_3834_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> var_3834_strides_0 = const()[name = string("op_3834_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_3834_pad_0 = const()[name = string("op_3834_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> var_3834_dilations_0 = const()[name = string("op_3834_dilations_0"), val = tensor<int32, [1]>([1])]; |
| int32 var_3834_groups_0 = const()[name = string("op_3834_groups_0"), val = int32(1)]; |
| tensor<fp16, [96, 96, 1]> audio_decoder_4_block_3_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [96, 96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114120512))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114129792))))[name = string("audio_decoder_4_block_3_conv2_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [96]> audio_decoder_4_block_3_conv2_conv_bias_to_fp16 = const()[name = string("audio_decoder_4_block_3_conv2_conv_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114130368)))]; |
| tensor<fp16, [1, 96, 240000]> var_3834_cast_fp16 = conv(bias = audio_decoder_4_block_3_conv2_conv_bias_to_fp16, dilations = var_3834_dilations_0, groups = var_3834_groups_0, pad = var_3834_pad_0, pad_type = var_3834_pad_type_0, strides = var_3834_strides_0, weight = audio_decoder_4_block_3_conv2_conv_weight_to_fp16_palettized, x = hidden_state_91_cast_fp16)[name = string("op_3834_cast_fp16")]; |
| tensor<fp16, [1, 96, 240000]> hidden_states_69_cast_fp16 = add(x = var_3834_cast_fp16, y = hidden_states_65_cast_fp16)[name = string("hidden_states_69_cast_fp16")]; |
| tensor<fp16, [1, 96, 1]> alpha_107_to_fp16 = const()[name = string("alpha_107_to_fp16"), val = tensor<fp16, [1, 96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114130624)))]; |
| tensor<fp16, [1, 96, 240000]> var_3854_cast_fp16 = mul(x = hidden_states_69_cast_fp16, y = alpha_107_to_fp16)[name = string("op_3854_cast_fp16")]; |
| tensor<fp16, [1, 96, 240000]> var_3855_cast_fp16 = sin(x = var_3854_cast_fp16)[name = string("op_3855_cast_fp16")]; |
| fp16 var_3651_promoted_5_to_fp16 = const()[name = string("op_3651_promoted_5_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 96, 240000]> var_3856_cast_fp16 = pow(x = var_3855_cast_fp16, y = var_3651_promoted_5_to_fp16)[name = string("op_3856_cast_fp16")]; |
| tensor<fp16, [1, 96, 1]> var_3851_to_fp16 = const()[name = string("op_3851_to_fp16"), val = tensor<fp16, [1, 96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114130880)))]; |
| tensor<fp16, [1, 96, 240000]> var_3857_cast_fp16 = mul(x = var_3851_to_fp16, y = var_3856_cast_fp16)[name = string("op_3857_cast_fp16")]; |
| tensor<fp16, [1, 96, 240000]> hidden_state_95_cast_fp16 = add(x = hidden_states_69_cast_fp16, y = var_3857_cast_fp16)[name = string("hidden_state_95_cast_fp16")]; |
| tensor<int32, [6]> input_185_pad_0 = const()[name = string("input_185_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 54, 0])]; |
| string input_185_mode_0 = const()[name = string("input_185_mode_0"), val = string("constant")]; |
| fp16 const_162_to_fp16 = const()[name = string("const_162_to_fp16"), val = fp16(0x0p+0)]; |
| tensor<fp16, [1, 96, 240054]> input_185_cast_fp16 = pad(constant_val = const_162_to_fp16, mode = input_185_mode_0, pad = input_185_pad_0, x = hidden_state_95_cast_fp16)[name = string("input_185_cast_fp16")]; |
| string var_3872_pad_type_0 = const()[name = string("op_3872_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> var_3872_dilations_0 = const()[name = string("op_3872_dilations_0"), val = tensor<int32, [1]>([9])]; |
| tensor<int32, [1]> var_3872_strides_0 = const()[name = string("op_3872_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_3872_pad_0 = const()[name = string("op_3872_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| int32 var_3872_groups_0 = const()[name = string("op_3872_groups_0"), val = int32(1)]; |
| tensor<fp16, [96, 96, 7]> audio_decoder_4_block_4_conv1_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [96, 96, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114131136))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114195712))))[name = string("audio_decoder_4_block_4_conv1_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [96]> audio_decoder_4_block_4_conv1_conv_bias_to_fp16 = const()[name = string("audio_decoder_4_block_4_conv1_conv_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114196288)))]; |
| tensor<fp16, [1, 96, 240000]> var_3872_cast_fp16 = conv(bias = audio_decoder_4_block_4_conv1_conv_bias_to_fp16, dilations = var_3872_dilations_0, groups = var_3872_groups_0, pad = var_3872_pad_0, pad_type = var_3872_pad_type_0, strides = var_3872_strides_0, weight = audio_decoder_4_block_4_conv1_conv_weight_to_fp16_palettized, x = input_185_cast_fp16)[name = string("op_3872_cast_fp16")]; |
| tensor<fp16, [1, 96, 1]> alpha_111_to_fp16 = const()[name = string("alpha_111_to_fp16"), val = tensor<fp16, [1, 96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114196544)))]; |
| tensor<fp16, [1, 96, 240000]> var_3887_cast_fp16 = mul(x = var_3872_cast_fp16, y = alpha_111_to_fp16)[name = string("op_3887_cast_fp16")]; |
| tensor<fp16, [1, 96, 240000]> var_3888_cast_fp16 = sin(x = var_3887_cast_fp16)[name = string("op_3888_cast_fp16")]; |
| fp16 var_3651_promoted_6_to_fp16 = const()[name = string("op_3651_promoted_6_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 96, 240000]> var_3889_cast_fp16 = pow(x = var_3888_cast_fp16, y = var_3651_promoted_6_to_fp16)[name = string("op_3889_cast_fp16")]; |
| tensor<fp16, [1, 96, 1]> var_3884_to_fp16 = const()[name = string("op_3884_to_fp16"), val = tensor<fp16, [1, 96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114196800)))]; |
| tensor<fp16, [1, 96, 240000]> var_3890_cast_fp16 = mul(x = var_3884_to_fp16, y = var_3889_cast_fp16)[name = string("op_3890_cast_fp16")]; |
| tensor<fp16, [1, 96, 240000]> hidden_state_97_cast_fp16 = add(x = var_3872_cast_fp16, y = var_3890_cast_fp16)[name = string("hidden_state_97_cast_fp16")]; |
| string var_3905_pad_type_0 = const()[name = string("op_3905_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> var_3905_strides_0 = const()[name = string("op_3905_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_3905_pad_0 = const()[name = string("op_3905_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> var_3905_dilations_0 = const()[name = string("op_3905_dilations_0"), val = tensor<int32, [1]>([1])]; |
| int32 var_3905_groups_0 = const()[name = string("op_3905_groups_0"), val = int32(1)]; |
| tensor<fp16, [96, 96, 1]> audio_decoder_4_block_4_conv2_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [96, 96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114197056))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114206336))))[name = string("audio_decoder_4_block_4_conv2_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [96]> audio_decoder_4_block_4_conv2_conv_bias_to_fp16 = const()[name = string("audio_decoder_4_block_4_conv2_conv_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114206912)))]; |
| tensor<fp16, [1, 96, 240000]> var_3905_cast_fp16 = conv(bias = audio_decoder_4_block_4_conv2_conv_bias_to_fp16, dilations = var_3905_dilations_0, groups = var_3905_groups_0, pad = var_3905_pad_0, pad_type = var_3905_pad_type_0, strides = var_3905_strides_0, weight = audio_decoder_4_block_4_conv2_conv_weight_to_fp16_palettized, x = hidden_state_97_cast_fp16)[name = string("op_3905_cast_fp16")]; |
| tensor<fp16, [1, 96, 240000]> hidden_states_cast_fp16 = add(x = var_3905_cast_fp16, y = hidden_states_69_cast_fp16)[name = string("hidden_states_cast_fp16")]; |
| tensor<fp16, [1, 96, 1]> alpha_115_to_fp16 = const()[name = string("alpha_115_to_fp16"), val = tensor<fp16, [1, 96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114207168)))]; |
| tensor<fp16, [1, 96, 240000]> var_3925_cast_fp16 = mul(x = hidden_states_cast_fp16, y = alpha_115_to_fp16)[name = string("op_3925_cast_fp16")]; |
| tensor<fp16, [1, 96, 240000]> var_3926_cast_fp16 = sin(x = var_3925_cast_fp16)[name = string("op_3926_cast_fp16")]; |
| fp16 var_3908_promoted_to_fp16 = const()[name = string("op_3908_promoted_to_fp16"), val = fp16(0x1p+1)]; |
| tensor<fp16, [1, 96, 240000]> var_3927_cast_fp16 = pow(x = var_3926_cast_fp16, y = var_3908_promoted_to_fp16)[name = string("op_3927_cast_fp16")]; |
| tensor<fp16, [1, 96, 1]> var_3922_to_fp16 = const()[name = string("op_3922_to_fp16"), val = tensor<fp16, [1, 96, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114207424)))]; |
| tensor<fp16, [1, 96, 240000]> var_3928_cast_fp16 = mul(x = var_3922_to_fp16, y = var_3927_cast_fp16)[name = string("op_3928_cast_fp16")]; |
| tensor<fp16, [1, 96, 240000]> hidden_state_cast_fp16 = add(x = hidden_states_cast_fp16, y = var_3928_cast_fp16)[name = string("hidden_state_cast_fp16")]; |
| tensor<int32, [6]> input_pad_0 = const()[name = string("input_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 6, 0])]; |
| string input_mode_0 = const()[name = string("input_mode_0"), val = string("constant")]; |
| fp16 const_166_to_fp16 = const()[name = string("const_166_to_fp16"), val = fp16(0x0p+0)]; |
| tensor<fp16, [1, 96, 240006]> input_cast_fp16 = pad(constant_val = const_166_to_fp16, mode = input_mode_0, pad = input_pad_0, x = hidden_state_cast_fp16)[name = string("input_cast_fp16")]; |
| string var_3952_pad_type_0 = const()[name = string("op_3952_pad_type_0"), val = string("valid")]; |
| tensor<int32, [1]> var_3952_strides_0 = const()[name = string("op_3952_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_3952_pad_0 = const()[name = string("op_3952_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> var_3952_dilations_0 = const()[name = string("op_3952_dilations_0"), val = tensor<int32, [1]>([1])]; |
| int32 var_3952_groups_0 = const()[name = string("op_3952_groups_0"), val = int32(1)]; |
| tensor<fp16, [1, 96, 7]> audio_decoder_6_conv_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [1, 96, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114207680))), lut = tensor<fp16, [1, 1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114208448))))[name = string("audio_decoder_6_conv_weight_to_fp16_palettized")]; |
| tensor<fp16, [1]> audio_decoder_6_conv_bias_to_fp16 = const()[name = string("audio_decoder_6_conv_bias_to_fp16"), val = tensor<fp16, [1]>([-0x1.1p-19])]; |
| tensor<fp16, [1, 1, 240000]> var_3952_cast_fp16 = conv(bias = audio_decoder_6_conv_bias_to_fp16, dilations = var_3952_dilations_0, groups = var_3952_groups_0, pad = var_3952_pad_0, pad_type = var_3952_pad_type_0, strides = var_3952_strides_0, weight = audio_decoder_6_conv_weight_to_fp16_palettized, x = input_cast_fp16)[name = string("op_3952_cast_fp16")]; |
| fp16 var_3954_promoted_to_fp16 = const()[name = string("op_3954_promoted_to_fp16"), val = fp16(-0x1p+0)]; |
| fp16 var_3955_promoted_to_fp16 = const()[name = string("op_3955_promoted_to_fp16"), val = fp16(0x1p+0)]; |
| tensor<fp16, [1, 1, 240000]> audio = clip(alpha = var_3954_promoted_to_fp16, beta = var_3955_promoted_to_fp16, x = var_3952_cast_fp16)[name = string("clip_0_cast_fp16")]; |
| } -> (audio); |
| } |
