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1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 | program(1.3)
[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.7.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
{
func main<ios18>(tensor<int32, [1, ?]> all_tokens) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"all_tokens", [1, 500]}}), ("RangeDims", {{"all_tokens", [[1, 1], [1, 2048]]}})))] {
tensor<fp32, [6561, 512]> input_embedding_weight = const()[name = string("input_embedding_weight"), val = tensor<fp32, [6561, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
tensor<fp32, [512]> encoder_embed_out_0_bias = const()[name = string("encoder_embed_out_0_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13437056)))];
tensor<fp32, [512, 512]> encoder_embed_out_0_weight = const()[name = string("encoder_embed_out_0_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13439168)))];
tensor<fp32, [512]> encoder_embed_out_1_bias = const()[name = string("encoder_embed_out_1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14487808)))];
tensor<fp32, [512]> encoder_embed_out_1_weight = const()[name = string("encoder_embed_out_1_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14489920)))];
tensor<fp32, [512]> encoder_pre_lookahead_layer_conv1_bias = const()[name = string("encoder_pre_lookahead_layer_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14492032)))];
tensor<fp32, [512, 512, 4]> encoder_pre_lookahead_layer_conv1_weight = const()[name = string("encoder_pre_lookahead_layer_conv1_weight"), val = tensor<fp32, [512, 512, 4]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14494144)))];
tensor<fp32, [512]> encoder_pre_lookahead_layer_conv2_bias = const()[name = string("encoder_pre_lookahead_layer_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18688512)))];
tensor<fp32, [512, 512, 3]> encoder_pre_lookahead_layer_conv2_weight = const()[name = string("encoder_pre_lookahead_layer_conv2_weight"), val = tensor<fp32, [512, 512, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18690624)))];
tensor<fp32, [512]> encoder_encoders_0_norm_mha_bias = const()[name = string("encoder_encoders_0_norm_mha_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21836416)))];
tensor<fp32, [512]> encoder_encoders_0_norm_mha_weight = const()[name = string("encoder_encoders_0_norm_mha_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21838528)))];
tensor<fp32, [512]> encoder_encoders_0_self_attn_linear_q_bias = const()[name = string("encoder_encoders_0_self_attn_linear_q_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21840640)))];
tensor<fp32, [512, 512]> encoder_encoders_0_self_attn_linear_q_weight = const()[name = string("encoder_encoders_0_self_attn_linear_q_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21842752)))];
tensor<fp32, [512]> encoder_encoders_0_self_attn_linear_k_bias = const()[name = string("encoder_encoders_0_self_attn_linear_k_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22891392)))];
tensor<fp32, [512, 512]> encoder_encoders_0_self_attn_linear_k_weight = const()[name = string("encoder_encoders_0_self_attn_linear_k_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22893504)))];
tensor<fp32, [512]> encoder_encoders_0_self_attn_linear_v_bias = const()[name = string("encoder_encoders_0_self_attn_linear_v_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23942144)))];
tensor<fp32, [512, 512]> encoder_encoders_0_self_attn_linear_v_weight = const()[name = string("encoder_encoders_0_self_attn_linear_v_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23944256)))];
tensor<fp32, [512, 512]> encoder_encoders_0_self_attn_linear_pos_weight = const()[name = string("encoder_encoders_0_self_attn_linear_pos_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24992896)))];
tensor<fp32, [512]> encoder_encoders_0_self_attn_linear_out_bias = const()[name = string("encoder_encoders_0_self_attn_linear_out_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26041536)))];
tensor<fp32, [512, 512]> encoder_encoders_0_self_attn_linear_out_weight = const()[name = string("encoder_encoders_0_self_attn_linear_out_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26043648)))];
tensor<fp32, [512]> encoder_encoders_0_norm_ff_bias = const()[name = string("encoder_encoders_0_norm_ff_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27092288)))];
tensor<fp32, [512]> encoder_encoders_0_norm_ff_weight = const()[name = string("encoder_encoders_0_norm_ff_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27094400)))];
tensor<fp32, [2048]> encoder_encoders_0_feed_forward_w_1_bias = const()[name = string("encoder_encoders_0_feed_forward_w_1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27096512)))];
tensor<fp32, [2048, 512]> encoder_encoders_0_feed_forward_w_1_weight = const()[name = string("encoder_encoders_0_feed_forward_w_1_weight"), val = tensor<fp32, [2048, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27104768)))];
tensor<fp32, [512]> encoder_encoders_0_feed_forward_w_2_bias = const()[name = string("encoder_encoders_0_feed_forward_w_2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31299136)))];
tensor<fp32, [512, 2048]> encoder_encoders_0_feed_forward_w_2_weight = const()[name = string("encoder_encoders_0_feed_forward_w_2_weight"), val = tensor<fp32, [512, 2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31301248)))];
tensor<fp32, [512]> encoder_encoders_1_norm_mha_bias = const()[name = string("encoder_encoders_1_norm_mha_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35495616)))];
tensor<fp32, [512]> encoder_encoders_1_norm_mha_weight = const()[name = string("encoder_encoders_1_norm_mha_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35497728)))];
tensor<fp32, [512]> encoder_encoders_1_self_attn_linear_q_bias = const()[name = string("encoder_encoders_1_self_attn_linear_q_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35499840)))];
tensor<fp32, [512, 512]> encoder_encoders_1_self_attn_linear_q_weight = const()[name = string("encoder_encoders_1_self_attn_linear_q_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35501952)))];
tensor<fp32, [512]> encoder_encoders_1_self_attn_linear_k_bias = const()[name = string("encoder_encoders_1_self_attn_linear_k_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36550592)))];
tensor<fp32, [512, 512]> encoder_encoders_1_self_attn_linear_k_weight = const()[name = string("encoder_encoders_1_self_attn_linear_k_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36552704)))];
tensor<fp32, [512]> encoder_encoders_1_self_attn_linear_v_bias = const()[name = string("encoder_encoders_1_self_attn_linear_v_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37601344)))];
tensor<fp32, [512, 512]> encoder_encoders_1_self_attn_linear_v_weight = const()[name = string("encoder_encoders_1_self_attn_linear_v_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37603456)))];
tensor<fp32, [512, 512]> encoder_encoders_1_self_attn_linear_pos_weight = const()[name = string("encoder_encoders_1_self_attn_linear_pos_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38652096)))];
tensor<fp32, [512]> encoder_encoders_1_self_attn_linear_out_bias = const()[name = string("encoder_encoders_1_self_attn_linear_out_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39700736)))];
tensor<fp32, [512, 512]> encoder_encoders_1_self_attn_linear_out_weight = const()[name = string("encoder_encoders_1_self_attn_linear_out_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39702848)))];
tensor<fp32, [512]> encoder_encoders_1_norm_ff_bias = const()[name = string("encoder_encoders_1_norm_ff_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40751488)))];
tensor<fp32, [512]> encoder_encoders_1_norm_ff_weight = const()[name = string("encoder_encoders_1_norm_ff_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40753600)))];
tensor<fp32, [2048]> encoder_encoders_1_feed_forward_w_1_bias = const()[name = string("encoder_encoders_1_feed_forward_w_1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40755712)))];
tensor<fp32, [2048, 512]> encoder_encoders_1_feed_forward_w_1_weight = const()[name = string("encoder_encoders_1_feed_forward_w_1_weight"), val = tensor<fp32, [2048, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40763968)))];
tensor<fp32, [512]> encoder_encoders_1_feed_forward_w_2_bias = const()[name = string("encoder_encoders_1_feed_forward_w_2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44958336)))];
tensor<fp32, [512, 2048]> encoder_encoders_1_feed_forward_w_2_weight = const()[name = string("encoder_encoders_1_feed_forward_w_2_weight"), val = tensor<fp32, [512, 2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44960448)))];
tensor<fp32, [512]> encoder_encoders_2_norm_mha_bias = const()[name = string("encoder_encoders_2_norm_mha_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49154816)))];
tensor<fp32, [512]> encoder_encoders_2_norm_mha_weight = const()[name = string("encoder_encoders_2_norm_mha_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49156928)))];
tensor<fp32, [512]> encoder_encoders_2_self_attn_linear_q_bias = const()[name = string("encoder_encoders_2_self_attn_linear_q_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49159040)))];
tensor<fp32, [512, 512]> encoder_encoders_2_self_attn_linear_q_weight = const()[name = string("encoder_encoders_2_self_attn_linear_q_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(49161152)))];
tensor<fp32, [512]> encoder_encoders_2_self_attn_linear_k_bias = const()[name = string("encoder_encoders_2_self_attn_linear_k_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50209792)))];
tensor<fp32, [512, 512]> encoder_encoders_2_self_attn_linear_k_weight = const()[name = string("encoder_encoders_2_self_attn_linear_k_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50211904)))];
tensor<fp32, [512]> encoder_encoders_2_self_attn_linear_v_bias = const()[name = string("encoder_encoders_2_self_attn_linear_v_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51260544)))];
tensor<fp32, [512, 512]> encoder_encoders_2_self_attn_linear_v_weight = const()[name = string("encoder_encoders_2_self_attn_linear_v_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51262656)))];
tensor<fp32, [512, 512]> encoder_encoders_2_self_attn_linear_pos_weight = const()[name = string("encoder_encoders_2_self_attn_linear_pos_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52311296)))];
tensor<fp32, [512]> encoder_encoders_2_self_attn_linear_out_bias = const()[name = string("encoder_encoders_2_self_attn_linear_out_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53359936)))];
tensor<fp32, [512, 512]> encoder_encoders_2_self_attn_linear_out_weight = const()[name = string("encoder_encoders_2_self_attn_linear_out_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53362048)))];
tensor<fp32, [512]> encoder_encoders_2_norm_ff_bias = const()[name = string("encoder_encoders_2_norm_ff_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54410688)))];
tensor<fp32, [512]> encoder_encoders_2_norm_ff_weight = const()[name = string("encoder_encoders_2_norm_ff_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54412800)))];
tensor<fp32, [2048]> encoder_encoders_2_feed_forward_w_1_bias = const()[name = string("encoder_encoders_2_feed_forward_w_1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54414912)))];
tensor<fp32, [2048, 512]> encoder_encoders_2_feed_forward_w_1_weight = const()[name = string("encoder_encoders_2_feed_forward_w_1_weight"), val = tensor<fp32, [2048, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54423168)))];
tensor<fp32, [512]> encoder_encoders_2_feed_forward_w_2_bias = const()[name = string("encoder_encoders_2_feed_forward_w_2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58617536)))];
tensor<fp32, [512, 2048]> encoder_encoders_2_feed_forward_w_2_weight = const()[name = string("encoder_encoders_2_feed_forward_w_2_weight"), val = tensor<fp32, [512, 2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58619648)))];
tensor<fp32, [512]> encoder_encoders_3_norm_mha_bias = const()[name = string("encoder_encoders_3_norm_mha_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62814016)))];
tensor<fp32, [512]> encoder_encoders_3_norm_mha_weight = const()[name = string("encoder_encoders_3_norm_mha_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62816128)))];
tensor<fp32, [512]> encoder_encoders_3_self_attn_linear_q_bias = const()[name = string("encoder_encoders_3_self_attn_linear_q_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62818240)))];
tensor<fp32, [512, 512]> encoder_encoders_3_self_attn_linear_q_weight = const()[name = string("encoder_encoders_3_self_attn_linear_q_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(62820352)))];
tensor<fp32, [512]> encoder_encoders_3_self_attn_linear_k_bias = const()[name = string("encoder_encoders_3_self_attn_linear_k_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63868992)))];
tensor<fp32, [512, 512]> encoder_encoders_3_self_attn_linear_k_weight = const()[name = string("encoder_encoders_3_self_attn_linear_k_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63871104)))];
tensor<fp32, [512]> encoder_encoders_3_self_attn_linear_v_bias = const()[name = string("encoder_encoders_3_self_attn_linear_v_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64919744)))];
tensor<fp32, [512, 512]> encoder_encoders_3_self_attn_linear_v_weight = const()[name = string("encoder_encoders_3_self_attn_linear_v_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64921856)))];
tensor<fp32, [512, 512]> encoder_encoders_3_self_attn_linear_pos_weight = const()[name = string("encoder_encoders_3_self_attn_linear_pos_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(65970496)))];
tensor<fp32, [512]> encoder_encoders_3_self_attn_linear_out_bias = const()[name = string("encoder_encoders_3_self_attn_linear_out_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67019136)))];
tensor<fp32, [512, 512]> encoder_encoders_3_self_attn_linear_out_weight = const()[name = string("encoder_encoders_3_self_attn_linear_out_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(67021248)))];
tensor<fp32, [512]> encoder_encoders_3_norm_ff_bias = const()[name = string("encoder_encoders_3_norm_ff_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68069888)))];
tensor<fp32, [512]> encoder_encoders_3_norm_ff_weight = const()[name = string("encoder_encoders_3_norm_ff_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68072000)))];
tensor<fp32, [2048]> encoder_encoders_3_feed_forward_w_1_bias = const()[name = string("encoder_encoders_3_feed_forward_w_1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68074112)))];
tensor<fp32, [2048, 512]> encoder_encoders_3_feed_forward_w_1_weight = const()[name = string("encoder_encoders_3_feed_forward_w_1_weight"), val = tensor<fp32, [2048, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68082368)))];
tensor<fp32, [512]> encoder_encoders_3_feed_forward_w_2_bias = const()[name = string("encoder_encoders_3_feed_forward_w_2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72276736)))];
tensor<fp32, [512, 2048]> encoder_encoders_3_feed_forward_w_2_weight = const()[name = string("encoder_encoders_3_feed_forward_w_2_weight"), val = tensor<fp32, [512, 2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72278848)))];
tensor<fp32, [512]> encoder_encoders_4_norm_mha_bias = const()[name = string("encoder_encoders_4_norm_mha_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76473216)))];
tensor<fp32, [512]> encoder_encoders_4_norm_mha_weight = const()[name = string("encoder_encoders_4_norm_mha_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76475328)))];
tensor<fp32, [512]> encoder_encoders_4_self_attn_linear_q_bias = const()[name = string("encoder_encoders_4_self_attn_linear_q_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76477440)))];
tensor<fp32, [512, 512]> encoder_encoders_4_self_attn_linear_q_weight = const()[name = string("encoder_encoders_4_self_attn_linear_q_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76479552)))];
tensor<fp32, [512]> encoder_encoders_4_self_attn_linear_k_bias = const()[name = string("encoder_encoders_4_self_attn_linear_k_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77528192)))];
tensor<fp32, [512, 512]> encoder_encoders_4_self_attn_linear_k_weight = const()[name = string("encoder_encoders_4_self_attn_linear_k_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(77530304)))];
tensor<fp32, [512]> encoder_encoders_4_self_attn_linear_v_bias = const()[name = string("encoder_encoders_4_self_attn_linear_v_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78578944)))];
tensor<fp32, [512, 512]> encoder_encoders_4_self_attn_linear_v_weight = const()[name = string("encoder_encoders_4_self_attn_linear_v_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78581056)))];
tensor<fp32, [512, 512]> encoder_encoders_4_self_attn_linear_pos_weight = const()[name = string("encoder_encoders_4_self_attn_linear_pos_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79629696)))];
tensor<fp32, [512]> encoder_encoders_4_self_attn_linear_out_bias = const()[name = string("encoder_encoders_4_self_attn_linear_out_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80678336)))];
tensor<fp32, [512, 512]> encoder_encoders_4_self_attn_linear_out_weight = const()[name = string("encoder_encoders_4_self_attn_linear_out_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80680448)))];
tensor<fp32, [512]> encoder_encoders_4_norm_ff_bias = const()[name = string("encoder_encoders_4_norm_ff_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81729088)))];
tensor<fp32, [512]> encoder_encoders_4_norm_ff_weight = const()[name = string("encoder_encoders_4_norm_ff_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81731200)))];
tensor<fp32, [2048]> encoder_encoders_4_feed_forward_w_1_bias = const()[name = string("encoder_encoders_4_feed_forward_w_1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81733312)))];
tensor<fp32, [2048, 512]> encoder_encoders_4_feed_forward_w_1_weight = const()[name = string("encoder_encoders_4_feed_forward_w_1_weight"), val = tensor<fp32, [2048, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(81741568)))];
tensor<fp32, [512]> encoder_encoders_4_feed_forward_w_2_bias = const()[name = string("encoder_encoders_4_feed_forward_w_2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85935936)))];
tensor<fp32, [512, 2048]> encoder_encoders_4_feed_forward_w_2_weight = const()[name = string("encoder_encoders_4_feed_forward_w_2_weight"), val = tensor<fp32, [512, 2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85938048)))];
tensor<fp32, [512]> encoder_encoders_5_norm_mha_bias = const()[name = string("encoder_encoders_5_norm_mha_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90132416)))];
tensor<fp32, [512]> encoder_encoders_5_norm_mha_weight = const()[name = string("encoder_encoders_5_norm_mha_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90134528)))];
tensor<fp32, [512]> encoder_encoders_5_self_attn_linear_q_bias = const()[name = string("encoder_encoders_5_self_attn_linear_q_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90136640)))];
tensor<fp32, [512, 512]> encoder_encoders_5_self_attn_linear_q_weight = const()[name = string("encoder_encoders_5_self_attn_linear_q_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90138752)))];
tensor<fp32, [512]> encoder_encoders_5_self_attn_linear_k_bias = const()[name = string("encoder_encoders_5_self_attn_linear_k_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91187392)))];
tensor<fp32, [512, 512]> encoder_encoders_5_self_attn_linear_k_weight = const()[name = string("encoder_encoders_5_self_attn_linear_k_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(91189504)))];
tensor<fp32, [512]> encoder_encoders_5_self_attn_linear_v_bias = const()[name = string("encoder_encoders_5_self_attn_linear_v_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92238144)))];
tensor<fp32, [512, 512]> encoder_encoders_5_self_attn_linear_v_weight = const()[name = string("encoder_encoders_5_self_attn_linear_v_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(92240256)))];
tensor<fp32, [512, 512]> encoder_encoders_5_self_attn_linear_pos_weight = const()[name = string("encoder_encoders_5_self_attn_linear_pos_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(93288896)))];
tensor<fp32, [512]> encoder_encoders_5_self_attn_linear_out_bias = const()[name = string("encoder_encoders_5_self_attn_linear_out_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94337536)))];
tensor<fp32, [512, 512]> encoder_encoders_5_self_attn_linear_out_weight = const()[name = string("encoder_encoders_5_self_attn_linear_out_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(94339648)))];
tensor<fp32, [512]> encoder_encoders_5_norm_ff_bias = const()[name = string("encoder_encoders_5_norm_ff_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95388288)))];
tensor<fp32, [512]> encoder_encoders_5_norm_ff_weight = const()[name = string("encoder_encoders_5_norm_ff_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95390400)))];
tensor<fp32, [2048]> encoder_encoders_5_feed_forward_w_1_bias = const()[name = string("encoder_encoders_5_feed_forward_w_1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95392512)))];
tensor<fp32, [2048, 512]> encoder_encoders_5_feed_forward_w_1_weight = const()[name = string("encoder_encoders_5_feed_forward_w_1_weight"), val = tensor<fp32, [2048, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(95400768)))];
tensor<fp32, [512]> encoder_encoders_5_feed_forward_w_2_bias = const()[name = string("encoder_encoders_5_feed_forward_w_2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(99595136)))];
tensor<fp32, [512, 2048]> encoder_encoders_5_feed_forward_w_2_weight = const()[name = string("encoder_encoders_5_feed_forward_w_2_weight"), val = tensor<fp32, [512, 2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(99597248)))];
tensor<fp32, [512]> encoder_up_layer_conv_bias = const()[name = string("encoder_up_layer_conv_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103791616)))];
tensor<fp32, [512, 512, 5]> encoder_up_layer_conv_weight = const()[name = string("encoder_up_layer_conv_weight"), val = tensor<fp32, [512, 512, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103793728)))];
tensor<fp32, [512]> encoder_up_embed_out_0_bias = const()[name = string("encoder_up_embed_out_0_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109036672)))];
tensor<fp32, [512, 512]> encoder_up_embed_out_0_weight = const()[name = string("encoder_up_embed_out_0_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(109038784)))];
tensor<fp32, [512]> encoder_up_embed_out_1_bias = const()[name = string("encoder_up_embed_out_1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110087424)))];
tensor<fp32, [512]> encoder_up_embed_out_1_weight = const()[name = string("encoder_up_embed_out_1_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110089536)))];
tensor<fp32, [512]> encoder_up_encoders_0_norm_mha_bias = const()[name = string("encoder_up_encoders_0_norm_mha_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110091648)))];
tensor<fp32, [512]> encoder_up_encoders_0_norm_mha_weight = const()[name = string("encoder_up_encoders_0_norm_mha_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110093760)))];
tensor<fp32, [512]> encoder_up_encoders_0_self_attn_linear_q_bias = const()[name = string("encoder_up_encoders_0_self_attn_linear_q_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110095872)))];
tensor<fp32, [512, 512]> encoder_up_encoders_0_self_attn_linear_q_weight = const()[name = string("encoder_up_encoders_0_self_attn_linear_q_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(110097984)))];
tensor<fp32, [512]> encoder_up_encoders_0_self_attn_linear_k_bias = const()[name = string("encoder_up_encoders_0_self_attn_linear_k_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111146624)))];
tensor<fp32, [512, 512]> encoder_up_encoders_0_self_attn_linear_k_weight = const()[name = string("encoder_up_encoders_0_self_attn_linear_k_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(111148736)))];
tensor<fp32, [512]> encoder_up_encoders_0_self_attn_linear_v_bias = const()[name = string("encoder_up_encoders_0_self_attn_linear_v_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112197376)))];
tensor<fp32, [512, 512]> encoder_up_encoders_0_self_attn_linear_v_weight = const()[name = string("encoder_up_encoders_0_self_attn_linear_v_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112199488)))];
tensor<fp32, [512, 512]> encoder_up_encoders_0_self_attn_linear_pos_weight = const()[name = string("encoder_up_encoders_0_self_attn_linear_pos_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(113248128)))];
tensor<fp32, [512]> encoder_up_encoders_0_self_attn_linear_out_bias = const()[name = string("encoder_up_encoders_0_self_attn_linear_out_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114296768)))];
tensor<fp32, [512, 512]> encoder_up_encoders_0_self_attn_linear_out_weight = const()[name = string("encoder_up_encoders_0_self_attn_linear_out_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114298880)))];
tensor<fp32, [512]> encoder_up_encoders_0_norm_ff_bias = const()[name = string("encoder_up_encoders_0_norm_ff_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115347520)))];
tensor<fp32, [512]> encoder_up_encoders_0_norm_ff_weight = const()[name = string("encoder_up_encoders_0_norm_ff_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115349632)))];
tensor<fp32, [2048]> encoder_up_encoders_0_feed_forward_w_1_bias = const()[name = string("encoder_up_encoders_0_feed_forward_w_1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115351744)))];
tensor<fp32, [2048, 512]> encoder_up_encoders_0_feed_forward_w_1_weight = const()[name = string("encoder_up_encoders_0_feed_forward_w_1_weight"), val = tensor<fp32, [2048, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115360000)))];
tensor<fp32, [512]> encoder_up_encoders_0_feed_forward_w_2_bias = const()[name = string("encoder_up_encoders_0_feed_forward_w_2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119554368)))];
tensor<fp32, [512, 2048]> encoder_up_encoders_0_feed_forward_w_2_weight = const()[name = string("encoder_up_encoders_0_feed_forward_w_2_weight"), val = tensor<fp32, [512, 2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(119556480)))];
tensor<fp32, [512]> encoder_up_encoders_1_norm_mha_bias = const()[name = string("encoder_up_encoders_1_norm_mha_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123750848)))];
tensor<fp32, [512]> encoder_up_encoders_1_norm_mha_weight = const()[name = string("encoder_up_encoders_1_norm_mha_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123752960)))];
tensor<fp32, [512]> encoder_up_encoders_1_self_attn_linear_q_bias = const()[name = string("encoder_up_encoders_1_self_attn_linear_q_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123755072)))];
tensor<fp32, [512, 512]> encoder_up_encoders_1_self_attn_linear_q_weight = const()[name = string("encoder_up_encoders_1_self_attn_linear_q_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123757184)))];
tensor<fp32, [512]> encoder_up_encoders_1_self_attn_linear_k_bias = const()[name = string("encoder_up_encoders_1_self_attn_linear_k_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(124805824)))];
tensor<fp32, [512, 512]> encoder_up_encoders_1_self_attn_linear_k_weight = const()[name = string("encoder_up_encoders_1_self_attn_linear_k_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(124807936)))];
tensor<fp32, [512]> encoder_up_encoders_1_self_attn_linear_v_bias = const()[name = string("encoder_up_encoders_1_self_attn_linear_v_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125856576)))];
tensor<fp32, [512, 512]> encoder_up_encoders_1_self_attn_linear_v_weight = const()[name = string("encoder_up_encoders_1_self_attn_linear_v_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125858688)))];
tensor<fp32, [512, 512]> encoder_up_encoders_1_self_attn_linear_pos_weight = const()[name = string("encoder_up_encoders_1_self_attn_linear_pos_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126907328)))];
tensor<fp32, [512]> encoder_up_encoders_1_self_attn_linear_out_bias = const()[name = string("encoder_up_encoders_1_self_attn_linear_out_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127955968)))];
tensor<fp32, [512, 512]> encoder_up_encoders_1_self_attn_linear_out_weight = const()[name = string("encoder_up_encoders_1_self_attn_linear_out_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(127958080)))];
tensor<fp32, [512]> encoder_up_encoders_1_norm_ff_bias = const()[name = string("encoder_up_encoders_1_norm_ff_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129006720)))];
tensor<fp32, [512]> encoder_up_encoders_1_norm_ff_weight = const()[name = string("encoder_up_encoders_1_norm_ff_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129008832)))];
tensor<fp32, [2048]> encoder_up_encoders_1_feed_forward_w_1_bias = const()[name = string("encoder_up_encoders_1_feed_forward_w_1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129010944)))];
tensor<fp32, [2048, 512]> encoder_up_encoders_1_feed_forward_w_1_weight = const()[name = string("encoder_up_encoders_1_feed_forward_w_1_weight"), val = tensor<fp32, [2048, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129019200)))];
tensor<fp32, [512]> encoder_up_encoders_1_feed_forward_w_2_bias = const()[name = string("encoder_up_encoders_1_feed_forward_w_2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133213568)))];
tensor<fp32, [512, 2048]> encoder_up_encoders_1_feed_forward_w_2_weight = const()[name = string("encoder_up_encoders_1_feed_forward_w_2_weight"), val = tensor<fp32, [512, 2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133215680)))];
tensor<fp32, [512]> encoder_up_encoders_2_norm_mha_bias = const()[name = string("encoder_up_encoders_2_norm_mha_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(137410048)))];
tensor<fp32, [512]> encoder_up_encoders_2_norm_mha_weight = const()[name = string("encoder_up_encoders_2_norm_mha_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(137412160)))];
tensor<fp32, [512]> encoder_up_encoders_2_self_attn_linear_q_bias = const()[name = string("encoder_up_encoders_2_self_attn_linear_q_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(137414272)))];
tensor<fp32, [512, 512]> encoder_up_encoders_2_self_attn_linear_q_weight = const()[name = string("encoder_up_encoders_2_self_attn_linear_q_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(137416384)))];
tensor<fp32, [512]> encoder_up_encoders_2_self_attn_linear_k_bias = const()[name = string("encoder_up_encoders_2_self_attn_linear_k_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138465024)))];
tensor<fp32, [512, 512]> encoder_up_encoders_2_self_attn_linear_k_weight = const()[name = string("encoder_up_encoders_2_self_attn_linear_k_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(138467136)))];
tensor<fp32, [512]> encoder_up_encoders_2_self_attn_linear_v_bias = const()[name = string("encoder_up_encoders_2_self_attn_linear_v_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139515776)))];
tensor<fp32, [512, 512]> encoder_up_encoders_2_self_attn_linear_v_weight = const()[name = string("encoder_up_encoders_2_self_attn_linear_v_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139517888)))];
tensor<fp32, [512, 512]> encoder_up_encoders_2_self_attn_linear_pos_weight = const()[name = string("encoder_up_encoders_2_self_attn_linear_pos_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140566528)))];
tensor<fp32, [512]> encoder_up_encoders_2_self_attn_linear_out_bias = const()[name = string("encoder_up_encoders_2_self_attn_linear_out_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141615168)))];
tensor<fp32, [512, 512]> encoder_up_encoders_2_self_attn_linear_out_weight = const()[name = string("encoder_up_encoders_2_self_attn_linear_out_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(141617280)))];
tensor<fp32, [512]> encoder_up_encoders_2_norm_ff_bias = const()[name = string("encoder_up_encoders_2_norm_ff_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142665920)))];
tensor<fp32, [512]> encoder_up_encoders_2_norm_ff_weight = const()[name = string("encoder_up_encoders_2_norm_ff_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142668032)))];
tensor<fp32, [2048]> encoder_up_encoders_2_feed_forward_w_1_bias = const()[name = string("encoder_up_encoders_2_feed_forward_w_1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142670144)))];
tensor<fp32, [2048, 512]> encoder_up_encoders_2_feed_forward_w_1_weight = const()[name = string("encoder_up_encoders_2_feed_forward_w_1_weight"), val = tensor<fp32, [2048, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(142678400)))];
tensor<fp32, [512]> encoder_up_encoders_2_feed_forward_w_2_bias = const()[name = string("encoder_up_encoders_2_feed_forward_w_2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146872768)))];
tensor<fp32, [512, 2048]> encoder_up_encoders_2_feed_forward_w_2_weight = const()[name = string("encoder_up_encoders_2_feed_forward_w_2_weight"), val = tensor<fp32, [512, 2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(146874880)))];
tensor<fp32, [512]> encoder_up_encoders_3_norm_mha_bias = const()[name = string("encoder_up_encoders_3_norm_mha_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151069248)))];
tensor<fp32, [512]> encoder_up_encoders_3_norm_mha_weight = const()[name = string("encoder_up_encoders_3_norm_mha_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151071360)))];
tensor<fp32, [512]> encoder_up_encoders_3_self_attn_linear_q_bias = const()[name = string("encoder_up_encoders_3_self_attn_linear_q_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151073472)))];
tensor<fp32, [512, 512]> encoder_up_encoders_3_self_attn_linear_q_weight = const()[name = string("encoder_up_encoders_3_self_attn_linear_q_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151075584)))];
tensor<fp32, [512]> encoder_up_encoders_3_self_attn_linear_k_bias = const()[name = string("encoder_up_encoders_3_self_attn_linear_k_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(152124224)))];
tensor<fp32, [512, 512]> encoder_up_encoders_3_self_attn_linear_k_weight = const()[name = string("encoder_up_encoders_3_self_attn_linear_k_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(152126336)))];
tensor<fp32, [512]> encoder_up_encoders_3_self_attn_linear_v_bias = const()[name = string("encoder_up_encoders_3_self_attn_linear_v_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(153174976)))];
tensor<fp32, [512, 512]> encoder_up_encoders_3_self_attn_linear_v_weight = const()[name = string("encoder_up_encoders_3_self_attn_linear_v_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(153177088)))];
tensor<fp32, [512, 512]> encoder_up_encoders_3_self_attn_linear_pos_weight = const()[name = string("encoder_up_encoders_3_self_attn_linear_pos_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154225728)))];
tensor<fp32, [512]> encoder_up_encoders_3_self_attn_linear_out_bias = const()[name = string("encoder_up_encoders_3_self_attn_linear_out_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155274368)))];
tensor<fp32, [512, 512]> encoder_up_encoders_3_self_attn_linear_out_weight = const()[name = string("encoder_up_encoders_3_self_attn_linear_out_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155276480)))];
tensor<fp32, [512]> encoder_up_encoders_3_norm_ff_bias = const()[name = string("encoder_up_encoders_3_norm_ff_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156325120)))];
tensor<fp32, [512]> encoder_up_encoders_3_norm_ff_weight = const()[name = string("encoder_up_encoders_3_norm_ff_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156327232)))];
tensor<fp32, [2048]> encoder_up_encoders_3_feed_forward_w_1_bias = const()[name = string("encoder_up_encoders_3_feed_forward_w_1_bias"), val = tensor<fp32, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156329344)))];
tensor<fp32, [2048, 512]> encoder_up_encoders_3_feed_forward_w_1_weight = const()[name = string("encoder_up_encoders_3_feed_forward_w_1_weight"), val = tensor<fp32, [2048, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156337600)))];
tensor<fp32, [512]> encoder_up_encoders_3_feed_forward_w_2_bias = const()[name = string("encoder_up_encoders_3_feed_forward_w_2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160531968)))];
tensor<fp32, [512, 2048]> encoder_up_encoders_3_feed_forward_w_2_weight = const()[name = string("encoder_up_encoders_3_feed_forward_w_2_weight"), val = tensor<fp32, [512, 2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(160534080)))];
tensor<fp32, [512]> encoder_after_norm_bias = const()[name = string("encoder_after_norm_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164728448)))];
tensor<fp32, [512]> encoder_after_norm_weight = const()[name = string("encoder_after_norm_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164730560)))];
tensor<fp32, [80]> encoder_proj_bias = const()[name = string("encoder_proj_bias"), val = tensor<fp32, [80]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164732672)))];
tensor<fp32, [80, 512]> encoder_proj_weight = const()[name = string("encoder_proj_weight"), val = tensor<fp32, [80, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164733056)))];
int32 var_33_batch_dims_0 = const()[name = string("op_33_batch_dims_0"), val = int32(0)];
bool var_33_validate_indices_0 = const()[name = string("op_33_validate_indices_0"), val = bool(false)];
int32 greater_equal_1_y_0 = const()[name = string("greater_equal_1_y_0"), val = int32(0)];
tensor<bool, [1, ?]> greater_equal_1 = greater_equal(x = all_tokens, y = greater_equal_1_y_0)[name = string("greater_equal_1")];
int32 slice_by_index_1 = const()[name = string("slice_by_index_1"), val = int32(6561)];
tensor<int32, [1, ?]> add_1 = add(x = all_tokens, y = slice_by_index_1)[name = string("add_1")];
tensor<int32, [1, ?]> select_3 = select(a = all_tokens, b = add_1, cond = greater_equal_1)[name = string("select_3")];
int32 var_33_axis_1 = const()[name = string("op_33_axis_1"), val = int32(0)];
tensor<fp32, [1, ?, 512]> var_33 = gather(axis = var_33_axis_1, batch_dims = var_33_batch_dims_0, indices = select_3, validate_indices = var_33_validate_indices_0, x = input_embedding_weight)[name = string("op_33")];
fp32 var_36 = const()[name = string("op_36"), val = fp32(0x1.197998p-40)];
fp32 var_37 = const()[name = string("op_37"), val = fp32(-0x1.ff933cp+127)];
fp32 var_46 = const()[name = string("op_46"), val = fp32(0x0p+0)];
fp32 var_47 = const()[name = string("op_47"), val = fp32(0x1.47ae14p-7)];
fp32 var_50 = const()[name = string("op_50"), val = fp32(0x1.4f8b58p-17)];
int32 var_53 = const()[name = string("op_53"), val = int32(2)];
tensor<fp32, [1, 9999, 512]> var_54 = const()[name = string("op_54"), val = tensor<fp32, [1, 9999, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164896960)))];
int32 var_55 = const()[name = string("op_55"), val = int32(-1)];
int32 var_57 = const()[name = string("op_57"), val = int32(0)];
int32 var_61 = const()[name = string("op_61"), val = int32(1)];
tensor<int32, [3]> var_87_shape = shape(x = var_33)[name = string("op_87_shape")];
int32 gather_1_batch_dims_0 = const()[name = string("gather_1_batch_dims_0"), val = int32(0)];
bool gather_1_validate_indices_0 = const()[name = string("gather_1_validate_indices_0"), val = bool(false)];
int32 select_4 = const()[name = string("select_4"), val = int32(1)];
int32 gather_1_axis_1 = const()[name = string("gather_1_axis_1"), val = int32(0)];
int32 gather_1 = gather(axis = gather_1_axis_1, batch_dims = gather_1_batch_dims_0, indices = select_4, validate_indices = gather_1_validate_indices_0, x = var_87_shape)[name = string("gather_1")];
int32 const_2 = const()[name = string("const_2"), val = int32(1)];
int32 const_3 = const()[name = string("const_3"), val = int32(1)];
tensor<int32, [?]> seq_range_1 = range_1d(end = gather_1, start = var_57, step = const_3)[name = string("seq_range_1")];
tensor<int32, [1]> var_91_axes_0 = const()[name = string("op_91_axes_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1, ?]> var_91 = expand_dims(axes = var_91_axes_0, x = seq_range_1)[name = string("op_91")];
int32 concat_1_axis_0 = const()[name = string("concat_1_axis_0"), val = int32(0)];
bool concat_1_interleave_0 = const()[name = string("concat_1_interleave_0"), val = bool(false)];
tensor<int32, [2]> concat_1 = concat(axis = concat_1_axis_0, interleave = concat_1_interleave_0, values = (const_2, gather_1))[name = string("concat_1")];
tensor<int32, [2]> shape_0 = shape(x = var_91)[name = string("shape_0")];
int32 equal_0_y_0 = const()[name = string("equal_0_y_0"), val = int32(-1)];
tensor<bool, [2]> equal_0 = equal(x = concat_1, y = equal_0_y_0)[name = string("equal_0")];
tensor<int32, [2]> select_0 = select(a = shape_0, b = concat_1, cond = equal_0)[name = string("select_0")];
tensor<int32, [2]> real_div_0 = real_div(x = select_0, y = shape_0)[name = string("real_div_0")];
tensor<int32, [?, ?]> seq_range_expand_1 = tile(reps = real_div_0, x = var_91)[name = string("seq_range_expand_1")];
tensor<int32, [1, 1]> seq_length_expand_1 = const()[name = string("seq_length_expand_1"), val = tensor<int32, [1, 1]>([[500]])];
tensor<bool, [?, ?]> var_95 = greater_equal(x = seq_range_expand_1, y = seq_length_expand_1)[name = string("op_95")];
tensor<int32, [1]> var_96_axes_0 = const()[name = string("op_96_axes_0"), val = tensor<int32, [1]>([1])];
tensor<bool, [?, 1, ?]> var_96 = expand_dims(axes = var_96_axes_0, x = var_95)[name = string("op_96")];
tensor<bool, [?, 1, ?]> masks_1 = logical_not(x = var_96)[name = string("masks_1")];
tensor<fp32, [1, ?, 512]> input_3 = linear(bias = encoder_embed_out_0_bias, weight = encoder_embed_out_0_weight, x = var_33)[name = string("linear_0")];
tensor<int32, [1]> input_5_axes_0 = const()[name = string("input_5_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp32, [1, ?, 512]> input_5 = layer_norm(axes = input_5_axes_0, beta = encoder_embed_out_1_bias, epsilon = var_50, gamma = encoder_embed_out_1_weight, x = input_3)[name = string("input_5")];
fp32 var_109 = const()[name = string("op_109"), val = fp32(0x1.6a09e6p+4)];
tensor<fp32, [1, ?, 512]> x_3 = mul(x = input_5, y = var_109)[name = string("x_3")];
tensor<int32, [3]> var_111_shape = shape(x = x_3)[name = string("op_111_shape")];
int32 gather_2_batch_dims_0 = const()[name = string("gather_2_batch_dims_0"), val = int32(0)];
bool gather_2_validate_indices_0 = const()[name = string("gather_2_validate_indices_0"), val = bool(false)];
int32 select_5 = const()[name = string("select_5"), val = int32(1)];
int32 gather_2_axis_1 = const()[name = string("gather_2_axis_1"), val = int32(0)];
int32 gather_2 = gather(axis = gather_2_axis_1, batch_dims = gather_2_batch_dims_0, indices = select_5, validate_indices = gather_2_validate_indices_0, x = var_111_shape)[name = string("gather_2")];
tensor<fp32, [1]> var_115 = const()[name = string("op_115"), val = tensor<fp32, [1]>([0x1.387p+12])];
string gather_2_promoted_dtype_0 = const()[name = string("gather_2_promoted_dtype_0"), val = string("fp32")];
fp32 gather_2_promoted = cast(dtype = gather_2_promoted_dtype_0, x = gather_2)[name = string("cast_130")];
tensor<fp32, [1]> var_116 = sub(x = var_115, y = gather_2_promoted)[name = string("op_116")];
fp32 var_117_promoted = const()[name = string("op_117_promoted"), val = fp32(0x1p+0)];
tensor<fp32, [1]> var_118 = add(x = var_116, y = var_117_promoted)[name = string("op_118")];
fp32 var_119_item = squeeze(x = var_118)[name = string("op_119_item")];
string var_119_dtype_0 = const()[name = string("op_119_dtype_0"), val = string("int32")];
tensor<fp32, [1]> var_122 = const()[name = string("op_122"), val = tensor<fp32, [1]>([0x1.387p+12])];
tensor<fp32, [1]> var_123 = add(x = var_122, y = gather_2_promoted)[name = string("op_123")];
fp32 var_124_item = squeeze(x = var_123)[name = string("op_124_item")];
string var_124_dtype_0 = const()[name = string("op_124_dtype_0"), val = string("int32")];
int32 concat_2_values0_0 = const()[name = string("concat_2_values0_0"), val = int32(0)];
int32 concat_2_values2_0 = const()[name = string("concat_2_values2_0"), val = int32(0)];
int32 concat_2_axis_0 = const()[name = string("concat_2_axis_0"), val = int32(0)];
bool concat_2_interleave_0 = const()[name = string("concat_2_interleave_0"), val = bool(false)];
int32 var_119 = cast(dtype = var_119_dtype_0, x = var_119_item)[name = string("cast_129")];
tensor<int32, [3]> concat_2 = concat(axis = concat_2_axis_0, interleave = concat_2_interleave_0, values = (concat_2_values0_0, var_119, concat_2_values2_0))[name = string("concat_2")];
int32 concat_3_values0_0 = const()[name = string("concat_3_values0_0"), val = int32(1)];
int32 concat_3_values2_0 = const()[name = string("concat_3_values2_0"), val = int32(512)];
int32 concat_3_axis_0 = const()[name = string("concat_3_axis_0"), val = int32(0)];
bool concat_3_interleave_0 = const()[name = string("concat_3_interleave_0"), val = bool(false)];
int32 var_124 = cast(dtype = var_124_dtype_0, x = var_124_item)[name = string("cast_128")];
tensor<int32, [3]> concat_3 = concat(axis = concat_3_axis_0, interleave = concat_3_interleave_0, values = (concat_3_values0_0, var_124, concat_3_values2_0))[name = string("concat_3")];
tensor<bool, [3]> input_7_end_mask_0 = const()[name = string("input_7_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
tensor<fp32, [1, ?, ?]> input_7 = slice_by_index(begin = concat_2, end = concat_3, end_mask = input_7_end_mask_0, x = var_54)[name = string("input_7")];
tensor<int32, [3]> var_137_perm_0 = const()[name = string("op_137_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
fp32 const_6 = const()[name = string("const_6"), val = fp32(0x0p+0)];
tensor<int32, [6]> input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 0, 3])];
string input_11_mode_0 = const()[name = string("input_11_mode_0"), val = string("constant")];
tensor<fp32, [1, 512, ?]> var_137 = transpose(perm = var_137_perm_0, x = x_3)[name = string("transpose_164")];
tensor<fp32, [1, 512, ?]> input_11 = pad(constant_val = const_6, mode = input_11_mode_0, pad = input_11_pad_0, x = var_137)[name = string("input_11")];
string input_13_pad_type_0 = const()[name = string("input_13_pad_type_0"), val = string("valid")];
tensor<int32, [1]> input_13_strides_0 = const()[name = string("input_13_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> input_13_pad_0 = const()[name = string("input_13_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> input_13_dilations_0 = const()[name = string("input_13_dilations_0"), val = tensor<int32, [1]>([1])];
int32 input_13_groups_0 = const()[name = string("input_13_groups_0"), val = int32(1)];
tensor<fp32, [1, 512, ?]> input_13 = conv(bias = encoder_pre_lookahead_layer_conv1_bias, dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = encoder_pre_lookahead_layer_conv1_weight, x = input_11)[name = string("input_13")];
tensor<fp32, [1, 512, ?]> input_15 = leaky_relu(alpha = var_47, x = input_13)[name = string("input_15")];
fp32 const_7 = const()[name = string("const_7"), val = fp32(0x0p+0)];
tensor<int32, [6]> input_17_pad_0 = const()[name = string("input_17_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 0])];
string input_17_mode_0 = const()[name = string("input_17_mode_0"), val = string("constant")];
tensor<fp32, [1, 512, ?]> input_17 = pad(constant_val = const_7, mode = input_17_mode_0, pad = input_17_pad_0, x = input_15)[name = string("input_17")];
string outputs_1_pad_type_0 = const()[name = string("outputs_1_pad_type_0"), val = string("valid")];
tensor<int32, [1]> outputs_1_strides_0 = const()[name = string("outputs_1_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> outputs_1_pad_0 = const()[name = string("outputs_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> outputs_1_dilations_0 = const()[name = string("outputs_1_dilations_0"), val = tensor<int32, [1]>([1])];
int32 outputs_1_groups_0 = const()[name = string("outputs_1_groups_0"), val = int32(1)];
tensor<fp32, [1, 512, ?]> outputs_1 = conv(bias = encoder_pre_lookahead_layer_conv2_bias, dilations = outputs_1_dilations_0, groups = outputs_1_groups_0, pad = outputs_1_pad_0, pad_type = outputs_1_pad_type_0, strides = outputs_1_strides_0, weight = encoder_pre_lookahead_layer_conv2_weight, x = input_17)[name = string("outputs_1")];
tensor<int32, [3]> var_158_perm_0 = const()[name = string("op_158_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<fp32, [1, ?, 512]> var_158 = transpose(perm = var_158_perm_0, x = outputs_1)[name = string("transpose_163")];
tensor<fp32, [1, ?, 512]> input_19 = add(x = var_158, y = x_3)[name = string("input_19")];
tensor<int32, [1]> query_1_axes_0 = const()[name = string("query_1_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp32, [1, ?, 512]> query_1 = layer_norm(axes = query_1_axes_0, beta = encoder_encoders_0_norm_mha_bias, epsilon = var_36, gamma = encoder_encoders_0_norm_mha_weight, x = input_19)[name = string("query_1")];
tensor<fp32, [1, ?, 512]> var_179 = linear(bias = encoder_encoders_0_self_attn_linear_q_bias, weight = encoder_encoders_0_self_attn_linear_q_weight, x = query_1)[name = string("linear_1")];
tensor<int32, [4]> var_180 = const()[name = string("op_180"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> q_1 = reshape(shape = var_180, x = var_179)[name = string("q_1")];
tensor<fp32, [1, ?, 512]> var_184 = linear(bias = encoder_encoders_0_self_attn_linear_k_bias, weight = encoder_encoders_0_self_attn_linear_k_weight, x = query_1)[name = string("linear_2")];
tensor<int32, [4]> var_185 = const()[name = string("op_185"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> k_1 = reshape(shape = var_185, x = var_184)[name = string("k_1")];
tensor<fp32, [1, ?, 512]> var_189 = linear(bias = encoder_encoders_0_self_attn_linear_v_bias, weight = encoder_encoders_0_self_attn_linear_v_weight, x = query_1)[name = string("linear_3")];
tensor<int32, [4]> var_190 = const()[name = string("op_190"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> v_1 = reshape(shape = var_190, x = var_189)[name = string("v_1")];
tensor<int32, [4]> v_3_perm_0 = const()[name = string("v_3_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp32, [512]> linear_4_bias_0 = const()[name = string("linear_4_bias_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185374976)))];
tensor<fp32, [1, ?, 512]> var_198 = linear(bias = linear_4_bias_0, weight = encoder_encoders_0_self_attn_linear_pos_weight, x = input_7)[name = string("linear_4")];
tensor<int32, [4]> var_199 = const()[name = string("op_199"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> p_1 = reshape(shape = var_199, x = var_198)[name = string("p_1")];
tensor<fp32, [8, 64]> const_8 = const()[name = string("const_8"), val = tensor<fp32, [8, 64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185377088)))];
tensor<fp32, [1, ?, 8, 64]> var_203 = add(x = q_1, y = const_8)[name = string("op_203")];
tensor<fp32, [8, 64]> const_9 = const()[name = string("const_9"), val = tensor<fp32, [8, 64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185379200)))];
tensor<fp32, [1, ?, 8, 64]> var_206 = add(x = q_1, y = const_9)[name = string("op_206")];
bool matrix_ac_1_transpose_x_0 = const()[name = string("matrix_ac_1_transpose_x_0"), val = bool(false)];
bool matrix_ac_1_transpose_y_0 = const()[name = string("matrix_ac_1_transpose_y_0"), val = bool(false)];
tensor<int32, [4]> transpose_60_perm_0 = const()[name = string("transpose_60_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
tensor<int32, [4]> transpose_61_perm_0 = const()[name = string("transpose_61_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
tensor<fp32, [1, 8, 64, ?]> transpose_61 = transpose(perm = transpose_61_perm_0, x = k_1)[name = string("transpose_160")];
tensor<fp32, [1, 8, ?, 64]> transpose_60 = transpose(perm = transpose_60_perm_0, x = var_203)[name = string("transpose_161")];
tensor<fp32, [1, 8, ?, ?]> matrix_ac_1 = matmul(transpose_x = matrix_ac_1_transpose_x_0, transpose_y = matrix_ac_1_transpose_y_0, x = transpose_60, y = transpose_61)[name = string("matrix_ac_1")];
bool matrix_bd_1_transpose_x_0 = const()[name = string("matrix_bd_1_transpose_x_0"), val = bool(false)];
bool matrix_bd_1_transpose_y_0 = const()[name = string("matrix_bd_1_transpose_y_0"), val = bool(false)];
tensor<int32, [4]> transpose_62_perm_0 = const()[name = string("transpose_62_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
tensor<int32, [4]> transpose_63_perm_0 = const()[name = string("transpose_63_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
tensor<fp32, [1, 8, 64, ?]> transpose_63 = transpose(perm = transpose_63_perm_0, x = p_1)[name = string("transpose_158")];
tensor<fp32, [1, 8, ?, 64]> transpose_62 = transpose(perm = transpose_62_perm_0, x = var_206)[name = string("transpose_159")];
tensor<fp32, [1, 8, ?, ?]> matrix_bd_1 = matmul(transpose_x = matrix_bd_1_transpose_x_0, transpose_y = matrix_bd_1_transpose_y_0, x = transpose_62, y = transpose_63)[name = string("matrix_bd_1")];
tensor<int32, [4]> var_212_shape = shape(x = matrix_bd_1)[name = string("op_212_shape")];
int32 gather_5 = const()[name = string("gather_5"), val = int32(1)];
int32 gather_6 = const()[name = string("gather_6"), val = int32(8)];
int32 gather_7_batch_dims_0 = const()[name = string("gather_7_batch_dims_0"), val = int32(0)];
bool gather_7_validate_indices_0 = const()[name = string("gather_7_validate_indices_0"), val = bool(false)];
int32 select_6 = const()[name = string("select_6"), val = int32(2)];
int32 gather_7_axis_1 = const()[name = string("gather_7_axis_1"), val = int32(0)];
int32 gather_7 = gather(axis = gather_7_axis_1, batch_dims = gather_7_batch_dims_0, indices = select_6, validate_indices = gather_7_validate_indices_0, x = var_212_shape)[name = string("gather_7")];
int32 concat_4_axis_0 = const()[name = string("concat_4_axis_0"), val = int32(0)];
bool concat_4_interleave_0 = const()[name = string("concat_4_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_4 = concat(axis = concat_4_axis_0, interleave = concat_4_interleave_0, values = (gather_5, gather_6, gather_7, var_61))[name = string("concat_4")];
fp32 zero_pad_1_value_0 = const()[name = string("zero_pad_1_value_0"), val = fp32(0x0p+0)];
tensor<fp32, [1, 8, ?, 1]> zero_pad_1 = fill(shape = concat_4, value = zero_pad_1_value_0)[name = string("zero_pad_1")];
bool x_padded_1_interleave_0 = const()[name = string("x_padded_1_interleave_0"), val = bool(false)];
tensor<fp32, [1, 8, ?, ?]> x_padded_1 = concat(axis = var_55, interleave = x_padded_1_interleave_0, values = (zero_pad_1, matrix_bd_1))[name = string("x_padded_1")];
int32 gather_8 = const()[name = string("gather_8"), val = int32(1)];
int32 gather_9 = const()[name = string("gather_9"), val = int32(8)];
int32 gather_10_batch_dims_0 = const()[name = string("gather_10_batch_dims_0"), val = int32(0)];
bool gather_10_validate_indices_0 = const()[name = string("gather_10_validate_indices_0"), val = bool(false)];
int32 select_7 = const()[name = string("select_7"), val = int32(3)];
int32 gather_10_axis_1 = const()[name = string("gather_10_axis_1"), val = int32(0)];
int32 gather_10 = gather(axis = gather_10_axis_1, batch_dims = gather_10_batch_dims_0, indices = select_7, validate_indices = gather_10_validate_indices_0, x = var_212_shape)[name = string("gather_10")];
int32 var_223 = const()[name = string("op_223"), val = int32(1)];
int32 var_224 = add(x = gather_10, y = var_223)[name = string("op_224")];
int32 concat_5_axis_0 = const()[name = string("concat_5_axis_0"), val = int32(0)];
bool concat_5_interleave_0 = const()[name = string("concat_5_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_5 = concat(axis = concat_5_axis_0, interleave = concat_5_interleave_0, values = (gather_8, gather_9, var_224, gather_7))[name = string("concat_5")];
tensor<fp32, [1, 8, ?, ?]> x_padded_3 = reshape(shape = concat_5, x = x_padded_1)[name = string("x_padded_3")];
tensor<int32, [4]> var_231_begin_0 = const()[name = string("op_231_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
tensor<int32, [4]> var_231_end_0 = const()[name = string("op_231_end_0"), val = tensor<int32, [4]>([1, 8, 0, 0])];
tensor<bool, [4]> var_231_end_mask_0 = const()[name = string("op_231_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp32, [1, 8, ?, ?]> var_231 = slice_by_index(begin = var_231_begin_0, end = var_231_end_0, end_mask = var_231_end_mask_0, x = x_padded_3)[name = string("op_231")];
int32 gather_12 = const()[name = string("gather_12"), val = int32(1)];
int32 gather_13 = const()[name = string("gather_13"), val = int32(8)];
int32 concat_6_axis_0 = const()[name = string("concat_6_axis_0"), val = int32(0)];
bool concat_6_interleave_0 = const()[name = string("concat_6_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_6 = concat(axis = concat_6_axis_0, interleave = concat_6_interleave_0, values = (gather_12, gather_13, gather_7, gather_10))[name = string("concat_6")];
tensor<fp32, [1, 8, ?, ?]> var_237 = reshape(shape = concat_6, x = var_231)[name = string("op_237")];
int32 floor_div_2 = floor_div(x = gather_10, y = var_53)[name = string("floor_div_2")];
string var_240_dtype_0 = const()[name = string("op_240_dtype_0"), val = string("fp32")];
fp32 var_241_promoted = const()[name = string("op_241_promoted"), val = fp32(0x1p+0)];
fp32 var_240 = cast(dtype = var_240_dtype_0, x = floor_div_2)[name = string("cast_127")];
fp32 var_242 = add(x = var_240, y = var_241_promoted)[name = string("op_242")];
string var_243_dtype_0 = const()[name = string("op_243_dtype_0"), val = string("int32")];
int32 concat_7_values0_0 = const()[name = string("concat_7_values0_0"), val = int32(1)];
int32 concat_7_values1_0 = const()[name = string("concat_7_values1_0"), val = int32(8)];
int32 concat_7_values2_0 = const()[name = string("concat_7_values2_0"), val = int32(0)];
int32 concat_7_axis_0 = const()[name = string("concat_7_axis_0"), val = int32(0)];
bool concat_7_interleave_0 = const()[name = string("concat_7_interleave_0"), val = bool(false)];
int32 var_243 = cast(dtype = var_243_dtype_0, x = var_242)[name = string("cast_126")];
tensor<int32, [4]> concat_7 = concat(axis = concat_7_axis_0, interleave = concat_7_interleave_0, values = (concat_7_values0_0, concat_7_values1_0, concat_7_values2_0, var_243))[name = string("concat_7")];
tensor<int32, [4]> matrix_bd_3_begin_0 = const()[name = string("matrix_bd_3_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<bool, [4]> matrix_bd_3_end_mask_0 = const()[name = string("matrix_bd_3_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp32, [1, 8, ?, ?]> matrix_bd_3 = slice_by_index(begin = matrix_bd_3_begin_0, end = concat_7, end_mask = matrix_bd_3_end_mask_0, x = var_237)[name = string("matrix_bd_3")];
tensor<fp32, [1, 8, ?, ?]> var_248 = add(x = matrix_ac_1, y = matrix_bd_3)[name = string("op_248")];
fp32 _inversed_scores_1_y_0 = const()[name = string("_inversed_scores_1_y_0"), val = fp32(0x1p-3)];
tensor<fp32, [1, 8, ?, ?]> _inversed_scores_1 = mul(x = var_248, y = _inversed_scores_1_y_0)[name = string("_inversed_scores_1")];
tensor<int32, [1]> var_252_axes_0 = const()[name = string("op_252_axes_0"), val = tensor<int32, [1]>([1])];
tensor<bool, [?, 1, 1, ?]> var_252 = expand_dims(axes = var_252_axes_0, x = masks_1)[name = string("op_252")];
string cast_10_dtype_0 = const()[name = string("cast_10_dtype_0"), val = string("int32")];
tensor<int32, [?, 1, 1, ?]> cast_10 = cast(dtype = cast_10_dtype_0, x = var_252)[name = string("cast_125")];
tensor<bool, [?, 1, 1, ?]> mask_3 = equal(x = cast_10, y = var_57)[name = string("mask_3")];
tensor<int32, [4]> var_254_shape = shape(x = _inversed_scores_1)[name = string("op_254_shape")];
int32 gather_18_batch_dims_0 = const()[name = string("gather_18_batch_dims_0"), val = int32(0)];
bool gather_18_validate_indices_0 = const()[name = string("gather_18_validate_indices_0"), val = bool(false)];
int32 select_9 = const()[name = string("select_9"), val = int32(3)];
int32 gather_18_axis_1 = const()[name = string("gather_18_axis_1"), val = int32(0)];
int32 gather_18 = gather(axis = gather_18_axis_1, batch_dims = gather_18_batch_dims_0, indices = select_9, validate_indices = gather_18_validate_indices_0, x = var_254_shape)[name = string("gather_18")];
int32 concat_8_values0_0 = const()[name = string("concat_8_values0_0"), val = int32(0)];
int32 concat_8_values1_0 = const()[name = string("concat_8_values1_0"), val = int32(1)];
int32 concat_8_values2_0 = const()[name = string("concat_8_values2_0"), val = int32(1)];
int32 concat_8_axis_0 = const()[name = string("concat_8_axis_0"), val = int32(0)];
bool concat_8_interleave_0 = const()[name = string("concat_8_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_8 = concat(axis = concat_8_axis_0, interleave = concat_8_interleave_0, values = (concat_8_values0_0, concat_8_values1_0, concat_8_values2_0, gather_18))[name = string("concat_8")];
tensor<int32, [4]> mask_5_begin_0 = const()[name = string("mask_5_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<bool, [4]> mask_5_end_mask_0 = const()[name = string("mask_5_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<bool, [?, 1, 1, ?]> mask_5 = slice_by_index(begin = mask_5_begin_0, end = concat_8, end_mask = mask_5_end_mask_0, x = mask_3)[name = string("mask_5")];
tensor<fp32, [1, 8, ?, ?]> scores_3 = select(a = var_37, b = _inversed_scores_1, cond = mask_5)[name = string("scores_3")];
tensor<fp32, [1, 8, ?, ?]> var_260 = softmax(axis = var_55, x = scores_3)[name = string("op_260")];
tensor<fp32, [1, 8, ?, ?]> input_21 = select(a = var_46, b = var_260, cond = mask_5)[name = string("input_21")];
bool x_5_transpose_x_0 = const()[name = string("x_5_transpose_x_0"), val = bool(false)];
bool x_5_transpose_y_0 = const()[name = string("x_5_transpose_y_0"), val = bool(false)];
tensor<fp32, [1, 8, ?, 64]> v_3 = transpose(perm = v_3_perm_0, x = v_1)[name = string("transpose_162")];
tensor<fp32, [1, 8, ?, 64]> x_5 = matmul(transpose_x = x_5_transpose_x_0, transpose_y = x_5_transpose_y_0, x = input_21, y = v_3)[name = string("x_5")];
tensor<int32, [4]> var_264_perm_0 = const()[name = string("op_264_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_266 = const()[name = string("op_266"), val = tensor<int32, [3]>([1, -1, 512])];
tensor<fp32, [1, ?, 8, 64]> var_264 = transpose(perm = var_264_perm_0, x = x_5)[name = string("transpose_157")];
tensor<fp32, [1, ?, 512]> input_23 = reshape(shape = var_266, x = var_264)[name = string("input_23")];
tensor<fp32, [1, ?, 512]> input_25 = linear(bias = encoder_encoders_0_self_attn_linear_out_bias, weight = encoder_encoders_0_self_attn_linear_out_weight, x = input_23)[name = string("linear_5")];
tensor<fp32, [1, ?, 512]> input_27 = add(x = input_19, y = input_25)[name = string("input_27")];
tensor<int32, [1]> input_29_axes_0 = const()[name = string("input_29_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp32, [1, ?, 512]> input_29 = layer_norm(axes = input_29_axes_0, beta = encoder_encoders_0_norm_ff_bias, epsilon = var_36, gamma = encoder_encoders_0_norm_ff_weight, x = input_27)[name = string("input_29")];
tensor<fp32, [1, ?, 2048]> input_31 = linear(bias = encoder_encoders_0_feed_forward_w_1_bias, weight = encoder_encoders_0_feed_forward_w_1_weight, x = input_29)[name = string("linear_6")];
tensor<fp32, [1, ?, 2048]> input_33 = silu(x = input_31)[name = string("input_33")];
tensor<fp32, [1, ?, 512]> input_37 = linear(bias = encoder_encoders_0_feed_forward_w_2_bias, weight = encoder_encoders_0_feed_forward_w_2_weight, x = input_33)[name = string("linear_7")];
tensor<fp32, [1, ?, 512]> input_39 = add(x = input_27, y = input_37)[name = string("input_39")];
tensor<int32, [1]> query_3_axes_0 = const()[name = string("query_3_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp32, [1, ?, 512]> query_3 = layer_norm(axes = query_3_axes_0, beta = encoder_encoders_1_norm_mha_bias, epsilon = var_36, gamma = encoder_encoders_1_norm_mha_weight, x = input_39)[name = string("query_3")];
tensor<fp32, [1, ?, 512]> var_315 = linear(bias = encoder_encoders_1_self_attn_linear_q_bias, weight = encoder_encoders_1_self_attn_linear_q_weight, x = query_3)[name = string("linear_8")];
tensor<int32, [4]> var_316 = const()[name = string("op_316"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> q_7 = reshape(shape = var_316, x = var_315)[name = string("q_7")];
tensor<fp32, [1, ?, 512]> var_320 = linear(bias = encoder_encoders_1_self_attn_linear_k_bias, weight = encoder_encoders_1_self_attn_linear_k_weight, x = query_3)[name = string("linear_9")];
tensor<int32, [4]> var_321 = const()[name = string("op_321"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> k_5 = reshape(shape = var_321, x = var_320)[name = string("k_5")];
tensor<fp32, [1, ?, 512]> var_325 = linear(bias = encoder_encoders_1_self_attn_linear_v_bias, weight = encoder_encoders_1_self_attn_linear_v_weight, x = query_3)[name = string("linear_10")];
tensor<int32, [4]> var_326 = const()[name = string("op_326"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> v_5 = reshape(shape = var_326, x = var_325)[name = string("v_5")];
tensor<int32, [4]> v_7_perm_0 = const()[name = string("v_7_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp32, [1, ?, 512]> var_334 = linear(bias = linear_4_bias_0, weight = encoder_encoders_1_self_attn_linear_pos_weight, x = input_7)[name = string("linear_11")];
tensor<int32, [4]> var_335 = const()[name = string("op_335"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> p_5 = reshape(shape = var_335, x = var_334)[name = string("p_5")];
tensor<fp32, [8, 64]> const_10 = const()[name = string("const_10"), val = tensor<fp32, [8, 64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185381312)))];
tensor<fp32, [1, ?, 8, 64]> var_339 = add(x = q_7, y = const_10)[name = string("op_339")];
tensor<fp32, [8, 64]> const_11 = const()[name = string("const_11"), val = tensor<fp32, [8, 64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185383424)))];
tensor<fp32, [1, ?, 8, 64]> var_342 = add(x = q_7, y = const_11)[name = string("op_342")];
bool matrix_ac_3_transpose_x_0 = const()[name = string("matrix_ac_3_transpose_x_0"), val = bool(false)];
bool matrix_ac_3_transpose_y_0 = const()[name = string("matrix_ac_3_transpose_y_0"), val = bool(false)];
tensor<int32, [4]> transpose_64_perm_0 = const()[name = string("transpose_64_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
tensor<int32, [4]> transpose_65_perm_0 = const()[name = string("transpose_65_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
tensor<fp32, [1, 8, 64, ?]> transpose_65 = transpose(perm = transpose_65_perm_0, x = k_5)[name = string("transpose_154")];
tensor<fp32, [1, 8, ?, 64]> transpose_64 = transpose(perm = transpose_64_perm_0, x = var_339)[name = string("transpose_155")];
tensor<fp32, [1, 8, ?, ?]> matrix_ac_3 = matmul(transpose_x = matrix_ac_3_transpose_x_0, transpose_y = matrix_ac_3_transpose_y_0, x = transpose_64, y = transpose_65)[name = string("matrix_ac_3")];
bool matrix_bd_5_transpose_x_0 = const()[name = string("matrix_bd_5_transpose_x_0"), val = bool(false)];
bool matrix_bd_5_transpose_y_0 = const()[name = string("matrix_bd_5_transpose_y_0"), val = bool(false)];
tensor<int32, [4]> transpose_66_perm_0 = const()[name = string("transpose_66_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
tensor<int32, [4]> transpose_67_perm_0 = const()[name = string("transpose_67_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
tensor<fp32, [1, 8, 64, ?]> transpose_67 = transpose(perm = transpose_67_perm_0, x = p_5)[name = string("transpose_152")];
tensor<fp32, [1, 8, ?, 64]> transpose_66 = transpose(perm = transpose_66_perm_0, x = var_342)[name = string("transpose_153")];
tensor<fp32, [1, 8, ?, ?]> matrix_bd_5 = matmul(transpose_x = matrix_bd_5_transpose_x_0, transpose_y = matrix_bd_5_transpose_y_0, x = transpose_66, y = transpose_67)[name = string("matrix_bd_5")];
tensor<int32, [4]> var_348_shape = shape(x = matrix_bd_5)[name = string("op_348_shape")];
int32 gather_21 = const()[name = string("gather_21"), val = int32(1)];
int32 gather_22 = const()[name = string("gather_22"), val = int32(8)];
int32 gather_23_batch_dims_0 = const()[name = string("gather_23_batch_dims_0"), val = int32(0)];
bool gather_23_validate_indices_0 = const()[name = string("gather_23_validate_indices_0"), val = bool(false)];
int32 select_10 = const()[name = string("select_10"), val = int32(2)];
int32 gather_23_axis_1 = const()[name = string("gather_23_axis_1"), val = int32(0)];
int32 gather_23 = gather(axis = gather_23_axis_1, batch_dims = gather_23_batch_dims_0, indices = select_10, validate_indices = gather_23_validate_indices_0, x = var_348_shape)[name = string("gather_23")];
int32 concat_9_axis_0 = const()[name = string("concat_9_axis_0"), val = int32(0)];
bool concat_9_interleave_0 = const()[name = string("concat_9_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_9 = concat(axis = concat_9_axis_0, interleave = concat_9_interleave_0, values = (gather_21, gather_22, gather_23, var_61))[name = string("concat_9")];
fp32 zero_pad_3_value_0 = const()[name = string("zero_pad_3_value_0"), val = fp32(0x0p+0)];
tensor<fp32, [1, 8, ?, 1]> zero_pad_3 = fill(shape = concat_9, value = zero_pad_3_value_0)[name = string("zero_pad_3")];
bool x_padded_5_interleave_0 = const()[name = string("x_padded_5_interleave_0"), val = bool(false)];
tensor<fp32, [1, 8, ?, ?]> x_padded_5 = concat(axis = var_55, interleave = x_padded_5_interleave_0, values = (zero_pad_3, matrix_bd_5))[name = string("x_padded_5")];
int32 gather_24 = const()[name = string("gather_24"), val = int32(1)];
int32 gather_25 = const()[name = string("gather_25"), val = int32(8)];
int32 gather_26_batch_dims_0 = const()[name = string("gather_26_batch_dims_0"), val = int32(0)];
bool gather_26_validate_indices_0 = const()[name = string("gather_26_validate_indices_0"), val = bool(false)];
int32 select_11 = const()[name = string("select_11"), val = int32(3)];
int32 gather_26_axis_1 = const()[name = string("gather_26_axis_1"), val = int32(0)];
int32 gather_26 = gather(axis = gather_26_axis_1, batch_dims = gather_26_batch_dims_0, indices = select_11, validate_indices = gather_26_validate_indices_0, x = var_348_shape)[name = string("gather_26")];
int32 var_359 = const()[name = string("op_359"), val = int32(1)];
int32 var_360 = add(x = gather_26, y = var_359)[name = string("op_360")];
int32 concat_10_axis_0 = const()[name = string("concat_10_axis_0"), val = int32(0)];
bool concat_10_interleave_0 = const()[name = string("concat_10_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_10 = concat(axis = concat_10_axis_0, interleave = concat_10_interleave_0, values = (gather_24, gather_25, var_360, gather_23))[name = string("concat_10")];
tensor<fp32, [1, 8, ?, ?]> x_padded_7 = reshape(shape = concat_10, x = x_padded_5)[name = string("x_padded_7")];
tensor<int32, [4]> var_367_begin_0 = const()[name = string("op_367_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
tensor<int32, [4]> var_367_end_0 = const()[name = string("op_367_end_0"), val = tensor<int32, [4]>([1, 8, 0, 0])];
tensor<bool, [4]> var_367_end_mask_0 = const()[name = string("op_367_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp32, [1, 8, ?, ?]> var_367 = slice_by_index(begin = var_367_begin_0, end = var_367_end_0, end_mask = var_367_end_mask_0, x = x_padded_7)[name = string("op_367")];
int32 gather_28 = const()[name = string("gather_28"), val = int32(1)];
int32 gather_29 = const()[name = string("gather_29"), val = int32(8)];
int32 concat_11_axis_0 = const()[name = string("concat_11_axis_0"), val = int32(0)];
bool concat_11_interleave_0 = const()[name = string("concat_11_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_11 = concat(axis = concat_11_axis_0, interleave = concat_11_interleave_0, values = (gather_28, gather_29, gather_23, gather_26))[name = string("concat_11")];
tensor<fp32, [1, 8, ?, ?]> var_373 = reshape(shape = concat_11, x = var_367)[name = string("op_373")];
int32 floor_div_3 = floor_div(x = gather_26, y = var_53)[name = string("floor_div_3")];
string var_376_dtype_0 = const()[name = string("op_376_dtype_0"), val = string("fp32")];
fp32 var_377_promoted = const()[name = string("op_377_promoted"), val = fp32(0x1p+0)];
fp32 var_376 = cast(dtype = var_376_dtype_0, x = floor_div_3)[name = string("cast_124")];
fp32 var_378 = add(x = var_376, y = var_377_promoted)[name = string("op_378")];
string var_379_dtype_0 = const()[name = string("op_379_dtype_0"), val = string("int32")];
int32 concat_12_values0_0 = const()[name = string("concat_12_values0_0"), val = int32(1)];
int32 concat_12_values1_0 = const()[name = string("concat_12_values1_0"), val = int32(8)];
int32 concat_12_values2_0 = const()[name = string("concat_12_values2_0"), val = int32(0)];
int32 concat_12_axis_0 = const()[name = string("concat_12_axis_0"), val = int32(0)];
bool concat_12_interleave_0 = const()[name = string("concat_12_interleave_0"), val = bool(false)];
int32 var_379 = cast(dtype = var_379_dtype_0, x = var_378)[name = string("cast_123")];
tensor<int32, [4]> concat_12 = concat(axis = concat_12_axis_0, interleave = concat_12_interleave_0, values = (concat_12_values0_0, concat_12_values1_0, concat_12_values2_0, var_379))[name = string("concat_12")];
tensor<int32, [4]> matrix_bd_7_begin_0 = const()[name = string("matrix_bd_7_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<bool, [4]> matrix_bd_7_end_mask_0 = const()[name = string("matrix_bd_7_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp32, [1, 8, ?, ?]> matrix_bd_7 = slice_by_index(begin = matrix_bd_7_begin_0, end = concat_12, end_mask = matrix_bd_7_end_mask_0, x = var_373)[name = string("matrix_bd_7")];
tensor<fp32, [1, 8, ?, ?]> var_384 = add(x = matrix_ac_3, y = matrix_bd_7)[name = string("op_384")];
fp32 _inversed_scores_5_y_0 = const()[name = string("_inversed_scores_5_y_0"), val = fp32(0x1p-3)];
tensor<fp32, [1, 8, ?, ?]> _inversed_scores_5 = mul(x = var_384, y = _inversed_scores_5_y_0)[name = string("_inversed_scores_5")];
tensor<int32, [4]> var_390_shape = shape(x = _inversed_scores_5)[name = string("op_390_shape")];
int32 gather_34_batch_dims_0 = const()[name = string("gather_34_batch_dims_0"), val = int32(0)];
bool gather_34_validate_indices_0 = const()[name = string("gather_34_validate_indices_0"), val = bool(false)];
int32 select_13 = const()[name = string("select_13"), val = int32(3)];
int32 gather_34_axis_1 = const()[name = string("gather_34_axis_1"), val = int32(0)];
int32 gather_34 = gather(axis = gather_34_axis_1, batch_dims = gather_34_batch_dims_0, indices = select_13, validate_indices = gather_34_validate_indices_0, x = var_390_shape)[name = string("gather_34")];
int32 concat_13_values0_0 = const()[name = string("concat_13_values0_0"), val = int32(0)];
int32 concat_13_values1_0 = const()[name = string("concat_13_values1_0"), val = int32(1)];
int32 concat_13_values2_0 = const()[name = string("concat_13_values2_0"), val = int32(1)];
int32 concat_13_axis_0 = const()[name = string("concat_13_axis_0"), val = int32(0)];
bool concat_13_interleave_0 = const()[name = string("concat_13_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_13 = concat(axis = concat_13_axis_0, interleave = concat_13_interleave_0, values = (concat_13_values0_0, concat_13_values1_0, concat_13_values2_0, gather_34))[name = string("concat_13")];
tensor<int32, [4]> mask_9_begin_0 = const()[name = string("mask_9_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<bool, [4]> mask_9_end_mask_0 = const()[name = string("mask_9_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<bool, [?, 1, 1, ?]> mask_9 = slice_by_index(begin = mask_9_begin_0, end = concat_13, end_mask = mask_9_end_mask_0, x = mask_3)[name = string("mask_9")];
tensor<fp32, [1, 8, ?, ?]> scores_7 = select(a = var_37, b = _inversed_scores_5, cond = mask_9)[name = string("scores_7")];
tensor<fp32, [1, 8, ?, ?]> var_396 = softmax(axis = var_55, x = scores_7)[name = string("op_396")];
tensor<fp32, [1, 8, ?, ?]> input_41 = select(a = var_46, b = var_396, cond = mask_9)[name = string("input_41")];
bool x_7_transpose_x_0 = const()[name = string("x_7_transpose_x_0"), val = bool(false)];
bool x_7_transpose_y_0 = const()[name = string("x_7_transpose_y_0"), val = bool(false)];
tensor<fp32, [1, 8, ?, 64]> v_7 = transpose(perm = v_7_perm_0, x = v_5)[name = string("transpose_156")];
tensor<fp32, [1, 8, ?, 64]> x_7 = matmul(transpose_x = x_7_transpose_x_0, transpose_y = x_7_transpose_y_0, x = input_41, y = v_7)[name = string("x_7")];
tensor<int32, [4]> var_400_perm_0 = const()[name = string("op_400_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_402 = const()[name = string("op_402"), val = tensor<int32, [3]>([1, -1, 512])];
tensor<fp32, [1, ?, 8, 64]> var_400 = transpose(perm = var_400_perm_0, x = x_7)[name = string("transpose_151")];
tensor<fp32, [1, ?, 512]> input_43 = reshape(shape = var_402, x = var_400)[name = string("input_43")];
tensor<fp32, [1, ?, 512]> input_45 = linear(bias = encoder_encoders_1_self_attn_linear_out_bias, weight = encoder_encoders_1_self_attn_linear_out_weight, x = input_43)[name = string("linear_12")];
tensor<fp32, [1, ?, 512]> input_47 = add(x = input_39, y = input_45)[name = string("input_47")];
tensor<int32, [1]> input_49_axes_0 = const()[name = string("input_49_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp32, [1, ?, 512]> input_49 = layer_norm(axes = input_49_axes_0, beta = encoder_encoders_1_norm_ff_bias, epsilon = var_36, gamma = encoder_encoders_1_norm_ff_weight, x = input_47)[name = string("input_49")];
tensor<fp32, [1, ?, 2048]> input_51 = linear(bias = encoder_encoders_1_feed_forward_w_1_bias, weight = encoder_encoders_1_feed_forward_w_1_weight, x = input_49)[name = string("linear_13")];
tensor<fp32, [1, ?, 2048]> input_53 = silu(x = input_51)[name = string("input_53")];
tensor<fp32, [1, ?, 512]> input_57 = linear(bias = encoder_encoders_1_feed_forward_w_2_bias, weight = encoder_encoders_1_feed_forward_w_2_weight, x = input_53)[name = string("linear_14")];
tensor<fp32, [1, ?, 512]> input_59 = add(x = input_47, y = input_57)[name = string("input_59")];
tensor<int32, [1]> query_5_axes_0 = const()[name = string("query_5_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp32, [1, ?, 512]> query_5 = layer_norm(axes = query_5_axes_0, beta = encoder_encoders_2_norm_mha_bias, epsilon = var_36, gamma = encoder_encoders_2_norm_mha_weight, x = input_59)[name = string("query_5")];
tensor<fp32, [1, ?, 512]> var_445 = linear(bias = encoder_encoders_2_self_attn_linear_q_bias, weight = encoder_encoders_2_self_attn_linear_q_weight, x = query_5)[name = string("linear_15")];
tensor<int32, [4]> var_446 = const()[name = string("op_446"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> q_13 = reshape(shape = var_446, x = var_445)[name = string("q_13")];
tensor<fp32, [1, ?, 512]> var_450 = linear(bias = encoder_encoders_2_self_attn_linear_k_bias, weight = encoder_encoders_2_self_attn_linear_k_weight, x = query_5)[name = string("linear_16")];
tensor<int32, [4]> var_451 = const()[name = string("op_451"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> k_9 = reshape(shape = var_451, x = var_450)[name = string("k_9")];
tensor<fp32, [1, ?, 512]> var_455 = linear(bias = encoder_encoders_2_self_attn_linear_v_bias, weight = encoder_encoders_2_self_attn_linear_v_weight, x = query_5)[name = string("linear_17")];
tensor<int32, [4]> var_456 = const()[name = string("op_456"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> v_9 = reshape(shape = var_456, x = var_455)[name = string("v_9")];
tensor<int32, [4]> v_11_perm_0 = const()[name = string("v_11_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp32, [1, ?, 512]> var_464 = linear(bias = linear_4_bias_0, weight = encoder_encoders_2_self_attn_linear_pos_weight, x = input_7)[name = string("linear_18")];
tensor<int32, [4]> var_465 = const()[name = string("op_465"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> p_9 = reshape(shape = var_465, x = var_464)[name = string("p_9")];
tensor<fp32, [8, 64]> const_12 = const()[name = string("const_12"), val = tensor<fp32, [8, 64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185385536)))];
tensor<fp32, [1, ?, 8, 64]> var_469 = add(x = q_13, y = const_12)[name = string("op_469")];
tensor<fp32, [8, 64]> const_13 = const()[name = string("const_13"), val = tensor<fp32, [8, 64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185387648)))];
tensor<fp32, [1, ?, 8, 64]> var_472 = add(x = q_13, y = const_13)[name = string("op_472")];
bool matrix_ac_5_transpose_x_0 = const()[name = string("matrix_ac_5_transpose_x_0"), val = bool(false)];
bool matrix_ac_5_transpose_y_0 = const()[name = string("matrix_ac_5_transpose_y_0"), val = bool(false)];
tensor<int32, [4]> transpose_68_perm_0 = const()[name = string("transpose_68_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
tensor<int32, [4]> transpose_69_perm_0 = const()[name = string("transpose_69_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
tensor<fp32, [1, 8, 64, ?]> transpose_69 = transpose(perm = transpose_69_perm_0, x = k_9)[name = string("transpose_148")];
tensor<fp32, [1, 8, ?, 64]> transpose_68 = transpose(perm = transpose_68_perm_0, x = var_469)[name = string("transpose_149")];
tensor<fp32, [1, 8, ?, ?]> matrix_ac_5 = matmul(transpose_x = matrix_ac_5_transpose_x_0, transpose_y = matrix_ac_5_transpose_y_0, x = transpose_68, y = transpose_69)[name = string("matrix_ac_5")];
bool matrix_bd_9_transpose_x_0 = const()[name = string("matrix_bd_9_transpose_x_0"), val = bool(false)];
bool matrix_bd_9_transpose_y_0 = const()[name = string("matrix_bd_9_transpose_y_0"), val = bool(false)];
tensor<int32, [4]> transpose_70_perm_0 = const()[name = string("transpose_70_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
tensor<int32, [4]> transpose_71_perm_0 = const()[name = string("transpose_71_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
tensor<fp32, [1, 8, 64, ?]> transpose_71 = transpose(perm = transpose_71_perm_0, x = p_9)[name = string("transpose_146")];
tensor<fp32, [1, 8, ?, 64]> transpose_70 = transpose(perm = transpose_70_perm_0, x = var_472)[name = string("transpose_147")];
tensor<fp32, [1, 8, ?, ?]> matrix_bd_9 = matmul(transpose_x = matrix_bd_9_transpose_x_0, transpose_y = matrix_bd_9_transpose_y_0, x = transpose_70, y = transpose_71)[name = string("matrix_bd_9")];
tensor<int32, [4]> var_478_shape = shape(x = matrix_bd_9)[name = string("op_478_shape")];
int32 gather_37 = const()[name = string("gather_37"), val = int32(1)];
int32 gather_38 = const()[name = string("gather_38"), val = int32(8)];
int32 gather_39_batch_dims_0 = const()[name = string("gather_39_batch_dims_0"), val = int32(0)];
bool gather_39_validate_indices_0 = const()[name = string("gather_39_validate_indices_0"), val = bool(false)];
int32 select_14 = const()[name = string("select_14"), val = int32(2)];
int32 gather_39_axis_1 = const()[name = string("gather_39_axis_1"), val = int32(0)];
int32 gather_39 = gather(axis = gather_39_axis_1, batch_dims = gather_39_batch_dims_0, indices = select_14, validate_indices = gather_39_validate_indices_0, x = var_478_shape)[name = string("gather_39")];
int32 concat_14_axis_0 = const()[name = string("concat_14_axis_0"), val = int32(0)];
bool concat_14_interleave_0 = const()[name = string("concat_14_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_14 = concat(axis = concat_14_axis_0, interleave = concat_14_interleave_0, values = (gather_37, gather_38, gather_39, var_61))[name = string("concat_14")];
fp32 zero_pad_5_value_0 = const()[name = string("zero_pad_5_value_0"), val = fp32(0x0p+0)];
tensor<fp32, [1, 8, ?, 1]> zero_pad_5 = fill(shape = concat_14, value = zero_pad_5_value_0)[name = string("zero_pad_5")];
bool x_padded_9_interleave_0 = const()[name = string("x_padded_9_interleave_0"), val = bool(false)];
tensor<fp32, [1, 8, ?, ?]> x_padded_9 = concat(axis = var_55, interleave = x_padded_9_interleave_0, values = (zero_pad_5, matrix_bd_9))[name = string("x_padded_9")];
int32 gather_40 = const()[name = string("gather_40"), val = int32(1)];
int32 gather_41 = const()[name = string("gather_41"), val = int32(8)];
int32 gather_42_batch_dims_0 = const()[name = string("gather_42_batch_dims_0"), val = int32(0)];
bool gather_42_validate_indices_0 = const()[name = string("gather_42_validate_indices_0"), val = bool(false)];
int32 select_15 = const()[name = string("select_15"), val = int32(3)];
int32 gather_42_axis_1 = const()[name = string("gather_42_axis_1"), val = int32(0)];
int32 gather_42 = gather(axis = gather_42_axis_1, batch_dims = gather_42_batch_dims_0, indices = select_15, validate_indices = gather_42_validate_indices_0, x = var_478_shape)[name = string("gather_42")];
int32 var_489 = const()[name = string("op_489"), val = int32(1)];
int32 var_490 = add(x = gather_42, y = var_489)[name = string("op_490")];
int32 concat_15_axis_0 = const()[name = string("concat_15_axis_0"), val = int32(0)];
bool concat_15_interleave_0 = const()[name = string("concat_15_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_15 = concat(axis = concat_15_axis_0, interleave = concat_15_interleave_0, values = (gather_40, gather_41, var_490, gather_39))[name = string("concat_15")];
tensor<fp32, [1, 8, ?, ?]> x_padded_11 = reshape(shape = concat_15, x = x_padded_9)[name = string("x_padded_11")];
tensor<int32, [4]> var_497_begin_0 = const()[name = string("op_497_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
tensor<int32, [4]> var_497_end_0 = const()[name = string("op_497_end_0"), val = tensor<int32, [4]>([1, 8, 0, 0])];
tensor<bool, [4]> var_497_end_mask_0 = const()[name = string("op_497_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp32, [1, 8, ?, ?]> var_497 = slice_by_index(begin = var_497_begin_0, end = var_497_end_0, end_mask = var_497_end_mask_0, x = x_padded_11)[name = string("op_497")];
int32 gather_44 = const()[name = string("gather_44"), val = int32(1)];
int32 gather_45 = const()[name = string("gather_45"), val = int32(8)];
int32 concat_16_axis_0 = const()[name = string("concat_16_axis_0"), val = int32(0)];
bool concat_16_interleave_0 = const()[name = string("concat_16_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_16 = concat(axis = concat_16_axis_0, interleave = concat_16_interleave_0, values = (gather_44, gather_45, gather_39, gather_42))[name = string("concat_16")];
tensor<fp32, [1, 8, ?, ?]> var_503 = reshape(shape = concat_16, x = var_497)[name = string("op_503")];
int32 floor_div_4 = floor_div(x = gather_42, y = var_53)[name = string("floor_div_4")];
string var_506_dtype_0 = const()[name = string("op_506_dtype_0"), val = string("fp32")];
fp32 var_507_promoted = const()[name = string("op_507_promoted"), val = fp32(0x1p+0)];
fp32 var_506 = cast(dtype = var_506_dtype_0, x = floor_div_4)[name = string("cast_122")];
fp32 var_508 = add(x = var_506, y = var_507_promoted)[name = string("op_508")];
string var_509_dtype_0 = const()[name = string("op_509_dtype_0"), val = string("int32")];
int32 concat_17_values0_0 = const()[name = string("concat_17_values0_0"), val = int32(1)];
int32 concat_17_values1_0 = const()[name = string("concat_17_values1_0"), val = int32(8)];
int32 concat_17_values2_0 = const()[name = string("concat_17_values2_0"), val = int32(0)];
int32 concat_17_axis_0 = const()[name = string("concat_17_axis_0"), val = int32(0)];
bool concat_17_interleave_0 = const()[name = string("concat_17_interleave_0"), val = bool(false)];
int32 var_509 = cast(dtype = var_509_dtype_0, x = var_508)[name = string("cast_121")];
tensor<int32, [4]> concat_17 = concat(axis = concat_17_axis_0, interleave = concat_17_interleave_0, values = (concat_17_values0_0, concat_17_values1_0, concat_17_values2_0, var_509))[name = string("concat_17")];
tensor<int32, [4]> matrix_bd_11_begin_0 = const()[name = string("matrix_bd_11_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<bool, [4]> matrix_bd_11_end_mask_0 = const()[name = string("matrix_bd_11_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp32, [1, 8, ?, ?]> matrix_bd_11 = slice_by_index(begin = matrix_bd_11_begin_0, end = concat_17, end_mask = matrix_bd_11_end_mask_0, x = var_503)[name = string("matrix_bd_11")];
tensor<fp32, [1, 8, ?, ?]> var_514 = add(x = matrix_ac_5, y = matrix_bd_11)[name = string("op_514")];
fp32 _inversed_scores_9_y_0 = const()[name = string("_inversed_scores_9_y_0"), val = fp32(0x1p-3)];
tensor<fp32, [1, 8, ?, ?]> _inversed_scores_9 = mul(x = var_514, y = _inversed_scores_9_y_0)[name = string("_inversed_scores_9")];
tensor<int32, [4]> var_520_shape = shape(x = _inversed_scores_9)[name = string("op_520_shape")];
int32 gather_50_batch_dims_0 = const()[name = string("gather_50_batch_dims_0"), val = int32(0)];
bool gather_50_validate_indices_0 = const()[name = string("gather_50_validate_indices_0"), val = bool(false)];
int32 select_17 = const()[name = string("select_17"), val = int32(3)];
int32 gather_50_axis_1 = const()[name = string("gather_50_axis_1"), val = int32(0)];
int32 gather_50 = gather(axis = gather_50_axis_1, batch_dims = gather_50_batch_dims_0, indices = select_17, validate_indices = gather_50_validate_indices_0, x = var_520_shape)[name = string("gather_50")];
int32 concat_18_values0_0 = const()[name = string("concat_18_values0_0"), val = int32(0)];
int32 concat_18_values1_0 = const()[name = string("concat_18_values1_0"), val = int32(1)];
int32 concat_18_values2_0 = const()[name = string("concat_18_values2_0"), val = int32(1)];
int32 concat_18_axis_0 = const()[name = string("concat_18_axis_0"), val = int32(0)];
bool concat_18_interleave_0 = const()[name = string("concat_18_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_18 = concat(axis = concat_18_axis_0, interleave = concat_18_interleave_0, values = (concat_18_values0_0, concat_18_values1_0, concat_18_values2_0, gather_50))[name = string("concat_18")];
tensor<int32, [4]> mask_13_begin_0 = const()[name = string("mask_13_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<bool, [4]> mask_13_end_mask_0 = const()[name = string("mask_13_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<bool, [?, 1, 1, ?]> mask_13 = slice_by_index(begin = mask_13_begin_0, end = concat_18, end_mask = mask_13_end_mask_0, x = mask_3)[name = string("mask_13")];
tensor<fp32, [1, 8, ?, ?]> scores_11 = select(a = var_37, b = _inversed_scores_9, cond = mask_13)[name = string("scores_11")];
tensor<fp32, [1, 8, ?, ?]> var_526 = softmax(axis = var_55, x = scores_11)[name = string("op_526")];
tensor<fp32, [1, 8, ?, ?]> input_61 = select(a = var_46, b = var_526, cond = mask_13)[name = string("input_61")];
bool x_9_transpose_x_0 = const()[name = string("x_9_transpose_x_0"), val = bool(false)];
bool x_9_transpose_y_0 = const()[name = string("x_9_transpose_y_0"), val = bool(false)];
tensor<fp32, [1, 8, ?, 64]> v_11 = transpose(perm = v_11_perm_0, x = v_9)[name = string("transpose_150")];
tensor<fp32, [1, 8, ?, 64]> x_9 = matmul(transpose_x = x_9_transpose_x_0, transpose_y = x_9_transpose_y_0, x = input_61, y = v_11)[name = string("x_9")];
tensor<int32, [4]> var_530_perm_0 = const()[name = string("op_530_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_532 = const()[name = string("op_532"), val = tensor<int32, [3]>([1, -1, 512])];
tensor<fp32, [1, ?, 8, 64]> var_530 = transpose(perm = var_530_perm_0, x = x_9)[name = string("transpose_145")];
tensor<fp32, [1, ?, 512]> input_63 = reshape(shape = var_532, x = var_530)[name = string("input_63")];
tensor<fp32, [1, ?, 512]> input_65 = linear(bias = encoder_encoders_2_self_attn_linear_out_bias, weight = encoder_encoders_2_self_attn_linear_out_weight, x = input_63)[name = string("linear_19")];
tensor<fp32, [1, ?, 512]> input_67 = add(x = input_59, y = input_65)[name = string("input_67")];
tensor<int32, [1]> input_69_axes_0 = const()[name = string("input_69_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp32, [1, ?, 512]> input_69 = layer_norm(axes = input_69_axes_0, beta = encoder_encoders_2_norm_ff_bias, epsilon = var_36, gamma = encoder_encoders_2_norm_ff_weight, x = input_67)[name = string("input_69")];
tensor<fp32, [1, ?, 2048]> input_71 = linear(bias = encoder_encoders_2_feed_forward_w_1_bias, weight = encoder_encoders_2_feed_forward_w_1_weight, x = input_69)[name = string("linear_20")];
tensor<fp32, [1, ?, 2048]> input_73 = silu(x = input_71)[name = string("input_73")];
tensor<fp32, [1, ?, 512]> input_77 = linear(bias = encoder_encoders_2_feed_forward_w_2_bias, weight = encoder_encoders_2_feed_forward_w_2_weight, x = input_73)[name = string("linear_21")];
tensor<fp32, [1, ?, 512]> input_79 = add(x = input_67, y = input_77)[name = string("input_79")];
tensor<int32, [1]> query_7_axes_0 = const()[name = string("query_7_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp32, [1, ?, 512]> query_7 = layer_norm(axes = query_7_axes_0, beta = encoder_encoders_3_norm_mha_bias, epsilon = var_36, gamma = encoder_encoders_3_norm_mha_weight, x = input_79)[name = string("query_7")];
tensor<fp32, [1, ?, 512]> var_575 = linear(bias = encoder_encoders_3_self_attn_linear_q_bias, weight = encoder_encoders_3_self_attn_linear_q_weight, x = query_7)[name = string("linear_22")];
tensor<int32, [4]> var_576 = const()[name = string("op_576"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> q_19 = reshape(shape = var_576, x = var_575)[name = string("q_19")];
tensor<fp32, [1, ?, 512]> var_580 = linear(bias = encoder_encoders_3_self_attn_linear_k_bias, weight = encoder_encoders_3_self_attn_linear_k_weight, x = query_7)[name = string("linear_23")];
tensor<int32, [4]> var_581 = const()[name = string("op_581"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> k_13 = reshape(shape = var_581, x = var_580)[name = string("k_13")];
tensor<fp32, [1, ?, 512]> var_585 = linear(bias = encoder_encoders_3_self_attn_linear_v_bias, weight = encoder_encoders_3_self_attn_linear_v_weight, x = query_7)[name = string("linear_24")];
tensor<int32, [4]> var_586 = const()[name = string("op_586"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> v_13 = reshape(shape = var_586, x = var_585)[name = string("v_13")];
tensor<int32, [4]> v_15_perm_0 = const()[name = string("v_15_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp32, [1, ?, 512]> var_594 = linear(bias = linear_4_bias_0, weight = encoder_encoders_3_self_attn_linear_pos_weight, x = input_7)[name = string("linear_25")];
tensor<int32, [4]> var_595 = const()[name = string("op_595"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> p_13 = reshape(shape = var_595, x = var_594)[name = string("p_13")];
tensor<fp32, [8, 64]> const_14 = const()[name = string("const_14"), val = tensor<fp32, [8, 64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185389760)))];
tensor<fp32, [1, ?, 8, 64]> var_599 = add(x = q_19, y = const_14)[name = string("op_599")];
tensor<fp32, [8, 64]> const_15 = const()[name = string("const_15"), val = tensor<fp32, [8, 64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185391872)))];
tensor<fp32, [1, ?, 8, 64]> var_602 = add(x = q_19, y = const_15)[name = string("op_602")];
bool matrix_ac_7_transpose_x_0 = const()[name = string("matrix_ac_7_transpose_x_0"), val = bool(false)];
bool matrix_ac_7_transpose_y_0 = const()[name = string("matrix_ac_7_transpose_y_0"), val = bool(false)];
tensor<int32, [4]> transpose_72_perm_0 = const()[name = string("transpose_72_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
tensor<int32, [4]> transpose_73_perm_0 = const()[name = string("transpose_73_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
tensor<fp32, [1, 8, 64, ?]> transpose_73 = transpose(perm = transpose_73_perm_0, x = k_13)[name = string("transpose_142")];
tensor<fp32, [1, 8, ?, 64]> transpose_72 = transpose(perm = transpose_72_perm_0, x = var_599)[name = string("transpose_143")];
tensor<fp32, [1, 8, ?, ?]> matrix_ac_7 = matmul(transpose_x = matrix_ac_7_transpose_x_0, transpose_y = matrix_ac_7_transpose_y_0, x = transpose_72, y = transpose_73)[name = string("matrix_ac_7")];
bool matrix_bd_13_transpose_x_0 = const()[name = string("matrix_bd_13_transpose_x_0"), val = bool(false)];
bool matrix_bd_13_transpose_y_0 = const()[name = string("matrix_bd_13_transpose_y_0"), val = bool(false)];
tensor<int32, [4]> transpose_74_perm_0 = const()[name = string("transpose_74_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
tensor<int32, [4]> transpose_75_perm_0 = const()[name = string("transpose_75_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
tensor<fp32, [1, 8, 64, ?]> transpose_75 = transpose(perm = transpose_75_perm_0, x = p_13)[name = string("transpose_140")];
tensor<fp32, [1, 8, ?, 64]> transpose_74 = transpose(perm = transpose_74_perm_0, x = var_602)[name = string("transpose_141")];
tensor<fp32, [1, 8, ?, ?]> matrix_bd_13 = matmul(transpose_x = matrix_bd_13_transpose_x_0, transpose_y = matrix_bd_13_transpose_y_0, x = transpose_74, y = transpose_75)[name = string("matrix_bd_13")];
tensor<int32, [4]> var_608_shape = shape(x = matrix_bd_13)[name = string("op_608_shape")];
int32 gather_53 = const()[name = string("gather_53"), val = int32(1)];
int32 gather_54 = const()[name = string("gather_54"), val = int32(8)];
int32 gather_55_batch_dims_0 = const()[name = string("gather_55_batch_dims_0"), val = int32(0)];
bool gather_55_validate_indices_0 = const()[name = string("gather_55_validate_indices_0"), val = bool(false)];
int32 select_18 = const()[name = string("select_18"), val = int32(2)];
int32 gather_55_axis_1 = const()[name = string("gather_55_axis_1"), val = int32(0)];
int32 gather_55 = gather(axis = gather_55_axis_1, batch_dims = gather_55_batch_dims_0, indices = select_18, validate_indices = gather_55_validate_indices_0, x = var_608_shape)[name = string("gather_55")];
int32 concat_19_axis_0 = const()[name = string("concat_19_axis_0"), val = int32(0)];
bool concat_19_interleave_0 = const()[name = string("concat_19_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_19 = concat(axis = concat_19_axis_0, interleave = concat_19_interleave_0, values = (gather_53, gather_54, gather_55, var_61))[name = string("concat_19")];
fp32 zero_pad_7_value_0 = const()[name = string("zero_pad_7_value_0"), val = fp32(0x0p+0)];
tensor<fp32, [1, 8, ?, 1]> zero_pad_7 = fill(shape = concat_19, value = zero_pad_7_value_0)[name = string("zero_pad_7")];
bool x_padded_13_interleave_0 = const()[name = string("x_padded_13_interleave_0"), val = bool(false)];
tensor<fp32, [1, 8, ?, ?]> x_padded_13 = concat(axis = var_55, interleave = x_padded_13_interleave_0, values = (zero_pad_7, matrix_bd_13))[name = string("x_padded_13")];
int32 gather_56 = const()[name = string("gather_56"), val = int32(1)];
int32 gather_57 = const()[name = string("gather_57"), val = int32(8)];
int32 gather_58_batch_dims_0 = const()[name = string("gather_58_batch_dims_0"), val = int32(0)];
bool gather_58_validate_indices_0 = const()[name = string("gather_58_validate_indices_0"), val = bool(false)];
int32 select_19 = const()[name = string("select_19"), val = int32(3)];
int32 gather_58_axis_1 = const()[name = string("gather_58_axis_1"), val = int32(0)];
int32 gather_58 = gather(axis = gather_58_axis_1, batch_dims = gather_58_batch_dims_0, indices = select_19, validate_indices = gather_58_validate_indices_0, x = var_608_shape)[name = string("gather_58")];
int32 var_619 = const()[name = string("op_619"), val = int32(1)];
int32 var_620 = add(x = gather_58, y = var_619)[name = string("op_620")];
int32 concat_20_axis_0 = const()[name = string("concat_20_axis_0"), val = int32(0)];
bool concat_20_interleave_0 = const()[name = string("concat_20_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_20 = concat(axis = concat_20_axis_0, interleave = concat_20_interleave_0, values = (gather_56, gather_57, var_620, gather_55))[name = string("concat_20")];
tensor<fp32, [1, 8, ?, ?]> x_padded_15 = reshape(shape = concat_20, x = x_padded_13)[name = string("x_padded_15")];
tensor<int32, [4]> var_627_begin_0 = const()[name = string("op_627_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
tensor<int32, [4]> var_627_end_0 = const()[name = string("op_627_end_0"), val = tensor<int32, [4]>([1, 8, 0, 0])];
tensor<bool, [4]> var_627_end_mask_0 = const()[name = string("op_627_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp32, [1, 8, ?, ?]> var_627 = slice_by_index(begin = var_627_begin_0, end = var_627_end_0, end_mask = var_627_end_mask_0, x = x_padded_15)[name = string("op_627")];
int32 gather_60 = const()[name = string("gather_60"), val = int32(1)];
int32 gather_61 = const()[name = string("gather_61"), val = int32(8)];
int32 concat_21_axis_0 = const()[name = string("concat_21_axis_0"), val = int32(0)];
bool concat_21_interleave_0 = const()[name = string("concat_21_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_21 = concat(axis = concat_21_axis_0, interleave = concat_21_interleave_0, values = (gather_60, gather_61, gather_55, gather_58))[name = string("concat_21")];
tensor<fp32, [1, 8, ?, ?]> var_633 = reshape(shape = concat_21, x = var_627)[name = string("op_633")];
int32 floor_div_5 = floor_div(x = gather_58, y = var_53)[name = string("floor_div_5")];
string var_636_dtype_0 = const()[name = string("op_636_dtype_0"), val = string("fp32")];
fp32 var_637_promoted = const()[name = string("op_637_promoted"), val = fp32(0x1p+0)];
fp32 var_636 = cast(dtype = var_636_dtype_0, x = floor_div_5)[name = string("cast_120")];
fp32 var_638 = add(x = var_636, y = var_637_promoted)[name = string("op_638")];
string var_639_dtype_0 = const()[name = string("op_639_dtype_0"), val = string("int32")];
int32 concat_22_values0_0 = const()[name = string("concat_22_values0_0"), val = int32(1)];
int32 concat_22_values1_0 = const()[name = string("concat_22_values1_0"), val = int32(8)];
int32 concat_22_values2_0 = const()[name = string("concat_22_values2_0"), val = int32(0)];
int32 concat_22_axis_0 = const()[name = string("concat_22_axis_0"), val = int32(0)];
bool concat_22_interleave_0 = const()[name = string("concat_22_interleave_0"), val = bool(false)];
int32 var_639 = cast(dtype = var_639_dtype_0, x = var_638)[name = string("cast_119")];
tensor<int32, [4]> concat_22 = concat(axis = concat_22_axis_0, interleave = concat_22_interleave_0, values = (concat_22_values0_0, concat_22_values1_0, concat_22_values2_0, var_639))[name = string("concat_22")];
tensor<int32, [4]> matrix_bd_15_begin_0 = const()[name = string("matrix_bd_15_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<bool, [4]> matrix_bd_15_end_mask_0 = const()[name = string("matrix_bd_15_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp32, [1, 8, ?, ?]> matrix_bd_15 = slice_by_index(begin = matrix_bd_15_begin_0, end = concat_22, end_mask = matrix_bd_15_end_mask_0, x = var_633)[name = string("matrix_bd_15")];
tensor<fp32, [1, 8, ?, ?]> var_644 = add(x = matrix_ac_7, y = matrix_bd_15)[name = string("op_644")];
fp32 _inversed_scores_13_y_0 = const()[name = string("_inversed_scores_13_y_0"), val = fp32(0x1p-3)];
tensor<fp32, [1, 8, ?, ?]> _inversed_scores_13 = mul(x = var_644, y = _inversed_scores_13_y_0)[name = string("_inversed_scores_13")];
tensor<int32, [4]> var_650_shape = shape(x = _inversed_scores_13)[name = string("op_650_shape")];
int32 gather_66_batch_dims_0 = const()[name = string("gather_66_batch_dims_0"), val = int32(0)];
bool gather_66_validate_indices_0 = const()[name = string("gather_66_validate_indices_0"), val = bool(false)];
int32 select_21 = const()[name = string("select_21"), val = int32(3)];
int32 gather_66_axis_1 = const()[name = string("gather_66_axis_1"), val = int32(0)];
int32 gather_66 = gather(axis = gather_66_axis_1, batch_dims = gather_66_batch_dims_0, indices = select_21, validate_indices = gather_66_validate_indices_0, x = var_650_shape)[name = string("gather_66")];
int32 concat_23_values0_0 = const()[name = string("concat_23_values0_0"), val = int32(0)];
int32 concat_23_values1_0 = const()[name = string("concat_23_values1_0"), val = int32(1)];
int32 concat_23_values2_0 = const()[name = string("concat_23_values2_0"), val = int32(1)];
int32 concat_23_axis_0 = const()[name = string("concat_23_axis_0"), val = int32(0)];
bool concat_23_interleave_0 = const()[name = string("concat_23_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_23 = concat(axis = concat_23_axis_0, interleave = concat_23_interleave_0, values = (concat_23_values0_0, concat_23_values1_0, concat_23_values2_0, gather_66))[name = string("concat_23")];
tensor<int32, [4]> mask_17_begin_0 = const()[name = string("mask_17_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<bool, [4]> mask_17_end_mask_0 = const()[name = string("mask_17_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<bool, [?, 1, 1, ?]> mask_17 = slice_by_index(begin = mask_17_begin_0, end = concat_23, end_mask = mask_17_end_mask_0, x = mask_3)[name = string("mask_17")];
tensor<fp32, [1, 8, ?, ?]> scores_15 = select(a = var_37, b = _inversed_scores_13, cond = mask_17)[name = string("scores_15")];
tensor<fp32, [1, 8, ?, ?]> var_656 = softmax(axis = var_55, x = scores_15)[name = string("op_656")];
tensor<fp32, [1, 8, ?, ?]> input_81 = select(a = var_46, b = var_656, cond = mask_17)[name = string("input_81")];
bool x_11_transpose_x_0 = const()[name = string("x_11_transpose_x_0"), val = bool(false)];
bool x_11_transpose_y_0 = const()[name = string("x_11_transpose_y_0"), val = bool(false)];
tensor<fp32, [1, 8, ?, 64]> v_15 = transpose(perm = v_15_perm_0, x = v_13)[name = string("transpose_144")];
tensor<fp32, [1, 8, ?, 64]> x_11 = matmul(transpose_x = x_11_transpose_x_0, transpose_y = x_11_transpose_y_0, x = input_81, y = v_15)[name = string("x_11")];
tensor<int32, [4]> var_660_perm_0 = const()[name = string("op_660_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_662 = const()[name = string("op_662"), val = tensor<int32, [3]>([1, -1, 512])];
tensor<fp32, [1, ?, 8, 64]> var_660 = transpose(perm = var_660_perm_0, x = x_11)[name = string("transpose_139")];
tensor<fp32, [1, ?, 512]> input_83 = reshape(shape = var_662, x = var_660)[name = string("input_83")];
tensor<fp32, [1, ?, 512]> input_85 = linear(bias = encoder_encoders_3_self_attn_linear_out_bias, weight = encoder_encoders_3_self_attn_linear_out_weight, x = input_83)[name = string("linear_26")];
tensor<fp32, [1, ?, 512]> input_87 = add(x = input_79, y = input_85)[name = string("input_87")];
tensor<int32, [1]> input_89_axes_0 = const()[name = string("input_89_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp32, [1, ?, 512]> input_89 = layer_norm(axes = input_89_axes_0, beta = encoder_encoders_3_norm_ff_bias, epsilon = var_36, gamma = encoder_encoders_3_norm_ff_weight, x = input_87)[name = string("input_89")];
tensor<fp32, [1, ?, 2048]> input_91 = linear(bias = encoder_encoders_3_feed_forward_w_1_bias, weight = encoder_encoders_3_feed_forward_w_1_weight, x = input_89)[name = string("linear_27")];
tensor<fp32, [1, ?, 2048]> input_93 = silu(x = input_91)[name = string("input_93")];
tensor<fp32, [1, ?, 512]> input_97 = linear(bias = encoder_encoders_3_feed_forward_w_2_bias, weight = encoder_encoders_3_feed_forward_w_2_weight, x = input_93)[name = string("linear_28")];
tensor<fp32, [1, ?, 512]> input_99 = add(x = input_87, y = input_97)[name = string("input_99")];
tensor<int32, [1]> query_9_axes_0 = const()[name = string("query_9_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp32, [1, ?, 512]> query_9 = layer_norm(axes = query_9_axes_0, beta = encoder_encoders_4_norm_mha_bias, epsilon = var_36, gamma = encoder_encoders_4_norm_mha_weight, x = input_99)[name = string("query_9")];
tensor<fp32, [1, ?, 512]> var_705 = linear(bias = encoder_encoders_4_self_attn_linear_q_bias, weight = encoder_encoders_4_self_attn_linear_q_weight, x = query_9)[name = string("linear_29")];
tensor<int32, [4]> var_706 = const()[name = string("op_706"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> q_25 = reshape(shape = var_706, x = var_705)[name = string("q_25")];
tensor<fp32, [1, ?, 512]> var_710 = linear(bias = encoder_encoders_4_self_attn_linear_k_bias, weight = encoder_encoders_4_self_attn_linear_k_weight, x = query_9)[name = string("linear_30")];
tensor<int32, [4]> var_711 = const()[name = string("op_711"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> k_17 = reshape(shape = var_711, x = var_710)[name = string("k_17")];
tensor<fp32, [1, ?, 512]> var_715 = linear(bias = encoder_encoders_4_self_attn_linear_v_bias, weight = encoder_encoders_4_self_attn_linear_v_weight, x = query_9)[name = string("linear_31")];
tensor<int32, [4]> var_716 = const()[name = string("op_716"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> v_17 = reshape(shape = var_716, x = var_715)[name = string("v_17")];
tensor<int32, [4]> v_19_perm_0 = const()[name = string("v_19_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp32, [1, ?, 512]> var_724 = linear(bias = linear_4_bias_0, weight = encoder_encoders_4_self_attn_linear_pos_weight, x = input_7)[name = string("linear_32")];
tensor<int32, [4]> var_725 = const()[name = string("op_725"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> p_17 = reshape(shape = var_725, x = var_724)[name = string("p_17")];
tensor<fp32, [8, 64]> const_16 = const()[name = string("const_16"), val = tensor<fp32, [8, 64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185393984)))];
tensor<fp32, [1, ?, 8, 64]> var_729 = add(x = q_25, y = const_16)[name = string("op_729")];
tensor<fp32, [8, 64]> const_17 = const()[name = string("const_17"), val = tensor<fp32, [8, 64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185396096)))];
tensor<fp32, [1, ?, 8, 64]> var_732 = add(x = q_25, y = const_17)[name = string("op_732")];
bool matrix_ac_9_transpose_x_0 = const()[name = string("matrix_ac_9_transpose_x_0"), val = bool(false)];
bool matrix_ac_9_transpose_y_0 = const()[name = string("matrix_ac_9_transpose_y_0"), val = bool(false)];
tensor<int32, [4]> transpose_76_perm_0 = const()[name = string("transpose_76_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
tensor<int32, [4]> transpose_77_perm_0 = const()[name = string("transpose_77_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
tensor<fp32, [1, 8, 64, ?]> transpose_77 = transpose(perm = transpose_77_perm_0, x = k_17)[name = string("transpose_136")];
tensor<fp32, [1, 8, ?, 64]> transpose_76 = transpose(perm = transpose_76_perm_0, x = var_729)[name = string("transpose_137")];
tensor<fp32, [1, 8, ?, ?]> matrix_ac_9 = matmul(transpose_x = matrix_ac_9_transpose_x_0, transpose_y = matrix_ac_9_transpose_y_0, x = transpose_76, y = transpose_77)[name = string("matrix_ac_9")];
bool matrix_bd_17_transpose_x_0 = const()[name = string("matrix_bd_17_transpose_x_0"), val = bool(false)];
bool matrix_bd_17_transpose_y_0 = const()[name = string("matrix_bd_17_transpose_y_0"), val = bool(false)];
tensor<int32, [4]> transpose_78_perm_0 = const()[name = string("transpose_78_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
tensor<int32, [4]> transpose_79_perm_0 = const()[name = string("transpose_79_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
tensor<fp32, [1, 8, 64, ?]> transpose_79 = transpose(perm = transpose_79_perm_0, x = p_17)[name = string("transpose_134")];
tensor<fp32, [1, 8, ?, 64]> transpose_78 = transpose(perm = transpose_78_perm_0, x = var_732)[name = string("transpose_135")];
tensor<fp32, [1, 8, ?, ?]> matrix_bd_17 = matmul(transpose_x = matrix_bd_17_transpose_x_0, transpose_y = matrix_bd_17_transpose_y_0, x = transpose_78, y = transpose_79)[name = string("matrix_bd_17")];
tensor<int32, [4]> var_738_shape = shape(x = matrix_bd_17)[name = string("op_738_shape")];
int32 gather_69 = const()[name = string("gather_69"), val = int32(1)];
int32 gather_70 = const()[name = string("gather_70"), val = int32(8)];
int32 gather_71_batch_dims_0 = const()[name = string("gather_71_batch_dims_0"), val = int32(0)];
bool gather_71_validate_indices_0 = const()[name = string("gather_71_validate_indices_0"), val = bool(false)];
int32 select_22 = const()[name = string("select_22"), val = int32(2)];
int32 gather_71_axis_1 = const()[name = string("gather_71_axis_1"), val = int32(0)];
int32 gather_71 = gather(axis = gather_71_axis_1, batch_dims = gather_71_batch_dims_0, indices = select_22, validate_indices = gather_71_validate_indices_0, x = var_738_shape)[name = string("gather_71")];
int32 concat_24_axis_0 = const()[name = string("concat_24_axis_0"), val = int32(0)];
bool concat_24_interleave_0 = const()[name = string("concat_24_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_24 = concat(axis = concat_24_axis_0, interleave = concat_24_interleave_0, values = (gather_69, gather_70, gather_71, var_61))[name = string("concat_24")];
fp32 zero_pad_9_value_0 = const()[name = string("zero_pad_9_value_0"), val = fp32(0x0p+0)];
tensor<fp32, [1, 8, ?, 1]> zero_pad_9 = fill(shape = concat_24, value = zero_pad_9_value_0)[name = string("zero_pad_9")];
bool x_padded_17_interleave_0 = const()[name = string("x_padded_17_interleave_0"), val = bool(false)];
tensor<fp32, [1, 8, ?, ?]> x_padded_17 = concat(axis = var_55, interleave = x_padded_17_interleave_0, values = (zero_pad_9, matrix_bd_17))[name = string("x_padded_17")];
int32 gather_72 = const()[name = string("gather_72"), val = int32(1)];
int32 gather_73 = const()[name = string("gather_73"), val = int32(8)];
int32 gather_74_batch_dims_0 = const()[name = string("gather_74_batch_dims_0"), val = int32(0)];
bool gather_74_validate_indices_0 = const()[name = string("gather_74_validate_indices_0"), val = bool(false)];
int32 select_23 = const()[name = string("select_23"), val = int32(3)];
int32 gather_74_axis_1 = const()[name = string("gather_74_axis_1"), val = int32(0)];
int32 gather_74 = gather(axis = gather_74_axis_1, batch_dims = gather_74_batch_dims_0, indices = select_23, validate_indices = gather_74_validate_indices_0, x = var_738_shape)[name = string("gather_74")];
int32 var_749 = const()[name = string("op_749"), val = int32(1)];
int32 var_750 = add(x = gather_74, y = var_749)[name = string("op_750")];
int32 concat_25_axis_0 = const()[name = string("concat_25_axis_0"), val = int32(0)];
bool concat_25_interleave_0 = const()[name = string("concat_25_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_25 = concat(axis = concat_25_axis_0, interleave = concat_25_interleave_0, values = (gather_72, gather_73, var_750, gather_71))[name = string("concat_25")];
tensor<fp32, [1, 8, ?, ?]> x_padded_19 = reshape(shape = concat_25, x = x_padded_17)[name = string("x_padded_19")];
tensor<int32, [4]> var_757_begin_0 = const()[name = string("op_757_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
tensor<int32, [4]> var_757_end_0 = const()[name = string("op_757_end_0"), val = tensor<int32, [4]>([1, 8, 0, 0])];
tensor<bool, [4]> var_757_end_mask_0 = const()[name = string("op_757_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp32, [1, 8, ?, ?]> var_757 = slice_by_index(begin = var_757_begin_0, end = var_757_end_0, end_mask = var_757_end_mask_0, x = x_padded_19)[name = string("op_757")];
int32 gather_76 = const()[name = string("gather_76"), val = int32(1)];
int32 gather_77 = const()[name = string("gather_77"), val = int32(8)];
int32 concat_26_axis_0 = const()[name = string("concat_26_axis_0"), val = int32(0)];
bool concat_26_interleave_0 = const()[name = string("concat_26_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_26 = concat(axis = concat_26_axis_0, interleave = concat_26_interleave_0, values = (gather_76, gather_77, gather_71, gather_74))[name = string("concat_26")];
tensor<fp32, [1, 8, ?, ?]> var_763 = reshape(shape = concat_26, x = var_757)[name = string("op_763")];
int32 floor_div_6 = floor_div(x = gather_74, y = var_53)[name = string("floor_div_6")];
string var_766_dtype_0 = const()[name = string("op_766_dtype_0"), val = string("fp32")];
fp32 var_767_promoted = const()[name = string("op_767_promoted"), val = fp32(0x1p+0)];
fp32 var_766 = cast(dtype = var_766_dtype_0, x = floor_div_6)[name = string("cast_118")];
fp32 var_768 = add(x = var_766, y = var_767_promoted)[name = string("op_768")];
string var_769_dtype_0 = const()[name = string("op_769_dtype_0"), val = string("int32")];
int32 concat_27_values0_0 = const()[name = string("concat_27_values0_0"), val = int32(1)];
int32 concat_27_values1_0 = const()[name = string("concat_27_values1_0"), val = int32(8)];
int32 concat_27_values2_0 = const()[name = string("concat_27_values2_0"), val = int32(0)];
int32 concat_27_axis_0 = const()[name = string("concat_27_axis_0"), val = int32(0)];
bool concat_27_interleave_0 = const()[name = string("concat_27_interleave_0"), val = bool(false)];
int32 var_769 = cast(dtype = var_769_dtype_0, x = var_768)[name = string("cast_117")];
tensor<int32, [4]> concat_27 = concat(axis = concat_27_axis_0, interleave = concat_27_interleave_0, values = (concat_27_values0_0, concat_27_values1_0, concat_27_values2_0, var_769))[name = string("concat_27")];
tensor<int32, [4]> matrix_bd_19_begin_0 = const()[name = string("matrix_bd_19_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<bool, [4]> matrix_bd_19_end_mask_0 = const()[name = string("matrix_bd_19_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp32, [1, 8, ?, ?]> matrix_bd_19 = slice_by_index(begin = matrix_bd_19_begin_0, end = concat_27, end_mask = matrix_bd_19_end_mask_0, x = var_763)[name = string("matrix_bd_19")];
tensor<fp32, [1, 8, ?, ?]> var_774 = add(x = matrix_ac_9, y = matrix_bd_19)[name = string("op_774")];
fp32 _inversed_scores_17_y_0 = const()[name = string("_inversed_scores_17_y_0"), val = fp32(0x1p-3)];
tensor<fp32, [1, 8, ?, ?]> _inversed_scores_17 = mul(x = var_774, y = _inversed_scores_17_y_0)[name = string("_inversed_scores_17")];
tensor<int32, [4]> var_780_shape = shape(x = _inversed_scores_17)[name = string("op_780_shape")];
int32 gather_82_batch_dims_0 = const()[name = string("gather_82_batch_dims_0"), val = int32(0)];
bool gather_82_validate_indices_0 = const()[name = string("gather_82_validate_indices_0"), val = bool(false)];
int32 select_25 = const()[name = string("select_25"), val = int32(3)];
int32 gather_82_axis_1 = const()[name = string("gather_82_axis_1"), val = int32(0)];
int32 gather_82 = gather(axis = gather_82_axis_1, batch_dims = gather_82_batch_dims_0, indices = select_25, validate_indices = gather_82_validate_indices_0, x = var_780_shape)[name = string("gather_82")];
int32 concat_28_values0_0 = const()[name = string("concat_28_values0_0"), val = int32(0)];
int32 concat_28_values1_0 = const()[name = string("concat_28_values1_0"), val = int32(1)];
int32 concat_28_values2_0 = const()[name = string("concat_28_values2_0"), val = int32(1)];
int32 concat_28_axis_0 = const()[name = string("concat_28_axis_0"), val = int32(0)];
bool concat_28_interleave_0 = const()[name = string("concat_28_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_28 = concat(axis = concat_28_axis_0, interleave = concat_28_interleave_0, values = (concat_28_values0_0, concat_28_values1_0, concat_28_values2_0, gather_82))[name = string("concat_28")];
tensor<int32, [4]> mask_21_begin_0 = const()[name = string("mask_21_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<bool, [4]> mask_21_end_mask_0 = const()[name = string("mask_21_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<bool, [?, 1, 1, ?]> mask_21 = slice_by_index(begin = mask_21_begin_0, end = concat_28, end_mask = mask_21_end_mask_0, x = mask_3)[name = string("mask_21")];
tensor<fp32, [1, 8, ?, ?]> scores_19 = select(a = var_37, b = _inversed_scores_17, cond = mask_21)[name = string("scores_19")];
tensor<fp32, [1, 8, ?, ?]> var_786 = softmax(axis = var_55, x = scores_19)[name = string("op_786")];
tensor<fp32, [1, 8, ?, ?]> input_101 = select(a = var_46, b = var_786, cond = mask_21)[name = string("input_101")];
bool x_13_transpose_x_0 = const()[name = string("x_13_transpose_x_0"), val = bool(false)];
bool x_13_transpose_y_0 = const()[name = string("x_13_transpose_y_0"), val = bool(false)];
tensor<fp32, [1, 8, ?, 64]> v_19 = transpose(perm = v_19_perm_0, x = v_17)[name = string("transpose_138")];
tensor<fp32, [1, 8, ?, 64]> x_13 = matmul(transpose_x = x_13_transpose_x_0, transpose_y = x_13_transpose_y_0, x = input_101, y = v_19)[name = string("x_13")];
tensor<int32, [4]> var_790_perm_0 = const()[name = string("op_790_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_792 = const()[name = string("op_792"), val = tensor<int32, [3]>([1, -1, 512])];
tensor<fp32, [1, ?, 8, 64]> var_790 = transpose(perm = var_790_perm_0, x = x_13)[name = string("transpose_133")];
tensor<fp32, [1, ?, 512]> input_103 = reshape(shape = var_792, x = var_790)[name = string("input_103")];
tensor<fp32, [1, ?, 512]> input_105 = linear(bias = encoder_encoders_4_self_attn_linear_out_bias, weight = encoder_encoders_4_self_attn_linear_out_weight, x = input_103)[name = string("linear_33")];
tensor<fp32, [1, ?, 512]> input_107 = add(x = input_99, y = input_105)[name = string("input_107")];
tensor<int32, [1]> input_109_axes_0 = const()[name = string("input_109_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp32, [1, ?, 512]> input_109 = layer_norm(axes = input_109_axes_0, beta = encoder_encoders_4_norm_ff_bias, epsilon = var_36, gamma = encoder_encoders_4_norm_ff_weight, x = input_107)[name = string("input_109")];
tensor<fp32, [1, ?, 2048]> input_111 = linear(bias = encoder_encoders_4_feed_forward_w_1_bias, weight = encoder_encoders_4_feed_forward_w_1_weight, x = input_109)[name = string("linear_34")];
tensor<fp32, [1, ?, 2048]> input_113 = silu(x = input_111)[name = string("input_113")];
tensor<fp32, [1, ?, 512]> input_117 = linear(bias = encoder_encoders_4_feed_forward_w_2_bias, weight = encoder_encoders_4_feed_forward_w_2_weight, x = input_113)[name = string("linear_35")];
tensor<fp32, [1, ?, 512]> input_119 = add(x = input_107, y = input_117)[name = string("input_119")];
tensor<int32, [1]> query_11_axes_0 = const()[name = string("query_11_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp32, [1, ?, 512]> query_11 = layer_norm(axes = query_11_axes_0, beta = encoder_encoders_5_norm_mha_bias, epsilon = var_36, gamma = encoder_encoders_5_norm_mha_weight, x = input_119)[name = string("query_11")];
tensor<fp32, [1, ?, 512]> var_835 = linear(bias = encoder_encoders_5_self_attn_linear_q_bias, weight = encoder_encoders_5_self_attn_linear_q_weight, x = query_11)[name = string("linear_36")];
tensor<int32, [4]> var_836 = const()[name = string("op_836"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> q_31 = reshape(shape = var_836, x = var_835)[name = string("q_31")];
tensor<fp32, [1, ?, 512]> var_840 = linear(bias = encoder_encoders_5_self_attn_linear_k_bias, weight = encoder_encoders_5_self_attn_linear_k_weight, x = query_11)[name = string("linear_37")];
tensor<int32, [4]> var_841 = const()[name = string("op_841"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> k_21 = reshape(shape = var_841, x = var_840)[name = string("k_21")];
tensor<fp32, [1, ?, 512]> var_845 = linear(bias = encoder_encoders_5_self_attn_linear_v_bias, weight = encoder_encoders_5_self_attn_linear_v_weight, x = query_11)[name = string("linear_38")];
tensor<int32, [4]> var_846 = const()[name = string("op_846"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> v_21 = reshape(shape = var_846, x = var_845)[name = string("v_21")];
tensor<int32, [4]> v_23_perm_0 = const()[name = string("v_23_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp32, [1, ?, 512]> var_854 = linear(bias = linear_4_bias_0, weight = encoder_encoders_5_self_attn_linear_pos_weight, x = input_7)[name = string("linear_39")];
tensor<int32, [4]> var_855 = const()[name = string("op_855"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> p_21 = reshape(shape = var_855, x = var_854)[name = string("p_21")];
tensor<fp32, [8, 64]> const_18 = const()[name = string("const_18"), val = tensor<fp32, [8, 64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185398208)))];
tensor<fp32, [1, ?, 8, 64]> var_859 = add(x = q_31, y = const_18)[name = string("op_859")];
tensor<fp32, [8, 64]> const_19 = const()[name = string("const_19"), val = tensor<fp32, [8, 64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185400320)))];
tensor<fp32, [1, ?, 8, 64]> var_862 = add(x = q_31, y = const_19)[name = string("op_862")];
bool matrix_ac_11_transpose_x_0 = const()[name = string("matrix_ac_11_transpose_x_0"), val = bool(false)];
bool matrix_ac_11_transpose_y_0 = const()[name = string("matrix_ac_11_transpose_y_0"), val = bool(false)];
tensor<int32, [4]> transpose_80_perm_0 = const()[name = string("transpose_80_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
tensor<int32, [4]> transpose_81_perm_0 = const()[name = string("transpose_81_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
tensor<fp32, [1, 8, 64, ?]> transpose_81 = transpose(perm = transpose_81_perm_0, x = k_21)[name = string("transpose_130")];
tensor<fp32, [1, 8, ?, 64]> transpose_80 = transpose(perm = transpose_80_perm_0, x = var_859)[name = string("transpose_131")];
tensor<fp32, [1, 8, ?, ?]> matrix_ac_11 = matmul(transpose_x = matrix_ac_11_transpose_x_0, transpose_y = matrix_ac_11_transpose_y_0, x = transpose_80, y = transpose_81)[name = string("matrix_ac_11")];
bool matrix_bd_21_transpose_x_0 = const()[name = string("matrix_bd_21_transpose_x_0"), val = bool(false)];
bool matrix_bd_21_transpose_y_0 = const()[name = string("matrix_bd_21_transpose_y_0"), val = bool(false)];
tensor<int32, [4]> transpose_82_perm_0 = const()[name = string("transpose_82_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
tensor<int32, [4]> transpose_83_perm_0 = const()[name = string("transpose_83_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
tensor<fp32, [1, 8, 64, ?]> transpose_83 = transpose(perm = transpose_83_perm_0, x = p_21)[name = string("transpose_128")];
tensor<fp32, [1, 8, ?, 64]> transpose_82 = transpose(perm = transpose_82_perm_0, x = var_862)[name = string("transpose_129")];
tensor<fp32, [1, 8, ?, ?]> matrix_bd_21 = matmul(transpose_x = matrix_bd_21_transpose_x_0, transpose_y = matrix_bd_21_transpose_y_0, x = transpose_82, y = transpose_83)[name = string("matrix_bd_21")];
tensor<int32, [4]> var_868_shape = shape(x = matrix_bd_21)[name = string("op_868_shape")];
int32 gather_85 = const()[name = string("gather_85"), val = int32(1)];
int32 gather_86 = const()[name = string("gather_86"), val = int32(8)];
int32 gather_87_batch_dims_0 = const()[name = string("gather_87_batch_dims_0"), val = int32(0)];
bool gather_87_validate_indices_0 = const()[name = string("gather_87_validate_indices_0"), val = bool(false)];
int32 select_26 = const()[name = string("select_26"), val = int32(2)];
int32 gather_87_axis_1 = const()[name = string("gather_87_axis_1"), val = int32(0)];
int32 gather_87 = gather(axis = gather_87_axis_1, batch_dims = gather_87_batch_dims_0, indices = select_26, validate_indices = gather_87_validate_indices_0, x = var_868_shape)[name = string("gather_87")];
int32 concat_29_axis_0 = const()[name = string("concat_29_axis_0"), val = int32(0)];
bool concat_29_interleave_0 = const()[name = string("concat_29_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_29 = concat(axis = concat_29_axis_0, interleave = concat_29_interleave_0, values = (gather_85, gather_86, gather_87, var_61))[name = string("concat_29")];
fp32 zero_pad_11_value_0 = const()[name = string("zero_pad_11_value_0"), val = fp32(0x0p+0)];
tensor<fp32, [1, 8, ?, 1]> zero_pad_11 = fill(shape = concat_29, value = zero_pad_11_value_0)[name = string("zero_pad_11")];
bool x_padded_21_interleave_0 = const()[name = string("x_padded_21_interleave_0"), val = bool(false)];
tensor<fp32, [1, 8, ?, ?]> x_padded_21 = concat(axis = var_55, interleave = x_padded_21_interleave_0, values = (zero_pad_11, matrix_bd_21))[name = string("x_padded_21")];
int32 gather_88 = const()[name = string("gather_88"), val = int32(1)];
int32 gather_89 = const()[name = string("gather_89"), val = int32(8)];
int32 gather_90_batch_dims_0 = const()[name = string("gather_90_batch_dims_0"), val = int32(0)];
bool gather_90_validate_indices_0 = const()[name = string("gather_90_validate_indices_0"), val = bool(false)];
int32 select_27 = const()[name = string("select_27"), val = int32(3)];
int32 gather_90_axis_1 = const()[name = string("gather_90_axis_1"), val = int32(0)];
int32 gather_90 = gather(axis = gather_90_axis_1, batch_dims = gather_90_batch_dims_0, indices = select_27, validate_indices = gather_90_validate_indices_0, x = var_868_shape)[name = string("gather_90")];
int32 var_879 = const()[name = string("op_879"), val = int32(1)];
int32 var_880 = add(x = gather_90, y = var_879)[name = string("op_880")];
int32 concat_30_axis_0 = const()[name = string("concat_30_axis_0"), val = int32(0)];
bool concat_30_interleave_0 = const()[name = string("concat_30_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_30 = concat(axis = concat_30_axis_0, interleave = concat_30_interleave_0, values = (gather_88, gather_89, var_880, gather_87))[name = string("concat_30")];
tensor<fp32, [1, 8, ?, ?]> x_padded_23 = reshape(shape = concat_30, x = x_padded_21)[name = string("x_padded_23")];
tensor<int32, [4]> var_887_begin_0 = const()[name = string("op_887_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
tensor<int32, [4]> var_887_end_0 = const()[name = string("op_887_end_0"), val = tensor<int32, [4]>([1, 8, 0, 0])];
tensor<bool, [4]> var_887_end_mask_0 = const()[name = string("op_887_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp32, [1, 8, ?, ?]> var_887 = slice_by_index(begin = var_887_begin_0, end = var_887_end_0, end_mask = var_887_end_mask_0, x = x_padded_23)[name = string("op_887")];
int32 gather_92 = const()[name = string("gather_92"), val = int32(1)];
int32 gather_93 = const()[name = string("gather_93"), val = int32(8)];
int32 concat_31_axis_0 = const()[name = string("concat_31_axis_0"), val = int32(0)];
bool concat_31_interleave_0 = const()[name = string("concat_31_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_31 = concat(axis = concat_31_axis_0, interleave = concat_31_interleave_0, values = (gather_92, gather_93, gather_87, gather_90))[name = string("concat_31")];
tensor<fp32, [1, 8, ?, ?]> var_893 = reshape(shape = concat_31, x = var_887)[name = string("op_893")];
int32 floor_div_7 = floor_div(x = gather_90, y = var_53)[name = string("floor_div_7")];
string var_896_dtype_0 = const()[name = string("op_896_dtype_0"), val = string("fp32")];
fp32 var_897_promoted = const()[name = string("op_897_promoted"), val = fp32(0x1p+0)];
fp32 var_896 = cast(dtype = var_896_dtype_0, x = floor_div_7)[name = string("cast_116")];
fp32 var_898 = add(x = var_896, y = var_897_promoted)[name = string("op_898")];
string var_899_dtype_0 = const()[name = string("op_899_dtype_0"), val = string("int32")];
int32 concat_32_values0_0 = const()[name = string("concat_32_values0_0"), val = int32(1)];
int32 concat_32_values1_0 = const()[name = string("concat_32_values1_0"), val = int32(8)];
int32 concat_32_values2_0 = const()[name = string("concat_32_values2_0"), val = int32(0)];
int32 concat_32_axis_0 = const()[name = string("concat_32_axis_0"), val = int32(0)];
bool concat_32_interleave_0 = const()[name = string("concat_32_interleave_0"), val = bool(false)];
int32 var_899 = cast(dtype = var_899_dtype_0, x = var_898)[name = string("cast_115")];
tensor<int32, [4]> concat_32 = concat(axis = concat_32_axis_0, interleave = concat_32_interleave_0, values = (concat_32_values0_0, concat_32_values1_0, concat_32_values2_0, var_899))[name = string("concat_32")];
tensor<int32, [4]> matrix_bd_23_begin_0 = const()[name = string("matrix_bd_23_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<bool, [4]> matrix_bd_23_end_mask_0 = const()[name = string("matrix_bd_23_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp32, [1, 8, ?, ?]> matrix_bd_23 = slice_by_index(begin = matrix_bd_23_begin_0, end = concat_32, end_mask = matrix_bd_23_end_mask_0, x = var_893)[name = string("matrix_bd_23")];
tensor<fp32, [1, 8, ?, ?]> var_904 = add(x = matrix_ac_11, y = matrix_bd_23)[name = string("op_904")];
fp32 _inversed_scores_21_y_0 = const()[name = string("_inversed_scores_21_y_0"), val = fp32(0x1p-3)];
tensor<fp32, [1, 8, ?, ?]> _inversed_scores_21 = mul(x = var_904, y = _inversed_scores_21_y_0)[name = string("_inversed_scores_21")];
tensor<int32, [4]> var_910_shape = shape(x = _inversed_scores_21)[name = string("op_910_shape")];
int32 gather_98_batch_dims_0 = const()[name = string("gather_98_batch_dims_0"), val = int32(0)];
bool gather_98_validate_indices_0 = const()[name = string("gather_98_validate_indices_0"), val = bool(false)];
int32 select_29 = const()[name = string("select_29"), val = int32(3)];
int32 gather_98_axis_1 = const()[name = string("gather_98_axis_1"), val = int32(0)];
int32 gather_98 = gather(axis = gather_98_axis_1, batch_dims = gather_98_batch_dims_0, indices = select_29, validate_indices = gather_98_validate_indices_0, x = var_910_shape)[name = string("gather_98")];
int32 concat_33_values0_0 = const()[name = string("concat_33_values0_0"), val = int32(0)];
int32 concat_33_values1_0 = const()[name = string("concat_33_values1_0"), val = int32(1)];
int32 concat_33_values2_0 = const()[name = string("concat_33_values2_0"), val = int32(1)];
int32 concat_33_axis_0 = const()[name = string("concat_33_axis_0"), val = int32(0)];
bool concat_33_interleave_0 = const()[name = string("concat_33_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_33 = concat(axis = concat_33_axis_0, interleave = concat_33_interleave_0, values = (concat_33_values0_0, concat_33_values1_0, concat_33_values2_0, gather_98))[name = string("concat_33")];
tensor<int32, [4]> mask_25_begin_0 = const()[name = string("mask_25_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<bool, [4]> mask_25_end_mask_0 = const()[name = string("mask_25_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<bool, [?, 1, 1, ?]> mask_25 = slice_by_index(begin = mask_25_begin_0, end = concat_33, end_mask = mask_25_end_mask_0, x = mask_3)[name = string("mask_25")];
tensor<fp32, [1, 8, ?, ?]> scores_23 = select(a = var_37, b = _inversed_scores_21, cond = mask_25)[name = string("scores_23")];
tensor<fp32, [1, 8, ?, ?]> var_916 = softmax(axis = var_55, x = scores_23)[name = string("op_916")];
tensor<fp32, [1, 8, ?, ?]> input_121 = select(a = var_46, b = var_916, cond = mask_25)[name = string("input_121")];
bool x_15_transpose_x_0 = const()[name = string("x_15_transpose_x_0"), val = bool(false)];
bool x_15_transpose_y_0 = const()[name = string("x_15_transpose_y_0"), val = bool(false)];
tensor<fp32, [1, 8, ?, 64]> v_23 = transpose(perm = v_23_perm_0, x = v_21)[name = string("transpose_132")];
tensor<fp32, [1, 8, ?, 64]> x_15 = matmul(transpose_x = x_15_transpose_x_0, transpose_y = x_15_transpose_y_0, x = input_121, y = v_23)[name = string("x_15")];
tensor<int32, [4]> var_920_perm_0 = const()[name = string("op_920_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_922 = const()[name = string("op_922"), val = tensor<int32, [3]>([1, -1, 512])];
tensor<fp32, [1, ?, 8, 64]> var_920 = transpose(perm = var_920_perm_0, x = x_15)[name = string("transpose_127")];
tensor<fp32, [1, ?, 512]> input_123 = reshape(shape = var_922, x = var_920)[name = string("input_123")];
tensor<fp32, [1, ?, 512]> input_125 = linear(bias = encoder_encoders_5_self_attn_linear_out_bias, weight = encoder_encoders_5_self_attn_linear_out_weight, x = input_123)[name = string("linear_40")];
tensor<fp32, [1, ?, 512]> input_127 = add(x = input_119, y = input_125)[name = string("input_127")];
tensor<int32, [1]> input_129_axes_0 = const()[name = string("input_129_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp32, [1, ?, 512]> input_129 = layer_norm(axes = input_129_axes_0, beta = encoder_encoders_5_norm_ff_bias, epsilon = var_36, gamma = encoder_encoders_5_norm_ff_weight, x = input_127)[name = string("input_129")];
tensor<fp32, [1, ?, 2048]> input_131 = linear(bias = encoder_encoders_5_feed_forward_w_1_bias, weight = encoder_encoders_5_feed_forward_w_1_weight, x = input_129)[name = string("linear_41")];
tensor<fp32, [1, ?, 2048]> input_133 = silu(x = input_131)[name = string("input_133")];
tensor<fp32, [1, ?, 512]> input_137 = linear(bias = encoder_encoders_5_feed_forward_w_2_bias, weight = encoder_encoders_5_feed_forward_w_2_weight, x = input_133)[name = string("linear_42")];
tensor<fp32, [1, ?, 512]> xs_3 = add(x = input_127, y = input_137)[name = string("xs_3")];
tensor<int32, [3]> var_947_perm_0 = const()[name = string("op_947_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor<int32, [1]>([3])];
tensor<fp32, [1, 512, ?]> var_947 = transpose(perm = var_947_perm_0, x = xs_3)[name = string("transpose_126")];
tensor<fp32, [1, 512, ?, 1]> expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = var_947)[name = string("expand_dims_0")];
int32 upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_height_0"), val = int32(2)];
int32 upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = string("upsample_nearest_neighbor_0_scale_factor_width_0"), val = int32(1)];
tensor<fp32, [1, 512, ?, 1]> upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")];
tensor<int32, [1]> input_141_axes_0 = const()[name = string("input_141_axes_0"), val = tensor<int32, [1]>([3])];
tensor<fp32, [1, 512, ?]> input_141 = squeeze(axes = input_141_axes_0, x = upsample_nearest_neighbor_0)[name = string("input_141")];
fp32 const_20 = const()[name = string("const_20"), val = fp32(0x0p+0)];
tensor<int32, [6]> input_143_pad_0 = const()[name = string("input_143_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 4, 0])];
string input_143_mode_0 = const()[name = string("input_143_mode_0"), val = string("constant")];
tensor<fp32, [1, 512, ?]> input_143 = pad(constant_val = const_20, mode = input_143_mode_0, pad = input_143_pad_0, x = input_141)[name = string("input_143")];
string xs_5_pad_type_0 = const()[name = string("xs_5_pad_type_0"), val = string("valid")];
tensor<int32, [1]> xs_5_strides_0 = const()[name = string("xs_5_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> xs_5_pad_0 = const()[name = string("xs_5_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> xs_5_dilations_0 = const()[name = string("xs_5_dilations_0"), val = tensor<int32, [1]>([1])];
int32 xs_5_groups_0 = const()[name = string("xs_5_groups_0"), val = int32(1)];
tensor<fp32, [1, 512, ?]> xs_5 = conv(bias = encoder_up_layer_conv_bias, dilations = xs_5_dilations_0, groups = xs_5_groups_0, pad = xs_5_pad_0, pad_type = xs_5_pad_type_0, strides = xs_5_strides_0, weight = encoder_up_layer_conv_weight, x = input_143)[name = string("xs_5")];
tensor<int32, [3]> var_966_perm_0 = const()[name = string("op_966_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<fp32, [1, ?, 512]> var_966 = transpose(perm = var_966_perm_0, x = xs_5)[name = string("transpose_125")];
tensor<int32, [3]> var_968_shape = shape(x = var_966)[name = string("op_968_shape")];
int32 gather_99_batch_dims_0 = const()[name = string("gather_99_batch_dims_0"), val = int32(0)];
bool gather_99_validate_indices_0 = const()[name = string("gather_99_validate_indices_0"), val = bool(false)];
int32 select_30 = const()[name = string("select_30"), val = int32(1)];
int32 gather_99_axis_1 = const()[name = string("gather_99_axis_1"), val = int32(0)];
int32 gather_99 = gather(axis = gather_99_axis_1, batch_dims = gather_99_batch_dims_0, indices = select_30, validate_indices = gather_99_validate_indices_0, x = var_968_shape)[name = string("gather_99")];
int32 const_22 = const()[name = string("const_22"), val = int32(1)];
int32 const_23 = const()[name = string("const_23"), val = int32(1)];
tensor<int32, [?]> seq_range = range_1d(end = gather_99, start = var_57, step = const_23)[name = string("seq_range")];
tensor<int32, [1]> var_972_axes_0 = const()[name = string("op_972_axes_0"), val = tensor<int32, [1]>([0])];
tensor<int32, [1, ?]> var_972 = expand_dims(axes = var_972_axes_0, x = seq_range)[name = string("op_972")];
int32 concat_34_axis_0 = const()[name = string("concat_34_axis_0"), val = int32(0)];
bool concat_34_interleave_0 = const()[name = string("concat_34_interleave_0"), val = bool(false)];
tensor<int32, [2]> concat_34 = concat(axis = concat_34_axis_0, interleave = concat_34_interleave_0, values = (const_22, gather_99))[name = string("concat_34")];
tensor<int32, [2]> shape_1 = shape(x = var_972)[name = string("shape_1")];
int32 equal_1_y_0 = const()[name = string("equal_1_y_0"), val = int32(-1)];
tensor<bool, [2]> equal_1 = equal(x = concat_34, y = equal_1_y_0)[name = string("equal_1")];
tensor<int32, [2]> select_1 = select(a = shape_1, b = concat_34, cond = equal_1)[name = string("select_1")];
tensor<int32, [2]> real_div_1 = real_div(x = select_1, y = shape_1)[name = string("real_div_1")];
tensor<int32, [?, ?]> seq_range_expand = tile(reps = real_div_1, x = var_972)[name = string("seq_range_expand")];
tensor<int32, [1, 1]> seq_length_expand = const()[name = string("seq_length_expand"), val = tensor<int32, [1, 1]>([[1000]])];
tensor<bool, [?, ?]> var_976 = greater_equal(x = seq_range_expand, y = seq_length_expand)[name = string("op_976")];
tensor<int32, [1]> var_977_axes_0 = const()[name = string("op_977_axes_0"), val = tensor<int32, [1]>([1])];
tensor<bool, [?, 1, ?]> var_977 = expand_dims(axes = var_977_axes_0, x = var_976)[name = string("op_977")];
tensor<bool, [?, 1, ?]> masks = logical_not(x = var_977)[name = string("masks")];
tensor<fp32, [1, ?, 512]> input_145 = linear(bias = encoder_up_embed_out_0_bias, weight = encoder_up_embed_out_0_weight, x = var_966)[name = string("linear_43")];
tensor<int32, [1]> input_147_axes_0 = const()[name = string("input_147_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp32, [1, ?, 512]> input_147 = layer_norm(axes = input_147_axes_0, beta = encoder_up_embed_out_1_bias, epsilon = var_50, gamma = encoder_up_embed_out_1_weight, x = input_145)[name = string("input_147")];
fp32 var_990 = const()[name = string("op_990"), val = fp32(0x1.6a09e6p+4)];
tensor<fp32, [1, ?, 512]> x_19 = mul(x = input_147, y = var_990)[name = string("x_19")];
tensor<int32, [3]> var_992_shape = shape(x = x_19)[name = string("op_992_shape")];
int32 gather_100_batch_dims_0 = const()[name = string("gather_100_batch_dims_0"), val = int32(0)];
bool gather_100_validate_indices_0 = const()[name = string("gather_100_validate_indices_0"), val = bool(false)];
int32 select_31 = const()[name = string("select_31"), val = int32(1)];
int32 gather_100_axis_1 = const()[name = string("gather_100_axis_1"), val = int32(0)];
int32 gather_100 = gather(axis = gather_100_axis_1, batch_dims = gather_100_batch_dims_0, indices = select_31, validate_indices = gather_100_validate_indices_0, x = var_992_shape)[name = string("gather_100")];
tensor<fp32, [1]> var_996 = const()[name = string("op_996"), val = tensor<fp32, [1]>([0x1.387p+12])];
string gather_100_promoted_dtype_0 = const()[name = string("gather_100_promoted_dtype_0"), val = string("fp32")];
fp32 gather_100_promoted = cast(dtype = gather_100_promoted_dtype_0, x = gather_100)[name = string("cast_114")];
tensor<fp32, [1]> var_997 = sub(x = var_996, y = gather_100_promoted)[name = string("op_997")];
fp32 var_998_promoted = const()[name = string("op_998_promoted"), val = fp32(0x1p+0)];
tensor<fp32, [1]> var_999 = add(x = var_997, y = var_998_promoted)[name = string("op_999")];
fp32 var_1000_item = squeeze(x = var_999)[name = string("op_1000_item")];
string var_1000_dtype_0 = const()[name = string("op_1000_dtype_0"), val = string("int32")];
tensor<fp32, [1]> var_1003 = const()[name = string("op_1003"), val = tensor<fp32, [1]>([0x1.387p+12])];
tensor<fp32, [1]> var_1004 = add(x = var_1003, y = gather_100_promoted)[name = string("op_1004")];
fp32 var_1005_item = squeeze(x = var_1004)[name = string("op_1005_item")];
string var_1005_dtype_0 = const()[name = string("op_1005_dtype_0"), val = string("int32")];
int32 concat_35_values0_0 = const()[name = string("concat_35_values0_0"), val = int32(0)];
int32 concat_35_values2_0 = const()[name = string("concat_35_values2_0"), val = int32(0)];
int32 concat_35_axis_0 = const()[name = string("concat_35_axis_0"), val = int32(0)];
bool concat_35_interleave_0 = const()[name = string("concat_35_interleave_0"), val = bool(false)];
int32 var_1000 = cast(dtype = var_1000_dtype_0, x = var_1000_item)[name = string("cast_113")];
tensor<int32, [3]> concat_35 = concat(axis = concat_35_axis_0, interleave = concat_35_interleave_0, values = (concat_35_values0_0, var_1000, concat_35_values2_0))[name = string("concat_35")];
int32 concat_36_values0_0 = const()[name = string("concat_36_values0_0"), val = int32(1)];
int32 concat_36_values2_0 = const()[name = string("concat_36_values2_0"), val = int32(512)];
int32 concat_36_axis_0 = const()[name = string("concat_36_axis_0"), val = int32(0)];
bool concat_36_interleave_0 = const()[name = string("concat_36_interleave_0"), val = bool(false)];
int32 var_1005 = cast(dtype = var_1005_dtype_0, x = var_1005_item)[name = string("cast_112")];
tensor<int32, [3]> concat_36 = concat(axis = concat_36_axis_0, interleave = concat_36_interleave_0, values = (concat_36_values0_0, var_1005, concat_36_values2_0))[name = string("concat_36")];
tensor<bool, [3]> input_149_end_mask_0 = const()[name = string("input_149_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
tensor<fp32, [1, ?, ?]> input_149 = slice_by_index(begin = concat_35, end = concat_36, end_mask = input_149_end_mask_0, x = var_54)[name = string("input_149")];
tensor<int32, [1]> query_13_axes_0 = const()[name = string("query_13_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp32, [1, ?, 512]> query_13 = layer_norm(axes = query_13_axes_0, beta = encoder_up_encoders_0_norm_mha_bias, epsilon = var_36, gamma = encoder_up_encoders_0_norm_mha_weight, x = x_19)[name = string("query_13")];
tensor<fp32, [1, ?, 512]> var_1034 = linear(bias = encoder_up_encoders_0_self_attn_linear_q_bias, weight = encoder_up_encoders_0_self_attn_linear_q_weight, x = query_13)[name = string("linear_44")];
tensor<int32, [4]> var_1035 = const()[name = string("op_1035"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> q_37 = reshape(shape = var_1035, x = var_1034)[name = string("q_37")];
tensor<fp32, [1, ?, 512]> var_1039 = linear(bias = encoder_up_encoders_0_self_attn_linear_k_bias, weight = encoder_up_encoders_0_self_attn_linear_k_weight, x = query_13)[name = string("linear_45")];
tensor<int32, [4]> var_1040 = const()[name = string("op_1040"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> k_25 = reshape(shape = var_1040, x = var_1039)[name = string("k_25")];
tensor<fp32, [1, ?, 512]> var_1044 = linear(bias = encoder_up_encoders_0_self_attn_linear_v_bias, weight = encoder_up_encoders_0_self_attn_linear_v_weight, x = query_13)[name = string("linear_46")];
tensor<int32, [4]> var_1045 = const()[name = string("op_1045"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> v_25 = reshape(shape = var_1045, x = var_1044)[name = string("v_25")];
tensor<int32, [4]> v_27_perm_0 = const()[name = string("v_27_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp32, [1, ?, 512]> var_1053 = linear(bias = linear_4_bias_0, weight = encoder_up_encoders_0_self_attn_linear_pos_weight, x = input_149)[name = string("linear_47")];
tensor<int32, [4]> var_1054 = const()[name = string("op_1054"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> p_25 = reshape(shape = var_1054, x = var_1053)[name = string("p_25")];
tensor<fp32, [8, 64]> const_26 = const()[name = string("const_26"), val = tensor<fp32, [8, 64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185402432)))];
tensor<fp32, [1, ?, 8, 64]> var_1058 = add(x = q_37, y = const_26)[name = string("op_1058")];
tensor<fp32, [8, 64]> const_27 = const()[name = string("const_27"), val = tensor<fp32, [8, 64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185404544)))];
tensor<fp32, [1, ?, 8, 64]> var_1061 = add(x = q_37, y = const_27)[name = string("op_1061")];
bool matrix_ac_13_transpose_x_0 = const()[name = string("matrix_ac_13_transpose_x_0"), val = bool(false)];
bool matrix_ac_13_transpose_y_0 = const()[name = string("matrix_ac_13_transpose_y_0"), val = bool(false)];
tensor<int32, [4]> transpose_84_perm_0 = const()[name = string("transpose_84_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
tensor<int32, [4]> transpose_85_perm_0 = const()[name = string("transpose_85_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
tensor<fp32, [1, 8, 64, ?]> transpose_85 = transpose(perm = transpose_85_perm_0, x = k_25)[name = string("transpose_122")];
tensor<fp32, [1, 8, ?, 64]> transpose_84 = transpose(perm = transpose_84_perm_0, x = var_1058)[name = string("transpose_123")];
tensor<fp32, [1, 8, ?, ?]> matrix_ac_13 = matmul(transpose_x = matrix_ac_13_transpose_x_0, transpose_y = matrix_ac_13_transpose_y_0, x = transpose_84, y = transpose_85)[name = string("matrix_ac_13")];
bool matrix_bd_25_transpose_x_0 = const()[name = string("matrix_bd_25_transpose_x_0"), val = bool(false)];
bool matrix_bd_25_transpose_y_0 = const()[name = string("matrix_bd_25_transpose_y_0"), val = bool(false)];
tensor<int32, [4]> transpose_86_perm_0 = const()[name = string("transpose_86_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
tensor<int32, [4]> transpose_87_perm_0 = const()[name = string("transpose_87_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
tensor<fp32, [1, 8, 64, ?]> transpose_87 = transpose(perm = transpose_87_perm_0, x = p_25)[name = string("transpose_120")];
tensor<fp32, [1, 8, ?, 64]> transpose_86 = transpose(perm = transpose_86_perm_0, x = var_1061)[name = string("transpose_121")];
tensor<fp32, [1, 8, ?, ?]> matrix_bd_25 = matmul(transpose_x = matrix_bd_25_transpose_x_0, transpose_y = matrix_bd_25_transpose_y_0, x = transpose_86, y = transpose_87)[name = string("matrix_bd_25")];
tensor<int32, [4]> var_1067_shape = shape(x = matrix_bd_25)[name = string("op_1067_shape")];
int32 gather_103 = const()[name = string("gather_103"), val = int32(1)];
int32 gather_104 = const()[name = string("gather_104"), val = int32(8)];
int32 gather_105_batch_dims_0 = const()[name = string("gather_105_batch_dims_0"), val = int32(0)];
bool gather_105_validate_indices_0 = const()[name = string("gather_105_validate_indices_0"), val = bool(false)];
int32 select_32 = const()[name = string("select_32"), val = int32(2)];
int32 gather_105_axis_1 = const()[name = string("gather_105_axis_1"), val = int32(0)];
int32 gather_105 = gather(axis = gather_105_axis_1, batch_dims = gather_105_batch_dims_0, indices = select_32, validate_indices = gather_105_validate_indices_0, x = var_1067_shape)[name = string("gather_105")];
int32 concat_37_axis_0 = const()[name = string("concat_37_axis_0"), val = int32(0)];
bool concat_37_interleave_0 = const()[name = string("concat_37_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_37 = concat(axis = concat_37_axis_0, interleave = concat_37_interleave_0, values = (gather_103, gather_104, gather_105, var_61))[name = string("concat_37")];
fp32 zero_pad_13_value_0 = const()[name = string("zero_pad_13_value_0"), val = fp32(0x0p+0)];
tensor<fp32, [1, 8, ?, 1]> zero_pad_13 = fill(shape = concat_37, value = zero_pad_13_value_0)[name = string("zero_pad_13")];
bool x_padded_25_interleave_0 = const()[name = string("x_padded_25_interleave_0"), val = bool(false)];
tensor<fp32, [1, 8, ?, ?]> x_padded_25 = concat(axis = var_55, interleave = x_padded_25_interleave_0, values = (zero_pad_13, matrix_bd_25))[name = string("x_padded_25")];
int32 gather_106 = const()[name = string("gather_106"), val = int32(1)];
int32 gather_107 = const()[name = string("gather_107"), val = int32(8)];
int32 gather_108_batch_dims_0 = const()[name = string("gather_108_batch_dims_0"), val = int32(0)];
bool gather_108_validate_indices_0 = const()[name = string("gather_108_validate_indices_0"), val = bool(false)];
int32 select_33 = const()[name = string("select_33"), val = int32(3)];
int32 gather_108_axis_1 = const()[name = string("gather_108_axis_1"), val = int32(0)];
int32 gather_108 = gather(axis = gather_108_axis_1, batch_dims = gather_108_batch_dims_0, indices = select_33, validate_indices = gather_108_validate_indices_0, x = var_1067_shape)[name = string("gather_108")];
int32 var_1078 = const()[name = string("op_1078"), val = int32(1)];
int32 var_1079 = add(x = gather_108, y = var_1078)[name = string("op_1079")];
int32 concat_38_axis_0 = const()[name = string("concat_38_axis_0"), val = int32(0)];
bool concat_38_interleave_0 = const()[name = string("concat_38_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_38 = concat(axis = concat_38_axis_0, interleave = concat_38_interleave_0, values = (gather_106, gather_107, var_1079, gather_105))[name = string("concat_38")];
tensor<fp32, [1, 8, ?, ?]> x_padded_27 = reshape(shape = concat_38, x = x_padded_25)[name = string("x_padded_27")];
tensor<int32, [4]> var_1086_begin_0 = const()[name = string("op_1086_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
tensor<int32, [4]> var_1086_end_0 = const()[name = string("op_1086_end_0"), val = tensor<int32, [4]>([1, 8, 0, 0])];
tensor<bool, [4]> var_1086_end_mask_0 = const()[name = string("op_1086_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp32, [1, 8, ?, ?]> var_1086 = slice_by_index(begin = var_1086_begin_0, end = var_1086_end_0, end_mask = var_1086_end_mask_0, x = x_padded_27)[name = string("op_1086")];
int32 gather_110 = const()[name = string("gather_110"), val = int32(1)];
int32 gather_111 = const()[name = string("gather_111"), val = int32(8)];
int32 concat_39_axis_0 = const()[name = string("concat_39_axis_0"), val = int32(0)];
bool concat_39_interleave_0 = const()[name = string("concat_39_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_39 = concat(axis = concat_39_axis_0, interleave = concat_39_interleave_0, values = (gather_110, gather_111, gather_105, gather_108))[name = string("concat_39")];
tensor<fp32, [1, 8, ?, ?]> var_1092 = reshape(shape = concat_39, x = var_1086)[name = string("op_1092")];
int32 floor_div_10 = floor_div(x = gather_108, y = var_53)[name = string("floor_div_10")];
string var_1095_dtype_0 = const()[name = string("op_1095_dtype_0"), val = string("fp32")];
fp32 var_1096_promoted = const()[name = string("op_1096_promoted"), val = fp32(0x1p+0)];
fp32 var_1095 = cast(dtype = var_1095_dtype_0, x = floor_div_10)[name = string("cast_111")];
fp32 var_1097 = add(x = var_1095, y = var_1096_promoted)[name = string("op_1097")];
string var_1098_dtype_0 = const()[name = string("op_1098_dtype_0"), val = string("int32")];
int32 concat_40_values0_0 = const()[name = string("concat_40_values0_0"), val = int32(1)];
int32 concat_40_values1_0 = const()[name = string("concat_40_values1_0"), val = int32(8)];
int32 concat_40_values2_0 = const()[name = string("concat_40_values2_0"), val = int32(0)];
int32 concat_40_axis_0 = const()[name = string("concat_40_axis_0"), val = int32(0)];
bool concat_40_interleave_0 = const()[name = string("concat_40_interleave_0"), val = bool(false)];
int32 var_1098 = cast(dtype = var_1098_dtype_0, x = var_1097)[name = string("cast_110")];
tensor<int32, [4]> concat_40 = concat(axis = concat_40_axis_0, interleave = concat_40_interleave_0, values = (concat_40_values0_0, concat_40_values1_0, concat_40_values2_0, var_1098))[name = string("concat_40")];
tensor<int32, [4]> matrix_bd_27_begin_0 = const()[name = string("matrix_bd_27_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<bool, [4]> matrix_bd_27_end_mask_0 = const()[name = string("matrix_bd_27_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp32, [1, 8, ?, ?]> matrix_bd_27 = slice_by_index(begin = matrix_bd_27_begin_0, end = concat_40, end_mask = matrix_bd_27_end_mask_0, x = var_1092)[name = string("matrix_bd_27")];
tensor<fp32, [1, 8, ?, ?]> var_1103 = add(x = matrix_ac_13, y = matrix_bd_27)[name = string("op_1103")];
fp32 _inversed_scores_25_y_0 = const()[name = string("_inversed_scores_25_y_0"), val = fp32(0x1p-3)];
tensor<fp32, [1, 8, ?, ?]> _inversed_scores_25 = mul(x = var_1103, y = _inversed_scores_25_y_0)[name = string("_inversed_scores_25")];
tensor<int32, [1]> var_1107_axes_0 = const()[name = string("op_1107_axes_0"), val = tensor<int32, [1]>([1])];
tensor<bool, [?, 1, 1, ?]> var_1107 = expand_dims(axes = var_1107_axes_0, x = masks)[name = string("op_1107")];
string cast_71_dtype_0 = const()[name = string("cast_71_dtype_0"), val = string("int32")];
tensor<int32, [?, 1, 1, ?]> cast_71 = cast(dtype = cast_71_dtype_0, x = var_1107)[name = string("cast_109")];
tensor<bool, [?, 1, 1, ?]> mask_27 = equal(x = cast_71, y = var_57)[name = string("mask_27")];
tensor<int32, [4]> var_1109_shape = shape(x = _inversed_scores_25)[name = string("op_1109_shape")];
int32 gather_116_batch_dims_0 = const()[name = string("gather_116_batch_dims_0"), val = int32(0)];
bool gather_116_validate_indices_0 = const()[name = string("gather_116_validate_indices_0"), val = bool(false)];
int32 select_35 = const()[name = string("select_35"), val = int32(3)];
int32 gather_116_axis_1 = const()[name = string("gather_116_axis_1"), val = int32(0)];
int32 gather_116 = gather(axis = gather_116_axis_1, batch_dims = gather_116_batch_dims_0, indices = select_35, validate_indices = gather_116_validate_indices_0, x = var_1109_shape)[name = string("gather_116")];
int32 concat_41_values0_0 = const()[name = string("concat_41_values0_0"), val = int32(0)];
int32 concat_41_values1_0 = const()[name = string("concat_41_values1_0"), val = int32(1)];
int32 concat_41_values2_0 = const()[name = string("concat_41_values2_0"), val = int32(1)];
int32 concat_41_axis_0 = const()[name = string("concat_41_axis_0"), val = int32(0)];
bool concat_41_interleave_0 = const()[name = string("concat_41_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_41 = concat(axis = concat_41_axis_0, interleave = concat_41_interleave_0, values = (concat_41_values0_0, concat_41_values1_0, concat_41_values2_0, gather_116))[name = string("concat_41")];
tensor<int32, [4]> mask_29_begin_0 = const()[name = string("mask_29_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<bool, [4]> mask_29_end_mask_0 = const()[name = string("mask_29_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<bool, [?, 1, 1, ?]> mask_29 = slice_by_index(begin = mask_29_begin_0, end = concat_41, end_mask = mask_29_end_mask_0, x = mask_27)[name = string("mask_29")];
tensor<fp32, [1, 8, ?, ?]> scores_27 = select(a = var_37, b = _inversed_scores_25, cond = mask_29)[name = string("scores_27")];
tensor<fp32, [1, 8, ?, ?]> var_1115 = softmax(axis = var_55, x = scores_27)[name = string("op_1115")];
tensor<fp32, [1, 8, ?, ?]> input_153 = select(a = var_46, b = var_1115, cond = mask_29)[name = string("input_153")];
bool x_21_transpose_x_0 = const()[name = string("x_21_transpose_x_0"), val = bool(false)];
bool x_21_transpose_y_0 = const()[name = string("x_21_transpose_y_0"), val = bool(false)];
tensor<fp32, [1, 8, ?, 64]> v_27 = transpose(perm = v_27_perm_0, x = v_25)[name = string("transpose_124")];
tensor<fp32, [1, 8, ?, 64]> x_21 = matmul(transpose_x = x_21_transpose_x_0, transpose_y = x_21_transpose_y_0, x = input_153, y = v_27)[name = string("x_21")];
tensor<int32, [4]> var_1119_perm_0 = const()[name = string("op_1119_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_1121 = const()[name = string("op_1121"), val = tensor<int32, [3]>([1, -1, 512])];
tensor<fp32, [1, ?, 8, 64]> var_1119 = transpose(perm = var_1119_perm_0, x = x_21)[name = string("transpose_119")];
tensor<fp32, [1, ?, 512]> input_155 = reshape(shape = var_1121, x = var_1119)[name = string("input_155")];
tensor<fp32, [1, ?, 512]> input_157 = linear(bias = encoder_up_encoders_0_self_attn_linear_out_bias, weight = encoder_up_encoders_0_self_attn_linear_out_weight, x = input_155)[name = string("linear_48")];
tensor<fp32, [1, ?, 512]> input_159 = add(x = x_19, y = input_157)[name = string("input_159")];
tensor<int32, [1]> input_161_axes_0 = const()[name = string("input_161_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp32, [1, ?, 512]> input_161 = layer_norm(axes = input_161_axes_0, beta = encoder_up_encoders_0_norm_ff_bias, epsilon = var_36, gamma = encoder_up_encoders_0_norm_ff_weight, x = input_159)[name = string("input_161")];
tensor<fp32, [1, ?, 2048]> input_163 = linear(bias = encoder_up_encoders_0_feed_forward_w_1_bias, weight = encoder_up_encoders_0_feed_forward_w_1_weight, x = input_161)[name = string("linear_49")];
tensor<fp32, [1, ?, 2048]> input_165 = silu(x = input_163)[name = string("input_165")];
tensor<fp32, [1, ?, 512]> input_169 = linear(bias = encoder_up_encoders_0_feed_forward_w_2_bias, weight = encoder_up_encoders_0_feed_forward_w_2_weight, x = input_165)[name = string("linear_50")];
tensor<fp32, [1, ?, 512]> input_171 = add(x = input_159, y = input_169)[name = string("input_171")];
tensor<int32, [1]> query_15_axes_0 = const()[name = string("query_15_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp32, [1, ?, 512]> query_15 = layer_norm(axes = query_15_axes_0, beta = encoder_up_encoders_1_norm_mha_bias, epsilon = var_36, gamma = encoder_up_encoders_1_norm_mha_weight, x = input_171)[name = string("query_15")];
tensor<fp32, [1, ?, 512]> var_1164 = linear(bias = encoder_up_encoders_1_self_attn_linear_q_bias, weight = encoder_up_encoders_1_self_attn_linear_q_weight, x = query_15)[name = string("linear_51")];
tensor<int32, [4]> var_1165 = const()[name = string("op_1165"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> q_43 = reshape(shape = var_1165, x = var_1164)[name = string("q_43")];
tensor<fp32, [1, ?, 512]> var_1169 = linear(bias = encoder_up_encoders_1_self_attn_linear_k_bias, weight = encoder_up_encoders_1_self_attn_linear_k_weight, x = query_15)[name = string("linear_52")];
tensor<int32, [4]> var_1170 = const()[name = string("op_1170"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> k_29 = reshape(shape = var_1170, x = var_1169)[name = string("k_29")];
tensor<fp32, [1, ?, 512]> var_1174 = linear(bias = encoder_up_encoders_1_self_attn_linear_v_bias, weight = encoder_up_encoders_1_self_attn_linear_v_weight, x = query_15)[name = string("linear_53")];
tensor<int32, [4]> var_1175 = const()[name = string("op_1175"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> v_29 = reshape(shape = var_1175, x = var_1174)[name = string("v_29")];
tensor<int32, [4]> v_31_perm_0 = const()[name = string("v_31_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp32, [1, ?, 512]> var_1183 = linear(bias = linear_4_bias_0, weight = encoder_up_encoders_1_self_attn_linear_pos_weight, x = input_149)[name = string("linear_54")];
tensor<int32, [4]> var_1184 = const()[name = string("op_1184"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> p_29 = reshape(shape = var_1184, x = var_1183)[name = string("p_29")];
tensor<fp32, [8, 64]> const_28 = const()[name = string("const_28"), val = tensor<fp32, [8, 64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185406656)))];
tensor<fp32, [1, ?, 8, 64]> var_1188 = add(x = q_43, y = const_28)[name = string("op_1188")];
tensor<fp32, [8, 64]> const_29 = const()[name = string("const_29"), val = tensor<fp32, [8, 64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185408768)))];
tensor<fp32, [1, ?, 8, 64]> var_1191 = add(x = q_43, y = const_29)[name = string("op_1191")];
bool matrix_ac_15_transpose_x_0 = const()[name = string("matrix_ac_15_transpose_x_0"), val = bool(false)];
bool matrix_ac_15_transpose_y_0 = const()[name = string("matrix_ac_15_transpose_y_0"), val = bool(false)];
tensor<int32, [4]> transpose_88_perm_0 = const()[name = string("transpose_88_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
tensor<int32, [4]> transpose_89_perm_0 = const()[name = string("transpose_89_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
tensor<fp32, [1, 8, 64, ?]> transpose_89 = transpose(perm = transpose_89_perm_0, x = k_29)[name = string("transpose_116")];
tensor<fp32, [1, 8, ?, 64]> transpose_88 = transpose(perm = transpose_88_perm_0, x = var_1188)[name = string("transpose_117")];
tensor<fp32, [1, 8, ?, ?]> matrix_ac_15 = matmul(transpose_x = matrix_ac_15_transpose_x_0, transpose_y = matrix_ac_15_transpose_y_0, x = transpose_88, y = transpose_89)[name = string("matrix_ac_15")];
bool matrix_bd_29_transpose_x_0 = const()[name = string("matrix_bd_29_transpose_x_0"), val = bool(false)];
bool matrix_bd_29_transpose_y_0 = const()[name = string("matrix_bd_29_transpose_y_0"), val = bool(false)];
tensor<int32, [4]> transpose_90_perm_0 = const()[name = string("transpose_90_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
tensor<int32, [4]> transpose_91_perm_0 = const()[name = string("transpose_91_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
tensor<fp32, [1, 8, 64, ?]> transpose_91 = transpose(perm = transpose_91_perm_0, x = p_29)[name = string("transpose_114")];
tensor<fp32, [1, 8, ?, 64]> transpose_90 = transpose(perm = transpose_90_perm_0, x = var_1191)[name = string("transpose_115")];
tensor<fp32, [1, 8, ?, ?]> matrix_bd_29 = matmul(transpose_x = matrix_bd_29_transpose_x_0, transpose_y = matrix_bd_29_transpose_y_0, x = transpose_90, y = transpose_91)[name = string("matrix_bd_29")];
tensor<int32, [4]> var_1197_shape = shape(x = matrix_bd_29)[name = string("op_1197_shape")];
int32 gather_119 = const()[name = string("gather_119"), val = int32(1)];
int32 gather_120 = const()[name = string("gather_120"), val = int32(8)];
int32 gather_121_batch_dims_0 = const()[name = string("gather_121_batch_dims_0"), val = int32(0)];
bool gather_121_validate_indices_0 = const()[name = string("gather_121_validate_indices_0"), val = bool(false)];
int32 select_36 = const()[name = string("select_36"), val = int32(2)];
int32 gather_121_axis_1 = const()[name = string("gather_121_axis_1"), val = int32(0)];
int32 gather_121 = gather(axis = gather_121_axis_1, batch_dims = gather_121_batch_dims_0, indices = select_36, validate_indices = gather_121_validate_indices_0, x = var_1197_shape)[name = string("gather_121")];
int32 concat_42_axis_0 = const()[name = string("concat_42_axis_0"), val = int32(0)];
bool concat_42_interleave_0 = const()[name = string("concat_42_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_42 = concat(axis = concat_42_axis_0, interleave = concat_42_interleave_0, values = (gather_119, gather_120, gather_121, var_61))[name = string("concat_42")];
fp32 zero_pad_15_value_0 = const()[name = string("zero_pad_15_value_0"), val = fp32(0x0p+0)];
tensor<fp32, [1, 8, ?, 1]> zero_pad_15 = fill(shape = concat_42, value = zero_pad_15_value_0)[name = string("zero_pad_15")];
bool x_padded_29_interleave_0 = const()[name = string("x_padded_29_interleave_0"), val = bool(false)];
tensor<fp32, [1, 8, ?, ?]> x_padded_29 = concat(axis = var_55, interleave = x_padded_29_interleave_0, values = (zero_pad_15, matrix_bd_29))[name = string("x_padded_29")];
int32 gather_122 = const()[name = string("gather_122"), val = int32(1)];
int32 gather_123 = const()[name = string("gather_123"), val = int32(8)];
int32 gather_124_batch_dims_0 = const()[name = string("gather_124_batch_dims_0"), val = int32(0)];
bool gather_124_validate_indices_0 = const()[name = string("gather_124_validate_indices_0"), val = bool(false)];
int32 select_37 = const()[name = string("select_37"), val = int32(3)];
int32 gather_124_axis_1 = const()[name = string("gather_124_axis_1"), val = int32(0)];
int32 gather_124 = gather(axis = gather_124_axis_1, batch_dims = gather_124_batch_dims_0, indices = select_37, validate_indices = gather_124_validate_indices_0, x = var_1197_shape)[name = string("gather_124")];
int32 var_1208 = const()[name = string("op_1208"), val = int32(1)];
int32 var_1209 = add(x = gather_124, y = var_1208)[name = string("op_1209")];
int32 concat_43_axis_0 = const()[name = string("concat_43_axis_0"), val = int32(0)];
bool concat_43_interleave_0 = const()[name = string("concat_43_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_43 = concat(axis = concat_43_axis_0, interleave = concat_43_interleave_0, values = (gather_122, gather_123, var_1209, gather_121))[name = string("concat_43")];
tensor<fp32, [1, 8, ?, ?]> x_padded_31 = reshape(shape = concat_43, x = x_padded_29)[name = string("x_padded_31")];
tensor<int32, [4]> var_1216_begin_0 = const()[name = string("op_1216_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
tensor<int32, [4]> var_1216_end_0 = const()[name = string("op_1216_end_0"), val = tensor<int32, [4]>([1, 8, 0, 0])];
tensor<bool, [4]> var_1216_end_mask_0 = const()[name = string("op_1216_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp32, [1, 8, ?, ?]> var_1216 = slice_by_index(begin = var_1216_begin_0, end = var_1216_end_0, end_mask = var_1216_end_mask_0, x = x_padded_31)[name = string("op_1216")];
int32 gather_126 = const()[name = string("gather_126"), val = int32(1)];
int32 gather_127 = const()[name = string("gather_127"), val = int32(8)];
int32 concat_44_axis_0 = const()[name = string("concat_44_axis_0"), val = int32(0)];
bool concat_44_interleave_0 = const()[name = string("concat_44_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_44 = concat(axis = concat_44_axis_0, interleave = concat_44_interleave_0, values = (gather_126, gather_127, gather_121, gather_124))[name = string("concat_44")];
tensor<fp32, [1, 8, ?, ?]> var_1222 = reshape(shape = concat_44, x = var_1216)[name = string("op_1222")];
int32 floor_div_11 = floor_div(x = gather_124, y = var_53)[name = string("floor_div_11")];
string var_1225_dtype_0 = const()[name = string("op_1225_dtype_0"), val = string("fp32")];
fp32 var_1226_promoted = const()[name = string("op_1226_promoted"), val = fp32(0x1p+0)];
fp32 var_1225 = cast(dtype = var_1225_dtype_0, x = floor_div_11)[name = string("cast_108")];
fp32 var_1227 = add(x = var_1225, y = var_1226_promoted)[name = string("op_1227")];
string var_1228_dtype_0 = const()[name = string("op_1228_dtype_0"), val = string("int32")];
int32 concat_45_values0_0 = const()[name = string("concat_45_values0_0"), val = int32(1)];
int32 concat_45_values1_0 = const()[name = string("concat_45_values1_0"), val = int32(8)];
int32 concat_45_values2_0 = const()[name = string("concat_45_values2_0"), val = int32(0)];
int32 concat_45_axis_0 = const()[name = string("concat_45_axis_0"), val = int32(0)];
bool concat_45_interleave_0 = const()[name = string("concat_45_interleave_0"), val = bool(false)];
int32 var_1228 = cast(dtype = var_1228_dtype_0, x = var_1227)[name = string("cast_107")];
tensor<int32, [4]> concat_45 = concat(axis = concat_45_axis_0, interleave = concat_45_interleave_0, values = (concat_45_values0_0, concat_45_values1_0, concat_45_values2_0, var_1228))[name = string("concat_45")];
tensor<int32, [4]> matrix_bd_31_begin_0 = const()[name = string("matrix_bd_31_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<bool, [4]> matrix_bd_31_end_mask_0 = const()[name = string("matrix_bd_31_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp32, [1, 8, ?, ?]> matrix_bd_31 = slice_by_index(begin = matrix_bd_31_begin_0, end = concat_45, end_mask = matrix_bd_31_end_mask_0, x = var_1222)[name = string("matrix_bd_31")];
tensor<fp32, [1, 8, ?, ?]> var_1233 = add(x = matrix_ac_15, y = matrix_bd_31)[name = string("op_1233")];
fp32 _inversed_scores_29_y_0 = const()[name = string("_inversed_scores_29_y_0"), val = fp32(0x1p-3)];
tensor<fp32, [1, 8, ?, ?]> _inversed_scores_29 = mul(x = var_1233, y = _inversed_scores_29_y_0)[name = string("_inversed_scores_29")];
tensor<int32, [4]> var_1239_shape = shape(x = _inversed_scores_29)[name = string("op_1239_shape")];
int32 gather_132_batch_dims_0 = const()[name = string("gather_132_batch_dims_0"), val = int32(0)];
bool gather_132_validate_indices_0 = const()[name = string("gather_132_validate_indices_0"), val = bool(false)];
int32 select_39 = const()[name = string("select_39"), val = int32(3)];
int32 gather_132_axis_1 = const()[name = string("gather_132_axis_1"), val = int32(0)];
int32 gather_132 = gather(axis = gather_132_axis_1, batch_dims = gather_132_batch_dims_0, indices = select_39, validate_indices = gather_132_validate_indices_0, x = var_1239_shape)[name = string("gather_132")];
int32 concat_46_values0_0 = const()[name = string("concat_46_values0_0"), val = int32(0)];
int32 concat_46_values1_0 = const()[name = string("concat_46_values1_0"), val = int32(1)];
int32 concat_46_values2_0 = const()[name = string("concat_46_values2_0"), val = int32(1)];
int32 concat_46_axis_0 = const()[name = string("concat_46_axis_0"), val = int32(0)];
bool concat_46_interleave_0 = const()[name = string("concat_46_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_46 = concat(axis = concat_46_axis_0, interleave = concat_46_interleave_0, values = (concat_46_values0_0, concat_46_values1_0, concat_46_values2_0, gather_132))[name = string("concat_46")];
tensor<int32, [4]> mask_33_begin_0 = const()[name = string("mask_33_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<bool, [4]> mask_33_end_mask_0 = const()[name = string("mask_33_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<bool, [?, 1, 1, ?]> mask_33 = slice_by_index(begin = mask_33_begin_0, end = concat_46, end_mask = mask_33_end_mask_0, x = mask_27)[name = string("mask_33")];
tensor<fp32, [1, 8, ?, ?]> scores_31 = select(a = var_37, b = _inversed_scores_29, cond = mask_33)[name = string("scores_31")];
tensor<fp32, [1, 8, ?, ?]> var_1245 = softmax(axis = var_55, x = scores_31)[name = string("op_1245")];
tensor<fp32, [1, 8, ?, ?]> input_173 = select(a = var_46, b = var_1245, cond = mask_33)[name = string("input_173")];
bool x_23_transpose_x_0 = const()[name = string("x_23_transpose_x_0"), val = bool(false)];
bool x_23_transpose_y_0 = const()[name = string("x_23_transpose_y_0"), val = bool(false)];
tensor<fp32, [1, 8, ?, 64]> v_31 = transpose(perm = v_31_perm_0, x = v_29)[name = string("transpose_118")];
tensor<fp32, [1, 8, ?, 64]> x_23 = matmul(transpose_x = x_23_transpose_x_0, transpose_y = x_23_transpose_y_0, x = input_173, y = v_31)[name = string("x_23")];
tensor<int32, [4]> var_1249_perm_0 = const()[name = string("op_1249_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_1251 = const()[name = string("op_1251"), val = tensor<int32, [3]>([1, -1, 512])];
tensor<fp32, [1, ?, 8, 64]> var_1249 = transpose(perm = var_1249_perm_0, x = x_23)[name = string("transpose_113")];
tensor<fp32, [1, ?, 512]> input_175 = reshape(shape = var_1251, x = var_1249)[name = string("input_175")];
tensor<fp32, [1, ?, 512]> input_177 = linear(bias = encoder_up_encoders_1_self_attn_linear_out_bias, weight = encoder_up_encoders_1_self_attn_linear_out_weight, x = input_175)[name = string("linear_55")];
tensor<fp32, [1, ?, 512]> input_179 = add(x = input_171, y = input_177)[name = string("input_179")];
tensor<int32, [1]> input_181_axes_0 = const()[name = string("input_181_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp32, [1, ?, 512]> input_181 = layer_norm(axes = input_181_axes_0, beta = encoder_up_encoders_1_norm_ff_bias, epsilon = var_36, gamma = encoder_up_encoders_1_norm_ff_weight, x = input_179)[name = string("input_181")];
tensor<fp32, [1, ?, 2048]> input_183 = linear(bias = encoder_up_encoders_1_feed_forward_w_1_bias, weight = encoder_up_encoders_1_feed_forward_w_1_weight, x = input_181)[name = string("linear_56")];
tensor<fp32, [1, ?, 2048]> input_185 = silu(x = input_183)[name = string("input_185")];
tensor<fp32, [1, ?, 512]> input_189 = linear(bias = encoder_up_encoders_1_feed_forward_w_2_bias, weight = encoder_up_encoders_1_feed_forward_w_2_weight, x = input_185)[name = string("linear_57")];
tensor<fp32, [1, ?, 512]> input_191 = add(x = input_179, y = input_189)[name = string("input_191")];
tensor<int32, [1]> query_17_axes_0 = const()[name = string("query_17_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp32, [1, ?, 512]> query_17 = layer_norm(axes = query_17_axes_0, beta = encoder_up_encoders_2_norm_mha_bias, epsilon = var_36, gamma = encoder_up_encoders_2_norm_mha_weight, x = input_191)[name = string("query_17")];
tensor<fp32, [1, ?, 512]> var_1294 = linear(bias = encoder_up_encoders_2_self_attn_linear_q_bias, weight = encoder_up_encoders_2_self_attn_linear_q_weight, x = query_17)[name = string("linear_58")];
tensor<int32, [4]> var_1295 = const()[name = string("op_1295"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> q_49 = reshape(shape = var_1295, x = var_1294)[name = string("q_49")];
tensor<fp32, [1, ?, 512]> var_1299 = linear(bias = encoder_up_encoders_2_self_attn_linear_k_bias, weight = encoder_up_encoders_2_self_attn_linear_k_weight, x = query_17)[name = string("linear_59")];
tensor<int32, [4]> var_1300 = const()[name = string("op_1300"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> k_33 = reshape(shape = var_1300, x = var_1299)[name = string("k_33")];
tensor<fp32, [1, ?, 512]> var_1304 = linear(bias = encoder_up_encoders_2_self_attn_linear_v_bias, weight = encoder_up_encoders_2_self_attn_linear_v_weight, x = query_17)[name = string("linear_60")];
tensor<int32, [4]> var_1305 = const()[name = string("op_1305"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> v_33 = reshape(shape = var_1305, x = var_1304)[name = string("v_33")];
tensor<int32, [4]> v_35_perm_0 = const()[name = string("v_35_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp32, [1, ?, 512]> var_1313 = linear(bias = linear_4_bias_0, weight = encoder_up_encoders_2_self_attn_linear_pos_weight, x = input_149)[name = string("linear_61")];
tensor<int32, [4]> var_1314 = const()[name = string("op_1314"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> p_33 = reshape(shape = var_1314, x = var_1313)[name = string("p_33")];
tensor<fp32, [8, 64]> const_30 = const()[name = string("const_30"), val = tensor<fp32, [8, 64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185410880)))];
tensor<fp32, [1, ?, 8, 64]> var_1318 = add(x = q_49, y = const_30)[name = string("op_1318")];
tensor<fp32, [8, 64]> const_31 = const()[name = string("const_31"), val = tensor<fp32, [8, 64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185412992)))];
tensor<fp32, [1, ?, 8, 64]> var_1321 = add(x = q_49, y = const_31)[name = string("op_1321")];
bool matrix_ac_17_transpose_x_0 = const()[name = string("matrix_ac_17_transpose_x_0"), val = bool(false)];
bool matrix_ac_17_transpose_y_0 = const()[name = string("matrix_ac_17_transpose_y_0"), val = bool(false)];
tensor<int32, [4]> transpose_92_perm_0 = const()[name = string("transpose_92_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
tensor<int32, [4]> transpose_93_perm_0 = const()[name = string("transpose_93_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
tensor<fp32, [1, 8, 64, ?]> transpose_93 = transpose(perm = transpose_93_perm_0, x = k_33)[name = string("transpose_110")];
tensor<fp32, [1, 8, ?, 64]> transpose_92 = transpose(perm = transpose_92_perm_0, x = var_1318)[name = string("transpose_111")];
tensor<fp32, [1, 8, ?, ?]> matrix_ac_17 = matmul(transpose_x = matrix_ac_17_transpose_x_0, transpose_y = matrix_ac_17_transpose_y_0, x = transpose_92, y = transpose_93)[name = string("matrix_ac_17")];
bool matrix_bd_33_transpose_x_0 = const()[name = string("matrix_bd_33_transpose_x_0"), val = bool(false)];
bool matrix_bd_33_transpose_y_0 = const()[name = string("matrix_bd_33_transpose_y_0"), val = bool(false)];
tensor<int32, [4]> transpose_94_perm_0 = const()[name = string("transpose_94_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
tensor<int32, [4]> transpose_95_perm_0 = const()[name = string("transpose_95_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
tensor<fp32, [1, 8, 64, ?]> transpose_95 = transpose(perm = transpose_95_perm_0, x = p_33)[name = string("transpose_108")];
tensor<fp32, [1, 8, ?, 64]> transpose_94 = transpose(perm = transpose_94_perm_0, x = var_1321)[name = string("transpose_109")];
tensor<fp32, [1, 8, ?, ?]> matrix_bd_33 = matmul(transpose_x = matrix_bd_33_transpose_x_0, transpose_y = matrix_bd_33_transpose_y_0, x = transpose_94, y = transpose_95)[name = string("matrix_bd_33")];
tensor<int32, [4]> var_1327_shape = shape(x = matrix_bd_33)[name = string("op_1327_shape")];
int32 gather_135 = const()[name = string("gather_135"), val = int32(1)];
int32 gather_136 = const()[name = string("gather_136"), val = int32(8)];
int32 gather_137_batch_dims_0 = const()[name = string("gather_137_batch_dims_0"), val = int32(0)];
bool gather_137_validate_indices_0 = const()[name = string("gather_137_validate_indices_0"), val = bool(false)];
int32 select_40 = const()[name = string("select_40"), val = int32(2)];
int32 gather_137_axis_1 = const()[name = string("gather_137_axis_1"), val = int32(0)];
int32 gather_137 = gather(axis = gather_137_axis_1, batch_dims = gather_137_batch_dims_0, indices = select_40, validate_indices = gather_137_validate_indices_0, x = var_1327_shape)[name = string("gather_137")];
int32 concat_47_axis_0 = const()[name = string("concat_47_axis_0"), val = int32(0)];
bool concat_47_interleave_0 = const()[name = string("concat_47_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_47 = concat(axis = concat_47_axis_0, interleave = concat_47_interleave_0, values = (gather_135, gather_136, gather_137, var_61))[name = string("concat_47")];
fp32 zero_pad_17_value_0 = const()[name = string("zero_pad_17_value_0"), val = fp32(0x0p+0)];
tensor<fp32, [1, 8, ?, 1]> zero_pad_17 = fill(shape = concat_47, value = zero_pad_17_value_0)[name = string("zero_pad_17")];
bool x_padded_33_interleave_0 = const()[name = string("x_padded_33_interleave_0"), val = bool(false)];
tensor<fp32, [1, 8, ?, ?]> x_padded_33 = concat(axis = var_55, interleave = x_padded_33_interleave_0, values = (zero_pad_17, matrix_bd_33))[name = string("x_padded_33")];
int32 gather_138 = const()[name = string("gather_138"), val = int32(1)];
int32 gather_139 = const()[name = string("gather_139"), val = int32(8)];
int32 gather_140_batch_dims_0 = const()[name = string("gather_140_batch_dims_0"), val = int32(0)];
bool gather_140_validate_indices_0 = const()[name = string("gather_140_validate_indices_0"), val = bool(false)];
int32 select_41 = const()[name = string("select_41"), val = int32(3)];
int32 gather_140_axis_1 = const()[name = string("gather_140_axis_1"), val = int32(0)];
int32 gather_140 = gather(axis = gather_140_axis_1, batch_dims = gather_140_batch_dims_0, indices = select_41, validate_indices = gather_140_validate_indices_0, x = var_1327_shape)[name = string("gather_140")];
int32 var_1338 = const()[name = string("op_1338"), val = int32(1)];
int32 var_1339 = add(x = gather_140, y = var_1338)[name = string("op_1339")];
int32 concat_48_axis_0 = const()[name = string("concat_48_axis_0"), val = int32(0)];
bool concat_48_interleave_0 = const()[name = string("concat_48_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_48 = concat(axis = concat_48_axis_0, interleave = concat_48_interleave_0, values = (gather_138, gather_139, var_1339, gather_137))[name = string("concat_48")];
tensor<fp32, [1, 8, ?, ?]> x_padded_35 = reshape(shape = concat_48, x = x_padded_33)[name = string("x_padded_35")];
tensor<int32, [4]> var_1346_begin_0 = const()[name = string("op_1346_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
tensor<int32, [4]> var_1346_end_0 = const()[name = string("op_1346_end_0"), val = tensor<int32, [4]>([1, 8, 0, 0])];
tensor<bool, [4]> var_1346_end_mask_0 = const()[name = string("op_1346_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp32, [1, 8, ?, ?]> var_1346 = slice_by_index(begin = var_1346_begin_0, end = var_1346_end_0, end_mask = var_1346_end_mask_0, x = x_padded_35)[name = string("op_1346")];
int32 gather_142 = const()[name = string("gather_142"), val = int32(1)];
int32 gather_143 = const()[name = string("gather_143"), val = int32(8)];
int32 concat_49_axis_0 = const()[name = string("concat_49_axis_0"), val = int32(0)];
bool concat_49_interleave_0 = const()[name = string("concat_49_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_49 = concat(axis = concat_49_axis_0, interleave = concat_49_interleave_0, values = (gather_142, gather_143, gather_137, gather_140))[name = string("concat_49")];
tensor<fp32, [1, 8, ?, ?]> var_1352 = reshape(shape = concat_49, x = var_1346)[name = string("op_1352")];
int32 floor_div_12 = floor_div(x = gather_140, y = var_53)[name = string("floor_div_12")];
string var_1355_dtype_0 = const()[name = string("op_1355_dtype_0"), val = string("fp32")];
fp32 var_1356_promoted = const()[name = string("op_1356_promoted"), val = fp32(0x1p+0)];
fp32 var_1355 = cast(dtype = var_1355_dtype_0, x = floor_div_12)[name = string("cast_106")];
fp32 var_1357 = add(x = var_1355, y = var_1356_promoted)[name = string("op_1357")];
string var_1358_dtype_0 = const()[name = string("op_1358_dtype_0"), val = string("int32")];
int32 concat_50_values0_0 = const()[name = string("concat_50_values0_0"), val = int32(1)];
int32 concat_50_values1_0 = const()[name = string("concat_50_values1_0"), val = int32(8)];
int32 concat_50_values2_0 = const()[name = string("concat_50_values2_0"), val = int32(0)];
int32 concat_50_axis_0 = const()[name = string("concat_50_axis_0"), val = int32(0)];
bool concat_50_interleave_0 = const()[name = string("concat_50_interleave_0"), val = bool(false)];
int32 var_1358 = cast(dtype = var_1358_dtype_0, x = var_1357)[name = string("cast_105")];
tensor<int32, [4]> concat_50 = concat(axis = concat_50_axis_0, interleave = concat_50_interleave_0, values = (concat_50_values0_0, concat_50_values1_0, concat_50_values2_0, var_1358))[name = string("concat_50")];
tensor<int32, [4]> matrix_bd_35_begin_0 = const()[name = string("matrix_bd_35_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<bool, [4]> matrix_bd_35_end_mask_0 = const()[name = string("matrix_bd_35_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp32, [1, 8, ?, ?]> matrix_bd_35 = slice_by_index(begin = matrix_bd_35_begin_0, end = concat_50, end_mask = matrix_bd_35_end_mask_0, x = var_1352)[name = string("matrix_bd_35")];
tensor<fp32, [1, 8, ?, ?]> var_1363 = add(x = matrix_ac_17, y = matrix_bd_35)[name = string("op_1363")];
fp32 _inversed_scores_33_y_0 = const()[name = string("_inversed_scores_33_y_0"), val = fp32(0x1p-3)];
tensor<fp32, [1, 8, ?, ?]> _inversed_scores_33 = mul(x = var_1363, y = _inversed_scores_33_y_0)[name = string("_inversed_scores_33")];
tensor<int32, [4]> var_1369_shape = shape(x = _inversed_scores_33)[name = string("op_1369_shape")];
int32 gather_148_batch_dims_0 = const()[name = string("gather_148_batch_dims_0"), val = int32(0)];
bool gather_148_validate_indices_0 = const()[name = string("gather_148_validate_indices_0"), val = bool(false)];
int32 select_43 = const()[name = string("select_43"), val = int32(3)];
int32 gather_148_axis_1 = const()[name = string("gather_148_axis_1"), val = int32(0)];
int32 gather_148 = gather(axis = gather_148_axis_1, batch_dims = gather_148_batch_dims_0, indices = select_43, validate_indices = gather_148_validate_indices_0, x = var_1369_shape)[name = string("gather_148")];
int32 concat_51_values0_0 = const()[name = string("concat_51_values0_0"), val = int32(0)];
int32 concat_51_values1_0 = const()[name = string("concat_51_values1_0"), val = int32(1)];
int32 concat_51_values2_0 = const()[name = string("concat_51_values2_0"), val = int32(1)];
int32 concat_51_axis_0 = const()[name = string("concat_51_axis_0"), val = int32(0)];
bool concat_51_interleave_0 = const()[name = string("concat_51_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_51 = concat(axis = concat_51_axis_0, interleave = concat_51_interleave_0, values = (concat_51_values0_0, concat_51_values1_0, concat_51_values2_0, gather_148))[name = string("concat_51")];
tensor<int32, [4]> mask_37_begin_0 = const()[name = string("mask_37_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<bool, [4]> mask_37_end_mask_0 = const()[name = string("mask_37_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<bool, [?, 1, 1, ?]> mask_37 = slice_by_index(begin = mask_37_begin_0, end = concat_51, end_mask = mask_37_end_mask_0, x = mask_27)[name = string("mask_37")];
tensor<fp32, [1, 8, ?, ?]> scores_35 = select(a = var_37, b = _inversed_scores_33, cond = mask_37)[name = string("scores_35")];
tensor<fp32, [1, 8, ?, ?]> var_1375 = softmax(axis = var_55, x = scores_35)[name = string("op_1375")];
tensor<fp32, [1, 8, ?, ?]> input_193 = select(a = var_46, b = var_1375, cond = mask_37)[name = string("input_193")];
bool x_25_transpose_x_0 = const()[name = string("x_25_transpose_x_0"), val = bool(false)];
bool x_25_transpose_y_0 = const()[name = string("x_25_transpose_y_0"), val = bool(false)];
tensor<fp32, [1, 8, ?, 64]> v_35 = transpose(perm = v_35_perm_0, x = v_33)[name = string("transpose_112")];
tensor<fp32, [1, 8, ?, 64]> x_25 = matmul(transpose_x = x_25_transpose_x_0, transpose_y = x_25_transpose_y_0, x = input_193, y = v_35)[name = string("x_25")];
tensor<int32, [4]> var_1379_perm_0 = const()[name = string("op_1379_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_1381 = const()[name = string("op_1381"), val = tensor<int32, [3]>([1, -1, 512])];
tensor<fp32, [1, ?, 8, 64]> var_1379 = transpose(perm = var_1379_perm_0, x = x_25)[name = string("transpose_107")];
tensor<fp32, [1, ?, 512]> input_195 = reshape(shape = var_1381, x = var_1379)[name = string("input_195")];
tensor<fp32, [1, ?, 512]> input_197 = linear(bias = encoder_up_encoders_2_self_attn_linear_out_bias, weight = encoder_up_encoders_2_self_attn_linear_out_weight, x = input_195)[name = string("linear_62")];
tensor<fp32, [1, ?, 512]> input_199 = add(x = input_191, y = input_197)[name = string("input_199")];
tensor<int32, [1]> input_201_axes_0 = const()[name = string("input_201_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp32, [1, ?, 512]> input_201 = layer_norm(axes = input_201_axes_0, beta = encoder_up_encoders_2_norm_ff_bias, epsilon = var_36, gamma = encoder_up_encoders_2_norm_ff_weight, x = input_199)[name = string("input_201")];
tensor<fp32, [1, ?, 2048]> input_203 = linear(bias = encoder_up_encoders_2_feed_forward_w_1_bias, weight = encoder_up_encoders_2_feed_forward_w_1_weight, x = input_201)[name = string("linear_63")];
tensor<fp32, [1, ?, 2048]> input_205 = silu(x = input_203)[name = string("input_205")];
tensor<fp32, [1, ?, 512]> input_209 = linear(bias = encoder_up_encoders_2_feed_forward_w_2_bias, weight = encoder_up_encoders_2_feed_forward_w_2_weight, x = input_205)[name = string("linear_64")];
tensor<fp32, [1, ?, 512]> input_211 = add(x = input_199, y = input_209)[name = string("input_211")];
tensor<int32, [1]> query_axes_0 = const()[name = string("query_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp32, [1, ?, 512]> query = layer_norm(axes = query_axes_0, beta = encoder_up_encoders_3_norm_mha_bias, epsilon = var_36, gamma = encoder_up_encoders_3_norm_mha_weight, x = input_211)[name = string("query")];
tensor<fp32, [1, ?, 512]> var_1424 = linear(bias = encoder_up_encoders_3_self_attn_linear_q_bias, weight = encoder_up_encoders_3_self_attn_linear_q_weight, x = query)[name = string("linear_65")];
tensor<int32, [4]> var_1425 = const()[name = string("op_1425"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> q_55 = reshape(shape = var_1425, x = var_1424)[name = string("q_55")];
tensor<fp32, [1, ?, 512]> var_1429 = linear(bias = encoder_up_encoders_3_self_attn_linear_k_bias, weight = encoder_up_encoders_3_self_attn_linear_k_weight, x = query)[name = string("linear_66")];
tensor<int32, [4]> var_1430 = const()[name = string("op_1430"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> k_37 = reshape(shape = var_1430, x = var_1429)[name = string("k_37")];
tensor<fp32, [1, ?, 512]> var_1434 = linear(bias = encoder_up_encoders_3_self_attn_linear_v_bias, weight = encoder_up_encoders_3_self_attn_linear_v_weight, x = query)[name = string("linear_67")];
tensor<int32, [4]> var_1435 = const()[name = string("op_1435"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> v_37 = reshape(shape = var_1435, x = var_1434)[name = string("v_37")];
tensor<int32, [4]> v_perm_0 = const()[name = string("v_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp32, [1, ?, 512]> var_1443 = linear(bias = linear_4_bias_0, weight = encoder_up_encoders_3_self_attn_linear_pos_weight, x = input_149)[name = string("linear_68")];
tensor<int32, [4]> var_1444 = const()[name = string("op_1444"), val = tensor<int32, [4]>([1, -1, 8, 64])];
tensor<fp32, [1, ?, 8, 64]> p_37 = reshape(shape = var_1444, x = var_1443)[name = string("p_37")];
tensor<fp32, [8, 64]> const_32 = const()[name = string("const_32"), val = tensor<fp32, [8, 64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185415104)))];
tensor<fp32, [1, ?, 8, 64]> var_1448 = add(x = q_55, y = const_32)[name = string("op_1448")];
tensor<fp32, [8, 64]> const_33 = const()[name = string("const_33"), val = tensor<fp32, [8, 64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(185417216)))];
tensor<fp32, [1, ?, 8, 64]> var_1451 = add(x = q_55, y = const_33)[name = string("op_1451")];
bool matrix_ac_transpose_x_0 = const()[name = string("matrix_ac_transpose_x_0"), val = bool(false)];
bool matrix_ac_transpose_y_0 = const()[name = string("matrix_ac_transpose_y_0"), val = bool(false)];
tensor<int32, [4]> transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
tensor<int32, [4]> transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
tensor<fp32, [1, 8, 64, ?]> transpose_97 = transpose(perm = transpose_97_perm_0, x = k_37)[name = string("transpose_104")];
tensor<fp32, [1, 8, ?, 64]> transpose_96 = transpose(perm = transpose_96_perm_0, x = var_1448)[name = string("transpose_105")];
tensor<fp32, [1, 8, ?, ?]> matrix_ac = matmul(transpose_x = matrix_ac_transpose_x_0, transpose_y = matrix_ac_transpose_y_0, x = transpose_96, y = transpose_97)[name = string("matrix_ac")];
bool matrix_bd_37_transpose_x_0 = const()[name = string("matrix_bd_37_transpose_x_0"), val = bool(false)];
bool matrix_bd_37_transpose_y_0 = const()[name = string("matrix_bd_37_transpose_y_0"), val = bool(false)];
tensor<int32, [4]> transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])];
tensor<int32, [4]> transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])];
tensor<fp32, [1, 8, 64, ?]> transpose_99 = transpose(perm = transpose_99_perm_0, x = p_37)[name = string("transpose_102")];
tensor<fp32, [1, 8, ?, 64]> transpose_98 = transpose(perm = transpose_98_perm_0, x = var_1451)[name = string("transpose_103")];
tensor<fp32, [1, 8, ?, ?]> matrix_bd_37 = matmul(transpose_x = matrix_bd_37_transpose_x_0, transpose_y = matrix_bd_37_transpose_y_0, x = transpose_98, y = transpose_99)[name = string("matrix_bd_37")];
tensor<int32, [4]> var_1457_shape = shape(x = matrix_bd_37)[name = string("op_1457_shape")];
int32 gather_151 = const()[name = string("gather_151"), val = int32(1)];
int32 gather_152 = const()[name = string("gather_152"), val = int32(8)];
int32 gather_153_batch_dims_0 = const()[name = string("gather_153_batch_dims_0"), val = int32(0)];
bool gather_153_validate_indices_0 = const()[name = string("gather_153_validate_indices_0"), val = bool(false)];
int32 select_44 = const()[name = string("select_44"), val = int32(2)];
int32 gather_153_axis_1 = const()[name = string("gather_153_axis_1"), val = int32(0)];
int32 gather_153 = gather(axis = gather_153_axis_1, batch_dims = gather_153_batch_dims_0, indices = select_44, validate_indices = gather_153_validate_indices_0, x = var_1457_shape)[name = string("gather_153")];
int32 concat_52_axis_0 = const()[name = string("concat_52_axis_0"), val = int32(0)];
bool concat_52_interleave_0 = const()[name = string("concat_52_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_52 = concat(axis = concat_52_axis_0, interleave = concat_52_interleave_0, values = (gather_151, gather_152, gather_153, var_61))[name = string("concat_52")];
fp32 zero_pad_value_0 = const()[name = string("zero_pad_value_0"), val = fp32(0x0p+0)];
tensor<fp32, [1, 8, ?, 1]> zero_pad = fill(shape = concat_52, value = zero_pad_value_0)[name = string("zero_pad")];
bool x_padded_37_interleave_0 = const()[name = string("x_padded_37_interleave_0"), val = bool(false)];
tensor<fp32, [1, 8, ?, ?]> x_padded_37 = concat(axis = var_55, interleave = x_padded_37_interleave_0, values = (zero_pad, matrix_bd_37))[name = string("x_padded_37")];
int32 gather_154 = const()[name = string("gather_154"), val = int32(1)];
int32 gather_155 = const()[name = string("gather_155"), val = int32(8)];
int32 gather_156_batch_dims_0 = const()[name = string("gather_156_batch_dims_0"), val = int32(0)];
bool gather_156_validate_indices_0 = const()[name = string("gather_156_validate_indices_0"), val = bool(false)];
int32 select_45 = const()[name = string("select_45"), val = int32(3)];
int32 gather_156_axis_1 = const()[name = string("gather_156_axis_1"), val = int32(0)];
int32 gather_156 = gather(axis = gather_156_axis_1, batch_dims = gather_156_batch_dims_0, indices = select_45, validate_indices = gather_156_validate_indices_0, x = var_1457_shape)[name = string("gather_156")];
int32 var_1468 = const()[name = string("op_1468"), val = int32(1)];
int32 var_1469 = add(x = gather_156, y = var_1468)[name = string("op_1469")];
int32 concat_53_axis_0 = const()[name = string("concat_53_axis_0"), val = int32(0)];
bool concat_53_interleave_0 = const()[name = string("concat_53_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_53 = concat(axis = concat_53_axis_0, interleave = concat_53_interleave_0, values = (gather_154, gather_155, var_1469, gather_153))[name = string("concat_53")];
tensor<fp32, [1, 8, ?, ?]> x_padded = reshape(shape = concat_53, x = x_padded_37)[name = string("x_padded")];
tensor<int32, [4]> var_1476_begin_0 = const()[name = string("op_1476_begin_0"), val = tensor<int32, [4]>([0, 0, 1, 0])];
tensor<int32, [4]> var_1476_end_0 = const()[name = string("op_1476_end_0"), val = tensor<int32, [4]>([1, 8, 0, 0])];
tensor<bool, [4]> var_1476_end_mask_0 = const()[name = string("op_1476_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp32, [1, 8, ?, ?]> var_1476 = slice_by_index(begin = var_1476_begin_0, end = var_1476_end_0, end_mask = var_1476_end_mask_0, x = x_padded)[name = string("op_1476")];
int32 gather_158 = const()[name = string("gather_158"), val = int32(1)];
int32 gather_159 = const()[name = string("gather_159"), val = int32(8)];
int32 concat_54_axis_0 = const()[name = string("concat_54_axis_0"), val = int32(0)];
bool concat_54_interleave_0 = const()[name = string("concat_54_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_54 = concat(axis = concat_54_axis_0, interleave = concat_54_interleave_0, values = (gather_158, gather_159, gather_153, gather_156))[name = string("concat_54")];
tensor<fp32, [1, 8, ?, ?]> var_1482 = reshape(shape = concat_54, x = var_1476)[name = string("op_1482")];
int32 floor_div_13 = floor_div(x = gather_156, y = var_53)[name = string("floor_div_13")];
string var_1485_dtype_0 = const()[name = string("op_1485_dtype_0"), val = string("fp32")];
fp32 var_1486_promoted = const()[name = string("op_1486_promoted"), val = fp32(0x1p+0)];
fp32 var_1485 = cast(dtype = var_1485_dtype_0, x = floor_div_13)[name = string("cast_104")];
fp32 var_1487 = add(x = var_1485, y = var_1486_promoted)[name = string("op_1487")];
string var_1488_dtype_0 = const()[name = string("op_1488_dtype_0"), val = string("int32")];
int32 concat_55_values0_0 = const()[name = string("concat_55_values0_0"), val = int32(1)];
int32 concat_55_values1_0 = const()[name = string("concat_55_values1_0"), val = int32(8)];
int32 concat_55_values2_0 = const()[name = string("concat_55_values2_0"), val = int32(0)];
int32 concat_55_axis_0 = const()[name = string("concat_55_axis_0"), val = int32(0)];
bool concat_55_interleave_0 = const()[name = string("concat_55_interleave_0"), val = bool(false)];
int32 var_1488 = cast(dtype = var_1488_dtype_0, x = var_1487)[name = string("cast_103")];
tensor<int32, [4]> concat_55 = concat(axis = concat_55_axis_0, interleave = concat_55_interleave_0, values = (concat_55_values0_0, concat_55_values1_0, concat_55_values2_0, var_1488))[name = string("concat_55")];
tensor<int32, [4]> matrix_bd_begin_0 = const()[name = string("matrix_bd_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<bool, [4]> matrix_bd_end_mask_0 = const()[name = string("matrix_bd_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<fp32, [1, 8, ?, ?]> matrix_bd = slice_by_index(begin = matrix_bd_begin_0, end = concat_55, end_mask = matrix_bd_end_mask_0, x = var_1482)[name = string("matrix_bd")];
tensor<fp32, [1, 8, ?, ?]> var_1493 = add(x = matrix_ac, y = matrix_bd)[name = string("op_1493")];
fp32 _inversed_scores_37_y_0 = const()[name = string("_inversed_scores_37_y_0"), val = fp32(0x1p-3)];
tensor<fp32, [1, 8, ?, ?]> _inversed_scores_37 = mul(x = var_1493, y = _inversed_scores_37_y_0)[name = string("_inversed_scores_37")];
tensor<int32, [4]> var_1499_shape = shape(x = _inversed_scores_37)[name = string("op_1499_shape")];
int32 gather_164_batch_dims_0 = const()[name = string("gather_164_batch_dims_0"), val = int32(0)];
bool gather_164_validate_indices_0 = const()[name = string("gather_164_validate_indices_0"), val = bool(false)];
int32 select_47 = const()[name = string("select_47"), val = int32(3)];
int32 gather_164_axis_1 = const()[name = string("gather_164_axis_1"), val = int32(0)];
int32 gather_164 = gather(axis = gather_164_axis_1, batch_dims = gather_164_batch_dims_0, indices = select_47, validate_indices = gather_164_validate_indices_0, x = var_1499_shape)[name = string("gather_164")];
int32 concat_56_values0_0 = const()[name = string("concat_56_values0_0"), val = int32(0)];
int32 concat_56_values1_0 = const()[name = string("concat_56_values1_0"), val = int32(1)];
int32 concat_56_values2_0 = const()[name = string("concat_56_values2_0"), val = int32(1)];
int32 concat_56_axis_0 = const()[name = string("concat_56_axis_0"), val = int32(0)];
bool concat_56_interleave_0 = const()[name = string("concat_56_interleave_0"), val = bool(false)];
tensor<int32, [4]> concat_56 = concat(axis = concat_56_axis_0, interleave = concat_56_interleave_0, values = (concat_56_values0_0, concat_56_values1_0, concat_56_values2_0, gather_164))[name = string("concat_56")];
tensor<int32, [4]> mask_begin_0 = const()[name = string("mask_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<bool, [4]> mask_end_mask_0 = const()[name = string("mask_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
tensor<bool, [?, 1, 1, ?]> mask = slice_by_index(begin = mask_begin_0, end = concat_56, end_mask = mask_end_mask_0, x = mask_27)[name = string("mask")];
tensor<fp32, [1, 8, ?, ?]> scores = select(a = var_37, b = _inversed_scores_37, cond = mask)[name = string("scores")];
tensor<fp32, [1, 8, ?, ?]> var_1505 = softmax(axis = var_55, x = scores)[name = string("op_1505")];
tensor<fp32, [1, 8, ?, ?]> input_213 = select(a = var_46, b = var_1505, cond = mask)[name = string("input_213")];
bool x_transpose_x_0 = const()[name = string("x_transpose_x_0"), val = bool(false)];
bool x_transpose_y_0 = const()[name = string("x_transpose_y_0"), val = bool(false)];
tensor<fp32, [1, 8, ?, 64]> v = transpose(perm = v_perm_0, x = v_37)[name = string("transpose_106")];
tensor<fp32, [1, 8, ?, 64]> x = matmul(transpose_x = x_transpose_x_0, transpose_y = x_transpose_y_0, x = input_213, y = v)[name = string("x")];
tensor<int32, [4]> var_1509_perm_0 = const()[name = string("op_1509_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_1511 = const()[name = string("op_1511"), val = tensor<int32, [3]>([1, -1, 512])];
tensor<fp32, [1, ?, 8, 64]> var_1509 = transpose(perm = var_1509_perm_0, x = x)[name = string("transpose_101")];
tensor<fp32, [1, ?, 512]> input_215 = reshape(shape = var_1511, x = var_1509)[name = string("input_215")];
tensor<fp32, [1, ?, 512]> input_217 = linear(bias = encoder_up_encoders_3_self_attn_linear_out_bias, weight = encoder_up_encoders_3_self_attn_linear_out_weight, x = input_215)[name = string("linear_69")];
tensor<fp32, [1, ?, 512]> input_219 = add(x = input_211, y = input_217)[name = string("input_219")];
tensor<int32, [1]> input_221_axes_0 = const()[name = string("input_221_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp32, [1, ?, 512]> input_221 = layer_norm(axes = input_221_axes_0, beta = encoder_up_encoders_3_norm_ff_bias, epsilon = var_36, gamma = encoder_up_encoders_3_norm_ff_weight, x = input_219)[name = string("input_221")];
tensor<fp32, [1, ?, 2048]> input_223 = linear(bias = encoder_up_encoders_3_feed_forward_w_1_bias, weight = encoder_up_encoders_3_feed_forward_w_1_weight, x = input_221)[name = string("linear_70")];
tensor<fp32, [1, ?, 2048]> input_225 = silu(x = input_223)[name = string("input_225")];
tensor<fp32, [1, ?, 512]> input_229 = linear(bias = encoder_up_encoders_3_feed_forward_w_2_bias, weight = encoder_up_encoders_3_feed_forward_w_2_weight, x = input_225)[name = string("linear_71")];
tensor<fp32, [1, ?, 512]> input_231 = add(x = input_219, y = input_229)[name = string("input_231")];
tensor<int32, [1]> input_axes_0 = const()[name = string("input_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp32, [1, ?, 512]> input = layer_norm(axes = input_axes_0, beta = encoder_after_norm_bias, epsilon = var_50, gamma = encoder_after_norm_weight, x = input_231)[name = string("input")];
tensor<fp32, [1, ?, 80]> var_1542 = linear(bias = encoder_proj_bias, weight = encoder_proj_weight, x = input)[name = string("linear_72")];
tensor<int32, [3]> var_1545_perm_0 = const()[name = string("op_1545_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<fp32, [1, 80, ?]> encoder_proj = transpose(perm = var_1545_perm_0, x = var_1542)[name = string("transpose_100")];
} -> (encoder_proj);
} |