diff --git "a/openai_whisper-large-v3-v20240930_547MB/AudioEncoder.mlmodelc/model.mil" "b/openai_whisper-large-v3-v20240930_547MB/AudioEncoder.mlmodelc/model.mil" new file mode 100644--- /dev/null +++ "b/openai_whisper-large-v3-v20240930_547MB/AudioEncoder.mlmodelc/model.mil" @@ -0,0 +1,5573 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3304.5.2"}, {"coremlc-version", "3304.6.2"}})] +{ + func main(tensor melspectrogram_features) { + tensor var_106_pad_type_0 = const()[name = tensor("op_106_pad_type_0"), val = tensor("custom")]; + tensor var_106_pad_0 = const()[name = tensor("op_106_pad_0"), val = tensor([0, 0, 1, 1])]; + tensor var_106_strides_0 = const()[name = tensor("op_106_strides_0"), val = tensor([1, 1])]; + tensor var_106_dilations_0 = const()[name = tensor("op_106_dilations_0"), val = tensor([1, 1])]; + tensor var_106_groups_0 = const()[name = tensor("op_106_groups_0"), val = tensor(1)]; + tensor var_81_to_fp16 = const()[name = tensor("op_81_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor var_87_to_fp16 = const()[name = tensor("op_87_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(983168)))]; + tensor var_106_cast_fp16 = conv(bias = var_87_to_fp16, dilations = var_106_dilations_0, groups = var_106_groups_0, pad = var_106_pad_0, pad_type = var_106_pad_type_0, strides = var_106_strides_0, weight = var_81_to_fp16, x = melspectrogram_features)[name = tensor("op_106_cast_fp16")]; + tensor hidden_states_1_mode_0 = const()[name = tensor("hidden_states_1_mode_0"), val = tensor("EXACT")]; + tensor hidden_states_1_cast_fp16 = gelu(mode = hidden_states_1_mode_0, x = var_106_cast_fp16)[name = tensor("hidden_states_1_cast_fp16")]; + tensor var_146_pad_type_0 = const()[name = tensor("op_146_pad_type_0"), val = tensor("custom")]; + tensor var_146_pad_0 = const()[name = tensor("op_146_pad_0"), val = tensor([0, 0, 1, 1])]; + tensor var_146_strides_0 = const()[name = tensor("op_146_strides_0"), val = tensor([2, 2])]; + tensor var_146_dilations_0 = const()[name = tensor("op_146_dilations_0"), val = tensor([1, 1])]; + tensor var_146_groups_0 = const()[name = tensor("op_146_groups_0"), val = tensor(1)]; + tensor var_121_to_fp16 = const()[name = tensor("op_121_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(985792)))]; + tensor var_127_to_fp16 = const()[name = tensor("op_127_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10816256)))]; + tensor var_146_cast_fp16 = conv(bias = var_127_to_fp16, dilations = var_146_dilations_0, groups = var_146_groups_0, pad = var_146_pad_0, pad_type = var_146_pad_type_0, strides = var_146_strides_0, weight = var_121_to_fp16, x = hidden_states_1_cast_fp16)[name = tensor("op_146_cast_fp16")]; + tensor hidden_states_3_mode_0 = const()[name = tensor("hidden_states_3_mode_0"), val = tensor("EXACT")]; + tensor hidden_states_3_cast_fp16 = gelu(mode = hidden_states_3_mode_0, x = var_146_cast_fp16)[name = tensor("hidden_states_3_cast_fp16")]; + tensor var_164_to_fp16 = const()[name = tensor("op_164_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10818880)))]; + tensor inputs_1_cast_fp16 = add(x = hidden_states_3_cast_fp16, y = var_164_to_fp16)[name = tensor("inputs_1_cast_fp16")]; + tensor var_178 = const()[name = tensor("op_178"), val = tensor(3)]; + tensor out_1_axes_0 = const()[name = tensor("out_1_axes_0"), val = tensor([1])]; + tensor var_197_to_fp16 = const()[name = tensor("op_197_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_1_cast_fp16 = layer_norm(axes = out_1_axes_0, epsilon = var_197_to_fp16, x = inputs_1_cast_fp16)[name = tensor("out_1_cast_fp16")]; + tensor obj_1_mean_0_to_fp16 = const()[name = tensor("obj_1_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14658944)))]; + tensor obj_1_variance_0_to_fp16 = const()[name = tensor("obj_1_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14661568)))]; + tensor obj_1_gamma_0_to_fp16 = const()[name = tensor("obj_1_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14664192)))]; + tensor obj_1_beta_0_to_fp16 = const()[name = tensor("obj_1_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14666816)))]; + tensor obj_1_epsilon_0_to_fp16 = const()[name = tensor("obj_1_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_1_cast_fp16 = batch_norm(beta = obj_1_beta_0_to_fp16, epsilon = obj_1_epsilon_0_to_fp16, gamma = obj_1_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_1_cast_fp16)[name = tensor("obj_1_cast_fp16")]; + tensor pretrained_out_1_pad_type_0 = const()[name = tensor("pretrained_out_1_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_1_strides_0 = const()[name = tensor("pretrained_out_1_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_1_pad_0 = const()[name = tensor("pretrained_out_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_1_dilations_0 = const()[name = tensor("pretrained_out_1_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_1_groups_0 = const()[name = tensor("pretrained_out_1_groups_0"), val = tensor(1)]; + tensor layers_0_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14669440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15488704))), name = tensor("layers_0_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_0_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_0_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15488832)))]; + tensor pretrained_out_1_cast_fp16 = conv(bias = layers_0_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_1_dilations_0, groups = pretrained_out_1_groups_0, pad = pretrained_out_1_pad_0, pad_type = pretrained_out_1_pad_type_0, strides = pretrained_out_1_strides_0, weight = layers_0_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_1_cast_fp16)[name = tensor("pretrained_out_1_cast_fp16")]; + tensor input_1_pad_type_0 = const()[name = tensor("input_1_pad_type_0"), val = tensor("valid")]; + tensor input_1_strides_0 = const()[name = tensor("input_1_strides_0"), val = tensor([1, 1])]; + tensor input_1_pad_0 = const()[name = tensor("input_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_1_dilations_0 = const()[name = tensor("input_1_dilations_0"), val = tensor([1, 1])]; + tensor input_1_groups_0 = const()[name = tensor("input_1_groups_0"), val = tensor(1)]; + tensor layers_0_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_0_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15491456)))]; + tensor input_1_cast_fp16 = conv(dilations = input_1_dilations_0, groups = input_1_groups_0, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = input_1_strides_0, weight = layers_0_self_attn_q_proj_loraA_weight_to_fp16, x = obj_1_cast_fp16)[name = tensor("input_1_cast_fp16")]; + tensor lora_out_1_pad_type_0 = const()[name = tensor("lora_out_1_pad_type_0"), val = tensor("valid")]; + tensor lora_out_1_strides_0 = const()[name = tensor("lora_out_1_strides_0"), val = tensor([1, 1])]; + tensor lora_out_1_pad_0 = const()[name = tensor("lora_out_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_1_dilations_0 = const()[name = tensor("lora_out_1_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_1_groups_0 = const()[name = tensor("lora_out_1_groups_0"), val = tensor(1)]; + tensor layers_0_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_0_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15532480)))]; + tensor lora_out_1_cast_fp16 = conv(dilations = lora_out_1_dilations_0, groups = lora_out_1_groups_0, pad = lora_out_1_pad_0, pad_type = lora_out_1_pad_type_0, strides = lora_out_1_strides_0, weight = layers_0_self_attn_q_proj_loraB_weight_to_fp16, x = input_1_cast_fp16)[name = tensor("lora_out_1_cast_fp16")]; + tensor query_1_cast_fp16 = add(x = pretrained_out_1_cast_fp16, y = lora_out_1_cast_fp16)[name = tensor("query_1_cast_fp16")]; + tensor pretrained_out_3_pad_type_0 = const()[name = tensor("pretrained_out_3_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_3_strides_0 = const()[name = tensor("pretrained_out_3_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_3_pad_0 = const()[name = tensor("pretrained_out_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_3_dilations_0 = const()[name = tensor("pretrained_out_3_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_3_groups_0 = const()[name = tensor("pretrained_out_3_groups_0"), val = tensor(1)]; + tensor layers_0_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15573504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16392768))), name = tensor("layers_0_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_3_cast_fp16 = conv(dilations = pretrained_out_3_dilations_0, groups = pretrained_out_3_groups_0, pad = pretrained_out_3_pad_0, pad_type = pretrained_out_3_pad_type_0, strides = pretrained_out_3_strides_0, weight = layers_0_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_1_cast_fp16)[name = tensor("pretrained_out_3_cast_fp16")]; + tensor input_3_pad_type_0 = const()[name = tensor("input_3_pad_type_0"), val = tensor("valid")]; + tensor input_3_strides_0 = const()[name = tensor("input_3_strides_0"), val = tensor([1, 1])]; + tensor input_3_pad_0 = const()[name = tensor("input_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_3_dilations_0 = const()[name = tensor("input_3_dilations_0"), val = tensor([1, 1])]; + tensor input_3_groups_0 = const()[name = tensor("input_3_groups_0"), val = tensor(1)]; + tensor layers_0_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_0_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16392896)))]; + tensor input_3_cast_fp16 = conv(dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = layers_0_self_attn_k_proj_loraA_weight_to_fp16, x = obj_1_cast_fp16)[name = tensor("input_3_cast_fp16")]; + tensor lora_out_3_pad_type_0 = const()[name = tensor("lora_out_3_pad_type_0"), val = tensor("valid")]; + tensor lora_out_3_strides_0 = const()[name = tensor("lora_out_3_strides_0"), val = tensor([1, 1])]; + tensor lora_out_3_pad_0 = const()[name = tensor("lora_out_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_3_dilations_0 = const()[name = tensor("lora_out_3_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_3_groups_0 = const()[name = tensor("lora_out_3_groups_0"), val = tensor(1)]; + tensor layers_0_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_0_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16433920)))]; + tensor lora_out_3_cast_fp16 = conv(dilations = lora_out_3_dilations_0, groups = lora_out_3_groups_0, pad = lora_out_3_pad_0, pad_type = lora_out_3_pad_type_0, strides = lora_out_3_strides_0, weight = layers_0_self_attn_k_proj_loraB_weight_to_fp16, x = input_3_cast_fp16)[name = tensor("lora_out_3_cast_fp16")]; + tensor key_1_cast_fp16 = add(x = pretrained_out_3_cast_fp16, y = lora_out_3_cast_fp16)[name = tensor("key_1_cast_fp16")]; + tensor pretrained_out_5_pad_type_0 = const()[name = tensor("pretrained_out_5_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_5_strides_0 = const()[name = tensor("pretrained_out_5_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_5_pad_0 = const()[name = tensor("pretrained_out_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_5_dilations_0 = const()[name = tensor("pretrained_out_5_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_5_groups_0 = const()[name = tensor("pretrained_out_5_groups_0"), val = tensor(1)]; + tensor layers_0_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16474944))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17294208))), name = tensor("layers_0_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_0_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_0_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17294336)))]; + tensor pretrained_out_5_cast_fp16 = conv(bias = layers_0_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_5_dilations_0, groups = pretrained_out_5_groups_0, pad = pretrained_out_5_pad_0, pad_type = pretrained_out_5_pad_type_0, strides = pretrained_out_5_strides_0, weight = layers_0_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_1_cast_fp16)[name = tensor("pretrained_out_5_cast_fp16")]; + tensor input_5_pad_type_0 = const()[name = tensor("input_5_pad_type_0"), val = tensor("valid")]; + tensor input_5_strides_0 = const()[name = tensor("input_5_strides_0"), val = tensor([1, 1])]; + tensor input_5_pad_0 = const()[name = tensor("input_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_5_dilations_0 = const()[name = tensor("input_5_dilations_0"), val = tensor([1, 1])]; + tensor input_5_groups_0 = const()[name = tensor("input_5_groups_0"), val = tensor(1)]; + tensor layers_0_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_0_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17296960)))]; + tensor input_5_cast_fp16 = conv(dilations = input_5_dilations_0, groups = input_5_groups_0, pad = input_5_pad_0, pad_type = input_5_pad_type_0, strides = input_5_strides_0, weight = layers_0_self_attn_v_proj_loraA_weight_to_fp16, x = obj_1_cast_fp16)[name = tensor("input_5_cast_fp16")]; + tensor lora_out_5_pad_type_0 = const()[name = tensor("lora_out_5_pad_type_0"), val = tensor("valid")]; + tensor lora_out_5_strides_0 = const()[name = tensor("lora_out_5_strides_0"), val = tensor([1, 1])]; + tensor lora_out_5_pad_0 = const()[name = tensor("lora_out_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_5_dilations_0 = const()[name = tensor("lora_out_5_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_5_groups_0 = const()[name = tensor("lora_out_5_groups_0"), val = tensor(1)]; + tensor layers_0_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_0_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17337984)))]; + tensor lora_out_5_cast_fp16 = conv(dilations = lora_out_5_dilations_0, groups = lora_out_5_groups_0, pad = lora_out_5_pad_0, pad_type = lora_out_5_pad_type_0, strides = lora_out_5_strides_0, weight = layers_0_self_attn_v_proj_loraB_weight_to_fp16, x = input_5_cast_fp16)[name = tensor("lora_out_5_cast_fp16")]; + tensor value_1_cast_fp16 = add(x = pretrained_out_5_cast_fp16, y = lora_out_5_cast_fp16)[name = tensor("value_1_cast_fp16")]; + tensor var_280 = const()[name = tensor("op_280"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_1_cast_fp16 = reshape(shape = var_280, x = query_1_cast_fp16)[name = tensor("mh_q_1_cast_fp16")]; + tensor var_282_to_fp16 = const()[name = tensor("op_282_to_fp16"), val = tensor(0x1p-3)]; + tensor var_283_cast_fp16 = mul(x = mh_q_1_cast_fp16, y = var_282_to_fp16)[name = tensor("op_283_cast_fp16")]; + tensor var_284 = const()[name = tensor("op_284"), val = tensor([1, 20, 64, -1])]; + tensor var_285_cast_fp16 = reshape(shape = var_284, x = key_1_cast_fp16)[name = tensor("op_285_cast_fp16")]; + tensor mh_w_1_transpose_x_0 = const()[name = tensor("mh_w_1_transpose_x_0"), val = tensor(true)]; + tensor mh_w_1_transpose_y_0 = const()[name = tensor("mh_w_1_transpose_y_0"), val = tensor(false)]; + tensor mh_w_1_cast_fp16 = matmul(transpose_x = mh_w_1_transpose_x_0, transpose_y = mh_w_1_transpose_y_0, x = var_283_cast_fp16, y = var_285_cast_fp16)[name = tensor("mh_w_1_cast_fp16")]; + tensor var_288_cast_fp16 = softmax(axis = var_178, x = mh_w_1_cast_fp16)[name = tensor("op_288_cast_fp16")]; + tensor var_289 = const()[name = tensor("op_289"), val = tensor([1, 20, 64, -1])]; + tensor var_290_cast_fp16 = reshape(shape = var_289, x = value_1_cast_fp16)[name = tensor("op_290_cast_fp16")]; + tensor attn_1_transpose_x_0 = const()[name = tensor("attn_1_transpose_x_0"), val = tensor(false)]; + tensor attn_1_transpose_y_0 = const()[name = tensor("attn_1_transpose_y_0"), val = tensor(true)]; + tensor attn_1_cast_fp16 = matmul(transpose_x = attn_1_transpose_x_0, transpose_y = attn_1_transpose_y_0, x = var_290_cast_fp16, y = var_288_cast_fp16)[name = tensor("attn_1_cast_fp16")]; + tensor var_293 = const()[name = tensor("op_293"), val = tensor([1, 1280, 1, -1])]; + tensor input_7_cast_fp16 = reshape(shape = var_293, x = attn_1_cast_fp16)[name = tensor("input_7_cast_fp16")]; + tensor pretrained_out_7_pad_type_0 = const()[name = tensor("pretrained_out_7_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_7_strides_0 = const()[name = tensor("pretrained_out_7_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_7_pad_0 = const()[name = tensor("pretrained_out_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_7_dilations_0 = const()[name = tensor("pretrained_out_7_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_7_groups_0 = const()[name = tensor("pretrained_out_7_groups_0"), val = tensor(1)]; + tensor layers_0_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17379008))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18198272))), name = tensor("layers_0_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_0_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_0_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18198400)))]; + tensor pretrained_out_7_cast_fp16 = conv(bias = layers_0_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_7_dilations_0, groups = pretrained_out_7_groups_0, pad = pretrained_out_7_pad_0, pad_type = pretrained_out_7_pad_type_0, strides = pretrained_out_7_strides_0, weight = layers_0_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_7_cast_fp16)[name = tensor("pretrained_out_7_cast_fp16")]; + tensor input_9_pad_type_0 = const()[name = tensor("input_9_pad_type_0"), val = tensor("valid")]; + tensor input_9_strides_0 = const()[name = tensor("input_9_strides_0"), val = tensor([1, 1])]; + tensor input_9_pad_0 = const()[name = tensor("input_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_9_dilations_0 = const()[name = tensor("input_9_dilations_0"), val = tensor([1, 1])]; + tensor input_9_groups_0 = const()[name = tensor("input_9_groups_0"), val = tensor(1)]; + tensor layers_0_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_0_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18201024)))]; + tensor input_9_cast_fp16 = conv(dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = layers_0_self_attn_o_proj_loraA_weight_to_fp16, x = input_7_cast_fp16)[name = tensor("input_9_cast_fp16")]; + tensor lora_out_7_pad_type_0 = const()[name = tensor("lora_out_7_pad_type_0"), val = tensor("valid")]; + tensor lora_out_7_strides_0 = const()[name = tensor("lora_out_7_strides_0"), val = tensor([1, 1])]; + tensor lora_out_7_pad_0 = const()[name = tensor("lora_out_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_7_dilations_0 = const()[name = tensor("lora_out_7_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_7_groups_0 = const()[name = tensor("lora_out_7_groups_0"), val = tensor(1)]; + tensor layers_0_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_0_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18242048)))]; + tensor lora_out_7_cast_fp16 = conv(dilations = lora_out_7_dilations_0, groups = lora_out_7_groups_0, pad = lora_out_7_pad_0, pad_type = lora_out_7_pad_type_0, strides = lora_out_7_strides_0, weight = layers_0_self_attn_o_proj_loraB_weight_to_fp16, x = input_9_cast_fp16)[name = tensor("lora_out_7_cast_fp16")]; + tensor obj_3_cast_fp16 = add(x = pretrained_out_7_cast_fp16, y = lora_out_7_cast_fp16)[name = tensor("obj_3_cast_fp16")]; + tensor inputs_3_cast_fp16 = add(x = inputs_1_cast_fp16, y = obj_3_cast_fp16)[name = tensor("inputs_3_cast_fp16")]; + tensor out_3_axes_0 = const()[name = tensor("out_3_axes_0"), val = tensor([1])]; + tensor var_327_to_fp16 = const()[name = tensor("op_327_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_3_cast_fp16 = layer_norm(axes = out_3_axes_0, epsilon = var_327_to_fp16, x = inputs_3_cast_fp16)[name = tensor("out_3_cast_fp16")]; + tensor input_11_gamma_0_to_fp16 = const()[name = tensor("input_11_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18283072)))]; + tensor input_11_beta_0_to_fp16 = const()[name = tensor("input_11_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18285696)))]; + tensor input_11_epsilon_0_to_fp16 = const()[name = tensor("input_11_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_11_cast_fp16 = batch_norm(beta = input_11_beta_0_to_fp16, epsilon = input_11_epsilon_0_to_fp16, gamma = input_11_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_3_cast_fp16)[name = tensor("input_11_cast_fp16")]; + tensor pretrained_out_9_pad_type_0 = const()[name = tensor("pretrained_out_9_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_9_strides_0 = const()[name = tensor("pretrained_out_9_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_9_pad_0 = const()[name = tensor("pretrained_out_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_9_dilations_0 = const()[name = tensor("pretrained_out_9_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_9_groups_0 = const()[name = tensor("pretrained_out_9_groups_0"), val = tensor(1)]; + tensor layers_0_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18288320))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21565184))), name = tensor("layers_0_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_0_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_0_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21565312)))]; + tensor pretrained_out_9_cast_fp16 = conv(bias = layers_0_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_9_dilations_0, groups = pretrained_out_9_groups_0, pad = pretrained_out_9_pad_0, pad_type = pretrained_out_9_pad_type_0, strides = pretrained_out_9_strides_0, weight = layers_0_fc1_pretrained_weight_to_fp16_palettized, x = input_11_cast_fp16)[name = tensor("pretrained_out_9_cast_fp16")]; + tensor input_13_pad_type_0 = const()[name = tensor("input_13_pad_type_0"), val = tensor("valid")]; + tensor input_13_strides_0 = const()[name = tensor("input_13_strides_0"), val = tensor([1, 1])]; + tensor input_13_pad_0 = const()[name = tensor("input_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_13_dilations_0 = const()[name = tensor("input_13_dilations_0"), val = tensor([1, 1])]; + tensor input_13_groups_0 = const()[name = tensor("input_13_groups_0"), val = tensor(1)]; + tensor layers_0_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_0_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21575616)))]; + tensor input_13_cast_fp16 = conv(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 = layers_0_fc1_loraA_weight_to_fp16, x = input_11_cast_fp16)[name = tensor("input_13_cast_fp16")]; + tensor lora_out_9_pad_type_0 = const()[name = tensor("lora_out_9_pad_type_0"), val = tensor("valid")]; + tensor lora_out_9_strides_0 = const()[name = tensor("lora_out_9_strides_0"), val = tensor([1, 1])]; + tensor lora_out_9_pad_0 = const()[name = tensor("lora_out_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_9_dilations_0 = const()[name = tensor("lora_out_9_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_9_groups_0 = const()[name = tensor("lora_out_9_groups_0"), val = tensor(1)]; + tensor layers_0_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_0_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21616640)))]; + tensor lora_out_9_cast_fp16 = conv(dilations = lora_out_9_dilations_0, groups = lora_out_9_groups_0, pad = lora_out_9_pad_0, pad_type = lora_out_9_pad_type_0, strides = lora_out_9_strides_0, weight = layers_0_fc1_loraB_weight_to_fp16, x = input_13_cast_fp16)[name = tensor("lora_out_9_cast_fp16")]; + tensor input_15_cast_fp16 = add(x = pretrained_out_9_cast_fp16, y = lora_out_9_cast_fp16)[name = tensor("input_15_cast_fp16")]; + tensor input_17_mode_0 = const()[name = tensor("input_17_mode_0"), val = tensor("EXACT")]; + tensor input_17_cast_fp16 = gelu(mode = input_17_mode_0, x = input_15_cast_fp16)[name = tensor("input_17_cast_fp16")]; + tensor pretrained_out_11_pad_type_0 = const()[name = tensor("pretrained_out_11_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_11_strides_0 = const()[name = tensor("pretrained_out_11_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_11_pad_0 = const()[name = tensor("pretrained_out_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_11_dilations_0 = const()[name = tensor("pretrained_out_11_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_11_groups_0 = const()[name = tensor("pretrained_out_11_groups_0"), val = tensor(1)]; + tensor layers_0_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21780544))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25057408))), name = tensor("layers_0_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_0_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_0_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25057536)))]; + tensor pretrained_out_11_cast_fp16 = conv(bias = layers_0_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_11_dilations_0, groups = pretrained_out_11_groups_0, pad = pretrained_out_11_pad_0, pad_type = pretrained_out_11_pad_type_0, strides = pretrained_out_11_strides_0, weight = layers_0_fc2_pretrained_weight_to_fp16_palettized, x = input_17_cast_fp16)[name = tensor("pretrained_out_11_cast_fp16")]; + tensor input_19_pad_type_0 = const()[name = tensor("input_19_pad_type_0"), val = tensor("valid")]; + tensor input_19_strides_0 = const()[name = tensor("input_19_strides_0"), val = tensor([1, 1])]; + tensor input_19_pad_0 = const()[name = tensor("input_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_19_dilations_0 = const()[name = tensor("input_19_dilations_0"), val = tensor([1, 1])]; + tensor input_19_groups_0 = const()[name = tensor("input_19_groups_0"), val = tensor(1)]; + tensor layers_0_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_0_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25060160)))]; + tensor input_19_cast_fp16 = conv(dilations = input_19_dilations_0, groups = input_19_groups_0, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = input_19_strides_0, weight = layers_0_fc2_loraA_weight_to_fp16, x = input_17_cast_fp16)[name = tensor("input_19_cast_fp16")]; + tensor lora_out_11_pad_type_0 = const()[name = tensor("lora_out_11_pad_type_0"), val = tensor("valid")]; + tensor lora_out_11_strides_0 = const()[name = tensor("lora_out_11_strides_0"), val = tensor([1, 1])]; + tensor lora_out_11_pad_0 = const()[name = tensor("lora_out_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_11_dilations_0 = const()[name = tensor("lora_out_11_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_11_groups_0 = const()[name = tensor("lora_out_11_groups_0"), val = tensor(1)]; + tensor layers_0_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_0_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25224064)))]; + tensor lora_out_11_cast_fp16 = conv(dilations = lora_out_11_dilations_0, groups = lora_out_11_groups_0, pad = lora_out_11_pad_0, pad_type = lora_out_11_pad_type_0, strides = lora_out_11_strides_0, weight = layers_0_fc2_loraB_weight_to_fp16, x = input_19_cast_fp16)[name = tensor("lora_out_11_cast_fp16")]; + tensor hidden_states_5_cast_fp16 = add(x = pretrained_out_11_cast_fp16, y = lora_out_11_cast_fp16)[name = tensor("hidden_states_5_cast_fp16")]; + tensor inputs_5_cast_fp16 = add(x = inputs_3_cast_fp16, y = hidden_states_5_cast_fp16)[name = tensor("inputs_5_cast_fp16")]; + tensor var_392 = const()[name = tensor("op_392"), val = tensor(3)]; + tensor out_5_axes_0 = const()[name = tensor("out_5_axes_0"), val = tensor([1])]; + tensor var_411_to_fp16 = const()[name = tensor("op_411_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_5_cast_fp16 = layer_norm(axes = out_5_axes_0, epsilon = var_411_to_fp16, x = inputs_5_cast_fp16)[name = tensor("out_5_cast_fp16")]; + tensor obj_5_gamma_0_to_fp16 = const()[name = tensor("obj_5_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25265088)))]; + tensor obj_5_beta_0_to_fp16 = const()[name = tensor("obj_5_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25267712)))]; + tensor obj_5_epsilon_0_to_fp16 = const()[name = tensor("obj_5_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_5_cast_fp16 = batch_norm(beta = obj_5_beta_0_to_fp16, epsilon = obj_5_epsilon_0_to_fp16, gamma = obj_5_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_5_cast_fp16)[name = tensor("obj_5_cast_fp16")]; + tensor pretrained_out_13_pad_type_0 = const()[name = tensor("pretrained_out_13_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_13_strides_0 = const()[name = tensor("pretrained_out_13_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_13_pad_0 = const()[name = tensor("pretrained_out_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_13_dilations_0 = const()[name = tensor("pretrained_out_13_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_13_groups_0 = const()[name = tensor("pretrained_out_13_groups_0"), val = tensor(1)]; + tensor layers_1_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25270336))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26089600))), name = tensor("layers_1_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_1_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_1_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26089728)))]; + tensor pretrained_out_13_cast_fp16 = conv(bias = layers_1_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_13_dilations_0, groups = pretrained_out_13_groups_0, pad = pretrained_out_13_pad_0, pad_type = pretrained_out_13_pad_type_0, strides = pretrained_out_13_strides_0, weight = layers_1_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_5_cast_fp16)[name = tensor("pretrained_out_13_cast_fp16")]; + tensor input_21_pad_type_0 = const()[name = tensor("input_21_pad_type_0"), val = tensor("valid")]; + tensor input_21_strides_0 = const()[name = tensor("input_21_strides_0"), val = tensor([1, 1])]; + tensor input_21_pad_0 = const()[name = tensor("input_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_21_dilations_0 = const()[name = tensor("input_21_dilations_0"), val = tensor([1, 1])]; + tensor input_21_groups_0 = const()[name = tensor("input_21_groups_0"), val = tensor(1)]; + tensor layers_1_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_1_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26092352)))]; + tensor input_21_cast_fp16 = conv(dilations = input_21_dilations_0, groups = input_21_groups_0, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = input_21_strides_0, weight = layers_1_self_attn_q_proj_loraA_weight_to_fp16, x = obj_5_cast_fp16)[name = tensor("input_21_cast_fp16")]; + tensor lora_out_13_pad_type_0 = const()[name = tensor("lora_out_13_pad_type_0"), val = tensor("valid")]; + tensor lora_out_13_strides_0 = const()[name = tensor("lora_out_13_strides_0"), val = tensor([1, 1])]; + tensor lora_out_13_pad_0 = const()[name = tensor("lora_out_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_13_dilations_0 = const()[name = tensor("lora_out_13_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_13_groups_0 = const()[name = tensor("lora_out_13_groups_0"), val = tensor(1)]; + tensor layers_1_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_1_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26133376)))]; + tensor lora_out_13_cast_fp16 = conv(dilations = lora_out_13_dilations_0, groups = lora_out_13_groups_0, pad = lora_out_13_pad_0, pad_type = lora_out_13_pad_type_0, strides = lora_out_13_strides_0, weight = layers_1_self_attn_q_proj_loraB_weight_to_fp16, x = input_21_cast_fp16)[name = tensor("lora_out_13_cast_fp16")]; + tensor query_3_cast_fp16 = add(x = pretrained_out_13_cast_fp16, y = lora_out_13_cast_fp16)[name = tensor("query_3_cast_fp16")]; + tensor pretrained_out_15_pad_type_0 = const()[name = tensor("pretrained_out_15_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_15_strides_0 = const()[name = tensor("pretrained_out_15_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_15_pad_0 = const()[name = tensor("pretrained_out_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_15_dilations_0 = const()[name = tensor("pretrained_out_15_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_15_groups_0 = const()[name = tensor("pretrained_out_15_groups_0"), val = tensor(1)]; + tensor layers_1_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26174400))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26993664))), name = tensor("layers_1_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_15_cast_fp16 = conv(dilations = pretrained_out_15_dilations_0, groups = pretrained_out_15_groups_0, pad = pretrained_out_15_pad_0, pad_type = pretrained_out_15_pad_type_0, strides = pretrained_out_15_strides_0, weight = layers_1_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_5_cast_fp16)[name = tensor("pretrained_out_15_cast_fp16")]; + tensor input_23_pad_type_0 = const()[name = tensor("input_23_pad_type_0"), val = tensor("valid")]; + tensor input_23_strides_0 = const()[name = tensor("input_23_strides_0"), val = tensor([1, 1])]; + tensor input_23_pad_0 = const()[name = tensor("input_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_23_dilations_0 = const()[name = tensor("input_23_dilations_0"), val = tensor([1, 1])]; + tensor input_23_groups_0 = const()[name = tensor("input_23_groups_0"), val = tensor(1)]; + tensor layers_1_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_1_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26993792)))]; + tensor input_23_cast_fp16 = conv(dilations = input_23_dilations_0, groups = input_23_groups_0, pad = input_23_pad_0, pad_type = input_23_pad_type_0, strides = input_23_strides_0, weight = layers_1_self_attn_k_proj_loraA_weight_to_fp16, x = obj_5_cast_fp16)[name = tensor("input_23_cast_fp16")]; + tensor lora_out_15_pad_type_0 = const()[name = tensor("lora_out_15_pad_type_0"), val = tensor("valid")]; + tensor lora_out_15_strides_0 = const()[name = tensor("lora_out_15_strides_0"), val = tensor([1, 1])]; + tensor lora_out_15_pad_0 = const()[name = tensor("lora_out_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_15_dilations_0 = const()[name = tensor("lora_out_15_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_15_groups_0 = const()[name = tensor("lora_out_15_groups_0"), val = tensor(1)]; + tensor layers_1_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_1_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27034816)))]; + tensor lora_out_15_cast_fp16 = conv(dilations = lora_out_15_dilations_0, groups = lora_out_15_groups_0, pad = lora_out_15_pad_0, pad_type = lora_out_15_pad_type_0, strides = lora_out_15_strides_0, weight = layers_1_self_attn_k_proj_loraB_weight_to_fp16, x = input_23_cast_fp16)[name = tensor("lora_out_15_cast_fp16")]; + tensor key_3_cast_fp16 = add(x = pretrained_out_15_cast_fp16, y = lora_out_15_cast_fp16)[name = tensor("key_3_cast_fp16")]; + tensor pretrained_out_17_pad_type_0 = const()[name = tensor("pretrained_out_17_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_17_strides_0 = const()[name = tensor("pretrained_out_17_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_17_pad_0 = const()[name = tensor("pretrained_out_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_17_dilations_0 = const()[name = tensor("pretrained_out_17_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_17_groups_0 = const()[name = tensor("pretrained_out_17_groups_0"), val = tensor(1)]; + tensor layers_1_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27075840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27895104))), name = tensor("layers_1_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_1_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_1_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27895232)))]; + tensor pretrained_out_17_cast_fp16 = conv(bias = layers_1_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_17_dilations_0, groups = pretrained_out_17_groups_0, pad = pretrained_out_17_pad_0, pad_type = pretrained_out_17_pad_type_0, strides = pretrained_out_17_strides_0, weight = layers_1_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_5_cast_fp16)[name = tensor("pretrained_out_17_cast_fp16")]; + tensor input_25_pad_type_0 = const()[name = tensor("input_25_pad_type_0"), val = tensor("valid")]; + tensor input_25_strides_0 = const()[name = tensor("input_25_strides_0"), val = tensor([1, 1])]; + tensor input_25_pad_0 = const()[name = tensor("input_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_25_dilations_0 = const()[name = tensor("input_25_dilations_0"), val = tensor([1, 1])]; + tensor input_25_groups_0 = const()[name = tensor("input_25_groups_0"), val = tensor(1)]; + tensor layers_1_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_1_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27897856)))]; + tensor input_25_cast_fp16 = conv(dilations = input_25_dilations_0, groups = input_25_groups_0, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = input_25_strides_0, weight = layers_1_self_attn_v_proj_loraA_weight_to_fp16, x = obj_5_cast_fp16)[name = tensor("input_25_cast_fp16")]; + tensor lora_out_17_pad_type_0 = const()[name = tensor("lora_out_17_pad_type_0"), val = tensor("valid")]; + tensor lora_out_17_strides_0 = const()[name = tensor("lora_out_17_strides_0"), val = tensor([1, 1])]; + tensor lora_out_17_pad_0 = const()[name = tensor("lora_out_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_17_dilations_0 = const()[name = tensor("lora_out_17_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_17_groups_0 = const()[name = tensor("lora_out_17_groups_0"), val = tensor(1)]; + tensor layers_1_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_1_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27938880)))]; + tensor lora_out_17_cast_fp16 = conv(dilations = lora_out_17_dilations_0, groups = lora_out_17_groups_0, pad = lora_out_17_pad_0, pad_type = lora_out_17_pad_type_0, strides = lora_out_17_strides_0, weight = layers_1_self_attn_v_proj_loraB_weight_to_fp16, x = input_25_cast_fp16)[name = tensor("lora_out_17_cast_fp16")]; + tensor value_3_cast_fp16 = add(x = pretrained_out_17_cast_fp16, y = lora_out_17_cast_fp16)[name = tensor("value_3_cast_fp16")]; + tensor var_494 = const()[name = tensor("op_494"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_3_cast_fp16 = reshape(shape = var_494, x = query_3_cast_fp16)[name = tensor("mh_q_3_cast_fp16")]; + tensor var_496_to_fp16 = const()[name = tensor("op_496_to_fp16"), val = tensor(0x1p-3)]; + tensor var_497_cast_fp16 = mul(x = mh_q_3_cast_fp16, y = var_496_to_fp16)[name = tensor("op_497_cast_fp16")]; + tensor var_498 = const()[name = tensor("op_498"), val = tensor([1, 20, 64, -1])]; + tensor var_499_cast_fp16 = reshape(shape = var_498, x = key_3_cast_fp16)[name = tensor("op_499_cast_fp16")]; + tensor mh_w_3_transpose_x_0 = const()[name = tensor("mh_w_3_transpose_x_0"), val = tensor(true)]; + tensor mh_w_3_transpose_y_0 = const()[name = tensor("mh_w_3_transpose_y_0"), val = tensor(false)]; + tensor mh_w_3_cast_fp16 = matmul(transpose_x = mh_w_3_transpose_x_0, transpose_y = mh_w_3_transpose_y_0, x = var_497_cast_fp16, y = var_499_cast_fp16)[name = tensor("mh_w_3_cast_fp16")]; + tensor var_502_cast_fp16 = softmax(axis = var_392, x = mh_w_3_cast_fp16)[name = tensor("op_502_cast_fp16")]; + tensor var_503 = const()[name = tensor("op_503"), val = tensor([1, 20, 64, -1])]; + tensor var_504_cast_fp16 = reshape(shape = var_503, x = value_3_cast_fp16)[name = tensor("op_504_cast_fp16")]; + tensor attn_3_transpose_x_0 = const()[name = tensor("attn_3_transpose_x_0"), val = tensor(false)]; + tensor attn_3_transpose_y_0 = const()[name = tensor("attn_3_transpose_y_0"), val = tensor(true)]; + tensor attn_3_cast_fp16 = matmul(transpose_x = attn_3_transpose_x_0, transpose_y = attn_3_transpose_y_0, x = var_504_cast_fp16, y = var_502_cast_fp16)[name = tensor("attn_3_cast_fp16")]; + tensor var_507 = const()[name = tensor("op_507"), val = tensor([1, 1280, 1, -1])]; + tensor input_27_cast_fp16 = reshape(shape = var_507, x = attn_3_cast_fp16)[name = tensor("input_27_cast_fp16")]; + tensor pretrained_out_19_pad_type_0 = const()[name = tensor("pretrained_out_19_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_19_strides_0 = const()[name = tensor("pretrained_out_19_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_19_pad_0 = const()[name = tensor("pretrained_out_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_19_dilations_0 = const()[name = tensor("pretrained_out_19_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_19_groups_0 = const()[name = tensor("pretrained_out_19_groups_0"), val = tensor(1)]; + tensor layers_1_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27979904))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28799168))), name = tensor("layers_1_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_1_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_1_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28799296)))]; + tensor pretrained_out_19_cast_fp16 = conv(bias = layers_1_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_19_dilations_0, groups = pretrained_out_19_groups_0, pad = pretrained_out_19_pad_0, pad_type = pretrained_out_19_pad_type_0, strides = pretrained_out_19_strides_0, weight = layers_1_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_27_cast_fp16)[name = tensor("pretrained_out_19_cast_fp16")]; + tensor input_29_pad_type_0 = const()[name = tensor("input_29_pad_type_0"), val = tensor("valid")]; + tensor input_29_strides_0 = const()[name = tensor("input_29_strides_0"), val = tensor([1, 1])]; + tensor input_29_pad_0 = const()[name = tensor("input_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_29_dilations_0 = const()[name = tensor("input_29_dilations_0"), val = tensor([1, 1])]; + tensor input_29_groups_0 = const()[name = tensor("input_29_groups_0"), val = tensor(1)]; + tensor layers_1_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_1_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28801920)))]; + tensor input_29_cast_fp16 = conv(dilations = input_29_dilations_0, groups = input_29_groups_0, pad = input_29_pad_0, pad_type = input_29_pad_type_0, strides = input_29_strides_0, weight = layers_1_self_attn_o_proj_loraA_weight_to_fp16, x = input_27_cast_fp16)[name = tensor("input_29_cast_fp16")]; + tensor lora_out_19_pad_type_0 = const()[name = tensor("lora_out_19_pad_type_0"), val = tensor("valid")]; + tensor lora_out_19_strides_0 = const()[name = tensor("lora_out_19_strides_0"), val = tensor([1, 1])]; + tensor lora_out_19_pad_0 = const()[name = tensor("lora_out_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_19_dilations_0 = const()[name = tensor("lora_out_19_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_19_groups_0 = const()[name = tensor("lora_out_19_groups_0"), val = tensor(1)]; + tensor layers_1_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_1_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28842944)))]; + tensor lora_out_19_cast_fp16 = conv(dilations = lora_out_19_dilations_0, groups = lora_out_19_groups_0, pad = lora_out_19_pad_0, pad_type = lora_out_19_pad_type_0, strides = lora_out_19_strides_0, weight = layers_1_self_attn_o_proj_loraB_weight_to_fp16, x = input_29_cast_fp16)[name = tensor("lora_out_19_cast_fp16")]; + tensor obj_7_cast_fp16 = add(x = pretrained_out_19_cast_fp16, y = lora_out_19_cast_fp16)[name = tensor("obj_7_cast_fp16")]; + tensor inputs_7_cast_fp16 = add(x = inputs_5_cast_fp16, y = obj_7_cast_fp16)[name = tensor("inputs_7_cast_fp16")]; + tensor out_7_axes_0 = const()[name = tensor("out_7_axes_0"), val = tensor([1])]; + tensor var_541_to_fp16 = const()[name = tensor("op_541_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_7_cast_fp16 = layer_norm(axes = out_7_axes_0, epsilon = var_541_to_fp16, x = inputs_7_cast_fp16)[name = tensor("out_7_cast_fp16")]; + tensor input_31_gamma_0_to_fp16 = const()[name = tensor("input_31_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28883968)))]; + tensor input_31_beta_0_to_fp16 = const()[name = tensor("input_31_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28886592)))]; + tensor input_31_epsilon_0_to_fp16 = const()[name = tensor("input_31_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_31_cast_fp16 = batch_norm(beta = input_31_beta_0_to_fp16, epsilon = input_31_epsilon_0_to_fp16, gamma = input_31_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_7_cast_fp16)[name = tensor("input_31_cast_fp16")]; + tensor pretrained_out_21_pad_type_0 = const()[name = tensor("pretrained_out_21_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_21_strides_0 = const()[name = tensor("pretrained_out_21_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_21_pad_0 = const()[name = tensor("pretrained_out_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_21_dilations_0 = const()[name = tensor("pretrained_out_21_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_21_groups_0 = const()[name = tensor("pretrained_out_21_groups_0"), val = tensor(1)]; + tensor layers_1_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28889216))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32166080))), name = tensor("layers_1_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_1_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_1_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32166208)))]; + tensor pretrained_out_21_cast_fp16 = conv(bias = layers_1_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_21_dilations_0, groups = pretrained_out_21_groups_0, pad = pretrained_out_21_pad_0, pad_type = pretrained_out_21_pad_type_0, strides = pretrained_out_21_strides_0, weight = layers_1_fc1_pretrained_weight_to_fp16_palettized, x = input_31_cast_fp16)[name = tensor("pretrained_out_21_cast_fp16")]; + tensor input_33_pad_type_0 = const()[name = tensor("input_33_pad_type_0"), val = tensor("valid")]; + tensor input_33_strides_0 = const()[name = tensor("input_33_strides_0"), val = tensor([1, 1])]; + tensor input_33_pad_0 = const()[name = tensor("input_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_33_dilations_0 = const()[name = tensor("input_33_dilations_0"), val = tensor([1, 1])]; + tensor input_33_groups_0 = const()[name = tensor("input_33_groups_0"), val = tensor(1)]; + tensor layers_1_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_1_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32176512)))]; + tensor input_33_cast_fp16 = conv(dilations = input_33_dilations_0, groups = input_33_groups_0, pad = input_33_pad_0, pad_type = input_33_pad_type_0, strides = input_33_strides_0, weight = layers_1_fc1_loraA_weight_to_fp16, x = input_31_cast_fp16)[name = tensor("input_33_cast_fp16")]; + tensor lora_out_21_pad_type_0 = const()[name = tensor("lora_out_21_pad_type_0"), val = tensor("valid")]; + tensor lora_out_21_strides_0 = const()[name = tensor("lora_out_21_strides_0"), val = tensor([1, 1])]; + tensor lora_out_21_pad_0 = const()[name = tensor("lora_out_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_21_dilations_0 = const()[name = tensor("lora_out_21_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_21_groups_0 = const()[name = tensor("lora_out_21_groups_0"), val = tensor(1)]; + tensor layers_1_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_1_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32217536)))]; + tensor lora_out_21_cast_fp16 = conv(dilations = lora_out_21_dilations_0, groups = lora_out_21_groups_0, pad = lora_out_21_pad_0, pad_type = lora_out_21_pad_type_0, strides = lora_out_21_strides_0, weight = layers_1_fc1_loraB_weight_to_fp16, x = input_33_cast_fp16)[name = tensor("lora_out_21_cast_fp16")]; + tensor input_35_cast_fp16 = add(x = pretrained_out_21_cast_fp16, y = lora_out_21_cast_fp16)[name = tensor("input_35_cast_fp16")]; + tensor input_37_mode_0 = const()[name = tensor("input_37_mode_0"), val = tensor("EXACT")]; + tensor input_37_cast_fp16 = gelu(mode = input_37_mode_0, x = input_35_cast_fp16)[name = tensor("input_37_cast_fp16")]; + tensor pretrained_out_23_pad_type_0 = const()[name = tensor("pretrained_out_23_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_23_strides_0 = const()[name = tensor("pretrained_out_23_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_23_pad_0 = const()[name = tensor("pretrained_out_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_23_dilations_0 = const()[name = tensor("pretrained_out_23_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_23_groups_0 = const()[name = tensor("pretrained_out_23_groups_0"), val = tensor(1)]; + tensor layers_1_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32381440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35658304))), name = tensor("layers_1_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_1_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_1_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35658432)))]; + tensor pretrained_out_23_cast_fp16 = conv(bias = layers_1_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_23_dilations_0, groups = pretrained_out_23_groups_0, pad = pretrained_out_23_pad_0, pad_type = pretrained_out_23_pad_type_0, strides = pretrained_out_23_strides_0, weight = layers_1_fc2_pretrained_weight_to_fp16_palettized, x = input_37_cast_fp16)[name = tensor("pretrained_out_23_cast_fp16")]; + tensor input_39_pad_type_0 = const()[name = tensor("input_39_pad_type_0"), val = tensor("valid")]; + tensor input_39_strides_0 = const()[name = tensor("input_39_strides_0"), val = tensor([1, 1])]; + tensor input_39_pad_0 = const()[name = tensor("input_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_39_dilations_0 = const()[name = tensor("input_39_dilations_0"), val = tensor([1, 1])]; + tensor input_39_groups_0 = const()[name = tensor("input_39_groups_0"), val = tensor(1)]; + tensor layers_1_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_1_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35661056)))]; + tensor input_39_cast_fp16 = conv(dilations = input_39_dilations_0, groups = input_39_groups_0, pad = input_39_pad_0, pad_type = input_39_pad_type_0, strides = input_39_strides_0, weight = layers_1_fc2_loraA_weight_to_fp16, x = input_37_cast_fp16)[name = tensor("input_39_cast_fp16")]; + tensor lora_out_23_pad_type_0 = const()[name = tensor("lora_out_23_pad_type_0"), val = tensor("valid")]; + tensor lora_out_23_strides_0 = const()[name = tensor("lora_out_23_strides_0"), val = tensor([1, 1])]; + tensor lora_out_23_pad_0 = const()[name = tensor("lora_out_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_23_dilations_0 = const()[name = tensor("lora_out_23_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_23_groups_0 = const()[name = tensor("lora_out_23_groups_0"), val = tensor(1)]; + tensor layers_1_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_1_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35824960)))]; + tensor lora_out_23_cast_fp16 = conv(dilations = lora_out_23_dilations_0, groups = lora_out_23_groups_0, pad = lora_out_23_pad_0, pad_type = lora_out_23_pad_type_0, strides = lora_out_23_strides_0, weight = layers_1_fc2_loraB_weight_to_fp16, x = input_39_cast_fp16)[name = tensor("lora_out_23_cast_fp16")]; + tensor hidden_states_7_cast_fp16 = add(x = pretrained_out_23_cast_fp16, y = lora_out_23_cast_fp16)[name = tensor("hidden_states_7_cast_fp16")]; + tensor inputs_9_cast_fp16 = add(x = inputs_7_cast_fp16, y = hidden_states_7_cast_fp16)[name = tensor("inputs_9_cast_fp16")]; + tensor var_606 = const()[name = tensor("op_606"), val = tensor(3)]; + tensor out_9_axes_0 = const()[name = tensor("out_9_axes_0"), val = tensor([1])]; + tensor var_625_to_fp16 = const()[name = tensor("op_625_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_9_cast_fp16 = layer_norm(axes = out_9_axes_0, epsilon = var_625_to_fp16, x = inputs_9_cast_fp16)[name = tensor("out_9_cast_fp16")]; + tensor obj_9_gamma_0_to_fp16 = const()[name = tensor("obj_9_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35865984)))]; + tensor obj_9_beta_0_to_fp16 = const()[name = tensor("obj_9_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35868608)))]; + tensor obj_9_epsilon_0_to_fp16 = const()[name = tensor("obj_9_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_9_cast_fp16 = batch_norm(beta = obj_9_beta_0_to_fp16, epsilon = obj_9_epsilon_0_to_fp16, gamma = obj_9_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_9_cast_fp16)[name = tensor("obj_9_cast_fp16")]; + tensor pretrained_out_25_pad_type_0 = const()[name = tensor("pretrained_out_25_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_25_strides_0 = const()[name = tensor("pretrained_out_25_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_25_pad_0 = const()[name = tensor("pretrained_out_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_25_dilations_0 = const()[name = tensor("pretrained_out_25_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_25_groups_0 = const()[name = tensor("pretrained_out_25_groups_0"), val = tensor(1)]; + tensor layers_2_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35871232))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36690496))), name = tensor("layers_2_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_2_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_2_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36690624)))]; + tensor pretrained_out_25_cast_fp16 = conv(bias = layers_2_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_25_dilations_0, groups = pretrained_out_25_groups_0, pad = pretrained_out_25_pad_0, pad_type = pretrained_out_25_pad_type_0, strides = pretrained_out_25_strides_0, weight = layers_2_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_9_cast_fp16)[name = tensor("pretrained_out_25_cast_fp16")]; + tensor input_41_pad_type_0 = const()[name = tensor("input_41_pad_type_0"), val = tensor("valid")]; + tensor input_41_strides_0 = const()[name = tensor("input_41_strides_0"), val = tensor([1, 1])]; + tensor input_41_pad_0 = const()[name = tensor("input_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_41_dilations_0 = const()[name = tensor("input_41_dilations_0"), val = tensor([1, 1])]; + tensor input_41_groups_0 = const()[name = tensor("input_41_groups_0"), val = tensor(1)]; + tensor layers_2_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_2_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36693248)))]; + tensor input_41_cast_fp16 = conv(dilations = input_41_dilations_0, groups = input_41_groups_0, pad = input_41_pad_0, pad_type = input_41_pad_type_0, strides = input_41_strides_0, weight = layers_2_self_attn_q_proj_loraA_weight_to_fp16, x = obj_9_cast_fp16)[name = tensor("input_41_cast_fp16")]; + tensor lora_out_25_pad_type_0 = const()[name = tensor("lora_out_25_pad_type_0"), val = tensor("valid")]; + tensor lora_out_25_strides_0 = const()[name = tensor("lora_out_25_strides_0"), val = tensor([1, 1])]; + tensor lora_out_25_pad_0 = const()[name = tensor("lora_out_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_25_dilations_0 = const()[name = tensor("lora_out_25_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_25_groups_0 = const()[name = tensor("lora_out_25_groups_0"), val = tensor(1)]; + tensor layers_2_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_2_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36734272)))]; + tensor lora_out_25_cast_fp16 = conv(dilations = lora_out_25_dilations_0, groups = lora_out_25_groups_0, pad = lora_out_25_pad_0, pad_type = lora_out_25_pad_type_0, strides = lora_out_25_strides_0, weight = layers_2_self_attn_q_proj_loraB_weight_to_fp16, x = input_41_cast_fp16)[name = tensor("lora_out_25_cast_fp16")]; + tensor query_5_cast_fp16 = add(x = pretrained_out_25_cast_fp16, y = lora_out_25_cast_fp16)[name = tensor("query_5_cast_fp16")]; + tensor pretrained_out_27_pad_type_0 = const()[name = tensor("pretrained_out_27_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_27_strides_0 = const()[name = tensor("pretrained_out_27_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_27_pad_0 = const()[name = tensor("pretrained_out_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_27_dilations_0 = const()[name = tensor("pretrained_out_27_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_27_groups_0 = const()[name = tensor("pretrained_out_27_groups_0"), val = tensor(1)]; + tensor layers_2_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36775296))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37594560))), name = tensor("layers_2_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_27_cast_fp16 = conv(dilations = pretrained_out_27_dilations_0, groups = pretrained_out_27_groups_0, pad = pretrained_out_27_pad_0, pad_type = pretrained_out_27_pad_type_0, strides = pretrained_out_27_strides_0, weight = layers_2_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_9_cast_fp16)[name = tensor("pretrained_out_27_cast_fp16")]; + tensor input_43_pad_type_0 = const()[name = tensor("input_43_pad_type_0"), val = tensor("valid")]; + tensor input_43_strides_0 = const()[name = tensor("input_43_strides_0"), val = tensor([1, 1])]; + tensor input_43_pad_0 = const()[name = tensor("input_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_43_dilations_0 = const()[name = tensor("input_43_dilations_0"), val = tensor([1, 1])]; + tensor input_43_groups_0 = const()[name = tensor("input_43_groups_0"), val = tensor(1)]; + tensor layers_2_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_2_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37594688)))]; + tensor input_43_cast_fp16 = conv(dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = layers_2_self_attn_k_proj_loraA_weight_to_fp16, x = obj_9_cast_fp16)[name = tensor("input_43_cast_fp16")]; + tensor lora_out_27_pad_type_0 = const()[name = tensor("lora_out_27_pad_type_0"), val = tensor("valid")]; + tensor lora_out_27_strides_0 = const()[name = tensor("lora_out_27_strides_0"), val = tensor([1, 1])]; + tensor lora_out_27_pad_0 = const()[name = tensor("lora_out_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_27_dilations_0 = const()[name = tensor("lora_out_27_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_27_groups_0 = const()[name = tensor("lora_out_27_groups_0"), val = tensor(1)]; + tensor layers_2_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_2_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37635712)))]; + tensor lora_out_27_cast_fp16 = conv(dilations = lora_out_27_dilations_0, groups = lora_out_27_groups_0, pad = lora_out_27_pad_0, pad_type = lora_out_27_pad_type_0, strides = lora_out_27_strides_0, weight = layers_2_self_attn_k_proj_loraB_weight_to_fp16, x = input_43_cast_fp16)[name = tensor("lora_out_27_cast_fp16")]; + tensor key_5_cast_fp16 = add(x = pretrained_out_27_cast_fp16, y = lora_out_27_cast_fp16)[name = tensor("key_5_cast_fp16")]; + tensor pretrained_out_29_pad_type_0 = const()[name = tensor("pretrained_out_29_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_29_strides_0 = const()[name = tensor("pretrained_out_29_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_29_pad_0 = const()[name = tensor("pretrained_out_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_29_dilations_0 = const()[name = tensor("pretrained_out_29_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_29_groups_0 = const()[name = tensor("pretrained_out_29_groups_0"), val = tensor(1)]; + tensor layers_2_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37676736))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38496000))), name = tensor("layers_2_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_2_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_2_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38496128)))]; + tensor pretrained_out_29_cast_fp16 = conv(bias = layers_2_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_29_dilations_0, groups = pretrained_out_29_groups_0, pad = pretrained_out_29_pad_0, pad_type = pretrained_out_29_pad_type_0, strides = pretrained_out_29_strides_0, weight = layers_2_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_9_cast_fp16)[name = tensor("pretrained_out_29_cast_fp16")]; + tensor input_45_pad_type_0 = const()[name = tensor("input_45_pad_type_0"), val = tensor("valid")]; + tensor input_45_strides_0 = const()[name = tensor("input_45_strides_0"), val = tensor([1, 1])]; + tensor input_45_pad_0 = const()[name = tensor("input_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_45_dilations_0 = const()[name = tensor("input_45_dilations_0"), val = tensor([1, 1])]; + tensor input_45_groups_0 = const()[name = tensor("input_45_groups_0"), val = tensor(1)]; + tensor layers_2_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_2_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38498752)))]; + tensor input_45_cast_fp16 = conv(dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = layers_2_self_attn_v_proj_loraA_weight_to_fp16, x = obj_9_cast_fp16)[name = tensor("input_45_cast_fp16")]; + tensor lora_out_29_pad_type_0 = const()[name = tensor("lora_out_29_pad_type_0"), val = tensor("valid")]; + tensor lora_out_29_strides_0 = const()[name = tensor("lora_out_29_strides_0"), val = tensor([1, 1])]; + tensor lora_out_29_pad_0 = const()[name = tensor("lora_out_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_29_dilations_0 = const()[name = tensor("lora_out_29_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_29_groups_0 = const()[name = tensor("lora_out_29_groups_0"), val = tensor(1)]; + tensor layers_2_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_2_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38539776)))]; + tensor lora_out_29_cast_fp16 = conv(dilations = lora_out_29_dilations_0, groups = lora_out_29_groups_0, pad = lora_out_29_pad_0, pad_type = lora_out_29_pad_type_0, strides = lora_out_29_strides_0, weight = layers_2_self_attn_v_proj_loraB_weight_to_fp16, x = input_45_cast_fp16)[name = tensor("lora_out_29_cast_fp16")]; + tensor value_5_cast_fp16 = add(x = pretrained_out_29_cast_fp16, y = lora_out_29_cast_fp16)[name = tensor("value_5_cast_fp16")]; + tensor var_708 = const()[name = tensor("op_708"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_5_cast_fp16 = reshape(shape = var_708, x = query_5_cast_fp16)[name = tensor("mh_q_5_cast_fp16")]; + tensor var_710_to_fp16 = const()[name = tensor("op_710_to_fp16"), val = tensor(0x1p-3)]; + tensor var_711_cast_fp16 = mul(x = mh_q_5_cast_fp16, y = var_710_to_fp16)[name = tensor("op_711_cast_fp16")]; + tensor var_712 = const()[name = tensor("op_712"), val = tensor([1, 20, 64, -1])]; + tensor var_713_cast_fp16 = reshape(shape = var_712, x = key_5_cast_fp16)[name = tensor("op_713_cast_fp16")]; + tensor mh_w_5_transpose_x_0 = const()[name = tensor("mh_w_5_transpose_x_0"), val = tensor(true)]; + tensor mh_w_5_transpose_y_0 = const()[name = tensor("mh_w_5_transpose_y_0"), val = tensor(false)]; + tensor mh_w_5_cast_fp16 = matmul(transpose_x = mh_w_5_transpose_x_0, transpose_y = mh_w_5_transpose_y_0, x = var_711_cast_fp16, y = var_713_cast_fp16)[name = tensor("mh_w_5_cast_fp16")]; + tensor var_716_cast_fp16 = softmax(axis = var_606, x = mh_w_5_cast_fp16)[name = tensor("op_716_cast_fp16")]; + tensor var_717 = const()[name = tensor("op_717"), val = tensor([1, 20, 64, -1])]; + tensor var_718_cast_fp16 = reshape(shape = var_717, x = value_5_cast_fp16)[name = tensor("op_718_cast_fp16")]; + tensor attn_5_transpose_x_0 = const()[name = tensor("attn_5_transpose_x_0"), val = tensor(false)]; + tensor attn_5_transpose_y_0 = const()[name = tensor("attn_5_transpose_y_0"), val = tensor(true)]; + tensor attn_5_cast_fp16 = matmul(transpose_x = attn_5_transpose_x_0, transpose_y = attn_5_transpose_y_0, x = var_718_cast_fp16, y = var_716_cast_fp16)[name = tensor("attn_5_cast_fp16")]; + tensor var_721 = const()[name = tensor("op_721"), val = tensor([1, 1280, 1, -1])]; + tensor input_47_cast_fp16 = reshape(shape = var_721, x = attn_5_cast_fp16)[name = tensor("input_47_cast_fp16")]; + tensor pretrained_out_31_pad_type_0 = const()[name = tensor("pretrained_out_31_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_31_strides_0 = const()[name = tensor("pretrained_out_31_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_31_pad_0 = const()[name = tensor("pretrained_out_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_31_dilations_0 = const()[name = tensor("pretrained_out_31_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_31_groups_0 = const()[name = tensor("pretrained_out_31_groups_0"), val = tensor(1)]; + tensor layers_2_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38580800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39400064))), name = tensor("layers_2_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_2_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_2_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39400192)))]; + tensor pretrained_out_31_cast_fp16 = conv(bias = layers_2_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_31_dilations_0, groups = pretrained_out_31_groups_0, pad = pretrained_out_31_pad_0, pad_type = pretrained_out_31_pad_type_0, strides = pretrained_out_31_strides_0, weight = layers_2_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_47_cast_fp16)[name = tensor("pretrained_out_31_cast_fp16")]; + tensor input_49_pad_type_0 = const()[name = tensor("input_49_pad_type_0"), val = tensor("valid")]; + tensor input_49_strides_0 = const()[name = tensor("input_49_strides_0"), val = tensor([1, 1])]; + tensor input_49_pad_0 = const()[name = tensor("input_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_49_dilations_0 = const()[name = tensor("input_49_dilations_0"), val = tensor([1, 1])]; + tensor input_49_groups_0 = const()[name = tensor("input_49_groups_0"), val = tensor(1)]; + tensor layers_2_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_2_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39402816)))]; + tensor input_49_cast_fp16 = conv(dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = layers_2_self_attn_o_proj_loraA_weight_to_fp16, x = input_47_cast_fp16)[name = tensor("input_49_cast_fp16")]; + tensor lora_out_31_pad_type_0 = const()[name = tensor("lora_out_31_pad_type_0"), val = tensor("valid")]; + tensor lora_out_31_strides_0 = const()[name = tensor("lora_out_31_strides_0"), val = tensor([1, 1])]; + tensor lora_out_31_pad_0 = const()[name = tensor("lora_out_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_31_dilations_0 = const()[name = tensor("lora_out_31_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_31_groups_0 = const()[name = tensor("lora_out_31_groups_0"), val = tensor(1)]; + tensor layers_2_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_2_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39443840)))]; + tensor lora_out_31_cast_fp16 = conv(dilations = lora_out_31_dilations_0, groups = lora_out_31_groups_0, pad = lora_out_31_pad_0, pad_type = lora_out_31_pad_type_0, strides = lora_out_31_strides_0, weight = layers_2_self_attn_o_proj_loraB_weight_to_fp16, x = input_49_cast_fp16)[name = tensor("lora_out_31_cast_fp16")]; + tensor obj_11_cast_fp16 = add(x = pretrained_out_31_cast_fp16, y = lora_out_31_cast_fp16)[name = tensor("obj_11_cast_fp16")]; + tensor inputs_11_cast_fp16 = add(x = inputs_9_cast_fp16, y = obj_11_cast_fp16)[name = tensor("inputs_11_cast_fp16")]; + tensor out_11_axes_0 = const()[name = tensor("out_11_axes_0"), val = tensor([1])]; + tensor var_755_to_fp16 = const()[name = tensor("op_755_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_11_cast_fp16 = layer_norm(axes = out_11_axes_0, epsilon = var_755_to_fp16, x = inputs_11_cast_fp16)[name = tensor("out_11_cast_fp16")]; + tensor input_51_gamma_0_to_fp16 = const()[name = tensor("input_51_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39484864)))]; + tensor input_51_beta_0_to_fp16 = const()[name = tensor("input_51_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39487488)))]; + tensor input_51_epsilon_0_to_fp16 = const()[name = tensor("input_51_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_51_cast_fp16 = batch_norm(beta = input_51_beta_0_to_fp16, epsilon = input_51_epsilon_0_to_fp16, gamma = input_51_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_11_cast_fp16)[name = tensor("input_51_cast_fp16")]; + tensor pretrained_out_33_pad_type_0 = const()[name = tensor("pretrained_out_33_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_33_strides_0 = const()[name = tensor("pretrained_out_33_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_33_pad_0 = const()[name = tensor("pretrained_out_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_33_dilations_0 = const()[name = tensor("pretrained_out_33_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_33_groups_0 = const()[name = tensor("pretrained_out_33_groups_0"), val = tensor(1)]; + tensor layers_2_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39490112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42766976))), name = tensor("layers_2_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_2_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_2_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42767104)))]; + tensor pretrained_out_33_cast_fp16 = conv(bias = layers_2_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_33_dilations_0, groups = pretrained_out_33_groups_0, pad = pretrained_out_33_pad_0, pad_type = pretrained_out_33_pad_type_0, strides = pretrained_out_33_strides_0, weight = layers_2_fc1_pretrained_weight_to_fp16_palettized, x = input_51_cast_fp16)[name = tensor("pretrained_out_33_cast_fp16")]; + tensor input_53_pad_type_0 = const()[name = tensor("input_53_pad_type_0"), val = tensor("valid")]; + tensor input_53_strides_0 = const()[name = tensor("input_53_strides_0"), val = tensor([1, 1])]; + tensor input_53_pad_0 = const()[name = tensor("input_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_53_dilations_0 = const()[name = tensor("input_53_dilations_0"), val = tensor([1, 1])]; + tensor input_53_groups_0 = const()[name = tensor("input_53_groups_0"), val = tensor(1)]; + tensor layers_2_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_2_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42777408)))]; + tensor input_53_cast_fp16 = conv(dilations = input_53_dilations_0, groups = input_53_groups_0, pad = input_53_pad_0, pad_type = input_53_pad_type_0, strides = input_53_strides_0, weight = layers_2_fc1_loraA_weight_to_fp16, x = input_51_cast_fp16)[name = tensor("input_53_cast_fp16")]; + tensor lora_out_33_pad_type_0 = const()[name = tensor("lora_out_33_pad_type_0"), val = tensor("valid")]; + tensor lora_out_33_strides_0 = const()[name = tensor("lora_out_33_strides_0"), val = tensor([1, 1])]; + tensor lora_out_33_pad_0 = const()[name = tensor("lora_out_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_33_dilations_0 = const()[name = tensor("lora_out_33_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_33_groups_0 = const()[name = tensor("lora_out_33_groups_0"), val = tensor(1)]; + tensor layers_2_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_2_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42818432)))]; + tensor lora_out_33_cast_fp16 = conv(dilations = lora_out_33_dilations_0, groups = lora_out_33_groups_0, pad = lora_out_33_pad_0, pad_type = lora_out_33_pad_type_0, strides = lora_out_33_strides_0, weight = layers_2_fc1_loraB_weight_to_fp16, x = input_53_cast_fp16)[name = tensor("lora_out_33_cast_fp16")]; + tensor input_55_cast_fp16 = add(x = pretrained_out_33_cast_fp16, y = lora_out_33_cast_fp16)[name = tensor("input_55_cast_fp16")]; + tensor input_57_mode_0 = const()[name = tensor("input_57_mode_0"), val = tensor("EXACT")]; + tensor input_57_cast_fp16 = gelu(mode = input_57_mode_0, x = input_55_cast_fp16)[name = tensor("input_57_cast_fp16")]; + tensor pretrained_out_35_pad_type_0 = const()[name = tensor("pretrained_out_35_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_35_strides_0 = const()[name = tensor("pretrained_out_35_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_35_pad_0 = const()[name = tensor("pretrained_out_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_35_dilations_0 = const()[name = tensor("pretrained_out_35_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_35_groups_0 = const()[name = tensor("pretrained_out_35_groups_0"), val = tensor(1)]; + tensor layers_2_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42982336))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46259200))), name = tensor("layers_2_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_2_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_2_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46259328)))]; + tensor pretrained_out_35_cast_fp16 = conv(bias = layers_2_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_35_dilations_0, groups = pretrained_out_35_groups_0, pad = pretrained_out_35_pad_0, pad_type = pretrained_out_35_pad_type_0, strides = pretrained_out_35_strides_0, weight = layers_2_fc2_pretrained_weight_to_fp16_palettized, x = input_57_cast_fp16)[name = tensor("pretrained_out_35_cast_fp16")]; + tensor input_59_pad_type_0 = const()[name = tensor("input_59_pad_type_0"), val = tensor("valid")]; + tensor input_59_strides_0 = const()[name = tensor("input_59_strides_0"), val = tensor([1, 1])]; + tensor input_59_pad_0 = const()[name = tensor("input_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_59_dilations_0 = const()[name = tensor("input_59_dilations_0"), val = tensor([1, 1])]; + tensor input_59_groups_0 = const()[name = tensor("input_59_groups_0"), val = tensor(1)]; + tensor layers_2_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_2_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46261952)))]; + tensor input_59_cast_fp16 = conv(dilations = input_59_dilations_0, groups = input_59_groups_0, pad = input_59_pad_0, pad_type = input_59_pad_type_0, strides = input_59_strides_0, weight = layers_2_fc2_loraA_weight_to_fp16, x = input_57_cast_fp16)[name = tensor("input_59_cast_fp16")]; + tensor lora_out_35_pad_type_0 = const()[name = tensor("lora_out_35_pad_type_0"), val = tensor("valid")]; + tensor lora_out_35_strides_0 = const()[name = tensor("lora_out_35_strides_0"), val = tensor([1, 1])]; + tensor lora_out_35_pad_0 = const()[name = tensor("lora_out_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_35_dilations_0 = const()[name = tensor("lora_out_35_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_35_groups_0 = const()[name = tensor("lora_out_35_groups_0"), val = tensor(1)]; + tensor layers_2_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_2_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46425856)))]; + tensor lora_out_35_cast_fp16 = conv(dilations = lora_out_35_dilations_0, groups = lora_out_35_groups_0, pad = lora_out_35_pad_0, pad_type = lora_out_35_pad_type_0, strides = lora_out_35_strides_0, weight = layers_2_fc2_loraB_weight_to_fp16, x = input_59_cast_fp16)[name = tensor("lora_out_35_cast_fp16")]; + tensor hidden_states_9_cast_fp16 = add(x = pretrained_out_35_cast_fp16, y = lora_out_35_cast_fp16)[name = tensor("hidden_states_9_cast_fp16")]; + tensor inputs_13_cast_fp16 = add(x = inputs_11_cast_fp16, y = hidden_states_9_cast_fp16)[name = tensor("inputs_13_cast_fp16")]; + tensor var_820 = const()[name = tensor("op_820"), val = tensor(3)]; + tensor out_13_axes_0 = const()[name = tensor("out_13_axes_0"), val = tensor([1])]; + tensor var_839_to_fp16 = const()[name = tensor("op_839_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_13_cast_fp16 = layer_norm(axes = out_13_axes_0, epsilon = var_839_to_fp16, x = inputs_13_cast_fp16)[name = tensor("out_13_cast_fp16")]; + tensor obj_13_gamma_0_to_fp16 = const()[name = tensor("obj_13_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46466880)))]; + tensor obj_13_beta_0_to_fp16 = const()[name = tensor("obj_13_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46469504)))]; + tensor obj_13_epsilon_0_to_fp16 = const()[name = tensor("obj_13_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_13_cast_fp16 = batch_norm(beta = obj_13_beta_0_to_fp16, epsilon = obj_13_epsilon_0_to_fp16, gamma = obj_13_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_13_cast_fp16)[name = tensor("obj_13_cast_fp16")]; + tensor pretrained_out_37_pad_type_0 = const()[name = tensor("pretrained_out_37_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_37_strides_0 = const()[name = tensor("pretrained_out_37_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_37_pad_0 = const()[name = tensor("pretrained_out_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_37_dilations_0 = const()[name = tensor("pretrained_out_37_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_37_groups_0 = const()[name = tensor("pretrained_out_37_groups_0"), val = tensor(1)]; + tensor layers_3_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46472128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47291392))), name = tensor("layers_3_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_3_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_3_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47291520)))]; + tensor pretrained_out_37_cast_fp16 = conv(bias = layers_3_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_37_dilations_0, groups = pretrained_out_37_groups_0, pad = pretrained_out_37_pad_0, pad_type = pretrained_out_37_pad_type_0, strides = pretrained_out_37_strides_0, weight = layers_3_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_13_cast_fp16)[name = tensor("pretrained_out_37_cast_fp16")]; + tensor input_61_pad_type_0 = const()[name = tensor("input_61_pad_type_0"), val = tensor("valid")]; + tensor input_61_strides_0 = const()[name = tensor("input_61_strides_0"), val = tensor([1, 1])]; + tensor input_61_pad_0 = const()[name = tensor("input_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_61_dilations_0 = const()[name = tensor("input_61_dilations_0"), val = tensor([1, 1])]; + tensor input_61_groups_0 = const()[name = tensor("input_61_groups_0"), val = tensor(1)]; + tensor layers_3_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_3_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47294144)))]; + tensor input_61_cast_fp16 = conv(dilations = input_61_dilations_0, groups = input_61_groups_0, pad = input_61_pad_0, pad_type = input_61_pad_type_0, strides = input_61_strides_0, weight = layers_3_self_attn_q_proj_loraA_weight_to_fp16, x = obj_13_cast_fp16)[name = tensor("input_61_cast_fp16")]; + tensor lora_out_37_pad_type_0 = const()[name = tensor("lora_out_37_pad_type_0"), val = tensor("valid")]; + tensor lora_out_37_strides_0 = const()[name = tensor("lora_out_37_strides_0"), val = tensor([1, 1])]; + tensor lora_out_37_pad_0 = const()[name = tensor("lora_out_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_37_dilations_0 = const()[name = tensor("lora_out_37_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_37_groups_0 = const()[name = tensor("lora_out_37_groups_0"), val = tensor(1)]; + tensor layers_3_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_3_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47335168)))]; + tensor lora_out_37_cast_fp16 = conv(dilations = lora_out_37_dilations_0, groups = lora_out_37_groups_0, pad = lora_out_37_pad_0, pad_type = lora_out_37_pad_type_0, strides = lora_out_37_strides_0, weight = layers_3_self_attn_q_proj_loraB_weight_to_fp16, x = input_61_cast_fp16)[name = tensor("lora_out_37_cast_fp16")]; + tensor query_7_cast_fp16 = add(x = pretrained_out_37_cast_fp16, y = lora_out_37_cast_fp16)[name = tensor("query_7_cast_fp16")]; + tensor pretrained_out_39_pad_type_0 = const()[name = tensor("pretrained_out_39_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_39_strides_0 = const()[name = tensor("pretrained_out_39_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_39_pad_0 = const()[name = tensor("pretrained_out_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_39_dilations_0 = const()[name = tensor("pretrained_out_39_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_39_groups_0 = const()[name = tensor("pretrained_out_39_groups_0"), val = tensor(1)]; + tensor layers_3_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47376192))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48195456))), name = tensor("layers_3_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_39_cast_fp16 = conv(dilations = pretrained_out_39_dilations_0, groups = pretrained_out_39_groups_0, pad = pretrained_out_39_pad_0, pad_type = pretrained_out_39_pad_type_0, strides = pretrained_out_39_strides_0, weight = layers_3_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_13_cast_fp16)[name = tensor("pretrained_out_39_cast_fp16")]; + tensor input_63_pad_type_0 = const()[name = tensor("input_63_pad_type_0"), val = tensor("valid")]; + tensor input_63_strides_0 = const()[name = tensor("input_63_strides_0"), val = tensor([1, 1])]; + tensor input_63_pad_0 = const()[name = tensor("input_63_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_63_dilations_0 = const()[name = tensor("input_63_dilations_0"), val = tensor([1, 1])]; + tensor input_63_groups_0 = const()[name = tensor("input_63_groups_0"), val = tensor(1)]; + tensor layers_3_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_3_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48195584)))]; + tensor input_63_cast_fp16 = conv(dilations = input_63_dilations_0, groups = input_63_groups_0, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = input_63_strides_0, weight = layers_3_self_attn_k_proj_loraA_weight_to_fp16, x = obj_13_cast_fp16)[name = tensor("input_63_cast_fp16")]; + tensor lora_out_39_pad_type_0 = const()[name = tensor("lora_out_39_pad_type_0"), val = tensor("valid")]; + tensor lora_out_39_strides_0 = const()[name = tensor("lora_out_39_strides_0"), val = tensor([1, 1])]; + tensor lora_out_39_pad_0 = const()[name = tensor("lora_out_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_39_dilations_0 = const()[name = tensor("lora_out_39_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_39_groups_0 = const()[name = tensor("lora_out_39_groups_0"), val = tensor(1)]; + tensor layers_3_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_3_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48236608)))]; + tensor lora_out_39_cast_fp16 = conv(dilations = lora_out_39_dilations_0, groups = lora_out_39_groups_0, pad = lora_out_39_pad_0, pad_type = lora_out_39_pad_type_0, strides = lora_out_39_strides_0, weight = layers_3_self_attn_k_proj_loraB_weight_to_fp16, x = input_63_cast_fp16)[name = tensor("lora_out_39_cast_fp16")]; + tensor key_7_cast_fp16 = add(x = pretrained_out_39_cast_fp16, y = lora_out_39_cast_fp16)[name = tensor("key_7_cast_fp16")]; + tensor pretrained_out_41_pad_type_0 = const()[name = tensor("pretrained_out_41_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_41_strides_0 = const()[name = tensor("pretrained_out_41_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_41_pad_0 = const()[name = tensor("pretrained_out_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_41_dilations_0 = const()[name = tensor("pretrained_out_41_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_41_groups_0 = const()[name = tensor("pretrained_out_41_groups_0"), val = tensor(1)]; + tensor layers_3_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48277632))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49096896))), name = tensor("layers_3_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_3_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_3_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49097024)))]; + tensor pretrained_out_41_cast_fp16 = conv(bias = layers_3_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_41_dilations_0, groups = pretrained_out_41_groups_0, pad = pretrained_out_41_pad_0, pad_type = pretrained_out_41_pad_type_0, strides = pretrained_out_41_strides_0, weight = layers_3_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_13_cast_fp16)[name = tensor("pretrained_out_41_cast_fp16")]; + tensor input_65_pad_type_0 = const()[name = tensor("input_65_pad_type_0"), val = tensor("valid")]; + tensor input_65_strides_0 = const()[name = tensor("input_65_strides_0"), val = tensor([1, 1])]; + tensor input_65_pad_0 = const()[name = tensor("input_65_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_65_dilations_0 = const()[name = tensor("input_65_dilations_0"), val = tensor([1, 1])]; + tensor input_65_groups_0 = const()[name = tensor("input_65_groups_0"), val = tensor(1)]; + tensor layers_3_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_3_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49099648)))]; + tensor input_65_cast_fp16 = conv(dilations = input_65_dilations_0, groups = input_65_groups_0, pad = input_65_pad_0, pad_type = input_65_pad_type_0, strides = input_65_strides_0, weight = layers_3_self_attn_v_proj_loraA_weight_to_fp16, x = obj_13_cast_fp16)[name = tensor("input_65_cast_fp16")]; + tensor lora_out_41_pad_type_0 = const()[name = tensor("lora_out_41_pad_type_0"), val = tensor("valid")]; + tensor lora_out_41_strides_0 = const()[name = tensor("lora_out_41_strides_0"), val = tensor([1, 1])]; + tensor lora_out_41_pad_0 = const()[name = tensor("lora_out_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_41_dilations_0 = const()[name = tensor("lora_out_41_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_41_groups_0 = const()[name = tensor("lora_out_41_groups_0"), val = tensor(1)]; + tensor layers_3_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_3_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49140672)))]; + tensor lora_out_41_cast_fp16 = conv(dilations = lora_out_41_dilations_0, groups = lora_out_41_groups_0, pad = lora_out_41_pad_0, pad_type = lora_out_41_pad_type_0, strides = lora_out_41_strides_0, weight = layers_3_self_attn_v_proj_loraB_weight_to_fp16, x = input_65_cast_fp16)[name = tensor("lora_out_41_cast_fp16")]; + tensor value_7_cast_fp16 = add(x = pretrained_out_41_cast_fp16, y = lora_out_41_cast_fp16)[name = tensor("value_7_cast_fp16")]; + tensor var_922 = const()[name = tensor("op_922"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_7_cast_fp16 = reshape(shape = var_922, x = query_7_cast_fp16)[name = tensor("mh_q_7_cast_fp16")]; + tensor var_924_to_fp16 = const()[name = tensor("op_924_to_fp16"), val = tensor(0x1p-3)]; + tensor var_925_cast_fp16 = mul(x = mh_q_7_cast_fp16, y = var_924_to_fp16)[name = tensor("op_925_cast_fp16")]; + tensor var_926 = const()[name = tensor("op_926"), val = tensor([1, 20, 64, -1])]; + tensor var_927_cast_fp16 = reshape(shape = var_926, x = key_7_cast_fp16)[name = tensor("op_927_cast_fp16")]; + tensor mh_w_7_transpose_x_0 = const()[name = tensor("mh_w_7_transpose_x_0"), val = tensor(true)]; + tensor mh_w_7_transpose_y_0 = const()[name = tensor("mh_w_7_transpose_y_0"), val = tensor(false)]; + tensor mh_w_7_cast_fp16 = matmul(transpose_x = mh_w_7_transpose_x_0, transpose_y = mh_w_7_transpose_y_0, x = var_925_cast_fp16, y = var_927_cast_fp16)[name = tensor("mh_w_7_cast_fp16")]; + tensor var_930_cast_fp16 = softmax(axis = var_820, x = mh_w_7_cast_fp16)[name = tensor("op_930_cast_fp16")]; + tensor var_931 = const()[name = tensor("op_931"), val = tensor([1, 20, 64, -1])]; + tensor var_932_cast_fp16 = reshape(shape = var_931, x = value_7_cast_fp16)[name = tensor("op_932_cast_fp16")]; + tensor attn_7_transpose_x_0 = const()[name = tensor("attn_7_transpose_x_0"), val = tensor(false)]; + tensor attn_7_transpose_y_0 = const()[name = tensor("attn_7_transpose_y_0"), val = tensor(true)]; + tensor attn_7_cast_fp16 = matmul(transpose_x = attn_7_transpose_x_0, transpose_y = attn_7_transpose_y_0, x = var_932_cast_fp16, y = var_930_cast_fp16)[name = tensor("attn_7_cast_fp16")]; + tensor var_935 = const()[name = tensor("op_935"), val = tensor([1, 1280, 1, -1])]; + tensor input_67_cast_fp16 = reshape(shape = var_935, x = attn_7_cast_fp16)[name = tensor("input_67_cast_fp16")]; + tensor pretrained_out_43_pad_type_0 = const()[name = tensor("pretrained_out_43_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_43_strides_0 = const()[name = tensor("pretrained_out_43_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_43_pad_0 = const()[name = tensor("pretrained_out_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_43_dilations_0 = const()[name = tensor("pretrained_out_43_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_43_groups_0 = const()[name = tensor("pretrained_out_43_groups_0"), val = tensor(1)]; + tensor layers_3_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49181696))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50000960))), name = tensor("layers_3_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_3_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_3_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50001088)))]; + tensor pretrained_out_43_cast_fp16 = conv(bias = layers_3_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_43_dilations_0, groups = pretrained_out_43_groups_0, pad = pretrained_out_43_pad_0, pad_type = pretrained_out_43_pad_type_0, strides = pretrained_out_43_strides_0, weight = layers_3_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_67_cast_fp16)[name = tensor("pretrained_out_43_cast_fp16")]; + tensor input_69_pad_type_0 = const()[name = tensor("input_69_pad_type_0"), val = tensor("valid")]; + tensor input_69_strides_0 = const()[name = tensor("input_69_strides_0"), val = tensor([1, 1])]; + tensor input_69_pad_0 = const()[name = tensor("input_69_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_69_dilations_0 = const()[name = tensor("input_69_dilations_0"), val = tensor([1, 1])]; + tensor input_69_groups_0 = const()[name = tensor("input_69_groups_0"), val = tensor(1)]; + tensor layers_3_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_3_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50003712)))]; + tensor input_69_cast_fp16 = conv(dilations = input_69_dilations_0, groups = input_69_groups_0, pad = input_69_pad_0, pad_type = input_69_pad_type_0, strides = input_69_strides_0, weight = layers_3_self_attn_o_proj_loraA_weight_to_fp16, x = input_67_cast_fp16)[name = tensor("input_69_cast_fp16")]; + tensor lora_out_43_pad_type_0 = const()[name = tensor("lora_out_43_pad_type_0"), val = tensor("valid")]; + tensor lora_out_43_strides_0 = const()[name = tensor("lora_out_43_strides_0"), val = tensor([1, 1])]; + tensor lora_out_43_pad_0 = const()[name = tensor("lora_out_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_43_dilations_0 = const()[name = tensor("lora_out_43_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_43_groups_0 = const()[name = tensor("lora_out_43_groups_0"), val = tensor(1)]; + tensor layers_3_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_3_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50044736)))]; + tensor lora_out_43_cast_fp16 = conv(dilations = lora_out_43_dilations_0, groups = lora_out_43_groups_0, pad = lora_out_43_pad_0, pad_type = lora_out_43_pad_type_0, strides = lora_out_43_strides_0, weight = layers_3_self_attn_o_proj_loraB_weight_to_fp16, x = input_69_cast_fp16)[name = tensor("lora_out_43_cast_fp16")]; + tensor obj_15_cast_fp16 = add(x = pretrained_out_43_cast_fp16, y = lora_out_43_cast_fp16)[name = tensor("obj_15_cast_fp16")]; + tensor inputs_15_cast_fp16 = add(x = inputs_13_cast_fp16, y = obj_15_cast_fp16)[name = tensor("inputs_15_cast_fp16")]; + tensor out_15_axes_0 = const()[name = tensor("out_15_axes_0"), val = tensor([1])]; + tensor var_969_to_fp16 = const()[name = tensor("op_969_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_15_cast_fp16 = layer_norm(axes = out_15_axes_0, epsilon = var_969_to_fp16, x = inputs_15_cast_fp16)[name = tensor("out_15_cast_fp16")]; + tensor input_71_gamma_0_to_fp16 = const()[name = tensor("input_71_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50085760)))]; + tensor input_71_beta_0_to_fp16 = const()[name = tensor("input_71_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50088384)))]; + tensor input_71_epsilon_0_to_fp16 = const()[name = tensor("input_71_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_71_cast_fp16 = batch_norm(beta = input_71_beta_0_to_fp16, epsilon = input_71_epsilon_0_to_fp16, gamma = input_71_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_15_cast_fp16)[name = tensor("input_71_cast_fp16")]; + tensor pretrained_out_45_pad_type_0 = const()[name = tensor("pretrained_out_45_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_45_strides_0 = const()[name = tensor("pretrained_out_45_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_45_pad_0 = const()[name = tensor("pretrained_out_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_45_dilations_0 = const()[name = tensor("pretrained_out_45_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_45_groups_0 = const()[name = tensor("pretrained_out_45_groups_0"), val = tensor(1)]; + tensor layers_3_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50091008))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(53367872))), name = tensor("layers_3_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_3_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_3_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(53368000)))]; + tensor pretrained_out_45_cast_fp16 = conv(bias = layers_3_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_45_dilations_0, groups = pretrained_out_45_groups_0, pad = pretrained_out_45_pad_0, pad_type = pretrained_out_45_pad_type_0, strides = pretrained_out_45_strides_0, weight = layers_3_fc1_pretrained_weight_to_fp16_palettized, x = input_71_cast_fp16)[name = tensor("pretrained_out_45_cast_fp16")]; + tensor input_73_pad_type_0 = const()[name = tensor("input_73_pad_type_0"), val = tensor("valid")]; + tensor input_73_strides_0 = const()[name = tensor("input_73_strides_0"), val = tensor([1, 1])]; + tensor input_73_pad_0 = const()[name = tensor("input_73_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_73_dilations_0 = const()[name = tensor("input_73_dilations_0"), val = tensor([1, 1])]; + tensor input_73_groups_0 = const()[name = tensor("input_73_groups_0"), val = tensor(1)]; + tensor layers_3_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_3_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(53378304)))]; + tensor input_73_cast_fp16 = conv(dilations = input_73_dilations_0, groups = input_73_groups_0, pad = input_73_pad_0, pad_type = input_73_pad_type_0, strides = input_73_strides_0, weight = layers_3_fc1_loraA_weight_to_fp16, x = input_71_cast_fp16)[name = tensor("input_73_cast_fp16")]; + tensor lora_out_45_pad_type_0 = const()[name = tensor("lora_out_45_pad_type_0"), val = tensor("valid")]; + tensor lora_out_45_strides_0 = const()[name = tensor("lora_out_45_strides_0"), val = tensor([1, 1])]; + tensor lora_out_45_pad_0 = const()[name = tensor("lora_out_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_45_dilations_0 = const()[name = tensor("lora_out_45_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_45_groups_0 = const()[name = tensor("lora_out_45_groups_0"), val = tensor(1)]; + tensor layers_3_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_3_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(53419328)))]; + tensor lora_out_45_cast_fp16 = conv(dilations = lora_out_45_dilations_0, groups = lora_out_45_groups_0, pad = lora_out_45_pad_0, pad_type = lora_out_45_pad_type_0, strides = lora_out_45_strides_0, weight = layers_3_fc1_loraB_weight_to_fp16, x = input_73_cast_fp16)[name = tensor("lora_out_45_cast_fp16")]; + tensor input_75_cast_fp16 = add(x = pretrained_out_45_cast_fp16, y = lora_out_45_cast_fp16)[name = tensor("input_75_cast_fp16")]; + tensor input_77_mode_0 = const()[name = tensor("input_77_mode_0"), val = tensor("EXACT")]; + tensor input_77_cast_fp16 = gelu(mode = input_77_mode_0, x = input_75_cast_fp16)[name = tensor("input_77_cast_fp16")]; + tensor pretrained_out_47_pad_type_0 = const()[name = tensor("pretrained_out_47_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_47_strides_0 = const()[name = tensor("pretrained_out_47_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_47_pad_0 = const()[name = tensor("pretrained_out_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_47_dilations_0 = const()[name = tensor("pretrained_out_47_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_47_groups_0 = const()[name = tensor("pretrained_out_47_groups_0"), val = tensor(1)]; + tensor layers_3_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(53583232))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56860096))), name = tensor("layers_3_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_3_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_3_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56860224)))]; + tensor pretrained_out_47_cast_fp16 = conv(bias = layers_3_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_47_dilations_0, groups = pretrained_out_47_groups_0, pad = pretrained_out_47_pad_0, pad_type = pretrained_out_47_pad_type_0, strides = pretrained_out_47_strides_0, weight = layers_3_fc2_pretrained_weight_to_fp16_palettized, x = input_77_cast_fp16)[name = tensor("pretrained_out_47_cast_fp16")]; + tensor input_79_pad_type_0 = const()[name = tensor("input_79_pad_type_0"), val = tensor("valid")]; + tensor input_79_strides_0 = const()[name = tensor("input_79_strides_0"), val = tensor([1, 1])]; + tensor input_79_pad_0 = const()[name = tensor("input_79_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_79_dilations_0 = const()[name = tensor("input_79_dilations_0"), val = tensor([1, 1])]; + tensor input_79_groups_0 = const()[name = tensor("input_79_groups_0"), val = tensor(1)]; + tensor layers_3_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_3_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56862848)))]; + tensor input_79_cast_fp16 = conv(dilations = input_79_dilations_0, groups = input_79_groups_0, pad = input_79_pad_0, pad_type = input_79_pad_type_0, strides = input_79_strides_0, weight = layers_3_fc2_loraA_weight_to_fp16, x = input_77_cast_fp16)[name = tensor("input_79_cast_fp16")]; + tensor lora_out_47_pad_type_0 = const()[name = tensor("lora_out_47_pad_type_0"), val = tensor("valid")]; + tensor lora_out_47_strides_0 = const()[name = tensor("lora_out_47_strides_0"), val = tensor([1, 1])]; + tensor lora_out_47_pad_0 = const()[name = tensor("lora_out_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_47_dilations_0 = const()[name = tensor("lora_out_47_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_47_groups_0 = const()[name = tensor("lora_out_47_groups_0"), val = tensor(1)]; + tensor layers_3_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_3_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57026752)))]; + tensor lora_out_47_cast_fp16 = conv(dilations = lora_out_47_dilations_0, groups = lora_out_47_groups_0, pad = lora_out_47_pad_0, pad_type = lora_out_47_pad_type_0, strides = lora_out_47_strides_0, weight = layers_3_fc2_loraB_weight_to_fp16, x = input_79_cast_fp16)[name = tensor("lora_out_47_cast_fp16")]; + tensor hidden_states_11_cast_fp16 = add(x = pretrained_out_47_cast_fp16, y = lora_out_47_cast_fp16)[name = tensor("hidden_states_11_cast_fp16")]; + tensor inputs_17_cast_fp16 = add(x = inputs_15_cast_fp16, y = hidden_states_11_cast_fp16)[name = tensor("inputs_17_cast_fp16")]; + tensor var_1034 = const()[name = tensor("op_1034"), val = tensor(3)]; + tensor out_17_axes_0 = const()[name = tensor("out_17_axes_0"), val = tensor([1])]; + tensor var_1053_to_fp16 = const()[name = tensor("op_1053_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_17_cast_fp16 = layer_norm(axes = out_17_axes_0, epsilon = var_1053_to_fp16, x = inputs_17_cast_fp16)[name = tensor("out_17_cast_fp16")]; + tensor obj_17_gamma_0_to_fp16 = const()[name = tensor("obj_17_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57067776)))]; + tensor obj_17_beta_0_to_fp16 = const()[name = tensor("obj_17_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57070400)))]; + tensor obj_17_epsilon_0_to_fp16 = const()[name = tensor("obj_17_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_17_cast_fp16 = batch_norm(beta = obj_17_beta_0_to_fp16, epsilon = obj_17_epsilon_0_to_fp16, gamma = obj_17_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_17_cast_fp16)[name = tensor("obj_17_cast_fp16")]; + tensor pretrained_out_49_pad_type_0 = const()[name = tensor("pretrained_out_49_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_49_strides_0 = const()[name = tensor("pretrained_out_49_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_49_pad_0 = const()[name = tensor("pretrained_out_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_49_dilations_0 = const()[name = tensor("pretrained_out_49_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_49_groups_0 = const()[name = tensor("pretrained_out_49_groups_0"), val = tensor(1)]; + tensor layers_4_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57073024))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57892288))), name = tensor("layers_4_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_4_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_4_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57892416)))]; + tensor pretrained_out_49_cast_fp16 = conv(bias = layers_4_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_49_dilations_0, groups = pretrained_out_49_groups_0, pad = pretrained_out_49_pad_0, pad_type = pretrained_out_49_pad_type_0, strides = pretrained_out_49_strides_0, weight = layers_4_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_17_cast_fp16)[name = tensor("pretrained_out_49_cast_fp16")]; + tensor input_81_pad_type_0 = const()[name = tensor("input_81_pad_type_0"), val = tensor("valid")]; + tensor input_81_strides_0 = const()[name = tensor("input_81_strides_0"), val = tensor([1, 1])]; + tensor input_81_pad_0 = const()[name = tensor("input_81_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_81_dilations_0 = const()[name = tensor("input_81_dilations_0"), val = tensor([1, 1])]; + tensor input_81_groups_0 = const()[name = tensor("input_81_groups_0"), val = tensor(1)]; + tensor layers_4_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_4_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57895040)))]; + tensor input_81_cast_fp16 = conv(dilations = input_81_dilations_0, groups = input_81_groups_0, pad = input_81_pad_0, pad_type = input_81_pad_type_0, strides = input_81_strides_0, weight = layers_4_self_attn_q_proj_loraA_weight_to_fp16, x = obj_17_cast_fp16)[name = tensor("input_81_cast_fp16")]; + tensor lora_out_49_pad_type_0 = const()[name = tensor("lora_out_49_pad_type_0"), val = tensor("valid")]; + tensor lora_out_49_strides_0 = const()[name = tensor("lora_out_49_strides_0"), val = tensor([1, 1])]; + tensor lora_out_49_pad_0 = const()[name = tensor("lora_out_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_49_dilations_0 = const()[name = tensor("lora_out_49_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_49_groups_0 = const()[name = tensor("lora_out_49_groups_0"), val = tensor(1)]; + tensor layers_4_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_4_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57936064)))]; + tensor lora_out_49_cast_fp16 = conv(dilations = lora_out_49_dilations_0, groups = lora_out_49_groups_0, pad = lora_out_49_pad_0, pad_type = lora_out_49_pad_type_0, strides = lora_out_49_strides_0, weight = layers_4_self_attn_q_proj_loraB_weight_to_fp16, x = input_81_cast_fp16)[name = tensor("lora_out_49_cast_fp16")]; + tensor query_9_cast_fp16 = add(x = pretrained_out_49_cast_fp16, y = lora_out_49_cast_fp16)[name = tensor("query_9_cast_fp16")]; + tensor pretrained_out_51_pad_type_0 = const()[name = tensor("pretrained_out_51_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_51_strides_0 = const()[name = tensor("pretrained_out_51_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_51_pad_0 = const()[name = tensor("pretrained_out_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_51_dilations_0 = const()[name = tensor("pretrained_out_51_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_51_groups_0 = const()[name = tensor("pretrained_out_51_groups_0"), val = tensor(1)]; + tensor layers_4_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57977088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58796352))), name = tensor("layers_4_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_51_cast_fp16 = conv(dilations = pretrained_out_51_dilations_0, groups = pretrained_out_51_groups_0, pad = pretrained_out_51_pad_0, pad_type = pretrained_out_51_pad_type_0, strides = pretrained_out_51_strides_0, weight = layers_4_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_17_cast_fp16)[name = tensor("pretrained_out_51_cast_fp16")]; + tensor input_83_pad_type_0 = const()[name = tensor("input_83_pad_type_0"), val = tensor("valid")]; + tensor input_83_strides_0 = const()[name = tensor("input_83_strides_0"), val = tensor([1, 1])]; + tensor input_83_pad_0 = const()[name = tensor("input_83_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_83_dilations_0 = const()[name = tensor("input_83_dilations_0"), val = tensor([1, 1])]; + tensor input_83_groups_0 = const()[name = tensor("input_83_groups_0"), val = tensor(1)]; + tensor layers_4_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_4_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58796480)))]; + tensor input_83_cast_fp16 = conv(dilations = input_83_dilations_0, groups = input_83_groups_0, pad = input_83_pad_0, pad_type = input_83_pad_type_0, strides = input_83_strides_0, weight = layers_4_self_attn_k_proj_loraA_weight_to_fp16, x = obj_17_cast_fp16)[name = tensor("input_83_cast_fp16")]; + tensor lora_out_51_pad_type_0 = const()[name = tensor("lora_out_51_pad_type_0"), val = tensor("valid")]; + tensor lora_out_51_strides_0 = const()[name = tensor("lora_out_51_strides_0"), val = tensor([1, 1])]; + tensor lora_out_51_pad_0 = const()[name = tensor("lora_out_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_51_dilations_0 = const()[name = tensor("lora_out_51_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_51_groups_0 = const()[name = tensor("lora_out_51_groups_0"), val = tensor(1)]; + tensor layers_4_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_4_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58837504)))]; + tensor lora_out_51_cast_fp16 = conv(dilations = lora_out_51_dilations_0, groups = lora_out_51_groups_0, pad = lora_out_51_pad_0, pad_type = lora_out_51_pad_type_0, strides = lora_out_51_strides_0, weight = layers_4_self_attn_k_proj_loraB_weight_to_fp16, x = input_83_cast_fp16)[name = tensor("lora_out_51_cast_fp16")]; + tensor key_9_cast_fp16 = add(x = pretrained_out_51_cast_fp16, y = lora_out_51_cast_fp16)[name = tensor("key_9_cast_fp16")]; + tensor pretrained_out_53_pad_type_0 = const()[name = tensor("pretrained_out_53_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_53_strides_0 = const()[name = tensor("pretrained_out_53_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_53_pad_0 = const()[name = tensor("pretrained_out_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_53_dilations_0 = const()[name = tensor("pretrained_out_53_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_53_groups_0 = const()[name = tensor("pretrained_out_53_groups_0"), val = tensor(1)]; + tensor layers_4_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58878528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59697792))), name = tensor("layers_4_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_4_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_4_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59697920)))]; + tensor pretrained_out_53_cast_fp16 = conv(bias = layers_4_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_53_dilations_0, groups = pretrained_out_53_groups_0, pad = pretrained_out_53_pad_0, pad_type = pretrained_out_53_pad_type_0, strides = pretrained_out_53_strides_0, weight = layers_4_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_17_cast_fp16)[name = tensor("pretrained_out_53_cast_fp16")]; + tensor input_85_pad_type_0 = const()[name = tensor("input_85_pad_type_0"), val = tensor("valid")]; + tensor input_85_strides_0 = const()[name = tensor("input_85_strides_0"), val = tensor([1, 1])]; + tensor input_85_pad_0 = const()[name = tensor("input_85_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_85_dilations_0 = const()[name = tensor("input_85_dilations_0"), val = tensor([1, 1])]; + tensor input_85_groups_0 = const()[name = tensor("input_85_groups_0"), val = tensor(1)]; + tensor layers_4_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_4_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59700544)))]; + tensor input_85_cast_fp16 = conv(dilations = input_85_dilations_0, groups = input_85_groups_0, pad = input_85_pad_0, pad_type = input_85_pad_type_0, strides = input_85_strides_0, weight = layers_4_self_attn_v_proj_loraA_weight_to_fp16, x = obj_17_cast_fp16)[name = tensor("input_85_cast_fp16")]; + tensor lora_out_53_pad_type_0 = const()[name = tensor("lora_out_53_pad_type_0"), val = tensor("valid")]; + tensor lora_out_53_strides_0 = const()[name = tensor("lora_out_53_strides_0"), val = tensor([1, 1])]; + tensor lora_out_53_pad_0 = const()[name = tensor("lora_out_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_53_dilations_0 = const()[name = tensor("lora_out_53_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_53_groups_0 = const()[name = tensor("lora_out_53_groups_0"), val = tensor(1)]; + tensor layers_4_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_4_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59741568)))]; + tensor lora_out_53_cast_fp16 = conv(dilations = lora_out_53_dilations_0, groups = lora_out_53_groups_0, pad = lora_out_53_pad_0, pad_type = lora_out_53_pad_type_0, strides = lora_out_53_strides_0, weight = layers_4_self_attn_v_proj_loraB_weight_to_fp16, x = input_85_cast_fp16)[name = tensor("lora_out_53_cast_fp16")]; + tensor value_9_cast_fp16 = add(x = pretrained_out_53_cast_fp16, y = lora_out_53_cast_fp16)[name = tensor("value_9_cast_fp16")]; + tensor var_1136 = const()[name = tensor("op_1136"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_9_cast_fp16 = reshape(shape = var_1136, x = query_9_cast_fp16)[name = tensor("mh_q_9_cast_fp16")]; + tensor var_1138_to_fp16 = const()[name = tensor("op_1138_to_fp16"), val = tensor(0x1p-3)]; + tensor var_1139_cast_fp16 = mul(x = mh_q_9_cast_fp16, y = var_1138_to_fp16)[name = tensor("op_1139_cast_fp16")]; + tensor var_1140 = const()[name = tensor("op_1140"), val = tensor([1, 20, 64, -1])]; + tensor var_1141_cast_fp16 = reshape(shape = var_1140, x = key_9_cast_fp16)[name = tensor("op_1141_cast_fp16")]; + tensor mh_w_9_transpose_x_0 = const()[name = tensor("mh_w_9_transpose_x_0"), val = tensor(true)]; + tensor mh_w_9_transpose_y_0 = const()[name = tensor("mh_w_9_transpose_y_0"), val = tensor(false)]; + tensor mh_w_9_cast_fp16 = matmul(transpose_x = mh_w_9_transpose_x_0, transpose_y = mh_w_9_transpose_y_0, x = var_1139_cast_fp16, y = var_1141_cast_fp16)[name = tensor("mh_w_9_cast_fp16")]; + tensor var_1144_cast_fp16 = softmax(axis = var_1034, x = mh_w_9_cast_fp16)[name = tensor("op_1144_cast_fp16")]; + tensor var_1145 = const()[name = tensor("op_1145"), val = tensor([1, 20, 64, -1])]; + tensor var_1146_cast_fp16 = reshape(shape = var_1145, x = value_9_cast_fp16)[name = tensor("op_1146_cast_fp16")]; + tensor attn_9_transpose_x_0 = const()[name = tensor("attn_9_transpose_x_0"), val = tensor(false)]; + tensor attn_9_transpose_y_0 = const()[name = tensor("attn_9_transpose_y_0"), val = tensor(true)]; + tensor attn_9_cast_fp16 = matmul(transpose_x = attn_9_transpose_x_0, transpose_y = attn_9_transpose_y_0, x = var_1146_cast_fp16, y = var_1144_cast_fp16)[name = tensor("attn_9_cast_fp16")]; + tensor var_1149 = const()[name = tensor("op_1149"), val = tensor([1, 1280, 1, -1])]; + tensor input_87_cast_fp16 = reshape(shape = var_1149, x = attn_9_cast_fp16)[name = tensor("input_87_cast_fp16")]; + tensor pretrained_out_55_pad_type_0 = const()[name = tensor("pretrained_out_55_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_55_strides_0 = const()[name = tensor("pretrained_out_55_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_55_pad_0 = const()[name = tensor("pretrained_out_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_55_dilations_0 = const()[name = tensor("pretrained_out_55_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_55_groups_0 = const()[name = tensor("pretrained_out_55_groups_0"), val = tensor(1)]; + tensor layers_4_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59782592))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60601856))), name = tensor("layers_4_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_4_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_4_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60601984)))]; + tensor pretrained_out_55_cast_fp16 = conv(bias = layers_4_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_55_dilations_0, groups = pretrained_out_55_groups_0, pad = pretrained_out_55_pad_0, pad_type = pretrained_out_55_pad_type_0, strides = pretrained_out_55_strides_0, weight = layers_4_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_87_cast_fp16)[name = tensor("pretrained_out_55_cast_fp16")]; + tensor input_89_pad_type_0 = const()[name = tensor("input_89_pad_type_0"), val = tensor("valid")]; + tensor input_89_strides_0 = const()[name = tensor("input_89_strides_0"), val = tensor([1, 1])]; + tensor input_89_pad_0 = const()[name = tensor("input_89_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_89_dilations_0 = const()[name = tensor("input_89_dilations_0"), val = tensor([1, 1])]; + tensor input_89_groups_0 = const()[name = tensor("input_89_groups_0"), val = tensor(1)]; + tensor layers_4_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_4_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60604608)))]; + tensor input_89_cast_fp16 = conv(dilations = input_89_dilations_0, groups = input_89_groups_0, pad = input_89_pad_0, pad_type = input_89_pad_type_0, strides = input_89_strides_0, weight = layers_4_self_attn_o_proj_loraA_weight_to_fp16, x = input_87_cast_fp16)[name = tensor("input_89_cast_fp16")]; + tensor lora_out_55_pad_type_0 = const()[name = tensor("lora_out_55_pad_type_0"), val = tensor("valid")]; + tensor lora_out_55_strides_0 = const()[name = tensor("lora_out_55_strides_0"), val = tensor([1, 1])]; + tensor lora_out_55_pad_0 = const()[name = tensor("lora_out_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_55_dilations_0 = const()[name = tensor("lora_out_55_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_55_groups_0 = const()[name = tensor("lora_out_55_groups_0"), val = tensor(1)]; + tensor layers_4_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_4_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60645632)))]; + tensor lora_out_55_cast_fp16 = conv(dilations = lora_out_55_dilations_0, groups = lora_out_55_groups_0, pad = lora_out_55_pad_0, pad_type = lora_out_55_pad_type_0, strides = lora_out_55_strides_0, weight = layers_4_self_attn_o_proj_loraB_weight_to_fp16, x = input_89_cast_fp16)[name = tensor("lora_out_55_cast_fp16")]; + tensor obj_19_cast_fp16 = add(x = pretrained_out_55_cast_fp16, y = lora_out_55_cast_fp16)[name = tensor("obj_19_cast_fp16")]; + tensor inputs_19_cast_fp16 = add(x = inputs_17_cast_fp16, y = obj_19_cast_fp16)[name = tensor("inputs_19_cast_fp16")]; + tensor out_19_axes_0 = const()[name = tensor("out_19_axes_0"), val = tensor([1])]; + tensor var_1183_to_fp16 = const()[name = tensor("op_1183_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_19_cast_fp16 = layer_norm(axes = out_19_axes_0, epsilon = var_1183_to_fp16, x = inputs_19_cast_fp16)[name = tensor("out_19_cast_fp16")]; + tensor input_91_gamma_0_to_fp16 = const()[name = tensor("input_91_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60686656)))]; + tensor input_91_beta_0_to_fp16 = const()[name = tensor("input_91_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60689280)))]; + tensor input_91_epsilon_0_to_fp16 = const()[name = tensor("input_91_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_91_cast_fp16 = batch_norm(beta = input_91_beta_0_to_fp16, epsilon = input_91_epsilon_0_to_fp16, gamma = input_91_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_19_cast_fp16)[name = tensor("input_91_cast_fp16")]; + tensor pretrained_out_57_pad_type_0 = const()[name = tensor("pretrained_out_57_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_57_strides_0 = const()[name = tensor("pretrained_out_57_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_57_pad_0 = const()[name = tensor("pretrained_out_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_57_dilations_0 = const()[name = tensor("pretrained_out_57_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_57_groups_0 = const()[name = tensor("pretrained_out_57_groups_0"), val = tensor(1)]; + tensor layers_4_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60691904))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63968768))), name = tensor("layers_4_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_4_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_4_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63968896)))]; + tensor pretrained_out_57_cast_fp16 = conv(bias = layers_4_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_57_dilations_0, groups = pretrained_out_57_groups_0, pad = pretrained_out_57_pad_0, pad_type = pretrained_out_57_pad_type_0, strides = pretrained_out_57_strides_0, weight = layers_4_fc1_pretrained_weight_to_fp16_palettized, x = input_91_cast_fp16)[name = tensor("pretrained_out_57_cast_fp16")]; + tensor input_93_pad_type_0 = const()[name = tensor("input_93_pad_type_0"), val = tensor("valid")]; + tensor input_93_strides_0 = const()[name = tensor("input_93_strides_0"), val = tensor([1, 1])]; + tensor input_93_pad_0 = const()[name = tensor("input_93_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_93_dilations_0 = const()[name = tensor("input_93_dilations_0"), val = tensor([1, 1])]; + tensor input_93_groups_0 = const()[name = tensor("input_93_groups_0"), val = tensor(1)]; + tensor layers_4_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_4_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63979200)))]; + tensor input_93_cast_fp16 = conv(dilations = input_93_dilations_0, groups = input_93_groups_0, pad = input_93_pad_0, pad_type = input_93_pad_type_0, strides = input_93_strides_0, weight = layers_4_fc1_loraA_weight_to_fp16, x = input_91_cast_fp16)[name = tensor("input_93_cast_fp16")]; + tensor lora_out_57_pad_type_0 = const()[name = tensor("lora_out_57_pad_type_0"), val = tensor("valid")]; + tensor lora_out_57_strides_0 = const()[name = tensor("lora_out_57_strides_0"), val = tensor([1, 1])]; + tensor lora_out_57_pad_0 = const()[name = tensor("lora_out_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_57_dilations_0 = const()[name = tensor("lora_out_57_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_57_groups_0 = const()[name = tensor("lora_out_57_groups_0"), val = tensor(1)]; + tensor layers_4_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_4_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64020224)))]; + tensor lora_out_57_cast_fp16 = conv(dilations = lora_out_57_dilations_0, groups = lora_out_57_groups_0, pad = lora_out_57_pad_0, pad_type = lora_out_57_pad_type_0, strides = lora_out_57_strides_0, weight = layers_4_fc1_loraB_weight_to_fp16, x = input_93_cast_fp16)[name = tensor("lora_out_57_cast_fp16")]; + tensor input_95_cast_fp16 = add(x = pretrained_out_57_cast_fp16, y = lora_out_57_cast_fp16)[name = tensor("input_95_cast_fp16")]; + tensor input_97_mode_0 = const()[name = tensor("input_97_mode_0"), val = tensor("EXACT")]; + tensor input_97_cast_fp16 = gelu(mode = input_97_mode_0, x = input_95_cast_fp16)[name = tensor("input_97_cast_fp16")]; + tensor pretrained_out_59_pad_type_0 = const()[name = tensor("pretrained_out_59_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_59_strides_0 = const()[name = tensor("pretrained_out_59_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_59_pad_0 = const()[name = tensor("pretrained_out_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_59_dilations_0 = const()[name = tensor("pretrained_out_59_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_59_groups_0 = const()[name = tensor("pretrained_out_59_groups_0"), val = tensor(1)]; + tensor layers_4_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64184128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67460992))), name = tensor("layers_4_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_4_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_4_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67461120)))]; + tensor pretrained_out_59_cast_fp16 = conv(bias = layers_4_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_59_dilations_0, groups = pretrained_out_59_groups_0, pad = pretrained_out_59_pad_0, pad_type = pretrained_out_59_pad_type_0, strides = pretrained_out_59_strides_0, weight = layers_4_fc2_pretrained_weight_to_fp16_palettized, x = input_97_cast_fp16)[name = tensor("pretrained_out_59_cast_fp16")]; + tensor input_99_pad_type_0 = const()[name = tensor("input_99_pad_type_0"), val = tensor("valid")]; + tensor input_99_strides_0 = const()[name = tensor("input_99_strides_0"), val = tensor([1, 1])]; + tensor input_99_pad_0 = const()[name = tensor("input_99_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_99_dilations_0 = const()[name = tensor("input_99_dilations_0"), val = tensor([1, 1])]; + tensor input_99_groups_0 = const()[name = tensor("input_99_groups_0"), val = tensor(1)]; + tensor layers_4_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_4_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67463744)))]; + tensor input_99_cast_fp16 = conv(dilations = input_99_dilations_0, groups = input_99_groups_0, pad = input_99_pad_0, pad_type = input_99_pad_type_0, strides = input_99_strides_0, weight = layers_4_fc2_loraA_weight_to_fp16, x = input_97_cast_fp16)[name = tensor("input_99_cast_fp16")]; + tensor lora_out_59_pad_type_0 = const()[name = tensor("lora_out_59_pad_type_0"), val = tensor("valid")]; + tensor lora_out_59_strides_0 = const()[name = tensor("lora_out_59_strides_0"), val = tensor([1, 1])]; + tensor lora_out_59_pad_0 = const()[name = tensor("lora_out_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_59_dilations_0 = const()[name = tensor("lora_out_59_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_59_groups_0 = const()[name = tensor("lora_out_59_groups_0"), val = tensor(1)]; + tensor layers_4_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_4_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67627648)))]; + tensor lora_out_59_cast_fp16 = conv(dilations = lora_out_59_dilations_0, groups = lora_out_59_groups_0, pad = lora_out_59_pad_0, pad_type = lora_out_59_pad_type_0, strides = lora_out_59_strides_0, weight = layers_4_fc2_loraB_weight_to_fp16, x = input_99_cast_fp16)[name = tensor("lora_out_59_cast_fp16")]; + tensor hidden_states_13_cast_fp16 = add(x = pretrained_out_59_cast_fp16, y = lora_out_59_cast_fp16)[name = tensor("hidden_states_13_cast_fp16")]; + tensor inputs_21_cast_fp16 = add(x = inputs_19_cast_fp16, y = hidden_states_13_cast_fp16)[name = tensor("inputs_21_cast_fp16")]; + tensor var_1248 = const()[name = tensor("op_1248"), val = tensor(3)]; + tensor out_21_axes_0 = const()[name = tensor("out_21_axes_0"), val = tensor([1])]; + tensor var_1267_to_fp16 = const()[name = tensor("op_1267_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_21_cast_fp16 = layer_norm(axes = out_21_axes_0, epsilon = var_1267_to_fp16, x = inputs_21_cast_fp16)[name = tensor("out_21_cast_fp16")]; + tensor obj_21_gamma_0_to_fp16 = const()[name = tensor("obj_21_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67668672)))]; + tensor obj_21_beta_0_to_fp16 = const()[name = tensor("obj_21_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67671296)))]; + tensor obj_21_epsilon_0_to_fp16 = const()[name = tensor("obj_21_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_21_cast_fp16 = batch_norm(beta = obj_21_beta_0_to_fp16, epsilon = obj_21_epsilon_0_to_fp16, gamma = obj_21_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_21_cast_fp16)[name = tensor("obj_21_cast_fp16")]; + tensor pretrained_out_61_pad_type_0 = const()[name = tensor("pretrained_out_61_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_61_strides_0 = const()[name = tensor("pretrained_out_61_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_61_pad_0 = const()[name = tensor("pretrained_out_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_61_dilations_0 = const()[name = tensor("pretrained_out_61_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_61_groups_0 = const()[name = tensor("pretrained_out_61_groups_0"), val = tensor(1)]; + tensor layers_5_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67673920))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68493184))), name = tensor("layers_5_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_5_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_5_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68493312)))]; + tensor pretrained_out_61_cast_fp16 = conv(bias = layers_5_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_61_dilations_0, groups = pretrained_out_61_groups_0, pad = pretrained_out_61_pad_0, pad_type = pretrained_out_61_pad_type_0, strides = pretrained_out_61_strides_0, weight = layers_5_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_21_cast_fp16)[name = tensor("pretrained_out_61_cast_fp16")]; + tensor input_101_pad_type_0 = const()[name = tensor("input_101_pad_type_0"), val = tensor("valid")]; + tensor input_101_strides_0 = const()[name = tensor("input_101_strides_0"), val = tensor([1, 1])]; + tensor input_101_pad_0 = const()[name = tensor("input_101_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_101_dilations_0 = const()[name = tensor("input_101_dilations_0"), val = tensor([1, 1])]; + tensor input_101_groups_0 = const()[name = tensor("input_101_groups_0"), val = tensor(1)]; + tensor layers_5_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_5_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68495936)))]; + tensor input_101_cast_fp16 = conv(dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = layers_5_self_attn_q_proj_loraA_weight_to_fp16, x = obj_21_cast_fp16)[name = tensor("input_101_cast_fp16")]; + tensor lora_out_61_pad_type_0 = const()[name = tensor("lora_out_61_pad_type_0"), val = tensor("valid")]; + tensor lora_out_61_strides_0 = const()[name = tensor("lora_out_61_strides_0"), val = tensor([1, 1])]; + tensor lora_out_61_pad_0 = const()[name = tensor("lora_out_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_61_dilations_0 = const()[name = tensor("lora_out_61_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_61_groups_0 = const()[name = tensor("lora_out_61_groups_0"), val = tensor(1)]; + tensor layers_5_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_5_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68536960)))]; + tensor lora_out_61_cast_fp16 = conv(dilations = lora_out_61_dilations_0, groups = lora_out_61_groups_0, pad = lora_out_61_pad_0, pad_type = lora_out_61_pad_type_0, strides = lora_out_61_strides_0, weight = layers_5_self_attn_q_proj_loraB_weight_to_fp16, x = input_101_cast_fp16)[name = tensor("lora_out_61_cast_fp16")]; + tensor query_11_cast_fp16 = add(x = pretrained_out_61_cast_fp16, y = lora_out_61_cast_fp16)[name = tensor("query_11_cast_fp16")]; + tensor pretrained_out_63_pad_type_0 = const()[name = tensor("pretrained_out_63_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_63_strides_0 = const()[name = tensor("pretrained_out_63_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_63_pad_0 = const()[name = tensor("pretrained_out_63_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_63_dilations_0 = const()[name = tensor("pretrained_out_63_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_63_groups_0 = const()[name = tensor("pretrained_out_63_groups_0"), val = tensor(1)]; + tensor layers_5_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68577984))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69397248))), name = tensor("layers_5_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_63_cast_fp16 = conv(dilations = pretrained_out_63_dilations_0, groups = pretrained_out_63_groups_0, pad = pretrained_out_63_pad_0, pad_type = pretrained_out_63_pad_type_0, strides = pretrained_out_63_strides_0, weight = layers_5_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_21_cast_fp16)[name = tensor("pretrained_out_63_cast_fp16")]; + tensor input_103_pad_type_0 = const()[name = tensor("input_103_pad_type_0"), val = tensor("valid")]; + tensor input_103_strides_0 = const()[name = tensor("input_103_strides_0"), val = tensor([1, 1])]; + tensor input_103_pad_0 = const()[name = tensor("input_103_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_103_dilations_0 = const()[name = tensor("input_103_dilations_0"), val = tensor([1, 1])]; + tensor input_103_groups_0 = const()[name = tensor("input_103_groups_0"), val = tensor(1)]; + tensor layers_5_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_5_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69397376)))]; + tensor input_103_cast_fp16 = conv(dilations = input_103_dilations_0, groups = input_103_groups_0, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = input_103_strides_0, weight = layers_5_self_attn_k_proj_loraA_weight_to_fp16, x = obj_21_cast_fp16)[name = tensor("input_103_cast_fp16")]; + tensor lora_out_63_pad_type_0 = const()[name = tensor("lora_out_63_pad_type_0"), val = tensor("valid")]; + tensor lora_out_63_strides_0 = const()[name = tensor("lora_out_63_strides_0"), val = tensor([1, 1])]; + tensor lora_out_63_pad_0 = const()[name = tensor("lora_out_63_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_63_dilations_0 = const()[name = tensor("lora_out_63_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_63_groups_0 = const()[name = tensor("lora_out_63_groups_0"), val = tensor(1)]; + tensor layers_5_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_5_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69438400)))]; + tensor lora_out_63_cast_fp16 = conv(dilations = lora_out_63_dilations_0, groups = lora_out_63_groups_0, pad = lora_out_63_pad_0, pad_type = lora_out_63_pad_type_0, strides = lora_out_63_strides_0, weight = layers_5_self_attn_k_proj_loraB_weight_to_fp16, x = input_103_cast_fp16)[name = tensor("lora_out_63_cast_fp16")]; + tensor key_11_cast_fp16 = add(x = pretrained_out_63_cast_fp16, y = lora_out_63_cast_fp16)[name = tensor("key_11_cast_fp16")]; + tensor pretrained_out_65_pad_type_0 = const()[name = tensor("pretrained_out_65_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_65_strides_0 = const()[name = tensor("pretrained_out_65_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_65_pad_0 = const()[name = tensor("pretrained_out_65_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_65_dilations_0 = const()[name = tensor("pretrained_out_65_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_65_groups_0 = const()[name = tensor("pretrained_out_65_groups_0"), val = tensor(1)]; + tensor layers_5_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69479424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70298688))), name = tensor("layers_5_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_5_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_5_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70298816)))]; + tensor pretrained_out_65_cast_fp16 = conv(bias = layers_5_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_65_dilations_0, groups = pretrained_out_65_groups_0, pad = pretrained_out_65_pad_0, pad_type = pretrained_out_65_pad_type_0, strides = pretrained_out_65_strides_0, weight = layers_5_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_21_cast_fp16)[name = tensor("pretrained_out_65_cast_fp16")]; + tensor input_105_pad_type_0 = const()[name = tensor("input_105_pad_type_0"), val = tensor("valid")]; + tensor input_105_strides_0 = const()[name = tensor("input_105_strides_0"), val = tensor([1, 1])]; + tensor input_105_pad_0 = const()[name = tensor("input_105_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_105_dilations_0 = const()[name = tensor("input_105_dilations_0"), val = tensor([1, 1])]; + tensor input_105_groups_0 = const()[name = tensor("input_105_groups_0"), val = tensor(1)]; + tensor layers_5_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_5_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70301440)))]; + tensor input_105_cast_fp16 = conv(dilations = input_105_dilations_0, groups = input_105_groups_0, pad = input_105_pad_0, pad_type = input_105_pad_type_0, strides = input_105_strides_0, weight = layers_5_self_attn_v_proj_loraA_weight_to_fp16, x = obj_21_cast_fp16)[name = tensor("input_105_cast_fp16")]; + tensor lora_out_65_pad_type_0 = const()[name = tensor("lora_out_65_pad_type_0"), val = tensor("valid")]; + tensor lora_out_65_strides_0 = const()[name = tensor("lora_out_65_strides_0"), val = tensor([1, 1])]; + tensor lora_out_65_pad_0 = const()[name = tensor("lora_out_65_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_65_dilations_0 = const()[name = tensor("lora_out_65_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_65_groups_0 = const()[name = tensor("lora_out_65_groups_0"), val = tensor(1)]; + tensor layers_5_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_5_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70342464)))]; + tensor lora_out_65_cast_fp16 = conv(dilations = lora_out_65_dilations_0, groups = lora_out_65_groups_0, pad = lora_out_65_pad_0, pad_type = lora_out_65_pad_type_0, strides = lora_out_65_strides_0, weight = layers_5_self_attn_v_proj_loraB_weight_to_fp16, x = input_105_cast_fp16)[name = tensor("lora_out_65_cast_fp16")]; + tensor value_11_cast_fp16 = add(x = pretrained_out_65_cast_fp16, y = lora_out_65_cast_fp16)[name = tensor("value_11_cast_fp16")]; + tensor var_1350 = const()[name = tensor("op_1350"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_11_cast_fp16 = reshape(shape = var_1350, x = query_11_cast_fp16)[name = tensor("mh_q_11_cast_fp16")]; + tensor var_1352_to_fp16 = const()[name = tensor("op_1352_to_fp16"), val = tensor(0x1p-3)]; + tensor var_1353_cast_fp16 = mul(x = mh_q_11_cast_fp16, y = var_1352_to_fp16)[name = tensor("op_1353_cast_fp16")]; + tensor var_1354 = const()[name = tensor("op_1354"), val = tensor([1, 20, 64, -1])]; + tensor var_1355_cast_fp16 = reshape(shape = var_1354, x = key_11_cast_fp16)[name = tensor("op_1355_cast_fp16")]; + tensor mh_w_11_transpose_x_0 = const()[name = tensor("mh_w_11_transpose_x_0"), val = tensor(true)]; + tensor mh_w_11_transpose_y_0 = const()[name = tensor("mh_w_11_transpose_y_0"), val = tensor(false)]; + tensor mh_w_11_cast_fp16 = matmul(transpose_x = mh_w_11_transpose_x_0, transpose_y = mh_w_11_transpose_y_0, x = var_1353_cast_fp16, y = var_1355_cast_fp16)[name = tensor("mh_w_11_cast_fp16")]; + tensor var_1358_cast_fp16 = softmax(axis = var_1248, x = mh_w_11_cast_fp16)[name = tensor("op_1358_cast_fp16")]; + tensor var_1359 = const()[name = tensor("op_1359"), val = tensor([1, 20, 64, -1])]; + tensor var_1360_cast_fp16 = reshape(shape = var_1359, x = value_11_cast_fp16)[name = tensor("op_1360_cast_fp16")]; + tensor attn_11_transpose_x_0 = const()[name = tensor("attn_11_transpose_x_0"), val = tensor(false)]; + tensor attn_11_transpose_y_0 = const()[name = tensor("attn_11_transpose_y_0"), val = tensor(true)]; + tensor attn_11_cast_fp16 = matmul(transpose_x = attn_11_transpose_x_0, transpose_y = attn_11_transpose_y_0, x = var_1360_cast_fp16, y = var_1358_cast_fp16)[name = tensor("attn_11_cast_fp16")]; + tensor var_1363 = const()[name = tensor("op_1363"), val = tensor([1, 1280, 1, -1])]; + tensor input_107_cast_fp16 = reshape(shape = var_1363, x = attn_11_cast_fp16)[name = tensor("input_107_cast_fp16")]; + tensor pretrained_out_67_pad_type_0 = const()[name = tensor("pretrained_out_67_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_67_strides_0 = const()[name = tensor("pretrained_out_67_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_67_pad_0 = const()[name = tensor("pretrained_out_67_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_67_dilations_0 = const()[name = tensor("pretrained_out_67_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_67_groups_0 = const()[name = tensor("pretrained_out_67_groups_0"), val = tensor(1)]; + tensor layers_5_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70383488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71202752))), name = tensor("layers_5_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_5_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_5_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71202880)))]; + tensor pretrained_out_67_cast_fp16 = conv(bias = layers_5_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_67_dilations_0, groups = pretrained_out_67_groups_0, pad = pretrained_out_67_pad_0, pad_type = pretrained_out_67_pad_type_0, strides = pretrained_out_67_strides_0, weight = layers_5_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_107_cast_fp16)[name = tensor("pretrained_out_67_cast_fp16")]; + tensor input_109_pad_type_0 = const()[name = tensor("input_109_pad_type_0"), val = tensor("valid")]; + tensor input_109_strides_0 = const()[name = tensor("input_109_strides_0"), val = tensor([1, 1])]; + tensor input_109_pad_0 = const()[name = tensor("input_109_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_109_dilations_0 = const()[name = tensor("input_109_dilations_0"), val = tensor([1, 1])]; + tensor input_109_groups_0 = const()[name = tensor("input_109_groups_0"), val = tensor(1)]; + tensor layers_5_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_5_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71205504)))]; + tensor input_109_cast_fp16 = conv(dilations = input_109_dilations_0, groups = input_109_groups_0, pad = input_109_pad_0, pad_type = input_109_pad_type_0, strides = input_109_strides_0, weight = layers_5_self_attn_o_proj_loraA_weight_to_fp16, x = input_107_cast_fp16)[name = tensor("input_109_cast_fp16")]; + tensor lora_out_67_pad_type_0 = const()[name = tensor("lora_out_67_pad_type_0"), val = tensor("valid")]; + tensor lora_out_67_strides_0 = const()[name = tensor("lora_out_67_strides_0"), val = tensor([1, 1])]; + tensor lora_out_67_pad_0 = const()[name = tensor("lora_out_67_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_67_dilations_0 = const()[name = tensor("lora_out_67_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_67_groups_0 = const()[name = tensor("lora_out_67_groups_0"), val = tensor(1)]; + tensor layers_5_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_5_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71246528)))]; + tensor lora_out_67_cast_fp16 = conv(dilations = lora_out_67_dilations_0, groups = lora_out_67_groups_0, pad = lora_out_67_pad_0, pad_type = lora_out_67_pad_type_0, strides = lora_out_67_strides_0, weight = layers_5_self_attn_o_proj_loraB_weight_to_fp16, x = input_109_cast_fp16)[name = tensor("lora_out_67_cast_fp16")]; + tensor obj_23_cast_fp16 = add(x = pretrained_out_67_cast_fp16, y = lora_out_67_cast_fp16)[name = tensor("obj_23_cast_fp16")]; + tensor inputs_23_cast_fp16 = add(x = inputs_21_cast_fp16, y = obj_23_cast_fp16)[name = tensor("inputs_23_cast_fp16")]; + tensor out_23_axes_0 = const()[name = tensor("out_23_axes_0"), val = tensor([1])]; + tensor var_1397_to_fp16 = const()[name = tensor("op_1397_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_23_cast_fp16 = layer_norm(axes = out_23_axes_0, epsilon = var_1397_to_fp16, x = inputs_23_cast_fp16)[name = tensor("out_23_cast_fp16")]; + tensor input_111_gamma_0_to_fp16 = const()[name = tensor("input_111_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71287552)))]; + tensor input_111_beta_0_to_fp16 = const()[name = tensor("input_111_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71290176)))]; + tensor input_111_epsilon_0_to_fp16 = const()[name = tensor("input_111_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_111_cast_fp16 = batch_norm(beta = input_111_beta_0_to_fp16, epsilon = input_111_epsilon_0_to_fp16, gamma = input_111_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_23_cast_fp16)[name = tensor("input_111_cast_fp16")]; + tensor pretrained_out_69_pad_type_0 = const()[name = tensor("pretrained_out_69_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_69_strides_0 = const()[name = tensor("pretrained_out_69_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_69_pad_0 = const()[name = tensor("pretrained_out_69_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_69_dilations_0 = const()[name = tensor("pretrained_out_69_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_69_groups_0 = const()[name = tensor("pretrained_out_69_groups_0"), val = tensor(1)]; + tensor layers_5_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71292800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74569664))), name = tensor("layers_5_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_5_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_5_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74569792)))]; + tensor pretrained_out_69_cast_fp16 = conv(bias = layers_5_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_69_dilations_0, groups = pretrained_out_69_groups_0, pad = pretrained_out_69_pad_0, pad_type = pretrained_out_69_pad_type_0, strides = pretrained_out_69_strides_0, weight = layers_5_fc1_pretrained_weight_to_fp16_palettized, x = input_111_cast_fp16)[name = tensor("pretrained_out_69_cast_fp16")]; + tensor input_113_pad_type_0 = const()[name = tensor("input_113_pad_type_0"), val = tensor("valid")]; + tensor input_113_strides_0 = const()[name = tensor("input_113_strides_0"), val = tensor([1, 1])]; + tensor input_113_pad_0 = const()[name = tensor("input_113_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_113_dilations_0 = const()[name = tensor("input_113_dilations_0"), val = tensor([1, 1])]; + tensor input_113_groups_0 = const()[name = tensor("input_113_groups_0"), val = tensor(1)]; + tensor layers_5_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_5_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74580096)))]; + tensor input_113_cast_fp16 = conv(dilations = input_113_dilations_0, groups = input_113_groups_0, pad = input_113_pad_0, pad_type = input_113_pad_type_0, strides = input_113_strides_0, weight = layers_5_fc1_loraA_weight_to_fp16, x = input_111_cast_fp16)[name = tensor("input_113_cast_fp16")]; + tensor lora_out_69_pad_type_0 = const()[name = tensor("lora_out_69_pad_type_0"), val = tensor("valid")]; + tensor lora_out_69_strides_0 = const()[name = tensor("lora_out_69_strides_0"), val = tensor([1, 1])]; + tensor lora_out_69_pad_0 = const()[name = tensor("lora_out_69_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_69_dilations_0 = const()[name = tensor("lora_out_69_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_69_groups_0 = const()[name = tensor("lora_out_69_groups_0"), val = tensor(1)]; + tensor layers_5_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_5_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74621120)))]; + tensor lora_out_69_cast_fp16 = conv(dilations = lora_out_69_dilations_0, groups = lora_out_69_groups_0, pad = lora_out_69_pad_0, pad_type = lora_out_69_pad_type_0, strides = lora_out_69_strides_0, weight = layers_5_fc1_loraB_weight_to_fp16, x = input_113_cast_fp16)[name = tensor("lora_out_69_cast_fp16")]; + tensor input_115_cast_fp16 = add(x = pretrained_out_69_cast_fp16, y = lora_out_69_cast_fp16)[name = tensor("input_115_cast_fp16")]; + tensor input_117_mode_0 = const()[name = tensor("input_117_mode_0"), val = tensor("EXACT")]; + tensor input_117_cast_fp16 = gelu(mode = input_117_mode_0, x = input_115_cast_fp16)[name = tensor("input_117_cast_fp16")]; + tensor pretrained_out_71_pad_type_0 = const()[name = tensor("pretrained_out_71_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_71_strides_0 = const()[name = tensor("pretrained_out_71_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_71_pad_0 = const()[name = tensor("pretrained_out_71_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_71_dilations_0 = const()[name = tensor("pretrained_out_71_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_71_groups_0 = const()[name = tensor("pretrained_out_71_groups_0"), val = tensor(1)]; + tensor layers_5_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(74785024))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78061888))), name = tensor("layers_5_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_5_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_5_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78062016)))]; + tensor pretrained_out_71_cast_fp16 = conv(bias = layers_5_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_71_dilations_0, groups = pretrained_out_71_groups_0, pad = pretrained_out_71_pad_0, pad_type = pretrained_out_71_pad_type_0, strides = pretrained_out_71_strides_0, weight = layers_5_fc2_pretrained_weight_to_fp16_palettized, x = input_117_cast_fp16)[name = tensor("pretrained_out_71_cast_fp16")]; + tensor input_119_pad_type_0 = const()[name = tensor("input_119_pad_type_0"), val = tensor("valid")]; + tensor input_119_strides_0 = const()[name = tensor("input_119_strides_0"), val = tensor([1, 1])]; + tensor input_119_pad_0 = const()[name = tensor("input_119_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_119_dilations_0 = const()[name = tensor("input_119_dilations_0"), val = tensor([1, 1])]; + tensor input_119_groups_0 = const()[name = tensor("input_119_groups_0"), val = tensor(1)]; + tensor layers_5_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_5_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78064640)))]; + tensor input_119_cast_fp16 = conv(dilations = input_119_dilations_0, groups = input_119_groups_0, pad = input_119_pad_0, pad_type = input_119_pad_type_0, strides = input_119_strides_0, weight = layers_5_fc2_loraA_weight_to_fp16, x = input_117_cast_fp16)[name = tensor("input_119_cast_fp16")]; + tensor lora_out_71_pad_type_0 = const()[name = tensor("lora_out_71_pad_type_0"), val = tensor("valid")]; + tensor lora_out_71_strides_0 = const()[name = tensor("lora_out_71_strides_0"), val = tensor([1, 1])]; + tensor lora_out_71_pad_0 = const()[name = tensor("lora_out_71_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_71_dilations_0 = const()[name = tensor("lora_out_71_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_71_groups_0 = const()[name = tensor("lora_out_71_groups_0"), val = tensor(1)]; + tensor layers_5_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_5_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78228544)))]; + tensor lora_out_71_cast_fp16 = conv(dilations = lora_out_71_dilations_0, groups = lora_out_71_groups_0, pad = lora_out_71_pad_0, pad_type = lora_out_71_pad_type_0, strides = lora_out_71_strides_0, weight = layers_5_fc2_loraB_weight_to_fp16, x = input_119_cast_fp16)[name = tensor("lora_out_71_cast_fp16")]; + tensor hidden_states_15_cast_fp16 = add(x = pretrained_out_71_cast_fp16, y = lora_out_71_cast_fp16)[name = tensor("hidden_states_15_cast_fp16")]; + tensor inputs_25_cast_fp16 = add(x = inputs_23_cast_fp16, y = hidden_states_15_cast_fp16)[name = tensor("inputs_25_cast_fp16")]; + tensor var_1462 = const()[name = tensor("op_1462"), val = tensor(3)]; + tensor out_25_axes_0 = const()[name = tensor("out_25_axes_0"), val = tensor([1])]; + tensor var_1481_to_fp16 = const()[name = tensor("op_1481_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_25_cast_fp16 = layer_norm(axes = out_25_axes_0, epsilon = var_1481_to_fp16, x = inputs_25_cast_fp16)[name = tensor("out_25_cast_fp16")]; + tensor obj_25_gamma_0_to_fp16 = const()[name = tensor("obj_25_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78269568)))]; + tensor obj_25_beta_0_to_fp16 = const()[name = tensor("obj_25_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78272192)))]; + tensor obj_25_epsilon_0_to_fp16 = const()[name = tensor("obj_25_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_25_cast_fp16 = batch_norm(beta = obj_25_beta_0_to_fp16, epsilon = obj_25_epsilon_0_to_fp16, gamma = obj_25_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_25_cast_fp16)[name = tensor("obj_25_cast_fp16")]; + tensor pretrained_out_73_pad_type_0 = const()[name = tensor("pretrained_out_73_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_73_strides_0 = const()[name = tensor("pretrained_out_73_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_73_pad_0 = const()[name = tensor("pretrained_out_73_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_73_dilations_0 = const()[name = tensor("pretrained_out_73_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_73_groups_0 = const()[name = tensor("pretrained_out_73_groups_0"), val = tensor(1)]; + tensor layers_6_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78274816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79094080))), name = tensor("layers_6_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_6_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_6_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79094208)))]; + tensor pretrained_out_73_cast_fp16 = conv(bias = layers_6_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_73_dilations_0, groups = pretrained_out_73_groups_0, pad = pretrained_out_73_pad_0, pad_type = pretrained_out_73_pad_type_0, strides = pretrained_out_73_strides_0, weight = layers_6_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_25_cast_fp16)[name = tensor("pretrained_out_73_cast_fp16")]; + tensor input_121_pad_type_0 = const()[name = tensor("input_121_pad_type_0"), val = tensor("valid")]; + tensor input_121_strides_0 = const()[name = tensor("input_121_strides_0"), val = tensor([1, 1])]; + tensor input_121_pad_0 = const()[name = tensor("input_121_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_121_dilations_0 = const()[name = tensor("input_121_dilations_0"), val = tensor([1, 1])]; + tensor input_121_groups_0 = const()[name = tensor("input_121_groups_0"), val = tensor(1)]; + tensor layers_6_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_6_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79096832)))]; + tensor input_121_cast_fp16 = conv(dilations = input_121_dilations_0, groups = input_121_groups_0, pad = input_121_pad_0, pad_type = input_121_pad_type_0, strides = input_121_strides_0, weight = layers_6_self_attn_q_proj_loraA_weight_to_fp16, x = obj_25_cast_fp16)[name = tensor("input_121_cast_fp16")]; + tensor lora_out_73_pad_type_0 = const()[name = tensor("lora_out_73_pad_type_0"), val = tensor("valid")]; + tensor lora_out_73_strides_0 = const()[name = tensor("lora_out_73_strides_0"), val = tensor([1, 1])]; + tensor lora_out_73_pad_0 = const()[name = tensor("lora_out_73_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_73_dilations_0 = const()[name = tensor("lora_out_73_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_73_groups_0 = const()[name = tensor("lora_out_73_groups_0"), val = tensor(1)]; + tensor layers_6_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_6_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79137856)))]; + tensor lora_out_73_cast_fp16 = conv(dilations = lora_out_73_dilations_0, groups = lora_out_73_groups_0, pad = lora_out_73_pad_0, pad_type = lora_out_73_pad_type_0, strides = lora_out_73_strides_0, weight = layers_6_self_attn_q_proj_loraB_weight_to_fp16, x = input_121_cast_fp16)[name = tensor("lora_out_73_cast_fp16")]; + tensor query_13_cast_fp16 = add(x = pretrained_out_73_cast_fp16, y = lora_out_73_cast_fp16)[name = tensor("query_13_cast_fp16")]; + tensor pretrained_out_75_pad_type_0 = const()[name = tensor("pretrained_out_75_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_75_strides_0 = const()[name = tensor("pretrained_out_75_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_75_pad_0 = const()[name = tensor("pretrained_out_75_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_75_dilations_0 = const()[name = tensor("pretrained_out_75_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_75_groups_0 = const()[name = tensor("pretrained_out_75_groups_0"), val = tensor(1)]; + tensor layers_6_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79178880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79998144))), name = tensor("layers_6_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_75_cast_fp16 = conv(dilations = pretrained_out_75_dilations_0, groups = pretrained_out_75_groups_0, pad = pretrained_out_75_pad_0, pad_type = pretrained_out_75_pad_type_0, strides = pretrained_out_75_strides_0, weight = layers_6_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_25_cast_fp16)[name = tensor("pretrained_out_75_cast_fp16")]; + tensor input_123_pad_type_0 = const()[name = tensor("input_123_pad_type_0"), val = tensor("valid")]; + tensor input_123_strides_0 = const()[name = tensor("input_123_strides_0"), val = tensor([1, 1])]; + tensor input_123_pad_0 = const()[name = tensor("input_123_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_123_dilations_0 = const()[name = tensor("input_123_dilations_0"), val = tensor([1, 1])]; + tensor input_123_groups_0 = const()[name = tensor("input_123_groups_0"), val = tensor(1)]; + tensor layers_6_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_6_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79998272)))]; + tensor input_123_cast_fp16 = conv(dilations = input_123_dilations_0, groups = input_123_groups_0, pad = input_123_pad_0, pad_type = input_123_pad_type_0, strides = input_123_strides_0, weight = layers_6_self_attn_k_proj_loraA_weight_to_fp16, x = obj_25_cast_fp16)[name = tensor("input_123_cast_fp16")]; + tensor lora_out_75_pad_type_0 = const()[name = tensor("lora_out_75_pad_type_0"), val = tensor("valid")]; + tensor lora_out_75_strides_0 = const()[name = tensor("lora_out_75_strides_0"), val = tensor([1, 1])]; + tensor lora_out_75_pad_0 = const()[name = tensor("lora_out_75_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_75_dilations_0 = const()[name = tensor("lora_out_75_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_75_groups_0 = const()[name = tensor("lora_out_75_groups_0"), val = tensor(1)]; + tensor layers_6_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_6_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80039296)))]; + tensor lora_out_75_cast_fp16 = conv(dilations = lora_out_75_dilations_0, groups = lora_out_75_groups_0, pad = lora_out_75_pad_0, pad_type = lora_out_75_pad_type_0, strides = lora_out_75_strides_0, weight = layers_6_self_attn_k_proj_loraB_weight_to_fp16, x = input_123_cast_fp16)[name = tensor("lora_out_75_cast_fp16")]; + tensor key_13_cast_fp16 = add(x = pretrained_out_75_cast_fp16, y = lora_out_75_cast_fp16)[name = tensor("key_13_cast_fp16")]; + tensor pretrained_out_77_pad_type_0 = const()[name = tensor("pretrained_out_77_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_77_strides_0 = const()[name = tensor("pretrained_out_77_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_77_pad_0 = const()[name = tensor("pretrained_out_77_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_77_dilations_0 = const()[name = tensor("pretrained_out_77_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_77_groups_0 = const()[name = tensor("pretrained_out_77_groups_0"), val = tensor(1)]; + tensor layers_6_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80080320))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80899584))), name = tensor("layers_6_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_6_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_6_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80899712)))]; + tensor pretrained_out_77_cast_fp16 = conv(bias = layers_6_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_77_dilations_0, groups = pretrained_out_77_groups_0, pad = pretrained_out_77_pad_0, pad_type = pretrained_out_77_pad_type_0, strides = pretrained_out_77_strides_0, weight = layers_6_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_25_cast_fp16)[name = tensor("pretrained_out_77_cast_fp16")]; + tensor input_125_pad_type_0 = const()[name = tensor("input_125_pad_type_0"), val = tensor("valid")]; + tensor input_125_strides_0 = const()[name = tensor("input_125_strides_0"), val = tensor([1, 1])]; + tensor input_125_pad_0 = const()[name = tensor("input_125_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_125_dilations_0 = const()[name = tensor("input_125_dilations_0"), val = tensor([1, 1])]; + tensor input_125_groups_0 = const()[name = tensor("input_125_groups_0"), val = tensor(1)]; + tensor layers_6_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_6_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80902336)))]; + tensor input_125_cast_fp16 = conv(dilations = input_125_dilations_0, groups = input_125_groups_0, pad = input_125_pad_0, pad_type = input_125_pad_type_0, strides = input_125_strides_0, weight = layers_6_self_attn_v_proj_loraA_weight_to_fp16, x = obj_25_cast_fp16)[name = tensor("input_125_cast_fp16")]; + tensor lora_out_77_pad_type_0 = const()[name = tensor("lora_out_77_pad_type_0"), val = tensor("valid")]; + tensor lora_out_77_strides_0 = const()[name = tensor("lora_out_77_strides_0"), val = tensor([1, 1])]; + tensor lora_out_77_pad_0 = const()[name = tensor("lora_out_77_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_77_dilations_0 = const()[name = tensor("lora_out_77_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_77_groups_0 = const()[name = tensor("lora_out_77_groups_0"), val = tensor(1)]; + tensor layers_6_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_6_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80943360)))]; + tensor lora_out_77_cast_fp16 = conv(dilations = lora_out_77_dilations_0, groups = lora_out_77_groups_0, pad = lora_out_77_pad_0, pad_type = lora_out_77_pad_type_0, strides = lora_out_77_strides_0, weight = layers_6_self_attn_v_proj_loraB_weight_to_fp16, x = input_125_cast_fp16)[name = tensor("lora_out_77_cast_fp16")]; + tensor value_13_cast_fp16 = add(x = pretrained_out_77_cast_fp16, y = lora_out_77_cast_fp16)[name = tensor("value_13_cast_fp16")]; + tensor var_1564 = const()[name = tensor("op_1564"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_13_cast_fp16 = reshape(shape = var_1564, x = query_13_cast_fp16)[name = tensor("mh_q_13_cast_fp16")]; + tensor var_1566_to_fp16 = const()[name = tensor("op_1566_to_fp16"), val = tensor(0x1p-3)]; + tensor var_1567_cast_fp16 = mul(x = mh_q_13_cast_fp16, y = var_1566_to_fp16)[name = tensor("op_1567_cast_fp16")]; + tensor var_1568 = const()[name = tensor("op_1568"), val = tensor([1, 20, 64, -1])]; + tensor var_1569_cast_fp16 = reshape(shape = var_1568, x = key_13_cast_fp16)[name = tensor("op_1569_cast_fp16")]; + tensor mh_w_13_transpose_x_0 = const()[name = tensor("mh_w_13_transpose_x_0"), val = tensor(true)]; + tensor mh_w_13_transpose_y_0 = const()[name = tensor("mh_w_13_transpose_y_0"), val = tensor(false)]; + tensor mh_w_13_cast_fp16 = matmul(transpose_x = mh_w_13_transpose_x_0, transpose_y = mh_w_13_transpose_y_0, x = var_1567_cast_fp16, y = var_1569_cast_fp16)[name = tensor("mh_w_13_cast_fp16")]; + tensor var_1572_cast_fp16 = softmax(axis = var_1462, x = mh_w_13_cast_fp16)[name = tensor("op_1572_cast_fp16")]; + tensor var_1573 = const()[name = tensor("op_1573"), val = tensor([1, 20, 64, -1])]; + tensor var_1574_cast_fp16 = reshape(shape = var_1573, x = value_13_cast_fp16)[name = tensor("op_1574_cast_fp16")]; + tensor attn_13_transpose_x_0 = const()[name = tensor("attn_13_transpose_x_0"), val = tensor(false)]; + tensor attn_13_transpose_y_0 = const()[name = tensor("attn_13_transpose_y_0"), val = tensor(true)]; + tensor attn_13_cast_fp16 = matmul(transpose_x = attn_13_transpose_x_0, transpose_y = attn_13_transpose_y_0, x = var_1574_cast_fp16, y = var_1572_cast_fp16)[name = tensor("attn_13_cast_fp16")]; + tensor var_1577 = const()[name = tensor("op_1577"), val = tensor([1, 1280, 1, -1])]; + tensor input_127_cast_fp16 = reshape(shape = var_1577, x = attn_13_cast_fp16)[name = tensor("input_127_cast_fp16")]; + tensor pretrained_out_79_pad_type_0 = const()[name = tensor("pretrained_out_79_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_79_strides_0 = const()[name = tensor("pretrained_out_79_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_79_pad_0 = const()[name = tensor("pretrained_out_79_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_79_dilations_0 = const()[name = tensor("pretrained_out_79_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_79_groups_0 = const()[name = tensor("pretrained_out_79_groups_0"), val = tensor(1)]; + tensor layers_6_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80984384))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(81803648))), name = tensor("layers_6_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_6_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_6_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(81803776)))]; + tensor pretrained_out_79_cast_fp16 = conv(bias = layers_6_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_79_dilations_0, groups = pretrained_out_79_groups_0, pad = pretrained_out_79_pad_0, pad_type = pretrained_out_79_pad_type_0, strides = pretrained_out_79_strides_0, weight = layers_6_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_127_cast_fp16)[name = tensor("pretrained_out_79_cast_fp16")]; + tensor input_129_pad_type_0 = const()[name = tensor("input_129_pad_type_0"), val = tensor("valid")]; + tensor input_129_strides_0 = const()[name = tensor("input_129_strides_0"), val = tensor([1, 1])]; + tensor input_129_pad_0 = const()[name = tensor("input_129_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_129_dilations_0 = const()[name = tensor("input_129_dilations_0"), val = tensor([1, 1])]; + tensor input_129_groups_0 = const()[name = tensor("input_129_groups_0"), val = tensor(1)]; + tensor layers_6_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_6_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(81806400)))]; + tensor input_129_cast_fp16 = conv(dilations = input_129_dilations_0, groups = input_129_groups_0, pad = input_129_pad_0, pad_type = input_129_pad_type_0, strides = input_129_strides_0, weight = layers_6_self_attn_o_proj_loraA_weight_to_fp16, x = input_127_cast_fp16)[name = tensor("input_129_cast_fp16")]; + tensor lora_out_79_pad_type_0 = const()[name = tensor("lora_out_79_pad_type_0"), val = tensor("valid")]; + tensor lora_out_79_strides_0 = const()[name = tensor("lora_out_79_strides_0"), val = tensor([1, 1])]; + tensor lora_out_79_pad_0 = const()[name = tensor("lora_out_79_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_79_dilations_0 = const()[name = tensor("lora_out_79_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_79_groups_0 = const()[name = tensor("lora_out_79_groups_0"), val = tensor(1)]; + tensor layers_6_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_6_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(81847424)))]; + tensor lora_out_79_cast_fp16 = conv(dilations = lora_out_79_dilations_0, groups = lora_out_79_groups_0, pad = lora_out_79_pad_0, pad_type = lora_out_79_pad_type_0, strides = lora_out_79_strides_0, weight = layers_6_self_attn_o_proj_loraB_weight_to_fp16, x = input_129_cast_fp16)[name = tensor("lora_out_79_cast_fp16")]; + tensor obj_27_cast_fp16 = add(x = pretrained_out_79_cast_fp16, y = lora_out_79_cast_fp16)[name = tensor("obj_27_cast_fp16")]; + tensor inputs_27_cast_fp16 = add(x = inputs_25_cast_fp16, y = obj_27_cast_fp16)[name = tensor("inputs_27_cast_fp16")]; + tensor out_27_axes_0 = const()[name = tensor("out_27_axes_0"), val = tensor([1])]; + tensor var_1611_to_fp16 = const()[name = tensor("op_1611_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_27_cast_fp16 = layer_norm(axes = out_27_axes_0, epsilon = var_1611_to_fp16, x = inputs_27_cast_fp16)[name = tensor("out_27_cast_fp16")]; + tensor input_131_gamma_0_to_fp16 = const()[name = tensor("input_131_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(81888448)))]; + tensor input_131_beta_0_to_fp16 = const()[name = tensor("input_131_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(81891072)))]; + tensor input_131_epsilon_0_to_fp16 = const()[name = tensor("input_131_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_131_cast_fp16 = batch_norm(beta = input_131_beta_0_to_fp16, epsilon = input_131_epsilon_0_to_fp16, gamma = input_131_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_27_cast_fp16)[name = tensor("input_131_cast_fp16")]; + tensor pretrained_out_81_pad_type_0 = const()[name = tensor("pretrained_out_81_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_81_strides_0 = const()[name = tensor("pretrained_out_81_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_81_pad_0 = const()[name = tensor("pretrained_out_81_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_81_dilations_0 = const()[name = tensor("pretrained_out_81_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_81_groups_0 = const()[name = tensor("pretrained_out_81_groups_0"), val = tensor(1)]; + tensor layers_6_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(81893696))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85170560))), name = tensor("layers_6_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_6_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_6_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85170688)))]; + tensor pretrained_out_81_cast_fp16 = conv(bias = layers_6_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_81_dilations_0, groups = pretrained_out_81_groups_0, pad = pretrained_out_81_pad_0, pad_type = pretrained_out_81_pad_type_0, strides = pretrained_out_81_strides_0, weight = layers_6_fc1_pretrained_weight_to_fp16_palettized, x = input_131_cast_fp16)[name = tensor("pretrained_out_81_cast_fp16")]; + tensor input_133_pad_type_0 = const()[name = tensor("input_133_pad_type_0"), val = tensor("valid")]; + tensor input_133_strides_0 = const()[name = tensor("input_133_strides_0"), val = tensor([1, 1])]; + tensor input_133_pad_0 = const()[name = tensor("input_133_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_133_dilations_0 = const()[name = tensor("input_133_dilations_0"), val = tensor([1, 1])]; + tensor input_133_groups_0 = const()[name = tensor("input_133_groups_0"), val = tensor(1)]; + tensor layers_6_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_6_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85180992)))]; + tensor input_133_cast_fp16 = conv(dilations = input_133_dilations_0, groups = input_133_groups_0, pad = input_133_pad_0, pad_type = input_133_pad_type_0, strides = input_133_strides_0, weight = layers_6_fc1_loraA_weight_to_fp16, x = input_131_cast_fp16)[name = tensor("input_133_cast_fp16")]; + tensor lora_out_81_pad_type_0 = const()[name = tensor("lora_out_81_pad_type_0"), val = tensor("valid")]; + tensor lora_out_81_strides_0 = const()[name = tensor("lora_out_81_strides_0"), val = tensor([1, 1])]; + tensor lora_out_81_pad_0 = const()[name = tensor("lora_out_81_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_81_dilations_0 = const()[name = tensor("lora_out_81_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_81_groups_0 = const()[name = tensor("lora_out_81_groups_0"), val = tensor(1)]; + tensor layers_6_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_6_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85222016)))]; + tensor lora_out_81_cast_fp16 = conv(dilations = lora_out_81_dilations_0, groups = lora_out_81_groups_0, pad = lora_out_81_pad_0, pad_type = lora_out_81_pad_type_0, strides = lora_out_81_strides_0, weight = layers_6_fc1_loraB_weight_to_fp16, x = input_133_cast_fp16)[name = tensor("lora_out_81_cast_fp16")]; + tensor input_135_cast_fp16 = add(x = pretrained_out_81_cast_fp16, y = lora_out_81_cast_fp16)[name = tensor("input_135_cast_fp16")]; + tensor input_137_mode_0 = const()[name = tensor("input_137_mode_0"), val = tensor("EXACT")]; + tensor input_137_cast_fp16 = gelu(mode = input_137_mode_0, x = input_135_cast_fp16)[name = tensor("input_137_cast_fp16")]; + tensor pretrained_out_83_pad_type_0 = const()[name = tensor("pretrained_out_83_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_83_strides_0 = const()[name = tensor("pretrained_out_83_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_83_pad_0 = const()[name = tensor("pretrained_out_83_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_83_dilations_0 = const()[name = tensor("pretrained_out_83_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_83_groups_0 = const()[name = tensor("pretrained_out_83_groups_0"), val = tensor(1)]; + tensor layers_6_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85385920))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88662784))), name = tensor("layers_6_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_6_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_6_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88662912)))]; + tensor pretrained_out_83_cast_fp16 = conv(bias = layers_6_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_83_dilations_0, groups = pretrained_out_83_groups_0, pad = pretrained_out_83_pad_0, pad_type = pretrained_out_83_pad_type_0, strides = pretrained_out_83_strides_0, weight = layers_6_fc2_pretrained_weight_to_fp16_palettized, x = input_137_cast_fp16)[name = tensor("pretrained_out_83_cast_fp16")]; + tensor input_139_pad_type_0 = const()[name = tensor("input_139_pad_type_0"), val = tensor("valid")]; + tensor input_139_strides_0 = const()[name = tensor("input_139_strides_0"), val = tensor([1, 1])]; + tensor input_139_pad_0 = const()[name = tensor("input_139_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_139_dilations_0 = const()[name = tensor("input_139_dilations_0"), val = tensor([1, 1])]; + tensor input_139_groups_0 = const()[name = tensor("input_139_groups_0"), val = tensor(1)]; + tensor layers_6_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_6_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88665536)))]; + tensor input_139_cast_fp16 = conv(dilations = input_139_dilations_0, groups = input_139_groups_0, pad = input_139_pad_0, pad_type = input_139_pad_type_0, strides = input_139_strides_0, weight = layers_6_fc2_loraA_weight_to_fp16, x = input_137_cast_fp16)[name = tensor("input_139_cast_fp16")]; + tensor lora_out_83_pad_type_0 = const()[name = tensor("lora_out_83_pad_type_0"), val = tensor("valid")]; + tensor lora_out_83_strides_0 = const()[name = tensor("lora_out_83_strides_0"), val = tensor([1, 1])]; + tensor lora_out_83_pad_0 = const()[name = tensor("lora_out_83_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_83_dilations_0 = const()[name = tensor("lora_out_83_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_83_groups_0 = const()[name = tensor("lora_out_83_groups_0"), val = tensor(1)]; + tensor layers_6_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_6_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88829440)))]; + tensor lora_out_83_cast_fp16 = conv(dilations = lora_out_83_dilations_0, groups = lora_out_83_groups_0, pad = lora_out_83_pad_0, pad_type = lora_out_83_pad_type_0, strides = lora_out_83_strides_0, weight = layers_6_fc2_loraB_weight_to_fp16, x = input_139_cast_fp16)[name = tensor("lora_out_83_cast_fp16")]; + tensor hidden_states_17_cast_fp16 = add(x = pretrained_out_83_cast_fp16, y = lora_out_83_cast_fp16)[name = tensor("hidden_states_17_cast_fp16")]; + tensor inputs_29_cast_fp16 = add(x = inputs_27_cast_fp16, y = hidden_states_17_cast_fp16)[name = tensor("inputs_29_cast_fp16")]; + tensor var_1676 = const()[name = tensor("op_1676"), val = tensor(3)]; + tensor out_29_axes_0 = const()[name = tensor("out_29_axes_0"), val = tensor([1])]; + tensor var_1695_to_fp16 = const()[name = tensor("op_1695_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_29_cast_fp16 = layer_norm(axes = out_29_axes_0, epsilon = var_1695_to_fp16, x = inputs_29_cast_fp16)[name = tensor("out_29_cast_fp16")]; + tensor obj_29_gamma_0_to_fp16 = const()[name = tensor("obj_29_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88870464)))]; + tensor obj_29_beta_0_to_fp16 = const()[name = tensor("obj_29_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88873088)))]; + tensor obj_29_epsilon_0_to_fp16 = const()[name = tensor("obj_29_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_29_cast_fp16 = batch_norm(beta = obj_29_beta_0_to_fp16, epsilon = obj_29_epsilon_0_to_fp16, gamma = obj_29_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_29_cast_fp16)[name = tensor("obj_29_cast_fp16")]; + tensor pretrained_out_85_pad_type_0 = const()[name = tensor("pretrained_out_85_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_85_strides_0 = const()[name = tensor("pretrained_out_85_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_85_pad_0 = const()[name = tensor("pretrained_out_85_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_85_dilations_0 = const()[name = tensor("pretrained_out_85_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_85_groups_0 = const()[name = tensor("pretrained_out_85_groups_0"), val = tensor(1)]; + tensor layers_7_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(88875712))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89694976))), name = tensor("layers_7_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_7_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_7_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89695104)))]; + tensor pretrained_out_85_cast_fp16 = conv(bias = layers_7_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_85_dilations_0, groups = pretrained_out_85_groups_0, pad = pretrained_out_85_pad_0, pad_type = pretrained_out_85_pad_type_0, strides = pretrained_out_85_strides_0, weight = layers_7_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_29_cast_fp16)[name = tensor("pretrained_out_85_cast_fp16")]; + tensor input_141_pad_type_0 = const()[name = tensor("input_141_pad_type_0"), val = tensor("valid")]; + tensor input_141_strides_0 = const()[name = tensor("input_141_strides_0"), val = tensor([1, 1])]; + tensor input_141_pad_0 = const()[name = tensor("input_141_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_141_dilations_0 = const()[name = tensor("input_141_dilations_0"), val = tensor([1, 1])]; + tensor input_141_groups_0 = const()[name = tensor("input_141_groups_0"), val = tensor(1)]; + tensor layers_7_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_7_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89697728)))]; + tensor input_141_cast_fp16 = conv(dilations = input_141_dilations_0, groups = input_141_groups_0, pad = input_141_pad_0, pad_type = input_141_pad_type_0, strides = input_141_strides_0, weight = layers_7_self_attn_q_proj_loraA_weight_to_fp16, x = obj_29_cast_fp16)[name = tensor("input_141_cast_fp16")]; + tensor lora_out_85_pad_type_0 = const()[name = tensor("lora_out_85_pad_type_0"), val = tensor("valid")]; + tensor lora_out_85_strides_0 = const()[name = tensor("lora_out_85_strides_0"), val = tensor([1, 1])]; + tensor lora_out_85_pad_0 = const()[name = tensor("lora_out_85_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_85_dilations_0 = const()[name = tensor("lora_out_85_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_85_groups_0 = const()[name = tensor("lora_out_85_groups_0"), val = tensor(1)]; + tensor layers_7_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_7_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89738752)))]; + tensor lora_out_85_cast_fp16 = conv(dilations = lora_out_85_dilations_0, groups = lora_out_85_groups_0, pad = lora_out_85_pad_0, pad_type = lora_out_85_pad_type_0, strides = lora_out_85_strides_0, weight = layers_7_self_attn_q_proj_loraB_weight_to_fp16, x = input_141_cast_fp16)[name = tensor("lora_out_85_cast_fp16")]; + tensor query_15_cast_fp16 = add(x = pretrained_out_85_cast_fp16, y = lora_out_85_cast_fp16)[name = tensor("query_15_cast_fp16")]; + tensor pretrained_out_87_pad_type_0 = const()[name = tensor("pretrained_out_87_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_87_strides_0 = const()[name = tensor("pretrained_out_87_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_87_pad_0 = const()[name = tensor("pretrained_out_87_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_87_dilations_0 = const()[name = tensor("pretrained_out_87_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_87_groups_0 = const()[name = tensor("pretrained_out_87_groups_0"), val = tensor(1)]; + tensor layers_7_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89779776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90599040))), name = tensor("layers_7_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_87_cast_fp16 = conv(dilations = pretrained_out_87_dilations_0, groups = pretrained_out_87_groups_0, pad = pretrained_out_87_pad_0, pad_type = pretrained_out_87_pad_type_0, strides = pretrained_out_87_strides_0, weight = layers_7_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_29_cast_fp16)[name = tensor("pretrained_out_87_cast_fp16")]; + tensor input_143_pad_type_0 = const()[name = tensor("input_143_pad_type_0"), val = tensor("valid")]; + tensor input_143_strides_0 = const()[name = tensor("input_143_strides_0"), val = tensor([1, 1])]; + tensor input_143_pad_0 = const()[name = tensor("input_143_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_143_dilations_0 = const()[name = tensor("input_143_dilations_0"), val = tensor([1, 1])]; + tensor input_143_groups_0 = const()[name = tensor("input_143_groups_0"), val = tensor(1)]; + tensor layers_7_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_7_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90599168)))]; + tensor input_143_cast_fp16 = conv(dilations = input_143_dilations_0, groups = input_143_groups_0, pad = input_143_pad_0, pad_type = input_143_pad_type_0, strides = input_143_strides_0, weight = layers_7_self_attn_k_proj_loraA_weight_to_fp16, x = obj_29_cast_fp16)[name = tensor("input_143_cast_fp16")]; + tensor lora_out_87_pad_type_0 = const()[name = tensor("lora_out_87_pad_type_0"), val = tensor("valid")]; + tensor lora_out_87_strides_0 = const()[name = tensor("lora_out_87_strides_0"), val = tensor([1, 1])]; + tensor lora_out_87_pad_0 = const()[name = tensor("lora_out_87_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_87_dilations_0 = const()[name = tensor("lora_out_87_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_87_groups_0 = const()[name = tensor("lora_out_87_groups_0"), val = tensor(1)]; + tensor layers_7_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_7_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90640192)))]; + tensor lora_out_87_cast_fp16 = conv(dilations = lora_out_87_dilations_0, groups = lora_out_87_groups_0, pad = lora_out_87_pad_0, pad_type = lora_out_87_pad_type_0, strides = lora_out_87_strides_0, weight = layers_7_self_attn_k_proj_loraB_weight_to_fp16, x = input_143_cast_fp16)[name = tensor("lora_out_87_cast_fp16")]; + tensor key_15_cast_fp16 = add(x = pretrained_out_87_cast_fp16, y = lora_out_87_cast_fp16)[name = tensor("key_15_cast_fp16")]; + tensor pretrained_out_89_pad_type_0 = const()[name = tensor("pretrained_out_89_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_89_strides_0 = const()[name = tensor("pretrained_out_89_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_89_pad_0 = const()[name = tensor("pretrained_out_89_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_89_dilations_0 = const()[name = tensor("pretrained_out_89_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_89_groups_0 = const()[name = tensor("pretrained_out_89_groups_0"), val = tensor(1)]; + tensor layers_7_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90681216))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91500480))), name = tensor("layers_7_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_7_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_7_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91500608)))]; + tensor pretrained_out_89_cast_fp16 = conv(bias = layers_7_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_89_dilations_0, groups = pretrained_out_89_groups_0, pad = pretrained_out_89_pad_0, pad_type = pretrained_out_89_pad_type_0, strides = pretrained_out_89_strides_0, weight = layers_7_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_29_cast_fp16)[name = tensor("pretrained_out_89_cast_fp16")]; + tensor input_145_pad_type_0 = const()[name = tensor("input_145_pad_type_0"), val = tensor("valid")]; + tensor input_145_strides_0 = const()[name = tensor("input_145_strides_0"), val = tensor([1, 1])]; + tensor input_145_pad_0 = const()[name = tensor("input_145_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_145_dilations_0 = const()[name = tensor("input_145_dilations_0"), val = tensor([1, 1])]; + tensor input_145_groups_0 = const()[name = tensor("input_145_groups_0"), val = tensor(1)]; + tensor layers_7_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_7_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91503232)))]; + tensor input_145_cast_fp16 = conv(dilations = input_145_dilations_0, groups = input_145_groups_0, pad = input_145_pad_0, pad_type = input_145_pad_type_0, strides = input_145_strides_0, weight = layers_7_self_attn_v_proj_loraA_weight_to_fp16, x = obj_29_cast_fp16)[name = tensor("input_145_cast_fp16")]; + tensor lora_out_89_pad_type_0 = const()[name = tensor("lora_out_89_pad_type_0"), val = tensor("valid")]; + tensor lora_out_89_strides_0 = const()[name = tensor("lora_out_89_strides_0"), val = tensor([1, 1])]; + tensor lora_out_89_pad_0 = const()[name = tensor("lora_out_89_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_89_dilations_0 = const()[name = tensor("lora_out_89_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_89_groups_0 = const()[name = tensor("lora_out_89_groups_0"), val = tensor(1)]; + tensor layers_7_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_7_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91544256)))]; + tensor lora_out_89_cast_fp16 = conv(dilations = lora_out_89_dilations_0, groups = lora_out_89_groups_0, pad = lora_out_89_pad_0, pad_type = lora_out_89_pad_type_0, strides = lora_out_89_strides_0, weight = layers_7_self_attn_v_proj_loraB_weight_to_fp16, x = input_145_cast_fp16)[name = tensor("lora_out_89_cast_fp16")]; + tensor value_15_cast_fp16 = add(x = pretrained_out_89_cast_fp16, y = lora_out_89_cast_fp16)[name = tensor("value_15_cast_fp16")]; + tensor var_1778 = const()[name = tensor("op_1778"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_15_cast_fp16 = reshape(shape = var_1778, x = query_15_cast_fp16)[name = tensor("mh_q_15_cast_fp16")]; + tensor var_1780_to_fp16 = const()[name = tensor("op_1780_to_fp16"), val = tensor(0x1p-3)]; + tensor var_1781_cast_fp16 = mul(x = mh_q_15_cast_fp16, y = var_1780_to_fp16)[name = tensor("op_1781_cast_fp16")]; + tensor var_1782 = const()[name = tensor("op_1782"), val = tensor([1, 20, 64, -1])]; + tensor var_1783_cast_fp16 = reshape(shape = var_1782, x = key_15_cast_fp16)[name = tensor("op_1783_cast_fp16")]; + tensor mh_w_15_transpose_x_0 = const()[name = tensor("mh_w_15_transpose_x_0"), val = tensor(true)]; + tensor mh_w_15_transpose_y_0 = const()[name = tensor("mh_w_15_transpose_y_0"), val = tensor(false)]; + tensor mh_w_15_cast_fp16 = matmul(transpose_x = mh_w_15_transpose_x_0, transpose_y = mh_w_15_transpose_y_0, x = var_1781_cast_fp16, y = var_1783_cast_fp16)[name = tensor("mh_w_15_cast_fp16")]; + tensor var_1786_cast_fp16 = softmax(axis = var_1676, x = mh_w_15_cast_fp16)[name = tensor("op_1786_cast_fp16")]; + tensor var_1787 = const()[name = tensor("op_1787"), val = tensor([1, 20, 64, -1])]; + tensor var_1788_cast_fp16 = reshape(shape = var_1787, x = value_15_cast_fp16)[name = tensor("op_1788_cast_fp16")]; + tensor attn_15_transpose_x_0 = const()[name = tensor("attn_15_transpose_x_0"), val = tensor(false)]; + tensor attn_15_transpose_y_0 = const()[name = tensor("attn_15_transpose_y_0"), val = tensor(true)]; + tensor attn_15_cast_fp16 = matmul(transpose_x = attn_15_transpose_x_0, transpose_y = attn_15_transpose_y_0, x = var_1788_cast_fp16, y = var_1786_cast_fp16)[name = tensor("attn_15_cast_fp16")]; + tensor var_1791 = const()[name = tensor("op_1791"), val = tensor([1, 1280, 1, -1])]; + tensor input_147_cast_fp16 = reshape(shape = var_1791, x = attn_15_cast_fp16)[name = tensor("input_147_cast_fp16")]; + tensor pretrained_out_91_pad_type_0 = const()[name = tensor("pretrained_out_91_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_91_strides_0 = const()[name = tensor("pretrained_out_91_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_91_pad_0 = const()[name = tensor("pretrained_out_91_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_91_dilations_0 = const()[name = tensor("pretrained_out_91_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_91_groups_0 = const()[name = tensor("pretrained_out_91_groups_0"), val = tensor(1)]; + tensor layers_7_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91585280))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92404544))), name = tensor("layers_7_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_7_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_7_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92404672)))]; + tensor pretrained_out_91_cast_fp16 = conv(bias = layers_7_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_91_dilations_0, groups = pretrained_out_91_groups_0, pad = pretrained_out_91_pad_0, pad_type = pretrained_out_91_pad_type_0, strides = pretrained_out_91_strides_0, weight = layers_7_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_147_cast_fp16)[name = tensor("pretrained_out_91_cast_fp16")]; + tensor input_149_pad_type_0 = const()[name = tensor("input_149_pad_type_0"), val = tensor("valid")]; + tensor input_149_strides_0 = const()[name = tensor("input_149_strides_0"), val = tensor([1, 1])]; + tensor input_149_pad_0 = const()[name = tensor("input_149_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_149_dilations_0 = const()[name = tensor("input_149_dilations_0"), val = tensor([1, 1])]; + tensor input_149_groups_0 = const()[name = tensor("input_149_groups_0"), val = tensor(1)]; + tensor layers_7_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_7_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92407296)))]; + tensor input_149_cast_fp16 = conv(dilations = input_149_dilations_0, groups = input_149_groups_0, pad = input_149_pad_0, pad_type = input_149_pad_type_0, strides = input_149_strides_0, weight = layers_7_self_attn_o_proj_loraA_weight_to_fp16, x = input_147_cast_fp16)[name = tensor("input_149_cast_fp16")]; + tensor lora_out_91_pad_type_0 = const()[name = tensor("lora_out_91_pad_type_0"), val = tensor("valid")]; + tensor lora_out_91_strides_0 = const()[name = tensor("lora_out_91_strides_0"), val = tensor([1, 1])]; + tensor lora_out_91_pad_0 = const()[name = tensor("lora_out_91_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_91_dilations_0 = const()[name = tensor("lora_out_91_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_91_groups_0 = const()[name = tensor("lora_out_91_groups_0"), val = tensor(1)]; + tensor layers_7_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_7_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92448320)))]; + tensor lora_out_91_cast_fp16 = conv(dilations = lora_out_91_dilations_0, groups = lora_out_91_groups_0, pad = lora_out_91_pad_0, pad_type = lora_out_91_pad_type_0, strides = lora_out_91_strides_0, weight = layers_7_self_attn_o_proj_loraB_weight_to_fp16, x = input_149_cast_fp16)[name = tensor("lora_out_91_cast_fp16")]; + tensor obj_31_cast_fp16 = add(x = pretrained_out_91_cast_fp16, y = lora_out_91_cast_fp16)[name = tensor("obj_31_cast_fp16")]; + tensor inputs_31_cast_fp16 = add(x = inputs_29_cast_fp16, y = obj_31_cast_fp16)[name = tensor("inputs_31_cast_fp16")]; + tensor out_31_axes_0 = const()[name = tensor("out_31_axes_0"), val = tensor([1])]; + tensor var_1825_to_fp16 = const()[name = tensor("op_1825_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_31_cast_fp16 = layer_norm(axes = out_31_axes_0, epsilon = var_1825_to_fp16, x = inputs_31_cast_fp16)[name = tensor("out_31_cast_fp16")]; + tensor input_151_gamma_0_to_fp16 = const()[name = tensor("input_151_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92489344)))]; + tensor input_151_beta_0_to_fp16 = const()[name = tensor("input_151_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92491968)))]; + tensor input_151_epsilon_0_to_fp16 = const()[name = tensor("input_151_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_151_cast_fp16 = batch_norm(beta = input_151_beta_0_to_fp16, epsilon = input_151_epsilon_0_to_fp16, gamma = input_151_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_31_cast_fp16)[name = tensor("input_151_cast_fp16")]; + tensor pretrained_out_93_pad_type_0 = const()[name = tensor("pretrained_out_93_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_93_strides_0 = const()[name = tensor("pretrained_out_93_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_93_pad_0 = const()[name = tensor("pretrained_out_93_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_93_dilations_0 = const()[name = tensor("pretrained_out_93_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_93_groups_0 = const()[name = tensor("pretrained_out_93_groups_0"), val = tensor(1)]; + tensor layers_7_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92494592))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95771456))), name = tensor("layers_7_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_7_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_7_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95771584)))]; + tensor pretrained_out_93_cast_fp16 = conv(bias = layers_7_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_93_dilations_0, groups = pretrained_out_93_groups_0, pad = pretrained_out_93_pad_0, pad_type = pretrained_out_93_pad_type_0, strides = pretrained_out_93_strides_0, weight = layers_7_fc1_pretrained_weight_to_fp16_palettized, x = input_151_cast_fp16)[name = tensor("pretrained_out_93_cast_fp16")]; + tensor input_153_pad_type_0 = const()[name = tensor("input_153_pad_type_0"), val = tensor("valid")]; + tensor input_153_strides_0 = const()[name = tensor("input_153_strides_0"), val = tensor([1, 1])]; + tensor input_153_pad_0 = const()[name = tensor("input_153_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_153_dilations_0 = const()[name = tensor("input_153_dilations_0"), val = tensor([1, 1])]; + tensor input_153_groups_0 = const()[name = tensor("input_153_groups_0"), val = tensor(1)]; + tensor layers_7_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_7_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95781888)))]; + tensor input_153_cast_fp16 = conv(dilations = input_153_dilations_0, groups = input_153_groups_0, pad = input_153_pad_0, pad_type = input_153_pad_type_0, strides = input_153_strides_0, weight = layers_7_fc1_loraA_weight_to_fp16, x = input_151_cast_fp16)[name = tensor("input_153_cast_fp16")]; + tensor lora_out_93_pad_type_0 = const()[name = tensor("lora_out_93_pad_type_0"), val = tensor("valid")]; + tensor lora_out_93_strides_0 = const()[name = tensor("lora_out_93_strides_0"), val = tensor([1, 1])]; + tensor lora_out_93_pad_0 = const()[name = tensor("lora_out_93_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_93_dilations_0 = const()[name = tensor("lora_out_93_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_93_groups_0 = const()[name = tensor("lora_out_93_groups_0"), val = tensor(1)]; + tensor layers_7_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_7_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95822912)))]; + tensor lora_out_93_cast_fp16 = conv(dilations = lora_out_93_dilations_0, groups = lora_out_93_groups_0, pad = lora_out_93_pad_0, pad_type = lora_out_93_pad_type_0, strides = lora_out_93_strides_0, weight = layers_7_fc1_loraB_weight_to_fp16, x = input_153_cast_fp16)[name = tensor("lora_out_93_cast_fp16")]; + tensor input_155_cast_fp16 = add(x = pretrained_out_93_cast_fp16, y = lora_out_93_cast_fp16)[name = tensor("input_155_cast_fp16")]; + tensor input_157_mode_0 = const()[name = tensor("input_157_mode_0"), val = tensor("EXACT")]; + tensor input_157_cast_fp16 = gelu(mode = input_157_mode_0, x = input_155_cast_fp16)[name = tensor("input_157_cast_fp16")]; + tensor pretrained_out_95_pad_type_0 = const()[name = tensor("pretrained_out_95_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_95_strides_0 = const()[name = tensor("pretrained_out_95_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_95_pad_0 = const()[name = tensor("pretrained_out_95_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_95_dilations_0 = const()[name = tensor("pretrained_out_95_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_95_groups_0 = const()[name = tensor("pretrained_out_95_groups_0"), val = tensor(1)]; + tensor layers_7_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95986816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(99263680))), name = tensor("layers_7_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_7_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_7_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(99263808)))]; + tensor pretrained_out_95_cast_fp16 = conv(bias = layers_7_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_95_dilations_0, groups = pretrained_out_95_groups_0, pad = pretrained_out_95_pad_0, pad_type = pretrained_out_95_pad_type_0, strides = pretrained_out_95_strides_0, weight = layers_7_fc2_pretrained_weight_to_fp16_palettized, x = input_157_cast_fp16)[name = tensor("pretrained_out_95_cast_fp16")]; + tensor input_159_pad_type_0 = const()[name = tensor("input_159_pad_type_0"), val = tensor("valid")]; + tensor input_159_strides_0 = const()[name = tensor("input_159_strides_0"), val = tensor([1, 1])]; + tensor input_159_pad_0 = const()[name = tensor("input_159_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_159_dilations_0 = const()[name = tensor("input_159_dilations_0"), val = tensor([1, 1])]; + tensor input_159_groups_0 = const()[name = tensor("input_159_groups_0"), val = tensor(1)]; + tensor layers_7_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_7_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(99266432)))]; + tensor input_159_cast_fp16 = conv(dilations = input_159_dilations_0, groups = input_159_groups_0, pad = input_159_pad_0, pad_type = input_159_pad_type_0, strides = input_159_strides_0, weight = layers_7_fc2_loraA_weight_to_fp16, x = input_157_cast_fp16)[name = tensor("input_159_cast_fp16")]; + tensor lora_out_95_pad_type_0 = const()[name = tensor("lora_out_95_pad_type_0"), val = tensor("valid")]; + tensor lora_out_95_strides_0 = const()[name = tensor("lora_out_95_strides_0"), val = tensor([1, 1])]; + tensor lora_out_95_pad_0 = const()[name = tensor("lora_out_95_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_95_dilations_0 = const()[name = tensor("lora_out_95_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_95_groups_0 = const()[name = tensor("lora_out_95_groups_0"), val = tensor(1)]; + tensor layers_7_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_7_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(99430336)))]; + tensor lora_out_95_cast_fp16 = conv(dilations = lora_out_95_dilations_0, groups = lora_out_95_groups_0, pad = lora_out_95_pad_0, pad_type = lora_out_95_pad_type_0, strides = lora_out_95_strides_0, weight = layers_7_fc2_loraB_weight_to_fp16, x = input_159_cast_fp16)[name = tensor("lora_out_95_cast_fp16")]; + tensor hidden_states_19_cast_fp16 = add(x = pretrained_out_95_cast_fp16, y = lora_out_95_cast_fp16)[name = tensor("hidden_states_19_cast_fp16")]; + tensor inputs_33_cast_fp16 = add(x = inputs_31_cast_fp16, y = hidden_states_19_cast_fp16)[name = tensor("inputs_33_cast_fp16")]; + tensor var_1890 = const()[name = tensor("op_1890"), val = tensor(3)]; + tensor out_33_axes_0 = const()[name = tensor("out_33_axes_0"), val = tensor([1])]; + tensor var_1909_to_fp16 = const()[name = tensor("op_1909_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_33_cast_fp16 = layer_norm(axes = out_33_axes_0, epsilon = var_1909_to_fp16, x = inputs_33_cast_fp16)[name = tensor("out_33_cast_fp16")]; + tensor obj_33_gamma_0_to_fp16 = const()[name = tensor("obj_33_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(99471360)))]; + tensor obj_33_beta_0_to_fp16 = const()[name = tensor("obj_33_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(99473984)))]; + tensor obj_33_epsilon_0_to_fp16 = const()[name = tensor("obj_33_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_33_cast_fp16 = batch_norm(beta = obj_33_beta_0_to_fp16, epsilon = obj_33_epsilon_0_to_fp16, gamma = obj_33_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_33_cast_fp16)[name = tensor("obj_33_cast_fp16")]; + tensor pretrained_out_97_pad_type_0 = const()[name = tensor("pretrained_out_97_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_97_strides_0 = const()[name = tensor("pretrained_out_97_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_97_pad_0 = const()[name = tensor("pretrained_out_97_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_97_dilations_0 = const()[name = tensor("pretrained_out_97_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_97_groups_0 = const()[name = tensor("pretrained_out_97_groups_0"), val = tensor(1)]; + tensor layers_8_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(99476608))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100295872))), name = tensor("layers_8_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_8_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_8_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100296000)))]; + tensor pretrained_out_97_cast_fp16 = conv(bias = layers_8_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_97_dilations_0, groups = pretrained_out_97_groups_0, pad = pretrained_out_97_pad_0, pad_type = pretrained_out_97_pad_type_0, strides = pretrained_out_97_strides_0, weight = layers_8_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_33_cast_fp16)[name = tensor("pretrained_out_97_cast_fp16")]; + tensor input_161_pad_type_0 = const()[name = tensor("input_161_pad_type_0"), val = tensor("valid")]; + tensor input_161_strides_0 = const()[name = tensor("input_161_strides_0"), val = tensor([1, 1])]; + tensor input_161_pad_0 = const()[name = tensor("input_161_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_161_dilations_0 = const()[name = tensor("input_161_dilations_0"), val = tensor([1, 1])]; + tensor input_161_groups_0 = const()[name = tensor("input_161_groups_0"), val = tensor(1)]; + tensor layers_8_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_8_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100298624)))]; + tensor input_161_cast_fp16 = conv(dilations = input_161_dilations_0, groups = input_161_groups_0, pad = input_161_pad_0, pad_type = input_161_pad_type_0, strides = input_161_strides_0, weight = layers_8_self_attn_q_proj_loraA_weight_to_fp16, x = obj_33_cast_fp16)[name = tensor("input_161_cast_fp16")]; + tensor lora_out_97_pad_type_0 = const()[name = tensor("lora_out_97_pad_type_0"), val = tensor("valid")]; + tensor lora_out_97_strides_0 = const()[name = tensor("lora_out_97_strides_0"), val = tensor([1, 1])]; + tensor lora_out_97_pad_0 = const()[name = tensor("lora_out_97_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_97_dilations_0 = const()[name = tensor("lora_out_97_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_97_groups_0 = const()[name = tensor("lora_out_97_groups_0"), val = tensor(1)]; + tensor layers_8_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_8_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100339648)))]; + tensor lora_out_97_cast_fp16 = conv(dilations = lora_out_97_dilations_0, groups = lora_out_97_groups_0, pad = lora_out_97_pad_0, pad_type = lora_out_97_pad_type_0, strides = lora_out_97_strides_0, weight = layers_8_self_attn_q_proj_loraB_weight_to_fp16, x = input_161_cast_fp16)[name = tensor("lora_out_97_cast_fp16")]; + tensor query_17_cast_fp16 = add(x = pretrained_out_97_cast_fp16, y = lora_out_97_cast_fp16)[name = tensor("query_17_cast_fp16")]; + tensor pretrained_out_99_pad_type_0 = const()[name = tensor("pretrained_out_99_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_99_strides_0 = const()[name = tensor("pretrained_out_99_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_99_pad_0 = const()[name = tensor("pretrained_out_99_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_99_dilations_0 = const()[name = tensor("pretrained_out_99_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_99_groups_0 = const()[name = tensor("pretrained_out_99_groups_0"), val = tensor(1)]; + tensor layers_8_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100380672))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(101199936))), name = tensor("layers_8_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_99_cast_fp16 = conv(dilations = pretrained_out_99_dilations_0, groups = pretrained_out_99_groups_0, pad = pretrained_out_99_pad_0, pad_type = pretrained_out_99_pad_type_0, strides = pretrained_out_99_strides_0, weight = layers_8_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_33_cast_fp16)[name = tensor("pretrained_out_99_cast_fp16")]; + tensor input_163_pad_type_0 = const()[name = tensor("input_163_pad_type_0"), val = tensor("valid")]; + tensor input_163_strides_0 = const()[name = tensor("input_163_strides_0"), val = tensor([1, 1])]; + tensor input_163_pad_0 = const()[name = tensor("input_163_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_163_dilations_0 = const()[name = tensor("input_163_dilations_0"), val = tensor([1, 1])]; + tensor input_163_groups_0 = const()[name = tensor("input_163_groups_0"), val = tensor(1)]; + tensor layers_8_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_8_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(101200064)))]; + tensor input_163_cast_fp16 = conv(dilations = input_163_dilations_0, groups = input_163_groups_0, pad = input_163_pad_0, pad_type = input_163_pad_type_0, strides = input_163_strides_0, weight = layers_8_self_attn_k_proj_loraA_weight_to_fp16, x = obj_33_cast_fp16)[name = tensor("input_163_cast_fp16")]; + tensor lora_out_99_pad_type_0 = const()[name = tensor("lora_out_99_pad_type_0"), val = tensor("valid")]; + tensor lora_out_99_strides_0 = const()[name = tensor("lora_out_99_strides_0"), val = tensor([1, 1])]; + tensor lora_out_99_pad_0 = const()[name = tensor("lora_out_99_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_99_dilations_0 = const()[name = tensor("lora_out_99_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_99_groups_0 = const()[name = tensor("lora_out_99_groups_0"), val = tensor(1)]; + tensor layers_8_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_8_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(101241088)))]; + tensor lora_out_99_cast_fp16 = conv(dilations = lora_out_99_dilations_0, groups = lora_out_99_groups_0, pad = lora_out_99_pad_0, pad_type = lora_out_99_pad_type_0, strides = lora_out_99_strides_0, weight = layers_8_self_attn_k_proj_loraB_weight_to_fp16, x = input_163_cast_fp16)[name = tensor("lora_out_99_cast_fp16")]; + tensor key_17_cast_fp16 = add(x = pretrained_out_99_cast_fp16, y = lora_out_99_cast_fp16)[name = tensor("key_17_cast_fp16")]; + tensor pretrained_out_101_pad_type_0 = const()[name = tensor("pretrained_out_101_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_101_strides_0 = const()[name = tensor("pretrained_out_101_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_101_pad_0 = const()[name = tensor("pretrained_out_101_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_101_dilations_0 = const()[name = tensor("pretrained_out_101_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_101_groups_0 = const()[name = tensor("pretrained_out_101_groups_0"), val = tensor(1)]; + tensor layers_8_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(101282112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102101376))), name = tensor("layers_8_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_8_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_8_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102101504)))]; + tensor pretrained_out_101_cast_fp16 = conv(bias = layers_8_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_101_dilations_0, groups = pretrained_out_101_groups_0, pad = pretrained_out_101_pad_0, pad_type = pretrained_out_101_pad_type_0, strides = pretrained_out_101_strides_0, weight = layers_8_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_33_cast_fp16)[name = tensor("pretrained_out_101_cast_fp16")]; + tensor input_165_pad_type_0 = const()[name = tensor("input_165_pad_type_0"), val = tensor("valid")]; + tensor input_165_strides_0 = const()[name = tensor("input_165_strides_0"), val = tensor([1, 1])]; + tensor input_165_pad_0 = const()[name = tensor("input_165_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_165_dilations_0 = const()[name = tensor("input_165_dilations_0"), val = tensor([1, 1])]; + tensor input_165_groups_0 = const()[name = tensor("input_165_groups_0"), val = tensor(1)]; + tensor layers_8_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_8_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102104128)))]; + tensor input_165_cast_fp16 = conv(dilations = input_165_dilations_0, groups = input_165_groups_0, pad = input_165_pad_0, pad_type = input_165_pad_type_0, strides = input_165_strides_0, weight = layers_8_self_attn_v_proj_loraA_weight_to_fp16, x = obj_33_cast_fp16)[name = tensor("input_165_cast_fp16")]; + tensor lora_out_101_pad_type_0 = const()[name = tensor("lora_out_101_pad_type_0"), val = tensor("valid")]; + tensor lora_out_101_strides_0 = const()[name = tensor("lora_out_101_strides_0"), val = tensor([1, 1])]; + tensor lora_out_101_pad_0 = const()[name = tensor("lora_out_101_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_101_dilations_0 = const()[name = tensor("lora_out_101_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_101_groups_0 = const()[name = tensor("lora_out_101_groups_0"), val = tensor(1)]; + tensor layers_8_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_8_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102145152)))]; + tensor lora_out_101_cast_fp16 = conv(dilations = lora_out_101_dilations_0, groups = lora_out_101_groups_0, pad = lora_out_101_pad_0, pad_type = lora_out_101_pad_type_0, strides = lora_out_101_strides_0, weight = layers_8_self_attn_v_proj_loraB_weight_to_fp16, x = input_165_cast_fp16)[name = tensor("lora_out_101_cast_fp16")]; + tensor value_17_cast_fp16 = add(x = pretrained_out_101_cast_fp16, y = lora_out_101_cast_fp16)[name = tensor("value_17_cast_fp16")]; + tensor var_1992 = const()[name = tensor("op_1992"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_17_cast_fp16 = reshape(shape = var_1992, x = query_17_cast_fp16)[name = tensor("mh_q_17_cast_fp16")]; + tensor var_1994_to_fp16 = const()[name = tensor("op_1994_to_fp16"), val = tensor(0x1p-3)]; + tensor var_1995_cast_fp16 = mul(x = mh_q_17_cast_fp16, y = var_1994_to_fp16)[name = tensor("op_1995_cast_fp16")]; + tensor var_1996 = const()[name = tensor("op_1996"), val = tensor([1, 20, 64, -1])]; + tensor var_1997_cast_fp16 = reshape(shape = var_1996, x = key_17_cast_fp16)[name = tensor("op_1997_cast_fp16")]; + tensor mh_w_17_transpose_x_0 = const()[name = tensor("mh_w_17_transpose_x_0"), val = tensor(true)]; + tensor mh_w_17_transpose_y_0 = const()[name = tensor("mh_w_17_transpose_y_0"), val = tensor(false)]; + tensor mh_w_17_cast_fp16 = matmul(transpose_x = mh_w_17_transpose_x_0, transpose_y = mh_w_17_transpose_y_0, x = var_1995_cast_fp16, y = var_1997_cast_fp16)[name = tensor("mh_w_17_cast_fp16")]; + tensor var_2000_cast_fp16 = softmax(axis = var_1890, x = mh_w_17_cast_fp16)[name = tensor("op_2000_cast_fp16")]; + tensor var_2001 = const()[name = tensor("op_2001"), val = tensor([1, 20, 64, -1])]; + tensor var_2002_cast_fp16 = reshape(shape = var_2001, x = value_17_cast_fp16)[name = tensor("op_2002_cast_fp16")]; + tensor attn_17_transpose_x_0 = const()[name = tensor("attn_17_transpose_x_0"), val = tensor(false)]; + tensor attn_17_transpose_y_0 = const()[name = tensor("attn_17_transpose_y_0"), val = tensor(true)]; + tensor attn_17_cast_fp16 = matmul(transpose_x = attn_17_transpose_x_0, transpose_y = attn_17_transpose_y_0, x = var_2002_cast_fp16, y = var_2000_cast_fp16)[name = tensor("attn_17_cast_fp16")]; + tensor var_2005 = const()[name = tensor("op_2005"), val = tensor([1, 1280, 1, -1])]; + tensor input_167_cast_fp16 = reshape(shape = var_2005, x = attn_17_cast_fp16)[name = tensor("input_167_cast_fp16")]; + tensor pretrained_out_103_pad_type_0 = const()[name = tensor("pretrained_out_103_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_103_strides_0 = const()[name = tensor("pretrained_out_103_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_103_pad_0 = const()[name = tensor("pretrained_out_103_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_103_dilations_0 = const()[name = tensor("pretrained_out_103_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_103_groups_0 = const()[name = tensor("pretrained_out_103_groups_0"), val = tensor(1)]; + tensor layers_8_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102186176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(103005440))), name = tensor("layers_8_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_8_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_8_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(103005568)))]; + tensor pretrained_out_103_cast_fp16 = conv(bias = layers_8_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_103_dilations_0, groups = pretrained_out_103_groups_0, pad = pretrained_out_103_pad_0, pad_type = pretrained_out_103_pad_type_0, strides = pretrained_out_103_strides_0, weight = layers_8_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_167_cast_fp16)[name = tensor("pretrained_out_103_cast_fp16")]; + tensor input_169_pad_type_0 = const()[name = tensor("input_169_pad_type_0"), val = tensor("valid")]; + tensor input_169_strides_0 = const()[name = tensor("input_169_strides_0"), val = tensor([1, 1])]; + tensor input_169_pad_0 = const()[name = tensor("input_169_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_169_dilations_0 = const()[name = tensor("input_169_dilations_0"), val = tensor([1, 1])]; + tensor input_169_groups_0 = const()[name = tensor("input_169_groups_0"), val = tensor(1)]; + tensor layers_8_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_8_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(103008192)))]; + tensor input_169_cast_fp16 = conv(dilations = input_169_dilations_0, groups = input_169_groups_0, pad = input_169_pad_0, pad_type = input_169_pad_type_0, strides = input_169_strides_0, weight = layers_8_self_attn_o_proj_loraA_weight_to_fp16, x = input_167_cast_fp16)[name = tensor("input_169_cast_fp16")]; + tensor lora_out_103_pad_type_0 = const()[name = tensor("lora_out_103_pad_type_0"), val = tensor("valid")]; + tensor lora_out_103_strides_0 = const()[name = tensor("lora_out_103_strides_0"), val = tensor([1, 1])]; + tensor lora_out_103_pad_0 = const()[name = tensor("lora_out_103_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_103_dilations_0 = const()[name = tensor("lora_out_103_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_103_groups_0 = const()[name = tensor("lora_out_103_groups_0"), val = tensor(1)]; + tensor layers_8_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_8_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(103049216)))]; + tensor lora_out_103_cast_fp16 = conv(dilations = lora_out_103_dilations_0, groups = lora_out_103_groups_0, pad = lora_out_103_pad_0, pad_type = lora_out_103_pad_type_0, strides = lora_out_103_strides_0, weight = layers_8_self_attn_o_proj_loraB_weight_to_fp16, x = input_169_cast_fp16)[name = tensor("lora_out_103_cast_fp16")]; + tensor obj_35_cast_fp16 = add(x = pretrained_out_103_cast_fp16, y = lora_out_103_cast_fp16)[name = tensor("obj_35_cast_fp16")]; + tensor inputs_35_cast_fp16 = add(x = inputs_33_cast_fp16, y = obj_35_cast_fp16)[name = tensor("inputs_35_cast_fp16")]; + tensor out_35_axes_0 = const()[name = tensor("out_35_axes_0"), val = tensor([1])]; + tensor var_2039_to_fp16 = const()[name = tensor("op_2039_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_35_cast_fp16 = layer_norm(axes = out_35_axes_0, epsilon = var_2039_to_fp16, x = inputs_35_cast_fp16)[name = tensor("out_35_cast_fp16")]; + tensor input_171_gamma_0_to_fp16 = const()[name = tensor("input_171_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(103090240)))]; + tensor input_171_beta_0_to_fp16 = const()[name = tensor("input_171_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(103092864)))]; + tensor input_171_epsilon_0_to_fp16 = const()[name = tensor("input_171_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_171_cast_fp16 = batch_norm(beta = input_171_beta_0_to_fp16, epsilon = input_171_epsilon_0_to_fp16, gamma = input_171_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_35_cast_fp16)[name = tensor("input_171_cast_fp16")]; + tensor pretrained_out_105_pad_type_0 = const()[name = tensor("pretrained_out_105_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_105_strides_0 = const()[name = tensor("pretrained_out_105_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_105_pad_0 = const()[name = tensor("pretrained_out_105_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_105_dilations_0 = const()[name = tensor("pretrained_out_105_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_105_groups_0 = const()[name = tensor("pretrained_out_105_groups_0"), val = tensor(1)]; + tensor layers_8_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(103095488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106372352))), name = tensor("layers_8_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_8_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_8_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106372480)))]; + tensor pretrained_out_105_cast_fp16 = conv(bias = layers_8_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_105_dilations_0, groups = pretrained_out_105_groups_0, pad = pretrained_out_105_pad_0, pad_type = pretrained_out_105_pad_type_0, strides = pretrained_out_105_strides_0, weight = layers_8_fc1_pretrained_weight_to_fp16_palettized, x = input_171_cast_fp16)[name = tensor("pretrained_out_105_cast_fp16")]; + tensor input_173_pad_type_0 = const()[name = tensor("input_173_pad_type_0"), val = tensor("valid")]; + tensor input_173_strides_0 = const()[name = tensor("input_173_strides_0"), val = tensor([1, 1])]; + tensor input_173_pad_0 = const()[name = tensor("input_173_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_173_dilations_0 = const()[name = tensor("input_173_dilations_0"), val = tensor([1, 1])]; + tensor input_173_groups_0 = const()[name = tensor("input_173_groups_0"), val = tensor(1)]; + tensor layers_8_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_8_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106382784)))]; + tensor input_173_cast_fp16 = conv(dilations = input_173_dilations_0, groups = input_173_groups_0, pad = input_173_pad_0, pad_type = input_173_pad_type_0, strides = input_173_strides_0, weight = layers_8_fc1_loraA_weight_to_fp16, x = input_171_cast_fp16)[name = tensor("input_173_cast_fp16")]; + tensor lora_out_105_pad_type_0 = const()[name = tensor("lora_out_105_pad_type_0"), val = tensor("valid")]; + tensor lora_out_105_strides_0 = const()[name = tensor("lora_out_105_strides_0"), val = tensor([1, 1])]; + tensor lora_out_105_pad_0 = const()[name = tensor("lora_out_105_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_105_dilations_0 = const()[name = tensor("lora_out_105_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_105_groups_0 = const()[name = tensor("lora_out_105_groups_0"), val = tensor(1)]; + tensor layers_8_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_8_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106423808)))]; + tensor lora_out_105_cast_fp16 = conv(dilations = lora_out_105_dilations_0, groups = lora_out_105_groups_0, pad = lora_out_105_pad_0, pad_type = lora_out_105_pad_type_0, strides = lora_out_105_strides_0, weight = layers_8_fc1_loraB_weight_to_fp16, x = input_173_cast_fp16)[name = tensor("lora_out_105_cast_fp16")]; + tensor input_175_cast_fp16 = add(x = pretrained_out_105_cast_fp16, y = lora_out_105_cast_fp16)[name = tensor("input_175_cast_fp16")]; + tensor input_177_mode_0 = const()[name = tensor("input_177_mode_0"), val = tensor("EXACT")]; + tensor input_177_cast_fp16 = gelu(mode = input_177_mode_0, x = input_175_cast_fp16)[name = tensor("input_177_cast_fp16")]; + tensor pretrained_out_107_pad_type_0 = const()[name = tensor("pretrained_out_107_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_107_strides_0 = const()[name = tensor("pretrained_out_107_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_107_pad_0 = const()[name = tensor("pretrained_out_107_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_107_dilations_0 = const()[name = tensor("pretrained_out_107_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_107_groups_0 = const()[name = tensor("pretrained_out_107_groups_0"), val = tensor(1)]; + tensor layers_8_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106587712))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109864576))), name = tensor("layers_8_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_8_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_8_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109864704)))]; + tensor pretrained_out_107_cast_fp16 = conv(bias = layers_8_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_107_dilations_0, groups = pretrained_out_107_groups_0, pad = pretrained_out_107_pad_0, pad_type = pretrained_out_107_pad_type_0, strides = pretrained_out_107_strides_0, weight = layers_8_fc2_pretrained_weight_to_fp16_palettized, x = input_177_cast_fp16)[name = tensor("pretrained_out_107_cast_fp16")]; + tensor input_179_pad_type_0 = const()[name = tensor("input_179_pad_type_0"), val = tensor("valid")]; + tensor input_179_strides_0 = const()[name = tensor("input_179_strides_0"), val = tensor([1, 1])]; + tensor input_179_pad_0 = const()[name = tensor("input_179_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_179_dilations_0 = const()[name = tensor("input_179_dilations_0"), val = tensor([1, 1])]; + tensor input_179_groups_0 = const()[name = tensor("input_179_groups_0"), val = tensor(1)]; + tensor layers_8_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_8_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109867328)))]; + tensor input_179_cast_fp16 = conv(dilations = input_179_dilations_0, groups = input_179_groups_0, pad = input_179_pad_0, pad_type = input_179_pad_type_0, strides = input_179_strides_0, weight = layers_8_fc2_loraA_weight_to_fp16, x = input_177_cast_fp16)[name = tensor("input_179_cast_fp16")]; + tensor lora_out_107_pad_type_0 = const()[name = tensor("lora_out_107_pad_type_0"), val = tensor("valid")]; + tensor lora_out_107_strides_0 = const()[name = tensor("lora_out_107_strides_0"), val = tensor([1, 1])]; + tensor lora_out_107_pad_0 = const()[name = tensor("lora_out_107_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_107_dilations_0 = const()[name = tensor("lora_out_107_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_107_groups_0 = const()[name = tensor("lora_out_107_groups_0"), val = tensor(1)]; + tensor layers_8_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_8_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110031232)))]; + tensor lora_out_107_cast_fp16 = conv(dilations = lora_out_107_dilations_0, groups = lora_out_107_groups_0, pad = lora_out_107_pad_0, pad_type = lora_out_107_pad_type_0, strides = lora_out_107_strides_0, weight = layers_8_fc2_loraB_weight_to_fp16, x = input_179_cast_fp16)[name = tensor("lora_out_107_cast_fp16")]; + tensor hidden_states_21_cast_fp16 = add(x = pretrained_out_107_cast_fp16, y = lora_out_107_cast_fp16)[name = tensor("hidden_states_21_cast_fp16")]; + tensor inputs_37_cast_fp16 = add(x = inputs_35_cast_fp16, y = hidden_states_21_cast_fp16)[name = tensor("inputs_37_cast_fp16")]; + tensor var_2104 = const()[name = tensor("op_2104"), val = tensor(3)]; + tensor out_37_axes_0 = const()[name = tensor("out_37_axes_0"), val = tensor([1])]; + tensor var_2123_to_fp16 = const()[name = tensor("op_2123_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_37_cast_fp16 = layer_norm(axes = out_37_axes_0, epsilon = var_2123_to_fp16, x = inputs_37_cast_fp16)[name = tensor("out_37_cast_fp16")]; + tensor obj_37_gamma_0_to_fp16 = const()[name = tensor("obj_37_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110072256)))]; + tensor obj_37_beta_0_to_fp16 = const()[name = tensor("obj_37_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110074880)))]; + tensor obj_37_epsilon_0_to_fp16 = const()[name = tensor("obj_37_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_37_cast_fp16 = batch_norm(beta = obj_37_beta_0_to_fp16, epsilon = obj_37_epsilon_0_to_fp16, gamma = obj_37_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_37_cast_fp16)[name = tensor("obj_37_cast_fp16")]; + tensor pretrained_out_109_pad_type_0 = const()[name = tensor("pretrained_out_109_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_109_strides_0 = const()[name = tensor("pretrained_out_109_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_109_pad_0 = const()[name = tensor("pretrained_out_109_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_109_dilations_0 = const()[name = tensor("pretrained_out_109_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_109_groups_0 = const()[name = tensor("pretrained_out_109_groups_0"), val = tensor(1)]; + tensor layers_9_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110077504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110896768))), name = tensor("layers_9_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_9_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_9_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110896896)))]; + tensor pretrained_out_109_cast_fp16 = conv(bias = layers_9_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_109_dilations_0, groups = pretrained_out_109_groups_0, pad = pretrained_out_109_pad_0, pad_type = pretrained_out_109_pad_type_0, strides = pretrained_out_109_strides_0, weight = layers_9_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_37_cast_fp16)[name = tensor("pretrained_out_109_cast_fp16")]; + tensor input_181_pad_type_0 = const()[name = tensor("input_181_pad_type_0"), val = tensor("valid")]; + tensor input_181_strides_0 = const()[name = tensor("input_181_strides_0"), val = tensor([1, 1])]; + tensor input_181_pad_0 = const()[name = tensor("input_181_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_181_dilations_0 = const()[name = tensor("input_181_dilations_0"), val = tensor([1, 1])]; + tensor input_181_groups_0 = const()[name = tensor("input_181_groups_0"), val = tensor(1)]; + tensor layers_9_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_9_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110899520)))]; + tensor input_181_cast_fp16 = conv(dilations = input_181_dilations_0, groups = input_181_groups_0, pad = input_181_pad_0, pad_type = input_181_pad_type_0, strides = input_181_strides_0, weight = layers_9_self_attn_q_proj_loraA_weight_to_fp16, x = obj_37_cast_fp16)[name = tensor("input_181_cast_fp16")]; + tensor lora_out_109_pad_type_0 = const()[name = tensor("lora_out_109_pad_type_0"), val = tensor("valid")]; + tensor lora_out_109_strides_0 = const()[name = tensor("lora_out_109_strides_0"), val = tensor([1, 1])]; + tensor lora_out_109_pad_0 = const()[name = tensor("lora_out_109_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_109_dilations_0 = const()[name = tensor("lora_out_109_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_109_groups_0 = const()[name = tensor("lora_out_109_groups_0"), val = tensor(1)]; + tensor layers_9_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_9_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110940544)))]; + tensor lora_out_109_cast_fp16 = conv(dilations = lora_out_109_dilations_0, groups = lora_out_109_groups_0, pad = lora_out_109_pad_0, pad_type = lora_out_109_pad_type_0, strides = lora_out_109_strides_0, weight = layers_9_self_attn_q_proj_loraB_weight_to_fp16, x = input_181_cast_fp16)[name = tensor("lora_out_109_cast_fp16")]; + tensor query_19_cast_fp16 = add(x = pretrained_out_109_cast_fp16, y = lora_out_109_cast_fp16)[name = tensor("query_19_cast_fp16")]; + tensor pretrained_out_111_pad_type_0 = const()[name = tensor("pretrained_out_111_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_111_strides_0 = const()[name = tensor("pretrained_out_111_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_111_pad_0 = const()[name = tensor("pretrained_out_111_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_111_dilations_0 = const()[name = tensor("pretrained_out_111_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_111_groups_0 = const()[name = tensor("pretrained_out_111_groups_0"), val = tensor(1)]; + tensor layers_9_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(110981568))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111800832))), name = tensor("layers_9_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_111_cast_fp16 = conv(dilations = pretrained_out_111_dilations_0, groups = pretrained_out_111_groups_0, pad = pretrained_out_111_pad_0, pad_type = pretrained_out_111_pad_type_0, strides = pretrained_out_111_strides_0, weight = layers_9_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_37_cast_fp16)[name = tensor("pretrained_out_111_cast_fp16")]; + tensor input_183_pad_type_0 = const()[name = tensor("input_183_pad_type_0"), val = tensor("valid")]; + tensor input_183_strides_0 = const()[name = tensor("input_183_strides_0"), val = tensor([1, 1])]; + tensor input_183_pad_0 = const()[name = tensor("input_183_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_183_dilations_0 = const()[name = tensor("input_183_dilations_0"), val = tensor([1, 1])]; + tensor input_183_groups_0 = const()[name = tensor("input_183_groups_0"), val = tensor(1)]; + tensor layers_9_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_9_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111800960)))]; + tensor input_183_cast_fp16 = conv(dilations = input_183_dilations_0, groups = input_183_groups_0, pad = input_183_pad_0, pad_type = input_183_pad_type_0, strides = input_183_strides_0, weight = layers_9_self_attn_k_proj_loraA_weight_to_fp16, x = obj_37_cast_fp16)[name = tensor("input_183_cast_fp16")]; + tensor lora_out_111_pad_type_0 = const()[name = tensor("lora_out_111_pad_type_0"), val = tensor("valid")]; + tensor lora_out_111_strides_0 = const()[name = tensor("lora_out_111_strides_0"), val = tensor([1, 1])]; + tensor lora_out_111_pad_0 = const()[name = tensor("lora_out_111_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_111_dilations_0 = const()[name = tensor("lora_out_111_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_111_groups_0 = const()[name = tensor("lora_out_111_groups_0"), val = tensor(1)]; + tensor layers_9_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_9_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111841984)))]; + tensor lora_out_111_cast_fp16 = conv(dilations = lora_out_111_dilations_0, groups = lora_out_111_groups_0, pad = lora_out_111_pad_0, pad_type = lora_out_111_pad_type_0, strides = lora_out_111_strides_0, weight = layers_9_self_attn_k_proj_loraB_weight_to_fp16, x = input_183_cast_fp16)[name = tensor("lora_out_111_cast_fp16")]; + tensor key_19_cast_fp16 = add(x = pretrained_out_111_cast_fp16, y = lora_out_111_cast_fp16)[name = tensor("key_19_cast_fp16")]; + tensor pretrained_out_113_pad_type_0 = const()[name = tensor("pretrained_out_113_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_113_strides_0 = const()[name = tensor("pretrained_out_113_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_113_pad_0 = const()[name = tensor("pretrained_out_113_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_113_dilations_0 = const()[name = tensor("pretrained_out_113_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_113_groups_0 = const()[name = tensor("pretrained_out_113_groups_0"), val = tensor(1)]; + tensor layers_9_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111883008))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(112702272))), name = tensor("layers_9_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_9_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_9_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(112702400)))]; + tensor pretrained_out_113_cast_fp16 = conv(bias = layers_9_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_113_dilations_0, groups = pretrained_out_113_groups_0, pad = pretrained_out_113_pad_0, pad_type = pretrained_out_113_pad_type_0, strides = pretrained_out_113_strides_0, weight = layers_9_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_37_cast_fp16)[name = tensor("pretrained_out_113_cast_fp16")]; + tensor input_185_pad_type_0 = const()[name = tensor("input_185_pad_type_0"), val = tensor("valid")]; + tensor input_185_strides_0 = const()[name = tensor("input_185_strides_0"), val = tensor([1, 1])]; + tensor input_185_pad_0 = const()[name = tensor("input_185_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_185_dilations_0 = const()[name = tensor("input_185_dilations_0"), val = tensor([1, 1])]; + tensor input_185_groups_0 = const()[name = tensor("input_185_groups_0"), val = tensor(1)]; + tensor layers_9_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_9_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(112705024)))]; + tensor input_185_cast_fp16 = conv(dilations = input_185_dilations_0, groups = input_185_groups_0, pad = input_185_pad_0, pad_type = input_185_pad_type_0, strides = input_185_strides_0, weight = layers_9_self_attn_v_proj_loraA_weight_to_fp16, x = obj_37_cast_fp16)[name = tensor("input_185_cast_fp16")]; + tensor lora_out_113_pad_type_0 = const()[name = tensor("lora_out_113_pad_type_0"), val = tensor("valid")]; + tensor lora_out_113_strides_0 = const()[name = tensor("lora_out_113_strides_0"), val = tensor([1, 1])]; + tensor lora_out_113_pad_0 = const()[name = tensor("lora_out_113_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_113_dilations_0 = const()[name = tensor("lora_out_113_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_113_groups_0 = const()[name = tensor("lora_out_113_groups_0"), val = tensor(1)]; + tensor layers_9_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_9_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(112746048)))]; + tensor lora_out_113_cast_fp16 = conv(dilations = lora_out_113_dilations_0, groups = lora_out_113_groups_0, pad = lora_out_113_pad_0, pad_type = lora_out_113_pad_type_0, strides = lora_out_113_strides_0, weight = layers_9_self_attn_v_proj_loraB_weight_to_fp16, x = input_185_cast_fp16)[name = tensor("lora_out_113_cast_fp16")]; + tensor value_19_cast_fp16 = add(x = pretrained_out_113_cast_fp16, y = lora_out_113_cast_fp16)[name = tensor("value_19_cast_fp16")]; + tensor var_2206 = const()[name = tensor("op_2206"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_19_cast_fp16 = reshape(shape = var_2206, x = query_19_cast_fp16)[name = tensor("mh_q_19_cast_fp16")]; + tensor var_2208_to_fp16 = const()[name = tensor("op_2208_to_fp16"), val = tensor(0x1p-3)]; + tensor var_2209_cast_fp16 = mul(x = mh_q_19_cast_fp16, y = var_2208_to_fp16)[name = tensor("op_2209_cast_fp16")]; + tensor var_2210 = const()[name = tensor("op_2210"), val = tensor([1, 20, 64, -1])]; + tensor var_2211_cast_fp16 = reshape(shape = var_2210, x = key_19_cast_fp16)[name = tensor("op_2211_cast_fp16")]; + tensor mh_w_19_transpose_x_0 = const()[name = tensor("mh_w_19_transpose_x_0"), val = tensor(true)]; + tensor mh_w_19_transpose_y_0 = const()[name = tensor("mh_w_19_transpose_y_0"), val = tensor(false)]; + tensor mh_w_19_cast_fp16 = matmul(transpose_x = mh_w_19_transpose_x_0, transpose_y = mh_w_19_transpose_y_0, x = var_2209_cast_fp16, y = var_2211_cast_fp16)[name = tensor("mh_w_19_cast_fp16")]; + tensor var_2214_cast_fp16 = softmax(axis = var_2104, x = mh_w_19_cast_fp16)[name = tensor("op_2214_cast_fp16")]; + tensor var_2215 = const()[name = tensor("op_2215"), val = tensor([1, 20, 64, -1])]; + tensor var_2216_cast_fp16 = reshape(shape = var_2215, x = value_19_cast_fp16)[name = tensor("op_2216_cast_fp16")]; + tensor attn_19_transpose_x_0 = const()[name = tensor("attn_19_transpose_x_0"), val = tensor(false)]; + tensor attn_19_transpose_y_0 = const()[name = tensor("attn_19_transpose_y_0"), val = tensor(true)]; + tensor attn_19_cast_fp16 = matmul(transpose_x = attn_19_transpose_x_0, transpose_y = attn_19_transpose_y_0, x = var_2216_cast_fp16, y = var_2214_cast_fp16)[name = tensor("attn_19_cast_fp16")]; + tensor var_2219 = const()[name = tensor("op_2219"), val = tensor([1, 1280, 1, -1])]; + tensor input_187_cast_fp16 = reshape(shape = var_2219, x = attn_19_cast_fp16)[name = tensor("input_187_cast_fp16")]; + tensor pretrained_out_115_pad_type_0 = const()[name = tensor("pretrained_out_115_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_115_strides_0 = const()[name = tensor("pretrained_out_115_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_115_pad_0 = const()[name = tensor("pretrained_out_115_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_115_dilations_0 = const()[name = tensor("pretrained_out_115_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_115_groups_0 = const()[name = tensor("pretrained_out_115_groups_0"), val = tensor(1)]; + tensor layers_9_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(112787072))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113606336))), name = tensor("layers_9_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_9_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_9_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113606464)))]; + tensor pretrained_out_115_cast_fp16 = conv(bias = layers_9_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_115_dilations_0, groups = pretrained_out_115_groups_0, pad = pretrained_out_115_pad_0, pad_type = pretrained_out_115_pad_type_0, strides = pretrained_out_115_strides_0, weight = layers_9_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_187_cast_fp16)[name = tensor("pretrained_out_115_cast_fp16")]; + tensor input_189_pad_type_0 = const()[name = tensor("input_189_pad_type_0"), val = tensor("valid")]; + tensor input_189_strides_0 = const()[name = tensor("input_189_strides_0"), val = tensor([1, 1])]; + tensor input_189_pad_0 = const()[name = tensor("input_189_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_189_dilations_0 = const()[name = tensor("input_189_dilations_0"), val = tensor([1, 1])]; + tensor input_189_groups_0 = const()[name = tensor("input_189_groups_0"), val = tensor(1)]; + tensor layers_9_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_9_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113609088)))]; + tensor input_189_cast_fp16 = conv(dilations = input_189_dilations_0, groups = input_189_groups_0, pad = input_189_pad_0, pad_type = input_189_pad_type_0, strides = input_189_strides_0, weight = layers_9_self_attn_o_proj_loraA_weight_to_fp16, x = input_187_cast_fp16)[name = tensor("input_189_cast_fp16")]; + tensor lora_out_115_pad_type_0 = const()[name = tensor("lora_out_115_pad_type_0"), val = tensor("valid")]; + tensor lora_out_115_strides_0 = const()[name = tensor("lora_out_115_strides_0"), val = tensor([1, 1])]; + tensor lora_out_115_pad_0 = const()[name = tensor("lora_out_115_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_115_dilations_0 = const()[name = tensor("lora_out_115_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_115_groups_0 = const()[name = tensor("lora_out_115_groups_0"), val = tensor(1)]; + tensor layers_9_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_9_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113650112)))]; + tensor lora_out_115_cast_fp16 = conv(dilations = lora_out_115_dilations_0, groups = lora_out_115_groups_0, pad = lora_out_115_pad_0, pad_type = lora_out_115_pad_type_0, strides = lora_out_115_strides_0, weight = layers_9_self_attn_o_proj_loraB_weight_to_fp16, x = input_189_cast_fp16)[name = tensor("lora_out_115_cast_fp16")]; + tensor obj_39_cast_fp16 = add(x = pretrained_out_115_cast_fp16, y = lora_out_115_cast_fp16)[name = tensor("obj_39_cast_fp16")]; + tensor inputs_39_cast_fp16 = add(x = inputs_37_cast_fp16, y = obj_39_cast_fp16)[name = tensor("inputs_39_cast_fp16")]; + tensor out_39_axes_0 = const()[name = tensor("out_39_axes_0"), val = tensor([1])]; + tensor var_2253_to_fp16 = const()[name = tensor("op_2253_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_39_cast_fp16 = layer_norm(axes = out_39_axes_0, epsilon = var_2253_to_fp16, x = inputs_39_cast_fp16)[name = tensor("out_39_cast_fp16")]; + tensor input_191_gamma_0_to_fp16 = const()[name = tensor("input_191_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113691136)))]; + tensor input_191_beta_0_to_fp16 = const()[name = tensor("input_191_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113693760)))]; + tensor input_191_epsilon_0_to_fp16 = const()[name = tensor("input_191_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_191_cast_fp16 = batch_norm(beta = input_191_beta_0_to_fp16, epsilon = input_191_epsilon_0_to_fp16, gamma = input_191_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_39_cast_fp16)[name = tensor("input_191_cast_fp16")]; + tensor pretrained_out_117_pad_type_0 = const()[name = tensor("pretrained_out_117_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_117_strides_0 = const()[name = tensor("pretrained_out_117_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_117_pad_0 = const()[name = tensor("pretrained_out_117_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_117_dilations_0 = const()[name = tensor("pretrained_out_117_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_117_groups_0 = const()[name = tensor("pretrained_out_117_groups_0"), val = tensor(1)]; + tensor layers_9_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113696384))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(116973248))), name = tensor("layers_9_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_9_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_9_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(116973376)))]; + tensor pretrained_out_117_cast_fp16 = conv(bias = layers_9_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_117_dilations_0, groups = pretrained_out_117_groups_0, pad = pretrained_out_117_pad_0, pad_type = pretrained_out_117_pad_type_0, strides = pretrained_out_117_strides_0, weight = layers_9_fc1_pretrained_weight_to_fp16_palettized, x = input_191_cast_fp16)[name = tensor("pretrained_out_117_cast_fp16")]; + tensor input_193_pad_type_0 = const()[name = tensor("input_193_pad_type_0"), val = tensor("valid")]; + tensor input_193_strides_0 = const()[name = tensor("input_193_strides_0"), val = tensor([1, 1])]; + tensor input_193_pad_0 = const()[name = tensor("input_193_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_193_dilations_0 = const()[name = tensor("input_193_dilations_0"), val = tensor([1, 1])]; + tensor input_193_groups_0 = const()[name = tensor("input_193_groups_0"), val = tensor(1)]; + tensor layers_9_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_9_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(116983680)))]; + tensor input_193_cast_fp16 = conv(dilations = input_193_dilations_0, groups = input_193_groups_0, pad = input_193_pad_0, pad_type = input_193_pad_type_0, strides = input_193_strides_0, weight = layers_9_fc1_loraA_weight_to_fp16, x = input_191_cast_fp16)[name = tensor("input_193_cast_fp16")]; + tensor lora_out_117_pad_type_0 = const()[name = tensor("lora_out_117_pad_type_0"), val = tensor("valid")]; + tensor lora_out_117_strides_0 = const()[name = tensor("lora_out_117_strides_0"), val = tensor([1, 1])]; + tensor lora_out_117_pad_0 = const()[name = tensor("lora_out_117_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_117_dilations_0 = const()[name = tensor("lora_out_117_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_117_groups_0 = const()[name = tensor("lora_out_117_groups_0"), val = tensor(1)]; + tensor layers_9_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_9_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117024704)))]; + tensor lora_out_117_cast_fp16 = conv(dilations = lora_out_117_dilations_0, groups = lora_out_117_groups_0, pad = lora_out_117_pad_0, pad_type = lora_out_117_pad_type_0, strides = lora_out_117_strides_0, weight = layers_9_fc1_loraB_weight_to_fp16, x = input_193_cast_fp16)[name = tensor("lora_out_117_cast_fp16")]; + tensor input_195_cast_fp16 = add(x = pretrained_out_117_cast_fp16, y = lora_out_117_cast_fp16)[name = tensor("input_195_cast_fp16")]; + tensor input_197_mode_0 = const()[name = tensor("input_197_mode_0"), val = tensor("EXACT")]; + tensor input_197_cast_fp16 = gelu(mode = input_197_mode_0, x = input_195_cast_fp16)[name = tensor("input_197_cast_fp16")]; + tensor pretrained_out_119_pad_type_0 = const()[name = tensor("pretrained_out_119_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_119_strides_0 = const()[name = tensor("pretrained_out_119_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_119_pad_0 = const()[name = tensor("pretrained_out_119_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_119_dilations_0 = const()[name = tensor("pretrained_out_119_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_119_groups_0 = const()[name = tensor("pretrained_out_119_groups_0"), val = tensor(1)]; + tensor layers_9_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117188608))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120465472))), name = tensor("layers_9_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_9_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_9_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120465600)))]; + tensor pretrained_out_119_cast_fp16 = conv(bias = layers_9_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_119_dilations_0, groups = pretrained_out_119_groups_0, pad = pretrained_out_119_pad_0, pad_type = pretrained_out_119_pad_type_0, strides = pretrained_out_119_strides_0, weight = layers_9_fc2_pretrained_weight_to_fp16_palettized, x = input_197_cast_fp16)[name = tensor("pretrained_out_119_cast_fp16")]; + tensor input_199_pad_type_0 = const()[name = tensor("input_199_pad_type_0"), val = tensor("valid")]; + tensor input_199_strides_0 = const()[name = tensor("input_199_strides_0"), val = tensor([1, 1])]; + tensor input_199_pad_0 = const()[name = tensor("input_199_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_199_dilations_0 = const()[name = tensor("input_199_dilations_0"), val = tensor([1, 1])]; + tensor input_199_groups_0 = const()[name = tensor("input_199_groups_0"), val = tensor(1)]; + tensor layers_9_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_9_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120468224)))]; + tensor input_199_cast_fp16 = conv(dilations = input_199_dilations_0, groups = input_199_groups_0, pad = input_199_pad_0, pad_type = input_199_pad_type_0, strides = input_199_strides_0, weight = layers_9_fc2_loraA_weight_to_fp16, x = input_197_cast_fp16)[name = tensor("input_199_cast_fp16")]; + tensor lora_out_119_pad_type_0 = const()[name = tensor("lora_out_119_pad_type_0"), val = tensor("valid")]; + tensor lora_out_119_strides_0 = const()[name = tensor("lora_out_119_strides_0"), val = tensor([1, 1])]; + tensor lora_out_119_pad_0 = const()[name = tensor("lora_out_119_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_119_dilations_0 = const()[name = tensor("lora_out_119_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_119_groups_0 = const()[name = tensor("lora_out_119_groups_0"), val = tensor(1)]; + tensor layers_9_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_9_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120632128)))]; + tensor lora_out_119_cast_fp16 = conv(dilations = lora_out_119_dilations_0, groups = lora_out_119_groups_0, pad = lora_out_119_pad_0, pad_type = lora_out_119_pad_type_0, strides = lora_out_119_strides_0, weight = layers_9_fc2_loraB_weight_to_fp16, x = input_199_cast_fp16)[name = tensor("lora_out_119_cast_fp16")]; + tensor hidden_states_23_cast_fp16 = add(x = pretrained_out_119_cast_fp16, y = lora_out_119_cast_fp16)[name = tensor("hidden_states_23_cast_fp16")]; + tensor inputs_41_cast_fp16 = add(x = inputs_39_cast_fp16, y = hidden_states_23_cast_fp16)[name = tensor("inputs_41_cast_fp16")]; + tensor var_2318 = const()[name = tensor("op_2318"), val = tensor(3)]; + tensor out_41_axes_0 = const()[name = tensor("out_41_axes_0"), val = tensor([1])]; + tensor var_2337_to_fp16 = const()[name = tensor("op_2337_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_41_cast_fp16 = layer_norm(axes = out_41_axes_0, epsilon = var_2337_to_fp16, x = inputs_41_cast_fp16)[name = tensor("out_41_cast_fp16")]; + tensor obj_41_gamma_0_to_fp16 = const()[name = tensor("obj_41_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120673152)))]; + tensor obj_41_beta_0_to_fp16 = const()[name = tensor("obj_41_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120675776)))]; + tensor obj_41_epsilon_0_to_fp16 = const()[name = tensor("obj_41_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_41_cast_fp16 = batch_norm(beta = obj_41_beta_0_to_fp16, epsilon = obj_41_epsilon_0_to_fp16, gamma = obj_41_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_41_cast_fp16)[name = tensor("obj_41_cast_fp16")]; + tensor pretrained_out_121_pad_type_0 = const()[name = tensor("pretrained_out_121_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_121_strides_0 = const()[name = tensor("pretrained_out_121_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_121_pad_0 = const()[name = tensor("pretrained_out_121_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_121_dilations_0 = const()[name = tensor("pretrained_out_121_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_121_groups_0 = const()[name = tensor("pretrained_out_121_groups_0"), val = tensor(1)]; + tensor layers_10_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120678400))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121497664))), name = tensor("layers_10_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_10_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_10_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121497792)))]; + tensor pretrained_out_121_cast_fp16 = conv(bias = layers_10_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_121_dilations_0, groups = pretrained_out_121_groups_0, pad = pretrained_out_121_pad_0, pad_type = pretrained_out_121_pad_type_0, strides = pretrained_out_121_strides_0, weight = layers_10_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_41_cast_fp16)[name = tensor("pretrained_out_121_cast_fp16")]; + tensor input_201_pad_type_0 = const()[name = tensor("input_201_pad_type_0"), val = tensor("valid")]; + tensor input_201_strides_0 = const()[name = tensor("input_201_strides_0"), val = tensor([1, 1])]; + tensor input_201_pad_0 = const()[name = tensor("input_201_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_201_dilations_0 = const()[name = tensor("input_201_dilations_0"), val = tensor([1, 1])]; + tensor input_201_groups_0 = const()[name = tensor("input_201_groups_0"), val = tensor(1)]; + tensor layers_10_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_10_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121500416)))]; + tensor input_201_cast_fp16 = conv(dilations = input_201_dilations_0, groups = input_201_groups_0, pad = input_201_pad_0, pad_type = input_201_pad_type_0, strides = input_201_strides_0, weight = layers_10_self_attn_q_proj_loraA_weight_to_fp16, x = obj_41_cast_fp16)[name = tensor("input_201_cast_fp16")]; + tensor lora_out_121_pad_type_0 = const()[name = tensor("lora_out_121_pad_type_0"), val = tensor("valid")]; + tensor lora_out_121_strides_0 = const()[name = tensor("lora_out_121_strides_0"), val = tensor([1, 1])]; + tensor lora_out_121_pad_0 = const()[name = tensor("lora_out_121_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_121_dilations_0 = const()[name = tensor("lora_out_121_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_121_groups_0 = const()[name = tensor("lora_out_121_groups_0"), val = tensor(1)]; + tensor layers_10_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_10_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121541440)))]; + tensor lora_out_121_cast_fp16 = conv(dilations = lora_out_121_dilations_0, groups = lora_out_121_groups_0, pad = lora_out_121_pad_0, pad_type = lora_out_121_pad_type_0, strides = lora_out_121_strides_0, weight = layers_10_self_attn_q_proj_loraB_weight_to_fp16, x = input_201_cast_fp16)[name = tensor("lora_out_121_cast_fp16")]; + tensor query_21_cast_fp16 = add(x = pretrained_out_121_cast_fp16, y = lora_out_121_cast_fp16)[name = tensor("query_21_cast_fp16")]; + tensor pretrained_out_123_pad_type_0 = const()[name = tensor("pretrained_out_123_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_123_strides_0 = const()[name = tensor("pretrained_out_123_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_123_pad_0 = const()[name = tensor("pretrained_out_123_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_123_dilations_0 = const()[name = tensor("pretrained_out_123_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_123_groups_0 = const()[name = tensor("pretrained_out_123_groups_0"), val = tensor(1)]; + tensor layers_10_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121582464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(122401728))), name = tensor("layers_10_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_123_cast_fp16 = conv(dilations = pretrained_out_123_dilations_0, groups = pretrained_out_123_groups_0, pad = pretrained_out_123_pad_0, pad_type = pretrained_out_123_pad_type_0, strides = pretrained_out_123_strides_0, weight = layers_10_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_41_cast_fp16)[name = tensor("pretrained_out_123_cast_fp16")]; + tensor input_203_pad_type_0 = const()[name = tensor("input_203_pad_type_0"), val = tensor("valid")]; + tensor input_203_strides_0 = const()[name = tensor("input_203_strides_0"), val = tensor([1, 1])]; + tensor input_203_pad_0 = const()[name = tensor("input_203_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_203_dilations_0 = const()[name = tensor("input_203_dilations_0"), val = tensor([1, 1])]; + tensor input_203_groups_0 = const()[name = tensor("input_203_groups_0"), val = tensor(1)]; + tensor layers_10_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_10_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(122401856)))]; + tensor input_203_cast_fp16 = conv(dilations = input_203_dilations_0, groups = input_203_groups_0, pad = input_203_pad_0, pad_type = input_203_pad_type_0, strides = input_203_strides_0, weight = layers_10_self_attn_k_proj_loraA_weight_to_fp16, x = obj_41_cast_fp16)[name = tensor("input_203_cast_fp16")]; + tensor lora_out_123_pad_type_0 = const()[name = tensor("lora_out_123_pad_type_0"), val = tensor("valid")]; + tensor lora_out_123_strides_0 = const()[name = tensor("lora_out_123_strides_0"), val = tensor([1, 1])]; + tensor lora_out_123_pad_0 = const()[name = tensor("lora_out_123_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_123_dilations_0 = const()[name = tensor("lora_out_123_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_123_groups_0 = const()[name = tensor("lora_out_123_groups_0"), val = tensor(1)]; + tensor layers_10_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_10_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(122442880)))]; + tensor lora_out_123_cast_fp16 = conv(dilations = lora_out_123_dilations_0, groups = lora_out_123_groups_0, pad = lora_out_123_pad_0, pad_type = lora_out_123_pad_type_0, strides = lora_out_123_strides_0, weight = layers_10_self_attn_k_proj_loraB_weight_to_fp16, x = input_203_cast_fp16)[name = tensor("lora_out_123_cast_fp16")]; + tensor key_21_cast_fp16 = add(x = pretrained_out_123_cast_fp16, y = lora_out_123_cast_fp16)[name = tensor("key_21_cast_fp16")]; + tensor pretrained_out_125_pad_type_0 = const()[name = tensor("pretrained_out_125_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_125_strides_0 = const()[name = tensor("pretrained_out_125_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_125_pad_0 = const()[name = tensor("pretrained_out_125_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_125_dilations_0 = const()[name = tensor("pretrained_out_125_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_125_groups_0 = const()[name = tensor("pretrained_out_125_groups_0"), val = tensor(1)]; + tensor layers_10_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(122483904))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123303168))), name = tensor("layers_10_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_10_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_10_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123303296)))]; + tensor pretrained_out_125_cast_fp16 = conv(bias = layers_10_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_125_dilations_0, groups = pretrained_out_125_groups_0, pad = pretrained_out_125_pad_0, pad_type = pretrained_out_125_pad_type_0, strides = pretrained_out_125_strides_0, weight = layers_10_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_41_cast_fp16)[name = tensor("pretrained_out_125_cast_fp16")]; + tensor input_205_pad_type_0 = const()[name = tensor("input_205_pad_type_0"), val = tensor("valid")]; + tensor input_205_strides_0 = const()[name = tensor("input_205_strides_0"), val = tensor([1, 1])]; + tensor input_205_pad_0 = const()[name = tensor("input_205_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_205_dilations_0 = const()[name = tensor("input_205_dilations_0"), val = tensor([1, 1])]; + tensor input_205_groups_0 = const()[name = tensor("input_205_groups_0"), val = tensor(1)]; + tensor layers_10_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_10_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123305920)))]; + tensor input_205_cast_fp16 = conv(dilations = input_205_dilations_0, groups = input_205_groups_0, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = input_205_strides_0, weight = layers_10_self_attn_v_proj_loraA_weight_to_fp16, x = obj_41_cast_fp16)[name = tensor("input_205_cast_fp16")]; + tensor lora_out_125_pad_type_0 = const()[name = tensor("lora_out_125_pad_type_0"), val = tensor("valid")]; + tensor lora_out_125_strides_0 = const()[name = tensor("lora_out_125_strides_0"), val = tensor([1, 1])]; + tensor lora_out_125_pad_0 = const()[name = tensor("lora_out_125_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_125_dilations_0 = const()[name = tensor("lora_out_125_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_125_groups_0 = const()[name = tensor("lora_out_125_groups_0"), val = tensor(1)]; + tensor layers_10_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_10_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123346944)))]; + tensor lora_out_125_cast_fp16 = conv(dilations = lora_out_125_dilations_0, groups = lora_out_125_groups_0, pad = lora_out_125_pad_0, pad_type = lora_out_125_pad_type_0, strides = lora_out_125_strides_0, weight = layers_10_self_attn_v_proj_loraB_weight_to_fp16, x = input_205_cast_fp16)[name = tensor("lora_out_125_cast_fp16")]; + tensor value_21_cast_fp16 = add(x = pretrained_out_125_cast_fp16, y = lora_out_125_cast_fp16)[name = tensor("value_21_cast_fp16")]; + tensor var_2420 = const()[name = tensor("op_2420"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_21_cast_fp16 = reshape(shape = var_2420, x = query_21_cast_fp16)[name = tensor("mh_q_21_cast_fp16")]; + tensor var_2422_to_fp16 = const()[name = tensor("op_2422_to_fp16"), val = tensor(0x1p-3)]; + tensor var_2423_cast_fp16 = mul(x = mh_q_21_cast_fp16, y = var_2422_to_fp16)[name = tensor("op_2423_cast_fp16")]; + tensor var_2424 = const()[name = tensor("op_2424"), val = tensor([1, 20, 64, -1])]; + tensor var_2425_cast_fp16 = reshape(shape = var_2424, x = key_21_cast_fp16)[name = tensor("op_2425_cast_fp16")]; + tensor mh_w_21_transpose_x_0 = const()[name = tensor("mh_w_21_transpose_x_0"), val = tensor(true)]; + tensor mh_w_21_transpose_y_0 = const()[name = tensor("mh_w_21_transpose_y_0"), val = tensor(false)]; + tensor mh_w_21_cast_fp16 = matmul(transpose_x = mh_w_21_transpose_x_0, transpose_y = mh_w_21_transpose_y_0, x = var_2423_cast_fp16, y = var_2425_cast_fp16)[name = tensor("mh_w_21_cast_fp16")]; + tensor var_2428_cast_fp16 = softmax(axis = var_2318, x = mh_w_21_cast_fp16)[name = tensor("op_2428_cast_fp16")]; + tensor var_2429 = const()[name = tensor("op_2429"), val = tensor([1, 20, 64, -1])]; + tensor var_2430_cast_fp16 = reshape(shape = var_2429, x = value_21_cast_fp16)[name = tensor("op_2430_cast_fp16")]; + tensor attn_21_transpose_x_0 = const()[name = tensor("attn_21_transpose_x_0"), val = tensor(false)]; + tensor attn_21_transpose_y_0 = const()[name = tensor("attn_21_transpose_y_0"), val = tensor(true)]; + tensor attn_21_cast_fp16 = matmul(transpose_x = attn_21_transpose_x_0, transpose_y = attn_21_transpose_y_0, x = var_2430_cast_fp16, y = var_2428_cast_fp16)[name = tensor("attn_21_cast_fp16")]; + tensor var_2433 = const()[name = tensor("op_2433"), val = tensor([1, 1280, 1, -1])]; + tensor input_207_cast_fp16 = reshape(shape = var_2433, x = attn_21_cast_fp16)[name = tensor("input_207_cast_fp16")]; + tensor pretrained_out_127_pad_type_0 = const()[name = tensor("pretrained_out_127_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_127_strides_0 = const()[name = tensor("pretrained_out_127_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_127_pad_0 = const()[name = tensor("pretrained_out_127_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_127_dilations_0 = const()[name = tensor("pretrained_out_127_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_127_groups_0 = const()[name = tensor("pretrained_out_127_groups_0"), val = tensor(1)]; + tensor layers_10_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123387968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(124207232))), name = tensor("layers_10_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_10_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_10_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(124207360)))]; + tensor pretrained_out_127_cast_fp16 = conv(bias = layers_10_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_127_dilations_0, groups = pretrained_out_127_groups_0, pad = pretrained_out_127_pad_0, pad_type = pretrained_out_127_pad_type_0, strides = pretrained_out_127_strides_0, weight = layers_10_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_207_cast_fp16)[name = tensor("pretrained_out_127_cast_fp16")]; + tensor input_209_pad_type_0 = const()[name = tensor("input_209_pad_type_0"), val = tensor("valid")]; + tensor input_209_strides_0 = const()[name = tensor("input_209_strides_0"), val = tensor([1, 1])]; + tensor input_209_pad_0 = const()[name = tensor("input_209_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_209_dilations_0 = const()[name = tensor("input_209_dilations_0"), val = tensor([1, 1])]; + tensor input_209_groups_0 = const()[name = tensor("input_209_groups_0"), val = tensor(1)]; + tensor layers_10_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_10_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(124209984)))]; + tensor input_209_cast_fp16 = conv(dilations = input_209_dilations_0, groups = input_209_groups_0, pad = input_209_pad_0, pad_type = input_209_pad_type_0, strides = input_209_strides_0, weight = layers_10_self_attn_o_proj_loraA_weight_to_fp16, x = input_207_cast_fp16)[name = tensor("input_209_cast_fp16")]; + tensor lora_out_127_pad_type_0 = const()[name = tensor("lora_out_127_pad_type_0"), val = tensor("valid")]; + tensor lora_out_127_strides_0 = const()[name = tensor("lora_out_127_strides_0"), val = tensor([1, 1])]; + tensor lora_out_127_pad_0 = const()[name = tensor("lora_out_127_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_127_dilations_0 = const()[name = tensor("lora_out_127_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_127_groups_0 = const()[name = tensor("lora_out_127_groups_0"), val = tensor(1)]; + tensor layers_10_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_10_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(124251008)))]; + tensor lora_out_127_cast_fp16 = conv(dilations = lora_out_127_dilations_0, groups = lora_out_127_groups_0, pad = lora_out_127_pad_0, pad_type = lora_out_127_pad_type_0, strides = lora_out_127_strides_0, weight = layers_10_self_attn_o_proj_loraB_weight_to_fp16, x = input_209_cast_fp16)[name = tensor("lora_out_127_cast_fp16")]; + tensor obj_43_cast_fp16 = add(x = pretrained_out_127_cast_fp16, y = lora_out_127_cast_fp16)[name = tensor("obj_43_cast_fp16")]; + tensor inputs_43_cast_fp16 = add(x = inputs_41_cast_fp16, y = obj_43_cast_fp16)[name = tensor("inputs_43_cast_fp16")]; + tensor out_43_axes_0 = const()[name = tensor("out_43_axes_0"), val = tensor([1])]; + tensor var_2467_to_fp16 = const()[name = tensor("op_2467_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_43_cast_fp16 = layer_norm(axes = out_43_axes_0, epsilon = var_2467_to_fp16, x = inputs_43_cast_fp16)[name = tensor("out_43_cast_fp16")]; + tensor input_211_gamma_0_to_fp16 = const()[name = tensor("input_211_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(124292032)))]; + tensor input_211_beta_0_to_fp16 = const()[name = tensor("input_211_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(124294656)))]; + tensor input_211_epsilon_0_to_fp16 = const()[name = tensor("input_211_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_211_cast_fp16 = batch_norm(beta = input_211_beta_0_to_fp16, epsilon = input_211_epsilon_0_to_fp16, gamma = input_211_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_43_cast_fp16)[name = tensor("input_211_cast_fp16")]; + tensor pretrained_out_129_pad_type_0 = const()[name = tensor("pretrained_out_129_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_129_strides_0 = const()[name = tensor("pretrained_out_129_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_129_pad_0 = const()[name = tensor("pretrained_out_129_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_129_dilations_0 = const()[name = tensor("pretrained_out_129_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_129_groups_0 = const()[name = tensor("pretrained_out_129_groups_0"), val = tensor(1)]; + tensor layers_10_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(124297280))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127574144))), name = tensor("layers_10_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_10_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_10_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127574272)))]; + tensor pretrained_out_129_cast_fp16 = conv(bias = layers_10_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_129_dilations_0, groups = pretrained_out_129_groups_0, pad = pretrained_out_129_pad_0, pad_type = pretrained_out_129_pad_type_0, strides = pretrained_out_129_strides_0, weight = layers_10_fc1_pretrained_weight_to_fp16_palettized, x = input_211_cast_fp16)[name = tensor("pretrained_out_129_cast_fp16")]; + tensor input_213_pad_type_0 = const()[name = tensor("input_213_pad_type_0"), val = tensor("valid")]; + tensor input_213_strides_0 = const()[name = tensor("input_213_strides_0"), val = tensor([1, 1])]; + tensor input_213_pad_0 = const()[name = tensor("input_213_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_213_dilations_0 = const()[name = tensor("input_213_dilations_0"), val = tensor([1, 1])]; + tensor input_213_groups_0 = const()[name = tensor("input_213_groups_0"), val = tensor(1)]; + tensor layers_10_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_10_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127584576)))]; + tensor input_213_cast_fp16 = conv(dilations = input_213_dilations_0, groups = input_213_groups_0, pad = input_213_pad_0, pad_type = input_213_pad_type_0, strides = input_213_strides_0, weight = layers_10_fc1_loraA_weight_to_fp16, x = input_211_cast_fp16)[name = tensor("input_213_cast_fp16")]; + tensor lora_out_129_pad_type_0 = const()[name = tensor("lora_out_129_pad_type_0"), val = tensor("valid")]; + tensor lora_out_129_strides_0 = const()[name = tensor("lora_out_129_strides_0"), val = tensor([1, 1])]; + tensor lora_out_129_pad_0 = const()[name = tensor("lora_out_129_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_129_dilations_0 = const()[name = tensor("lora_out_129_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_129_groups_0 = const()[name = tensor("lora_out_129_groups_0"), val = tensor(1)]; + tensor layers_10_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_10_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127625600)))]; + tensor lora_out_129_cast_fp16 = conv(dilations = lora_out_129_dilations_0, groups = lora_out_129_groups_0, pad = lora_out_129_pad_0, pad_type = lora_out_129_pad_type_0, strides = lora_out_129_strides_0, weight = layers_10_fc1_loraB_weight_to_fp16, x = input_213_cast_fp16)[name = tensor("lora_out_129_cast_fp16")]; + tensor input_215_cast_fp16 = add(x = pretrained_out_129_cast_fp16, y = lora_out_129_cast_fp16)[name = tensor("input_215_cast_fp16")]; + tensor input_217_mode_0 = const()[name = tensor("input_217_mode_0"), val = tensor("EXACT")]; + tensor input_217_cast_fp16 = gelu(mode = input_217_mode_0, x = input_215_cast_fp16)[name = tensor("input_217_cast_fp16")]; + tensor pretrained_out_131_pad_type_0 = const()[name = tensor("pretrained_out_131_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_131_strides_0 = const()[name = tensor("pretrained_out_131_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_131_pad_0 = const()[name = tensor("pretrained_out_131_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_131_dilations_0 = const()[name = tensor("pretrained_out_131_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_131_groups_0 = const()[name = tensor("pretrained_out_131_groups_0"), val = tensor(1)]; + tensor layers_10_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127789504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131066368))), name = tensor("layers_10_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_10_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_10_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131066496)))]; + tensor pretrained_out_131_cast_fp16 = conv(bias = layers_10_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_131_dilations_0, groups = pretrained_out_131_groups_0, pad = pretrained_out_131_pad_0, pad_type = pretrained_out_131_pad_type_0, strides = pretrained_out_131_strides_0, weight = layers_10_fc2_pretrained_weight_to_fp16_palettized, x = input_217_cast_fp16)[name = tensor("pretrained_out_131_cast_fp16")]; + tensor input_219_pad_type_0 = const()[name = tensor("input_219_pad_type_0"), val = tensor("valid")]; + tensor input_219_strides_0 = const()[name = tensor("input_219_strides_0"), val = tensor([1, 1])]; + tensor input_219_pad_0 = const()[name = tensor("input_219_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_219_dilations_0 = const()[name = tensor("input_219_dilations_0"), val = tensor([1, 1])]; + tensor input_219_groups_0 = const()[name = tensor("input_219_groups_0"), val = tensor(1)]; + tensor layers_10_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_10_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131069120)))]; + tensor input_219_cast_fp16 = conv(dilations = input_219_dilations_0, groups = input_219_groups_0, pad = input_219_pad_0, pad_type = input_219_pad_type_0, strides = input_219_strides_0, weight = layers_10_fc2_loraA_weight_to_fp16, x = input_217_cast_fp16)[name = tensor("input_219_cast_fp16")]; + tensor lora_out_131_pad_type_0 = const()[name = tensor("lora_out_131_pad_type_0"), val = tensor("valid")]; + tensor lora_out_131_strides_0 = const()[name = tensor("lora_out_131_strides_0"), val = tensor([1, 1])]; + tensor lora_out_131_pad_0 = const()[name = tensor("lora_out_131_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_131_dilations_0 = const()[name = tensor("lora_out_131_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_131_groups_0 = const()[name = tensor("lora_out_131_groups_0"), val = tensor(1)]; + tensor layers_10_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_10_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131233024)))]; + tensor lora_out_131_cast_fp16 = conv(dilations = lora_out_131_dilations_0, groups = lora_out_131_groups_0, pad = lora_out_131_pad_0, pad_type = lora_out_131_pad_type_0, strides = lora_out_131_strides_0, weight = layers_10_fc2_loraB_weight_to_fp16, x = input_219_cast_fp16)[name = tensor("lora_out_131_cast_fp16")]; + tensor hidden_states_25_cast_fp16 = add(x = pretrained_out_131_cast_fp16, y = lora_out_131_cast_fp16)[name = tensor("hidden_states_25_cast_fp16")]; + tensor inputs_45_cast_fp16 = add(x = inputs_43_cast_fp16, y = hidden_states_25_cast_fp16)[name = tensor("inputs_45_cast_fp16")]; + tensor var_2532 = const()[name = tensor("op_2532"), val = tensor(3)]; + tensor out_45_axes_0 = const()[name = tensor("out_45_axes_0"), val = tensor([1])]; + tensor var_2551_to_fp16 = const()[name = tensor("op_2551_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_45_cast_fp16 = layer_norm(axes = out_45_axes_0, epsilon = var_2551_to_fp16, x = inputs_45_cast_fp16)[name = tensor("out_45_cast_fp16")]; + tensor obj_45_gamma_0_to_fp16 = const()[name = tensor("obj_45_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131274048)))]; + tensor obj_45_beta_0_to_fp16 = const()[name = tensor("obj_45_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131276672)))]; + tensor obj_45_epsilon_0_to_fp16 = const()[name = tensor("obj_45_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_45_cast_fp16 = batch_norm(beta = obj_45_beta_0_to_fp16, epsilon = obj_45_epsilon_0_to_fp16, gamma = obj_45_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_45_cast_fp16)[name = tensor("obj_45_cast_fp16")]; + tensor pretrained_out_133_pad_type_0 = const()[name = tensor("pretrained_out_133_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_133_strides_0 = const()[name = tensor("pretrained_out_133_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_133_pad_0 = const()[name = tensor("pretrained_out_133_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_133_dilations_0 = const()[name = tensor("pretrained_out_133_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_133_groups_0 = const()[name = tensor("pretrained_out_133_groups_0"), val = tensor(1)]; + tensor layers_11_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131279296))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132098560))), name = tensor("layers_11_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_11_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_11_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132098688)))]; + tensor pretrained_out_133_cast_fp16 = conv(bias = layers_11_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_133_dilations_0, groups = pretrained_out_133_groups_0, pad = pretrained_out_133_pad_0, pad_type = pretrained_out_133_pad_type_0, strides = pretrained_out_133_strides_0, weight = layers_11_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_45_cast_fp16)[name = tensor("pretrained_out_133_cast_fp16")]; + tensor input_221_pad_type_0 = const()[name = tensor("input_221_pad_type_0"), val = tensor("valid")]; + tensor input_221_strides_0 = const()[name = tensor("input_221_strides_0"), val = tensor([1, 1])]; + tensor input_221_pad_0 = const()[name = tensor("input_221_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_221_dilations_0 = const()[name = tensor("input_221_dilations_0"), val = tensor([1, 1])]; + tensor input_221_groups_0 = const()[name = tensor("input_221_groups_0"), val = tensor(1)]; + tensor layers_11_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_11_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132101312)))]; + tensor input_221_cast_fp16 = conv(dilations = input_221_dilations_0, groups = input_221_groups_0, pad = input_221_pad_0, pad_type = input_221_pad_type_0, strides = input_221_strides_0, weight = layers_11_self_attn_q_proj_loraA_weight_to_fp16, x = obj_45_cast_fp16)[name = tensor("input_221_cast_fp16")]; + tensor lora_out_133_pad_type_0 = const()[name = tensor("lora_out_133_pad_type_0"), val = tensor("valid")]; + tensor lora_out_133_strides_0 = const()[name = tensor("lora_out_133_strides_0"), val = tensor([1, 1])]; + tensor lora_out_133_pad_0 = const()[name = tensor("lora_out_133_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_133_dilations_0 = const()[name = tensor("lora_out_133_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_133_groups_0 = const()[name = tensor("lora_out_133_groups_0"), val = tensor(1)]; + tensor layers_11_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_11_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132142336)))]; + tensor lora_out_133_cast_fp16 = conv(dilations = lora_out_133_dilations_0, groups = lora_out_133_groups_0, pad = lora_out_133_pad_0, pad_type = lora_out_133_pad_type_0, strides = lora_out_133_strides_0, weight = layers_11_self_attn_q_proj_loraB_weight_to_fp16, x = input_221_cast_fp16)[name = tensor("lora_out_133_cast_fp16")]; + tensor query_23_cast_fp16 = add(x = pretrained_out_133_cast_fp16, y = lora_out_133_cast_fp16)[name = tensor("query_23_cast_fp16")]; + tensor pretrained_out_135_pad_type_0 = const()[name = tensor("pretrained_out_135_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_135_strides_0 = const()[name = tensor("pretrained_out_135_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_135_pad_0 = const()[name = tensor("pretrained_out_135_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_135_dilations_0 = const()[name = tensor("pretrained_out_135_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_135_groups_0 = const()[name = tensor("pretrained_out_135_groups_0"), val = tensor(1)]; + tensor layers_11_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132183360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133002624))), name = tensor("layers_11_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_135_cast_fp16 = conv(dilations = pretrained_out_135_dilations_0, groups = pretrained_out_135_groups_0, pad = pretrained_out_135_pad_0, pad_type = pretrained_out_135_pad_type_0, strides = pretrained_out_135_strides_0, weight = layers_11_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_45_cast_fp16)[name = tensor("pretrained_out_135_cast_fp16")]; + tensor input_223_pad_type_0 = const()[name = tensor("input_223_pad_type_0"), val = tensor("valid")]; + tensor input_223_strides_0 = const()[name = tensor("input_223_strides_0"), val = tensor([1, 1])]; + tensor input_223_pad_0 = const()[name = tensor("input_223_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_223_dilations_0 = const()[name = tensor("input_223_dilations_0"), val = tensor([1, 1])]; + tensor input_223_groups_0 = const()[name = tensor("input_223_groups_0"), val = tensor(1)]; + tensor layers_11_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_11_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133002752)))]; + tensor input_223_cast_fp16 = conv(dilations = input_223_dilations_0, groups = input_223_groups_0, pad = input_223_pad_0, pad_type = input_223_pad_type_0, strides = input_223_strides_0, weight = layers_11_self_attn_k_proj_loraA_weight_to_fp16, x = obj_45_cast_fp16)[name = tensor("input_223_cast_fp16")]; + tensor lora_out_135_pad_type_0 = const()[name = tensor("lora_out_135_pad_type_0"), val = tensor("valid")]; + tensor lora_out_135_strides_0 = const()[name = tensor("lora_out_135_strides_0"), val = tensor([1, 1])]; + tensor lora_out_135_pad_0 = const()[name = tensor("lora_out_135_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_135_dilations_0 = const()[name = tensor("lora_out_135_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_135_groups_0 = const()[name = tensor("lora_out_135_groups_0"), val = tensor(1)]; + tensor layers_11_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_11_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133043776)))]; + tensor lora_out_135_cast_fp16 = conv(dilations = lora_out_135_dilations_0, groups = lora_out_135_groups_0, pad = lora_out_135_pad_0, pad_type = lora_out_135_pad_type_0, strides = lora_out_135_strides_0, weight = layers_11_self_attn_k_proj_loraB_weight_to_fp16, x = input_223_cast_fp16)[name = tensor("lora_out_135_cast_fp16")]; + tensor key_23_cast_fp16 = add(x = pretrained_out_135_cast_fp16, y = lora_out_135_cast_fp16)[name = tensor("key_23_cast_fp16")]; + tensor pretrained_out_137_pad_type_0 = const()[name = tensor("pretrained_out_137_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_137_strides_0 = const()[name = tensor("pretrained_out_137_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_137_pad_0 = const()[name = tensor("pretrained_out_137_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_137_dilations_0 = const()[name = tensor("pretrained_out_137_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_137_groups_0 = const()[name = tensor("pretrained_out_137_groups_0"), val = tensor(1)]; + tensor layers_11_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133084800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133904064))), name = tensor("layers_11_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_11_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_11_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133904192)))]; + tensor pretrained_out_137_cast_fp16 = conv(bias = layers_11_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_137_dilations_0, groups = pretrained_out_137_groups_0, pad = pretrained_out_137_pad_0, pad_type = pretrained_out_137_pad_type_0, strides = pretrained_out_137_strides_0, weight = layers_11_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_45_cast_fp16)[name = tensor("pretrained_out_137_cast_fp16")]; + tensor input_225_pad_type_0 = const()[name = tensor("input_225_pad_type_0"), val = tensor("valid")]; + tensor input_225_strides_0 = const()[name = tensor("input_225_strides_0"), val = tensor([1, 1])]; + tensor input_225_pad_0 = const()[name = tensor("input_225_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_225_dilations_0 = const()[name = tensor("input_225_dilations_0"), val = tensor([1, 1])]; + tensor input_225_groups_0 = const()[name = tensor("input_225_groups_0"), val = tensor(1)]; + tensor layers_11_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_11_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133906816)))]; + tensor input_225_cast_fp16 = conv(dilations = input_225_dilations_0, groups = input_225_groups_0, pad = input_225_pad_0, pad_type = input_225_pad_type_0, strides = input_225_strides_0, weight = layers_11_self_attn_v_proj_loraA_weight_to_fp16, x = obj_45_cast_fp16)[name = tensor("input_225_cast_fp16")]; + tensor lora_out_137_pad_type_0 = const()[name = tensor("lora_out_137_pad_type_0"), val = tensor("valid")]; + tensor lora_out_137_strides_0 = const()[name = tensor("lora_out_137_strides_0"), val = tensor([1, 1])]; + tensor lora_out_137_pad_0 = const()[name = tensor("lora_out_137_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_137_dilations_0 = const()[name = tensor("lora_out_137_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_137_groups_0 = const()[name = tensor("lora_out_137_groups_0"), val = tensor(1)]; + tensor layers_11_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_11_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133947840)))]; + tensor lora_out_137_cast_fp16 = conv(dilations = lora_out_137_dilations_0, groups = lora_out_137_groups_0, pad = lora_out_137_pad_0, pad_type = lora_out_137_pad_type_0, strides = lora_out_137_strides_0, weight = layers_11_self_attn_v_proj_loraB_weight_to_fp16, x = input_225_cast_fp16)[name = tensor("lora_out_137_cast_fp16")]; + tensor value_23_cast_fp16 = add(x = pretrained_out_137_cast_fp16, y = lora_out_137_cast_fp16)[name = tensor("value_23_cast_fp16")]; + tensor var_2634 = const()[name = tensor("op_2634"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_23_cast_fp16 = reshape(shape = var_2634, x = query_23_cast_fp16)[name = tensor("mh_q_23_cast_fp16")]; + tensor var_2636_to_fp16 = const()[name = tensor("op_2636_to_fp16"), val = tensor(0x1p-3)]; + tensor var_2637_cast_fp16 = mul(x = mh_q_23_cast_fp16, y = var_2636_to_fp16)[name = tensor("op_2637_cast_fp16")]; + tensor var_2638 = const()[name = tensor("op_2638"), val = tensor([1, 20, 64, -1])]; + tensor var_2639_cast_fp16 = reshape(shape = var_2638, x = key_23_cast_fp16)[name = tensor("op_2639_cast_fp16")]; + tensor mh_w_23_transpose_x_0 = const()[name = tensor("mh_w_23_transpose_x_0"), val = tensor(true)]; + tensor mh_w_23_transpose_y_0 = const()[name = tensor("mh_w_23_transpose_y_0"), val = tensor(false)]; + tensor mh_w_23_cast_fp16 = matmul(transpose_x = mh_w_23_transpose_x_0, transpose_y = mh_w_23_transpose_y_0, x = var_2637_cast_fp16, y = var_2639_cast_fp16)[name = tensor("mh_w_23_cast_fp16")]; + tensor var_2642_cast_fp16 = softmax(axis = var_2532, x = mh_w_23_cast_fp16)[name = tensor("op_2642_cast_fp16")]; + tensor var_2643 = const()[name = tensor("op_2643"), val = tensor([1, 20, 64, -1])]; + tensor var_2644_cast_fp16 = reshape(shape = var_2643, x = value_23_cast_fp16)[name = tensor("op_2644_cast_fp16")]; + tensor attn_23_transpose_x_0 = const()[name = tensor("attn_23_transpose_x_0"), val = tensor(false)]; + tensor attn_23_transpose_y_0 = const()[name = tensor("attn_23_transpose_y_0"), val = tensor(true)]; + tensor attn_23_cast_fp16 = matmul(transpose_x = attn_23_transpose_x_0, transpose_y = attn_23_transpose_y_0, x = var_2644_cast_fp16, y = var_2642_cast_fp16)[name = tensor("attn_23_cast_fp16")]; + tensor var_2647 = const()[name = tensor("op_2647"), val = tensor([1, 1280, 1, -1])]; + tensor input_227_cast_fp16 = reshape(shape = var_2647, x = attn_23_cast_fp16)[name = tensor("input_227_cast_fp16")]; + tensor pretrained_out_139_pad_type_0 = const()[name = tensor("pretrained_out_139_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_139_strides_0 = const()[name = tensor("pretrained_out_139_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_139_pad_0 = const()[name = tensor("pretrained_out_139_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_139_dilations_0 = const()[name = tensor("pretrained_out_139_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_139_groups_0 = const()[name = tensor("pretrained_out_139_groups_0"), val = tensor(1)]; + tensor layers_11_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133988864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134808128))), name = tensor("layers_11_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_11_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_11_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134808256)))]; + tensor pretrained_out_139_cast_fp16 = conv(bias = layers_11_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_139_dilations_0, groups = pretrained_out_139_groups_0, pad = pretrained_out_139_pad_0, pad_type = pretrained_out_139_pad_type_0, strides = pretrained_out_139_strides_0, weight = layers_11_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_227_cast_fp16)[name = tensor("pretrained_out_139_cast_fp16")]; + tensor input_229_pad_type_0 = const()[name = tensor("input_229_pad_type_0"), val = tensor("valid")]; + tensor input_229_strides_0 = const()[name = tensor("input_229_strides_0"), val = tensor([1, 1])]; + tensor input_229_pad_0 = const()[name = tensor("input_229_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_229_dilations_0 = const()[name = tensor("input_229_dilations_0"), val = tensor([1, 1])]; + tensor input_229_groups_0 = const()[name = tensor("input_229_groups_0"), val = tensor(1)]; + tensor layers_11_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_11_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134810880)))]; + tensor input_229_cast_fp16 = conv(dilations = input_229_dilations_0, groups = input_229_groups_0, pad = input_229_pad_0, pad_type = input_229_pad_type_0, strides = input_229_strides_0, weight = layers_11_self_attn_o_proj_loraA_weight_to_fp16, x = input_227_cast_fp16)[name = tensor("input_229_cast_fp16")]; + tensor lora_out_139_pad_type_0 = const()[name = tensor("lora_out_139_pad_type_0"), val = tensor("valid")]; + tensor lora_out_139_strides_0 = const()[name = tensor("lora_out_139_strides_0"), val = tensor([1, 1])]; + tensor lora_out_139_pad_0 = const()[name = tensor("lora_out_139_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_139_dilations_0 = const()[name = tensor("lora_out_139_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_139_groups_0 = const()[name = tensor("lora_out_139_groups_0"), val = tensor(1)]; + tensor layers_11_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_11_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134851904)))]; + tensor lora_out_139_cast_fp16 = conv(dilations = lora_out_139_dilations_0, groups = lora_out_139_groups_0, pad = lora_out_139_pad_0, pad_type = lora_out_139_pad_type_0, strides = lora_out_139_strides_0, weight = layers_11_self_attn_o_proj_loraB_weight_to_fp16, x = input_229_cast_fp16)[name = tensor("lora_out_139_cast_fp16")]; + tensor obj_47_cast_fp16 = add(x = pretrained_out_139_cast_fp16, y = lora_out_139_cast_fp16)[name = tensor("obj_47_cast_fp16")]; + tensor inputs_47_cast_fp16 = add(x = inputs_45_cast_fp16, y = obj_47_cast_fp16)[name = tensor("inputs_47_cast_fp16")]; + tensor out_47_axes_0 = const()[name = tensor("out_47_axes_0"), val = tensor([1])]; + tensor var_2681_to_fp16 = const()[name = tensor("op_2681_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_47_cast_fp16 = layer_norm(axes = out_47_axes_0, epsilon = var_2681_to_fp16, x = inputs_47_cast_fp16)[name = tensor("out_47_cast_fp16")]; + tensor input_231_gamma_0_to_fp16 = const()[name = tensor("input_231_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134892928)))]; + tensor input_231_beta_0_to_fp16 = const()[name = tensor("input_231_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134895552)))]; + tensor input_231_epsilon_0_to_fp16 = const()[name = tensor("input_231_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_231_cast_fp16 = batch_norm(beta = input_231_beta_0_to_fp16, epsilon = input_231_epsilon_0_to_fp16, gamma = input_231_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_47_cast_fp16)[name = tensor("input_231_cast_fp16")]; + tensor pretrained_out_141_pad_type_0 = const()[name = tensor("pretrained_out_141_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_141_strides_0 = const()[name = tensor("pretrained_out_141_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_141_pad_0 = const()[name = tensor("pretrained_out_141_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_141_dilations_0 = const()[name = tensor("pretrained_out_141_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_141_groups_0 = const()[name = tensor("pretrained_out_141_groups_0"), val = tensor(1)]; + tensor layers_11_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134898176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(138175040))), name = tensor("layers_11_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_11_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_11_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(138175168)))]; + tensor pretrained_out_141_cast_fp16 = conv(bias = layers_11_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_141_dilations_0, groups = pretrained_out_141_groups_0, pad = pretrained_out_141_pad_0, pad_type = pretrained_out_141_pad_type_0, strides = pretrained_out_141_strides_0, weight = layers_11_fc1_pretrained_weight_to_fp16_palettized, x = input_231_cast_fp16)[name = tensor("pretrained_out_141_cast_fp16")]; + tensor input_233_pad_type_0 = const()[name = tensor("input_233_pad_type_0"), val = tensor("valid")]; + tensor input_233_strides_0 = const()[name = tensor("input_233_strides_0"), val = tensor([1, 1])]; + tensor input_233_pad_0 = const()[name = tensor("input_233_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_233_dilations_0 = const()[name = tensor("input_233_dilations_0"), val = tensor([1, 1])]; + tensor input_233_groups_0 = const()[name = tensor("input_233_groups_0"), val = tensor(1)]; + tensor layers_11_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_11_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(138185472)))]; + tensor input_233_cast_fp16 = conv(dilations = input_233_dilations_0, groups = input_233_groups_0, pad = input_233_pad_0, pad_type = input_233_pad_type_0, strides = input_233_strides_0, weight = layers_11_fc1_loraA_weight_to_fp16, x = input_231_cast_fp16)[name = tensor("input_233_cast_fp16")]; + tensor lora_out_141_pad_type_0 = const()[name = tensor("lora_out_141_pad_type_0"), val = tensor("valid")]; + tensor lora_out_141_strides_0 = const()[name = tensor("lora_out_141_strides_0"), val = tensor([1, 1])]; + tensor lora_out_141_pad_0 = const()[name = tensor("lora_out_141_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_141_dilations_0 = const()[name = tensor("lora_out_141_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_141_groups_0 = const()[name = tensor("lora_out_141_groups_0"), val = tensor(1)]; + tensor layers_11_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_11_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(138226496)))]; + tensor lora_out_141_cast_fp16 = conv(dilations = lora_out_141_dilations_0, groups = lora_out_141_groups_0, pad = lora_out_141_pad_0, pad_type = lora_out_141_pad_type_0, strides = lora_out_141_strides_0, weight = layers_11_fc1_loraB_weight_to_fp16, x = input_233_cast_fp16)[name = tensor("lora_out_141_cast_fp16")]; + tensor input_235_cast_fp16 = add(x = pretrained_out_141_cast_fp16, y = lora_out_141_cast_fp16)[name = tensor("input_235_cast_fp16")]; + tensor input_237_mode_0 = const()[name = tensor("input_237_mode_0"), val = tensor("EXACT")]; + tensor input_237_cast_fp16 = gelu(mode = input_237_mode_0, x = input_235_cast_fp16)[name = tensor("input_237_cast_fp16")]; + tensor pretrained_out_143_pad_type_0 = const()[name = tensor("pretrained_out_143_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_143_strides_0 = const()[name = tensor("pretrained_out_143_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_143_pad_0 = const()[name = tensor("pretrained_out_143_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_143_dilations_0 = const()[name = tensor("pretrained_out_143_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_143_groups_0 = const()[name = tensor("pretrained_out_143_groups_0"), val = tensor(1)]; + tensor layers_11_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(138390400))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141667264))), name = tensor("layers_11_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_11_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_11_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141667392)))]; + tensor pretrained_out_143_cast_fp16 = conv(bias = layers_11_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_143_dilations_0, groups = pretrained_out_143_groups_0, pad = pretrained_out_143_pad_0, pad_type = pretrained_out_143_pad_type_0, strides = pretrained_out_143_strides_0, weight = layers_11_fc2_pretrained_weight_to_fp16_palettized, x = input_237_cast_fp16)[name = tensor("pretrained_out_143_cast_fp16")]; + tensor input_239_pad_type_0 = const()[name = tensor("input_239_pad_type_0"), val = tensor("valid")]; + tensor input_239_strides_0 = const()[name = tensor("input_239_strides_0"), val = tensor([1, 1])]; + tensor input_239_pad_0 = const()[name = tensor("input_239_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_239_dilations_0 = const()[name = tensor("input_239_dilations_0"), val = tensor([1, 1])]; + tensor input_239_groups_0 = const()[name = tensor("input_239_groups_0"), val = tensor(1)]; + tensor layers_11_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_11_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141670016)))]; + tensor input_239_cast_fp16 = conv(dilations = input_239_dilations_0, groups = input_239_groups_0, pad = input_239_pad_0, pad_type = input_239_pad_type_0, strides = input_239_strides_0, weight = layers_11_fc2_loraA_weight_to_fp16, x = input_237_cast_fp16)[name = tensor("input_239_cast_fp16")]; + tensor lora_out_143_pad_type_0 = const()[name = tensor("lora_out_143_pad_type_0"), val = tensor("valid")]; + tensor lora_out_143_strides_0 = const()[name = tensor("lora_out_143_strides_0"), val = tensor([1, 1])]; + tensor lora_out_143_pad_0 = const()[name = tensor("lora_out_143_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_143_dilations_0 = const()[name = tensor("lora_out_143_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_143_groups_0 = const()[name = tensor("lora_out_143_groups_0"), val = tensor(1)]; + tensor layers_11_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_11_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141833920)))]; + tensor lora_out_143_cast_fp16 = conv(dilations = lora_out_143_dilations_0, groups = lora_out_143_groups_0, pad = lora_out_143_pad_0, pad_type = lora_out_143_pad_type_0, strides = lora_out_143_strides_0, weight = layers_11_fc2_loraB_weight_to_fp16, x = input_239_cast_fp16)[name = tensor("lora_out_143_cast_fp16")]; + tensor hidden_states_27_cast_fp16 = add(x = pretrained_out_143_cast_fp16, y = lora_out_143_cast_fp16)[name = tensor("hidden_states_27_cast_fp16")]; + tensor inputs_49_cast_fp16 = add(x = inputs_47_cast_fp16, y = hidden_states_27_cast_fp16)[name = tensor("inputs_49_cast_fp16")]; + tensor var_2746 = const()[name = tensor("op_2746"), val = tensor(3)]; + tensor out_49_axes_0 = const()[name = tensor("out_49_axes_0"), val = tensor([1])]; + tensor var_2765_to_fp16 = const()[name = tensor("op_2765_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_49_cast_fp16 = layer_norm(axes = out_49_axes_0, epsilon = var_2765_to_fp16, x = inputs_49_cast_fp16)[name = tensor("out_49_cast_fp16")]; + tensor obj_49_gamma_0_to_fp16 = const()[name = tensor("obj_49_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141874944)))]; + tensor obj_49_beta_0_to_fp16 = const()[name = tensor("obj_49_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141877568)))]; + tensor obj_49_epsilon_0_to_fp16 = const()[name = tensor("obj_49_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_49_cast_fp16 = batch_norm(beta = obj_49_beta_0_to_fp16, epsilon = obj_49_epsilon_0_to_fp16, gamma = obj_49_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_49_cast_fp16)[name = tensor("obj_49_cast_fp16")]; + tensor pretrained_out_145_pad_type_0 = const()[name = tensor("pretrained_out_145_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_145_strides_0 = const()[name = tensor("pretrained_out_145_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_145_pad_0 = const()[name = tensor("pretrained_out_145_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_145_dilations_0 = const()[name = tensor("pretrained_out_145_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_145_groups_0 = const()[name = tensor("pretrained_out_145_groups_0"), val = tensor(1)]; + tensor layers_12_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141880192))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142699456))), name = tensor("layers_12_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_12_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_12_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142699584)))]; + tensor pretrained_out_145_cast_fp16 = conv(bias = layers_12_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_145_dilations_0, groups = pretrained_out_145_groups_0, pad = pretrained_out_145_pad_0, pad_type = pretrained_out_145_pad_type_0, strides = pretrained_out_145_strides_0, weight = layers_12_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_49_cast_fp16)[name = tensor("pretrained_out_145_cast_fp16")]; + tensor input_241_pad_type_0 = const()[name = tensor("input_241_pad_type_0"), val = tensor("valid")]; + tensor input_241_strides_0 = const()[name = tensor("input_241_strides_0"), val = tensor([1, 1])]; + tensor input_241_pad_0 = const()[name = tensor("input_241_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_241_dilations_0 = const()[name = tensor("input_241_dilations_0"), val = tensor([1, 1])]; + tensor input_241_groups_0 = const()[name = tensor("input_241_groups_0"), val = tensor(1)]; + tensor layers_12_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_12_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142702208)))]; + tensor input_241_cast_fp16 = conv(dilations = input_241_dilations_0, groups = input_241_groups_0, pad = input_241_pad_0, pad_type = input_241_pad_type_0, strides = input_241_strides_0, weight = layers_12_self_attn_q_proj_loraA_weight_to_fp16, x = obj_49_cast_fp16)[name = tensor("input_241_cast_fp16")]; + tensor lora_out_145_pad_type_0 = const()[name = tensor("lora_out_145_pad_type_0"), val = tensor("valid")]; + tensor lora_out_145_strides_0 = const()[name = tensor("lora_out_145_strides_0"), val = tensor([1, 1])]; + tensor lora_out_145_pad_0 = const()[name = tensor("lora_out_145_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_145_dilations_0 = const()[name = tensor("lora_out_145_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_145_groups_0 = const()[name = tensor("lora_out_145_groups_0"), val = tensor(1)]; + tensor layers_12_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_12_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142743232)))]; + tensor lora_out_145_cast_fp16 = conv(dilations = lora_out_145_dilations_0, groups = lora_out_145_groups_0, pad = lora_out_145_pad_0, pad_type = lora_out_145_pad_type_0, strides = lora_out_145_strides_0, weight = layers_12_self_attn_q_proj_loraB_weight_to_fp16, x = input_241_cast_fp16)[name = tensor("lora_out_145_cast_fp16")]; + tensor query_25_cast_fp16 = add(x = pretrained_out_145_cast_fp16, y = lora_out_145_cast_fp16)[name = tensor("query_25_cast_fp16")]; + tensor pretrained_out_147_pad_type_0 = const()[name = tensor("pretrained_out_147_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_147_strides_0 = const()[name = tensor("pretrained_out_147_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_147_pad_0 = const()[name = tensor("pretrained_out_147_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_147_dilations_0 = const()[name = tensor("pretrained_out_147_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_147_groups_0 = const()[name = tensor("pretrained_out_147_groups_0"), val = tensor(1)]; + tensor layers_12_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142784256))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(143603520))), name = tensor("layers_12_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_147_cast_fp16 = conv(dilations = pretrained_out_147_dilations_0, groups = pretrained_out_147_groups_0, pad = pretrained_out_147_pad_0, pad_type = pretrained_out_147_pad_type_0, strides = pretrained_out_147_strides_0, weight = layers_12_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_49_cast_fp16)[name = tensor("pretrained_out_147_cast_fp16")]; + tensor input_243_pad_type_0 = const()[name = tensor("input_243_pad_type_0"), val = tensor("valid")]; + tensor input_243_strides_0 = const()[name = tensor("input_243_strides_0"), val = tensor([1, 1])]; + tensor input_243_pad_0 = const()[name = tensor("input_243_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_243_dilations_0 = const()[name = tensor("input_243_dilations_0"), val = tensor([1, 1])]; + tensor input_243_groups_0 = const()[name = tensor("input_243_groups_0"), val = tensor(1)]; + tensor layers_12_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_12_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(143603648)))]; + tensor input_243_cast_fp16 = conv(dilations = input_243_dilations_0, groups = input_243_groups_0, pad = input_243_pad_0, pad_type = input_243_pad_type_0, strides = input_243_strides_0, weight = layers_12_self_attn_k_proj_loraA_weight_to_fp16, x = obj_49_cast_fp16)[name = tensor("input_243_cast_fp16")]; + tensor lora_out_147_pad_type_0 = const()[name = tensor("lora_out_147_pad_type_0"), val = tensor("valid")]; + tensor lora_out_147_strides_0 = const()[name = tensor("lora_out_147_strides_0"), val = tensor([1, 1])]; + tensor lora_out_147_pad_0 = const()[name = tensor("lora_out_147_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_147_dilations_0 = const()[name = tensor("lora_out_147_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_147_groups_0 = const()[name = tensor("lora_out_147_groups_0"), val = tensor(1)]; + tensor layers_12_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_12_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(143644672)))]; + tensor lora_out_147_cast_fp16 = conv(dilations = lora_out_147_dilations_0, groups = lora_out_147_groups_0, pad = lora_out_147_pad_0, pad_type = lora_out_147_pad_type_0, strides = lora_out_147_strides_0, weight = layers_12_self_attn_k_proj_loraB_weight_to_fp16, x = input_243_cast_fp16)[name = tensor("lora_out_147_cast_fp16")]; + tensor key_25_cast_fp16 = add(x = pretrained_out_147_cast_fp16, y = lora_out_147_cast_fp16)[name = tensor("key_25_cast_fp16")]; + tensor pretrained_out_149_pad_type_0 = const()[name = tensor("pretrained_out_149_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_149_strides_0 = const()[name = tensor("pretrained_out_149_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_149_pad_0 = const()[name = tensor("pretrained_out_149_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_149_dilations_0 = const()[name = tensor("pretrained_out_149_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_149_groups_0 = const()[name = tensor("pretrained_out_149_groups_0"), val = tensor(1)]; + tensor layers_12_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(143685696))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144504960))), name = tensor("layers_12_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_12_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_12_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144505088)))]; + tensor pretrained_out_149_cast_fp16 = conv(bias = layers_12_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_149_dilations_0, groups = pretrained_out_149_groups_0, pad = pretrained_out_149_pad_0, pad_type = pretrained_out_149_pad_type_0, strides = pretrained_out_149_strides_0, weight = layers_12_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_49_cast_fp16)[name = tensor("pretrained_out_149_cast_fp16")]; + tensor input_245_pad_type_0 = const()[name = tensor("input_245_pad_type_0"), val = tensor("valid")]; + tensor input_245_strides_0 = const()[name = tensor("input_245_strides_0"), val = tensor([1, 1])]; + tensor input_245_pad_0 = const()[name = tensor("input_245_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_245_dilations_0 = const()[name = tensor("input_245_dilations_0"), val = tensor([1, 1])]; + tensor input_245_groups_0 = const()[name = tensor("input_245_groups_0"), val = tensor(1)]; + tensor layers_12_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_12_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144507712)))]; + tensor input_245_cast_fp16 = conv(dilations = input_245_dilations_0, groups = input_245_groups_0, pad = input_245_pad_0, pad_type = input_245_pad_type_0, strides = input_245_strides_0, weight = layers_12_self_attn_v_proj_loraA_weight_to_fp16, x = obj_49_cast_fp16)[name = tensor("input_245_cast_fp16")]; + tensor lora_out_149_pad_type_0 = const()[name = tensor("lora_out_149_pad_type_0"), val = tensor("valid")]; + tensor lora_out_149_strides_0 = const()[name = tensor("lora_out_149_strides_0"), val = tensor([1, 1])]; + tensor lora_out_149_pad_0 = const()[name = tensor("lora_out_149_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_149_dilations_0 = const()[name = tensor("lora_out_149_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_149_groups_0 = const()[name = tensor("lora_out_149_groups_0"), val = tensor(1)]; + tensor layers_12_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_12_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144548736)))]; + tensor lora_out_149_cast_fp16 = conv(dilations = lora_out_149_dilations_0, groups = lora_out_149_groups_0, pad = lora_out_149_pad_0, pad_type = lora_out_149_pad_type_0, strides = lora_out_149_strides_0, weight = layers_12_self_attn_v_proj_loraB_weight_to_fp16, x = input_245_cast_fp16)[name = tensor("lora_out_149_cast_fp16")]; + tensor value_25_cast_fp16 = add(x = pretrained_out_149_cast_fp16, y = lora_out_149_cast_fp16)[name = tensor("value_25_cast_fp16")]; + tensor var_2848 = const()[name = tensor("op_2848"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_25_cast_fp16 = reshape(shape = var_2848, x = query_25_cast_fp16)[name = tensor("mh_q_25_cast_fp16")]; + tensor var_2850_to_fp16 = const()[name = tensor("op_2850_to_fp16"), val = tensor(0x1p-3)]; + tensor var_2851_cast_fp16 = mul(x = mh_q_25_cast_fp16, y = var_2850_to_fp16)[name = tensor("op_2851_cast_fp16")]; + tensor var_2852 = const()[name = tensor("op_2852"), val = tensor([1, 20, 64, -1])]; + tensor var_2853_cast_fp16 = reshape(shape = var_2852, x = key_25_cast_fp16)[name = tensor("op_2853_cast_fp16")]; + tensor mh_w_25_transpose_x_0 = const()[name = tensor("mh_w_25_transpose_x_0"), val = tensor(true)]; + tensor mh_w_25_transpose_y_0 = const()[name = tensor("mh_w_25_transpose_y_0"), val = tensor(false)]; + tensor mh_w_25_cast_fp16 = matmul(transpose_x = mh_w_25_transpose_x_0, transpose_y = mh_w_25_transpose_y_0, x = var_2851_cast_fp16, y = var_2853_cast_fp16)[name = tensor("mh_w_25_cast_fp16")]; + tensor var_2856_cast_fp16 = softmax(axis = var_2746, x = mh_w_25_cast_fp16)[name = tensor("op_2856_cast_fp16")]; + tensor var_2857 = const()[name = tensor("op_2857"), val = tensor([1, 20, 64, -1])]; + tensor var_2858_cast_fp16 = reshape(shape = var_2857, x = value_25_cast_fp16)[name = tensor("op_2858_cast_fp16")]; + tensor attn_25_transpose_x_0 = const()[name = tensor("attn_25_transpose_x_0"), val = tensor(false)]; + tensor attn_25_transpose_y_0 = const()[name = tensor("attn_25_transpose_y_0"), val = tensor(true)]; + tensor attn_25_cast_fp16 = matmul(transpose_x = attn_25_transpose_x_0, transpose_y = attn_25_transpose_y_0, x = var_2858_cast_fp16, y = var_2856_cast_fp16)[name = tensor("attn_25_cast_fp16")]; + tensor var_2861 = const()[name = tensor("op_2861"), val = tensor([1, 1280, 1, -1])]; + tensor input_247_cast_fp16 = reshape(shape = var_2861, x = attn_25_cast_fp16)[name = tensor("input_247_cast_fp16")]; + tensor pretrained_out_151_pad_type_0 = const()[name = tensor("pretrained_out_151_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_151_strides_0 = const()[name = tensor("pretrained_out_151_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_151_pad_0 = const()[name = tensor("pretrained_out_151_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_151_dilations_0 = const()[name = tensor("pretrained_out_151_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_151_groups_0 = const()[name = tensor("pretrained_out_151_groups_0"), val = tensor(1)]; + tensor layers_12_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(144589760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145409024))), name = tensor("layers_12_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_12_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_12_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145409152)))]; + tensor pretrained_out_151_cast_fp16 = conv(bias = layers_12_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_151_dilations_0, groups = pretrained_out_151_groups_0, pad = pretrained_out_151_pad_0, pad_type = pretrained_out_151_pad_type_0, strides = pretrained_out_151_strides_0, weight = layers_12_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_247_cast_fp16)[name = tensor("pretrained_out_151_cast_fp16")]; + tensor input_249_pad_type_0 = const()[name = tensor("input_249_pad_type_0"), val = tensor("valid")]; + tensor input_249_strides_0 = const()[name = tensor("input_249_strides_0"), val = tensor([1, 1])]; + tensor input_249_pad_0 = const()[name = tensor("input_249_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_249_dilations_0 = const()[name = tensor("input_249_dilations_0"), val = tensor([1, 1])]; + tensor input_249_groups_0 = const()[name = tensor("input_249_groups_0"), val = tensor(1)]; + tensor layers_12_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_12_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145411776)))]; + tensor input_249_cast_fp16 = conv(dilations = input_249_dilations_0, groups = input_249_groups_0, pad = input_249_pad_0, pad_type = input_249_pad_type_0, strides = input_249_strides_0, weight = layers_12_self_attn_o_proj_loraA_weight_to_fp16, x = input_247_cast_fp16)[name = tensor("input_249_cast_fp16")]; + tensor lora_out_151_pad_type_0 = const()[name = tensor("lora_out_151_pad_type_0"), val = tensor("valid")]; + tensor lora_out_151_strides_0 = const()[name = tensor("lora_out_151_strides_0"), val = tensor([1, 1])]; + tensor lora_out_151_pad_0 = const()[name = tensor("lora_out_151_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_151_dilations_0 = const()[name = tensor("lora_out_151_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_151_groups_0 = const()[name = tensor("lora_out_151_groups_0"), val = tensor(1)]; + tensor layers_12_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_12_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145452800)))]; + tensor lora_out_151_cast_fp16 = conv(dilations = lora_out_151_dilations_0, groups = lora_out_151_groups_0, pad = lora_out_151_pad_0, pad_type = lora_out_151_pad_type_0, strides = lora_out_151_strides_0, weight = layers_12_self_attn_o_proj_loraB_weight_to_fp16, x = input_249_cast_fp16)[name = tensor("lora_out_151_cast_fp16")]; + tensor obj_51_cast_fp16 = add(x = pretrained_out_151_cast_fp16, y = lora_out_151_cast_fp16)[name = tensor("obj_51_cast_fp16")]; + tensor inputs_51_cast_fp16 = add(x = inputs_49_cast_fp16, y = obj_51_cast_fp16)[name = tensor("inputs_51_cast_fp16")]; + tensor out_51_axes_0 = const()[name = tensor("out_51_axes_0"), val = tensor([1])]; + tensor var_2895_to_fp16 = const()[name = tensor("op_2895_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_51_cast_fp16 = layer_norm(axes = out_51_axes_0, epsilon = var_2895_to_fp16, x = inputs_51_cast_fp16)[name = tensor("out_51_cast_fp16")]; + tensor input_251_gamma_0_to_fp16 = const()[name = tensor("input_251_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145493824)))]; + tensor input_251_beta_0_to_fp16 = const()[name = tensor("input_251_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145496448)))]; + tensor input_251_epsilon_0_to_fp16 = const()[name = tensor("input_251_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_251_cast_fp16 = batch_norm(beta = input_251_beta_0_to_fp16, epsilon = input_251_epsilon_0_to_fp16, gamma = input_251_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_51_cast_fp16)[name = tensor("input_251_cast_fp16")]; + tensor pretrained_out_153_pad_type_0 = const()[name = tensor("pretrained_out_153_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_153_strides_0 = const()[name = tensor("pretrained_out_153_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_153_pad_0 = const()[name = tensor("pretrained_out_153_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_153_dilations_0 = const()[name = tensor("pretrained_out_153_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_153_groups_0 = const()[name = tensor("pretrained_out_153_groups_0"), val = tensor(1)]; + tensor layers_12_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145499072))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(148775936))), name = tensor("layers_12_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_12_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_12_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(148776064)))]; + tensor pretrained_out_153_cast_fp16 = conv(bias = layers_12_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_153_dilations_0, groups = pretrained_out_153_groups_0, pad = pretrained_out_153_pad_0, pad_type = pretrained_out_153_pad_type_0, strides = pretrained_out_153_strides_0, weight = layers_12_fc1_pretrained_weight_to_fp16_palettized, x = input_251_cast_fp16)[name = tensor("pretrained_out_153_cast_fp16")]; + tensor input_253_pad_type_0 = const()[name = tensor("input_253_pad_type_0"), val = tensor("valid")]; + tensor input_253_strides_0 = const()[name = tensor("input_253_strides_0"), val = tensor([1, 1])]; + tensor input_253_pad_0 = const()[name = tensor("input_253_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_253_dilations_0 = const()[name = tensor("input_253_dilations_0"), val = tensor([1, 1])]; + tensor input_253_groups_0 = const()[name = tensor("input_253_groups_0"), val = tensor(1)]; + tensor layers_12_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_12_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(148786368)))]; + tensor input_253_cast_fp16 = conv(dilations = input_253_dilations_0, groups = input_253_groups_0, pad = input_253_pad_0, pad_type = input_253_pad_type_0, strides = input_253_strides_0, weight = layers_12_fc1_loraA_weight_to_fp16, x = input_251_cast_fp16)[name = tensor("input_253_cast_fp16")]; + tensor lora_out_153_pad_type_0 = const()[name = tensor("lora_out_153_pad_type_0"), val = tensor("valid")]; + tensor lora_out_153_strides_0 = const()[name = tensor("lora_out_153_strides_0"), val = tensor([1, 1])]; + tensor lora_out_153_pad_0 = const()[name = tensor("lora_out_153_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_153_dilations_0 = const()[name = tensor("lora_out_153_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_153_groups_0 = const()[name = tensor("lora_out_153_groups_0"), val = tensor(1)]; + tensor layers_12_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_12_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(148827392)))]; + tensor lora_out_153_cast_fp16 = conv(dilations = lora_out_153_dilations_0, groups = lora_out_153_groups_0, pad = lora_out_153_pad_0, pad_type = lora_out_153_pad_type_0, strides = lora_out_153_strides_0, weight = layers_12_fc1_loraB_weight_to_fp16, x = input_253_cast_fp16)[name = tensor("lora_out_153_cast_fp16")]; + tensor input_255_cast_fp16 = add(x = pretrained_out_153_cast_fp16, y = lora_out_153_cast_fp16)[name = tensor("input_255_cast_fp16")]; + tensor input_257_mode_0 = const()[name = tensor("input_257_mode_0"), val = tensor("EXACT")]; + tensor input_257_cast_fp16 = gelu(mode = input_257_mode_0, x = input_255_cast_fp16)[name = tensor("input_257_cast_fp16")]; + tensor pretrained_out_155_pad_type_0 = const()[name = tensor("pretrained_out_155_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_155_strides_0 = const()[name = tensor("pretrained_out_155_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_155_pad_0 = const()[name = tensor("pretrained_out_155_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_155_dilations_0 = const()[name = tensor("pretrained_out_155_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_155_groups_0 = const()[name = tensor("pretrained_out_155_groups_0"), val = tensor(1)]; + tensor layers_12_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(148991296))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152268160))), name = tensor("layers_12_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_12_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_12_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152268288)))]; + tensor pretrained_out_155_cast_fp16 = conv(bias = layers_12_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_155_dilations_0, groups = pretrained_out_155_groups_0, pad = pretrained_out_155_pad_0, pad_type = pretrained_out_155_pad_type_0, strides = pretrained_out_155_strides_0, weight = layers_12_fc2_pretrained_weight_to_fp16_palettized, x = input_257_cast_fp16)[name = tensor("pretrained_out_155_cast_fp16")]; + tensor input_259_pad_type_0 = const()[name = tensor("input_259_pad_type_0"), val = tensor("valid")]; + tensor input_259_strides_0 = const()[name = tensor("input_259_strides_0"), val = tensor([1, 1])]; + tensor input_259_pad_0 = const()[name = tensor("input_259_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_259_dilations_0 = const()[name = tensor("input_259_dilations_0"), val = tensor([1, 1])]; + tensor input_259_groups_0 = const()[name = tensor("input_259_groups_0"), val = tensor(1)]; + tensor layers_12_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_12_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152270912)))]; + tensor input_259_cast_fp16 = conv(dilations = input_259_dilations_0, groups = input_259_groups_0, pad = input_259_pad_0, pad_type = input_259_pad_type_0, strides = input_259_strides_0, weight = layers_12_fc2_loraA_weight_to_fp16, x = input_257_cast_fp16)[name = tensor("input_259_cast_fp16")]; + tensor lora_out_155_pad_type_0 = const()[name = tensor("lora_out_155_pad_type_0"), val = tensor("valid")]; + tensor lora_out_155_strides_0 = const()[name = tensor("lora_out_155_strides_0"), val = tensor([1, 1])]; + tensor lora_out_155_pad_0 = const()[name = tensor("lora_out_155_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_155_dilations_0 = const()[name = tensor("lora_out_155_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_155_groups_0 = const()[name = tensor("lora_out_155_groups_0"), val = tensor(1)]; + tensor layers_12_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_12_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152434816)))]; + tensor lora_out_155_cast_fp16 = conv(dilations = lora_out_155_dilations_0, groups = lora_out_155_groups_0, pad = lora_out_155_pad_0, pad_type = lora_out_155_pad_type_0, strides = lora_out_155_strides_0, weight = layers_12_fc2_loraB_weight_to_fp16, x = input_259_cast_fp16)[name = tensor("lora_out_155_cast_fp16")]; + tensor hidden_states_29_cast_fp16 = add(x = pretrained_out_155_cast_fp16, y = lora_out_155_cast_fp16)[name = tensor("hidden_states_29_cast_fp16")]; + tensor inputs_53_cast_fp16 = add(x = inputs_51_cast_fp16, y = hidden_states_29_cast_fp16)[name = tensor("inputs_53_cast_fp16")]; + tensor var_2960 = const()[name = tensor("op_2960"), val = tensor(3)]; + tensor out_53_axes_0 = const()[name = tensor("out_53_axes_0"), val = tensor([1])]; + tensor var_2979_to_fp16 = const()[name = tensor("op_2979_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_53_cast_fp16 = layer_norm(axes = out_53_axes_0, epsilon = var_2979_to_fp16, x = inputs_53_cast_fp16)[name = tensor("out_53_cast_fp16")]; + tensor obj_53_gamma_0_to_fp16 = const()[name = tensor("obj_53_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152475840)))]; + tensor obj_53_beta_0_to_fp16 = const()[name = tensor("obj_53_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152478464)))]; + tensor obj_53_epsilon_0_to_fp16 = const()[name = tensor("obj_53_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_53_cast_fp16 = batch_norm(beta = obj_53_beta_0_to_fp16, epsilon = obj_53_epsilon_0_to_fp16, gamma = obj_53_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_53_cast_fp16)[name = tensor("obj_53_cast_fp16")]; + tensor pretrained_out_157_pad_type_0 = const()[name = tensor("pretrained_out_157_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_157_strides_0 = const()[name = tensor("pretrained_out_157_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_157_pad_0 = const()[name = tensor("pretrained_out_157_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_157_dilations_0 = const()[name = tensor("pretrained_out_157_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_157_groups_0 = const()[name = tensor("pretrained_out_157_groups_0"), val = tensor(1)]; + tensor layers_13_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152481088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(153300352))), name = tensor("layers_13_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_13_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_13_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(153300480)))]; + tensor pretrained_out_157_cast_fp16 = conv(bias = layers_13_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_157_dilations_0, groups = pretrained_out_157_groups_0, pad = pretrained_out_157_pad_0, pad_type = pretrained_out_157_pad_type_0, strides = pretrained_out_157_strides_0, weight = layers_13_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_53_cast_fp16)[name = tensor("pretrained_out_157_cast_fp16")]; + tensor input_261_pad_type_0 = const()[name = tensor("input_261_pad_type_0"), val = tensor("valid")]; + tensor input_261_strides_0 = const()[name = tensor("input_261_strides_0"), val = tensor([1, 1])]; + tensor input_261_pad_0 = const()[name = tensor("input_261_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_261_dilations_0 = const()[name = tensor("input_261_dilations_0"), val = tensor([1, 1])]; + tensor input_261_groups_0 = const()[name = tensor("input_261_groups_0"), val = tensor(1)]; + tensor layers_13_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_13_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(153303104)))]; + tensor input_261_cast_fp16 = conv(dilations = input_261_dilations_0, groups = input_261_groups_0, pad = input_261_pad_0, pad_type = input_261_pad_type_0, strides = input_261_strides_0, weight = layers_13_self_attn_q_proj_loraA_weight_to_fp16, x = obj_53_cast_fp16)[name = tensor("input_261_cast_fp16")]; + tensor lora_out_157_pad_type_0 = const()[name = tensor("lora_out_157_pad_type_0"), val = tensor("valid")]; + tensor lora_out_157_strides_0 = const()[name = tensor("lora_out_157_strides_0"), val = tensor([1, 1])]; + tensor lora_out_157_pad_0 = const()[name = tensor("lora_out_157_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_157_dilations_0 = const()[name = tensor("lora_out_157_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_157_groups_0 = const()[name = tensor("lora_out_157_groups_0"), val = tensor(1)]; + tensor layers_13_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_13_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(153344128)))]; + tensor lora_out_157_cast_fp16 = conv(dilations = lora_out_157_dilations_0, groups = lora_out_157_groups_0, pad = lora_out_157_pad_0, pad_type = lora_out_157_pad_type_0, strides = lora_out_157_strides_0, weight = layers_13_self_attn_q_proj_loraB_weight_to_fp16, x = input_261_cast_fp16)[name = tensor("lora_out_157_cast_fp16")]; + tensor query_27_cast_fp16 = add(x = pretrained_out_157_cast_fp16, y = lora_out_157_cast_fp16)[name = tensor("query_27_cast_fp16")]; + tensor pretrained_out_159_pad_type_0 = const()[name = tensor("pretrained_out_159_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_159_strides_0 = const()[name = tensor("pretrained_out_159_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_159_pad_0 = const()[name = tensor("pretrained_out_159_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_159_dilations_0 = const()[name = tensor("pretrained_out_159_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_159_groups_0 = const()[name = tensor("pretrained_out_159_groups_0"), val = tensor(1)]; + tensor layers_13_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(153385152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154204416))), name = tensor("layers_13_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_159_cast_fp16 = conv(dilations = pretrained_out_159_dilations_0, groups = pretrained_out_159_groups_0, pad = pretrained_out_159_pad_0, pad_type = pretrained_out_159_pad_type_0, strides = pretrained_out_159_strides_0, weight = layers_13_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_53_cast_fp16)[name = tensor("pretrained_out_159_cast_fp16")]; + tensor input_263_pad_type_0 = const()[name = tensor("input_263_pad_type_0"), val = tensor("valid")]; + tensor input_263_strides_0 = const()[name = tensor("input_263_strides_0"), val = tensor([1, 1])]; + tensor input_263_pad_0 = const()[name = tensor("input_263_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_263_dilations_0 = const()[name = tensor("input_263_dilations_0"), val = tensor([1, 1])]; + tensor input_263_groups_0 = const()[name = tensor("input_263_groups_0"), val = tensor(1)]; + tensor layers_13_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_13_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154204544)))]; + tensor input_263_cast_fp16 = conv(dilations = input_263_dilations_0, groups = input_263_groups_0, pad = input_263_pad_0, pad_type = input_263_pad_type_0, strides = input_263_strides_0, weight = layers_13_self_attn_k_proj_loraA_weight_to_fp16, x = obj_53_cast_fp16)[name = tensor("input_263_cast_fp16")]; + tensor lora_out_159_pad_type_0 = const()[name = tensor("lora_out_159_pad_type_0"), val = tensor("valid")]; + tensor lora_out_159_strides_0 = const()[name = tensor("lora_out_159_strides_0"), val = tensor([1, 1])]; + tensor lora_out_159_pad_0 = const()[name = tensor("lora_out_159_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_159_dilations_0 = const()[name = tensor("lora_out_159_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_159_groups_0 = const()[name = tensor("lora_out_159_groups_0"), val = tensor(1)]; + tensor layers_13_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_13_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154245568)))]; + tensor lora_out_159_cast_fp16 = conv(dilations = lora_out_159_dilations_0, groups = lora_out_159_groups_0, pad = lora_out_159_pad_0, pad_type = lora_out_159_pad_type_0, strides = lora_out_159_strides_0, weight = layers_13_self_attn_k_proj_loraB_weight_to_fp16, x = input_263_cast_fp16)[name = tensor("lora_out_159_cast_fp16")]; + tensor key_27_cast_fp16 = add(x = pretrained_out_159_cast_fp16, y = lora_out_159_cast_fp16)[name = tensor("key_27_cast_fp16")]; + tensor pretrained_out_161_pad_type_0 = const()[name = tensor("pretrained_out_161_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_161_strides_0 = const()[name = tensor("pretrained_out_161_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_161_pad_0 = const()[name = tensor("pretrained_out_161_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_161_dilations_0 = const()[name = tensor("pretrained_out_161_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_161_groups_0 = const()[name = tensor("pretrained_out_161_groups_0"), val = tensor(1)]; + tensor layers_13_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154286592))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(155105856))), name = tensor("layers_13_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_13_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_13_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(155105984)))]; + tensor pretrained_out_161_cast_fp16 = conv(bias = layers_13_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_161_dilations_0, groups = pretrained_out_161_groups_0, pad = pretrained_out_161_pad_0, pad_type = pretrained_out_161_pad_type_0, strides = pretrained_out_161_strides_0, weight = layers_13_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_53_cast_fp16)[name = tensor("pretrained_out_161_cast_fp16")]; + tensor input_265_pad_type_0 = const()[name = tensor("input_265_pad_type_0"), val = tensor("valid")]; + tensor input_265_strides_0 = const()[name = tensor("input_265_strides_0"), val = tensor([1, 1])]; + tensor input_265_pad_0 = const()[name = tensor("input_265_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_265_dilations_0 = const()[name = tensor("input_265_dilations_0"), val = tensor([1, 1])]; + tensor input_265_groups_0 = const()[name = tensor("input_265_groups_0"), val = tensor(1)]; + tensor layers_13_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_13_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(155108608)))]; + tensor input_265_cast_fp16 = conv(dilations = input_265_dilations_0, groups = input_265_groups_0, pad = input_265_pad_0, pad_type = input_265_pad_type_0, strides = input_265_strides_0, weight = layers_13_self_attn_v_proj_loraA_weight_to_fp16, x = obj_53_cast_fp16)[name = tensor("input_265_cast_fp16")]; + tensor lora_out_161_pad_type_0 = const()[name = tensor("lora_out_161_pad_type_0"), val = tensor("valid")]; + tensor lora_out_161_strides_0 = const()[name = tensor("lora_out_161_strides_0"), val = tensor([1, 1])]; + tensor lora_out_161_pad_0 = const()[name = tensor("lora_out_161_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_161_dilations_0 = const()[name = tensor("lora_out_161_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_161_groups_0 = const()[name = tensor("lora_out_161_groups_0"), val = tensor(1)]; + tensor layers_13_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_13_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(155149632)))]; + tensor lora_out_161_cast_fp16 = conv(dilations = lora_out_161_dilations_0, groups = lora_out_161_groups_0, pad = lora_out_161_pad_0, pad_type = lora_out_161_pad_type_0, strides = lora_out_161_strides_0, weight = layers_13_self_attn_v_proj_loraB_weight_to_fp16, x = input_265_cast_fp16)[name = tensor("lora_out_161_cast_fp16")]; + tensor value_27_cast_fp16 = add(x = pretrained_out_161_cast_fp16, y = lora_out_161_cast_fp16)[name = tensor("value_27_cast_fp16")]; + tensor var_3062 = const()[name = tensor("op_3062"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_27_cast_fp16 = reshape(shape = var_3062, x = query_27_cast_fp16)[name = tensor("mh_q_27_cast_fp16")]; + tensor var_3064_to_fp16 = const()[name = tensor("op_3064_to_fp16"), val = tensor(0x1p-3)]; + tensor var_3065_cast_fp16 = mul(x = mh_q_27_cast_fp16, y = var_3064_to_fp16)[name = tensor("op_3065_cast_fp16")]; + tensor var_3066 = const()[name = tensor("op_3066"), val = tensor([1, 20, 64, -1])]; + tensor var_3067_cast_fp16 = reshape(shape = var_3066, x = key_27_cast_fp16)[name = tensor("op_3067_cast_fp16")]; + tensor mh_w_27_transpose_x_0 = const()[name = tensor("mh_w_27_transpose_x_0"), val = tensor(true)]; + tensor mh_w_27_transpose_y_0 = const()[name = tensor("mh_w_27_transpose_y_0"), val = tensor(false)]; + tensor mh_w_27_cast_fp16 = matmul(transpose_x = mh_w_27_transpose_x_0, transpose_y = mh_w_27_transpose_y_0, x = var_3065_cast_fp16, y = var_3067_cast_fp16)[name = tensor("mh_w_27_cast_fp16")]; + tensor var_3070_cast_fp16 = softmax(axis = var_2960, x = mh_w_27_cast_fp16)[name = tensor("op_3070_cast_fp16")]; + tensor var_3071 = const()[name = tensor("op_3071"), val = tensor([1, 20, 64, -1])]; + tensor var_3072_cast_fp16 = reshape(shape = var_3071, x = value_27_cast_fp16)[name = tensor("op_3072_cast_fp16")]; + tensor attn_27_transpose_x_0 = const()[name = tensor("attn_27_transpose_x_0"), val = tensor(false)]; + tensor attn_27_transpose_y_0 = const()[name = tensor("attn_27_transpose_y_0"), val = tensor(true)]; + tensor attn_27_cast_fp16 = matmul(transpose_x = attn_27_transpose_x_0, transpose_y = attn_27_transpose_y_0, x = var_3072_cast_fp16, y = var_3070_cast_fp16)[name = tensor("attn_27_cast_fp16")]; + tensor var_3075 = const()[name = tensor("op_3075"), val = tensor([1, 1280, 1, -1])]; + tensor input_267_cast_fp16 = reshape(shape = var_3075, x = attn_27_cast_fp16)[name = tensor("input_267_cast_fp16")]; + tensor pretrained_out_163_pad_type_0 = const()[name = tensor("pretrained_out_163_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_163_strides_0 = const()[name = tensor("pretrained_out_163_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_163_pad_0 = const()[name = tensor("pretrained_out_163_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_163_dilations_0 = const()[name = tensor("pretrained_out_163_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_163_groups_0 = const()[name = tensor("pretrained_out_163_groups_0"), val = tensor(1)]; + tensor layers_13_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(155190656))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156009920))), name = tensor("layers_13_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_13_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_13_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156010048)))]; + tensor pretrained_out_163_cast_fp16 = conv(bias = layers_13_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_163_dilations_0, groups = pretrained_out_163_groups_0, pad = pretrained_out_163_pad_0, pad_type = pretrained_out_163_pad_type_0, strides = pretrained_out_163_strides_0, weight = layers_13_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_267_cast_fp16)[name = tensor("pretrained_out_163_cast_fp16")]; + tensor input_269_pad_type_0 = const()[name = tensor("input_269_pad_type_0"), val = tensor("valid")]; + tensor input_269_strides_0 = const()[name = tensor("input_269_strides_0"), val = tensor([1, 1])]; + tensor input_269_pad_0 = const()[name = tensor("input_269_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_269_dilations_0 = const()[name = tensor("input_269_dilations_0"), val = tensor([1, 1])]; + tensor input_269_groups_0 = const()[name = tensor("input_269_groups_0"), val = tensor(1)]; + tensor layers_13_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_13_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156012672)))]; + tensor input_269_cast_fp16 = conv(dilations = input_269_dilations_0, groups = input_269_groups_0, pad = input_269_pad_0, pad_type = input_269_pad_type_0, strides = input_269_strides_0, weight = layers_13_self_attn_o_proj_loraA_weight_to_fp16, x = input_267_cast_fp16)[name = tensor("input_269_cast_fp16")]; + tensor lora_out_163_pad_type_0 = const()[name = tensor("lora_out_163_pad_type_0"), val = tensor("valid")]; + tensor lora_out_163_strides_0 = const()[name = tensor("lora_out_163_strides_0"), val = tensor([1, 1])]; + tensor lora_out_163_pad_0 = const()[name = tensor("lora_out_163_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_163_dilations_0 = const()[name = tensor("lora_out_163_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_163_groups_0 = const()[name = tensor("lora_out_163_groups_0"), val = tensor(1)]; + tensor layers_13_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_13_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156053696)))]; + tensor lora_out_163_cast_fp16 = conv(dilations = lora_out_163_dilations_0, groups = lora_out_163_groups_0, pad = lora_out_163_pad_0, pad_type = lora_out_163_pad_type_0, strides = lora_out_163_strides_0, weight = layers_13_self_attn_o_proj_loraB_weight_to_fp16, x = input_269_cast_fp16)[name = tensor("lora_out_163_cast_fp16")]; + tensor obj_55_cast_fp16 = add(x = pretrained_out_163_cast_fp16, y = lora_out_163_cast_fp16)[name = tensor("obj_55_cast_fp16")]; + tensor inputs_55_cast_fp16 = add(x = inputs_53_cast_fp16, y = obj_55_cast_fp16)[name = tensor("inputs_55_cast_fp16")]; + tensor out_55_axes_0 = const()[name = tensor("out_55_axes_0"), val = tensor([1])]; + tensor var_3109_to_fp16 = const()[name = tensor("op_3109_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_55_cast_fp16 = layer_norm(axes = out_55_axes_0, epsilon = var_3109_to_fp16, x = inputs_55_cast_fp16)[name = tensor("out_55_cast_fp16")]; + tensor input_271_gamma_0_to_fp16 = const()[name = tensor("input_271_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156094720)))]; + tensor input_271_beta_0_to_fp16 = const()[name = tensor("input_271_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156097344)))]; + tensor input_271_epsilon_0_to_fp16 = const()[name = tensor("input_271_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_271_cast_fp16 = batch_norm(beta = input_271_beta_0_to_fp16, epsilon = input_271_epsilon_0_to_fp16, gamma = input_271_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_55_cast_fp16)[name = tensor("input_271_cast_fp16")]; + tensor pretrained_out_165_pad_type_0 = const()[name = tensor("pretrained_out_165_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_165_strides_0 = const()[name = tensor("pretrained_out_165_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_165_pad_0 = const()[name = tensor("pretrained_out_165_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_165_dilations_0 = const()[name = tensor("pretrained_out_165_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_165_groups_0 = const()[name = tensor("pretrained_out_165_groups_0"), val = tensor(1)]; + tensor layers_13_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156099968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159376832))), name = tensor("layers_13_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_13_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_13_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159376960)))]; + tensor pretrained_out_165_cast_fp16 = conv(bias = layers_13_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_165_dilations_0, groups = pretrained_out_165_groups_0, pad = pretrained_out_165_pad_0, pad_type = pretrained_out_165_pad_type_0, strides = pretrained_out_165_strides_0, weight = layers_13_fc1_pretrained_weight_to_fp16_palettized, x = input_271_cast_fp16)[name = tensor("pretrained_out_165_cast_fp16")]; + tensor input_273_pad_type_0 = const()[name = tensor("input_273_pad_type_0"), val = tensor("valid")]; + tensor input_273_strides_0 = const()[name = tensor("input_273_strides_0"), val = tensor([1, 1])]; + tensor input_273_pad_0 = const()[name = tensor("input_273_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_273_dilations_0 = const()[name = tensor("input_273_dilations_0"), val = tensor([1, 1])]; + tensor input_273_groups_0 = const()[name = tensor("input_273_groups_0"), val = tensor(1)]; + tensor layers_13_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_13_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159387264)))]; + tensor input_273_cast_fp16 = conv(dilations = input_273_dilations_0, groups = input_273_groups_0, pad = input_273_pad_0, pad_type = input_273_pad_type_0, strides = input_273_strides_0, weight = layers_13_fc1_loraA_weight_to_fp16, x = input_271_cast_fp16)[name = tensor("input_273_cast_fp16")]; + tensor lora_out_165_pad_type_0 = const()[name = tensor("lora_out_165_pad_type_0"), val = tensor("valid")]; + tensor lora_out_165_strides_0 = const()[name = tensor("lora_out_165_strides_0"), val = tensor([1, 1])]; + tensor lora_out_165_pad_0 = const()[name = tensor("lora_out_165_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_165_dilations_0 = const()[name = tensor("lora_out_165_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_165_groups_0 = const()[name = tensor("lora_out_165_groups_0"), val = tensor(1)]; + tensor layers_13_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_13_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159428288)))]; + tensor lora_out_165_cast_fp16 = conv(dilations = lora_out_165_dilations_0, groups = lora_out_165_groups_0, pad = lora_out_165_pad_0, pad_type = lora_out_165_pad_type_0, strides = lora_out_165_strides_0, weight = layers_13_fc1_loraB_weight_to_fp16, x = input_273_cast_fp16)[name = tensor("lora_out_165_cast_fp16")]; + tensor input_275_cast_fp16 = add(x = pretrained_out_165_cast_fp16, y = lora_out_165_cast_fp16)[name = tensor("input_275_cast_fp16")]; + tensor input_277_mode_0 = const()[name = tensor("input_277_mode_0"), val = tensor("EXACT")]; + tensor input_277_cast_fp16 = gelu(mode = input_277_mode_0, x = input_275_cast_fp16)[name = tensor("input_277_cast_fp16")]; + tensor pretrained_out_167_pad_type_0 = const()[name = tensor("pretrained_out_167_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_167_strides_0 = const()[name = tensor("pretrained_out_167_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_167_pad_0 = const()[name = tensor("pretrained_out_167_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_167_dilations_0 = const()[name = tensor("pretrained_out_167_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_167_groups_0 = const()[name = tensor("pretrained_out_167_groups_0"), val = tensor(1)]; + tensor layers_13_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159592192))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(162869056))), name = tensor("layers_13_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_13_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_13_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(162869184)))]; + tensor pretrained_out_167_cast_fp16 = conv(bias = layers_13_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_167_dilations_0, groups = pretrained_out_167_groups_0, pad = pretrained_out_167_pad_0, pad_type = pretrained_out_167_pad_type_0, strides = pretrained_out_167_strides_0, weight = layers_13_fc2_pretrained_weight_to_fp16_palettized, x = input_277_cast_fp16)[name = tensor("pretrained_out_167_cast_fp16")]; + tensor input_279_pad_type_0 = const()[name = tensor("input_279_pad_type_0"), val = tensor("valid")]; + tensor input_279_strides_0 = const()[name = tensor("input_279_strides_0"), val = tensor([1, 1])]; + tensor input_279_pad_0 = const()[name = tensor("input_279_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_279_dilations_0 = const()[name = tensor("input_279_dilations_0"), val = tensor([1, 1])]; + tensor input_279_groups_0 = const()[name = tensor("input_279_groups_0"), val = tensor(1)]; + tensor layers_13_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_13_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(162871808)))]; + tensor input_279_cast_fp16 = conv(dilations = input_279_dilations_0, groups = input_279_groups_0, pad = input_279_pad_0, pad_type = input_279_pad_type_0, strides = input_279_strides_0, weight = layers_13_fc2_loraA_weight_to_fp16, x = input_277_cast_fp16)[name = tensor("input_279_cast_fp16")]; + tensor lora_out_167_pad_type_0 = const()[name = tensor("lora_out_167_pad_type_0"), val = tensor("valid")]; + tensor lora_out_167_strides_0 = const()[name = tensor("lora_out_167_strides_0"), val = tensor([1, 1])]; + tensor lora_out_167_pad_0 = const()[name = tensor("lora_out_167_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_167_dilations_0 = const()[name = tensor("lora_out_167_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_167_groups_0 = const()[name = tensor("lora_out_167_groups_0"), val = tensor(1)]; + tensor layers_13_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_13_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163035712)))]; + tensor lora_out_167_cast_fp16 = conv(dilations = lora_out_167_dilations_0, groups = lora_out_167_groups_0, pad = lora_out_167_pad_0, pad_type = lora_out_167_pad_type_0, strides = lora_out_167_strides_0, weight = layers_13_fc2_loraB_weight_to_fp16, x = input_279_cast_fp16)[name = tensor("lora_out_167_cast_fp16")]; + tensor hidden_states_31_cast_fp16 = add(x = pretrained_out_167_cast_fp16, y = lora_out_167_cast_fp16)[name = tensor("hidden_states_31_cast_fp16")]; + tensor inputs_57_cast_fp16 = add(x = inputs_55_cast_fp16, y = hidden_states_31_cast_fp16)[name = tensor("inputs_57_cast_fp16")]; + tensor var_3174 = const()[name = tensor("op_3174"), val = tensor(3)]; + tensor out_57_axes_0 = const()[name = tensor("out_57_axes_0"), val = tensor([1])]; + tensor var_3193_to_fp16 = const()[name = tensor("op_3193_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_57_cast_fp16 = layer_norm(axes = out_57_axes_0, epsilon = var_3193_to_fp16, x = inputs_57_cast_fp16)[name = tensor("out_57_cast_fp16")]; + tensor obj_57_gamma_0_to_fp16 = const()[name = tensor("obj_57_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163076736)))]; + tensor obj_57_beta_0_to_fp16 = const()[name = tensor("obj_57_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163079360)))]; + tensor obj_57_epsilon_0_to_fp16 = const()[name = tensor("obj_57_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_57_cast_fp16 = batch_norm(beta = obj_57_beta_0_to_fp16, epsilon = obj_57_epsilon_0_to_fp16, gamma = obj_57_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_57_cast_fp16)[name = tensor("obj_57_cast_fp16")]; + tensor pretrained_out_169_pad_type_0 = const()[name = tensor("pretrained_out_169_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_169_strides_0 = const()[name = tensor("pretrained_out_169_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_169_pad_0 = const()[name = tensor("pretrained_out_169_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_169_dilations_0 = const()[name = tensor("pretrained_out_169_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_169_groups_0 = const()[name = tensor("pretrained_out_169_groups_0"), val = tensor(1)]; + tensor layers_14_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163081984))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163901248))), name = tensor("layers_14_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_14_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_14_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163901376)))]; + tensor pretrained_out_169_cast_fp16 = conv(bias = layers_14_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_169_dilations_0, groups = pretrained_out_169_groups_0, pad = pretrained_out_169_pad_0, pad_type = pretrained_out_169_pad_type_0, strides = pretrained_out_169_strides_0, weight = layers_14_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_57_cast_fp16)[name = tensor("pretrained_out_169_cast_fp16")]; + tensor input_281_pad_type_0 = const()[name = tensor("input_281_pad_type_0"), val = tensor("valid")]; + tensor input_281_strides_0 = const()[name = tensor("input_281_strides_0"), val = tensor([1, 1])]; + tensor input_281_pad_0 = const()[name = tensor("input_281_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_281_dilations_0 = const()[name = tensor("input_281_dilations_0"), val = tensor([1, 1])]; + tensor input_281_groups_0 = const()[name = tensor("input_281_groups_0"), val = tensor(1)]; + tensor layers_14_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_14_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163904000)))]; + tensor input_281_cast_fp16 = conv(dilations = input_281_dilations_0, groups = input_281_groups_0, pad = input_281_pad_0, pad_type = input_281_pad_type_0, strides = input_281_strides_0, weight = layers_14_self_attn_q_proj_loraA_weight_to_fp16, x = obj_57_cast_fp16)[name = tensor("input_281_cast_fp16")]; + tensor lora_out_169_pad_type_0 = const()[name = tensor("lora_out_169_pad_type_0"), val = tensor("valid")]; + tensor lora_out_169_strides_0 = const()[name = tensor("lora_out_169_strides_0"), val = tensor([1, 1])]; + tensor lora_out_169_pad_0 = const()[name = tensor("lora_out_169_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_169_dilations_0 = const()[name = tensor("lora_out_169_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_169_groups_0 = const()[name = tensor("lora_out_169_groups_0"), val = tensor(1)]; + tensor layers_14_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_14_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163945024)))]; + tensor lora_out_169_cast_fp16 = conv(dilations = lora_out_169_dilations_0, groups = lora_out_169_groups_0, pad = lora_out_169_pad_0, pad_type = lora_out_169_pad_type_0, strides = lora_out_169_strides_0, weight = layers_14_self_attn_q_proj_loraB_weight_to_fp16, x = input_281_cast_fp16)[name = tensor("lora_out_169_cast_fp16")]; + tensor query_29_cast_fp16 = add(x = pretrained_out_169_cast_fp16, y = lora_out_169_cast_fp16)[name = tensor("query_29_cast_fp16")]; + tensor pretrained_out_171_pad_type_0 = const()[name = tensor("pretrained_out_171_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_171_strides_0 = const()[name = tensor("pretrained_out_171_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_171_pad_0 = const()[name = tensor("pretrained_out_171_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_171_dilations_0 = const()[name = tensor("pretrained_out_171_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_171_groups_0 = const()[name = tensor("pretrained_out_171_groups_0"), val = tensor(1)]; + tensor layers_14_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163986048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(164805312))), name = tensor("layers_14_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_171_cast_fp16 = conv(dilations = pretrained_out_171_dilations_0, groups = pretrained_out_171_groups_0, pad = pretrained_out_171_pad_0, pad_type = pretrained_out_171_pad_type_0, strides = pretrained_out_171_strides_0, weight = layers_14_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_57_cast_fp16)[name = tensor("pretrained_out_171_cast_fp16")]; + tensor input_283_pad_type_0 = const()[name = tensor("input_283_pad_type_0"), val = tensor("valid")]; + tensor input_283_strides_0 = const()[name = tensor("input_283_strides_0"), val = tensor([1, 1])]; + tensor input_283_pad_0 = const()[name = tensor("input_283_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_283_dilations_0 = const()[name = tensor("input_283_dilations_0"), val = tensor([1, 1])]; + tensor input_283_groups_0 = const()[name = tensor("input_283_groups_0"), val = tensor(1)]; + tensor layers_14_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_14_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(164805440)))]; + tensor input_283_cast_fp16 = conv(dilations = input_283_dilations_0, groups = input_283_groups_0, pad = input_283_pad_0, pad_type = input_283_pad_type_0, strides = input_283_strides_0, weight = layers_14_self_attn_k_proj_loraA_weight_to_fp16, x = obj_57_cast_fp16)[name = tensor("input_283_cast_fp16")]; + tensor lora_out_171_pad_type_0 = const()[name = tensor("lora_out_171_pad_type_0"), val = tensor("valid")]; + tensor lora_out_171_strides_0 = const()[name = tensor("lora_out_171_strides_0"), val = tensor([1, 1])]; + tensor lora_out_171_pad_0 = const()[name = tensor("lora_out_171_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_171_dilations_0 = const()[name = tensor("lora_out_171_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_171_groups_0 = const()[name = tensor("lora_out_171_groups_0"), val = tensor(1)]; + tensor layers_14_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_14_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(164846464)))]; + tensor lora_out_171_cast_fp16 = conv(dilations = lora_out_171_dilations_0, groups = lora_out_171_groups_0, pad = lora_out_171_pad_0, pad_type = lora_out_171_pad_type_0, strides = lora_out_171_strides_0, weight = layers_14_self_attn_k_proj_loraB_weight_to_fp16, x = input_283_cast_fp16)[name = tensor("lora_out_171_cast_fp16")]; + tensor key_29_cast_fp16 = add(x = pretrained_out_171_cast_fp16, y = lora_out_171_cast_fp16)[name = tensor("key_29_cast_fp16")]; + tensor pretrained_out_173_pad_type_0 = const()[name = tensor("pretrained_out_173_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_173_strides_0 = const()[name = tensor("pretrained_out_173_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_173_pad_0 = const()[name = tensor("pretrained_out_173_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_173_dilations_0 = const()[name = tensor("pretrained_out_173_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_173_groups_0 = const()[name = tensor("pretrained_out_173_groups_0"), val = tensor(1)]; + tensor layers_14_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(164887488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165706752))), name = tensor("layers_14_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_14_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_14_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165706880)))]; + tensor pretrained_out_173_cast_fp16 = conv(bias = layers_14_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_173_dilations_0, groups = pretrained_out_173_groups_0, pad = pretrained_out_173_pad_0, pad_type = pretrained_out_173_pad_type_0, strides = pretrained_out_173_strides_0, weight = layers_14_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_57_cast_fp16)[name = tensor("pretrained_out_173_cast_fp16")]; + tensor input_285_pad_type_0 = const()[name = tensor("input_285_pad_type_0"), val = tensor("valid")]; + tensor input_285_strides_0 = const()[name = tensor("input_285_strides_0"), val = tensor([1, 1])]; + tensor input_285_pad_0 = const()[name = tensor("input_285_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_285_dilations_0 = const()[name = tensor("input_285_dilations_0"), val = tensor([1, 1])]; + tensor input_285_groups_0 = const()[name = tensor("input_285_groups_0"), val = tensor(1)]; + tensor layers_14_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_14_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165709504)))]; + tensor input_285_cast_fp16 = conv(dilations = input_285_dilations_0, groups = input_285_groups_0, pad = input_285_pad_0, pad_type = input_285_pad_type_0, strides = input_285_strides_0, weight = layers_14_self_attn_v_proj_loraA_weight_to_fp16, x = obj_57_cast_fp16)[name = tensor("input_285_cast_fp16")]; + tensor lora_out_173_pad_type_0 = const()[name = tensor("lora_out_173_pad_type_0"), val = tensor("valid")]; + tensor lora_out_173_strides_0 = const()[name = tensor("lora_out_173_strides_0"), val = tensor([1, 1])]; + tensor lora_out_173_pad_0 = const()[name = tensor("lora_out_173_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_173_dilations_0 = const()[name = tensor("lora_out_173_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_173_groups_0 = const()[name = tensor("lora_out_173_groups_0"), val = tensor(1)]; + tensor layers_14_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_14_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165750528)))]; + tensor lora_out_173_cast_fp16 = conv(dilations = lora_out_173_dilations_0, groups = lora_out_173_groups_0, pad = lora_out_173_pad_0, pad_type = lora_out_173_pad_type_0, strides = lora_out_173_strides_0, weight = layers_14_self_attn_v_proj_loraB_weight_to_fp16, x = input_285_cast_fp16)[name = tensor("lora_out_173_cast_fp16")]; + tensor value_29_cast_fp16 = add(x = pretrained_out_173_cast_fp16, y = lora_out_173_cast_fp16)[name = tensor("value_29_cast_fp16")]; + tensor var_3276 = const()[name = tensor("op_3276"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_29_cast_fp16 = reshape(shape = var_3276, x = query_29_cast_fp16)[name = tensor("mh_q_29_cast_fp16")]; + tensor var_3278_to_fp16 = const()[name = tensor("op_3278_to_fp16"), val = tensor(0x1p-3)]; + tensor var_3279_cast_fp16 = mul(x = mh_q_29_cast_fp16, y = var_3278_to_fp16)[name = tensor("op_3279_cast_fp16")]; + tensor var_3280 = const()[name = tensor("op_3280"), val = tensor([1, 20, 64, -1])]; + tensor var_3281_cast_fp16 = reshape(shape = var_3280, x = key_29_cast_fp16)[name = tensor("op_3281_cast_fp16")]; + tensor mh_w_29_transpose_x_0 = const()[name = tensor("mh_w_29_transpose_x_0"), val = tensor(true)]; + tensor mh_w_29_transpose_y_0 = const()[name = tensor("mh_w_29_transpose_y_0"), val = tensor(false)]; + tensor mh_w_29_cast_fp16 = matmul(transpose_x = mh_w_29_transpose_x_0, transpose_y = mh_w_29_transpose_y_0, x = var_3279_cast_fp16, y = var_3281_cast_fp16)[name = tensor("mh_w_29_cast_fp16")]; + tensor var_3284_cast_fp16 = softmax(axis = var_3174, x = mh_w_29_cast_fp16)[name = tensor("op_3284_cast_fp16")]; + tensor var_3285 = const()[name = tensor("op_3285"), val = tensor([1, 20, 64, -1])]; + tensor var_3286_cast_fp16 = reshape(shape = var_3285, x = value_29_cast_fp16)[name = tensor("op_3286_cast_fp16")]; + tensor attn_29_transpose_x_0 = const()[name = tensor("attn_29_transpose_x_0"), val = tensor(false)]; + tensor attn_29_transpose_y_0 = const()[name = tensor("attn_29_transpose_y_0"), val = tensor(true)]; + tensor attn_29_cast_fp16 = matmul(transpose_x = attn_29_transpose_x_0, transpose_y = attn_29_transpose_y_0, x = var_3286_cast_fp16, y = var_3284_cast_fp16)[name = tensor("attn_29_cast_fp16")]; + tensor var_3289 = const()[name = tensor("op_3289"), val = tensor([1, 1280, 1, -1])]; + tensor input_287_cast_fp16 = reshape(shape = var_3289, x = attn_29_cast_fp16)[name = tensor("input_287_cast_fp16")]; + tensor pretrained_out_175_pad_type_0 = const()[name = tensor("pretrained_out_175_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_175_strides_0 = const()[name = tensor("pretrained_out_175_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_175_pad_0 = const()[name = tensor("pretrained_out_175_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_175_dilations_0 = const()[name = tensor("pretrained_out_175_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_175_groups_0 = const()[name = tensor("pretrained_out_175_groups_0"), val = tensor(1)]; + tensor layers_14_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165791552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(166610816))), name = tensor("layers_14_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_14_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_14_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(166610944)))]; + tensor pretrained_out_175_cast_fp16 = conv(bias = layers_14_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_175_dilations_0, groups = pretrained_out_175_groups_0, pad = pretrained_out_175_pad_0, pad_type = pretrained_out_175_pad_type_0, strides = pretrained_out_175_strides_0, weight = layers_14_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_287_cast_fp16)[name = tensor("pretrained_out_175_cast_fp16")]; + tensor input_289_pad_type_0 = const()[name = tensor("input_289_pad_type_0"), val = tensor("valid")]; + tensor input_289_strides_0 = const()[name = tensor("input_289_strides_0"), val = tensor([1, 1])]; + tensor input_289_pad_0 = const()[name = tensor("input_289_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_289_dilations_0 = const()[name = tensor("input_289_dilations_0"), val = tensor([1, 1])]; + tensor input_289_groups_0 = const()[name = tensor("input_289_groups_0"), val = tensor(1)]; + tensor layers_14_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_14_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(166613568)))]; + tensor input_289_cast_fp16 = conv(dilations = input_289_dilations_0, groups = input_289_groups_0, pad = input_289_pad_0, pad_type = input_289_pad_type_0, strides = input_289_strides_0, weight = layers_14_self_attn_o_proj_loraA_weight_to_fp16, x = input_287_cast_fp16)[name = tensor("input_289_cast_fp16")]; + tensor lora_out_175_pad_type_0 = const()[name = tensor("lora_out_175_pad_type_0"), val = tensor("valid")]; + tensor lora_out_175_strides_0 = const()[name = tensor("lora_out_175_strides_0"), val = tensor([1, 1])]; + tensor lora_out_175_pad_0 = const()[name = tensor("lora_out_175_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_175_dilations_0 = const()[name = tensor("lora_out_175_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_175_groups_0 = const()[name = tensor("lora_out_175_groups_0"), val = tensor(1)]; + tensor layers_14_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_14_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(166654592)))]; + tensor lora_out_175_cast_fp16 = conv(dilations = lora_out_175_dilations_0, groups = lora_out_175_groups_0, pad = lora_out_175_pad_0, pad_type = lora_out_175_pad_type_0, strides = lora_out_175_strides_0, weight = layers_14_self_attn_o_proj_loraB_weight_to_fp16, x = input_289_cast_fp16)[name = tensor("lora_out_175_cast_fp16")]; + tensor obj_59_cast_fp16 = add(x = pretrained_out_175_cast_fp16, y = lora_out_175_cast_fp16)[name = tensor("obj_59_cast_fp16")]; + tensor inputs_59_cast_fp16 = add(x = inputs_57_cast_fp16, y = obj_59_cast_fp16)[name = tensor("inputs_59_cast_fp16")]; + tensor out_59_axes_0 = const()[name = tensor("out_59_axes_0"), val = tensor([1])]; + tensor var_3323_to_fp16 = const()[name = tensor("op_3323_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_59_cast_fp16 = layer_norm(axes = out_59_axes_0, epsilon = var_3323_to_fp16, x = inputs_59_cast_fp16)[name = tensor("out_59_cast_fp16")]; + tensor input_291_gamma_0_to_fp16 = const()[name = tensor("input_291_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(166695616)))]; + tensor input_291_beta_0_to_fp16 = const()[name = tensor("input_291_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(166698240)))]; + tensor input_291_epsilon_0_to_fp16 = const()[name = tensor("input_291_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_291_cast_fp16 = batch_norm(beta = input_291_beta_0_to_fp16, epsilon = input_291_epsilon_0_to_fp16, gamma = input_291_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_59_cast_fp16)[name = tensor("input_291_cast_fp16")]; + tensor pretrained_out_177_pad_type_0 = const()[name = tensor("pretrained_out_177_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_177_strides_0 = const()[name = tensor("pretrained_out_177_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_177_pad_0 = const()[name = tensor("pretrained_out_177_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_177_dilations_0 = const()[name = tensor("pretrained_out_177_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_177_groups_0 = const()[name = tensor("pretrained_out_177_groups_0"), val = tensor(1)]; + tensor layers_14_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(166700864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169977728))), name = tensor("layers_14_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_14_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_14_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169977856)))]; + tensor pretrained_out_177_cast_fp16 = conv(bias = layers_14_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_177_dilations_0, groups = pretrained_out_177_groups_0, pad = pretrained_out_177_pad_0, pad_type = pretrained_out_177_pad_type_0, strides = pretrained_out_177_strides_0, weight = layers_14_fc1_pretrained_weight_to_fp16_palettized, x = input_291_cast_fp16)[name = tensor("pretrained_out_177_cast_fp16")]; + tensor input_293_pad_type_0 = const()[name = tensor("input_293_pad_type_0"), val = tensor("valid")]; + tensor input_293_strides_0 = const()[name = tensor("input_293_strides_0"), val = tensor([1, 1])]; + tensor input_293_pad_0 = const()[name = tensor("input_293_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_293_dilations_0 = const()[name = tensor("input_293_dilations_0"), val = tensor([1, 1])]; + tensor input_293_groups_0 = const()[name = tensor("input_293_groups_0"), val = tensor(1)]; + tensor layers_14_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_14_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169988160)))]; + tensor input_293_cast_fp16 = conv(dilations = input_293_dilations_0, groups = input_293_groups_0, pad = input_293_pad_0, pad_type = input_293_pad_type_0, strides = input_293_strides_0, weight = layers_14_fc1_loraA_weight_to_fp16, x = input_291_cast_fp16)[name = tensor("input_293_cast_fp16")]; + tensor lora_out_177_pad_type_0 = const()[name = tensor("lora_out_177_pad_type_0"), val = tensor("valid")]; + tensor lora_out_177_strides_0 = const()[name = tensor("lora_out_177_strides_0"), val = tensor([1, 1])]; + tensor lora_out_177_pad_0 = const()[name = tensor("lora_out_177_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_177_dilations_0 = const()[name = tensor("lora_out_177_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_177_groups_0 = const()[name = tensor("lora_out_177_groups_0"), val = tensor(1)]; + tensor layers_14_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_14_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170029184)))]; + tensor lora_out_177_cast_fp16 = conv(dilations = lora_out_177_dilations_0, groups = lora_out_177_groups_0, pad = lora_out_177_pad_0, pad_type = lora_out_177_pad_type_0, strides = lora_out_177_strides_0, weight = layers_14_fc1_loraB_weight_to_fp16, x = input_293_cast_fp16)[name = tensor("lora_out_177_cast_fp16")]; + tensor input_295_cast_fp16 = add(x = pretrained_out_177_cast_fp16, y = lora_out_177_cast_fp16)[name = tensor("input_295_cast_fp16")]; + tensor input_297_mode_0 = const()[name = tensor("input_297_mode_0"), val = tensor("EXACT")]; + tensor input_297_cast_fp16 = gelu(mode = input_297_mode_0, x = input_295_cast_fp16)[name = tensor("input_297_cast_fp16")]; + tensor pretrained_out_179_pad_type_0 = const()[name = tensor("pretrained_out_179_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_179_strides_0 = const()[name = tensor("pretrained_out_179_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_179_pad_0 = const()[name = tensor("pretrained_out_179_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_179_dilations_0 = const()[name = tensor("pretrained_out_179_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_179_groups_0 = const()[name = tensor("pretrained_out_179_groups_0"), val = tensor(1)]; + tensor layers_14_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170193088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173469952))), name = tensor("layers_14_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_14_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_14_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173470080)))]; + tensor pretrained_out_179_cast_fp16 = conv(bias = layers_14_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_179_dilations_0, groups = pretrained_out_179_groups_0, pad = pretrained_out_179_pad_0, pad_type = pretrained_out_179_pad_type_0, strides = pretrained_out_179_strides_0, weight = layers_14_fc2_pretrained_weight_to_fp16_palettized, x = input_297_cast_fp16)[name = tensor("pretrained_out_179_cast_fp16")]; + tensor input_299_pad_type_0 = const()[name = tensor("input_299_pad_type_0"), val = tensor("valid")]; + tensor input_299_strides_0 = const()[name = tensor("input_299_strides_0"), val = tensor([1, 1])]; + tensor input_299_pad_0 = const()[name = tensor("input_299_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_299_dilations_0 = const()[name = tensor("input_299_dilations_0"), val = tensor([1, 1])]; + tensor input_299_groups_0 = const()[name = tensor("input_299_groups_0"), val = tensor(1)]; + tensor layers_14_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_14_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173472704)))]; + tensor input_299_cast_fp16 = conv(dilations = input_299_dilations_0, groups = input_299_groups_0, pad = input_299_pad_0, pad_type = input_299_pad_type_0, strides = input_299_strides_0, weight = layers_14_fc2_loraA_weight_to_fp16, x = input_297_cast_fp16)[name = tensor("input_299_cast_fp16")]; + tensor lora_out_179_pad_type_0 = const()[name = tensor("lora_out_179_pad_type_0"), val = tensor("valid")]; + tensor lora_out_179_strides_0 = const()[name = tensor("lora_out_179_strides_0"), val = tensor([1, 1])]; + tensor lora_out_179_pad_0 = const()[name = tensor("lora_out_179_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_179_dilations_0 = const()[name = tensor("lora_out_179_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_179_groups_0 = const()[name = tensor("lora_out_179_groups_0"), val = tensor(1)]; + tensor layers_14_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_14_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173636608)))]; + tensor lora_out_179_cast_fp16 = conv(dilations = lora_out_179_dilations_0, groups = lora_out_179_groups_0, pad = lora_out_179_pad_0, pad_type = lora_out_179_pad_type_0, strides = lora_out_179_strides_0, weight = layers_14_fc2_loraB_weight_to_fp16, x = input_299_cast_fp16)[name = tensor("lora_out_179_cast_fp16")]; + tensor hidden_states_33_cast_fp16 = add(x = pretrained_out_179_cast_fp16, y = lora_out_179_cast_fp16)[name = tensor("hidden_states_33_cast_fp16")]; + tensor inputs_61_cast_fp16 = add(x = inputs_59_cast_fp16, y = hidden_states_33_cast_fp16)[name = tensor("inputs_61_cast_fp16")]; + tensor var_3388 = const()[name = tensor("op_3388"), val = tensor(3)]; + tensor out_61_axes_0 = const()[name = tensor("out_61_axes_0"), val = tensor([1])]; + tensor var_3407_to_fp16 = const()[name = tensor("op_3407_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_61_cast_fp16 = layer_norm(axes = out_61_axes_0, epsilon = var_3407_to_fp16, x = inputs_61_cast_fp16)[name = tensor("out_61_cast_fp16")]; + tensor obj_61_gamma_0_to_fp16 = const()[name = tensor("obj_61_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173677632)))]; + tensor obj_61_beta_0_to_fp16 = const()[name = tensor("obj_61_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173680256)))]; + tensor obj_61_epsilon_0_to_fp16 = const()[name = tensor("obj_61_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_61_cast_fp16 = batch_norm(beta = obj_61_beta_0_to_fp16, epsilon = obj_61_epsilon_0_to_fp16, gamma = obj_61_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_61_cast_fp16)[name = tensor("obj_61_cast_fp16")]; + tensor pretrained_out_181_pad_type_0 = const()[name = tensor("pretrained_out_181_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_181_strides_0 = const()[name = tensor("pretrained_out_181_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_181_pad_0 = const()[name = tensor("pretrained_out_181_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_181_dilations_0 = const()[name = tensor("pretrained_out_181_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_181_groups_0 = const()[name = tensor("pretrained_out_181_groups_0"), val = tensor(1)]; + tensor layers_15_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(173682880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(174502144))), name = tensor("layers_15_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_15_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_15_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(174502272)))]; + tensor pretrained_out_181_cast_fp16 = conv(bias = layers_15_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_181_dilations_0, groups = pretrained_out_181_groups_0, pad = pretrained_out_181_pad_0, pad_type = pretrained_out_181_pad_type_0, strides = pretrained_out_181_strides_0, weight = layers_15_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_61_cast_fp16)[name = tensor("pretrained_out_181_cast_fp16")]; + tensor input_301_pad_type_0 = const()[name = tensor("input_301_pad_type_0"), val = tensor("valid")]; + tensor input_301_strides_0 = const()[name = tensor("input_301_strides_0"), val = tensor([1, 1])]; + tensor input_301_pad_0 = const()[name = tensor("input_301_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_301_dilations_0 = const()[name = tensor("input_301_dilations_0"), val = tensor([1, 1])]; + tensor input_301_groups_0 = const()[name = tensor("input_301_groups_0"), val = tensor(1)]; + tensor layers_15_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_15_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(174504896)))]; + tensor input_301_cast_fp16 = conv(dilations = input_301_dilations_0, groups = input_301_groups_0, pad = input_301_pad_0, pad_type = input_301_pad_type_0, strides = input_301_strides_0, weight = layers_15_self_attn_q_proj_loraA_weight_to_fp16, x = obj_61_cast_fp16)[name = tensor("input_301_cast_fp16")]; + tensor lora_out_181_pad_type_0 = const()[name = tensor("lora_out_181_pad_type_0"), val = tensor("valid")]; + tensor lora_out_181_strides_0 = const()[name = tensor("lora_out_181_strides_0"), val = tensor([1, 1])]; + tensor lora_out_181_pad_0 = const()[name = tensor("lora_out_181_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_181_dilations_0 = const()[name = tensor("lora_out_181_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_181_groups_0 = const()[name = tensor("lora_out_181_groups_0"), val = tensor(1)]; + tensor layers_15_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_15_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(174545920)))]; + tensor lora_out_181_cast_fp16 = conv(dilations = lora_out_181_dilations_0, groups = lora_out_181_groups_0, pad = lora_out_181_pad_0, pad_type = lora_out_181_pad_type_0, strides = lora_out_181_strides_0, weight = layers_15_self_attn_q_proj_loraB_weight_to_fp16, x = input_301_cast_fp16)[name = tensor("lora_out_181_cast_fp16")]; + tensor query_31_cast_fp16 = add(x = pretrained_out_181_cast_fp16, y = lora_out_181_cast_fp16)[name = tensor("query_31_cast_fp16")]; + tensor pretrained_out_183_pad_type_0 = const()[name = tensor("pretrained_out_183_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_183_strides_0 = const()[name = tensor("pretrained_out_183_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_183_pad_0 = const()[name = tensor("pretrained_out_183_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_183_dilations_0 = const()[name = tensor("pretrained_out_183_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_183_groups_0 = const()[name = tensor("pretrained_out_183_groups_0"), val = tensor(1)]; + tensor layers_15_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(174586944))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175406208))), name = tensor("layers_15_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_183_cast_fp16 = conv(dilations = pretrained_out_183_dilations_0, groups = pretrained_out_183_groups_0, pad = pretrained_out_183_pad_0, pad_type = pretrained_out_183_pad_type_0, strides = pretrained_out_183_strides_0, weight = layers_15_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_61_cast_fp16)[name = tensor("pretrained_out_183_cast_fp16")]; + tensor input_303_pad_type_0 = const()[name = tensor("input_303_pad_type_0"), val = tensor("valid")]; + tensor input_303_strides_0 = const()[name = tensor("input_303_strides_0"), val = tensor([1, 1])]; + tensor input_303_pad_0 = const()[name = tensor("input_303_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_303_dilations_0 = const()[name = tensor("input_303_dilations_0"), val = tensor([1, 1])]; + tensor input_303_groups_0 = const()[name = tensor("input_303_groups_0"), val = tensor(1)]; + tensor layers_15_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_15_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175406336)))]; + tensor input_303_cast_fp16 = conv(dilations = input_303_dilations_0, groups = input_303_groups_0, pad = input_303_pad_0, pad_type = input_303_pad_type_0, strides = input_303_strides_0, weight = layers_15_self_attn_k_proj_loraA_weight_to_fp16, x = obj_61_cast_fp16)[name = tensor("input_303_cast_fp16")]; + tensor lora_out_183_pad_type_0 = const()[name = tensor("lora_out_183_pad_type_0"), val = tensor("valid")]; + tensor lora_out_183_strides_0 = const()[name = tensor("lora_out_183_strides_0"), val = tensor([1, 1])]; + tensor lora_out_183_pad_0 = const()[name = tensor("lora_out_183_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_183_dilations_0 = const()[name = tensor("lora_out_183_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_183_groups_0 = const()[name = tensor("lora_out_183_groups_0"), val = tensor(1)]; + tensor layers_15_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_15_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175447360)))]; + tensor lora_out_183_cast_fp16 = conv(dilations = lora_out_183_dilations_0, groups = lora_out_183_groups_0, pad = lora_out_183_pad_0, pad_type = lora_out_183_pad_type_0, strides = lora_out_183_strides_0, weight = layers_15_self_attn_k_proj_loraB_weight_to_fp16, x = input_303_cast_fp16)[name = tensor("lora_out_183_cast_fp16")]; + tensor key_31_cast_fp16 = add(x = pretrained_out_183_cast_fp16, y = lora_out_183_cast_fp16)[name = tensor("key_31_cast_fp16")]; + tensor pretrained_out_185_pad_type_0 = const()[name = tensor("pretrained_out_185_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_185_strides_0 = const()[name = tensor("pretrained_out_185_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_185_pad_0 = const()[name = tensor("pretrained_out_185_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_185_dilations_0 = const()[name = tensor("pretrained_out_185_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_185_groups_0 = const()[name = tensor("pretrained_out_185_groups_0"), val = tensor(1)]; + tensor layers_15_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175488384))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(176307648))), name = tensor("layers_15_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_15_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_15_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(176307776)))]; + tensor pretrained_out_185_cast_fp16 = conv(bias = layers_15_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_185_dilations_0, groups = pretrained_out_185_groups_0, pad = pretrained_out_185_pad_0, pad_type = pretrained_out_185_pad_type_0, strides = pretrained_out_185_strides_0, weight = layers_15_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_61_cast_fp16)[name = tensor("pretrained_out_185_cast_fp16")]; + tensor input_305_pad_type_0 = const()[name = tensor("input_305_pad_type_0"), val = tensor("valid")]; + tensor input_305_strides_0 = const()[name = tensor("input_305_strides_0"), val = tensor([1, 1])]; + tensor input_305_pad_0 = const()[name = tensor("input_305_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_305_dilations_0 = const()[name = tensor("input_305_dilations_0"), val = tensor([1, 1])]; + tensor input_305_groups_0 = const()[name = tensor("input_305_groups_0"), val = tensor(1)]; + tensor layers_15_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_15_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(176310400)))]; + tensor input_305_cast_fp16 = conv(dilations = input_305_dilations_0, groups = input_305_groups_0, pad = input_305_pad_0, pad_type = input_305_pad_type_0, strides = input_305_strides_0, weight = layers_15_self_attn_v_proj_loraA_weight_to_fp16, x = obj_61_cast_fp16)[name = tensor("input_305_cast_fp16")]; + tensor lora_out_185_pad_type_0 = const()[name = tensor("lora_out_185_pad_type_0"), val = tensor("valid")]; + tensor lora_out_185_strides_0 = const()[name = tensor("lora_out_185_strides_0"), val = tensor([1, 1])]; + tensor lora_out_185_pad_0 = const()[name = tensor("lora_out_185_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_185_dilations_0 = const()[name = tensor("lora_out_185_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_185_groups_0 = const()[name = tensor("lora_out_185_groups_0"), val = tensor(1)]; + tensor layers_15_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_15_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(176351424)))]; + tensor lora_out_185_cast_fp16 = conv(dilations = lora_out_185_dilations_0, groups = lora_out_185_groups_0, pad = lora_out_185_pad_0, pad_type = lora_out_185_pad_type_0, strides = lora_out_185_strides_0, weight = layers_15_self_attn_v_proj_loraB_weight_to_fp16, x = input_305_cast_fp16)[name = tensor("lora_out_185_cast_fp16")]; + tensor value_31_cast_fp16 = add(x = pretrained_out_185_cast_fp16, y = lora_out_185_cast_fp16)[name = tensor("value_31_cast_fp16")]; + tensor var_3490 = const()[name = tensor("op_3490"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_31_cast_fp16 = reshape(shape = var_3490, x = query_31_cast_fp16)[name = tensor("mh_q_31_cast_fp16")]; + tensor var_3492_to_fp16 = const()[name = tensor("op_3492_to_fp16"), val = tensor(0x1p-3)]; + tensor var_3493_cast_fp16 = mul(x = mh_q_31_cast_fp16, y = var_3492_to_fp16)[name = tensor("op_3493_cast_fp16")]; + tensor var_3494 = const()[name = tensor("op_3494"), val = tensor([1, 20, 64, -1])]; + tensor var_3495_cast_fp16 = reshape(shape = var_3494, x = key_31_cast_fp16)[name = tensor("op_3495_cast_fp16")]; + tensor mh_w_31_transpose_x_0 = const()[name = tensor("mh_w_31_transpose_x_0"), val = tensor(true)]; + tensor mh_w_31_transpose_y_0 = const()[name = tensor("mh_w_31_transpose_y_0"), val = tensor(false)]; + tensor mh_w_31_cast_fp16 = matmul(transpose_x = mh_w_31_transpose_x_0, transpose_y = mh_w_31_transpose_y_0, x = var_3493_cast_fp16, y = var_3495_cast_fp16)[name = tensor("mh_w_31_cast_fp16")]; + tensor var_3498_cast_fp16 = softmax(axis = var_3388, x = mh_w_31_cast_fp16)[name = tensor("op_3498_cast_fp16")]; + tensor var_3499 = const()[name = tensor("op_3499"), val = tensor([1, 20, 64, -1])]; + tensor var_3500_cast_fp16 = reshape(shape = var_3499, x = value_31_cast_fp16)[name = tensor("op_3500_cast_fp16")]; + tensor attn_31_transpose_x_0 = const()[name = tensor("attn_31_transpose_x_0"), val = tensor(false)]; + tensor attn_31_transpose_y_0 = const()[name = tensor("attn_31_transpose_y_0"), val = tensor(true)]; + tensor attn_31_cast_fp16 = matmul(transpose_x = attn_31_transpose_x_0, transpose_y = attn_31_transpose_y_0, x = var_3500_cast_fp16, y = var_3498_cast_fp16)[name = tensor("attn_31_cast_fp16")]; + tensor var_3503 = const()[name = tensor("op_3503"), val = tensor([1, 1280, 1, -1])]; + tensor input_307_cast_fp16 = reshape(shape = var_3503, x = attn_31_cast_fp16)[name = tensor("input_307_cast_fp16")]; + tensor pretrained_out_187_pad_type_0 = const()[name = tensor("pretrained_out_187_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_187_strides_0 = const()[name = tensor("pretrained_out_187_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_187_pad_0 = const()[name = tensor("pretrained_out_187_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_187_dilations_0 = const()[name = tensor("pretrained_out_187_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_187_groups_0 = const()[name = tensor("pretrained_out_187_groups_0"), val = tensor(1)]; + tensor layers_15_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(176392448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177211712))), name = tensor("layers_15_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_15_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_15_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177211840)))]; + tensor pretrained_out_187_cast_fp16 = conv(bias = layers_15_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_187_dilations_0, groups = pretrained_out_187_groups_0, pad = pretrained_out_187_pad_0, pad_type = pretrained_out_187_pad_type_0, strides = pretrained_out_187_strides_0, weight = layers_15_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_307_cast_fp16)[name = tensor("pretrained_out_187_cast_fp16")]; + tensor input_309_pad_type_0 = const()[name = tensor("input_309_pad_type_0"), val = tensor("valid")]; + tensor input_309_strides_0 = const()[name = tensor("input_309_strides_0"), val = tensor([1, 1])]; + tensor input_309_pad_0 = const()[name = tensor("input_309_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_309_dilations_0 = const()[name = tensor("input_309_dilations_0"), val = tensor([1, 1])]; + tensor input_309_groups_0 = const()[name = tensor("input_309_groups_0"), val = tensor(1)]; + tensor layers_15_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_15_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177214464)))]; + tensor input_309_cast_fp16 = conv(dilations = input_309_dilations_0, groups = input_309_groups_0, pad = input_309_pad_0, pad_type = input_309_pad_type_0, strides = input_309_strides_0, weight = layers_15_self_attn_o_proj_loraA_weight_to_fp16, x = input_307_cast_fp16)[name = tensor("input_309_cast_fp16")]; + tensor lora_out_187_pad_type_0 = const()[name = tensor("lora_out_187_pad_type_0"), val = tensor("valid")]; + tensor lora_out_187_strides_0 = const()[name = tensor("lora_out_187_strides_0"), val = tensor([1, 1])]; + tensor lora_out_187_pad_0 = const()[name = tensor("lora_out_187_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_187_dilations_0 = const()[name = tensor("lora_out_187_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_187_groups_0 = const()[name = tensor("lora_out_187_groups_0"), val = tensor(1)]; + tensor layers_15_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_15_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177255488)))]; + tensor lora_out_187_cast_fp16 = conv(dilations = lora_out_187_dilations_0, groups = lora_out_187_groups_0, pad = lora_out_187_pad_0, pad_type = lora_out_187_pad_type_0, strides = lora_out_187_strides_0, weight = layers_15_self_attn_o_proj_loraB_weight_to_fp16, x = input_309_cast_fp16)[name = tensor("lora_out_187_cast_fp16")]; + tensor obj_63_cast_fp16 = add(x = pretrained_out_187_cast_fp16, y = lora_out_187_cast_fp16)[name = tensor("obj_63_cast_fp16")]; + tensor inputs_63_cast_fp16 = add(x = inputs_61_cast_fp16, y = obj_63_cast_fp16)[name = tensor("inputs_63_cast_fp16")]; + tensor out_63_axes_0 = const()[name = tensor("out_63_axes_0"), val = tensor([1])]; + tensor var_3537_to_fp16 = const()[name = tensor("op_3537_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_63_cast_fp16 = layer_norm(axes = out_63_axes_0, epsilon = var_3537_to_fp16, x = inputs_63_cast_fp16)[name = tensor("out_63_cast_fp16")]; + tensor input_311_gamma_0_to_fp16 = const()[name = tensor("input_311_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177296512)))]; + tensor input_311_beta_0_to_fp16 = const()[name = tensor("input_311_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177299136)))]; + tensor input_311_epsilon_0_to_fp16 = const()[name = tensor("input_311_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_311_cast_fp16 = batch_norm(beta = input_311_beta_0_to_fp16, epsilon = input_311_epsilon_0_to_fp16, gamma = input_311_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_63_cast_fp16)[name = tensor("input_311_cast_fp16")]; + tensor pretrained_out_189_pad_type_0 = const()[name = tensor("pretrained_out_189_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_189_strides_0 = const()[name = tensor("pretrained_out_189_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_189_pad_0 = const()[name = tensor("pretrained_out_189_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_189_dilations_0 = const()[name = tensor("pretrained_out_189_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_189_groups_0 = const()[name = tensor("pretrained_out_189_groups_0"), val = tensor(1)]; + tensor layers_15_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177301760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(180578624))), name = tensor("layers_15_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_15_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_15_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(180578752)))]; + tensor pretrained_out_189_cast_fp16 = conv(bias = layers_15_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_189_dilations_0, groups = pretrained_out_189_groups_0, pad = pretrained_out_189_pad_0, pad_type = pretrained_out_189_pad_type_0, strides = pretrained_out_189_strides_0, weight = layers_15_fc1_pretrained_weight_to_fp16_palettized, x = input_311_cast_fp16)[name = tensor("pretrained_out_189_cast_fp16")]; + tensor input_313_pad_type_0 = const()[name = tensor("input_313_pad_type_0"), val = tensor("valid")]; + tensor input_313_strides_0 = const()[name = tensor("input_313_strides_0"), val = tensor([1, 1])]; + tensor input_313_pad_0 = const()[name = tensor("input_313_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_313_dilations_0 = const()[name = tensor("input_313_dilations_0"), val = tensor([1, 1])]; + tensor input_313_groups_0 = const()[name = tensor("input_313_groups_0"), val = tensor(1)]; + tensor layers_15_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_15_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(180589056)))]; + tensor input_313_cast_fp16 = conv(dilations = input_313_dilations_0, groups = input_313_groups_0, pad = input_313_pad_0, pad_type = input_313_pad_type_0, strides = input_313_strides_0, weight = layers_15_fc1_loraA_weight_to_fp16, x = input_311_cast_fp16)[name = tensor("input_313_cast_fp16")]; + tensor lora_out_189_pad_type_0 = const()[name = tensor("lora_out_189_pad_type_0"), val = tensor("valid")]; + tensor lora_out_189_strides_0 = const()[name = tensor("lora_out_189_strides_0"), val = tensor([1, 1])]; + tensor lora_out_189_pad_0 = const()[name = tensor("lora_out_189_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_189_dilations_0 = const()[name = tensor("lora_out_189_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_189_groups_0 = const()[name = tensor("lora_out_189_groups_0"), val = tensor(1)]; + tensor layers_15_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_15_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(180630080)))]; + tensor lora_out_189_cast_fp16 = conv(dilations = lora_out_189_dilations_0, groups = lora_out_189_groups_0, pad = lora_out_189_pad_0, pad_type = lora_out_189_pad_type_0, strides = lora_out_189_strides_0, weight = layers_15_fc1_loraB_weight_to_fp16, x = input_313_cast_fp16)[name = tensor("lora_out_189_cast_fp16")]; + tensor input_315_cast_fp16 = add(x = pretrained_out_189_cast_fp16, y = lora_out_189_cast_fp16)[name = tensor("input_315_cast_fp16")]; + tensor input_317_mode_0 = const()[name = tensor("input_317_mode_0"), val = tensor("EXACT")]; + tensor input_317_cast_fp16 = gelu(mode = input_317_mode_0, x = input_315_cast_fp16)[name = tensor("input_317_cast_fp16")]; + tensor pretrained_out_191_pad_type_0 = const()[name = tensor("pretrained_out_191_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_191_strides_0 = const()[name = tensor("pretrained_out_191_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_191_pad_0 = const()[name = tensor("pretrained_out_191_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_191_dilations_0 = const()[name = tensor("pretrained_out_191_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_191_groups_0 = const()[name = tensor("pretrained_out_191_groups_0"), val = tensor(1)]; + tensor layers_15_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(180793984))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184070848))), name = tensor("layers_15_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_15_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_15_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184070976)))]; + tensor pretrained_out_191_cast_fp16 = conv(bias = layers_15_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_191_dilations_0, groups = pretrained_out_191_groups_0, pad = pretrained_out_191_pad_0, pad_type = pretrained_out_191_pad_type_0, strides = pretrained_out_191_strides_0, weight = layers_15_fc2_pretrained_weight_to_fp16_palettized, x = input_317_cast_fp16)[name = tensor("pretrained_out_191_cast_fp16")]; + tensor input_319_pad_type_0 = const()[name = tensor("input_319_pad_type_0"), val = tensor("valid")]; + tensor input_319_strides_0 = const()[name = tensor("input_319_strides_0"), val = tensor([1, 1])]; + tensor input_319_pad_0 = const()[name = tensor("input_319_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_319_dilations_0 = const()[name = tensor("input_319_dilations_0"), val = tensor([1, 1])]; + tensor input_319_groups_0 = const()[name = tensor("input_319_groups_0"), val = tensor(1)]; + tensor layers_15_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_15_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184073600)))]; + tensor input_319_cast_fp16 = conv(dilations = input_319_dilations_0, groups = input_319_groups_0, pad = input_319_pad_0, pad_type = input_319_pad_type_0, strides = input_319_strides_0, weight = layers_15_fc2_loraA_weight_to_fp16, x = input_317_cast_fp16)[name = tensor("input_319_cast_fp16")]; + tensor lora_out_191_pad_type_0 = const()[name = tensor("lora_out_191_pad_type_0"), val = tensor("valid")]; + tensor lora_out_191_strides_0 = const()[name = tensor("lora_out_191_strides_0"), val = tensor([1, 1])]; + tensor lora_out_191_pad_0 = const()[name = tensor("lora_out_191_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_191_dilations_0 = const()[name = tensor("lora_out_191_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_191_groups_0 = const()[name = tensor("lora_out_191_groups_0"), val = tensor(1)]; + tensor layers_15_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_15_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184237504)))]; + tensor lora_out_191_cast_fp16 = conv(dilations = lora_out_191_dilations_0, groups = lora_out_191_groups_0, pad = lora_out_191_pad_0, pad_type = lora_out_191_pad_type_0, strides = lora_out_191_strides_0, weight = layers_15_fc2_loraB_weight_to_fp16, x = input_319_cast_fp16)[name = tensor("lora_out_191_cast_fp16")]; + tensor hidden_states_35_cast_fp16 = add(x = pretrained_out_191_cast_fp16, y = lora_out_191_cast_fp16)[name = tensor("hidden_states_35_cast_fp16")]; + tensor inputs_65_cast_fp16 = add(x = inputs_63_cast_fp16, y = hidden_states_35_cast_fp16)[name = tensor("inputs_65_cast_fp16")]; + tensor var_3602 = const()[name = tensor("op_3602"), val = tensor(3)]; + tensor out_65_axes_0 = const()[name = tensor("out_65_axes_0"), val = tensor([1])]; + tensor var_3621_to_fp16 = const()[name = tensor("op_3621_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_65_cast_fp16 = layer_norm(axes = out_65_axes_0, epsilon = var_3621_to_fp16, x = inputs_65_cast_fp16)[name = tensor("out_65_cast_fp16")]; + tensor obj_65_gamma_0_to_fp16 = const()[name = tensor("obj_65_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184278528)))]; + tensor obj_65_beta_0_to_fp16 = const()[name = tensor("obj_65_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184281152)))]; + tensor obj_65_epsilon_0_to_fp16 = const()[name = tensor("obj_65_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_65_cast_fp16 = batch_norm(beta = obj_65_beta_0_to_fp16, epsilon = obj_65_epsilon_0_to_fp16, gamma = obj_65_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_65_cast_fp16)[name = tensor("obj_65_cast_fp16")]; + tensor pretrained_out_193_pad_type_0 = const()[name = tensor("pretrained_out_193_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_193_strides_0 = const()[name = tensor("pretrained_out_193_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_193_pad_0 = const()[name = tensor("pretrained_out_193_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_193_dilations_0 = const()[name = tensor("pretrained_out_193_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_193_groups_0 = const()[name = tensor("pretrained_out_193_groups_0"), val = tensor(1)]; + tensor layers_16_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184283776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185103040))), name = tensor("layers_16_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_16_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_16_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185103168)))]; + tensor pretrained_out_193_cast_fp16 = conv(bias = layers_16_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_193_dilations_0, groups = pretrained_out_193_groups_0, pad = pretrained_out_193_pad_0, pad_type = pretrained_out_193_pad_type_0, strides = pretrained_out_193_strides_0, weight = layers_16_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_65_cast_fp16)[name = tensor("pretrained_out_193_cast_fp16")]; + tensor input_321_pad_type_0 = const()[name = tensor("input_321_pad_type_0"), val = tensor("valid")]; + tensor input_321_strides_0 = const()[name = tensor("input_321_strides_0"), val = tensor([1, 1])]; + tensor input_321_pad_0 = const()[name = tensor("input_321_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_321_dilations_0 = const()[name = tensor("input_321_dilations_0"), val = tensor([1, 1])]; + tensor input_321_groups_0 = const()[name = tensor("input_321_groups_0"), val = tensor(1)]; + tensor layers_16_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_16_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185105792)))]; + tensor input_321_cast_fp16 = conv(dilations = input_321_dilations_0, groups = input_321_groups_0, pad = input_321_pad_0, pad_type = input_321_pad_type_0, strides = input_321_strides_0, weight = layers_16_self_attn_q_proj_loraA_weight_to_fp16, x = obj_65_cast_fp16)[name = tensor("input_321_cast_fp16")]; + tensor lora_out_193_pad_type_0 = const()[name = tensor("lora_out_193_pad_type_0"), val = tensor("valid")]; + tensor lora_out_193_strides_0 = const()[name = tensor("lora_out_193_strides_0"), val = tensor([1, 1])]; + tensor lora_out_193_pad_0 = const()[name = tensor("lora_out_193_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_193_dilations_0 = const()[name = tensor("lora_out_193_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_193_groups_0 = const()[name = tensor("lora_out_193_groups_0"), val = tensor(1)]; + tensor layers_16_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_16_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185146816)))]; + tensor lora_out_193_cast_fp16 = conv(dilations = lora_out_193_dilations_0, groups = lora_out_193_groups_0, pad = lora_out_193_pad_0, pad_type = lora_out_193_pad_type_0, strides = lora_out_193_strides_0, weight = layers_16_self_attn_q_proj_loraB_weight_to_fp16, x = input_321_cast_fp16)[name = tensor("lora_out_193_cast_fp16")]; + tensor query_33_cast_fp16 = add(x = pretrained_out_193_cast_fp16, y = lora_out_193_cast_fp16)[name = tensor("query_33_cast_fp16")]; + tensor pretrained_out_195_pad_type_0 = const()[name = tensor("pretrained_out_195_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_195_strides_0 = const()[name = tensor("pretrained_out_195_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_195_pad_0 = const()[name = tensor("pretrained_out_195_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_195_dilations_0 = const()[name = tensor("pretrained_out_195_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_195_groups_0 = const()[name = tensor("pretrained_out_195_groups_0"), val = tensor(1)]; + tensor layers_16_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(185187840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(186007104))), name = tensor("layers_16_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_195_cast_fp16 = conv(dilations = pretrained_out_195_dilations_0, groups = pretrained_out_195_groups_0, pad = pretrained_out_195_pad_0, pad_type = pretrained_out_195_pad_type_0, strides = pretrained_out_195_strides_0, weight = layers_16_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_65_cast_fp16)[name = tensor("pretrained_out_195_cast_fp16")]; + tensor input_323_pad_type_0 = const()[name = tensor("input_323_pad_type_0"), val = tensor("valid")]; + tensor input_323_strides_0 = const()[name = tensor("input_323_strides_0"), val = tensor([1, 1])]; + tensor input_323_pad_0 = const()[name = tensor("input_323_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_323_dilations_0 = const()[name = tensor("input_323_dilations_0"), val = tensor([1, 1])]; + tensor input_323_groups_0 = const()[name = tensor("input_323_groups_0"), val = tensor(1)]; + tensor layers_16_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_16_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(186007232)))]; + tensor input_323_cast_fp16 = conv(dilations = input_323_dilations_0, groups = input_323_groups_0, pad = input_323_pad_0, pad_type = input_323_pad_type_0, strides = input_323_strides_0, weight = layers_16_self_attn_k_proj_loraA_weight_to_fp16, x = obj_65_cast_fp16)[name = tensor("input_323_cast_fp16")]; + tensor lora_out_195_pad_type_0 = const()[name = tensor("lora_out_195_pad_type_0"), val = tensor("valid")]; + tensor lora_out_195_strides_0 = const()[name = tensor("lora_out_195_strides_0"), val = tensor([1, 1])]; + tensor lora_out_195_pad_0 = const()[name = tensor("lora_out_195_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_195_dilations_0 = const()[name = tensor("lora_out_195_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_195_groups_0 = const()[name = tensor("lora_out_195_groups_0"), val = tensor(1)]; + tensor layers_16_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_16_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(186048256)))]; + tensor lora_out_195_cast_fp16 = conv(dilations = lora_out_195_dilations_0, groups = lora_out_195_groups_0, pad = lora_out_195_pad_0, pad_type = lora_out_195_pad_type_0, strides = lora_out_195_strides_0, weight = layers_16_self_attn_k_proj_loraB_weight_to_fp16, x = input_323_cast_fp16)[name = tensor("lora_out_195_cast_fp16")]; + tensor key_33_cast_fp16 = add(x = pretrained_out_195_cast_fp16, y = lora_out_195_cast_fp16)[name = tensor("key_33_cast_fp16")]; + tensor pretrained_out_197_pad_type_0 = const()[name = tensor("pretrained_out_197_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_197_strides_0 = const()[name = tensor("pretrained_out_197_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_197_pad_0 = const()[name = tensor("pretrained_out_197_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_197_dilations_0 = const()[name = tensor("pretrained_out_197_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_197_groups_0 = const()[name = tensor("pretrained_out_197_groups_0"), val = tensor(1)]; + tensor layers_16_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(186089280))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(186908544))), name = tensor("layers_16_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_16_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_16_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(186908672)))]; + tensor pretrained_out_197_cast_fp16 = conv(bias = layers_16_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_197_dilations_0, groups = pretrained_out_197_groups_0, pad = pretrained_out_197_pad_0, pad_type = pretrained_out_197_pad_type_0, strides = pretrained_out_197_strides_0, weight = layers_16_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_65_cast_fp16)[name = tensor("pretrained_out_197_cast_fp16")]; + tensor input_325_pad_type_0 = const()[name = tensor("input_325_pad_type_0"), val = tensor("valid")]; + tensor input_325_strides_0 = const()[name = tensor("input_325_strides_0"), val = tensor([1, 1])]; + tensor input_325_pad_0 = const()[name = tensor("input_325_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_325_dilations_0 = const()[name = tensor("input_325_dilations_0"), val = tensor([1, 1])]; + tensor input_325_groups_0 = const()[name = tensor("input_325_groups_0"), val = tensor(1)]; + tensor layers_16_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_16_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(186911296)))]; + tensor input_325_cast_fp16 = conv(dilations = input_325_dilations_0, groups = input_325_groups_0, pad = input_325_pad_0, pad_type = input_325_pad_type_0, strides = input_325_strides_0, weight = layers_16_self_attn_v_proj_loraA_weight_to_fp16, x = obj_65_cast_fp16)[name = tensor("input_325_cast_fp16")]; + tensor lora_out_197_pad_type_0 = const()[name = tensor("lora_out_197_pad_type_0"), val = tensor("valid")]; + tensor lora_out_197_strides_0 = const()[name = tensor("lora_out_197_strides_0"), val = tensor([1, 1])]; + tensor lora_out_197_pad_0 = const()[name = tensor("lora_out_197_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_197_dilations_0 = const()[name = tensor("lora_out_197_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_197_groups_0 = const()[name = tensor("lora_out_197_groups_0"), val = tensor(1)]; + tensor layers_16_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_16_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(186952320)))]; + tensor lora_out_197_cast_fp16 = conv(dilations = lora_out_197_dilations_0, groups = lora_out_197_groups_0, pad = lora_out_197_pad_0, pad_type = lora_out_197_pad_type_0, strides = lora_out_197_strides_0, weight = layers_16_self_attn_v_proj_loraB_weight_to_fp16, x = input_325_cast_fp16)[name = tensor("lora_out_197_cast_fp16")]; + tensor value_33_cast_fp16 = add(x = pretrained_out_197_cast_fp16, y = lora_out_197_cast_fp16)[name = tensor("value_33_cast_fp16")]; + tensor var_3704 = const()[name = tensor("op_3704"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_33_cast_fp16 = reshape(shape = var_3704, x = query_33_cast_fp16)[name = tensor("mh_q_33_cast_fp16")]; + tensor var_3706_to_fp16 = const()[name = tensor("op_3706_to_fp16"), val = tensor(0x1p-3)]; + tensor var_3707_cast_fp16 = mul(x = mh_q_33_cast_fp16, y = var_3706_to_fp16)[name = tensor("op_3707_cast_fp16")]; + tensor var_3708 = const()[name = tensor("op_3708"), val = tensor([1, 20, 64, -1])]; + tensor var_3709_cast_fp16 = reshape(shape = var_3708, x = key_33_cast_fp16)[name = tensor("op_3709_cast_fp16")]; + tensor mh_w_33_transpose_x_0 = const()[name = tensor("mh_w_33_transpose_x_0"), val = tensor(true)]; + tensor mh_w_33_transpose_y_0 = const()[name = tensor("mh_w_33_transpose_y_0"), val = tensor(false)]; + tensor mh_w_33_cast_fp16 = matmul(transpose_x = mh_w_33_transpose_x_0, transpose_y = mh_w_33_transpose_y_0, x = var_3707_cast_fp16, y = var_3709_cast_fp16)[name = tensor("mh_w_33_cast_fp16")]; + tensor var_3712_cast_fp16 = softmax(axis = var_3602, x = mh_w_33_cast_fp16)[name = tensor("op_3712_cast_fp16")]; + tensor var_3713 = const()[name = tensor("op_3713"), val = tensor([1, 20, 64, -1])]; + tensor var_3714_cast_fp16 = reshape(shape = var_3713, x = value_33_cast_fp16)[name = tensor("op_3714_cast_fp16")]; + tensor attn_33_transpose_x_0 = const()[name = tensor("attn_33_transpose_x_0"), val = tensor(false)]; + tensor attn_33_transpose_y_0 = const()[name = tensor("attn_33_transpose_y_0"), val = tensor(true)]; + tensor attn_33_cast_fp16 = matmul(transpose_x = attn_33_transpose_x_0, transpose_y = attn_33_transpose_y_0, x = var_3714_cast_fp16, y = var_3712_cast_fp16)[name = tensor("attn_33_cast_fp16")]; + tensor var_3717 = const()[name = tensor("op_3717"), val = tensor([1, 1280, 1, -1])]; + tensor input_327_cast_fp16 = reshape(shape = var_3717, x = attn_33_cast_fp16)[name = tensor("input_327_cast_fp16")]; + tensor pretrained_out_199_pad_type_0 = const()[name = tensor("pretrained_out_199_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_199_strides_0 = const()[name = tensor("pretrained_out_199_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_199_pad_0 = const()[name = tensor("pretrained_out_199_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_199_dilations_0 = const()[name = tensor("pretrained_out_199_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_199_groups_0 = const()[name = tensor("pretrained_out_199_groups_0"), val = tensor(1)]; + tensor layers_16_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(186993344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(187812608))), name = tensor("layers_16_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_16_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_16_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(187812736)))]; + tensor pretrained_out_199_cast_fp16 = conv(bias = layers_16_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_199_dilations_0, groups = pretrained_out_199_groups_0, pad = pretrained_out_199_pad_0, pad_type = pretrained_out_199_pad_type_0, strides = pretrained_out_199_strides_0, weight = layers_16_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_327_cast_fp16)[name = tensor("pretrained_out_199_cast_fp16")]; + tensor input_329_pad_type_0 = const()[name = tensor("input_329_pad_type_0"), val = tensor("valid")]; + tensor input_329_strides_0 = const()[name = tensor("input_329_strides_0"), val = tensor([1, 1])]; + tensor input_329_pad_0 = const()[name = tensor("input_329_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_329_dilations_0 = const()[name = tensor("input_329_dilations_0"), val = tensor([1, 1])]; + tensor input_329_groups_0 = const()[name = tensor("input_329_groups_0"), val = tensor(1)]; + tensor layers_16_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_16_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(187815360)))]; + tensor input_329_cast_fp16 = conv(dilations = input_329_dilations_0, groups = input_329_groups_0, pad = input_329_pad_0, pad_type = input_329_pad_type_0, strides = input_329_strides_0, weight = layers_16_self_attn_o_proj_loraA_weight_to_fp16, x = input_327_cast_fp16)[name = tensor("input_329_cast_fp16")]; + tensor lora_out_199_pad_type_0 = const()[name = tensor("lora_out_199_pad_type_0"), val = tensor("valid")]; + tensor lora_out_199_strides_0 = const()[name = tensor("lora_out_199_strides_0"), val = tensor([1, 1])]; + tensor lora_out_199_pad_0 = const()[name = tensor("lora_out_199_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_199_dilations_0 = const()[name = tensor("lora_out_199_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_199_groups_0 = const()[name = tensor("lora_out_199_groups_0"), val = tensor(1)]; + tensor layers_16_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_16_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(187856384)))]; + tensor lora_out_199_cast_fp16 = conv(dilations = lora_out_199_dilations_0, groups = lora_out_199_groups_0, pad = lora_out_199_pad_0, pad_type = lora_out_199_pad_type_0, strides = lora_out_199_strides_0, weight = layers_16_self_attn_o_proj_loraB_weight_to_fp16, x = input_329_cast_fp16)[name = tensor("lora_out_199_cast_fp16")]; + tensor obj_67_cast_fp16 = add(x = pretrained_out_199_cast_fp16, y = lora_out_199_cast_fp16)[name = tensor("obj_67_cast_fp16")]; + tensor inputs_67_cast_fp16 = add(x = inputs_65_cast_fp16, y = obj_67_cast_fp16)[name = tensor("inputs_67_cast_fp16")]; + tensor out_67_axes_0 = const()[name = tensor("out_67_axes_0"), val = tensor([1])]; + tensor var_3751_to_fp16 = const()[name = tensor("op_3751_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_67_cast_fp16 = layer_norm(axes = out_67_axes_0, epsilon = var_3751_to_fp16, x = inputs_67_cast_fp16)[name = tensor("out_67_cast_fp16")]; + tensor input_331_gamma_0_to_fp16 = const()[name = tensor("input_331_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(187897408)))]; + tensor input_331_beta_0_to_fp16 = const()[name = tensor("input_331_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(187900032)))]; + tensor input_331_epsilon_0_to_fp16 = const()[name = tensor("input_331_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_331_cast_fp16 = batch_norm(beta = input_331_beta_0_to_fp16, epsilon = input_331_epsilon_0_to_fp16, gamma = input_331_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_67_cast_fp16)[name = tensor("input_331_cast_fp16")]; + tensor pretrained_out_201_pad_type_0 = const()[name = tensor("pretrained_out_201_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_201_strides_0 = const()[name = tensor("pretrained_out_201_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_201_pad_0 = const()[name = tensor("pretrained_out_201_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_201_dilations_0 = const()[name = tensor("pretrained_out_201_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_201_groups_0 = const()[name = tensor("pretrained_out_201_groups_0"), val = tensor(1)]; + tensor layers_16_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(187902656))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(191179520))), name = tensor("layers_16_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_16_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_16_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(191179648)))]; + tensor pretrained_out_201_cast_fp16 = conv(bias = layers_16_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_201_dilations_0, groups = pretrained_out_201_groups_0, pad = pretrained_out_201_pad_0, pad_type = pretrained_out_201_pad_type_0, strides = pretrained_out_201_strides_0, weight = layers_16_fc1_pretrained_weight_to_fp16_palettized, x = input_331_cast_fp16)[name = tensor("pretrained_out_201_cast_fp16")]; + tensor input_333_pad_type_0 = const()[name = tensor("input_333_pad_type_0"), val = tensor("valid")]; + tensor input_333_strides_0 = const()[name = tensor("input_333_strides_0"), val = tensor([1, 1])]; + tensor input_333_pad_0 = const()[name = tensor("input_333_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_333_dilations_0 = const()[name = tensor("input_333_dilations_0"), val = tensor([1, 1])]; + tensor input_333_groups_0 = const()[name = tensor("input_333_groups_0"), val = tensor(1)]; + tensor layers_16_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_16_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(191189952)))]; + tensor input_333_cast_fp16 = conv(dilations = input_333_dilations_0, groups = input_333_groups_0, pad = input_333_pad_0, pad_type = input_333_pad_type_0, strides = input_333_strides_0, weight = layers_16_fc1_loraA_weight_to_fp16, x = input_331_cast_fp16)[name = tensor("input_333_cast_fp16")]; + tensor lora_out_201_pad_type_0 = const()[name = tensor("lora_out_201_pad_type_0"), val = tensor("valid")]; + tensor lora_out_201_strides_0 = const()[name = tensor("lora_out_201_strides_0"), val = tensor([1, 1])]; + tensor lora_out_201_pad_0 = const()[name = tensor("lora_out_201_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_201_dilations_0 = const()[name = tensor("lora_out_201_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_201_groups_0 = const()[name = tensor("lora_out_201_groups_0"), val = tensor(1)]; + tensor layers_16_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_16_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(191230976)))]; + tensor lora_out_201_cast_fp16 = conv(dilations = lora_out_201_dilations_0, groups = lora_out_201_groups_0, pad = lora_out_201_pad_0, pad_type = lora_out_201_pad_type_0, strides = lora_out_201_strides_0, weight = layers_16_fc1_loraB_weight_to_fp16, x = input_333_cast_fp16)[name = tensor("lora_out_201_cast_fp16")]; + tensor input_335_cast_fp16 = add(x = pretrained_out_201_cast_fp16, y = lora_out_201_cast_fp16)[name = tensor("input_335_cast_fp16")]; + tensor input_337_mode_0 = const()[name = tensor("input_337_mode_0"), val = tensor("EXACT")]; + tensor input_337_cast_fp16 = gelu(mode = input_337_mode_0, x = input_335_cast_fp16)[name = tensor("input_337_cast_fp16")]; + tensor pretrained_out_203_pad_type_0 = const()[name = tensor("pretrained_out_203_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_203_strides_0 = const()[name = tensor("pretrained_out_203_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_203_pad_0 = const()[name = tensor("pretrained_out_203_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_203_dilations_0 = const()[name = tensor("pretrained_out_203_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_203_groups_0 = const()[name = tensor("pretrained_out_203_groups_0"), val = tensor(1)]; + tensor layers_16_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(191394880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194671744))), name = tensor("layers_16_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_16_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_16_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194671872)))]; + tensor pretrained_out_203_cast_fp16 = conv(bias = layers_16_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_203_dilations_0, groups = pretrained_out_203_groups_0, pad = pretrained_out_203_pad_0, pad_type = pretrained_out_203_pad_type_0, strides = pretrained_out_203_strides_0, weight = layers_16_fc2_pretrained_weight_to_fp16_palettized, x = input_337_cast_fp16)[name = tensor("pretrained_out_203_cast_fp16")]; + tensor input_339_pad_type_0 = const()[name = tensor("input_339_pad_type_0"), val = tensor("valid")]; + tensor input_339_strides_0 = const()[name = tensor("input_339_strides_0"), val = tensor([1, 1])]; + tensor input_339_pad_0 = const()[name = tensor("input_339_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_339_dilations_0 = const()[name = tensor("input_339_dilations_0"), val = tensor([1, 1])]; + tensor input_339_groups_0 = const()[name = tensor("input_339_groups_0"), val = tensor(1)]; + tensor layers_16_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_16_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194674496)))]; + tensor input_339_cast_fp16 = conv(dilations = input_339_dilations_0, groups = input_339_groups_0, pad = input_339_pad_0, pad_type = input_339_pad_type_0, strides = input_339_strides_0, weight = layers_16_fc2_loraA_weight_to_fp16, x = input_337_cast_fp16)[name = tensor("input_339_cast_fp16")]; + tensor lora_out_203_pad_type_0 = const()[name = tensor("lora_out_203_pad_type_0"), val = tensor("valid")]; + tensor lora_out_203_strides_0 = const()[name = tensor("lora_out_203_strides_0"), val = tensor([1, 1])]; + tensor lora_out_203_pad_0 = const()[name = tensor("lora_out_203_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_203_dilations_0 = const()[name = tensor("lora_out_203_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_203_groups_0 = const()[name = tensor("lora_out_203_groups_0"), val = tensor(1)]; + tensor layers_16_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_16_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194838400)))]; + tensor lora_out_203_cast_fp16 = conv(dilations = lora_out_203_dilations_0, groups = lora_out_203_groups_0, pad = lora_out_203_pad_0, pad_type = lora_out_203_pad_type_0, strides = lora_out_203_strides_0, weight = layers_16_fc2_loraB_weight_to_fp16, x = input_339_cast_fp16)[name = tensor("lora_out_203_cast_fp16")]; + tensor hidden_states_37_cast_fp16 = add(x = pretrained_out_203_cast_fp16, y = lora_out_203_cast_fp16)[name = tensor("hidden_states_37_cast_fp16")]; + tensor inputs_69_cast_fp16 = add(x = inputs_67_cast_fp16, y = hidden_states_37_cast_fp16)[name = tensor("inputs_69_cast_fp16")]; + tensor var_3816 = const()[name = tensor("op_3816"), val = tensor(3)]; + tensor out_69_axes_0 = const()[name = tensor("out_69_axes_0"), val = tensor([1])]; + tensor var_3835_to_fp16 = const()[name = tensor("op_3835_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_69_cast_fp16 = layer_norm(axes = out_69_axes_0, epsilon = var_3835_to_fp16, x = inputs_69_cast_fp16)[name = tensor("out_69_cast_fp16")]; + tensor obj_69_gamma_0_to_fp16 = const()[name = tensor("obj_69_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194879424)))]; + tensor obj_69_beta_0_to_fp16 = const()[name = tensor("obj_69_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194882048)))]; + tensor obj_69_epsilon_0_to_fp16 = const()[name = tensor("obj_69_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_69_cast_fp16 = batch_norm(beta = obj_69_beta_0_to_fp16, epsilon = obj_69_epsilon_0_to_fp16, gamma = obj_69_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_69_cast_fp16)[name = tensor("obj_69_cast_fp16")]; + tensor pretrained_out_205_pad_type_0 = const()[name = tensor("pretrained_out_205_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_205_strides_0 = const()[name = tensor("pretrained_out_205_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_205_pad_0 = const()[name = tensor("pretrained_out_205_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_205_dilations_0 = const()[name = tensor("pretrained_out_205_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_205_groups_0 = const()[name = tensor("pretrained_out_205_groups_0"), val = tensor(1)]; + tensor layers_17_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194884672))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195703936))), name = tensor("layers_17_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_17_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_17_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195704064)))]; + tensor pretrained_out_205_cast_fp16 = conv(bias = layers_17_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_205_dilations_0, groups = pretrained_out_205_groups_0, pad = pretrained_out_205_pad_0, pad_type = pretrained_out_205_pad_type_0, strides = pretrained_out_205_strides_0, weight = layers_17_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_69_cast_fp16)[name = tensor("pretrained_out_205_cast_fp16")]; + tensor input_341_pad_type_0 = const()[name = tensor("input_341_pad_type_0"), val = tensor("valid")]; + tensor input_341_strides_0 = const()[name = tensor("input_341_strides_0"), val = tensor([1, 1])]; + tensor input_341_pad_0 = const()[name = tensor("input_341_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_341_dilations_0 = const()[name = tensor("input_341_dilations_0"), val = tensor([1, 1])]; + tensor input_341_groups_0 = const()[name = tensor("input_341_groups_0"), val = tensor(1)]; + tensor layers_17_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_17_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195706688)))]; + tensor input_341_cast_fp16 = conv(dilations = input_341_dilations_0, groups = input_341_groups_0, pad = input_341_pad_0, pad_type = input_341_pad_type_0, strides = input_341_strides_0, weight = layers_17_self_attn_q_proj_loraA_weight_to_fp16, x = obj_69_cast_fp16)[name = tensor("input_341_cast_fp16")]; + tensor lora_out_205_pad_type_0 = const()[name = tensor("lora_out_205_pad_type_0"), val = tensor("valid")]; + tensor lora_out_205_strides_0 = const()[name = tensor("lora_out_205_strides_0"), val = tensor([1, 1])]; + tensor lora_out_205_pad_0 = const()[name = tensor("lora_out_205_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_205_dilations_0 = const()[name = tensor("lora_out_205_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_205_groups_0 = const()[name = tensor("lora_out_205_groups_0"), val = tensor(1)]; + tensor layers_17_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_17_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195747712)))]; + tensor lora_out_205_cast_fp16 = conv(dilations = lora_out_205_dilations_0, groups = lora_out_205_groups_0, pad = lora_out_205_pad_0, pad_type = lora_out_205_pad_type_0, strides = lora_out_205_strides_0, weight = layers_17_self_attn_q_proj_loraB_weight_to_fp16, x = input_341_cast_fp16)[name = tensor("lora_out_205_cast_fp16")]; + tensor query_35_cast_fp16 = add(x = pretrained_out_205_cast_fp16, y = lora_out_205_cast_fp16)[name = tensor("query_35_cast_fp16")]; + tensor pretrained_out_207_pad_type_0 = const()[name = tensor("pretrained_out_207_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_207_strides_0 = const()[name = tensor("pretrained_out_207_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_207_pad_0 = const()[name = tensor("pretrained_out_207_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_207_dilations_0 = const()[name = tensor("pretrained_out_207_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_207_groups_0 = const()[name = tensor("pretrained_out_207_groups_0"), val = tensor(1)]; + tensor layers_17_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195788736))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196608000))), name = tensor("layers_17_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_207_cast_fp16 = conv(dilations = pretrained_out_207_dilations_0, groups = pretrained_out_207_groups_0, pad = pretrained_out_207_pad_0, pad_type = pretrained_out_207_pad_type_0, strides = pretrained_out_207_strides_0, weight = layers_17_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_69_cast_fp16)[name = tensor("pretrained_out_207_cast_fp16")]; + tensor input_343_pad_type_0 = const()[name = tensor("input_343_pad_type_0"), val = tensor("valid")]; + tensor input_343_strides_0 = const()[name = tensor("input_343_strides_0"), val = tensor([1, 1])]; + tensor input_343_pad_0 = const()[name = tensor("input_343_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_343_dilations_0 = const()[name = tensor("input_343_dilations_0"), val = tensor([1, 1])]; + tensor input_343_groups_0 = const()[name = tensor("input_343_groups_0"), val = tensor(1)]; + tensor layers_17_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_17_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196608128)))]; + tensor input_343_cast_fp16 = conv(dilations = input_343_dilations_0, groups = input_343_groups_0, pad = input_343_pad_0, pad_type = input_343_pad_type_0, strides = input_343_strides_0, weight = layers_17_self_attn_k_proj_loraA_weight_to_fp16, x = obj_69_cast_fp16)[name = tensor("input_343_cast_fp16")]; + tensor lora_out_207_pad_type_0 = const()[name = tensor("lora_out_207_pad_type_0"), val = tensor("valid")]; + tensor lora_out_207_strides_0 = const()[name = tensor("lora_out_207_strides_0"), val = tensor([1, 1])]; + tensor lora_out_207_pad_0 = const()[name = tensor("lora_out_207_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_207_dilations_0 = const()[name = tensor("lora_out_207_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_207_groups_0 = const()[name = tensor("lora_out_207_groups_0"), val = tensor(1)]; + tensor layers_17_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_17_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196649152)))]; + tensor lora_out_207_cast_fp16 = conv(dilations = lora_out_207_dilations_0, groups = lora_out_207_groups_0, pad = lora_out_207_pad_0, pad_type = lora_out_207_pad_type_0, strides = lora_out_207_strides_0, weight = layers_17_self_attn_k_proj_loraB_weight_to_fp16, x = input_343_cast_fp16)[name = tensor("lora_out_207_cast_fp16")]; + tensor key_35_cast_fp16 = add(x = pretrained_out_207_cast_fp16, y = lora_out_207_cast_fp16)[name = tensor("key_35_cast_fp16")]; + tensor pretrained_out_209_pad_type_0 = const()[name = tensor("pretrained_out_209_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_209_strides_0 = const()[name = tensor("pretrained_out_209_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_209_pad_0 = const()[name = tensor("pretrained_out_209_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_209_dilations_0 = const()[name = tensor("pretrained_out_209_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_209_groups_0 = const()[name = tensor("pretrained_out_209_groups_0"), val = tensor(1)]; + tensor layers_17_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196690176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197509440))), name = tensor("layers_17_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_17_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_17_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197509568)))]; + tensor pretrained_out_209_cast_fp16 = conv(bias = layers_17_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_209_dilations_0, groups = pretrained_out_209_groups_0, pad = pretrained_out_209_pad_0, pad_type = pretrained_out_209_pad_type_0, strides = pretrained_out_209_strides_0, weight = layers_17_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_69_cast_fp16)[name = tensor("pretrained_out_209_cast_fp16")]; + tensor input_345_pad_type_0 = const()[name = tensor("input_345_pad_type_0"), val = tensor("valid")]; + tensor input_345_strides_0 = const()[name = tensor("input_345_strides_0"), val = tensor([1, 1])]; + tensor input_345_pad_0 = const()[name = tensor("input_345_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_345_dilations_0 = const()[name = tensor("input_345_dilations_0"), val = tensor([1, 1])]; + tensor input_345_groups_0 = const()[name = tensor("input_345_groups_0"), val = tensor(1)]; + tensor layers_17_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_17_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197512192)))]; + tensor input_345_cast_fp16 = conv(dilations = input_345_dilations_0, groups = input_345_groups_0, pad = input_345_pad_0, pad_type = input_345_pad_type_0, strides = input_345_strides_0, weight = layers_17_self_attn_v_proj_loraA_weight_to_fp16, x = obj_69_cast_fp16)[name = tensor("input_345_cast_fp16")]; + tensor lora_out_209_pad_type_0 = const()[name = tensor("lora_out_209_pad_type_0"), val = tensor("valid")]; + tensor lora_out_209_strides_0 = const()[name = tensor("lora_out_209_strides_0"), val = tensor([1, 1])]; + tensor lora_out_209_pad_0 = const()[name = tensor("lora_out_209_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_209_dilations_0 = const()[name = tensor("lora_out_209_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_209_groups_0 = const()[name = tensor("lora_out_209_groups_0"), val = tensor(1)]; + tensor layers_17_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_17_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197553216)))]; + tensor lora_out_209_cast_fp16 = conv(dilations = lora_out_209_dilations_0, groups = lora_out_209_groups_0, pad = lora_out_209_pad_0, pad_type = lora_out_209_pad_type_0, strides = lora_out_209_strides_0, weight = layers_17_self_attn_v_proj_loraB_weight_to_fp16, x = input_345_cast_fp16)[name = tensor("lora_out_209_cast_fp16")]; + tensor value_35_cast_fp16 = add(x = pretrained_out_209_cast_fp16, y = lora_out_209_cast_fp16)[name = tensor("value_35_cast_fp16")]; + tensor var_3918 = const()[name = tensor("op_3918"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_35_cast_fp16 = reshape(shape = var_3918, x = query_35_cast_fp16)[name = tensor("mh_q_35_cast_fp16")]; + tensor var_3920_to_fp16 = const()[name = tensor("op_3920_to_fp16"), val = tensor(0x1p-3)]; + tensor var_3921_cast_fp16 = mul(x = mh_q_35_cast_fp16, y = var_3920_to_fp16)[name = tensor("op_3921_cast_fp16")]; + tensor var_3922 = const()[name = tensor("op_3922"), val = tensor([1, 20, 64, -1])]; + tensor var_3923_cast_fp16 = reshape(shape = var_3922, x = key_35_cast_fp16)[name = tensor("op_3923_cast_fp16")]; + tensor mh_w_35_transpose_x_0 = const()[name = tensor("mh_w_35_transpose_x_0"), val = tensor(true)]; + tensor mh_w_35_transpose_y_0 = const()[name = tensor("mh_w_35_transpose_y_0"), val = tensor(false)]; + tensor mh_w_35_cast_fp16 = matmul(transpose_x = mh_w_35_transpose_x_0, transpose_y = mh_w_35_transpose_y_0, x = var_3921_cast_fp16, y = var_3923_cast_fp16)[name = tensor("mh_w_35_cast_fp16")]; + tensor var_3926_cast_fp16 = softmax(axis = var_3816, x = mh_w_35_cast_fp16)[name = tensor("op_3926_cast_fp16")]; + tensor var_3927 = const()[name = tensor("op_3927"), val = tensor([1, 20, 64, -1])]; + tensor var_3928_cast_fp16 = reshape(shape = var_3927, x = value_35_cast_fp16)[name = tensor("op_3928_cast_fp16")]; + tensor attn_35_transpose_x_0 = const()[name = tensor("attn_35_transpose_x_0"), val = tensor(false)]; + tensor attn_35_transpose_y_0 = const()[name = tensor("attn_35_transpose_y_0"), val = tensor(true)]; + tensor attn_35_cast_fp16 = matmul(transpose_x = attn_35_transpose_x_0, transpose_y = attn_35_transpose_y_0, x = var_3928_cast_fp16, y = var_3926_cast_fp16)[name = tensor("attn_35_cast_fp16")]; + tensor var_3931 = const()[name = tensor("op_3931"), val = tensor([1, 1280, 1, -1])]; + tensor input_347_cast_fp16 = reshape(shape = var_3931, x = attn_35_cast_fp16)[name = tensor("input_347_cast_fp16")]; + tensor pretrained_out_211_pad_type_0 = const()[name = tensor("pretrained_out_211_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_211_strides_0 = const()[name = tensor("pretrained_out_211_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_211_pad_0 = const()[name = tensor("pretrained_out_211_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_211_dilations_0 = const()[name = tensor("pretrained_out_211_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_211_groups_0 = const()[name = tensor("pretrained_out_211_groups_0"), val = tensor(1)]; + tensor layers_17_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197594240))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198413504))), name = tensor("layers_17_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_17_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_17_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198413632)))]; + tensor pretrained_out_211_cast_fp16 = conv(bias = layers_17_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_211_dilations_0, groups = pretrained_out_211_groups_0, pad = pretrained_out_211_pad_0, pad_type = pretrained_out_211_pad_type_0, strides = pretrained_out_211_strides_0, weight = layers_17_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_347_cast_fp16)[name = tensor("pretrained_out_211_cast_fp16")]; + tensor input_349_pad_type_0 = const()[name = tensor("input_349_pad_type_0"), val = tensor("valid")]; + tensor input_349_strides_0 = const()[name = tensor("input_349_strides_0"), val = tensor([1, 1])]; + tensor input_349_pad_0 = const()[name = tensor("input_349_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_349_dilations_0 = const()[name = tensor("input_349_dilations_0"), val = tensor([1, 1])]; + tensor input_349_groups_0 = const()[name = tensor("input_349_groups_0"), val = tensor(1)]; + tensor layers_17_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_17_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198416256)))]; + tensor input_349_cast_fp16 = conv(dilations = input_349_dilations_0, groups = input_349_groups_0, pad = input_349_pad_0, pad_type = input_349_pad_type_0, strides = input_349_strides_0, weight = layers_17_self_attn_o_proj_loraA_weight_to_fp16, x = input_347_cast_fp16)[name = tensor("input_349_cast_fp16")]; + tensor lora_out_211_pad_type_0 = const()[name = tensor("lora_out_211_pad_type_0"), val = tensor("valid")]; + tensor lora_out_211_strides_0 = const()[name = tensor("lora_out_211_strides_0"), val = tensor([1, 1])]; + tensor lora_out_211_pad_0 = const()[name = tensor("lora_out_211_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_211_dilations_0 = const()[name = tensor("lora_out_211_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_211_groups_0 = const()[name = tensor("lora_out_211_groups_0"), val = tensor(1)]; + tensor layers_17_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_17_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198457280)))]; + tensor lora_out_211_cast_fp16 = conv(dilations = lora_out_211_dilations_0, groups = lora_out_211_groups_0, pad = lora_out_211_pad_0, pad_type = lora_out_211_pad_type_0, strides = lora_out_211_strides_0, weight = layers_17_self_attn_o_proj_loraB_weight_to_fp16, x = input_349_cast_fp16)[name = tensor("lora_out_211_cast_fp16")]; + tensor obj_71_cast_fp16 = add(x = pretrained_out_211_cast_fp16, y = lora_out_211_cast_fp16)[name = tensor("obj_71_cast_fp16")]; + tensor inputs_71_cast_fp16 = add(x = inputs_69_cast_fp16, y = obj_71_cast_fp16)[name = tensor("inputs_71_cast_fp16")]; + tensor out_71_axes_0 = const()[name = tensor("out_71_axes_0"), val = tensor([1])]; + tensor var_3965_to_fp16 = const()[name = tensor("op_3965_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_71_cast_fp16 = layer_norm(axes = out_71_axes_0, epsilon = var_3965_to_fp16, x = inputs_71_cast_fp16)[name = tensor("out_71_cast_fp16")]; + tensor input_351_gamma_0_to_fp16 = const()[name = tensor("input_351_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198498304)))]; + tensor input_351_beta_0_to_fp16 = const()[name = tensor("input_351_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198500928)))]; + tensor input_351_epsilon_0_to_fp16 = const()[name = tensor("input_351_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_351_cast_fp16 = batch_norm(beta = input_351_beta_0_to_fp16, epsilon = input_351_epsilon_0_to_fp16, gamma = input_351_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_71_cast_fp16)[name = tensor("input_351_cast_fp16")]; + tensor pretrained_out_213_pad_type_0 = const()[name = tensor("pretrained_out_213_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_213_strides_0 = const()[name = tensor("pretrained_out_213_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_213_pad_0 = const()[name = tensor("pretrained_out_213_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_213_dilations_0 = const()[name = tensor("pretrained_out_213_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_213_groups_0 = const()[name = tensor("pretrained_out_213_groups_0"), val = tensor(1)]; + tensor layers_17_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198503552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201780416))), name = tensor("layers_17_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_17_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_17_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201780544)))]; + tensor pretrained_out_213_cast_fp16 = conv(bias = layers_17_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_213_dilations_0, groups = pretrained_out_213_groups_0, pad = pretrained_out_213_pad_0, pad_type = pretrained_out_213_pad_type_0, strides = pretrained_out_213_strides_0, weight = layers_17_fc1_pretrained_weight_to_fp16_palettized, x = input_351_cast_fp16)[name = tensor("pretrained_out_213_cast_fp16")]; + tensor input_353_pad_type_0 = const()[name = tensor("input_353_pad_type_0"), val = tensor("valid")]; + tensor input_353_strides_0 = const()[name = tensor("input_353_strides_0"), val = tensor([1, 1])]; + tensor input_353_pad_0 = const()[name = tensor("input_353_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_353_dilations_0 = const()[name = tensor("input_353_dilations_0"), val = tensor([1, 1])]; + tensor input_353_groups_0 = const()[name = tensor("input_353_groups_0"), val = tensor(1)]; + tensor layers_17_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_17_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201790848)))]; + tensor input_353_cast_fp16 = conv(dilations = input_353_dilations_0, groups = input_353_groups_0, pad = input_353_pad_0, pad_type = input_353_pad_type_0, strides = input_353_strides_0, weight = layers_17_fc1_loraA_weight_to_fp16, x = input_351_cast_fp16)[name = tensor("input_353_cast_fp16")]; + tensor lora_out_213_pad_type_0 = const()[name = tensor("lora_out_213_pad_type_0"), val = tensor("valid")]; + tensor lora_out_213_strides_0 = const()[name = tensor("lora_out_213_strides_0"), val = tensor([1, 1])]; + tensor lora_out_213_pad_0 = const()[name = tensor("lora_out_213_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_213_dilations_0 = const()[name = tensor("lora_out_213_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_213_groups_0 = const()[name = tensor("lora_out_213_groups_0"), val = tensor(1)]; + tensor layers_17_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_17_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201831872)))]; + tensor lora_out_213_cast_fp16 = conv(dilations = lora_out_213_dilations_0, groups = lora_out_213_groups_0, pad = lora_out_213_pad_0, pad_type = lora_out_213_pad_type_0, strides = lora_out_213_strides_0, weight = layers_17_fc1_loraB_weight_to_fp16, x = input_353_cast_fp16)[name = tensor("lora_out_213_cast_fp16")]; + tensor input_355_cast_fp16 = add(x = pretrained_out_213_cast_fp16, y = lora_out_213_cast_fp16)[name = tensor("input_355_cast_fp16")]; + tensor input_357_mode_0 = const()[name = tensor("input_357_mode_0"), val = tensor("EXACT")]; + tensor input_357_cast_fp16 = gelu(mode = input_357_mode_0, x = input_355_cast_fp16)[name = tensor("input_357_cast_fp16")]; + tensor pretrained_out_215_pad_type_0 = const()[name = tensor("pretrained_out_215_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_215_strides_0 = const()[name = tensor("pretrained_out_215_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_215_pad_0 = const()[name = tensor("pretrained_out_215_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_215_dilations_0 = const()[name = tensor("pretrained_out_215_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_215_groups_0 = const()[name = tensor("pretrained_out_215_groups_0"), val = tensor(1)]; + tensor layers_17_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201995776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205272640))), name = tensor("layers_17_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_17_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_17_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205272768)))]; + tensor pretrained_out_215_cast_fp16 = conv(bias = layers_17_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_215_dilations_0, groups = pretrained_out_215_groups_0, pad = pretrained_out_215_pad_0, pad_type = pretrained_out_215_pad_type_0, strides = pretrained_out_215_strides_0, weight = layers_17_fc2_pretrained_weight_to_fp16_palettized, x = input_357_cast_fp16)[name = tensor("pretrained_out_215_cast_fp16")]; + tensor input_359_pad_type_0 = const()[name = tensor("input_359_pad_type_0"), val = tensor("valid")]; + tensor input_359_strides_0 = const()[name = tensor("input_359_strides_0"), val = tensor([1, 1])]; + tensor input_359_pad_0 = const()[name = tensor("input_359_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_359_dilations_0 = const()[name = tensor("input_359_dilations_0"), val = tensor([1, 1])]; + tensor input_359_groups_0 = const()[name = tensor("input_359_groups_0"), val = tensor(1)]; + tensor layers_17_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_17_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205275392)))]; + tensor input_359_cast_fp16 = conv(dilations = input_359_dilations_0, groups = input_359_groups_0, pad = input_359_pad_0, pad_type = input_359_pad_type_0, strides = input_359_strides_0, weight = layers_17_fc2_loraA_weight_to_fp16, x = input_357_cast_fp16)[name = tensor("input_359_cast_fp16")]; + tensor lora_out_215_pad_type_0 = const()[name = tensor("lora_out_215_pad_type_0"), val = tensor("valid")]; + tensor lora_out_215_strides_0 = const()[name = tensor("lora_out_215_strides_0"), val = tensor([1, 1])]; + tensor lora_out_215_pad_0 = const()[name = tensor("lora_out_215_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_215_dilations_0 = const()[name = tensor("lora_out_215_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_215_groups_0 = const()[name = tensor("lora_out_215_groups_0"), val = tensor(1)]; + tensor layers_17_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_17_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205439296)))]; + tensor lora_out_215_cast_fp16 = conv(dilations = lora_out_215_dilations_0, groups = lora_out_215_groups_0, pad = lora_out_215_pad_0, pad_type = lora_out_215_pad_type_0, strides = lora_out_215_strides_0, weight = layers_17_fc2_loraB_weight_to_fp16, x = input_359_cast_fp16)[name = tensor("lora_out_215_cast_fp16")]; + tensor hidden_states_39_cast_fp16 = add(x = pretrained_out_215_cast_fp16, y = lora_out_215_cast_fp16)[name = tensor("hidden_states_39_cast_fp16")]; + tensor inputs_73_cast_fp16 = add(x = inputs_71_cast_fp16, y = hidden_states_39_cast_fp16)[name = tensor("inputs_73_cast_fp16")]; + tensor var_4030 = const()[name = tensor("op_4030"), val = tensor(3)]; + tensor out_73_axes_0 = const()[name = tensor("out_73_axes_0"), val = tensor([1])]; + tensor var_4049_to_fp16 = const()[name = tensor("op_4049_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_73_cast_fp16 = layer_norm(axes = out_73_axes_0, epsilon = var_4049_to_fp16, x = inputs_73_cast_fp16)[name = tensor("out_73_cast_fp16")]; + tensor obj_73_gamma_0_to_fp16 = const()[name = tensor("obj_73_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205480320)))]; + tensor obj_73_beta_0_to_fp16 = const()[name = tensor("obj_73_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205482944)))]; + tensor obj_73_epsilon_0_to_fp16 = const()[name = tensor("obj_73_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_73_cast_fp16 = batch_norm(beta = obj_73_beta_0_to_fp16, epsilon = obj_73_epsilon_0_to_fp16, gamma = obj_73_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_73_cast_fp16)[name = tensor("obj_73_cast_fp16")]; + tensor pretrained_out_217_pad_type_0 = const()[name = tensor("pretrained_out_217_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_217_strides_0 = const()[name = tensor("pretrained_out_217_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_217_pad_0 = const()[name = tensor("pretrained_out_217_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_217_dilations_0 = const()[name = tensor("pretrained_out_217_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_217_groups_0 = const()[name = tensor("pretrained_out_217_groups_0"), val = tensor(1)]; + tensor layers_18_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205485568))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206304832))), name = tensor("layers_18_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_18_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_18_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206304960)))]; + tensor pretrained_out_217_cast_fp16 = conv(bias = layers_18_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_217_dilations_0, groups = pretrained_out_217_groups_0, pad = pretrained_out_217_pad_0, pad_type = pretrained_out_217_pad_type_0, strides = pretrained_out_217_strides_0, weight = layers_18_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_73_cast_fp16)[name = tensor("pretrained_out_217_cast_fp16")]; + tensor input_361_pad_type_0 = const()[name = tensor("input_361_pad_type_0"), val = tensor("valid")]; + tensor input_361_strides_0 = const()[name = tensor("input_361_strides_0"), val = tensor([1, 1])]; + tensor input_361_pad_0 = const()[name = tensor("input_361_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_361_dilations_0 = const()[name = tensor("input_361_dilations_0"), val = tensor([1, 1])]; + tensor input_361_groups_0 = const()[name = tensor("input_361_groups_0"), val = tensor(1)]; + tensor layers_18_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_18_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206307584)))]; + tensor input_361_cast_fp16 = conv(dilations = input_361_dilations_0, groups = input_361_groups_0, pad = input_361_pad_0, pad_type = input_361_pad_type_0, strides = input_361_strides_0, weight = layers_18_self_attn_q_proj_loraA_weight_to_fp16, x = obj_73_cast_fp16)[name = tensor("input_361_cast_fp16")]; + tensor lora_out_217_pad_type_0 = const()[name = tensor("lora_out_217_pad_type_0"), val = tensor("valid")]; + tensor lora_out_217_strides_0 = const()[name = tensor("lora_out_217_strides_0"), val = tensor([1, 1])]; + tensor lora_out_217_pad_0 = const()[name = tensor("lora_out_217_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_217_dilations_0 = const()[name = tensor("lora_out_217_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_217_groups_0 = const()[name = tensor("lora_out_217_groups_0"), val = tensor(1)]; + tensor layers_18_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_18_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206348608)))]; + tensor lora_out_217_cast_fp16 = conv(dilations = lora_out_217_dilations_0, groups = lora_out_217_groups_0, pad = lora_out_217_pad_0, pad_type = lora_out_217_pad_type_0, strides = lora_out_217_strides_0, weight = layers_18_self_attn_q_proj_loraB_weight_to_fp16, x = input_361_cast_fp16)[name = tensor("lora_out_217_cast_fp16")]; + tensor query_37_cast_fp16 = add(x = pretrained_out_217_cast_fp16, y = lora_out_217_cast_fp16)[name = tensor("query_37_cast_fp16")]; + tensor pretrained_out_219_pad_type_0 = const()[name = tensor("pretrained_out_219_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_219_strides_0 = const()[name = tensor("pretrained_out_219_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_219_pad_0 = const()[name = tensor("pretrained_out_219_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_219_dilations_0 = const()[name = tensor("pretrained_out_219_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_219_groups_0 = const()[name = tensor("pretrained_out_219_groups_0"), val = tensor(1)]; + tensor layers_18_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(206389632))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207208896))), name = tensor("layers_18_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_219_cast_fp16 = conv(dilations = pretrained_out_219_dilations_0, groups = pretrained_out_219_groups_0, pad = pretrained_out_219_pad_0, pad_type = pretrained_out_219_pad_type_0, strides = pretrained_out_219_strides_0, weight = layers_18_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_73_cast_fp16)[name = tensor("pretrained_out_219_cast_fp16")]; + tensor input_363_pad_type_0 = const()[name = tensor("input_363_pad_type_0"), val = tensor("valid")]; + tensor input_363_strides_0 = const()[name = tensor("input_363_strides_0"), val = tensor([1, 1])]; + tensor input_363_pad_0 = const()[name = tensor("input_363_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_363_dilations_0 = const()[name = tensor("input_363_dilations_0"), val = tensor([1, 1])]; + tensor input_363_groups_0 = const()[name = tensor("input_363_groups_0"), val = tensor(1)]; + tensor layers_18_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_18_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207209024)))]; + tensor input_363_cast_fp16 = conv(dilations = input_363_dilations_0, groups = input_363_groups_0, pad = input_363_pad_0, pad_type = input_363_pad_type_0, strides = input_363_strides_0, weight = layers_18_self_attn_k_proj_loraA_weight_to_fp16, x = obj_73_cast_fp16)[name = tensor("input_363_cast_fp16")]; + tensor lora_out_219_pad_type_0 = const()[name = tensor("lora_out_219_pad_type_0"), val = tensor("valid")]; + tensor lora_out_219_strides_0 = const()[name = tensor("lora_out_219_strides_0"), val = tensor([1, 1])]; + tensor lora_out_219_pad_0 = const()[name = tensor("lora_out_219_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_219_dilations_0 = const()[name = tensor("lora_out_219_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_219_groups_0 = const()[name = tensor("lora_out_219_groups_0"), val = tensor(1)]; + tensor layers_18_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_18_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207250048)))]; + tensor lora_out_219_cast_fp16 = conv(dilations = lora_out_219_dilations_0, groups = lora_out_219_groups_0, pad = lora_out_219_pad_0, pad_type = lora_out_219_pad_type_0, strides = lora_out_219_strides_0, weight = layers_18_self_attn_k_proj_loraB_weight_to_fp16, x = input_363_cast_fp16)[name = tensor("lora_out_219_cast_fp16")]; + tensor key_37_cast_fp16 = add(x = pretrained_out_219_cast_fp16, y = lora_out_219_cast_fp16)[name = tensor("key_37_cast_fp16")]; + tensor pretrained_out_221_pad_type_0 = const()[name = tensor("pretrained_out_221_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_221_strides_0 = const()[name = tensor("pretrained_out_221_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_221_pad_0 = const()[name = tensor("pretrained_out_221_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_221_dilations_0 = const()[name = tensor("pretrained_out_221_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_221_groups_0 = const()[name = tensor("pretrained_out_221_groups_0"), val = tensor(1)]; + tensor layers_18_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207291072))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208110336))), name = tensor("layers_18_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_18_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_18_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208110464)))]; + tensor pretrained_out_221_cast_fp16 = conv(bias = layers_18_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_221_dilations_0, groups = pretrained_out_221_groups_0, pad = pretrained_out_221_pad_0, pad_type = pretrained_out_221_pad_type_0, strides = pretrained_out_221_strides_0, weight = layers_18_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_73_cast_fp16)[name = tensor("pretrained_out_221_cast_fp16")]; + tensor input_365_pad_type_0 = const()[name = tensor("input_365_pad_type_0"), val = tensor("valid")]; + tensor input_365_strides_0 = const()[name = tensor("input_365_strides_0"), val = tensor([1, 1])]; + tensor input_365_pad_0 = const()[name = tensor("input_365_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_365_dilations_0 = const()[name = tensor("input_365_dilations_0"), val = tensor([1, 1])]; + tensor input_365_groups_0 = const()[name = tensor("input_365_groups_0"), val = tensor(1)]; + tensor layers_18_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_18_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208113088)))]; + tensor input_365_cast_fp16 = conv(dilations = input_365_dilations_0, groups = input_365_groups_0, pad = input_365_pad_0, pad_type = input_365_pad_type_0, strides = input_365_strides_0, weight = layers_18_self_attn_v_proj_loraA_weight_to_fp16, x = obj_73_cast_fp16)[name = tensor("input_365_cast_fp16")]; + tensor lora_out_221_pad_type_0 = const()[name = tensor("lora_out_221_pad_type_0"), val = tensor("valid")]; + tensor lora_out_221_strides_0 = const()[name = tensor("lora_out_221_strides_0"), val = tensor([1, 1])]; + tensor lora_out_221_pad_0 = const()[name = tensor("lora_out_221_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_221_dilations_0 = const()[name = tensor("lora_out_221_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_221_groups_0 = const()[name = tensor("lora_out_221_groups_0"), val = tensor(1)]; + tensor layers_18_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_18_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208154112)))]; + tensor lora_out_221_cast_fp16 = conv(dilations = lora_out_221_dilations_0, groups = lora_out_221_groups_0, pad = lora_out_221_pad_0, pad_type = lora_out_221_pad_type_0, strides = lora_out_221_strides_0, weight = layers_18_self_attn_v_proj_loraB_weight_to_fp16, x = input_365_cast_fp16)[name = tensor("lora_out_221_cast_fp16")]; + tensor value_37_cast_fp16 = add(x = pretrained_out_221_cast_fp16, y = lora_out_221_cast_fp16)[name = tensor("value_37_cast_fp16")]; + tensor var_4132 = const()[name = tensor("op_4132"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_37_cast_fp16 = reshape(shape = var_4132, x = query_37_cast_fp16)[name = tensor("mh_q_37_cast_fp16")]; + tensor var_4134_to_fp16 = const()[name = tensor("op_4134_to_fp16"), val = tensor(0x1p-3)]; + tensor var_4135_cast_fp16 = mul(x = mh_q_37_cast_fp16, y = var_4134_to_fp16)[name = tensor("op_4135_cast_fp16")]; + tensor var_4136 = const()[name = tensor("op_4136"), val = tensor([1, 20, 64, -1])]; + tensor var_4137_cast_fp16 = reshape(shape = var_4136, x = key_37_cast_fp16)[name = tensor("op_4137_cast_fp16")]; + tensor mh_w_37_transpose_x_0 = const()[name = tensor("mh_w_37_transpose_x_0"), val = tensor(true)]; + tensor mh_w_37_transpose_y_0 = const()[name = tensor("mh_w_37_transpose_y_0"), val = tensor(false)]; + tensor mh_w_37_cast_fp16 = matmul(transpose_x = mh_w_37_transpose_x_0, transpose_y = mh_w_37_transpose_y_0, x = var_4135_cast_fp16, y = var_4137_cast_fp16)[name = tensor("mh_w_37_cast_fp16")]; + tensor var_4140_cast_fp16 = softmax(axis = var_4030, x = mh_w_37_cast_fp16)[name = tensor("op_4140_cast_fp16")]; + tensor var_4141 = const()[name = tensor("op_4141"), val = tensor([1, 20, 64, -1])]; + tensor var_4142_cast_fp16 = reshape(shape = var_4141, x = value_37_cast_fp16)[name = tensor("op_4142_cast_fp16")]; + tensor attn_37_transpose_x_0 = const()[name = tensor("attn_37_transpose_x_0"), val = tensor(false)]; + tensor attn_37_transpose_y_0 = const()[name = tensor("attn_37_transpose_y_0"), val = tensor(true)]; + tensor attn_37_cast_fp16 = matmul(transpose_x = attn_37_transpose_x_0, transpose_y = attn_37_transpose_y_0, x = var_4142_cast_fp16, y = var_4140_cast_fp16)[name = tensor("attn_37_cast_fp16")]; + tensor var_4145 = const()[name = tensor("op_4145"), val = tensor([1, 1280, 1, -1])]; + tensor input_367_cast_fp16 = reshape(shape = var_4145, x = attn_37_cast_fp16)[name = tensor("input_367_cast_fp16")]; + tensor pretrained_out_223_pad_type_0 = const()[name = tensor("pretrained_out_223_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_223_strides_0 = const()[name = tensor("pretrained_out_223_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_223_pad_0 = const()[name = tensor("pretrained_out_223_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_223_dilations_0 = const()[name = tensor("pretrained_out_223_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_223_groups_0 = const()[name = tensor("pretrained_out_223_groups_0"), val = tensor(1)]; + tensor layers_18_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208195136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209014400))), name = tensor("layers_18_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_18_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_18_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209014528)))]; + tensor pretrained_out_223_cast_fp16 = conv(bias = layers_18_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_223_dilations_0, groups = pretrained_out_223_groups_0, pad = pretrained_out_223_pad_0, pad_type = pretrained_out_223_pad_type_0, strides = pretrained_out_223_strides_0, weight = layers_18_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_367_cast_fp16)[name = tensor("pretrained_out_223_cast_fp16")]; + tensor input_369_pad_type_0 = const()[name = tensor("input_369_pad_type_0"), val = tensor("valid")]; + tensor input_369_strides_0 = const()[name = tensor("input_369_strides_0"), val = tensor([1, 1])]; + tensor input_369_pad_0 = const()[name = tensor("input_369_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_369_dilations_0 = const()[name = tensor("input_369_dilations_0"), val = tensor([1, 1])]; + tensor input_369_groups_0 = const()[name = tensor("input_369_groups_0"), val = tensor(1)]; + tensor layers_18_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_18_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209017152)))]; + tensor input_369_cast_fp16 = conv(dilations = input_369_dilations_0, groups = input_369_groups_0, pad = input_369_pad_0, pad_type = input_369_pad_type_0, strides = input_369_strides_0, weight = layers_18_self_attn_o_proj_loraA_weight_to_fp16, x = input_367_cast_fp16)[name = tensor("input_369_cast_fp16")]; + tensor lora_out_223_pad_type_0 = const()[name = tensor("lora_out_223_pad_type_0"), val = tensor("valid")]; + tensor lora_out_223_strides_0 = const()[name = tensor("lora_out_223_strides_0"), val = tensor([1, 1])]; + tensor lora_out_223_pad_0 = const()[name = tensor("lora_out_223_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_223_dilations_0 = const()[name = tensor("lora_out_223_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_223_groups_0 = const()[name = tensor("lora_out_223_groups_0"), val = tensor(1)]; + tensor layers_18_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_18_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209058176)))]; + tensor lora_out_223_cast_fp16 = conv(dilations = lora_out_223_dilations_0, groups = lora_out_223_groups_0, pad = lora_out_223_pad_0, pad_type = lora_out_223_pad_type_0, strides = lora_out_223_strides_0, weight = layers_18_self_attn_o_proj_loraB_weight_to_fp16, x = input_369_cast_fp16)[name = tensor("lora_out_223_cast_fp16")]; + tensor obj_75_cast_fp16 = add(x = pretrained_out_223_cast_fp16, y = lora_out_223_cast_fp16)[name = tensor("obj_75_cast_fp16")]; + tensor inputs_75_cast_fp16 = add(x = inputs_73_cast_fp16, y = obj_75_cast_fp16)[name = tensor("inputs_75_cast_fp16")]; + tensor out_75_axes_0 = const()[name = tensor("out_75_axes_0"), val = tensor([1])]; + tensor var_4179_to_fp16 = const()[name = tensor("op_4179_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_75_cast_fp16 = layer_norm(axes = out_75_axes_0, epsilon = var_4179_to_fp16, x = inputs_75_cast_fp16)[name = tensor("out_75_cast_fp16")]; + tensor input_371_gamma_0_to_fp16 = const()[name = tensor("input_371_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209099200)))]; + tensor input_371_beta_0_to_fp16 = const()[name = tensor("input_371_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209101824)))]; + tensor input_371_epsilon_0_to_fp16 = const()[name = tensor("input_371_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_371_cast_fp16 = batch_norm(beta = input_371_beta_0_to_fp16, epsilon = input_371_epsilon_0_to_fp16, gamma = input_371_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_75_cast_fp16)[name = tensor("input_371_cast_fp16")]; + tensor pretrained_out_225_pad_type_0 = const()[name = tensor("pretrained_out_225_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_225_strides_0 = const()[name = tensor("pretrained_out_225_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_225_pad_0 = const()[name = tensor("pretrained_out_225_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_225_dilations_0 = const()[name = tensor("pretrained_out_225_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_225_groups_0 = const()[name = tensor("pretrained_out_225_groups_0"), val = tensor(1)]; + tensor layers_18_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(209104448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212381312))), name = tensor("layers_18_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_18_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_18_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212381440)))]; + tensor pretrained_out_225_cast_fp16 = conv(bias = layers_18_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_225_dilations_0, groups = pretrained_out_225_groups_0, pad = pretrained_out_225_pad_0, pad_type = pretrained_out_225_pad_type_0, strides = pretrained_out_225_strides_0, weight = layers_18_fc1_pretrained_weight_to_fp16_palettized, x = input_371_cast_fp16)[name = tensor("pretrained_out_225_cast_fp16")]; + tensor input_373_pad_type_0 = const()[name = tensor("input_373_pad_type_0"), val = tensor("valid")]; + tensor input_373_strides_0 = const()[name = tensor("input_373_strides_0"), val = tensor([1, 1])]; + tensor input_373_pad_0 = const()[name = tensor("input_373_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_373_dilations_0 = const()[name = tensor("input_373_dilations_0"), val = tensor([1, 1])]; + tensor input_373_groups_0 = const()[name = tensor("input_373_groups_0"), val = tensor(1)]; + tensor layers_18_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_18_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212391744)))]; + tensor input_373_cast_fp16 = conv(dilations = input_373_dilations_0, groups = input_373_groups_0, pad = input_373_pad_0, pad_type = input_373_pad_type_0, strides = input_373_strides_0, weight = layers_18_fc1_loraA_weight_to_fp16, x = input_371_cast_fp16)[name = tensor("input_373_cast_fp16")]; + tensor lora_out_225_pad_type_0 = const()[name = tensor("lora_out_225_pad_type_0"), val = tensor("valid")]; + tensor lora_out_225_strides_0 = const()[name = tensor("lora_out_225_strides_0"), val = tensor([1, 1])]; + tensor lora_out_225_pad_0 = const()[name = tensor("lora_out_225_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_225_dilations_0 = const()[name = tensor("lora_out_225_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_225_groups_0 = const()[name = tensor("lora_out_225_groups_0"), val = tensor(1)]; + tensor layers_18_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_18_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212432768)))]; + tensor lora_out_225_cast_fp16 = conv(dilations = lora_out_225_dilations_0, groups = lora_out_225_groups_0, pad = lora_out_225_pad_0, pad_type = lora_out_225_pad_type_0, strides = lora_out_225_strides_0, weight = layers_18_fc1_loraB_weight_to_fp16, x = input_373_cast_fp16)[name = tensor("lora_out_225_cast_fp16")]; + tensor input_375_cast_fp16 = add(x = pretrained_out_225_cast_fp16, y = lora_out_225_cast_fp16)[name = tensor("input_375_cast_fp16")]; + tensor input_377_mode_0 = const()[name = tensor("input_377_mode_0"), val = tensor("EXACT")]; + tensor input_377_cast_fp16 = gelu(mode = input_377_mode_0, x = input_375_cast_fp16)[name = tensor("input_377_cast_fp16")]; + tensor pretrained_out_227_pad_type_0 = const()[name = tensor("pretrained_out_227_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_227_strides_0 = const()[name = tensor("pretrained_out_227_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_227_pad_0 = const()[name = tensor("pretrained_out_227_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_227_dilations_0 = const()[name = tensor("pretrained_out_227_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_227_groups_0 = const()[name = tensor("pretrained_out_227_groups_0"), val = tensor(1)]; + tensor layers_18_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212596672))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215873536))), name = tensor("layers_18_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_18_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_18_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215873664)))]; + tensor pretrained_out_227_cast_fp16 = conv(bias = layers_18_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_227_dilations_0, groups = pretrained_out_227_groups_0, pad = pretrained_out_227_pad_0, pad_type = pretrained_out_227_pad_type_0, strides = pretrained_out_227_strides_0, weight = layers_18_fc2_pretrained_weight_to_fp16_palettized, x = input_377_cast_fp16)[name = tensor("pretrained_out_227_cast_fp16")]; + tensor input_379_pad_type_0 = const()[name = tensor("input_379_pad_type_0"), val = tensor("valid")]; + tensor input_379_strides_0 = const()[name = tensor("input_379_strides_0"), val = tensor([1, 1])]; + tensor input_379_pad_0 = const()[name = tensor("input_379_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_379_dilations_0 = const()[name = tensor("input_379_dilations_0"), val = tensor([1, 1])]; + tensor input_379_groups_0 = const()[name = tensor("input_379_groups_0"), val = tensor(1)]; + tensor layers_18_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_18_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215876288)))]; + tensor input_379_cast_fp16 = conv(dilations = input_379_dilations_0, groups = input_379_groups_0, pad = input_379_pad_0, pad_type = input_379_pad_type_0, strides = input_379_strides_0, weight = layers_18_fc2_loraA_weight_to_fp16, x = input_377_cast_fp16)[name = tensor("input_379_cast_fp16")]; + tensor lora_out_227_pad_type_0 = const()[name = tensor("lora_out_227_pad_type_0"), val = tensor("valid")]; + tensor lora_out_227_strides_0 = const()[name = tensor("lora_out_227_strides_0"), val = tensor([1, 1])]; + tensor lora_out_227_pad_0 = const()[name = tensor("lora_out_227_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_227_dilations_0 = const()[name = tensor("lora_out_227_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_227_groups_0 = const()[name = tensor("lora_out_227_groups_0"), val = tensor(1)]; + tensor layers_18_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_18_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216040192)))]; + tensor lora_out_227_cast_fp16 = conv(dilations = lora_out_227_dilations_0, groups = lora_out_227_groups_0, pad = lora_out_227_pad_0, pad_type = lora_out_227_pad_type_0, strides = lora_out_227_strides_0, weight = layers_18_fc2_loraB_weight_to_fp16, x = input_379_cast_fp16)[name = tensor("lora_out_227_cast_fp16")]; + tensor hidden_states_41_cast_fp16 = add(x = pretrained_out_227_cast_fp16, y = lora_out_227_cast_fp16)[name = tensor("hidden_states_41_cast_fp16")]; + tensor inputs_77_cast_fp16 = add(x = inputs_75_cast_fp16, y = hidden_states_41_cast_fp16)[name = tensor("inputs_77_cast_fp16")]; + tensor var_4244 = const()[name = tensor("op_4244"), val = tensor(3)]; + tensor out_77_axes_0 = const()[name = tensor("out_77_axes_0"), val = tensor([1])]; + tensor var_4263_to_fp16 = const()[name = tensor("op_4263_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_77_cast_fp16 = layer_norm(axes = out_77_axes_0, epsilon = var_4263_to_fp16, x = inputs_77_cast_fp16)[name = tensor("out_77_cast_fp16")]; + tensor obj_77_gamma_0_to_fp16 = const()[name = tensor("obj_77_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216081216)))]; + tensor obj_77_beta_0_to_fp16 = const()[name = tensor("obj_77_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216083840)))]; + tensor obj_77_epsilon_0_to_fp16 = const()[name = tensor("obj_77_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_77_cast_fp16 = batch_norm(beta = obj_77_beta_0_to_fp16, epsilon = obj_77_epsilon_0_to_fp16, gamma = obj_77_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_77_cast_fp16)[name = tensor("obj_77_cast_fp16")]; + tensor pretrained_out_229_pad_type_0 = const()[name = tensor("pretrained_out_229_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_229_strides_0 = const()[name = tensor("pretrained_out_229_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_229_pad_0 = const()[name = tensor("pretrained_out_229_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_229_dilations_0 = const()[name = tensor("pretrained_out_229_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_229_groups_0 = const()[name = tensor("pretrained_out_229_groups_0"), val = tensor(1)]; + tensor layers_19_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216086464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216905728))), name = tensor("layers_19_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_19_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_19_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216905856)))]; + tensor pretrained_out_229_cast_fp16 = conv(bias = layers_19_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_229_dilations_0, groups = pretrained_out_229_groups_0, pad = pretrained_out_229_pad_0, pad_type = pretrained_out_229_pad_type_0, strides = pretrained_out_229_strides_0, weight = layers_19_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_77_cast_fp16)[name = tensor("pretrained_out_229_cast_fp16")]; + tensor input_381_pad_type_0 = const()[name = tensor("input_381_pad_type_0"), val = tensor("valid")]; + tensor input_381_strides_0 = const()[name = tensor("input_381_strides_0"), val = tensor([1, 1])]; + tensor input_381_pad_0 = const()[name = tensor("input_381_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_381_dilations_0 = const()[name = tensor("input_381_dilations_0"), val = tensor([1, 1])]; + tensor input_381_groups_0 = const()[name = tensor("input_381_groups_0"), val = tensor(1)]; + tensor layers_19_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_19_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216908480)))]; + tensor input_381_cast_fp16 = conv(dilations = input_381_dilations_0, groups = input_381_groups_0, pad = input_381_pad_0, pad_type = input_381_pad_type_0, strides = input_381_strides_0, weight = layers_19_self_attn_q_proj_loraA_weight_to_fp16, x = obj_77_cast_fp16)[name = tensor("input_381_cast_fp16")]; + tensor lora_out_229_pad_type_0 = const()[name = tensor("lora_out_229_pad_type_0"), val = tensor("valid")]; + tensor lora_out_229_strides_0 = const()[name = tensor("lora_out_229_strides_0"), val = tensor([1, 1])]; + tensor lora_out_229_pad_0 = const()[name = tensor("lora_out_229_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_229_dilations_0 = const()[name = tensor("lora_out_229_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_229_groups_0 = const()[name = tensor("lora_out_229_groups_0"), val = tensor(1)]; + tensor layers_19_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_19_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216949504)))]; + tensor lora_out_229_cast_fp16 = conv(dilations = lora_out_229_dilations_0, groups = lora_out_229_groups_0, pad = lora_out_229_pad_0, pad_type = lora_out_229_pad_type_0, strides = lora_out_229_strides_0, weight = layers_19_self_attn_q_proj_loraB_weight_to_fp16, x = input_381_cast_fp16)[name = tensor("lora_out_229_cast_fp16")]; + tensor query_39_cast_fp16 = add(x = pretrained_out_229_cast_fp16, y = lora_out_229_cast_fp16)[name = tensor("query_39_cast_fp16")]; + tensor pretrained_out_231_pad_type_0 = const()[name = tensor("pretrained_out_231_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_231_strides_0 = const()[name = tensor("pretrained_out_231_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_231_pad_0 = const()[name = tensor("pretrained_out_231_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_231_dilations_0 = const()[name = tensor("pretrained_out_231_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_231_groups_0 = const()[name = tensor("pretrained_out_231_groups_0"), val = tensor(1)]; + tensor layers_19_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216990528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217809792))), name = tensor("layers_19_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_231_cast_fp16 = conv(dilations = pretrained_out_231_dilations_0, groups = pretrained_out_231_groups_0, pad = pretrained_out_231_pad_0, pad_type = pretrained_out_231_pad_type_0, strides = pretrained_out_231_strides_0, weight = layers_19_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_77_cast_fp16)[name = tensor("pretrained_out_231_cast_fp16")]; + tensor input_383_pad_type_0 = const()[name = tensor("input_383_pad_type_0"), val = tensor("valid")]; + tensor input_383_strides_0 = const()[name = tensor("input_383_strides_0"), val = tensor([1, 1])]; + tensor input_383_pad_0 = const()[name = tensor("input_383_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_383_dilations_0 = const()[name = tensor("input_383_dilations_0"), val = tensor([1, 1])]; + tensor input_383_groups_0 = const()[name = tensor("input_383_groups_0"), val = tensor(1)]; + tensor layers_19_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_19_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217809920)))]; + tensor input_383_cast_fp16 = conv(dilations = input_383_dilations_0, groups = input_383_groups_0, pad = input_383_pad_0, pad_type = input_383_pad_type_0, strides = input_383_strides_0, weight = layers_19_self_attn_k_proj_loraA_weight_to_fp16, x = obj_77_cast_fp16)[name = tensor("input_383_cast_fp16")]; + tensor lora_out_231_pad_type_0 = const()[name = tensor("lora_out_231_pad_type_0"), val = tensor("valid")]; + tensor lora_out_231_strides_0 = const()[name = tensor("lora_out_231_strides_0"), val = tensor([1, 1])]; + tensor lora_out_231_pad_0 = const()[name = tensor("lora_out_231_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_231_dilations_0 = const()[name = tensor("lora_out_231_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_231_groups_0 = const()[name = tensor("lora_out_231_groups_0"), val = tensor(1)]; + tensor layers_19_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_19_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217850944)))]; + tensor lora_out_231_cast_fp16 = conv(dilations = lora_out_231_dilations_0, groups = lora_out_231_groups_0, pad = lora_out_231_pad_0, pad_type = lora_out_231_pad_type_0, strides = lora_out_231_strides_0, weight = layers_19_self_attn_k_proj_loraB_weight_to_fp16, x = input_383_cast_fp16)[name = tensor("lora_out_231_cast_fp16")]; + tensor key_39_cast_fp16 = add(x = pretrained_out_231_cast_fp16, y = lora_out_231_cast_fp16)[name = tensor("key_39_cast_fp16")]; + tensor pretrained_out_233_pad_type_0 = const()[name = tensor("pretrained_out_233_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_233_strides_0 = const()[name = tensor("pretrained_out_233_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_233_pad_0 = const()[name = tensor("pretrained_out_233_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_233_dilations_0 = const()[name = tensor("pretrained_out_233_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_233_groups_0 = const()[name = tensor("pretrained_out_233_groups_0"), val = tensor(1)]; + tensor layers_19_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217891968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218711232))), name = tensor("layers_19_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_19_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_19_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218711360)))]; + tensor pretrained_out_233_cast_fp16 = conv(bias = layers_19_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_233_dilations_0, groups = pretrained_out_233_groups_0, pad = pretrained_out_233_pad_0, pad_type = pretrained_out_233_pad_type_0, strides = pretrained_out_233_strides_0, weight = layers_19_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_77_cast_fp16)[name = tensor("pretrained_out_233_cast_fp16")]; + tensor input_385_pad_type_0 = const()[name = tensor("input_385_pad_type_0"), val = tensor("valid")]; + tensor input_385_strides_0 = const()[name = tensor("input_385_strides_0"), val = tensor([1, 1])]; + tensor input_385_pad_0 = const()[name = tensor("input_385_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_385_dilations_0 = const()[name = tensor("input_385_dilations_0"), val = tensor([1, 1])]; + tensor input_385_groups_0 = const()[name = tensor("input_385_groups_0"), val = tensor(1)]; + tensor layers_19_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_19_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218713984)))]; + tensor input_385_cast_fp16 = conv(dilations = input_385_dilations_0, groups = input_385_groups_0, pad = input_385_pad_0, pad_type = input_385_pad_type_0, strides = input_385_strides_0, weight = layers_19_self_attn_v_proj_loraA_weight_to_fp16, x = obj_77_cast_fp16)[name = tensor("input_385_cast_fp16")]; + tensor lora_out_233_pad_type_0 = const()[name = tensor("lora_out_233_pad_type_0"), val = tensor("valid")]; + tensor lora_out_233_strides_0 = const()[name = tensor("lora_out_233_strides_0"), val = tensor([1, 1])]; + tensor lora_out_233_pad_0 = const()[name = tensor("lora_out_233_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_233_dilations_0 = const()[name = tensor("lora_out_233_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_233_groups_0 = const()[name = tensor("lora_out_233_groups_0"), val = tensor(1)]; + tensor layers_19_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_19_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218755008)))]; + tensor lora_out_233_cast_fp16 = conv(dilations = lora_out_233_dilations_0, groups = lora_out_233_groups_0, pad = lora_out_233_pad_0, pad_type = lora_out_233_pad_type_0, strides = lora_out_233_strides_0, weight = layers_19_self_attn_v_proj_loraB_weight_to_fp16, x = input_385_cast_fp16)[name = tensor("lora_out_233_cast_fp16")]; + tensor value_39_cast_fp16 = add(x = pretrained_out_233_cast_fp16, y = lora_out_233_cast_fp16)[name = tensor("value_39_cast_fp16")]; + tensor var_4346 = const()[name = tensor("op_4346"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_39_cast_fp16 = reshape(shape = var_4346, x = query_39_cast_fp16)[name = tensor("mh_q_39_cast_fp16")]; + tensor var_4348_to_fp16 = const()[name = tensor("op_4348_to_fp16"), val = tensor(0x1p-3)]; + tensor var_4349_cast_fp16 = mul(x = mh_q_39_cast_fp16, y = var_4348_to_fp16)[name = tensor("op_4349_cast_fp16")]; + tensor var_4350 = const()[name = tensor("op_4350"), val = tensor([1, 20, 64, -1])]; + tensor var_4351_cast_fp16 = reshape(shape = var_4350, x = key_39_cast_fp16)[name = tensor("op_4351_cast_fp16")]; + tensor mh_w_39_transpose_x_0 = const()[name = tensor("mh_w_39_transpose_x_0"), val = tensor(true)]; + tensor mh_w_39_transpose_y_0 = const()[name = tensor("mh_w_39_transpose_y_0"), val = tensor(false)]; + tensor mh_w_39_cast_fp16 = matmul(transpose_x = mh_w_39_transpose_x_0, transpose_y = mh_w_39_transpose_y_0, x = var_4349_cast_fp16, y = var_4351_cast_fp16)[name = tensor("mh_w_39_cast_fp16")]; + tensor var_4354_cast_fp16 = softmax(axis = var_4244, x = mh_w_39_cast_fp16)[name = tensor("op_4354_cast_fp16")]; + tensor var_4355 = const()[name = tensor("op_4355"), val = tensor([1, 20, 64, -1])]; + tensor var_4356_cast_fp16 = reshape(shape = var_4355, x = value_39_cast_fp16)[name = tensor("op_4356_cast_fp16")]; + tensor attn_39_transpose_x_0 = const()[name = tensor("attn_39_transpose_x_0"), val = tensor(false)]; + tensor attn_39_transpose_y_0 = const()[name = tensor("attn_39_transpose_y_0"), val = tensor(true)]; + tensor attn_39_cast_fp16 = matmul(transpose_x = attn_39_transpose_x_0, transpose_y = attn_39_transpose_y_0, x = var_4356_cast_fp16, y = var_4354_cast_fp16)[name = tensor("attn_39_cast_fp16")]; + tensor var_4359 = const()[name = tensor("op_4359"), val = tensor([1, 1280, 1, -1])]; + tensor input_387_cast_fp16 = reshape(shape = var_4359, x = attn_39_cast_fp16)[name = tensor("input_387_cast_fp16")]; + tensor pretrained_out_235_pad_type_0 = const()[name = tensor("pretrained_out_235_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_235_strides_0 = const()[name = tensor("pretrained_out_235_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_235_pad_0 = const()[name = tensor("pretrained_out_235_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_235_dilations_0 = const()[name = tensor("pretrained_out_235_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_235_groups_0 = const()[name = tensor("pretrained_out_235_groups_0"), val = tensor(1)]; + tensor layers_19_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218796032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(219615296))), name = tensor("layers_19_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_19_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_19_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(219615424)))]; + tensor pretrained_out_235_cast_fp16 = conv(bias = layers_19_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_235_dilations_0, groups = pretrained_out_235_groups_0, pad = pretrained_out_235_pad_0, pad_type = pretrained_out_235_pad_type_0, strides = pretrained_out_235_strides_0, weight = layers_19_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_387_cast_fp16)[name = tensor("pretrained_out_235_cast_fp16")]; + tensor input_389_pad_type_0 = const()[name = tensor("input_389_pad_type_0"), val = tensor("valid")]; + tensor input_389_strides_0 = const()[name = tensor("input_389_strides_0"), val = tensor([1, 1])]; + tensor input_389_pad_0 = const()[name = tensor("input_389_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_389_dilations_0 = const()[name = tensor("input_389_dilations_0"), val = tensor([1, 1])]; + tensor input_389_groups_0 = const()[name = tensor("input_389_groups_0"), val = tensor(1)]; + tensor layers_19_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_19_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(219618048)))]; + tensor input_389_cast_fp16 = conv(dilations = input_389_dilations_0, groups = input_389_groups_0, pad = input_389_pad_0, pad_type = input_389_pad_type_0, strides = input_389_strides_0, weight = layers_19_self_attn_o_proj_loraA_weight_to_fp16, x = input_387_cast_fp16)[name = tensor("input_389_cast_fp16")]; + tensor lora_out_235_pad_type_0 = const()[name = tensor("lora_out_235_pad_type_0"), val = tensor("valid")]; + tensor lora_out_235_strides_0 = const()[name = tensor("lora_out_235_strides_0"), val = tensor([1, 1])]; + tensor lora_out_235_pad_0 = const()[name = tensor("lora_out_235_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_235_dilations_0 = const()[name = tensor("lora_out_235_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_235_groups_0 = const()[name = tensor("lora_out_235_groups_0"), val = tensor(1)]; + tensor layers_19_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_19_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(219659072)))]; + tensor lora_out_235_cast_fp16 = conv(dilations = lora_out_235_dilations_0, groups = lora_out_235_groups_0, pad = lora_out_235_pad_0, pad_type = lora_out_235_pad_type_0, strides = lora_out_235_strides_0, weight = layers_19_self_attn_o_proj_loraB_weight_to_fp16, x = input_389_cast_fp16)[name = tensor("lora_out_235_cast_fp16")]; + tensor obj_79_cast_fp16 = add(x = pretrained_out_235_cast_fp16, y = lora_out_235_cast_fp16)[name = tensor("obj_79_cast_fp16")]; + tensor inputs_79_cast_fp16 = add(x = inputs_77_cast_fp16, y = obj_79_cast_fp16)[name = tensor("inputs_79_cast_fp16")]; + tensor out_79_axes_0 = const()[name = tensor("out_79_axes_0"), val = tensor([1])]; + tensor var_4393_to_fp16 = const()[name = tensor("op_4393_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_79_cast_fp16 = layer_norm(axes = out_79_axes_0, epsilon = var_4393_to_fp16, x = inputs_79_cast_fp16)[name = tensor("out_79_cast_fp16")]; + tensor input_391_gamma_0_to_fp16 = const()[name = tensor("input_391_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(219700096)))]; + tensor input_391_beta_0_to_fp16 = const()[name = tensor("input_391_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(219702720)))]; + tensor input_391_epsilon_0_to_fp16 = const()[name = tensor("input_391_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_391_cast_fp16 = batch_norm(beta = input_391_beta_0_to_fp16, epsilon = input_391_epsilon_0_to_fp16, gamma = input_391_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_79_cast_fp16)[name = tensor("input_391_cast_fp16")]; + tensor pretrained_out_237_pad_type_0 = const()[name = tensor("pretrained_out_237_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_237_strides_0 = const()[name = tensor("pretrained_out_237_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_237_pad_0 = const()[name = tensor("pretrained_out_237_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_237_dilations_0 = const()[name = tensor("pretrained_out_237_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_237_groups_0 = const()[name = tensor("pretrained_out_237_groups_0"), val = tensor(1)]; + tensor layers_19_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(219705344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(222982208))), name = tensor("layers_19_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_19_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_19_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(222982336)))]; + tensor pretrained_out_237_cast_fp16 = conv(bias = layers_19_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_237_dilations_0, groups = pretrained_out_237_groups_0, pad = pretrained_out_237_pad_0, pad_type = pretrained_out_237_pad_type_0, strides = pretrained_out_237_strides_0, weight = layers_19_fc1_pretrained_weight_to_fp16_palettized, x = input_391_cast_fp16)[name = tensor("pretrained_out_237_cast_fp16")]; + tensor input_393_pad_type_0 = const()[name = tensor("input_393_pad_type_0"), val = tensor("valid")]; + tensor input_393_strides_0 = const()[name = tensor("input_393_strides_0"), val = tensor([1, 1])]; + tensor input_393_pad_0 = const()[name = tensor("input_393_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_393_dilations_0 = const()[name = tensor("input_393_dilations_0"), val = tensor([1, 1])]; + tensor input_393_groups_0 = const()[name = tensor("input_393_groups_0"), val = tensor(1)]; + tensor layers_19_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_19_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(222992640)))]; + tensor input_393_cast_fp16 = conv(dilations = input_393_dilations_0, groups = input_393_groups_0, pad = input_393_pad_0, pad_type = input_393_pad_type_0, strides = input_393_strides_0, weight = layers_19_fc1_loraA_weight_to_fp16, x = input_391_cast_fp16)[name = tensor("input_393_cast_fp16")]; + tensor lora_out_237_pad_type_0 = const()[name = tensor("lora_out_237_pad_type_0"), val = tensor("valid")]; + tensor lora_out_237_strides_0 = const()[name = tensor("lora_out_237_strides_0"), val = tensor([1, 1])]; + tensor lora_out_237_pad_0 = const()[name = tensor("lora_out_237_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_237_dilations_0 = const()[name = tensor("lora_out_237_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_237_groups_0 = const()[name = tensor("lora_out_237_groups_0"), val = tensor(1)]; + tensor layers_19_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_19_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(223033664)))]; + tensor lora_out_237_cast_fp16 = conv(dilations = lora_out_237_dilations_0, groups = lora_out_237_groups_0, pad = lora_out_237_pad_0, pad_type = lora_out_237_pad_type_0, strides = lora_out_237_strides_0, weight = layers_19_fc1_loraB_weight_to_fp16, x = input_393_cast_fp16)[name = tensor("lora_out_237_cast_fp16")]; + tensor input_395_cast_fp16 = add(x = pretrained_out_237_cast_fp16, y = lora_out_237_cast_fp16)[name = tensor("input_395_cast_fp16")]; + tensor input_397_mode_0 = const()[name = tensor("input_397_mode_0"), val = tensor("EXACT")]; + tensor input_397_cast_fp16 = gelu(mode = input_397_mode_0, x = input_395_cast_fp16)[name = tensor("input_397_cast_fp16")]; + tensor pretrained_out_239_pad_type_0 = const()[name = tensor("pretrained_out_239_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_239_strides_0 = const()[name = tensor("pretrained_out_239_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_239_pad_0 = const()[name = tensor("pretrained_out_239_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_239_dilations_0 = const()[name = tensor("pretrained_out_239_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_239_groups_0 = const()[name = tensor("pretrained_out_239_groups_0"), val = tensor(1)]; + tensor layers_19_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(223197568))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(226474432))), name = tensor("layers_19_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_19_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_19_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(226474560)))]; + tensor pretrained_out_239_cast_fp16 = conv(bias = layers_19_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_239_dilations_0, groups = pretrained_out_239_groups_0, pad = pretrained_out_239_pad_0, pad_type = pretrained_out_239_pad_type_0, strides = pretrained_out_239_strides_0, weight = layers_19_fc2_pretrained_weight_to_fp16_palettized, x = input_397_cast_fp16)[name = tensor("pretrained_out_239_cast_fp16")]; + tensor input_399_pad_type_0 = const()[name = tensor("input_399_pad_type_0"), val = tensor("valid")]; + tensor input_399_strides_0 = const()[name = tensor("input_399_strides_0"), val = tensor([1, 1])]; + tensor input_399_pad_0 = const()[name = tensor("input_399_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_399_dilations_0 = const()[name = tensor("input_399_dilations_0"), val = tensor([1, 1])]; + tensor input_399_groups_0 = const()[name = tensor("input_399_groups_0"), val = tensor(1)]; + tensor layers_19_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_19_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(226477184)))]; + tensor input_399_cast_fp16 = conv(dilations = input_399_dilations_0, groups = input_399_groups_0, pad = input_399_pad_0, pad_type = input_399_pad_type_0, strides = input_399_strides_0, weight = layers_19_fc2_loraA_weight_to_fp16, x = input_397_cast_fp16)[name = tensor("input_399_cast_fp16")]; + tensor lora_out_239_pad_type_0 = const()[name = tensor("lora_out_239_pad_type_0"), val = tensor("valid")]; + tensor lora_out_239_strides_0 = const()[name = tensor("lora_out_239_strides_0"), val = tensor([1, 1])]; + tensor lora_out_239_pad_0 = const()[name = tensor("lora_out_239_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_239_dilations_0 = const()[name = tensor("lora_out_239_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_239_groups_0 = const()[name = tensor("lora_out_239_groups_0"), val = tensor(1)]; + tensor layers_19_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_19_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(226641088)))]; + tensor lora_out_239_cast_fp16 = conv(dilations = lora_out_239_dilations_0, groups = lora_out_239_groups_0, pad = lora_out_239_pad_0, pad_type = lora_out_239_pad_type_0, strides = lora_out_239_strides_0, weight = layers_19_fc2_loraB_weight_to_fp16, x = input_399_cast_fp16)[name = tensor("lora_out_239_cast_fp16")]; + tensor hidden_states_43_cast_fp16 = add(x = pretrained_out_239_cast_fp16, y = lora_out_239_cast_fp16)[name = tensor("hidden_states_43_cast_fp16")]; + tensor inputs_81_cast_fp16 = add(x = inputs_79_cast_fp16, y = hidden_states_43_cast_fp16)[name = tensor("inputs_81_cast_fp16")]; + tensor var_4458 = const()[name = tensor("op_4458"), val = tensor(3)]; + tensor out_81_axes_0 = const()[name = tensor("out_81_axes_0"), val = tensor([1])]; + tensor var_4477_to_fp16 = const()[name = tensor("op_4477_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_81_cast_fp16 = layer_norm(axes = out_81_axes_0, epsilon = var_4477_to_fp16, x = inputs_81_cast_fp16)[name = tensor("out_81_cast_fp16")]; + tensor obj_81_gamma_0_to_fp16 = const()[name = tensor("obj_81_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(226682112)))]; + tensor obj_81_beta_0_to_fp16 = const()[name = tensor("obj_81_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(226684736)))]; + tensor obj_81_epsilon_0_to_fp16 = const()[name = tensor("obj_81_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_81_cast_fp16 = batch_norm(beta = obj_81_beta_0_to_fp16, epsilon = obj_81_epsilon_0_to_fp16, gamma = obj_81_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_81_cast_fp16)[name = tensor("obj_81_cast_fp16")]; + tensor pretrained_out_241_pad_type_0 = const()[name = tensor("pretrained_out_241_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_241_strides_0 = const()[name = tensor("pretrained_out_241_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_241_pad_0 = const()[name = tensor("pretrained_out_241_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_241_dilations_0 = const()[name = tensor("pretrained_out_241_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_241_groups_0 = const()[name = tensor("pretrained_out_241_groups_0"), val = tensor(1)]; + tensor layers_20_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(226687360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(227506624))), name = tensor("layers_20_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_20_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_20_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(227506752)))]; + tensor pretrained_out_241_cast_fp16 = conv(bias = layers_20_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_241_dilations_0, groups = pretrained_out_241_groups_0, pad = pretrained_out_241_pad_0, pad_type = pretrained_out_241_pad_type_0, strides = pretrained_out_241_strides_0, weight = layers_20_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_81_cast_fp16)[name = tensor("pretrained_out_241_cast_fp16")]; + tensor input_401_pad_type_0 = const()[name = tensor("input_401_pad_type_0"), val = tensor("valid")]; + tensor input_401_strides_0 = const()[name = tensor("input_401_strides_0"), val = tensor([1, 1])]; + tensor input_401_pad_0 = const()[name = tensor("input_401_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_401_dilations_0 = const()[name = tensor("input_401_dilations_0"), val = tensor([1, 1])]; + tensor input_401_groups_0 = const()[name = tensor("input_401_groups_0"), val = tensor(1)]; + tensor layers_20_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_20_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(227509376)))]; + tensor input_401_cast_fp16 = conv(dilations = input_401_dilations_0, groups = input_401_groups_0, pad = input_401_pad_0, pad_type = input_401_pad_type_0, strides = input_401_strides_0, weight = layers_20_self_attn_q_proj_loraA_weight_to_fp16, x = obj_81_cast_fp16)[name = tensor("input_401_cast_fp16")]; + tensor lora_out_241_pad_type_0 = const()[name = tensor("lora_out_241_pad_type_0"), val = tensor("valid")]; + tensor lora_out_241_strides_0 = const()[name = tensor("lora_out_241_strides_0"), val = tensor([1, 1])]; + tensor lora_out_241_pad_0 = const()[name = tensor("lora_out_241_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_241_dilations_0 = const()[name = tensor("lora_out_241_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_241_groups_0 = const()[name = tensor("lora_out_241_groups_0"), val = tensor(1)]; + tensor layers_20_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_20_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(227550400)))]; + tensor lora_out_241_cast_fp16 = conv(dilations = lora_out_241_dilations_0, groups = lora_out_241_groups_0, pad = lora_out_241_pad_0, pad_type = lora_out_241_pad_type_0, strides = lora_out_241_strides_0, weight = layers_20_self_attn_q_proj_loraB_weight_to_fp16, x = input_401_cast_fp16)[name = tensor("lora_out_241_cast_fp16")]; + tensor query_41_cast_fp16 = add(x = pretrained_out_241_cast_fp16, y = lora_out_241_cast_fp16)[name = tensor("query_41_cast_fp16")]; + tensor pretrained_out_243_pad_type_0 = const()[name = tensor("pretrained_out_243_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_243_strides_0 = const()[name = tensor("pretrained_out_243_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_243_pad_0 = const()[name = tensor("pretrained_out_243_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_243_dilations_0 = const()[name = tensor("pretrained_out_243_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_243_groups_0 = const()[name = tensor("pretrained_out_243_groups_0"), val = tensor(1)]; + tensor layers_20_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(227591424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(228410688))), name = tensor("layers_20_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_243_cast_fp16 = conv(dilations = pretrained_out_243_dilations_0, groups = pretrained_out_243_groups_0, pad = pretrained_out_243_pad_0, pad_type = pretrained_out_243_pad_type_0, strides = pretrained_out_243_strides_0, weight = layers_20_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_81_cast_fp16)[name = tensor("pretrained_out_243_cast_fp16")]; + tensor input_403_pad_type_0 = const()[name = tensor("input_403_pad_type_0"), val = tensor("valid")]; + tensor input_403_strides_0 = const()[name = tensor("input_403_strides_0"), val = tensor([1, 1])]; + tensor input_403_pad_0 = const()[name = tensor("input_403_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_403_dilations_0 = const()[name = tensor("input_403_dilations_0"), val = tensor([1, 1])]; + tensor input_403_groups_0 = const()[name = tensor("input_403_groups_0"), val = tensor(1)]; + tensor layers_20_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_20_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(228410816)))]; + tensor input_403_cast_fp16 = conv(dilations = input_403_dilations_0, groups = input_403_groups_0, pad = input_403_pad_0, pad_type = input_403_pad_type_0, strides = input_403_strides_0, weight = layers_20_self_attn_k_proj_loraA_weight_to_fp16, x = obj_81_cast_fp16)[name = tensor("input_403_cast_fp16")]; + tensor lora_out_243_pad_type_0 = const()[name = tensor("lora_out_243_pad_type_0"), val = tensor("valid")]; + tensor lora_out_243_strides_0 = const()[name = tensor("lora_out_243_strides_0"), val = tensor([1, 1])]; + tensor lora_out_243_pad_0 = const()[name = tensor("lora_out_243_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_243_dilations_0 = const()[name = tensor("lora_out_243_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_243_groups_0 = const()[name = tensor("lora_out_243_groups_0"), val = tensor(1)]; + tensor layers_20_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_20_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(228451840)))]; + tensor lora_out_243_cast_fp16 = conv(dilations = lora_out_243_dilations_0, groups = lora_out_243_groups_0, pad = lora_out_243_pad_0, pad_type = lora_out_243_pad_type_0, strides = lora_out_243_strides_0, weight = layers_20_self_attn_k_proj_loraB_weight_to_fp16, x = input_403_cast_fp16)[name = tensor("lora_out_243_cast_fp16")]; + tensor key_41_cast_fp16 = add(x = pretrained_out_243_cast_fp16, y = lora_out_243_cast_fp16)[name = tensor("key_41_cast_fp16")]; + tensor pretrained_out_245_pad_type_0 = const()[name = tensor("pretrained_out_245_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_245_strides_0 = const()[name = tensor("pretrained_out_245_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_245_pad_0 = const()[name = tensor("pretrained_out_245_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_245_dilations_0 = const()[name = tensor("pretrained_out_245_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_245_groups_0 = const()[name = tensor("pretrained_out_245_groups_0"), val = tensor(1)]; + tensor layers_20_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(228492864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(229312128))), name = tensor("layers_20_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_20_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_20_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(229312256)))]; + tensor pretrained_out_245_cast_fp16 = conv(bias = layers_20_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_245_dilations_0, groups = pretrained_out_245_groups_0, pad = pretrained_out_245_pad_0, pad_type = pretrained_out_245_pad_type_0, strides = pretrained_out_245_strides_0, weight = layers_20_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_81_cast_fp16)[name = tensor("pretrained_out_245_cast_fp16")]; + tensor input_405_pad_type_0 = const()[name = tensor("input_405_pad_type_0"), val = tensor("valid")]; + tensor input_405_strides_0 = const()[name = tensor("input_405_strides_0"), val = tensor([1, 1])]; + tensor input_405_pad_0 = const()[name = tensor("input_405_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_405_dilations_0 = const()[name = tensor("input_405_dilations_0"), val = tensor([1, 1])]; + tensor input_405_groups_0 = const()[name = tensor("input_405_groups_0"), val = tensor(1)]; + tensor layers_20_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_20_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(229314880)))]; + tensor input_405_cast_fp16 = conv(dilations = input_405_dilations_0, groups = input_405_groups_0, pad = input_405_pad_0, pad_type = input_405_pad_type_0, strides = input_405_strides_0, weight = layers_20_self_attn_v_proj_loraA_weight_to_fp16, x = obj_81_cast_fp16)[name = tensor("input_405_cast_fp16")]; + tensor lora_out_245_pad_type_0 = const()[name = tensor("lora_out_245_pad_type_0"), val = tensor("valid")]; + tensor lora_out_245_strides_0 = const()[name = tensor("lora_out_245_strides_0"), val = tensor([1, 1])]; + tensor lora_out_245_pad_0 = const()[name = tensor("lora_out_245_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_245_dilations_0 = const()[name = tensor("lora_out_245_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_245_groups_0 = const()[name = tensor("lora_out_245_groups_0"), val = tensor(1)]; + tensor layers_20_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_20_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(229355904)))]; + tensor lora_out_245_cast_fp16 = conv(dilations = lora_out_245_dilations_0, groups = lora_out_245_groups_0, pad = lora_out_245_pad_0, pad_type = lora_out_245_pad_type_0, strides = lora_out_245_strides_0, weight = layers_20_self_attn_v_proj_loraB_weight_to_fp16, x = input_405_cast_fp16)[name = tensor("lora_out_245_cast_fp16")]; + tensor value_41_cast_fp16 = add(x = pretrained_out_245_cast_fp16, y = lora_out_245_cast_fp16)[name = tensor("value_41_cast_fp16")]; + tensor var_4560 = const()[name = tensor("op_4560"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_41_cast_fp16 = reshape(shape = var_4560, x = query_41_cast_fp16)[name = tensor("mh_q_41_cast_fp16")]; + tensor var_4562_to_fp16 = const()[name = tensor("op_4562_to_fp16"), val = tensor(0x1p-3)]; + tensor var_4563_cast_fp16 = mul(x = mh_q_41_cast_fp16, y = var_4562_to_fp16)[name = tensor("op_4563_cast_fp16")]; + tensor var_4564 = const()[name = tensor("op_4564"), val = tensor([1, 20, 64, -1])]; + tensor var_4565_cast_fp16 = reshape(shape = var_4564, x = key_41_cast_fp16)[name = tensor("op_4565_cast_fp16")]; + tensor mh_w_41_transpose_x_0 = const()[name = tensor("mh_w_41_transpose_x_0"), val = tensor(true)]; + tensor mh_w_41_transpose_y_0 = const()[name = tensor("mh_w_41_transpose_y_0"), val = tensor(false)]; + tensor mh_w_41_cast_fp16 = matmul(transpose_x = mh_w_41_transpose_x_0, transpose_y = mh_w_41_transpose_y_0, x = var_4563_cast_fp16, y = var_4565_cast_fp16)[name = tensor("mh_w_41_cast_fp16")]; + tensor var_4568_cast_fp16 = softmax(axis = var_4458, x = mh_w_41_cast_fp16)[name = tensor("op_4568_cast_fp16")]; + tensor var_4569 = const()[name = tensor("op_4569"), val = tensor([1, 20, 64, -1])]; + tensor var_4570_cast_fp16 = reshape(shape = var_4569, x = value_41_cast_fp16)[name = tensor("op_4570_cast_fp16")]; + tensor attn_41_transpose_x_0 = const()[name = tensor("attn_41_transpose_x_0"), val = tensor(false)]; + tensor attn_41_transpose_y_0 = const()[name = tensor("attn_41_transpose_y_0"), val = tensor(true)]; + tensor attn_41_cast_fp16 = matmul(transpose_x = attn_41_transpose_x_0, transpose_y = attn_41_transpose_y_0, x = var_4570_cast_fp16, y = var_4568_cast_fp16)[name = tensor("attn_41_cast_fp16")]; + tensor var_4573 = const()[name = tensor("op_4573"), val = tensor([1, 1280, 1, -1])]; + tensor input_407_cast_fp16 = reshape(shape = var_4573, x = attn_41_cast_fp16)[name = tensor("input_407_cast_fp16")]; + tensor pretrained_out_247_pad_type_0 = const()[name = tensor("pretrained_out_247_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_247_strides_0 = const()[name = tensor("pretrained_out_247_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_247_pad_0 = const()[name = tensor("pretrained_out_247_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_247_dilations_0 = const()[name = tensor("pretrained_out_247_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_247_groups_0 = const()[name = tensor("pretrained_out_247_groups_0"), val = tensor(1)]; + tensor layers_20_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(229396928))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(230216192))), name = tensor("layers_20_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_20_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_20_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(230216320)))]; + tensor pretrained_out_247_cast_fp16 = conv(bias = layers_20_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_247_dilations_0, groups = pretrained_out_247_groups_0, pad = pretrained_out_247_pad_0, pad_type = pretrained_out_247_pad_type_0, strides = pretrained_out_247_strides_0, weight = layers_20_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_407_cast_fp16)[name = tensor("pretrained_out_247_cast_fp16")]; + tensor input_409_pad_type_0 = const()[name = tensor("input_409_pad_type_0"), val = tensor("valid")]; + tensor input_409_strides_0 = const()[name = tensor("input_409_strides_0"), val = tensor([1, 1])]; + tensor input_409_pad_0 = const()[name = tensor("input_409_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_409_dilations_0 = const()[name = tensor("input_409_dilations_0"), val = tensor([1, 1])]; + tensor input_409_groups_0 = const()[name = tensor("input_409_groups_0"), val = tensor(1)]; + tensor layers_20_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_20_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(230218944)))]; + tensor input_409_cast_fp16 = conv(dilations = input_409_dilations_0, groups = input_409_groups_0, pad = input_409_pad_0, pad_type = input_409_pad_type_0, strides = input_409_strides_0, weight = layers_20_self_attn_o_proj_loraA_weight_to_fp16, x = input_407_cast_fp16)[name = tensor("input_409_cast_fp16")]; + tensor lora_out_247_pad_type_0 = const()[name = tensor("lora_out_247_pad_type_0"), val = tensor("valid")]; + tensor lora_out_247_strides_0 = const()[name = tensor("lora_out_247_strides_0"), val = tensor([1, 1])]; + tensor lora_out_247_pad_0 = const()[name = tensor("lora_out_247_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_247_dilations_0 = const()[name = tensor("lora_out_247_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_247_groups_0 = const()[name = tensor("lora_out_247_groups_0"), val = tensor(1)]; + tensor layers_20_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_20_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(230259968)))]; + tensor lora_out_247_cast_fp16 = conv(dilations = lora_out_247_dilations_0, groups = lora_out_247_groups_0, pad = lora_out_247_pad_0, pad_type = lora_out_247_pad_type_0, strides = lora_out_247_strides_0, weight = layers_20_self_attn_o_proj_loraB_weight_to_fp16, x = input_409_cast_fp16)[name = tensor("lora_out_247_cast_fp16")]; + tensor obj_83_cast_fp16 = add(x = pretrained_out_247_cast_fp16, y = lora_out_247_cast_fp16)[name = tensor("obj_83_cast_fp16")]; + tensor inputs_83_cast_fp16 = add(x = inputs_81_cast_fp16, y = obj_83_cast_fp16)[name = tensor("inputs_83_cast_fp16")]; + tensor out_83_axes_0 = const()[name = tensor("out_83_axes_0"), val = tensor([1])]; + tensor var_4607_to_fp16 = const()[name = tensor("op_4607_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_83_cast_fp16 = layer_norm(axes = out_83_axes_0, epsilon = var_4607_to_fp16, x = inputs_83_cast_fp16)[name = tensor("out_83_cast_fp16")]; + tensor input_411_gamma_0_to_fp16 = const()[name = tensor("input_411_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(230300992)))]; + tensor input_411_beta_0_to_fp16 = const()[name = tensor("input_411_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(230303616)))]; + tensor input_411_epsilon_0_to_fp16 = const()[name = tensor("input_411_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_411_cast_fp16 = batch_norm(beta = input_411_beta_0_to_fp16, epsilon = input_411_epsilon_0_to_fp16, gamma = input_411_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_83_cast_fp16)[name = tensor("input_411_cast_fp16")]; + tensor pretrained_out_249_pad_type_0 = const()[name = tensor("pretrained_out_249_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_249_strides_0 = const()[name = tensor("pretrained_out_249_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_249_pad_0 = const()[name = tensor("pretrained_out_249_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_249_dilations_0 = const()[name = tensor("pretrained_out_249_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_249_groups_0 = const()[name = tensor("pretrained_out_249_groups_0"), val = tensor(1)]; + tensor layers_20_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(230306240))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(233583104))), name = tensor("layers_20_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_20_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_20_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(233583232)))]; + tensor pretrained_out_249_cast_fp16 = conv(bias = layers_20_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_249_dilations_0, groups = pretrained_out_249_groups_0, pad = pretrained_out_249_pad_0, pad_type = pretrained_out_249_pad_type_0, strides = pretrained_out_249_strides_0, weight = layers_20_fc1_pretrained_weight_to_fp16_palettized, x = input_411_cast_fp16)[name = tensor("pretrained_out_249_cast_fp16")]; + tensor input_413_pad_type_0 = const()[name = tensor("input_413_pad_type_0"), val = tensor("valid")]; + tensor input_413_strides_0 = const()[name = tensor("input_413_strides_0"), val = tensor([1, 1])]; + tensor input_413_pad_0 = const()[name = tensor("input_413_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_413_dilations_0 = const()[name = tensor("input_413_dilations_0"), val = tensor([1, 1])]; + tensor input_413_groups_0 = const()[name = tensor("input_413_groups_0"), val = tensor(1)]; + tensor layers_20_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_20_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(233593536)))]; + tensor input_413_cast_fp16 = conv(dilations = input_413_dilations_0, groups = input_413_groups_0, pad = input_413_pad_0, pad_type = input_413_pad_type_0, strides = input_413_strides_0, weight = layers_20_fc1_loraA_weight_to_fp16, x = input_411_cast_fp16)[name = tensor("input_413_cast_fp16")]; + tensor lora_out_249_pad_type_0 = const()[name = tensor("lora_out_249_pad_type_0"), val = tensor("valid")]; + tensor lora_out_249_strides_0 = const()[name = tensor("lora_out_249_strides_0"), val = tensor([1, 1])]; + tensor lora_out_249_pad_0 = const()[name = tensor("lora_out_249_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_249_dilations_0 = const()[name = tensor("lora_out_249_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_249_groups_0 = const()[name = tensor("lora_out_249_groups_0"), val = tensor(1)]; + tensor layers_20_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_20_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(233634560)))]; + tensor lora_out_249_cast_fp16 = conv(dilations = lora_out_249_dilations_0, groups = lora_out_249_groups_0, pad = lora_out_249_pad_0, pad_type = lora_out_249_pad_type_0, strides = lora_out_249_strides_0, weight = layers_20_fc1_loraB_weight_to_fp16, x = input_413_cast_fp16)[name = tensor("lora_out_249_cast_fp16")]; + tensor input_415_cast_fp16 = add(x = pretrained_out_249_cast_fp16, y = lora_out_249_cast_fp16)[name = tensor("input_415_cast_fp16")]; + tensor input_417_mode_0 = const()[name = tensor("input_417_mode_0"), val = tensor("EXACT")]; + tensor input_417_cast_fp16 = gelu(mode = input_417_mode_0, x = input_415_cast_fp16)[name = tensor("input_417_cast_fp16")]; + tensor pretrained_out_251_pad_type_0 = const()[name = tensor("pretrained_out_251_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_251_strides_0 = const()[name = tensor("pretrained_out_251_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_251_pad_0 = const()[name = tensor("pretrained_out_251_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_251_dilations_0 = const()[name = tensor("pretrained_out_251_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_251_groups_0 = const()[name = tensor("pretrained_out_251_groups_0"), val = tensor(1)]; + tensor layers_20_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(233798464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(237075328))), name = tensor("layers_20_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_20_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_20_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(237075456)))]; + tensor pretrained_out_251_cast_fp16 = conv(bias = layers_20_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_251_dilations_0, groups = pretrained_out_251_groups_0, pad = pretrained_out_251_pad_0, pad_type = pretrained_out_251_pad_type_0, strides = pretrained_out_251_strides_0, weight = layers_20_fc2_pretrained_weight_to_fp16_palettized, x = input_417_cast_fp16)[name = tensor("pretrained_out_251_cast_fp16")]; + tensor input_419_pad_type_0 = const()[name = tensor("input_419_pad_type_0"), val = tensor("valid")]; + tensor input_419_strides_0 = const()[name = tensor("input_419_strides_0"), val = tensor([1, 1])]; + tensor input_419_pad_0 = const()[name = tensor("input_419_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_419_dilations_0 = const()[name = tensor("input_419_dilations_0"), val = tensor([1, 1])]; + tensor input_419_groups_0 = const()[name = tensor("input_419_groups_0"), val = tensor(1)]; + tensor layers_20_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_20_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(237078080)))]; + tensor input_419_cast_fp16 = conv(dilations = input_419_dilations_0, groups = input_419_groups_0, pad = input_419_pad_0, pad_type = input_419_pad_type_0, strides = input_419_strides_0, weight = layers_20_fc2_loraA_weight_to_fp16, x = input_417_cast_fp16)[name = tensor("input_419_cast_fp16")]; + tensor lora_out_251_pad_type_0 = const()[name = tensor("lora_out_251_pad_type_0"), val = tensor("valid")]; + tensor lora_out_251_strides_0 = const()[name = tensor("lora_out_251_strides_0"), val = tensor([1, 1])]; + tensor lora_out_251_pad_0 = const()[name = tensor("lora_out_251_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_251_dilations_0 = const()[name = tensor("lora_out_251_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_251_groups_0 = const()[name = tensor("lora_out_251_groups_0"), val = tensor(1)]; + tensor layers_20_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_20_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(237241984)))]; + tensor lora_out_251_cast_fp16 = conv(dilations = lora_out_251_dilations_0, groups = lora_out_251_groups_0, pad = lora_out_251_pad_0, pad_type = lora_out_251_pad_type_0, strides = lora_out_251_strides_0, weight = layers_20_fc2_loraB_weight_to_fp16, x = input_419_cast_fp16)[name = tensor("lora_out_251_cast_fp16")]; + tensor hidden_states_45_cast_fp16 = add(x = pretrained_out_251_cast_fp16, y = lora_out_251_cast_fp16)[name = tensor("hidden_states_45_cast_fp16")]; + tensor inputs_85_cast_fp16 = add(x = inputs_83_cast_fp16, y = hidden_states_45_cast_fp16)[name = tensor("inputs_85_cast_fp16")]; + tensor var_4672 = const()[name = tensor("op_4672"), val = tensor(3)]; + tensor out_85_axes_0 = const()[name = tensor("out_85_axes_0"), val = tensor([1])]; + tensor var_4691_to_fp16 = const()[name = tensor("op_4691_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_85_cast_fp16 = layer_norm(axes = out_85_axes_0, epsilon = var_4691_to_fp16, x = inputs_85_cast_fp16)[name = tensor("out_85_cast_fp16")]; + tensor obj_85_gamma_0_to_fp16 = const()[name = tensor("obj_85_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(237283008)))]; + tensor obj_85_beta_0_to_fp16 = const()[name = tensor("obj_85_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(237285632)))]; + tensor obj_85_epsilon_0_to_fp16 = const()[name = tensor("obj_85_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_85_cast_fp16 = batch_norm(beta = obj_85_beta_0_to_fp16, epsilon = obj_85_epsilon_0_to_fp16, gamma = obj_85_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_85_cast_fp16)[name = tensor("obj_85_cast_fp16")]; + tensor pretrained_out_253_pad_type_0 = const()[name = tensor("pretrained_out_253_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_253_strides_0 = const()[name = tensor("pretrained_out_253_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_253_pad_0 = const()[name = tensor("pretrained_out_253_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_253_dilations_0 = const()[name = tensor("pretrained_out_253_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_253_groups_0 = const()[name = tensor("pretrained_out_253_groups_0"), val = tensor(1)]; + tensor layers_21_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(237288256))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(238107520))), name = tensor("layers_21_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_21_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_21_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(238107648)))]; + tensor pretrained_out_253_cast_fp16 = conv(bias = layers_21_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_253_dilations_0, groups = pretrained_out_253_groups_0, pad = pretrained_out_253_pad_0, pad_type = pretrained_out_253_pad_type_0, strides = pretrained_out_253_strides_0, weight = layers_21_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_85_cast_fp16)[name = tensor("pretrained_out_253_cast_fp16")]; + tensor input_421_pad_type_0 = const()[name = tensor("input_421_pad_type_0"), val = tensor("valid")]; + tensor input_421_strides_0 = const()[name = tensor("input_421_strides_0"), val = tensor([1, 1])]; + tensor input_421_pad_0 = const()[name = tensor("input_421_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_421_dilations_0 = const()[name = tensor("input_421_dilations_0"), val = tensor([1, 1])]; + tensor input_421_groups_0 = const()[name = tensor("input_421_groups_0"), val = tensor(1)]; + tensor layers_21_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_21_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(238110272)))]; + tensor input_421_cast_fp16 = conv(dilations = input_421_dilations_0, groups = input_421_groups_0, pad = input_421_pad_0, pad_type = input_421_pad_type_0, strides = input_421_strides_0, weight = layers_21_self_attn_q_proj_loraA_weight_to_fp16, x = obj_85_cast_fp16)[name = tensor("input_421_cast_fp16")]; + tensor lora_out_253_pad_type_0 = const()[name = tensor("lora_out_253_pad_type_0"), val = tensor("valid")]; + tensor lora_out_253_strides_0 = const()[name = tensor("lora_out_253_strides_0"), val = tensor([1, 1])]; + tensor lora_out_253_pad_0 = const()[name = tensor("lora_out_253_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_253_dilations_0 = const()[name = tensor("lora_out_253_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_253_groups_0 = const()[name = tensor("lora_out_253_groups_0"), val = tensor(1)]; + tensor layers_21_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_21_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(238151296)))]; + tensor lora_out_253_cast_fp16 = conv(dilations = lora_out_253_dilations_0, groups = lora_out_253_groups_0, pad = lora_out_253_pad_0, pad_type = lora_out_253_pad_type_0, strides = lora_out_253_strides_0, weight = layers_21_self_attn_q_proj_loraB_weight_to_fp16, x = input_421_cast_fp16)[name = tensor("lora_out_253_cast_fp16")]; + tensor query_43_cast_fp16 = add(x = pretrained_out_253_cast_fp16, y = lora_out_253_cast_fp16)[name = tensor("query_43_cast_fp16")]; + tensor pretrained_out_255_pad_type_0 = const()[name = tensor("pretrained_out_255_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_255_strides_0 = const()[name = tensor("pretrained_out_255_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_255_pad_0 = const()[name = tensor("pretrained_out_255_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_255_dilations_0 = const()[name = tensor("pretrained_out_255_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_255_groups_0 = const()[name = tensor("pretrained_out_255_groups_0"), val = tensor(1)]; + tensor layers_21_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(238192320))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(239011584))), name = tensor("layers_21_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_255_cast_fp16 = conv(dilations = pretrained_out_255_dilations_0, groups = pretrained_out_255_groups_0, pad = pretrained_out_255_pad_0, pad_type = pretrained_out_255_pad_type_0, strides = pretrained_out_255_strides_0, weight = layers_21_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_85_cast_fp16)[name = tensor("pretrained_out_255_cast_fp16")]; + tensor input_423_pad_type_0 = const()[name = tensor("input_423_pad_type_0"), val = tensor("valid")]; + tensor input_423_strides_0 = const()[name = tensor("input_423_strides_0"), val = tensor([1, 1])]; + tensor input_423_pad_0 = const()[name = tensor("input_423_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_423_dilations_0 = const()[name = tensor("input_423_dilations_0"), val = tensor([1, 1])]; + tensor input_423_groups_0 = const()[name = tensor("input_423_groups_0"), val = tensor(1)]; + tensor layers_21_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_21_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(239011712)))]; + tensor input_423_cast_fp16 = conv(dilations = input_423_dilations_0, groups = input_423_groups_0, pad = input_423_pad_0, pad_type = input_423_pad_type_0, strides = input_423_strides_0, weight = layers_21_self_attn_k_proj_loraA_weight_to_fp16, x = obj_85_cast_fp16)[name = tensor("input_423_cast_fp16")]; + tensor lora_out_255_pad_type_0 = const()[name = tensor("lora_out_255_pad_type_0"), val = tensor("valid")]; + tensor lora_out_255_strides_0 = const()[name = tensor("lora_out_255_strides_0"), val = tensor([1, 1])]; + tensor lora_out_255_pad_0 = const()[name = tensor("lora_out_255_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_255_dilations_0 = const()[name = tensor("lora_out_255_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_255_groups_0 = const()[name = tensor("lora_out_255_groups_0"), val = tensor(1)]; + tensor layers_21_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_21_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(239052736)))]; + tensor lora_out_255_cast_fp16 = conv(dilations = lora_out_255_dilations_0, groups = lora_out_255_groups_0, pad = lora_out_255_pad_0, pad_type = lora_out_255_pad_type_0, strides = lora_out_255_strides_0, weight = layers_21_self_attn_k_proj_loraB_weight_to_fp16, x = input_423_cast_fp16)[name = tensor("lora_out_255_cast_fp16")]; + tensor key_43_cast_fp16 = add(x = pretrained_out_255_cast_fp16, y = lora_out_255_cast_fp16)[name = tensor("key_43_cast_fp16")]; + tensor pretrained_out_257_pad_type_0 = const()[name = tensor("pretrained_out_257_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_257_strides_0 = const()[name = tensor("pretrained_out_257_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_257_pad_0 = const()[name = tensor("pretrained_out_257_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_257_dilations_0 = const()[name = tensor("pretrained_out_257_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_257_groups_0 = const()[name = tensor("pretrained_out_257_groups_0"), val = tensor(1)]; + tensor layers_21_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(239093760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(239913024))), name = tensor("layers_21_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_21_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_21_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(239913152)))]; + tensor pretrained_out_257_cast_fp16 = conv(bias = layers_21_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_257_dilations_0, groups = pretrained_out_257_groups_0, pad = pretrained_out_257_pad_0, pad_type = pretrained_out_257_pad_type_0, strides = pretrained_out_257_strides_0, weight = layers_21_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_85_cast_fp16)[name = tensor("pretrained_out_257_cast_fp16")]; + tensor input_425_pad_type_0 = const()[name = tensor("input_425_pad_type_0"), val = tensor("valid")]; + tensor input_425_strides_0 = const()[name = tensor("input_425_strides_0"), val = tensor([1, 1])]; + tensor input_425_pad_0 = const()[name = tensor("input_425_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_425_dilations_0 = const()[name = tensor("input_425_dilations_0"), val = tensor([1, 1])]; + tensor input_425_groups_0 = const()[name = tensor("input_425_groups_0"), val = tensor(1)]; + tensor layers_21_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_21_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(239915776)))]; + tensor input_425_cast_fp16 = conv(dilations = input_425_dilations_0, groups = input_425_groups_0, pad = input_425_pad_0, pad_type = input_425_pad_type_0, strides = input_425_strides_0, weight = layers_21_self_attn_v_proj_loraA_weight_to_fp16, x = obj_85_cast_fp16)[name = tensor("input_425_cast_fp16")]; + tensor lora_out_257_pad_type_0 = const()[name = tensor("lora_out_257_pad_type_0"), val = tensor("valid")]; + tensor lora_out_257_strides_0 = const()[name = tensor("lora_out_257_strides_0"), val = tensor([1, 1])]; + tensor lora_out_257_pad_0 = const()[name = tensor("lora_out_257_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_257_dilations_0 = const()[name = tensor("lora_out_257_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_257_groups_0 = const()[name = tensor("lora_out_257_groups_0"), val = tensor(1)]; + tensor layers_21_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_21_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(239956800)))]; + tensor lora_out_257_cast_fp16 = conv(dilations = lora_out_257_dilations_0, groups = lora_out_257_groups_0, pad = lora_out_257_pad_0, pad_type = lora_out_257_pad_type_0, strides = lora_out_257_strides_0, weight = layers_21_self_attn_v_proj_loraB_weight_to_fp16, x = input_425_cast_fp16)[name = tensor("lora_out_257_cast_fp16")]; + tensor value_43_cast_fp16 = add(x = pretrained_out_257_cast_fp16, y = lora_out_257_cast_fp16)[name = tensor("value_43_cast_fp16")]; + tensor var_4774 = const()[name = tensor("op_4774"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_43_cast_fp16 = reshape(shape = var_4774, x = query_43_cast_fp16)[name = tensor("mh_q_43_cast_fp16")]; + tensor var_4776_to_fp16 = const()[name = tensor("op_4776_to_fp16"), val = tensor(0x1p-3)]; + tensor var_4777_cast_fp16 = mul(x = mh_q_43_cast_fp16, y = var_4776_to_fp16)[name = tensor("op_4777_cast_fp16")]; + tensor var_4778 = const()[name = tensor("op_4778"), val = tensor([1, 20, 64, -1])]; + tensor var_4779_cast_fp16 = reshape(shape = var_4778, x = key_43_cast_fp16)[name = tensor("op_4779_cast_fp16")]; + tensor mh_w_43_transpose_x_0 = const()[name = tensor("mh_w_43_transpose_x_0"), val = tensor(true)]; + tensor mh_w_43_transpose_y_0 = const()[name = tensor("mh_w_43_transpose_y_0"), val = tensor(false)]; + tensor mh_w_43_cast_fp16 = matmul(transpose_x = mh_w_43_transpose_x_0, transpose_y = mh_w_43_transpose_y_0, x = var_4777_cast_fp16, y = var_4779_cast_fp16)[name = tensor("mh_w_43_cast_fp16")]; + tensor var_4782_cast_fp16 = softmax(axis = var_4672, x = mh_w_43_cast_fp16)[name = tensor("op_4782_cast_fp16")]; + tensor var_4783 = const()[name = tensor("op_4783"), val = tensor([1, 20, 64, -1])]; + tensor var_4784_cast_fp16 = reshape(shape = var_4783, x = value_43_cast_fp16)[name = tensor("op_4784_cast_fp16")]; + tensor attn_43_transpose_x_0 = const()[name = tensor("attn_43_transpose_x_0"), val = tensor(false)]; + tensor attn_43_transpose_y_0 = const()[name = tensor("attn_43_transpose_y_0"), val = tensor(true)]; + tensor attn_43_cast_fp16 = matmul(transpose_x = attn_43_transpose_x_0, transpose_y = attn_43_transpose_y_0, x = var_4784_cast_fp16, y = var_4782_cast_fp16)[name = tensor("attn_43_cast_fp16")]; + tensor var_4787 = const()[name = tensor("op_4787"), val = tensor([1, 1280, 1, -1])]; + tensor input_427_cast_fp16 = reshape(shape = var_4787, x = attn_43_cast_fp16)[name = tensor("input_427_cast_fp16")]; + tensor pretrained_out_259_pad_type_0 = const()[name = tensor("pretrained_out_259_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_259_strides_0 = const()[name = tensor("pretrained_out_259_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_259_pad_0 = const()[name = tensor("pretrained_out_259_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_259_dilations_0 = const()[name = tensor("pretrained_out_259_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_259_groups_0 = const()[name = tensor("pretrained_out_259_groups_0"), val = tensor(1)]; + tensor layers_21_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(239997824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(240817088))), name = tensor("layers_21_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_21_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_21_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(240817216)))]; + tensor pretrained_out_259_cast_fp16 = conv(bias = layers_21_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_259_dilations_0, groups = pretrained_out_259_groups_0, pad = pretrained_out_259_pad_0, pad_type = pretrained_out_259_pad_type_0, strides = pretrained_out_259_strides_0, weight = layers_21_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_427_cast_fp16)[name = tensor("pretrained_out_259_cast_fp16")]; + tensor input_429_pad_type_0 = const()[name = tensor("input_429_pad_type_0"), val = tensor("valid")]; + tensor input_429_strides_0 = const()[name = tensor("input_429_strides_0"), val = tensor([1, 1])]; + tensor input_429_pad_0 = const()[name = tensor("input_429_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_429_dilations_0 = const()[name = tensor("input_429_dilations_0"), val = tensor([1, 1])]; + tensor input_429_groups_0 = const()[name = tensor("input_429_groups_0"), val = tensor(1)]; + tensor layers_21_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_21_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(240819840)))]; + tensor input_429_cast_fp16 = conv(dilations = input_429_dilations_0, groups = input_429_groups_0, pad = input_429_pad_0, pad_type = input_429_pad_type_0, strides = input_429_strides_0, weight = layers_21_self_attn_o_proj_loraA_weight_to_fp16, x = input_427_cast_fp16)[name = tensor("input_429_cast_fp16")]; + tensor lora_out_259_pad_type_0 = const()[name = tensor("lora_out_259_pad_type_0"), val = tensor("valid")]; + tensor lora_out_259_strides_0 = const()[name = tensor("lora_out_259_strides_0"), val = tensor([1, 1])]; + tensor lora_out_259_pad_0 = const()[name = tensor("lora_out_259_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_259_dilations_0 = const()[name = tensor("lora_out_259_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_259_groups_0 = const()[name = tensor("lora_out_259_groups_0"), val = tensor(1)]; + tensor layers_21_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_21_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(240860864)))]; + tensor lora_out_259_cast_fp16 = conv(dilations = lora_out_259_dilations_0, groups = lora_out_259_groups_0, pad = lora_out_259_pad_0, pad_type = lora_out_259_pad_type_0, strides = lora_out_259_strides_0, weight = layers_21_self_attn_o_proj_loraB_weight_to_fp16, x = input_429_cast_fp16)[name = tensor("lora_out_259_cast_fp16")]; + tensor obj_87_cast_fp16 = add(x = pretrained_out_259_cast_fp16, y = lora_out_259_cast_fp16)[name = tensor("obj_87_cast_fp16")]; + tensor inputs_87_cast_fp16 = add(x = inputs_85_cast_fp16, y = obj_87_cast_fp16)[name = tensor("inputs_87_cast_fp16")]; + tensor out_87_axes_0 = const()[name = tensor("out_87_axes_0"), val = tensor([1])]; + tensor var_4821_to_fp16 = const()[name = tensor("op_4821_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_87_cast_fp16 = layer_norm(axes = out_87_axes_0, epsilon = var_4821_to_fp16, x = inputs_87_cast_fp16)[name = tensor("out_87_cast_fp16")]; + tensor input_431_gamma_0_to_fp16 = const()[name = tensor("input_431_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(240901888)))]; + tensor input_431_beta_0_to_fp16 = const()[name = tensor("input_431_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(240904512)))]; + tensor input_431_epsilon_0_to_fp16 = const()[name = tensor("input_431_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_431_cast_fp16 = batch_norm(beta = input_431_beta_0_to_fp16, epsilon = input_431_epsilon_0_to_fp16, gamma = input_431_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_87_cast_fp16)[name = tensor("input_431_cast_fp16")]; + tensor pretrained_out_261_pad_type_0 = const()[name = tensor("pretrained_out_261_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_261_strides_0 = const()[name = tensor("pretrained_out_261_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_261_pad_0 = const()[name = tensor("pretrained_out_261_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_261_dilations_0 = const()[name = tensor("pretrained_out_261_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_261_groups_0 = const()[name = tensor("pretrained_out_261_groups_0"), val = tensor(1)]; + tensor layers_21_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(240907136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(244184000))), name = tensor("layers_21_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_21_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_21_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(244184128)))]; + tensor pretrained_out_261_cast_fp16 = conv(bias = layers_21_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_261_dilations_0, groups = pretrained_out_261_groups_0, pad = pretrained_out_261_pad_0, pad_type = pretrained_out_261_pad_type_0, strides = pretrained_out_261_strides_0, weight = layers_21_fc1_pretrained_weight_to_fp16_palettized, x = input_431_cast_fp16)[name = tensor("pretrained_out_261_cast_fp16")]; + tensor input_433_pad_type_0 = const()[name = tensor("input_433_pad_type_0"), val = tensor("valid")]; + tensor input_433_strides_0 = const()[name = tensor("input_433_strides_0"), val = tensor([1, 1])]; + tensor input_433_pad_0 = const()[name = tensor("input_433_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_433_dilations_0 = const()[name = tensor("input_433_dilations_0"), val = tensor([1, 1])]; + tensor input_433_groups_0 = const()[name = tensor("input_433_groups_0"), val = tensor(1)]; + tensor layers_21_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_21_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(244194432)))]; + tensor input_433_cast_fp16 = conv(dilations = input_433_dilations_0, groups = input_433_groups_0, pad = input_433_pad_0, pad_type = input_433_pad_type_0, strides = input_433_strides_0, weight = layers_21_fc1_loraA_weight_to_fp16, x = input_431_cast_fp16)[name = tensor("input_433_cast_fp16")]; + tensor lora_out_261_pad_type_0 = const()[name = tensor("lora_out_261_pad_type_0"), val = tensor("valid")]; + tensor lora_out_261_strides_0 = const()[name = tensor("lora_out_261_strides_0"), val = tensor([1, 1])]; + tensor lora_out_261_pad_0 = const()[name = tensor("lora_out_261_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_261_dilations_0 = const()[name = tensor("lora_out_261_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_261_groups_0 = const()[name = tensor("lora_out_261_groups_0"), val = tensor(1)]; + tensor layers_21_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_21_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(244235456)))]; + tensor lora_out_261_cast_fp16 = conv(dilations = lora_out_261_dilations_0, groups = lora_out_261_groups_0, pad = lora_out_261_pad_0, pad_type = lora_out_261_pad_type_0, strides = lora_out_261_strides_0, weight = layers_21_fc1_loraB_weight_to_fp16, x = input_433_cast_fp16)[name = tensor("lora_out_261_cast_fp16")]; + tensor input_435_cast_fp16 = add(x = pretrained_out_261_cast_fp16, y = lora_out_261_cast_fp16)[name = tensor("input_435_cast_fp16")]; + tensor input_437_mode_0 = const()[name = tensor("input_437_mode_0"), val = tensor("EXACT")]; + tensor input_437_cast_fp16 = gelu(mode = input_437_mode_0, x = input_435_cast_fp16)[name = tensor("input_437_cast_fp16")]; + tensor pretrained_out_263_pad_type_0 = const()[name = tensor("pretrained_out_263_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_263_strides_0 = const()[name = tensor("pretrained_out_263_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_263_pad_0 = const()[name = tensor("pretrained_out_263_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_263_dilations_0 = const()[name = tensor("pretrained_out_263_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_263_groups_0 = const()[name = tensor("pretrained_out_263_groups_0"), val = tensor(1)]; + tensor layers_21_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(244399360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(247676224))), name = tensor("layers_21_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_21_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_21_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(247676352)))]; + tensor pretrained_out_263_cast_fp16 = conv(bias = layers_21_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_263_dilations_0, groups = pretrained_out_263_groups_0, pad = pretrained_out_263_pad_0, pad_type = pretrained_out_263_pad_type_0, strides = pretrained_out_263_strides_0, weight = layers_21_fc2_pretrained_weight_to_fp16_palettized, x = input_437_cast_fp16)[name = tensor("pretrained_out_263_cast_fp16")]; + tensor input_439_pad_type_0 = const()[name = tensor("input_439_pad_type_0"), val = tensor("valid")]; + tensor input_439_strides_0 = const()[name = tensor("input_439_strides_0"), val = tensor([1, 1])]; + tensor input_439_pad_0 = const()[name = tensor("input_439_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_439_dilations_0 = const()[name = tensor("input_439_dilations_0"), val = tensor([1, 1])]; + tensor input_439_groups_0 = const()[name = tensor("input_439_groups_0"), val = tensor(1)]; + tensor layers_21_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_21_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(247678976)))]; + tensor input_439_cast_fp16 = conv(dilations = input_439_dilations_0, groups = input_439_groups_0, pad = input_439_pad_0, pad_type = input_439_pad_type_0, strides = input_439_strides_0, weight = layers_21_fc2_loraA_weight_to_fp16, x = input_437_cast_fp16)[name = tensor("input_439_cast_fp16")]; + tensor lora_out_263_pad_type_0 = const()[name = tensor("lora_out_263_pad_type_0"), val = tensor("valid")]; + tensor lora_out_263_strides_0 = const()[name = tensor("lora_out_263_strides_0"), val = tensor([1, 1])]; + tensor lora_out_263_pad_0 = const()[name = tensor("lora_out_263_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_263_dilations_0 = const()[name = tensor("lora_out_263_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_263_groups_0 = const()[name = tensor("lora_out_263_groups_0"), val = tensor(1)]; + tensor layers_21_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_21_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(247842880)))]; + tensor lora_out_263_cast_fp16 = conv(dilations = lora_out_263_dilations_0, groups = lora_out_263_groups_0, pad = lora_out_263_pad_0, pad_type = lora_out_263_pad_type_0, strides = lora_out_263_strides_0, weight = layers_21_fc2_loraB_weight_to_fp16, x = input_439_cast_fp16)[name = tensor("lora_out_263_cast_fp16")]; + tensor hidden_states_47_cast_fp16 = add(x = pretrained_out_263_cast_fp16, y = lora_out_263_cast_fp16)[name = tensor("hidden_states_47_cast_fp16")]; + tensor inputs_89_cast_fp16 = add(x = inputs_87_cast_fp16, y = hidden_states_47_cast_fp16)[name = tensor("inputs_89_cast_fp16")]; + tensor var_4886 = const()[name = tensor("op_4886"), val = tensor(3)]; + tensor out_89_axes_0 = const()[name = tensor("out_89_axes_0"), val = tensor([1])]; + tensor var_4905_to_fp16 = const()[name = tensor("op_4905_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_89_cast_fp16 = layer_norm(axes = out_89_axes_0, epsilon = var_4905_to_fp16, x = inputs_89_cast_fp16)[name = tensor("out_89_cast_fp16")]; + tensor obj_89_gamma_0_to_fp16 = const()[name = tensor("obj_89_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(247883904)))]; + tensor obj_89_beta_0_to_fp16 = const()[name = tensor("obj_89_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(247886528)))]; + tensor obj_89_epsilon_0_to_fp16 = const()[name = tensor("obj_89_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_89_cast_fp16 = batch_norm(beta = obj_89_beta_0_to_fp16, epsilon = obj_89_epsilon_0_to_fp16, gamma = obj_89_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_89_cast_fp16)[name = tensor("obj_89_cast_fp16")]; + tensor pretrained_out_265_pad_type_0 = const()[name = tensor("pretrained_out_265_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_265_strides_0 = const()[name = tensor("pretrained_out_265_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_265_pad_0 = const()[name = tensor("pretrained_out_265_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_265_dilations_0 = const()[name = tensor("pretrained_out_265_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_265_groups_0 = const()[name = tensor("pretrained_out_265_groups_0"), val = tensor(1)]; + tensor layers_22_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(247889152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(248708416))), name = tensor("layers_22_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_22_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_22_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(248708544)))]; + tensor pretrained_out_265_cast_fp16 = conv(bias = layers_22_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_265_dilations_0, groups = pretrained_out_265_groups_0, pad = pretrained_out_265_pad_0, pad_type = pretrained_out_265_pad_type_0, strides = pretrained_out_265_strides_0, weight = layers_22_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_89_cast_fp16)[name = tensor("pretrained_out_265_cast_fp16")]; + tensor input_441_pad_type_0 = const()[name = tensor("input_441_pad_type_0"), val = tensor("valid")]; + tensor input_441_strides_0 = const()[name = tensor("input_441_strides_0"), val = tensor([1, 1])]; + tensor input_441_pad_0 = const()[name = tensor("input_441_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_441_dilations_0 = const()[name = tensor("input_441_dilations_0"), val = tensor([1, 1])]; + tensor input_441_groups_0 = const()[name = tensor("input_441_groups_0"), val = tensor(1)]; + tensor layers_22_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_22_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(248711168)))]; + tensor input_441_cast_fp16 = conv(dilations = input_441_dilations_0, groups = input_441_groups_0, pad = input_441_pad_0, pad_type = input_441_pad_type_0, strides = input_441_strides_0, weight = layers_22_self_attn_q_proj_loraA_weight_to_fp16, x = obj_89_cast_fp16)[name = tensor("input_441_cast_fp16")]; + tensor lora_out_265_pad_type_0 = const()[name = tensor("lora_out_265_pad_type_0"), val = tensor("valid")]; + tensor lora_out_265_strides_0 = const()[name = tensor("lora_out_265_strides_0"), val = tensor([1, 1])]; + tensor lora_out_265_pad_0 = const()[name = tensor("lora_out_265_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_265_dilations_0 = const()[name = tensor("lora_out_265_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_265_groups_0 = const()[name = tensor("lora_out_265_groups_0"), val = tensor(1)]; + tensor layers_22_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_22_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(248752192)))]; + tensor lora_out_265_cast_fp16 = conv(dilations = lora_out_265_dilations_0, groups = lora_out_265_groups_0, pad = lora_out_265_pad_0, pad_type = lora_out_265_pad_type_0, strides = lora_out_265_strides_0, weight = layers_22_self_attn_q_proj_loraB_weight_to_fp16, x = input_441_cast_fp16)[name = tensor("lora_out_265_cast_fp16")]; + tensor query_45_cast_fp16 = add(x = pretrained_out_265_cast_fp16, y = lora_out_265_cast_fp16)[name = tensor("query_45_cast_fp16")]; + tensor pretrained_out_267_pad_type_0 = const()[name = tensor("pretrained_out_267_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_267_strides_0 = const()[name = tensor("pretrained_out_267_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_267_pad_0 = const()[name = tensor("pretrained_out_267_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_267_dilations_0 = const()[name = tensor("pretrained_out_267_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_267_groups_0 = const()[name = tensor("pretrained_out_267_groups_0"), val = tensor(1)]; + tensor layers_22_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(248793216))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(249612480))), name = tensor("layers_22_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_267_cast_fp16 = conv(dilations = pretrained_out_267_dilations_0, groups = pretrained_out_267_groups_0, pad = pretrained_out_267_pad_0, pad_type = pretrained_out_267_pad_type_0, strides = pretrained_out_267_strides_0, weight = layers_22_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_89_cast_fp16)[name = tensor("pretrained_out_267_cast_fp16")]; + tensor input_443_pad_type_0 = const()[name = tensor("input_443_pad_type_0"), val = tensor("valid")]; + tensor input_443_strides_0 = const()[name = tensor("input_443_strides_0"), val = tensor([1, 1])]; + tensor input_443_pad_0 = const()[name = tensor("input_443_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_443_dilations_0 = const()[name = tensor("input_443_dilations_0"), val = tensor([1, 1])]; + tensor input_443_groups_0 = const()[name = tensor("input_443_groups_0"), val = tensor(1)]; + tensor layers_22_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_22_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(249612608)))]; + tensor input_443_cast_fp16 = conv(dilations = input_443_dilations_0, groups = input_443_groups_0, pad = input_443_pad_0, pad_type = input_443_pad_type_0, strides = input_443_strides_0, weight = layers_22_self_attn_k_proj_loraA_weight_to_fp16, x = obj_89_cast_fp16)[name = tensor("input_443_cast_fp16")]; + tensor lora_out_267_pad_type_0 = const()[name = tensor("lora_out_267_pad_type_0"), val = tensor("valid")]; + tensor lora_out_267_strides_0 = const()[name = tensor("lora_out_267_strides_0"), val = tensor([1, 1])]; + tensor lora_out_267_pad_0 = const()[name = tensor("lora_out_267_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_267_dilations_0 = const()[name = tensor("lora_out_267_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_267_groups_0 = const()[name = tensor("lora_out_267_groups_0"), val = tensor(1)]; + tensor layers_22_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_22_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(249653632)))]; + tensor lora_out_267_cast_fp16 = conv(dilations = lora_out_267_dilations_0, groups = lora_out_267_groups_0, pad = lora_out_267_pad_0, pad_type = lora_out_267_pad_type_0, strides = lora_out_267_strides_0, weight = layers_22_self_attn_k_proj_loraB_weight_to_fp16, x = input_443_cast_fp16)[name = tensor("lora_out_267_cast_fp16")]; + tensor key_45_cast_fp16 = add(x = pretrained_out_267_cast_fp16, y = lora_out_267_cast_fp16)[name = tensor("key_45_cast_fp16")]; + tensor pretrained_out_269_pad_type_0 = const()[name = tensor("pretrained_out_269_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_269_strides_0 = const()[name = tensor("pretrained_out_269_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_269_pad_0 = const()[name = tensor("pretrained_out_269_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_269_dilations_0 = const()[name = tensor("pretrained_out_269_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_269_groups_0 = const()[name = tensor("pretrained_out_269_groups_0"), val = tensor(1)]; + tensor layers_22_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(249694656))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(250513920))), name = tensor("layers_22_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_22_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_22_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(250514048)))]; + tensor pretrained_out_269_cast_fp16 = conv(bias = layers_22_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_269_dilations_0, groups = pretrained_out_269_groups_0, pad = pretrained_out_269_pad_0, pad_type = pretrained_out_269_pad_type_0, strides = pretrained_out_269_strides_0, weight = layers_22_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_89_cast_fp16)[name = tensor("pretrained_out_269_cast_fp16")]; + tensor input_445_pad_type_0 = const()[name = tensor("input_445_pad_type_0"), val = tensor("valid")]; + tensor input_445_strides_0 = const()[name = tensor("input_445_strides_0"), val = tensor([1, 1])]; + tensor input_445_pad_0 = const()[name = tensor("input_445_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_445_dilations_0 = const()[name = tensor("input_445_dilations_0"), val = tensor([1, 1])]; + tensor input_445_groups_0 = const()[name = tensor("input_445_groups_0"), val = tensor(1)]; + tensor layers_22_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_22_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(250516672)))]; + tensor input_445_cast_fp16 = conv(dilations = input_445_dilations_0, groups = input_445_groups_0, pad = input_445_pad_0, pad_type = input_445_pad_type_0, strides = input_445_strides_0, weight = layers_22_self_attn_v_proj_loraA_weight_to_fp16, x = obj_89_cast_fp16)[name = tensor("input_445_cast_fp16")]; + tensor lora_out_269_pad_type_0 = const()[name = tensor("lora_out_269_pad_type_0"), val = tensor("valid")]; + tensor lora_out_269_strides_0 = const()[name = tensor("lora_out_269_strides_0"), val = tensor([1, 1])]; + tensor lora_out_269_pad_0 = const()[name = tensor("lora_out_269_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_269_dilations_0 = const()[name = tensor("lora_out_269_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_269_groups_0 = const()[name = tensor("lora_out_269_groups_0"), val = tensor(1)]; + tensor layers_22_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_22_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(250557696)))]; + tensor lora_out_269_cast_fp16 = conv(dilations = lora_out_269_dilations_0, groups = lora_out_269_groups_0, pad = lora_out_269_pad_0, pad_type = lora_out_269_pad_type_0, strides = lora_out_269_strides_0, weight = layers_22_self_attn_v_proj_loraB_weight_to_fp16, x = input_445_cast_fp16)[name = tensor("lora_out_269_cast_fp16")]; + tensor value_45_cast_fp16 = add(x = pretrained_out_269_cast_fp16, y = lora_out_269_cast_fp16)[name = tensor("value_45_cast_fp16")]; + tensor var_4988 = const()[name = tensor("op_4988"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_45_cast_fp16 = reshape(shape = var_4988, x = query_45_cast_fp16)[name = tensor("mh_q_45_cast_fp16")]; + tensor var_4990_to_fp16 = const()[name = tensor("op_4990_to_fp16"), val = tensor(0x1p-3)]; + tensor var_4991_cast_fp16 = mul(x = mh_q_45_cast_fp16, y = var_4990_to_fp16)[name = tensor("op_4991_cast_fp16")]; + tensor var_4992 = const()[name = tensor("op_4992"), val = tensor([1, 20, 64, -1])]; + tensor var_4993_cast_fp16 = reshape(shape = var_4992, x = key_45_cast_fp16)[name = tensor("op_4993_cast_fp16")]; + tensor mh_w_45_transpose_x_0 = const()[name = tensor("mh_w_45_transpose_x_0"), val = tensor(true)]; + tensor mh_w_45_transpose_y_0 = const()[name = tensor("mh_w_45_transpose_y_0"), val = tensor(false)]; + tensor mh_w_45_cast_fp16 = matmul(transpose_x = mh_w_45_transpose_x_0, transpose_y = mh_w_45_transpose_y_0, x = var_4991_cast_fp16, y = var_4993_cast_fp16)[name = tensor("mh_w_45_cast_fp16")]; + tensor var_4996_cast_fp16 = softmax(axis = var_4886, x = mh_w_45_cast_fp16)[name = tensor("op_4996_cast_fp16")]; + tensor var_4997 = const()[name = tensor("op_4997"), val = tensor([1, 20, 64, -1])]; + tensor var_4998_cast_fp16 = reshape(shape = var_4997, x = value_45_cast_fp16)[name = tensor("op_4998_cast_fp16")]; + tensor attn_45_transpose_x_0 = const()[name = tensor("attn_45_transpose_x_0"), val = tensor(false)]; + tensor attn_45_transpose_y_0 = const()[name = tensor("attn_45_transpose_y_0"), val = tensor(true)]; + tensor attn_45_cast_fp16 = matmul(transpose_x = attn_45_transpose_x_0, transpose_y = attn_45_transpose_y_0, x = var_4998_cast_fp16, y = var_4996_cast_fp16)[name = tensor("attn_45_cast_fp16")]; + tensor var_5001 = const()[name = tensor("op_5001"), val = tensor([1, 1280, 1, -1])]; + tensor input_447_cast_fp16 = reshape(shape = var_5001, x = attn_45_cast_fp16)[name = tensor("input_447_cast_fp16")]; + tensor pretrained_out_271_pad_type_0 = const()[name = tensor("pretrained_out_271_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_271_strides_0 = const()[name = tensor("pretrained_out_271_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_271_pad_0 = const()[name = tensor("pretrained_out_271_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_271_dilations_0 = const()[name = tensor("pretrained_out_271_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_271_groups_0 = const()[name = tensor("pretrained_out_271_groups_0"), val = tensor(1)]; + tensor layers_22_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(250598720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251417984))), name = tensor("layers_22_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_22_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_22_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251418112)))]; + tensor pretrained_out_271_cast_fp16 = conv(bias = layers_22_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_271_dilations_0, groups = pretrained_out_271_groups_0, pad = pretrained_out_271_pad_0, pad_type = pretrained_out_271_pad_type_0, strides = pretrained_out_271_strides_0, weight = layers_22_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_447_cast_fp16)[name = tensor("pretrained_out_271_cast_fp16")]; + tensor input_449_pad_type_0 = const()[name = tensor("input_449_pad_type_0"), val = tensor("valid")]; + tensor input_449_strides_0 = const()[name = tensor("input_449_strides_0"), val = tensor([1, 1])]; + tensor input_449_pad_0 = const()[name = tensor("input_449_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_449_dilations_0 = const()[name = tensor("input_449_dilations_0"), val = tensor([1, 1])]; + tensor input_449_groups_0 = const()[name = tensor("input_449_groups_0"), val = tensor(1)]; + tensor layers_22_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_22_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251420736)))]; + tensor input_449_cast_fp16 = conv(dilations = input_449_dilations_0, groups = input_449_groups_0, pad = input_449_pad_0, pad_type = input_449_pad_type_0, strides = input_449_strides_0, weight = layers_22_self_attn_o_proj_loraA_weight_to_fp16, x = input_447_cast_fp16)[name = tensor("input_449_cast_fp16")]; + tensor lora_out_271_pad_type_0 = const()[name = tensor("lora_out_271_pad_type_0"), val = tensor("valid")]; + tensor lora_out_271_strides_0 = const()[name = tensor("lora_out_271_strides_0"), val = tensor([1, 1])]; + tensor lora_out_271_pad_0 = const()[name = tensor("lora_out_271_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_271_dilations_0 = const()[name = tensor("lora_out_271_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_271_groups_0 = const()[name = tensor("lora_out_271_groups_0"), val = tensor(1)]; + tensor layers_22_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_22_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251461760)))]; + tensor lora_out_271_cast_fp16 = conv(dilations = lora_out_271_dilations_0, groups = lora_out_271_groups_0, pad = lora_out_271_pad_0, pad_type = lora_out_271_pad_type_0, strides = lora_out_271_strides_0, weight = layers_22_self_attn_o_proj_loraB_weight_to_fp16, x = input_449_cast_fp16)[name = tensor("lora_out_271_cast_fp16")]; + tensor obj_91_cast_fp16 = add(x = pretrained_out_271_cast_fp16, y = lora_out_271_cast_fp16)[name = tensor("obj_91_cast_fp16")]; + tensor inputs_91_cast_fp16 = add(x = inputs_89_cast_fp16, y = obj_91_cast_fp16)[name = tensor("inputs_91_cast_fp16")]; + tensor out_91_axes_0 = const()[name = tensor("out_91_axes_0"), val = tensor([1])]; + tensor var_5035_to_fp16 = const()[name = tensor("op_5035_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_91_cast_fp16 = layer_norm(axes = out_91_axes_0, epsilon = var_5035_to_fp16, x = inputs_91_cast_fp16)[name = tensor("out_91_cast_fp16")]; + tensor input_451_gamma_0_to_fp16 = const()[name = tensor("input_451_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251502784)))]; + tensor input_451_beta_0_to_fp16 = const()[name = tensor("input_451_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251505408)))]; + tensor input_451_epsilon_0_to_fp16 = const()[name = tensor("input_451_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_451_cast_fp16 = batch_norm(beta = input_451_beta_0_to_fp16, epsilon = input_451_epsilon_0_to_fp16, gamma = input_451_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_91_cast_fp16)[name = tensor("input_451_cast_fp16")]; + tensor pretrained_out_273_pad_type_0 = const()[name = tensor("pretrained_out_273_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_273_strides_0 = const()[name = tensor("pretrained_out_273_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_273_pad_0 = const()[name = tensor("pretrained_out_273_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_273_dilations_0 = const()[name = tensor("pretrained_out_273_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_273_groups_0 = const()[name = tensor("pretrained_out_273_groups_0"), val = tensor(1)]; + tensor layers_22_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251508032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(254784896))), name = tensor("layers_22_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_22_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_22_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(254785024)))]; + tensor pretrained_out_273_cast_fp16 = conv(bias = layers_22_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_273_dilations_0, groups = pretrained_out_273_groups_0, pad = pretrained_out_273_pad_0, pad_type = pretrained_out_273_pad_type_0, strides = pretrained_out_273_strides_0, weight = layers_22_fc1_pretrained_weight_to_fp16_palettized, x = input_451_cast_fp16)[name = tensor("pretrained_out_273_cast_fp16")]; + tensor input_453_pad_type_0 = const()[name = tensor("input_453_pad_type_0"), val = tensor("valid")]; + tensor input_453_strides_0 = const()[name = tensor("input_453_strides_0"), val = tensor([1, 1])]; + tensor input_453_pad_0 = const()[name = tensor("input_453_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_453_dilations_0 = const()[name = tensor("input_453_dilations_0"), val = tensor([1, 1])]; + tensor input_453_groups_0 = const()[name = tensor("input_453_groups_0"), val = tensor(1)]; + tensor layers_22_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_22_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(254795328)))]; + tensor input_453_cast_fp16 = conv(dilations = input_453_dilations_0, groups = input_453_groups_0, pad = input_453_pad_0, pad_type = input_453_pad_type_0, strides = input_453_strides_0, weight = layers_22_fc1_loraA_weight_to_fp16, x = input_451_cast_fp16)[name = tensor("input_453_cast_fp16")]; + tensor lora_out_273_pad_type_0 = const()[name = tensor("lora_out_273_pad_type_0"), val = tensor("valid")]; + tensor lora_out_273_strides_0 = const()[name = tensor("lora_out_273_strides_0"), val = tensor([1, 1])]; + tensor lora_out_273_pad_0 = const()[name = tensor("lora_out_273_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_273_dilations_0 = const()[name = tensor("lora_out_273_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_273_groups_0 = const()[name = tensor("lora_out_273_groups_0"), val = tensor(1)]; + tensor layers_22_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_22_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(254836352)))]; + tensor lora_out_273_cast_fp16 = conv(dilations = lora_out_273_dilations_0, groups = lora_out_273_groups_0, pad = lora_out_273_pad_0, pad_type = lora_out_273_pad_type_0, strides = lora_out_273_strides_0, weight = layers_22_fc1_loraB_weight_to_fp16, x = input_453_cast_fp16)[name = tensor("lora_out_273_cast_fp16")]; + tensor input_455_cast_fp16 = add(x = pretrained_out_273_cast_fp16, y = lora_out_273_cast_fp16)[name = tensor("input_455_cast_fp16")]; + tensor input_457_mode_0 = const()[name = tensor("input_457_mode_0"), val = tensor("EXACT")]; + tensor input_457_cast_fp16 = gelu(mode = input_457_mode_0, x = input_455_cast_fp16)[name = tensor("input_457_cast_fp16")]; + tensor pretrained_out_275_pad_type_0 = const()[name = tensor("pretrained_out_275_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_275_strides_0 = const()[name = tensor("pretrained_out_275_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_275_pad_0 = const()[name = tensor("pretrained_out_275_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_275_dilations_0 = const()[name = tensor("pretrained_out_275_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_275_groups_0 = const()[name = tensor("pretrained_out_275_groups_0"), val = tensor(1)]; + tensor layers_22_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(255000256))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(258277120))), name = tensor("layers_22_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_22_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_22_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(258277248)))]; + tensor pretrained_out_275_cast_fp16 = conv(bias = layers_22_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_275_dilations_0, groups = pretrained_out_275_groups_0, pad = pretrained_out_275_pad_0, pad_type = pretrained_out_275_pad_type_0, strides = pretrained_out_275_strides_0, weight = layers_22_fc2_pretrained_weight_to_fp16_palettized, x = input_457_cast_fp16)[name = tensor("pretrained_out_275_cast_fp16")]; + tensor input_459_pad_type_0 = const()[name = tensor("input_459_pad_type_0"), val = tensor("valid")]; + tensor input_459_strides_0 = const()[name = tensor("input_459_strides_0"), val = tensor([1, 1])]; + tensor input_459_pad_0 = const()[name = tensor("input_459_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_459_dilations_0 = const()[name = tensor("input_459_dilations_0"), val = tensor([1, 1])]; + tensor input_459_groups_0 = const()[name = tensor("input_459_groups_0"), val = tensor(1)]; + tensor layers_22_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_22_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(258279872)))]; + tensor input_459_cast_fp16 = conv(dilations = input_459_dilations_0, groups = input_459_groups_0, pad = input_459_pad_0, pad_type = input_459_pad_type_0, strides = input_459_strides_0, weight = layers_22_fc2_loraA_weight_to_fp16, x = input_457_cast_fp16)[name = tensor("input_459_cast_fp16")]; + tensor lora_out_275_pad_type_0 = const()[name = tensor("lora_out_275_pad_type_0"), val = tensor("valid")]; + tensor lora_out_275_strides_0 = const()[name = tensor("lora_out_275_strides_0"), val = tensor([1, 1])]; + tensor lora_out_275_pad_0 = const()[name = tensor("lora_out_275_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_275_dilations_0 = const()[name = tensor("lora_out_275_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_275_groups_0 = const()[name = tensor("lora_out_275_groups_0"), val = tensor(1)]; + tensor layers_22_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_22_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(258443776)))]; + tensor lora_out_275_cast_fp16 = conv(dilations = lora_out_275_dilations_0, groups = lora_out_275_groups_0, pad = lora_out_275_pad_0, pad_type = lora_out_275_pad_type_0, strides = lora_out_275_strides_0, weight = layers_22_fc2_loraB_weight_to_fp16, x = input_459_cast_fp16)[name = tensor("lora_out_275_cast_fp16")]; + tensor hidden_states_49_cast_fp16 = add(x = pretrained_out_275_cast_fp16, y = lora_out_275_cast_fp16)[name = tensor("hidden_states_49_cast_fp16")]; + tensor inputs_93_cast_fp16 = add(x = inputs_91_cast_fp16, y = hidden_states_49_cast_fp16)[name = tensor("inputs_93_cast_fp16")]; + tensor var_5100 = const()[name = tensor("op_5100"), val = tensor(3)]; + tensor out_93_axes_0 = const()[name = tensor("out_93_axes_0"), val = tensor([1])]; + tensor var_5119_to_fp16 = const()[name = tensor("op_5119_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_93_cast_fp16 = layer_norm(axes = out_93_axes_0, epsilon = var_5119_to_fp16, x = inputs_93_cast_fp16)[name = tensor("out_93_cast_fp16")]; + tensor obj_93_gamma_0_to_fp16 = const()[name = tensor("obj_93_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(258484800)))]; + tensor obj_93_beta_0_to_fp16 = const()[name = tensor("obj_93_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(258487424)))]; + tensor obj_93_epsilon_0_to_fp16 = const()[name = tensor("obj_93_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_93_cast_fp16 = batch_norm(beta = obj_93_beta_0_to_fp16, epsilon = obj_93_epsilon_0_to_fp16, gamma = obj_93_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_93_cast_fp16)[name = tensor("obj_93_cast_fp16")]; + tensor pretrained_out_277_pad_type_0 = const()[name = tensor("pretrained_out_277_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_277_strides_0 = const()[name = tensor("pretrained_out_277_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_277_pad_0 = const()[name = tensor("pretrained_out_277_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_277_dilations_0 = const()[name = tensor("pretrained_out_277_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_277_groups_0 = const()[name = tensor("pretrained_out_277_groups_0"), val = tensor(1)]; + tensor layers_23_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(258490048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(259309312))), name = tensor("layers_23_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_23_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_23_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(259309440)))]; + tensor pretrained_out_277_cast_fp16 = conv(bias = layers_23_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_277_dilations_0, groups = pretrained_out_277_groups_0, pad = pretrained_out_277_pad_0, pad_type = pretrained_out_277_pad_type_0, strides = pretrained_out_277_strides_0, weight = layers_23_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_93_cast_fp16)[name = tensor("pretrained_out_277_cast_fp16")]; + tensor input_461_pad_type_0 = const()[name = tensor("input_461_pad_type_0"), val = tensor("valid")]; + tensor input_461_strides_0 = const()[name = tensor("input_461_strides_0"), val = tensor([1, 1])]; + tensor input_461_pad_0 = const()[name = tensor("input_461_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_461_dilations_0 = const()[name = tensor("input_461_dilations_0"), val = tensor([1, 1])]; + tensor input_461_groups_0 = const()[name = tensor("input_461_groups_0"), val = tensor(1)]; + tensor layers_23_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_23_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(259312064)))]; + tensor input_461_cast_fp16 = conv(dilations = input_461_dilations_0, groups = input_461_groups_0, pad = input_461_pad_0, pad_type = input_461_pad_type_0, strides = input_461_strides_0, weight = layers_23_self_attn_q_proj_loraA_weight_to_fp16, x = obj_93_cast_fp16)[name = tensor("input_461_cast_fp16")]; + tensor lora_out_277_pad_type_0 = const()[name = tensor("lora_out_277_pad_type_0"), val = tensor("valid")]; + tensor lora_out_277_strides_0 = const()[name = tensor("lora_out_277_strides_0"), val = tensor([1, 1])]; + tensor lora_out_277_pad_0 = const()[name = tensor("lora_out_277_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_277_dilations_0 = const()[name = tensor("lora_out_277_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_277_groups_0 = const()[name = tensor("lora_out_277_groups_0"), val = tensor(1)]; + tensor layers_23_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_23_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(259353088)))]; + tensor lora_out_277_cast_fp16 = conv(dilations = lora_out_277_dilations_0, groups = lora_out_277_groups_0, pad = lora_out_277_pad_0, pad_type = lora_out_277_pad_type_0, strides = lora_out_277_strides_0, weight = layers_23_self_attn_q_proj_loraB_weight_to_fp16, x = input_461_cast_fp16)[name = tensor("lora_out_277_cast_fp16")]; + tensor query_47_cast_fp16 = add(x = pretrained_out_277_cast_fp16, y = lora_out_277_cast_fp16)[name = tensor("query_47_cast_fp16")]; + tensor pretrained_out_279_pad_type_0 = const()[name = tensor("pretrained_out_279_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_279_strides_0 = const()[name = tensor("pretrained_out_279_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_279_pad_0 = const()[name = tensor("pretrained_out_279_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_279_dilations_0 = const()[name = tensor("pretrained_out_279_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_279_groups_0 = const()[name = tensor("pretrained_out_279_groups_0"), val = tensor(1)]; + tensor layers_23_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(259394112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(260213376))), name = tensor("layers_23_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_279_cast_fp16 = conv(dilations = pretrained_out_279_dilations_0, groups = pretrained_out_279_groups_0, pad = pretrained_out_279_pad_0, pad_type = pretrained_out_279_pad_type_0, strides = pretrained_out_279_strides_0, weight = layers_23_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_93_cast_fp16)[name = tensor("pretrained_out_279_cast_fp16")]; + tensor input_463_pad_type_0 = const()[name = tensor("input_463_pad_type_0"), val = tensor("valid")]; + tensor input_463_strides_0 = const()[name = tensor("input_463_strides_0"), val = tensor([1, 1])]; + tensor input_463_pad_0 = const()[name = tensor("input_463_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_463_dilations_0 = const()[name = tensor("input_463_dilations_0"), val = tensor([1, 1])]; + tensor input_463_groups_0 = const()[name = tensor("input_463_groups_0"), val = tensor(1)]; + tensor layers_23_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_23_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(260213504)))]; + tensor input_463_cast_fp16 = conv(dilations = input_463_dilations_0, groups = input_463_groups_0, pad = input_463_pad_0, pad_type = input_463_pad_type_0, strides = input_463_strides_0, weight = layers_23_self_attn_k_proj_loraA_weight_to_fp16, x = obj_93_cast_fp16)[name = tensor("input_463_cast_fp16")]; + tensor lora_out_279_pad_type_0 = const()[name = tensor("lora_out_279_pad_type_0"), val = tensor("valid")]; + tensor lora_out_279_strides_0 = const()[name = tensor("lora_out_279_strides_0"), val = tensor([1, 1])]; + tensor lora_out_279_pad_0 = const()[name = tensor("lora_out_279_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_279_dilations_0 = const()[name = tensor("lora_out_279_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_279_groups_0 = const()[name = tensor("lora_out_279_groups_0"), val = tensor(1)]; + tensor layers_23_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_23_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(260254528)))]; + tensor lora_out_279_cast_fp16 = conv(dilations = lora_out_279_dilations_0, groups = lora_out_279_groups_0, pad = lora_out_279_pad_0, pad_type = lora_out_279_pad_type_0, strides = lora_out_279_strides_0, weight = layers_23_self_attn_k_proj_loraB_weight_to_fp16, x = input_463_cast_fp16)[name = tensor("lora_out_279_cast_fp16")]; + tensor key_47_cast_fp16 = add(x = pretrained_out_279_cast_fp16, y = lora_out_279_cast_fp16)[name = tensor("key_47_cast_fp16")]; + tensor pretrained_out_281_pad_type_0 = const()[name = tensor("pretrained_out_281_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_281_strides_0 = const()[name = tensor("pretrained_out_281_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_281_pad_0 = const()[name = tensor("pretrained_out_281_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_281_dilations_0 = const()[name = tensor("pretrained_out_281_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_281_groups_0 = const()[name = tensor("pretrained_out_281_groups_0"), val = tensor(1)]; + tensor layers_23_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(260295552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261114816))), name = tensor("layers_23_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_23_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_23_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261114944)))]; + tensor pretrained_out_281_cast_fp16 = conv(bias = layers_23_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_281_dilations_0, groups = pretrained_out_281_groups_0, pad = pretrained_out_281_pad_0, pad_type = pretrained_out_281_pad_type_0, strides = pretrained_out_281_strides_0, weight = layers_23_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_93_cast_fp16)[name = tensor("pretrained_out_281_cast_fp16")]; + tensor input_465_pad_type_0 = const()[name = tensor("input_465_pad_type_0"), val = tensor("valid")]; + tensor input_465_strides_0 = const()[name = tensor("input_465_strides_0"), val = tensor([1, 1])]; + tensor input_465_pad_0 = const()[name = tensor("input_465_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_465_dilations_0 = const()[name = tensor("input_465_dilations_0"), val = tensor([1, 1])]; + tensor input_465_groups_0 = const()[name = tensor("input_465_groups_0"), val = tensor(1)]; + tensor layers_23_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_23_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261117568)))]; + tensor input_465_cast_fp16 = conv(dilations = input_465_dilations_0, groups = input_465_groups_0, pad = input_465_pad_0, pad_type = input_465_pad_type_0, strides = input_465_strides_0, weight = layers_23_self_attn_v_proj_loraA_weight_to_fp16, x = obj_93_cast_fp16)[name = tensor("input_465_cast_fp16")]; + tensor lora_out_281_pad_type_0 = const()[name = tensor("lora_out_281_pad_type_0"), val = tensor("valid")]; + tensor lora_out_281_strides_0 = const()[name = tensor("lora_out_281_strides_0"), val = tensor([1, 1])]; + tensor lora_out_281_pad_0 = const()[name = tensor("lora_out_281_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_281_dilations_0 = const()[name = tensor("lora_out_281_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_281_groups_0 = const()[name = tensor("lora_out_281_groups_0"), val = tensor(1)]; + tensor layers_23_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_23_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261158592)))]; + tensor lora_out_281_cast_fp16 = conv(dilations = lora_out_281_dilations_0, groups = lora_out_281_groups_0, pad = lora_out_281_pad_0, pad_type = lora_out_281_pad_type_0, strides = lora_out_281_strides_0, weight = layers_23_self_attn_v_proj_loraB_weight_to_fp16, x = input_465_cast_fp16)[name = tensor("lora_out_281_cast_fp16")]; + tensor value_47_cast_fp16 = add(x = pretrained_out_281_cast_fp16, y = lora_out_281_cast_fp16)[name = tensor("value_47_cast_fp16")]; + tensor var_5202 = const()[name = tensor("op_5202"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_47_cast_fp16 = reshape(shape = var_5202, x = query_47_cast_fp16)[name = tensor("mh_q_47_cast_fp16")]; + tensor var_5204_to_fp16 = const()[name = tensor("op_5204_to_fp16"), val = tensor(0x1p-3)]; + tensor var_5205_cast_fp16 = mul(x = mh_q_47_cast_fp16, y = var_5204_to_fp16)[name = tensor("op_5205_cast_fp16")]; + tensor var_5206 = const()[name = tensor("op_5206"), val = tensor([1, 20, 64, -1])]; + tensor var_5207_cast_fp16 = reshape(shape = var_5206, x = key_47_cast_fp16)[name = tensor("op_5207_cast_fp16")]; + tensor mh_w_47_transpose_x_0 = const()[name = tensor("mh_w_47_transpose_x_0"), val = tensor(true)]; + tensor mh_w_47_transpose_y_0 = const()[name = tensor("mh_w_47_transpose_y_0"), val = tensor(false)]; + tensor mh_w_47_cast_fp16 = matmul(transpose_x = mh_w_47_transpose_x_0, transpose_y = mh_w_47_transpose_y_0, x = var_5205_cast_fp16, y = var_5207_cast_fp16)[name = tensor("mh_w_47_cast_fp16")]; + tensor var_5210_cast_fp16 = softmax(axis = var_5100, x = mh_w_47_cast_fp16)[name = tensor("op_5210_cast_fp16")]; + tensor var_5211 = const()[name = tensor("op_5211"), val = tensor([1, 20, 64, -1])]; + tensor var_5212_cast_fp16 = reshape(shape = var_5211, x = value_47_cast_fp16)[name = tensor("op_5212_cast_fp16")]; + tensor attn_47_transpose_x_0 = const()[name = tensor("attn_47_transpose_x_0"), val = tensor(false)]; + tensor attn_47_transpose_y_0 = const()[name = tensor("attn_47_transpose_y_0"), val = tensor(true)]; + tensor attn_47_cast_fp16 = matmul(transpose_x = attn_47_transpose_x_0, transpose_y = attn_47_transpose_y_0, x = var_5212_cast_fp16, y = var_5210_cast_fp16)[name = tensor("attn_47_cast_fp16")]; + tensor var_5215 = const()[name = tensor("op_5215"), val = tensor([1, 1280, 1, -1])]; + tensor input_467_cast_fp16 = reshape(shape = var_5215, x = attn_47_cast_fp16)[name = tensor("input_467_cast_fp16")]; + tensor pretrained_out_283_pad_type_0 = const()[name = tensor("pretrained_out_283_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_283_strides_0 = const()[name = tensor("pretrained_out_283_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_283_pad_0 = const()[name = tensor("pretrained_out_283_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_283_dilations_0 = const()[name = tensor("pretrained_out_283_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_283_groups_0 = const()[name = tensor("pretrained_out_283_groups_0"), val = tensor(1)]; + tensor layers_23_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261199616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(262018880))), name = tensor("layers_23_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_23_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_23_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(262019008)))]; + tensor pretrained_out_283_cast_fp16 = conv(bias = layers_23_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_283_dilations_0, groups = pretrained_out_283_groups_0, pad = pretrained_out_283_pad_0, pad_type = pretrained_out_283_pad_type_0, strides = pretrained_out_283_strides_0, weight = layers_23_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_467_cast_fp16)[name = tensor("pretrained_out_283_cast_fp16")]; + tensor input_469_pad_type_0 = const()[name = tensor("input_469_pad_type_0"), val = tensor("valid")]; + tensor input_469_strides_0 = const()[name = tensor("input_469_strides_0"), val = tensor([1, 1])]; + tensor input_469_pad_0 = const()[name = tensor("input_469_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_469_dilations_0 = const()[name = tensor("input_469_dilations_0"), val = tensor([1, 1])]; + tensor input_469_groups_0 = const()[name = tensor("input_469_groups_0"), val = tensor(1)]; + tensor layers_23_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_23_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(262021632)))]; + tensor input_469_cast_fp16 = conv(dilations = input_469_dilations_0, groups = input_469_groups_0, pad = input_469_pad_0, pad_type = input_469_pad_type_0, strides = input_469_strides_0, weight = layers_23_self_attn_o_proj_loraA_weight_to_fp16, x = input_467_cast_fp16)[name = tensor("input_469_cast_fp16")]; + tensor lora_out_283_pad_type_0 = const()[name = tensor("lora_out_283_pad_type_0"), val = tensor("valid")]; + tensor lora_out_283_strides_0 = const()[name = tensor("lora_out_283_strides_0"), val = tensor([1, 1])]; + tensor lora_out_283_pad_0 = const()[name = tensor("lora_out_283_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_283_dilations_0 = const()[name = tensor("lora_out_283_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_283_groups_0 = const()[name = tensor("lora_out_283_groups_0"), val = tensor(1)]; + tensor layers_23_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_23_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(262062656)))]; + tensor lora_out_283_cast_fp16 = conv(dilations = lora_out_283_dilations_0, groups = lora_out_283_groups_0, pad = lora_out_283_pad_0, pad_type = lora_out_283_pad_type_0, strides = lora_out_283_strides_0, weight = layers_23_self_attn_o_proj_loraB_weight_to_fp16, x = input_469_cast_fp16)[name = tensor("lora_out_283_cast_fp16")]; + tensor obj_95_cast_fp16 = add(x = pretrained_out_283_cast_fp16, y = lora_out_283_cast_fp16)[name = tensor("obj_95_cast_fp16")]; + tensor inputs_95_cast_fp16 = add(x = inputs_93_cast_fp16, y = obj_95_cast_fp16)[name = tensor("inputs_95_cast_fp16")]; + tensor out_95_axes_0 = const()[name = tensor("out_95_axes_0"), val = tensor([1])]; + tensor var_5249_to_fp16 = const()[name = tensor("op_5249_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_95_cast_fp16 = layer_norm(axes = out_95_axes_0, epsilon = var_5249_to_fp16, x = inputs_95_cast_fp16)[name = tensor("out_95_cast_fp16")]; + tensor input_471_gamma_0_to_fp16 = const()[name = tensor("input_471_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(262103680)))]; + tensor input_471_beta_0_to_fp16 = const()[name = tensor("input_471_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(262106304)))]; + tensor input_471_epsilon_0_to_fp16 = const()[name = tensor("input_471_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_471_cast_fp16 = batch_norm(beta = input_471_beta_0_to_fp16, epsilon = input_471_epsilon_0_to_fp16, gamma = input_471_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_95_cast_fp16)[name = tensor("input_471_cast_fp16")]; + tensor pretrained_out_285_pad_type_0 = const()[name = tensor("pretrained_out_285_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_285_strides_0 = const()[name = tensor("pretrained_out_285_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_285_pad_0 = const()[name = tensor("pretrained_out_285_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_285_dilations_0 = const()[name = tensor("pretrained_out_285_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_285_groups_0 = const()[name = tensor("pretrained_out_285_groups_0"), val = tensor(1)]; + tensor layers_23_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(262108928))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(265385792))), name = tensor("layers_23_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_23_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_23_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(265385920)))]; + tensor pretrained_out_285_cast_fp16 = conv(bias = layers_23_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_285_dilations_0, groups = pretrained_out_285_groups_0, pad = pretrained_out_285_pad_0, pad_type = pretrained_out_285_pad_type_0, strides = pretrained_out_285_strides_0, weight = layers_23_fc1_pretrained_weight_to_fp16_palettized, x = input_471_cast_fp16)[name = tensor("pretrained_out_285_cast_fp16")]; + tensor input_473_pad_type_0 = const()[name = tensor("input_473_pad_type_0"), val = tensor("valid")]; + tensor input_473_strides_0 = const()[name = tensor("input_473_strides_0"), val = tensor([1, 1])]; + tensor input_473_pad_0 = const()[name = tensor("input_473_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_473_dilations_0 = const()[name = tensor("input_473_dilations_0"), val = tensor([1, 1])]; + tensor input_473_groups_0 = const()[name = tensor("input_473_groups_0"), val = tensor(1)]; + tensor layers_23_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_23_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(265396224)))]; + tensor input_473_cast_fp16 = conv(dilations = input_473_dilations_0, groups = input_473_groups_0, pad = input_473_pad_0, pad_type = input_473_pad_type_0, strides = input_473_strides_0, weight = layers_23_fc1_loraA_weight_to_fp16, x = input_471_cast_fp16)[name = tensor("input_473_cast_fp16")]; + tensor lora_out_285_pad_type_0 = const()[name = tensor("lora_out_285_pad_type_0"), val = tensor("valid")]; + tensor lora_out_285_strides_0 = const()[name = tensor("lora_out_285_strides_0"), val = tensor([1, 1])]; + tensor lora_out_285_pad_0 = const()[name = tensor("lora_out_285_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_285_dilations_0 = const()[name = tensor("lora_out_285_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_285_groups_0 = const()[name = tensor("lora_out_285_groups_0"), val = tensor(1)]; + tensor layers_23_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_23_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(265437248)))]; + tensor lora_out_285_cast_fp16 = conv(dilations = lora_out_285_dilations_0, groups = lora_out_285_groups_0, pad = lora_out_285_pad_0, pad_type = lora_out_285_pad_type_0, strides = lora_out_285_strides_0, weight = layers_23_fc1_loraB_weight_to_fp16, x = input_473_cast_fp16)[name = tensor("lora_out_285_cast_fp16")]; + tensor input_475_cast_fp16 = add(x = pretrained_out_285_cast_fp16, y = lora_out_285_cast_fp16)[name = tensor("input_475_cast_fp16")]; + tensor input_477_mode_0 = const()[name = tensor("input_477_mode_0"), val = tensor("EXACT")]; + tensor input_477_cast_fp16 = gelu(mode = input_477_mode_0, x = input_475_cast_fp16)[name = tensor("input_477_cast_fp16")]; + tensor pretrained_out_287_pad_type_0 = const()[name = tensor("pretrained_out_287_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_287_strides_0 = const()[name = tensor("pretrained_out_287_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_287_pad_0 = const()[name = tensor("pretrained_out_287_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_287_dilations_0 = const()[name = tensor("pretrained_out_287_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_287_groups_0 = const()[name = tensor("pretrained_out_287_groups_0"), val = tensor(1)]; + tensor layers_23_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(265601152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268878016))), name = tensor("layers_23_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_23_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_23_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268878144)))]; + tensor pretrained_out_287_cast_fp16 = conv(bias = layers_23_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_287_dilations_0, groups = pretrained_out_287_groups_0, pad = pretrained_out_287_pad_0, pad_type = pretrained_out_287_pad_type_0, strides = pretrained_out_287_strides_0, weight = layers_23_fc2_pretrained_weight_to_fp16_palettized, x = input_477_cast_fp16)[name = tensor("pretrained_out_287_cast_fp16")]; + tensor input_479_pad_type_0 = const()[name = tensor("input_479_pad_type_0"), val = tensor("valid")]; + tensor input_479_strides_0 = const()[name = tensor("input_479_strides_0"), val = tensor([1, 1])]; + tensor input_479_pad_0 = const()[name = tensor("input_479_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_479_dilations_0 = const()[name = tensor("input_479_dilations_0"), val = tensor([1, 1])]; + tensor input_479_groups_0 = const()[name = tensor("input_479_groups_0"), val = tensor(1)]; + tensor layers_23_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_23_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(268880768)))]; + tensor input_479_cast_fp16 = conv(dilations = input_479_dilations_0, groups = input_479_groups_0, pad = input_479_pad_0, pad_type = input_479_pad_type_0, strides = input_479_strides_0, weight = layers_23_fc2_loraA_weight_to_fp16, x = input_477_cast_fp16)[name = tensor("input_479_cast_fp16")]; + tensor lora_out_287_pad_type_0 = const()[name = tensor("lora_out_287_pad_type_0"), val = tensor("valid")]; + tensor lora_out_287_strides_0 = const()[name = tensor("lora_out_287_strides_0"), val = tensor([1, 1])]; + tensor lora_out_287_pad_0 = const()[name = tensor("lora_out_287_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_287_dilations_0 = const()[name = tensor("lora_out_287_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_287_groups_0 = const()[name = tensor("lora_out_287_groups_0"), val = tensor(1)]; + tensor layers_23_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_23_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(269044672)))]; + tensor lora_out_287_cast_fp16 = conv(dilations = lora_out_287_dilations_0, groups = lora_out_287_groups_0, pad = lora_out_287_pad_0, pad_type = lora_out_287_pad_type_0, strides = lora_out_287_strides_0, weight = layers_23_fc2_loraB_weight_to_fp16, x = input_479_cast_fp16)[name = tensor("lora_out_287_cast_fp16")]; + tensor hidden_states_51_cast_fp16 = add(x = pretrained_out_287_cast_fp16, y = lora_out_287_cast_fp16)[name = tensor("hidden_states_51_cast_fp16")]; + tensor inputs_97_cast_fp16 = add(x = inputs_95_cast_fp16, y = hidden_states_51_cast_fp16)[name = tensor("inputs_97_cast_fp16")]; + tensor var_5314 = const()[name = tensor("op_5314"), val = tensor(3)]; + tensor out_97_axes_0 = const()[name = tensor("out_97_axes_0"), val = tensor([1])]; + tensor var_5333_to_fp16 = const()[name = tensor("op_5333_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_97_cast_fp16 = layer_norm(axes = out_97_axes_0, epsilon = var_5333_to_fp16, x = inputs_97_cast_fp16)[name = tensor("out_97_cast_fp16")]; + tensor obj_97_gamma_0_to_fp16 = const()[name = tensor("obj_97_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(269085696)))]; + tensor obj_97_beta_0_to_fp16 = const()[name = tensor("obj_97_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(269088320)))]; + tensor obj_97_epsilon_0_to_fp16 = const()[name = tensor("obj_97_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_97_cast_fp16 = batch_norm(beta = obj_97_beta_0_to_fp16, epsilon = obj_97_epsilon_0_to_fp16, gamma = obj_97_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_97_cast_fp16)[name = tensor("obj_97_cast_fp16")]; + tensor pretrained_out_289_pad_type_0 = const()[name = tensor("pretrained_out_289_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_289_strides_0 = const()[name = tensor("pretrained_out_289_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_289_pad_0 = const()[name = tensor("pretrained_out_289_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_289_dilations_0 = const()[name = tensor("pretrained_out_289_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_289_groups_0 = const()[name = tensor("pretrained_out_289_groups_0"), val = tensor(1)]; + tensor layers_24_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(269090944))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(269910208))), name = tensor("layers_24_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_24_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_24_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(269910336)))]; + tensor pretrained_out_289_cast_fp16 = conv(bias = layers_24_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_289_dilations_0, groups = pretrained_out_289_groups_0, pad = pretrained_out_289_pad_0, pad_type = pretrained_out_289_pad_type_0, strides = pretrained_out_289_strides_0, weight = layers_24_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_97_cast_fp16)[name = tensor("pretrained_out_289_cast_fp16")]; + tensor input_481_pad_type_0 = const()[name = tensor("input_481_pad_type_0"), val = tensor("valid")]; + tensor input_481_strides_0 = const()[name = tensor("input_481_strides_0"), val = tensor([1, 1])]; + tensor input_481_pad_0 = const()[name = tensor("input_481_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_481_dilations_0 = const()[name = tensor("input_481_dilations_0"), val = tensor([1, 1])]; + tensor input_481_groups_0 = const()[name = tensor("input_481_groups_0"), val = tensor(1)]; + tensor layers_24_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_24_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(269912960)))]; + tensor input_481_cast_fp16 = conv(dilations = input_481_dilations_0, groups = input_481_groups_0, pad = input_481_pad_0, pad_type = input_481_pad_type_0, strides = input_481_strides_0, weight = layers_24_self_attn_q_proj_loraA_weight_to_fp16, x = obj_97_cast_fp16)[name = tensor("input_481_cast_fp16")]; + tensor lora_out_289_pad_type_0 = const()[name = tensor("lora_out_289_pad_type_0"), val = tensor("valid")]; + tensor lora_out_289_strides_0 = const()[name = tensor("lora_out_289_strides_0"), val = tensor([1, 1])]; + tensor lora_out_289_pad_0 = const()[name = tensor("lora_out_289_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_289_dilations_0 = const()[name = tensor("lora_out_289_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_289_groups_0 = const()[name = tensor("lora_out_289_groups_0"), val = tensor(1)]; + tensor layers_24_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_24_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(269953984)))]; + tensor lora_out_289_cast_fp16 = conv(dilations = lora_out_289_dilations_0, groups = lora_out_289_groups_0, pad = lora_out_289_pad_0, pad_type = lora_out_289_pad_type_0, strides = lora_out_289_strides_0, weight = layers_24_self_attn_q_proj_loraB_weight_to_fp16, x = input_481_cast_fp16)[name = tensor("lora_out_289_cast_fp16")]; + tensor query_49_cast_fp16 = add(x = pretrained_out_289_cast_fp16, y = lora_out_289_cast_fp16)[name = tensor("query_49_cast_fp16")]; + tensor pretrained_out_291_pad_type_0 = const()[name = tensor("pretrained_out_291_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_291_strides_0 = const()[name = tensor("pretrained_out_291_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_291_pad_0 = const()[name = tensor("pretrained_out_291_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_291_dilations_0 = const()[name = tensor("pretrained_out_291_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_291_groups_0 = const()[name = tensor("pretrained_out_291_groups_0"), val = tensor(1)]; + tensor layers_24_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(269995008))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(270814272))), name = tensor("layers_24_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_291_cast_fp16 = conv(dilations = pretrained_out_291_dilations_0, groups = pretrained_out_291_groups_0, pad = pretrained_out_291_pad_0, pad_type = pretrained_out_291_pad_type_0, strides = pretrained_out_291_strides_0, weight = layers_24_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_97_cast_fp16)[name = tensor("pretrained_out_291_cast_fp16")]; + tensor input_483_pad_type_0 = const()[name = tensor("input_483_pad_type_0"), val = tensor("valid")]; + tensor input_483_strides_0 = const()[name = tensor("input_483_strides_0"), val = tensor([1, 1])]; + tensor input_483_pad_0 = const()[name = tensor("input_483_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_483_dilations_0 = const()[name = tensor("input_483_dilations_0"), val = tensor([1, 1])]; + tensor input_483_groups_0 = const()[name = tensor("input_483_groups_0"), val = tensor(1)]; + tensor layers_24_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_24_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(270814400)))]; + tensor input_483_cast_fp16 = conv(dilations = input_483_dilations_0, groups = input_483_groups_0, pad = input_483_pad_0, pad_type = input_483_pad_type_0, strides = input_483_strides_0, weight = layers_24_self_attn_k_proj_loraA_weight_to_fp16, x = obj_97_cast_fp16)[name = tensor("input_483_cast_fp16")]; + tensor lora_out_291_pad_type_0 = const()[name = tensor("lora_out_291_pad_type_0"), val = tensor("valid")]; + tensor lora_out_291_strides_0 = const()[name = tensor("lora_out_291_strides_0"), val = tensor([1, 1])]; + tensor lora_out_291_pad_0 = const()[name = tensor("lora_out_291_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_291_dilations_0 = const()[name = tensor("lora_out_291_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_291_groups_0 = const()[name = tensor("lora_out_291_groups_0"), val = tensor(1)]; + tensor layers_24_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_24_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(270855424)))]; + tensor lora_out_291_cast_fp16 = conv(dilations = lora_out_291_dilations_0, groups = lora_out_291_groups_0, pad = lora_out_291_pad_0, pad_type = lora_out_291_pad_type_0, strides = lora_out_291_strides_0, weight = layers_24_self_attn_k_proj_loraB_weight_to_fp16, x = input_483_cast_fp16)[name = tensor("lora_out_291_cast_fp16")]; + tensor key_49_cast_fp16 = add(x = pretrained_out_291_cast_fp16, y = lora_out_291_cast_fp16)[name = tensor("key_49_cast_fp16")]; + tensor pretrained_out_293_pad_type_0 = const()[name = tensor("pretrained_out_293_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_293_strides_0 = const()[name = tensor("pretrained_out_293_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_293_pad_0 = const()[name = tensor("pretrained_out_293_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_293_dilations_0 = const()[name = tensor("pretrained_out_293_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_293_groups_0 = const()[name = tensor("pretrained_out_293_groups_0"), val = tensor(1)]; + tensor layers_24_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(270896448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(271715712))), name = tensor("layers_24_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_24_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_24_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(271715840)))]; + tensor pretrained_out_293_cast_fp16 = conv(bias = layers_24_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_293_dilations_0, groups = pretrained_out_293_groups_0, pad = pretrained_out_293_pad_0, pad_type = pretrained_out_293_pad_type_0, strides = pretrained_out_293_strides_0, weight = layers_24_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_97_cast_fp16)[name = tensor("pretrained_out_293_cast_fp16")]; + tensor input_485_pad_type_0 = const()[name = tensor("input_485_pad_type_0"), val = tensor("valid")]; + tensor input_485_strides_0 = const()[name = tensor("input_485_strides_0"), val = tensor([1, 1])]; + tensor input_485_pad_0 = const()[name = tensor("input_485_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_485_dilations_0 = const()[name = tensor("input_485_dilations_0"), val = tensor([1, 1])]; + tensor input_485_groups_0 = const()[name = tensor("input_485_groups_0"), val = tensor(1)]; + tensor layers_24_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_24_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(271718464)))]; + tensor input_485_cast_fp16 = conv(dilations = input_485_dilations_0, groups = input_485_groups_0, pad = input_485_pad_0, pad_type = input_485_pad_type_0, strides = input_485_strides_0, weight = layers_24_self_attn_v_proj_loraA_weight_to_fp16, x = obj_97_cast_fp16)[name = tensor("input_485_cast_fp16")]; + tensor lora_out_293_pad_type_0 = const()[name = tensor("lora_out_293_pad_type_0"), val = tensor("valid")]; + tensor lora_out_293_strides_0 = const()[name = tensor("lora_out_293_strides_0"), val = tensor([1, 1])]; + tensor lora_out_293_pad_0 = const()[name = tensor("lora_out_293_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_293_dilations_0 = const()[name = tensor("lora_out_293_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_293_groups_0 = const()[name = tensor("lora_out_293_groups_0"), val = tensor(1)]; + tensor layers_24_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_24_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(271759488)))]; + tensor lora_out_293_cast_fp16 = conv(dilations = lora_out_293_dilations_0, groups = lora_out_293_groups_0, pad = lora_out_293_pad_0, pad_type = lora_out_293_pad_type_0, strides = lora_out_293_strides_0, weight = layers_24_self_attn_v_proj_loraB_weight_to_fp16, x = input_485_cast_fp16)[name = tensor("lora_out_293_cast_fp16")]; + tensor value_49_cast_fp16 = add(x = pretrained_out_293_cast_fp16, y = lora_out_293_cast_fp16)[name = tensor("value_49_cast_fp16")]; + tensor var_5416 = const()[name = tensor("op_5416"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_49_cast_fp16 = reshape(shape = var_5416, x = query_49_cast_fp16)[name = tensor("mh_q_49_cast_fp16")]; + tensor var_5418_to_fp16 = const()[name = tensor("op_5418_to_fp16"), val = tensor(0x1p-3)]; + tensor var_5419_cast_fp16 = mul(x = mh_q_49_cast_fp16, y = var_5418_to_fp16)[name = tensor("op_5419_cast_fp16")]; + tensor var_5420 = const()[name = tensor("op_5420"), val = tensor([1, 20, 64, -1])]; + tensor var_5421_cast_fp16 = reshape(shape = var_5420, x = key_49_cast_fp16)[name = tensor("op_5421_cast_fp16")]; + tensor mh_w_49_transpose_x_0 = const()[name = tensor("mh_w_49_transpose_x_0"), val = tensor(true)]; + tensor mh_w_49_transpose_y_0 = const()[name = tensor("mh_w_49_transpose_y_0"), val = tensor(false)]; + tensor mh_w_49_cast_fp16 = matmul(transpose_x = mh_w_49_transpose_x_0, transpose_y = mh_w_49_transpose_y_0, x = var_5419_cast_fp16, y = var_5421_cast_fp16)[name = tensor("mh_w_49_cast_fp16")]; + tensor var_5424_cast_fp16 = softmax(axis = var_5314, x = mh_w_49_cast_fp16)[name = tensor("op_5424_cast_fp16")]; + tensor var_5425 = const()[name = tensor("op_5425"), val = tensor([1, 20, 64, -1])]; + tensor var_5426_cast_fp16 = reshape(shape = var_5425, x = value_49_cast_fp16)[name = tensor("op_5426_cast_fp16")]; + tensor attn_49_transpose_x_0 = const()[name = tensor("attn_49_transpose_x_0"), val = tensor(false)]; + tensor attn_49_transpose_y_0 = const()[name = tensor("attn_49_transpose_y_0"), val = tensor(true)]; + tensor attn_49_cast_fp16 = matmul(transpose_x = attn_49_transpose_x_0, transpose_y = attn_49_transpose_y_0, x = var_5426_cast_fp16, y = var_5424_cast_fp16)[name = tensor("attn_49_cast_fp16")]; + tensor var_5429 = const()[name = tensor("op_5429"), val = tensor([1, 1280, 1, -1])]; + tensor input_487_cast_fp16 = reshape(shape = var_5429, x = attn_49_cast_fp16)[name = tensor("input_487_cast_fp16")]; + tensor pretrained_out_295_pad_type_0 = const()[name = tensor("pretrained_out_295_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_295_strides_0 = const()[name = tensor("pretrained_out_295_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_295_pad_0 = const()[name = tensor("pretrained_out_295_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_295_dilations_0 = const()[name = tensor("pretrained_out_295_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_295_groups_0 = const()[name = tensor("pretrained_out_295_groups_0"), val = tensor(1)]; + tensor layers_24_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(271800512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(272619776))), name = tensor("layers_24_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_24_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_24_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(272619904)))]; + tensor pretrained_out_295_cast_fp16 = conv(bias = layers_24_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_295_dilations_0, groups = pretrained_out_295_groups_0, pad = pretrained_out_295_pad_0, pad_type = pretrained_out_295_pad_type_0, strides = pretrained_out_295_strides_0, weight = layers_24_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_487_cast_fp16)[name = tensor("pretrained_out_295_cast_fp16")]; + tensor input_489_pad_type_0 = const()[name = tensor("input_489_pad_type_0"), val = tensor("valid")]; + tensor input_489_strides_0 = const()[name = tensor("input_489_strides_0"), val = tensor([1, 1])]; + tensor input_489_pad_0 = const()[name = tensor("input_489_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_489_dilations_0 = const()[name = tensor("input_489_dilations_0"), val = tensor([1, 1])]; + tensor input_489_groups_0 = const()[name = tensor("input_489_groups_0"), val = tensor(1)]; + tensor layers_24_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_24_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(272622528)))]; + tensor input_489_cast_fp16 = conv(dilations = input_489_dilations_0, groups = input_489_groups_0, pad = input_489_pad_0, pad_type = input_489_pad_type_0, strides = input_489_strides_0, weight = layers_24_self_attn_o_proj_loraA_weight_to_fp16, x = input_487_cast_fp16)[name = tensor("input_489_cast_fp16")]; + tensor lora_out_295_pad_type_0 = const()[name = tensor("lora_out_295_pad_type_0"), val = tensor("valid")]; + tensor lora_out_295_strides_0 = const()[name = tensor("lora_out_295_strides_0"), val = tensor([1, 1])]; + tensor lora_out_295_pad_0 = const()[name = tensor("lora_out_295_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_295_dilations_0 = const()[name = tensor("lora_out_295_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_295_groups_0 = const()[name = tensor("lora_out_295_groups_0"), val = tensor(1)]; + tensor layers_24_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_24_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(272663552)))]; + tensor lora_out_295_cast_fp16 = conv(dilations = lora_out_295_dilations_0, groups = lora_out_295_groups_0, pad = lora_out_295_pad_0, pad_type = lora_out_295_pad_type_0, strides = lora_out_295_strides_0, weight = layers_24_self_attn_o_proj_loraB_weight_to_fp16, x = input_489_cast_fp16)[name = tensor("lora_out_295_cast_fp16")]; + tensor obj_99_cast_fp16 = add(x = pretrained_out_295_cast_fp16, y = lora_out_295_cast_fp16)[name = tensor("obj_99_cast_fp16")]; + tensor inputs_99_cast_fp16 = add(x = inputs_97_cast_fp16, y = obj_99_cast_fp16)[name = tensor("inputs_99_cast_fp16")]; + tensor out_99_axes_0 = const()[name = tensor("out_99_axes_0"), val = tensor([1])]; + tensor var_5463_to_fp16 = const()[name = tensor("op_5463_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_99_cast_fp16 = layer_norm(axes = out_99_axes_0, epsilon = var_5463_to_fp16, x = inputs_99_cast_fp16)[name = tensor("out_99_cast_fp16")]; + tensor input_491_gamma_0_to_fp16 = const()[name = tensor("input_491_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(272704576)))]; + tensor input_491_beta_0_to_fp16 = const()[name = tensor("input_491_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(272707200)))]; + tensor input_491_epsilon_0_to_fp16 = const()[name = tensor("input_491_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_491_cast_fp16 = batch_norm(beta = input_491_beta_0_to_fp16, epsilon = input_491_epsilon_0_to_fp16, gamma = input_491_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_99_cast_fp16)[name = tensor("input_491_cast_fp16")]; + tensor pretrained_out_297_pad_type_0 = const()[name = tensor("pretrained_out_297_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_297_strides_0 = const()[name = tensor("pretrained_out_297_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_297_pad_0 = const()[name = tensor("pretrained_out_297_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_297_dilations_0 = const()[name = tensor("pretrained_out_297_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_297_groups_0 = const()[name = tensor("pretrained_out_297_groups_0"), val = tensor(1)]; + tensor layers_24_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(272709824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(275986688))), name = tensor("layers_24_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_24_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_24_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(275986816)))]; + tensor pretrained_out_297_cast_fp16 = conv(bias = layers_24_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_297_dilations_0, groups = pretrained_out_297_groups_0, pad = pretrained_out_297_pad_0, pad_type = pretrained_out_297_pad_type_0, strides = pretrained_out_297_strides_0, weight = layers_24_fc1_pretrained_weight_to_fp16_palettized, x = input_491_cast_fp16)[name = tensor("pretrained_out_297_cast_fp16")]; + tensor input_493_pad_type_0 = const()[name = tensor("input_493_pad_type_0"), val = tensor("valid")]; + tensor input_493_strides_0 = const()[name = tensor("input_493_strides_0"), val = tensor([1, 1])]; + tensor input_493_pad_0 = const()[name = tensor("input_493_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_493_dilations_0 = const()[name = tensor("input_493_dilations_0"), val = tensor([1, 1])]; + tensor input_493_groups_0 = const()[name = tensor("input_493_groups_0"), val = tensor(1)]; + tensor layers_24_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_24_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(275997120)))]; + tensor input_493_cast_fp16 = conv(dilations = input_493_dilations_0, groups = input_493_groups_0, pad = input_493_pad_0, pad_type = input_493_pad_type_0, strides = input_493_strides_0, weight = layers_24_fc1_loraA_weight_to_fp16, x = input_491_cast_fp16)[name = tensor("input_493_cast_fp16")]; + tensor lora_out_297_pad_type_0 = const()[name = tensor("lora_out_297_pad_type_0"), val = tensor("valid")]; + tensor lora_out_297_strides_0 = const()[name = tensor("lora_out_297_strides_0"), val = tensor([1, 1])]; + tensor lora_out_297_pad_0 = const()[name = tensor("lora_out_297_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_297_dilations_0 = const()[name = tensor("lora_out_297_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_297_groups_0 = const()[name = tensor("lora_out_297_groups_0"), val = tensor(1)]; + tensor layers_24_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_24_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(276038144)))]; + tensor lora_out_297_cast_fp16 = conv(dilations = lora_out_297_dilations_0, groups = lora_out_297_groups_0, pad = lora_out_297_pad_0, pad_type = lora_out_297_pad_type_0, strides = lora_out_297_strides_0, weight = layers_24_fc1_loraB_weight_to_fp16, x = input_493_cast_fp16)[name = tensor("lora_out_297_cast_fp16")]; + tensor input_495_cast_fp16 = add(x = pretrained_out_297_cast_fp16, y = lora_out_297_cast_fp16)[name = tensor("input_495_cast_fp16")]; + tensor input_497_mode_0 = const()[name = tensor("input_497_mode_0"), val = tensor("EXACT")]; + tensor input_497_cast_fp16 = gelu(mode = input_497_mode_0, x = input_495_cast_fp16)[name = tensor("input_497_cast_fp16")]; + tensor pretrained_out_299_pad_type_0 = const()[name = tensor("pretrained_out_299_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_299_strides_0 = const()[name = tensor("pretrained_out_299_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_299_pad_0 = const()[name = tensor("pretrained_out_299_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_299_dilations_0 = const()[name = tensor("pretrained_out_299_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_299_groups_0 = const()[name = tensor("pretrained_out_299_groups_0"), val = tensor(1)]; + tensor layers_24_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(276202048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(279478912))), name = tensor("layers_24_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_24_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_24_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(279479040)))]; + tensor pretrained_out_299_cast_fp16 = conv(bias = layers_24_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_299_dilations_0, groups = pretrained_out_299_groups_0, pad = pretrained_out_299_pad_0, pad_type = pretrained_out_299_pad_type_0, strides = pretrained_out_299_strides_0, weight = layers_24_fc2_pretrained_weight_to_fp16_palettized, x = input_497_cast_fp16)[name = tensor("pretrained_out_299_cast_fp16")]; + tensor input_499_pad_type_0 = const()[name = tensor("input_499_pad_type_0"), val = tensor("valid")]; + tensor input_499_strides_0 = const()[name = tensor("input_499_strides_0"), val = tensor([1, 1])]; + tensor input_499_pad_0 = const()[name = tensor("input_499_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_499_dilations_0 = const()[name = tensor("input_499_dilations_0"), val = tensor([1, 1])]; + tensor input_499_groups_0 = const()[name = tensor("input_499_groups_0"), val = tensor(1)]; + tensor layers_24_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_24_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(279481664)))]; + tensor input_499_cast_fp16 = conv(dilations = input_499_dilations_0, groups = input_499_groups_0, pad = input_499_pad_0, pad_type = input_499_pad_type_0, strides = input_499_strides_0, weight = layers_24_fc2_loraA_weight_to_fp16, x = input_497_cast_fp16)[name = tensor("input_499_cast_fp16")]; + tensor lora_out_299_pad_type_0 = const()[name = tensor("lora_out_299_pad_type_0"), val = tensor("valid")]; + tensor lora_out_299_strides_0 = const()[name = tensor("lora_out_299_strides_0"), val = tensor([1, 1])]; + tensor lora_out_299_pad_0 = const()[name = tensor("lora_out_299_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_299_dilations_0 = const()[name = tensor("lora_out_299_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_299_groups_0 = const()[name = tensor("lora_out_299_groups_0"), val = tensor(1)]; + tensor layers_24_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_24_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(279645568)))]; + tensor lora_out_299_cast_fp16 = conv(dilations = lora_out_299_dilations_0, groups = lora_out_299_groups_0, pad = lora_out_299_pad_0, pad_type = lora_out_299_pad_type_0, strides = lora_out_299_strides_0, weight = layers_24_fc2_loraB_weight_to_fp16, x = input_499_cast_fp16)[name = tensor("lora_out_299_cast_fp16")]; + tensor hidden_states_53_cast_fp16 = add(x = pretrained_out_299_cast_fp16, y = lora_out_299_cast_fp16)[name = tensor("hidden_states_53_cast_fp16")]; + tensor inputs_101_cast_fp16 = add(x = inputs_99_cast_fp16, y = hidden_states_53_cast_fp16)[name = tensor("inputs_101_cast_fp16")]; + tensor var_5528 = const()[name = tensor("op_5528"), val = tensor(3)]; + tensor out_101_axes_0 = const()[name = tensor("out_101_axes_0"), val = tensor([1])]; + tensor var_5547_to_fp16 = const()[name = tensor("op_5547_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_101_cast_fp16 = layer_norm(axes = out_101_axes_0, epsilon = var_5547_to_fp16, x = inputs_101_cast_fp16)[name = tensor("out_101_cast_fp16")]; + tensor obj_101_gamma_0_to_fp16 = const()[name = tensor("obj_101_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(279686592)))]; + tensor obj_101_beta_0_to_fp16 = const()[name = tensor("obj_101_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(279689216)))]; + tensor obj_101_epsilon_0_to_fp16 = const()[name = tensor("obj_101_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_101_cast_fp16 = batch_norm(beta = obj_101_beta_0_to_fp16, epsilon = obj_101_epsilon_0_to_fp16, gamma = obj_101_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_101_cast_fp16)[name = tensor("obj_101_cast_fp16")]; + tensor pretrained_out_301_pad_type_0 = const()[name = tensor("pretrained_out_301_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_301_strides_0 = const()[name = tensor("pretrained_out_301_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_301_pad_0 = const()[name = tensor("pretrained_out_301_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_301_dilations_0 = const()[name = tensor("pretrained_out_301_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_301_groups_0 = const()[name = tensor("pretrained_out_301_groups_0"), val = tensor(1)]; + tensor layers_25_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(279691840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(280511104))), name = tensor("layers_25_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_25_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_25_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(280511232)))]; + tensor pretrained_out_301_cast_fp16 = conv(bias = layers_25_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_301_dilations_0, groups = pretrained_out_301_groups_0, pad = pretrained_out_301_pad_0, pad_type = pretrained_out_301_pad_type_0, strides = pretrained_out_301_strides_0, weight = layers_25_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_101_cast_fp16)[name = tensor("pretrained_out_301_cast_fp16")]; + tensor input_501_pad_type_0 = const()[name = tensor("input_501_pad_type_0"), val = tensor("valid")]; + tensor input_501_strides_0 = const()[name = tensor("input_501_strides_0"), val = tensor([1, 1])]; + tensor input_501_pad_0 = const()[name = tensor("input_501_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_501_dilations_0 = const()[name = tensor("input_501_dilations_0"), val = tensor([1, 1])]; + tensor input_501_groups_0 = const()[name = tensor("input_501_groups_0"), val = tensor(1)]; + tensor layers_25_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_25_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(280513856)))]; + tensor input_501_cast_fp16 = conv(dilations = input_501_dilations_0, groups = input_501_groups_0, pad = input_501_pad_0, pad_type = input_501_pad_type_0, strides = input_501_strides_0, weight = layers_25_self_attn_q_proj_loraA_weight_to_fp16, x = obj_101_cast_fp16)[name = tensor("input_501_cast_fp16")]; + tensor lora_out_301_pad_type_0 = const()[name = tensor("lora_out_301_pad_type_0"), val = tensor("valid")]; + tensor lora_out_301_strides_0 = const()[name = tensor("lora_out_301_strides_0"), val = tensor([1, 1])]; + tensor lora_out_301_pad_0 = const()[name = tensor("lora_out_301_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_301_dilations_0 = const()[name = tensor("lora_out_301_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_301_groups_0 = const()[name = tensor("lora_out_301_groups_0"), val = tensor(1)]; + tensor layers_25_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_25_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(280554880)))]; + tensor lora_out_301_cast_fp16 = conv(dilations = lora_out_301_dilations_0, groups = lora_out_301_groups_0, pad = lora_out_301_pad_0, pad_type = lora_out_301_pad_type_0, strides = lora_out_301_strides_0, weight = layers_25_self_attn_q_proj_loraB_weight_to_fp16, x = input_501_cast_fp16)[name = tensor("lora_out_301_cast_fp16")]; + tensor query_51_cast_fp16 = add(x = pretrained_out_301_cast_fp16, y = lora_out_301_cast_fp16)[name = tensor("query_51_cast_fp16")]; + tensor pretrained_out_303_pad_type_0 = const()[name = tensor("pretrained_out_303_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_303_strides_0 = const()[name = tensor("pretrained_out_303_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_303_pad_0 = const()[name = tensor("pretrained_out_303_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_303_dilations_0 = const()[name = tensor("pretrained_out_303_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_303_groups_0 = const()[name = tensor("pretrained_out_303_groups_0"), val = tensor(1)]; + tensor layers_25_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(280595904))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(281415168))), name = tensor("layers_25_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_303_cast_fp16 = conv(dilations = pretrained_out_303_dilations_0, groups = pretrained_out_303_groups_0, pad = pretrained_out_303_pad_0, pad_type = pretrained_out_303_pad_type_0, strides = pretrained_out_303_strides_0, weight = layers_25_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_101_cast_fp16)[name = tensor("pretrained_out_303_cast_fp16")]; + tensor input_503_pad_type_0 = const()[name = tensor("input_503_pad_type_0"), val = tensor("valid")]; + tensor input_503_strides_0 = const()[name = tensor("input_503_strides_0"), val = tensor([1, 1])]; + tensor input_503_pad_0 = const()[name = tensor("input_503_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_503_dilations_0 = const()[name = tensor("input_503_dilations_0"), val = tensor([1, 1])]; + tensor input_503_groups_0 = const()[name = tensor("input_503_groups_0"), val = tensor(1)]; + tensor layers_25_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_25_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(281415296)))]; + tensor input_503_cast_fp16 = conv(dilations = input_503_dilations_0, groups = input_503_groups_0, pad = input_503_pad_0, pad_type = input_503_pad_type_0, strides = input_503_strides_0, weight = layers_25_self_attn_k_proj_loraA_weight_to_fp16, x = obj_101_cast_fp16)[name = tensor("input_503_cast_fp16")]; + tensor lora_out_303_pad_type_0 = const()[name = tensor("lora_out_303_pad_type_0"), val = tensor("valid")]; + tensor lora_out_303_strides_0 = const()[name = tensor("lora_out_303_strides_0"), val = tensor([1, 1])]; + tensor lora_out_303_pad_0 = const()[name = tensor("lora_out_303_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_303_dilations_0 = const()[name = tensor("lora_out_303_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_303_groups_0 = const()[name = tensor("lora_out_303_groups_0"), val = tensor(1)]; + tensor layers_25_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_25_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(281456320)))]; + tensor lora_out_303_cast_fp16 = conv(dilations = lora_out_303_dilations_0, groups = lora_out_303_groups_0, pad = lora_out_303_pad_0, pad_type = lora_out_303_pad_type_0, strides = lora_out_303_strides_0, weight = layers_25_self_attn_k_proj_loraB_weight_to_fp16, x = input_503_cast_fp16)[name = tensor("lora_out_303_cast_fp16")]; + tensor key_51_cast_fp16 = add(x = pretrained_out_303_cast_fp16, y = lora_out_303_cast_fp16)[name = tensor("key_51_cast_fp16")]; + tensor pretrained_out_305_pad_type_0 = const()[name = tensor("pretrained_out_305_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_305_strides_0 = const()[name = tensor("pretrained_out_305_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_305_pad_0 = const()[name = tensor("pretrained_out_305_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_305_dilations_0 = const()[name = tensor("pretrained_out_305_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_305_groups_0 = const()[name = tensor("pretrained_out_305_groups_0"), val = tensor(1)]; + tensor layers_25_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(281497344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(282316608))), name = tensor("layers_25_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_25_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_25_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(282316736)))]; + tensor pretrained_out_305_cast_fp16 = conv(bias = layers_25_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_305_dilations_0, groups = pretrained_out_305_groups_0, pad = pretrained_out_305_pad_0, pad_type = pretrained_out_305_pad_type_0, strides = pretrained_out_305_strides_0, weight = layers_25_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_101_cast_fp16)[name = tensor("pretrained_out_305_cast_fp16")]; + tensor input_505_pad_type_0 = const()[name = tensor("input_505_pad_type_0"), val = tensor("valid")]; + tensor input_505_strides_0 = const()[name = tensor("input_505_strides_0"), val = tensor([1, 1])]; + tensor input_505_pad_0 = const()[name = tensor("input_505_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_505_dilations_0 = const()[name = tensor("input_505_dilations_0"), val = tensor([1, 1])]; + tensor input_505_groups_0 = const()[name = tensor("input_505_groups_0"), val = tensor(1)]; + tensor layers_25_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_25_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(282319360)))]; + tensor input_505_cast_fp16 = conv(dilations = input_505_dilations_0, groups = input_505_groups_0, pad = input_505_pad_0, pad_type = input_505_pad_type_0, strides = input_505_strides_0, weight = layers_25_self_attn_v_proj_loraA_weight_to_fp16, x = obj_101_cast_fp16)[name = tensor("input_505_cast_fp16")]; + tensor lora_out_305_pad_type_0 = const()[name = tensor("lora_out_305_pad_type_0"), val = tensor("valid")]; + tensor lora_out_305_strides_0 = const()[name = tensor("lora_out_305_strides_0"), val = tensor([1, 1])]; + tensor lora_out_305_pad_0 = const()[name = tensor("lora_out_305_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_305_dilations_0 = const()[name = tensor("lora_out_305_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_305_groups_0 = const()[name = tensor("lora_out_305_groups_0"), val = tensor(1)]; + tensor layers_25_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_25_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(282360384)))]; + tensor lora_out_305_cast_fp16 = conv(dilations = lora_out_305_dilations_0, groups = lora_out_305_groups_0, pad = lora_out_305_pad_0, pad_type = lora_out_305_pad_type_0, strides = lora_out_305_strides_0, weight = layers_25_self_attn_v_proj_loraB_weight_to_fp16, x = input_505_cast_fp16)[name = tensor("lora_out_305_cast_fp16")]; + tensor value_51_cast_fp16 = add(x = pretrained_out_305_cast_fp16, y = lora_out_305_cast_fp16)[name = tensor("value_51_cast_fp16")]; + tensor var_5630 = const()[name = tensor("op_5630"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_51_cast_fp16 = reshape(shape = var_5630, x = query_51_cast_fp16)[name = tensor("mh_q_51_cast_fp16")]; + tensor var_5632_to_fp16 = const()[name = tensor("op_5632_to_fp16"), val = tensor(0x1p-3)]; + tensor var_5633_cast_fp16 = mul(x = mh_q_51_cast_fp16, y = var_5632_to_fp16)[name = tensor("op_5633_cast_fp16")]; + tensor var_5634 = const()[name = tensor("op_5634"), val = tensor([1, 20, 64, -1])]; + tensor var_5635_cast_fp16 = reshape(shape = var_5634, x = key_51_cast_fp16)[name = tensor("op_5635_cast_fp16")]; + tensor mh_w_51_transpose_x_0 = const()[name = tensor("mh_w_51_transpose_x_0"), val = tensor(true)]; + tensor mh_w_51_transpose_y_0 = const()[name = tensor("mh_w_51_transpose_y_0"), val = tensor(false)]; + tensor mh_w_51_cast_fp16 = matmul(transpose_x = mh_w_51_transpose_x_0, transpose_y = mh_w_51_transpose_y_0, x = var_5633_cast_fp16, y = var_5635_cast_fp16)[name = tensor("mh_w_51_cast_fp16")]; + tensor var_5638_cast_fp16 = softmax(axis = var_5528, x = mh_w_51_cast_fp16)[name = tensor("op_5638_cast_fp16")]; + tensor var_5639 = const()[name = tensor("op_5639"), val = tensor([1, 20, 64, -1])]; + tensor var_5640_cast_fp16 = reshape(shape = var_5639, x = value_51_cast_fp16)[name = tensor("op_5640_cast_fp16")]; + tensor attn_51_transpose_x_0 = const()[name = tensor("attn_51_transpose_x_0"), val = tensor(false)]; + tensor attn_51_transpose_y_0 = const()[name = tensor("attn_51_transpose_y_0"), val = tensor(true)]; + tensor attn_51_cast_fp16 = matmul(transpose_x = attn_51_transpose_x_0, transpose_y = attn_51_transpose_y_0, x = var_5640_cast_fp16, y = var_5638_cast_fp16)[name = tensor("attn_51_cast_fp16")]; + tensor var_5643 = const()[name = tensor("op_5643"), val = tensor([1, 1280, 1, -1])]; + tensor input_507_cast_fp16 = reshape(shape = var_5643, x = attn_51_cast_fp16)[name = tensor("input_507_cast_fp16")]; + tensor pretrained_out_307_pad_type_0 = const()[name = tensor("pretrained_out_307_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_307_strides_0 = const()[name = tensor("pretrained_out_307_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_307_pad_0 = const()[name = tensor("pretrained_out_307_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_307_dilations_0 = const()[name = tensor("pretrained_out_307_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_307_groups_0 = const()[name = tensor("pretrained_out_307_groups_0"), val = tensor(1)]; + tensor layers_25_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(282401408))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283220672))), name = tensor("layers_25_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_25_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_25_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283220800)))]; + tensor pretrained_out_307_cast_fp16 = conv(bias = layers_25_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_307_dilations_0, groups = pretrained_out_307_groups_0, pad = pretrained_out_307_pad_0, pad_type = pretrained_out_307_pad_type_0, strides = pretrained_out_307_strides_0, weight = layers_25_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_507_cast_fp16)[name = tensor("pretrained_out_307_cast_fp16")]; + tensor input_509_pad_type_0 = const()[name = tensor("input_509_pad_type_0"), val = tensor("valid")]; + tensor input_509_strides_0 = const()[name = tensor("input_509_strides_0"), val = tensor([1, 1])]; + tensor input_509_pad_0 = const()[name = tensor("input_509_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_509_dilations_0 = const()[name = tensor("input_509_dilations_0"), val = tensor([1, 1])]; + tensor input_509_groups_0 = const()[name = tensor("input_509_groups_0"), val = tensor(1)]; + tensor layers_25_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_25_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283223424)))]; + tensor input_509_cast_fp16 = conv(dilations = input_509_dilations_0, groups = input_509_groups_0, pad = input_509_pad_0, pad_type = input_509_pad_type_0, strides = input_509_strides_0, weight = layers_25_self_attn_o_proj_loraA_weight_to_fp16, x = input_507_cast_fp16)[name = tensor("input_509_cast_fp16")]; + tensor lora_out_307_pad_type_0 = const()[name = tensor("lora_out_307_pad_type_0"), val = tensor("valid")]; + tensor lora_out_307_strides_0 = const()[name = tensor("lora_out_307_strides_0"), val = tensor([1, 1])]; + tensor lora_out_307_pad_0 = const()[name = tensor("lora_out_307_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_307_dilations_0 = const()[name = tensor("lora_out_307_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_307_groups_0 = const()[name = tensor("lora_out_307_groups_0"), val = tensor(1)]; + tensor layers_25_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_25_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283264448)))]; + tensor lora_out_307_cast_fp16 = conv(dilations = lora_out_307_dilations_0, groups = lora_out_307_groups_0, pad = lora_out_307_pad_0, pad_type = lora_out_307_pad_type_0, strides = lora_out_307_strides_0, weight = layers_25_self_attn_o_proj_loraB_weight_to_fp16, x = input_509_cast_fp16)[name = tensor("lora_out_307_cast_fp16")]; + tensor obj_103_cast_fp16 = add(x = pretrained_out_307_cast_fp16, y = lora_out_307_cast_fp16)[name = tensor("obj_103_cast_fp16")]; + tensor inputs_103_cast_fp16 = add(x = inputs_101_cast_fp16, y = obj_103_cast_fp16)[name = tensor("inputs_103_cast_fp16")]; + tensor out_103_axes_0 = const()[name = tensor("out_103_axes_0"), val = tensor([1])]; + tensor var_5677_to_fp16 = const()[name = tensor("op_5677_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_103_cast_fp16 = layer_norm(axes = out_103_axes_0, epsilon = var_5677_to_fp16, x = inputs_103_cast_fp16)[name = tensor("out_103_cast_fp16")]; + tensor input_511_gamma_0_to_fp16 = const()[name = tensor("input_511_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283305472)))]; + tensor input_511_beta_0_to_fp16 = const()[name = tensor("input_511_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283308096)))]; + tensor input_511_epsilon_0_to_fp16 = const()[name = tensor("input_511_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_511_cast_fp16 = batch_norm(beta = input_511_beta_0_to_fp16, epsilon = input_511_epsilon_0_to_fp16, gamma = input_511_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_103_cast_fp16)[name = tensor("input_511_cast_fp16")]; + tensor pretrained_out_309_pad_type_0 = const()[name = tensor("pretrained_out_309_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_309_strides_0 = const()[name = tensor("pretrained_out_309_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_309_pad_0 = const()[name = tensor("pretrained_out_309_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_309_dilations_0 = const()[name = tensor("pretrained_out_309_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_309_groups_0 = const()[name = tensor("pretrained_out_309_groups_0"), val = tensor(1)]; + tensor layers_25_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283310720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(286587584))), name = tensor("layers_25_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_25_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_25_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(286587712)))]; + tensor pretrained_out_309_cast_fp16 = conv(bias = layers_25_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_309_dilations_0, groups = pretrained_out_309_groups_0, pad = pretrained_out_309_pad_0, pad_type = pretrained_out_309_pad_type_0, strides = pretrained_out_309_strides_0, weight = layers_25_fc1_pretrained_weight_to_fp16_palettized, x = input_511_cast_fp16)[name = tensor("pretrained_out_309_cast_fp16")]; + tensor input_513_pad_type_0 = const()[name = tensor("input_513_pad_type_0"), val = tensor("valid")]; + tensor input_513_strides_0 = const()[name = tensor("input_513_strides_0"), val = tensor([1, 1])]; + tensor input_513_pad_0 = const()[name = tensor("input_513_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_513_dilations_0 = const()[name = tensor("input_513_dilations_0"), val = tensor([1, 1])]; + tensor input_513_groups_0 = const()[name = tensor("input_513_groups_0"), val = tensor(1)]; + tensor layers_25_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_25_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(286598016)))]; + tensor input_513_cast_fp16 = conv(dilations = input_513_dilations_0, groups = input_513_groups_0, pad = input_513_pad_0, pad_type = input_513_pad_type_0, strides = input_513_strides_0, weight = layers_25_fc1_loraA_weight_to_fp16, x = input_511_cast_fp16)[name = tensor("input_513_cast_fp16")]; + tensor lora_out_309_pad_type_0 = const()[name = tensor("lora_out_309_pad_type_0"), val = tensor("valid")]; + tensor lora_out_309_strides_0 = const()[name = tensor("lora_out_309_strides_0"), val = tensor([1, 1])]; + tensor lora_out_309_pad_0 = const()[name = tensor("lora_out_309_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_309_dilations_0 = const()[name = tensor("lora_out_309_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_309_groups_0 = const()[name = tensor("lora_out_309_groups_0"), val = tensor(1)]; + tensor layers_25_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_25_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(286639040)))]; + tensor lora_out_309_cast_fp16 = conv(dilations = lora_out_309_dilations_0, groups = lora_out_309_groups_0, pad = lora_out_309_pad_0, pad_type = lora_out_309_pad_type_0, strides = lora_out_309_strides_0, weight = layers_25_fc1_loraB_weight_to_fp16, x = input_513_cast_fp16)[name = tensor("lora_out_309_cast_fp16")]; + tensor input_515_cast_fp16 = add(x = pretrained_out_309_cast_fp16, y = lora_out_309_cast_fp16)[name = tensor("input_515_cast_fp16")]; + tensor input_517_mode_0 = const()[name = tensor("input_517_mode_0"), val = tensor("EXACT")]; + tensor input_517_cast_fp16 = gelu(mode = input_517_mode_0, x = input_515_cast_fp16)[name = tensor("input_517_cast_fp16")]; + tensor pretrained_out_311_pad_type_0 = const()[name = tensor("pretrained_out_311_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_311_strides_0 = const()[name = tensor("pretrained_out_311_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_311_pad_0 = const()[name = tensor("pretrained_out_311_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_311_dilations_0 = const()[name = tensor("pretrained_out_311_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_311_groups_0 = const()[name = tensor("pretrained_out_311_groups_0"), val = tensor(1)]; + tensor layers_25_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(286802944))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(290079808))), name = tensor("layers_25_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_25_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_25_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(290079936)))]; + tensor pretrained_out_311_cast_fp16 = conv(bias = layers_25_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_311_dilations_0, groups = pretrained_out_311_groups_0, pad = pretrained_out_311_pad_0, pad_type = pretrained_out_311_pad_type_0, strides = pretrained_out_311_strides_0, weight = layers_25_fc2_pretrained_weight_to_fp16_palettized, x = input_517_cast_fp16)[name = tensor("pretrained_out_311_cast_fp16")]; + tensor input_519_pad_type_0 = const()[name = tensor("input_519_pad_type_0"), val = tensor("valid")]; + tensor input_519_strides_0 = const()[name = tensor("input_519_strides_0"), val = tensor([1, 1])]; + tensor input_519_pad_0 = const()[name = tensor("input_519_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_519_dilations_0 = const()[name = tensor("input_519_dilations_0"), val = tensor([1, 1])]; + tensor input_519_groups_0 = const()[name = tensor("input_519_groups_0"), val = tensor(1)]; + tensor layers_25_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_25_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(290082560)))]; + tensor input_519_cast_fp16 = conv(dilations = input_519_dilations_0, groups = input_519_groups_0, pad = input_519_pad_0, pad_type = input_519_pad_type_0, strides = input_519_strides_0, weight = layers_25_fc2_loraA_weight_to_fp16, x = input_517_cast_fp16)[name = tensor("input_519_cast_fp16")]; + tensor lora_out_311_pad_type_0 = const()[name = tensor("lora_out_311_pad_type_0"), val = tensor("valid")]; + tensor lora_out_311_strides_0 = const()[name = tensor("lora_out_311_strides_0"), val = tensor([1, 1])]; + tensor lora_out_311_pad_0 = const()[name = tensor("lora_out_311_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_311_dilations_0 = const()[name = tensor("lora_out_311_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_311_groups_0 = const()[name = tensor("lora_out_311_groups_0"), val = tensor(1)]; + tensor layers_25_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_25_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(290246464)))]; + tensor lora_out_311_cast_fp16 = conv(dilations = lora_out_311_dilations_0, groups = lora_out_311_groups_0, pad = lora_out_311_pad_0, pad_type = lora_out_311_pad_type_0, strides = lora_out_311_strides_0, weight = layers_25_fc2_loraB_weight_to_fp16, x = input_519_cast_fp16)[name = tensor("lora_out_311_cast_fp16")]; + tensor hidden_states_55_cast_fp16 = add(x = pretrained_out_311_cast_fp16, y = lora_out_311_cast_fp16)[name = tensor("hidden_states_55_cast_fp16")]; + tensor inputs_105_cast_fp16 = add(x = inputs_103_cast_fp16, y = hidden_states_55_cast_fp16)[name = tensor("inputs_105_cast_fp16")]; + tensor var_5742 = const()[name = tensor("op_5742"), val = tensor(3)]; + tensor out_105_axes_0 = const()[name = tensor("out_105_axes_0"), val = tensor([1])]; + tensor var_5761_to_fp16 = const()[name = tensor("op_5761_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_105_cast_fp16 = layer_norm(axes = out_105_axes_0, epsilon = var_5761_to_fp16, x = inputs_105_cast_fp16)[name = tensor("out_105_cast_fp16")]; + tensor obj_105_gamma_0_to_fp16 = const()[name = tensor("obj_105_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(290287488)))]; + tensor obj_105_beta_0_to_fp16 = const()[name = tensor("obj_105_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(290290112)))]; + tensor obj_105_epsilon_0_to_fp16 = const()[name = tensor("obj_105_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_105_cast_fp16 = batch_norm(beta = obj_105_beta_0_to_fp16, epsilon = obj_105_epsilon_0_to_fp16, gamma = obj_105_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_105_cast_fp16)[name = tensor("obj_105_cast_fp16")]; + tensor pretrained_out_313_pad_type_0 = const()[name = tensor("pretrained_out_313_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_313_strides_0 = const()[name = tensor("pretrained_out_313_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_313_pad_0 = const()[name = tensor("pretrained_out_313_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_313_dilations_0 = const()[name = tensor("pretrained_out_313_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_313_groups_0 = const()[name = tensor("pretrained_out_313_groups_0"), val = tensor(1)]; + tensor layers_26_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(290292736))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(291112000))), name = tensor("layers_26_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_26_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_26_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(291112128)))]; + tensor pretrained_out_313_cast_fp16 = conv(bias = layers_26_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_313_dilations_0, groups = pretrained_out_313_groups_0, pad = pretrained_out_313_pad_0, pad_type = pretrained_out_313_pad_type_0, strides = pretrained_out_313_strides_0, weight = layers_26_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_105_cast_fp16)[name = tensor("pretrained_out_313_cast_fp16")]; + tensor input_521_pad_type_0 = const()[name = tensor("input_521_pad_type_0"), val = tensor("valid")]; + tensor input_521_strides_0 = const()[name = tensor("input_521_strides_0"), val = tensor([1, 1])]; + tensor input_521_pad_0 = const()[name = tensor("input_521_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_521_dilations_0 = const()[name = tensor("input_521_dilations_0"), val = tensor([1, 1])]; + tensor input_521_groups_0 = const()[name = tensor("input_521_groups_0"), val = tensor(1)]; + tensor layers_26_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_26_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(291114752)))]; + tensor input_521_cast_fp16 = conv(dilations = input_521_dilations_0, groups = input_521_groups_0, pad = input_521_pad_0, pad_type = input_521_pad_type_0, strides = input_521_strides_0, weight = layers_26_self_attn_q_proj_loraA_weight_to_fp16, x = obj_105_cast_fp16)[name = tensor("input_521_cast_fp16")]; + tensor lora_out_313_pad_type_0 = const()[name = tensor("lora_out_313_pad_type_0"), val = tensor("valid")]; + tensor lora_out_313_strides_0 = const()[name = tensor("lora_out_313_strides_0"), val = tensor([1, 1])]; + tensor lora_out_313_pad_0 = const()[name = tensor("lora_out_313_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_313_dilations_0 = const()[name = tensor("lora_out_313_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_313_groups_0 = const()[name = tensor("lora_out_313_groups_0"), val = tensor(1)]; + tensor layers_26_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_26_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(291155776)))]; + tensor lora_out_313_cast_fp16 = conv(dilations = lora_out_313_dilations_0, groups = lora_out_313_groups_0, pad = lora_out_313_pad_0, pad_type = lora_out_313_pad_type_0, strides = lora_out_313_strides_0, weight = layers_26_self_attn_q_proj_loraB_weight_to_fp16, x = input_521_cast_fp16)[name = tensor("lora_out_313_cast_fp16")]; + tensor query_53_cast_fp16 = add(x = pretrained_out_313_cast_fp16, y = lora_out_313_cast_fp16)[name = tensor("query_53_cast_fp16")]; + tensor pretrained_out_315_pad_type_0 = const()[name = tensor("pretrained_out_315_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_315_strides_0 = const()[name = tensor("pretrained_out_315_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_315_pad_0 = const()[name = tensor("pretrained_out_315_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_315_dilations_0 = const()[name = tensor("pretrained_out_315_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_315_groups_0 = const()[name = tensor("pretrained_out_315_groups_0"), val = tensor(1)]; + tensor layers_26_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(291196800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(292016064))), name = tensor("layers_26_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_315_cast_fp16 = conv(dilations = pretrained_out_315_dilations_0, groups = pretrained_out_315_groups_0, pad = pretrained_out_315_pad_0, pad_type = pretrained_out_315_pad_type_0, strides = pretrained_out_315_strides_0, weight = layers_26_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_105_cast_fp16)[name = tensor("pretrained_out_315_cast_fp16")]; + tensor input_523_pad_type_0 = const()[name = tensor("input_523_pad_type_0"), val = tensor("valid")]; + tensor input_523_strides_0 = const()[name = tensor("input_523_strides_0"), val = tensor([1, 1])]; + tensor input_523_pad_0 = const()[name = tensor("input_523_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_523_dilations_0 = const()[name = tensor("input_523_dilations_0"), val = tensor([1, 1])]; + tensor input_523_groups_0 = const()[name = tensor("input_523_groups_0"), val = tensor(1)]; + tensor layers_26_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_26_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(292016192)))]; + tensor input_523_cast_fp16 = conv(dilations = input_523_dilations_0, groups = input_523_groups_0, pad = input_523_pad_0, pad_type = input_523_pad_type_0, strides = input_523_strides_0, weight = layers_26_self_attn_k_proj_loraA_weight_to_fp16, x = obj_105_cast_fp16)[name = tensor("input_523_cast_fp16")]; + tensor lora_out_315_pad_type_0 = const()[name = tensor("lora_out_315_pad_type_0"), val = tensor("valid")]; + tensor lora_out_315_strides_0 = const()[name = tensor("lora_out_315_strides_0"), val = tensor([1, 1])]; + tensor lora_out_315_pad_0 = const()[name = tensor("lora_out_315_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_315_dilations_0 = const()[name = tensor("lora_out_315_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_315_groups_0 = const()[name = tensor("lora_out_315_groups_0"), val = tensor(1)]; + tensor layers_26_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_26_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(292057216)))]; + tensor lora_out_315_cast_fp16 = conv(dilations = lora_out_315_dilations_0, groups = lora_out_315_groups_0, pad = lora_out_315_pad_0, pad_type = lora_out_315_pad_type_0, strides = lora_out_315_strides_0, weight = layers_26_self_attn_k_proj_loraB_weight_to_fp16, x = input_523_cast_fp16)[name = tensor("lora_out_315_cast_fp16")]; + tensor key_53_cast_fp16 = add(x = pretrained_out_315_cast_fp16, y = lora_out_315_cast_fp16)[name = tensor("key_53_cast_fp16")]; + tensor pretrained_out_317_pad_type_0 = const()[name = tensor("pretrained_out_317_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_317_strides_0 = const()[name = tensor("pretrained_out_317_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_317_pad_0 = const()[name = tensor("pretrained_out_317_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_317_dilations_0 = const()[name = tensor("pretrained_out_317_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_317_groups_0 = const()[name = tensor("pretrained_out_317_groups_0"), val = tensor(1)]; + tensor layers_26_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(292098240))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(292917504))), name = tensor("layers_26_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_26_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_26_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(292917632)))]; + tensor pretrained_out_317_cast_fp16 = conv(bias = layers_26_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_317_dilations_0, groups = pretrained_out_317_groups_0, pad = pretrained_out_317_pad_0, pad_type = pretrained_out_317_pad_type_0, strides = pretrained_out_317_strides_0, weight = layers_26_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_105_cast_fp16)[name = tensor("pretrained_out_317_cast_fp16")]; + tensor input_525_pad_type_0 = const()[name = tensor("input_525_pad_type_0"), val = tensor("valid")]; + tensor input_525_strides_0 = const()[name = tensor("input_525_strides_0"), val = tensor([1, 1])]; + tensor input_525_pad_0 = const()[name = tensor("input_525_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_525_dilations_0 = const()[name = tensor("input_525_dilations_0"), val = tensor([1, 1])]; + tensor input_525_groups_0 = const()[name = tensor("input_525_groups_0"), val = tensor(1)]; + tensor layers_26_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_26_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(292920256)))]; + tensor input_525_cast_fp16 = conv(dilations = input_525_dilations_0, groups = input_525_groups_0, pad = input_525_pad_0, pad_type = input_525_pad_type_0, strides = input_525_strides_0, weight = layers_26_self_attn_v_proj_loraA_weight_to_fp16, x = obj_105_cast_fp16)[name = tensor("input_525_cast_fp16")]; + tensor lora_out_317_pad_type_0 = const()[name = tensor("lora_out_317_pad_type_0"), val = tensor("valid")]; + tensor lora_out_317_strides_0 = const()[name = tensor("lora_out_317_strides_0"), val = tensor([1, 1])]; + tensor lora_out_317_pad_0 = const()[name = tensor("lora_out_317_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_317_dilations_0 = const()[name = tensor("lora_out_317_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_317_groups_0 = const()[name = tensor("lora_out_317_groups_0"), val = tensor(1)]; + tensor layers_26_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_26_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(292961280)))]; + tensor lora_out_317_cast_fp16 = conv(dilations = lora_out_317_dilations_0, groups = lora_out_317_groups_0, pad = lora_out_317_pad_0, pad_type = lora_out_317_pad_type_0, strides = lora_out_317_strides_0, weight = layers_26_self_attn_v_proj_loraB_weight_to_fp16, x = input_525_cast_fp16)[name = tensor("lora_out_317_cast_fp16")]; + tensor value_53_cast_fp16 = add(x = pretrained_out_317_cast_fp16, y = lora_out_317_cast_fp16)[name = tensor("value_53_cast_fp16")]; + tensor var_5844 = const()[name = tensor("op_5844"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_53_cast_fp16 = reshape(shape = var_5844, x = query_53_cast_fp16)[name = tensor("mh_q_53_cast_fp16")]; + tensor var_5846_to_fp16 = const()[name = tensor("op_5846_to_fp16"), val = tensor(0x1p-3)]; + tensor var_5847_cast_fp16 = mul(x = mh_q_53_cast_fp16, y = var_5846_to_fp16)[name = tensor("op_5847_cast_fp16")]; + tensor var_5848 = const()[name = tensor("op_5848"), val = tensor([1, 20, 64, -1])]; + tensor var_5849_cast_fp16 = reshape(shape = var_5848, x = key_53_cast_fp16)[name = tensor("op_5849_cast_fp16")]; + tensor mh_w_53_transpose_x_0 = const()[name = tensor("mh_w_53_transpose_x_0"), val = tensor(true)]; + tensor mh_w_53_transpose_y_0 = const()[name = tensor("mh_w_53_transpose_y_0"), val = tensor(false)]; + tensor mh_w_53_cast_fp16 = matmul(transpose_x = mh_w_53_transpose_x_0, transpose_y = mh_w_53_transpose_y_0, x = var_5847_cast_fp16, y = var_5849_cast_fp16)[name = tensor("mh_w_53_cast_fp16")]; + tensor var_5852_cast_fp16 = softmax(axis = var_5742, x = mh_w_53_cast_fp16)[name = tensor("op_5852_cast_fp16")]; + tensor var_5853 = const()[name = tensor("op_5853"), val = tensor([1, 20, 64, -1])]; + tensor var_5854_cast_fp16 = reshape(shape = var_5853, x = value_53_cast_fp16)[name = tensor("op_5854_cast_fp16")]; + tensor attn_53_transpose_x_0 = const()[name = tensor("attn_53_transpose_x_0"), val = tensor(false)]; + tensor attn_53_transpose_y_0 = const()[name = tensor("attn_53_transpose_y_0"), val = tensor(true)]; + tensor attn_53_cast_fp16 = matmul(transpose_x = attn_53_transpose_x_0, transpose_y = attn_53_transpose_y_0, x = var_5854_cast_fp16, y = var_5852_cast_fp16)[name = tensor("attn_53_cast_fp16")]; + tensor var_5857 = const()[name = tensor("op_5857"), val = tensor([1, 1280, 1, -1])]; + tensor input_527_cast_fp16 = reshape(shape = var_5857, x = attn_53_cast_fp16)[name = tensor("input_527_cast_fp16")]; + tensor pretrained_out_319_pad_type_0 = const()[name = tensor("pretrained_out_319_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_319_strides_0 = const()[name = tensor("pretrained_out_319_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_319_pad_0 = const()[name = tensor("pretrained_out_319_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_319_dilations_0 = const()[name = tensor("pretrained_out_319_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_319_groups_0 = const()[name = tensor("pretrained_out_319_groups_0"), val = tensor(1)]; + tensor layers_26_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(293002304))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(293821568))), name = tensor("layers_26_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_26_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_26_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(293821696)))]; + tensor pretrained_out_319_cast_fp16 = conv(bias = layers_26_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_319_dilations_0, groups = pretrained_out_319_groups_0, pad = pretrained_out_319_pad_0, pad_type = pretrained_out_319_pad_type_0, strides = pretrained_out_319_strides_0, weight = layers_26_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_527_cast_fp16)[name = tensor("pretrained_out_319_cast_fp16")]; + tensor input_529_pad_type_0 = const()[name = tensor("input_529_pad_type_0"), val = tensor("valid")]; + tensor input_529_strides_0 = const()[name = tensor("input_529_strides_0"), val = tensor([1, 1])]; + tensor input_529_pad_0 = const()[name = tensor("input_529_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_529_dilations_0 = const()[name = tensor("input_529_dilations_0"), val = tensor([1, 1])]; + tensor input_529_groups_0 = const()[name = tensor("input_529_groups_0"), val = tensor(1)]; + tensor layers_26_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_26_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(293824320)))]; + tensor input_529_cast_fp16 = conv(dilations = input_529_dilations_0, groups = input_529_groups_0, pad = input_529_pad_0, pad_type = input_529_pad_type_0, strides = input_529_strides_0, weight = layers_26_self_attn_o_proj_loraA_weight_to_fp16, x = input_527_cast_fp16)[name = tensor("input_529_cast_fp16")]; + tensor lora_out_319_pad_type_0 = const()[name = tensor("lora_out_319_pad_type_0"), val = tensor("valid")]; + tensor lora_out_319_strides_0 = const()[name = tensor("lora_out_319_strides_0"), val = tensor([1, 1])]; + tensor lora_out_319_pad_0 = const()[name = tensor("lora_out_319_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_319_dilations_0 = const()[name = tensor("lora_out_319_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_319_groups_0 = const()[name = tensor("lora_out_319_groups_0"), val = tensor(1)]; + tensor layers_26_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_26_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(293865344)))]; + tensor lora_out_319_cast_fp16 = conv(dilations = lora_out_319_dilations_0, groups = lora_out_319_groups_0, pad = lora_out_319_pad_0, pad_type = lora_out_319_pad_type_0, strides = lora_out_319_strides_0, weight = layers_26_self_attn_o_proj_loraB_weight_to_fp16, x = input_529_cast_fp16)[name = tensor("lora_out_319_cast_fp16")]; + tensor obj_107_cast_fp16 = add(x = pretrained_out_319_cast_fp16, y = lora_out_319_cast_fp16)[name = tensor("obj_107_cast_fp16")]; + tensor inputs_107_cast_fp16 = add(x = inputs_105_cast_fp16, y = obj_107_cast_fp16)[name = tensor("inputs_107_cast_fp16")]; + tensor out_107_axes_0 = const()[name = tensor("out_107_axes_0"), val = tensor([1])]; + tensor var_5891_to_fp16 = const()[name = tensor("op_5891_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_107_cast_fp16 = layer_norm(axes = out_107_axes_0, epsilon = var_5891_to_fp16, x = inputs_107_cast_fp16)[name = tensor("out_107_cast_fp16")]; + tensor input_531_gamma_0_to_fp16 = const()[name = tensor("input_531_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(293906368)))]; + tensor input_531_beta_0_to_fp16 = const()[name = tensor("input_531_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(293908992)))]; + tensor input_531_epsilon_0_to_fp16 = const()[name = tensor("input_531_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_531_cast_fp16 = batch_norm(beta = input_531_beta_0_to_fp16, epsilon = input_531_epsilon_0_to_fp16, gamma = input_531_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_107_cast_fp16)[name = tensor("input_531_cast_fp16")]; + tensor pretrained_out_321_pad_type_0 = const()[name = tensor("pretrained_out_321_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_321_strides_0 = const()[name = tensor("pretrained_out_321_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_321_pad_0 = const()[name = tensor("pretrained_out_321_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_321_dilations_0 = const()[name = tensor("pretrained_out_321_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_321_groups_0 = const()[name = tensor("pretrained_out_321_groups_0"), val = tensor(1)]; + tensor layers_26_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(293911616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297188480))), name = tensor("layers_26_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_26_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_26_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297188608)))]; + tensor pretrained_out_321_cast_fp16 = conv(bias = layers_26_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_321_dilations_0, groups = pretrained_out_321_groups_0, pad = pretrained_out_321_pad_0, pad_type = pretrained_out_321_pad_type_0, strides = pretrained_out_321_strides_0, weight = layers_26_fc1_pretrained_weight_to_fp16_palettized, x = input_531_cast_fp16)[name = tensor("pretrained_out_321_cast_fp16")]; + tensor input_533_pad_type_0 = const()[name = tensor("input_533_pad_type_0"), val = tensor("valid")]; + tensor input_533_strides_0 = const()[name = tensor("input_533_strides_0"), val = tensor([1, 1])]; + tensor input_533_pad_0 = const()[name = tensor("input_533_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_533_dilations_0 = const()[name = tensor("input_533_dilations_0"), val = tensor([1, 1])]; + tensor input_533_groups_0 = const()[name = tensor("input_533_groups_0"), val = tensor(1)]; + tensor layers_26_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_26_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297198912)))]; + tensor input_533_cast_fp16 = conv(dilations = input_533_dilations_0, groups = input_533_groups_0, pad = input_533_pad_0, pad_type = input_533_pad_type_0, strides = input_533_strides_0, weight = layers_26_fc1_loraA_weight_to_fp16, x = input_531_cast_fp16)[name = tensor("input_533_cast_fp16")]; + tensor lora_out_321_pad_type_0 = const()[name = tensor("lora_out_321_pad_type_0"), val = tensor("valid")]; + tensor lora_out_321_strides_0 = const()[name = tensor("lora_out_321_strides_0"), val = tensor([1, 1])]; + tensor lora_out_321_pad_0 = const()[name = tensor("lora_out_321_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_321_dilations_0 = const()[name = tensor("lora_out_321_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_321_groups_0 = const()[name = tensor("lora_out_321_groups_0"), val = tensor(1)]; + tensor layers_26_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_26_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297239936)))]; + tensor lora_out_321_cast_fp16 = conv(dilations = lora_out_321_dilations_0, groups = lora_out_321_groups_0, pad = lora_out_321_pad_0, pad_type = lora_out_321_pad_type_0, strides = lora_out_321_strides_0, weight = layers_26_fc1_loraB_weight_to_fp16, x = input_533_cast_fp16)[name = tensor("lora_out_321_cast_fp16")]; + tensor input_535_cast_fp16 = add(x = pretrained_out_321_cast_fp16, y = lora_out_321_cast_fp16)[name = tensor("input_535_cast_fp16")]; + tensor input_537_mode_0 = const()[name = tensor("input_537_mode_0"), val = tensor("EXACT")]; + tensor input_537_cast_fp16 = gelu(mode = input_537_mode_0, x = input_535_cast_fp16)[name = tensor("input_537_cast_fp16")]; + tensor pretrained_out_323_pad_type_0 = const()[name = tensor("pretrained_out_323_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_323_strides_0 = const()[name = tensor("pretrained_out_323_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_323_pad_0 = const()[name = tensor("pretrained_out_323_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_323_dilations_0 = const()[name = tensor("pretrained_out_323_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_323_groups_0 = const()[name = tensor("pretrained_out_323_groups_0"), val = tensor(1)]; + tensor layers_26_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297403840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(300680704))), name = tensor("layers_26_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_26_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_26_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(300680832)))]; + tensor pretrained_out_323_cast_fp16 = conv(bias = layers_26_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_323_dilations_0, groups = pretrained_out_323_groups_0, pad = pretrained_out_323_pad_0, pad_type = pretrained_out_323_pad_type_0, strides = pretrained_out_323_strides_0, weight = layers_26_fc2_pretrained_weight_to_fp16_palettized, x = input_537_cast_fp16)[name = tensor("pretrained_out_323_cast_fp16")]; + tensor input_539_pad_type_0 = const()[name = tensor("input_539_pad_type_0"), val = tensor("valid")]; + tensor input_539_strides_0 = const()[name = tensor("input_539_strides_0"), val = tensor([1, 1])]; + tensor input_539_pad_0 = const()[name = tensor("input_539_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_539_dilations_0 = const()[name = tensor("input_539_dilations_0"), val = tensor([1, 1])]; + tensor input_539_groups_0 = const()[name = tensor("input_539_groups_0"), val = tensor(1)]; + tensor layers_26_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_26_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(300683456)))]; + tensor input_539_cast_fp16 = conv(dilations = input_539_dilations_0, groups = input_539_groups_0, pad = input_539_pad_0, pad_type = input_539_pad_type_0, strides = input_539_strides_0, weight = layers_26_fc2_loraA_weight_to_fp16, x = input_537_cast_fp16)[name = tensor("input_539_cast_fp16")]; + tensor lora_out_323_pad_type_0 = const()[name = tensor("lora_out_323_pad_type_0"), val = tensor("valid")]; + tensor lora_out_323_strides_0 = const()[name = tensor("lora_out_323_strides_0"), val = tensor([1, 1])]; + tensor lora_out_323_pad_0 = const()[name = tensor("lora_out_323_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_323_dilations_0 = const()[name = tensor("lora_out_323_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_323_groups_0 = const()[name = tensor("lora_out_323_groups_0"), val = tensor(1)]; + tensor layers_26_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_26_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(300847360)))]; + tensor lora_out_323_cast_fp16 = conv(dilations = lora_out_323_dilations_0, groups = lora_out_323_groups_0, pad = lora_out_323_pad_0, pad_type = lora_out_323_pad_type_0, strides = lora_out_323_strides_0, weight = layers_26_fc2_loraB_weight_to_fp16, x = input_539_cast_fp16)[name = tensor("lora_out_323_cast_fp16")]; + tensor hidden_states_57_cast_fp16 = add(x = pretrained_out_323_cast_fp16, y = lora_out_323_cast_fp16)[name = tensor("hidden_states_57_cast_fp16")]; + tensor inputs_109_cast_fp16 = add(x = inputs_107_cast_fp16, y = hidden_states_57_cast_fp16)[name = tensor("inputs_109_cast_fp16")]; + tensor var_5956 = const()[name = tensor("op_5956"), val = tensor(3)]; + tensor out_109_axes_0 = const()[name = tensor("out_109_axes_0"), val = tensor([1])]; + tensor var_5975_to_fp16 = const()[name = tensor("op_5975_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_109_cast_fp16 = layer_norm(axes = out_109_axes_0, epsilon = var_5975_to_fp16, x = inputs_109_cast_fp16)[name = tensor("out_109_cast_fp16")]; + tensor obj_109_gamma_0_to_fp16 = const()[name = tensor("obj_109_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(300888384)))]; + tensor obj_109_beta_0_to_fp16 = const()[name = tensor("obj_109_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(300891008)))]; + tensor obj_109_epsilon_0_to_fp16 = const()[name = tensor("obj_109_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_109_cast_fp16 = batch_norm(beta = obj_109_beta_0_to_fp16, epsilon = obj_109_epsilon_0_to_fp16, gamma = obj_109_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_109_cast_fp16)[name = tensor("obj_109_cast_fp16")]; + tensor pretrained_out_325_pad_type_0 = const()[name = tensor("pretrained_out_325_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_325_strides_0 = const()[name = tensor("pretrained_out_325_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_325_pad_0 = const()[name = tensor("pretrained_out_325_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_325_dilations_0 = const()[name = tensor("pretrained_out_325_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_325_groups_0 = const()[name = tensor("pretrained_out_325_groups_0"), val = tensor(1)]; + tensor layers_27_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(300893632))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(301712896))), name = tensor("layers_27_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_27_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_27_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(301713024)))]; + tensor pretrained_out_325_cast_fp16 = conv(bias = layers_27_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_325_dilations_0, groups = pretrained_out_325_groups_0, pad = pretrained_out_325_pad_0, pad_type = pretrained_out_325_pad_type_0, strides = pretrained_out_325_strides_0, weight = layers_27_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_109_cast_fp16)[name = tensor("pretrained_out_325_cast_fp16")]; + tensor input_541_pad_type_0 = const()[name = tensor("input_541_pad_type_0"), val = tensor("valid")]; + tensor input_541_strides_0 = const()[name = tensor("input_541_strides_0"), val = tensor([1, 1])]; + tensor input_541_pad_0 = const()[name = tensor("input_541_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_541_dilations_0 = const()[name = tensor("input_541_dilations_0"), val = tensor([1, 1])]; + tensor input_541_groups_0 = const()[name = tensor("input_541_groups_0"), val = tensor(1)]; + tensor layers_27_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_27_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(301715648)))]; + tensor input_541_cast_fp16 = conv(dilations = input_541_dilations_0, groups = input_541_groups_0, pad = input_541_pad_0, pad_type = input_541_pad_type_0, strides = input_541_strides_0, weight = layers_27_self_attn_q_proj_loraA_weight_to_fp16, x = obj_109_cast_fp16)[name = tensor("input_541_cast_fp16")]; + tensor lora_out_325_pad_type_0 = const()[name = tensor("lora_out_325_pad_type_0"), val = tensor("valid")]; + tensor lora_out_325_strides_0 = const()[name = tensor("lora_out_325_strides_0"), val = tensor([1, 1])]; + tensor lora_out_325_pad_0 = const()[name = tensor("lora_out_325_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_325_dilations_0 = const()[name = tensor("lora_out_325_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_325_groups_0 = const()[name = tensor("lora_out_325_groups_0"), val = tensor(1)]; + tensor layers_27_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_27_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(301756672)))]; + tensor lora_out_325_cast_fp16 = conv(dilations = lora_out_325_dilations_0, groups = lora_out_325_groups_0, pad = lora_out_325_pad_0, pad_type = lora_out_325_pad_type_0, strides = lora_out_325_strides_0, weight = layers_27_self_attn_q_proj_loraB_weight_to_fp16, x = input_541_cast_fp16)[name = tensor("lora_out_325_cast_fp16")]; + tensor query_55_cast_fp16 = add(x = pretrained_out_325_cast_fp16, y = lora_out_325_cast_fp16)[name = tensor("query_55_cast_fp16")]; + tensor pretrained_out_327_pad_type_0 = const()[name = tensor("pretrained_out_327_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_327_strides_0 = const()[name = tensor("pretrained_out_327_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_327_pad_0 = const()[name = tensor("pretrained_out_327_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_327_dilations_0 = const()[name = tensor("pretrained_out_327_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_327_groups_0 = const()[name = tensor("pretrained_out_327_groups_0"), val = tensor(1)]; + tensor layers_27_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(301797696))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(302616960))), name = tensor("layers_27_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_327_cast_fp16 = conv(dilations = pretrained_out_327_dilations_0, groups = pretrained_out_327_groups_0, pad = pretrained_out_327_pad_0, pad_type = pretrained_out_327_pad_type_0, strides = pretrained_out_327_strides_0, weight = layers_27_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_109_cast_fp16)[name = tensor("pretrained_out_327_cast_fp16")]; + tensor input_543_pad_type_0 = const()[name = tensor("input_543_pad_type_0"), val = tensor("valid")]; + tensor input_543_strides_0 = const()[name = tensor("input_543_strides_0"), val = tensor([1, 1])]; + tensor input_543_pad_0 = const()[name = tensor("input_543_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_543_dilations_0 = const()[name = tensor("input_543_dilations_0"), val = tensor([1, 1])]; + tensor input_543_groups_0 = const()[name = tensor("input_543_groups_0"), val = tensor(1)]; + tensor layers_27_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_27_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(302617088)))]; + tensor input_543_cast_fp16 = conv(dilations = input_543_dilations_0, groups = input_543_groups_0, pad = input_543_pad_0, pad_type = input_543_pad_type_0, strides = input_543_strides_0, weight = layers_27_self_attn_k_proj_loraA_weight_to_fp16, x = obj_109_cast_fp16)[name = tensor("input_543_cast_fp16")]; + tensor lora_out_327_pad_type_0 = const()[name = tensor("lora_out_327_pad_type_0"), val = tensor("valid")]; + tensor lora_out_327_strides_0 = const()[name = tensor("lora_out_327_strides_0"), val = tensor([1, 1])]; + tensor lora_out_327_pad_0 = const()[name = tensor("lora_out_327_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_327_dilations_0 = const()[name = tensor("lora_out_327_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_327_groups_0 = const()[name = tensor("lora_out_327_groups_0"), val = tensor(1)]; + tensor layers_27_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_27_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(302658112)))]; + tensor lora_out_327_cast_fp16 = conv(dilations = lora_out_327_dilations_0, groups = lora_out_327_groups_0, pad = lora_out_327_pad_0, pad_type = lora_out_327_pad_type_0, strides = lora_out_327_strides_0, weight = layers_27_self_attn_k_proj_loraB_weight_to_fp16, x = input_543_cast_fp16)[name = tensor("lora_out_327_cast_fp16")]; + tensor key_55_cast_fp16 = add(x = pretrained_out_327_cast_fp16, y = lora_out_327_cast_fp16)[name = tensor("key_55_cast_fp16")]; + tensor pretrained_out_329_pad_type_0 = const()[name = tensor("pretrained_out_329_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_329_strides_0 = const()[name = tensor("pretrained_out_329_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_329_pad_0 = const()[name = tensor("pretrained_out_329_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_329_dilations_0 = const()[name = tensor("pretrained_out_329_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_329_groups_0 = const()[name = tensor("pretrained_out_329_groups_0"), val = tensor(1)]; + tensor layers_27_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(302699136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(303518400))), name = tensor("layers_27_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_27_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_27_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(303518528)))]; + tensor pretrained_out_329_cast_fp16 = conv(bias = layers_27_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_329_dilations_0, groups = pretrained_out_329_groups_0, pad = pretrained_out_329_pad_0, pad_type = pretrained_out_329_pad_type_0, strides = pretrained_out_329_strides_0, weight = layers_27_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_109_cast_fp16)[name = tensor("pretrained_out_329_cast_fp16")]; + tensor input_545_pad_type_0 = const()[name = tensor("input_545_pad_type_0"), val = tensor("valid")]; + tensor input_545_strides_0 = const()[name = tensor("input_545_strides_0"), val = tensor([1, 1])]; + tensor input_545_pad_0 = const()[name = tensor("input_545_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_545_dilations_0 = const()[name = tensor("input_545_dilations_0"), val = tensor([1, 1])]; + tensor input_545_groups_0 = const()[name = tensor("input_545_groups_0"), val = tensor(1)]; + tensor layers_27_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_27_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(303521152)))]; + tensor input_545_cast_fp16 = conv(dilations = input_545_dilations_0, groups = input_545_groups_0, pad = input_545_pad_0, pad_type = input_545_pad_type_0, strides = input_545_strides_0, weight = layers_27_self_attn_v_proj_loraA_weight_to_fp16, x = obj_109_cast_fp16)[name = tensor("input_545_cast_fp16")]; + tensor lora_out_329_pad_type_0 = const()[name = tensor("lora_out_329_pad_type_0"), val = tensor("valid")]; + tensor lora_out_329_strides_0 = const()[name = tensor("lora_out_329_strides_0"), val = tensor([1, 1])]; + tensor lora_out_329_pad_0 = const()[name = tensor("lora_out_329_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_329_dilations_0 = const()[name = tensor("lora_out_329_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_329_groups_0 = const()[name = tensor("lora_out_329_groups_0"), val = tensor(1)]; + tensor layers_27_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_27_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(303562176)))]; + tensor lora_out_329_cast_fp16 = conv(dilations = lora_out_329_dilations_0, groups = lora_out_329_groups_0, pad = lora_out_329_pad_0, pad_type = lora_out_329_pad_type_0, strides = lora_out_329_strides_0, weight = layers_27_self_attn_v_proj_loraB_weight_to_fp16, x = input_545_cast_fp16)[name = tensor("lora_out_329_cast_fp16")]; + tensor value_55_cast_fp16 = add(x = pretrained_out_329_cast_fp16, y = lora_out_329_cast_fp16)[name = tensor("value_55_cast_fp16")]; + tensor var_6058 = const()[name = tensor("op_6058"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_55_cast_fp16 = reshape(shape = var_6058, x = query_55_cast_fp16)[name = tensor("mh_q_55_cast_fp16")]; + tensor var_6060_to_fp16 = const()[name = tensor("op_6060_to_fp16"), val = tensor(0x1p-3)]; + tensor var_6061_cast_fp16 = mul(x = mh_q_55_cast_fp16, y = var_6060_to_fp16)[name = tensor("op_6061_cast_fp16")]; + tensor var_6062 = const()[name = tensor("op_6062"), val = tensor([1, 20, 64, -1])]; + tensor var_6063_cast_fp16 = reshape(shape = var_6062, x = key_55_cast_fp16)[name = tensor("op_6063_cast_fp16")]; + tensor mh_w_55_transpose_x_0 = const()[name = tensor("mh_w_55_transpose_x_0"), val = tensor(true)]; + tensor mh_w_55_transpose_y_0 = const()[name = tensor("mh_w_55_transpose_y_0"), val = tensor(false)]; + tensor mh_w_55_cast_fp16 = matmul(transpose_x = mh_w_55_transpose_x_0, transpose_y = mh_w_55_transpose_y_0, x = var_6061_cast_fp16, y = var_6063_cast_fp16)[name = tensor("mh_w_55_cast_fp16")]; + tensor var_6066_cast_fp16 = softmax(axis = var_5956, x = mh_w_55_cast_fp16)[name = tensor("op_6066_cast_fp16")]; + tensor var_6067 = const()[name = tensor("op_6067"), val = tensor([1, 20, 64, -1])]; + tensor var_6068_cast_fp16 = reshape(shape = var_6067, x = value_55_cast_fp16)[name = tensor("op_6068_cast_fp16")]; + tensor attn_55_transpose_x_0 = const()[name = tensor("attn_55_transpose_x_0"), val = tensor(false)]; + tensor attn_55_transpose_y_0 = const()[name = tensor("attn_55_transpose_y_0"), val = tensor(true)]; + tensor attn_55_cast_fp16 = matmul(transpose_x = attn_55_transpose_x_0, transpose_y = attn_55_transpose_y_0, x = var_6068_cast_fp16, y = var_6066_cast_fp16)[name = tensor("attn_55_cast_fp16")]; + tensor var_6071 = const()[name = tensor("op_6071"), val = tensor([1, 1280, 1, -1])]; + tensor input_547_cast_fp16 = reshape(shape = var_6071, x = attn_55_cast_fp16)[name = tensor("input_547_cast_fp16")]; + tensor pretrained_out_331_pad_type_0 = const()[name = tensor("pretrained_out_331_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_331_strides_0 = const()[name = tensor("pretrained_out_331_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_331_pad_0 = const()[name = tensor("pretrained_out_331_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_331_dilations_0 = const()[name = tensor("pretrained_out_331_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_331_groups_0 = const()[name = tensor("pretrained_out_331_groups_0"), val = tensor(1)]; + tensor layers_27_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(303603200))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304422464))), name = tensor("layers_27_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_27_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_27_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304422592)))]; + tensor pretrained_out_331_cast_fp16 = conv(bias = layers_27_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_331_dilations_0, groups = pretrained_out_331_groups_0, pad = pretrained_out_331_pad_0, pad_type = pretrained_out_331_pad_type_0, strides = pretrained_out_331_strides_0, weight = layers_27_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_547_cast_fp16)[name = tensor("pretrained_out_331_cast_fp16")]; + tensor input_549_pad_type_0 = const()[name = tensor("input_549_pad_type_0"), val = tensor("valid")]; + tensor input_549_strides_0 = const()[name = tensor("input_549_strides_0"), val = tensor([1, 1])]; + tensor input_549_pad_0 = const()[name = tensor("input_549_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_549_dilations_0 = const()[name = tensor("input_549_dilations_0"), val = tensor([1, 1])]; + tensor input_549_groups_0 = const()[name = tensor("input_549_groups_0"), val = tensor(1)]; + tensor layers_27_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_27_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304425216)))]; + tensor input_549_cast_fp16 = conv(dilations = input_549_dilations_0, groups = input_549_groups_0, pad = input_549_pad_0, pad_type = input_549_pad_type_0, strides = input_549_strides_0, weight = layers_27_self_attn_o_proj_loraA_weight_to_fp16, x = input_547_cast_fp16)[name = tensor("input_549_cast_fp16")]; + tensor lora_out_331_pad_type_0 = const()[name = tensor("lora_out_331_pad_type_0"), val = tensor("valid")]; + tensor lora_out_331_strides_0 = const()[name = tensor("lora_out_331_strides_0"), val = tensor([1, 1])]; + tensor lora_out_331_pad_0 = const()[name = tensor("lora_out_331_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_331_dilations_0 = const()[name = tensor("lora_out_331_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_331_groups_0 = const()[name = tensor("lora_out_331_groups_0"), val = tensor(1)]; + tensor layers_27_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_27_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304466240)))]; + tensor lora_out_331_cast_fp16 = conv(dilations = lora_out_331_dilations_0, groups = lora_out_331_groups_0, pad = lora_out_331_pad_0, pad_type = lora_out_331_pad_type_0, strides = lora_out_331_strides_0, weight = layers_27_self_attn_o_proj_loraB_weight_to_fp16, x = input_549_cast_fp16)[name = tensor("lora_out_331_cast_fp16")]; + tensor obj_111_cast_fp16 = add(x = pretrained_out_331_cast_fp16, y = lora_out_331_cast_fp16)[name = tensor("obj_111_cast_fp16")]; + tensor inputs_111_cast_fp16 = add(x = inputs_109_cast_fp16, y = obj_111_cast_fp16)[name = tensor("inputs_111_cast_fp16")]; + tensor out_111_axes_0 = const()[name = tensor("out_111_axes_0"), val = tensor([1])]; + tensor var_6105_to_fp16 = const()[name = tensor("op_6105_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_111_cast_fp16 = layer_norm(axes = out_111_axes_0, epsilon = var_6105_to_fp16, x = inputs_111_cast_fp16)[name = tensor("out_111_cast_fp16")]; + tensor input_551_gamma_0_to_fp16 = const()[name = tensor("input_551_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304507264)))]; + tensor input_551_beta_0_to_fp16 = const()[name = tensor("input_551_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304509888)))]; + tensor input_551_epsilon_0_to_fp16 = const()[name = tensor("input_551_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_551_cast_fp16 = batch_norm(beta = input_551_beta_0_to_fp16, epsilon = input_551_epsilon_0_to_fp16, gamma = input_551_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_111_cast_fp16)[name = tensor("input_551_cast_fp16")]; + tensor pretrained_out_333_pad_type_0 = const()[name = tensor("pretrained_out_333_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_333_strides_0 = const()[name = tensor("pretrained_out_333_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_333_pad_0 = const()[name = tensor("pretrained_out_333_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_333_dilations_0 = const()[name = tensor("pretrained_out_333_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_333_groups_0 = const()[name = tensor("pretrained_out_333_groups_0"), val = tensor(1)]; + tensor layers_27_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304512512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(307789376))), name = tensor("layers_27_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_27_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_27_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(307789504)))]; + tensor pretrained_out_333_cast_fp16 = conv(bias = layers_27_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_333_dilations_0, groups = pretrained_out_333_groups_0, pad = pretrained_out_333_pad_0, pad_type = pretrained_out_333_pad_type_0, strides = pretrained_out_333_strides_0, weight = layers_27_fc1_pretrained_weight_to_fp16_palettized, x = input_551_cast_fp16)[name = tensor("pretrained_out_333_cast_fp16")]; + tensor input_553_pad_type_0 = const()[name = tensor("input_553_pad_type_0"), val = tensor("valid")]; + tensor input_553_strides_0 = const()[name = tensor("input_553_strides_0"), val = tensor([1, 1])]; + tensor input_553_pad_0 = const()[name = tensor("input_553_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_553_dilations_0 = const()[name = tensor("input_553_dilations_0"), val = tensor([1, 1])]; + tensor input_553_groups_0 = const()[name = tensor("input_553_groups_0"), val = tensor(1)]; + tensor layers_27_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_27_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(307799808)))]; + tensor input_553_cast_fp16 = conv(dilations = input_553_dilations_0, groups = input_553_groups_0, pad = input_553_pad_0, pad_type = input_553_pad_type_0, strides = input_553_strides_0, weight = layers_27_fc1_loraA_weight_to_fp16, x = input_551_cast_fp16)[name = tensor("input_553_cast_fp16")]; + tensor lora_out_333_pad_type_0 = const()[name = tensor("lora_out_333_pad_type_0"), val = tensor("valid")]; + tensor lora_out_333_strides_0 = const()[name = tensor("lora_out_333_strides_0"), val = tensor([1, 1])]; + tensor lora_out_333_pad_0 = const()[name = tensor("lora_out_333_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_333_dilations_0 = const()[name = tensor("lora_out_333_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_333_groups_0 = const()[name = tensor("lora_out_333_groups_0"), val = tensor(1)]; + tensor layers_27_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_27_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(307840832)))]; + tensor lora_out_333_cast_fp16 = conv(dilations = lora_out_333_dilations_0, groups = lora_out_333_groups_0, pad = lora_out_333_pad_0, pad_type = lora_out_333_pad_type_0, strides = lora_out_333_strides_0, weight = layers_27_fc1_loraB_weight_to_fp16, x = input_553_cast_fp16)[name = tensor("lora_out_333_cast_fp16")]; + tensor input_555_cast_fp16 = add(x = pretrained_out_333_cast_fp16, y = lora_out_333_cast_fp16)[name = tensor("input_555_cast_fp16")]; + tensor input_557_mode_0 = const()[name = tensor("input_557_mode_0"), val = tensor("EXACT")]; + tensor input_557_cast_fp16 = gelu(mode = input_557_mode_0, x = input_555_cast_fp16)[name = tensor("input_557_cast_fp16")]; + tensor pretrained_out_335_pad_type_0 = const()[name = tensor("pretrained_out_335_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_335_strides_0 = const()[name = tensor("pretrained_out_335_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_335_pad_0 = const()[name = tensor("pretrained_out_335_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_335_dilations_0 = const()[name = tensor("pretrained_out_335_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_335_groups_0 = const()[name = tensor("pretrained_out_335_groups_0"), val = tensor(1)]; + tensor layers_27_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(308004736))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311281600))), name = tensor("layers_27_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_27_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_27_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311281728)))]; + tensor pretrained_out_335_cast_fp16 = conv(bias = layers_27_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_335_dilations_0, groups = pretrained_out_335_groups_0, pad = pretrained_out_335_pad_0, pad_type = pretrained_out_335_pad_type_0, strides = pretrained_out_335_strides_0, weight = layers_27_fc2_pretrained_weight_to_fp16_palettized, x = input_557_cast_fp16)[name = tensor("pretrained_out_335_cast_fp16")]; + tensor input_559_pad_type_0 = const()[name = tensor("input_559_pad_type_0"), val = tensor("valid")]; + tensor input_559_strides_0 = const()[name = tensor("input_559_strides_0"), val = tensor([1, 1])]; + tensor input_559_pad_0 = const()[name = tensor("input_559_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_559_dilations_0 = const()[name = tensor("input_559_dilations_0"), val = tensor([1, 1])]; + tensor input_559_groups_0 = const()[name = tensor("input_559_groups_0"), val = tensor(1)]; + tensor layers_27_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_27_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311284352)))]; + tensor input_559_cast_fp16 = conv(dilations = input_559_dilations_0, groups = input_559_groups_0, pad = input_559_pad_0, pad_type = input_559_pad_type_0, strides = input_559_strides_0, weight = layers_27_fc2_loraA_weight_to_fp16, x = input_557_cast_fp16)[name = tensor("input_559_cast_fp16")]; + tensor lora_out_335_pad_type_0 = const()[name = tensor("lora_out_335_pad_type_0"), val = tensor("valid")]; + tensor lora_out_335_strides_0 = const()[name = tensor("lora_out_335_strides_0"), val = tensor([1, 1])]; + tensor lora_out_335_pad_0 = const()[name = tensor("lora_out_335_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_335_dilations_0 = const()[name = tensor("lora_out_335_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_335_groups_0 = const()[name = tensor("lora_out_335_groups_0"), val = tensor(1)]; + tensor layers_27_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_27_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311448256)))]; + tensor lora_out_335_cast_fp16 = conv(dilations = lora_out_335_dilations_0, groups = lora_out_335_groups_0, pad = lora_out_335_pad_0, pad_type = lora_out_335_pad_type_0, strides = lora_out_335_strides_0, weight = layers_27_fc2_loraB_weight_to_fp16, x = input_559_cast_fp16)[name = tensor("lora_out_335_cast_fp16")]; + tensor hidden_states_59_cast_fp16 = add(x = pretrained_out_335_cast_fp16, y = lora_out_335_cast_fp16)[name = tensor("hidden_states_59_cast_fp16")]; + tensor inputs_113_cast_fp16 = add(x = inputs_111_cast_fp16, y = hidden_states_59_cast_fp16)[name = tensor("inputs_113_cast_fp16")]; + tensor var_6170 = const()[name = tensor("op_6170"), val = tensor(3)]; + tensor out_113_axes_0 = const()[name = tensor("out_113_axes_0"), val = tensor([1])]; + tensor var_6189_to_fp16 = const()[name = tensor("op_6189_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_113_cast_fp16 = layer_norm(axes = out_113_axes_0, epsilon = var_6189_to_fp16, x = inputs_113_cast_fp16)[name = tensor("out_113_cast_fp16")]; + tensor obj_113_gamma_0_to_fp16 = const()[name = tensor("obj_113_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311489280)))]; + tensor obj_113_beta_0_to_fp16 = const()[name = tensor("obj_113_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311491904)))]; + tensor obj_113_epsilon_0_to_fp16 = const()[name = tensor("obj_113_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_113_cast_fp16 = batch_norm(beta = obj_113_beta_0_to_fp16, epsilon = obj_113_epsilon_0_to_fp16, gamma = obj_113_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_113_cast_fp16)[name = tensor("obj_113_cast_fp16")]; + tensor pretrained_out_337_pad_type_0 = const()[name = tensor("pretrained_out_337_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_337_strides_0 = const()[name = tensor("pretrained_out_337_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_337_pad_0 = const()[name = tensor("pretrained_out_337_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_337_dilations_0 = const()[name = tensor("pretrained_out_337_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_337_groups_0 = const()[name = tensor("pretrained_out_337_groups_0"), val = tensor(1)]; + tensor layers_28_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(311494528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(312313792))), name = tensor("layers_28_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_28_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_28_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(312313920)))]; + tensor pretrained_out_337_cast_fp16 = conv(bias = layers_28_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_337_dilations_0, groups = pretrained_out_337_groups_0, pad = pretrained_out_337_pad_0, pad_type = pretrained_out_337_pad_type_0, strides = pretrained_out_337_strides_0, weight = layers_28_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_113_cast_fp16)[name = tensor("pretrained_out_337_cast_fp16")]; + tensor input_561_pad_type_0 = const()[name = tensor("input_561_pad_type_0"), val = tensor("valid")]; + tensor input_561_strides_0 = const()[name = tensor("input_561_strides_0"), val = tensor([1, 1])]; + tensor input_561_pad_0 = const()[name = tensor("input_561_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_561_dilations_0 = const()[name = tensor("input_561_dilations_0"), val = tensor([1, 1])]; + tensor input_561_groups_0 = const()[name = tensor("input_561_groups_0"), val = tensor(1)]; + tensor layers_28_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_28_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(312316544)))]; + tensor input_561_cast_fp16 = conv(dilations = input_561_dilations_0, groups = input_561_groups_0, pad = input_561_pad_0, pad_type = input_561_pad_type_0, strides = input_561_strides_0, weight = layers_28_self_attn_q_proj_loraA_weight_to_fp16, x = obj_113_cast_fp16)[name = tensor("input_561_cast_fp16")]; + tensor lora_out_337_pad_type_0 = const()[name = tensor("lora_out_337_pad_type_0"), val = tensor("valid")]; + tensor lora_out_337_strides_0 = const()[name = tensor("lora_out_337_strides_0"), val = tensor([1, 1])]; + tensor lora_out_337_pad_0 = const()[name = tensor("lora_out_337_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_337_dilations_0 = const()[name = tensor("lora_out_337_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_337_groups_0 = const()[name = tensor("lora_out_337_groups_0"), val = tensor(1)]; + tensor layers_28_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_28_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(312357568)))]; + tensor lora_out_337_cast_fp16 = conv(dilations = lora_out_337_dilations_0, groups = lora_out_337_groups_0, pad = lora_out_337_pad_0, pad_type = lora_out_337_pad_type_0, strides = lora_out_337_strides_0, weight = layers_28_self_attn_q_proj_loraB_weight_to_fp16, x = input_561_cast_fp16)[name = tensor("lora_out_337_cast_fp16")]; + tensor query_57_cast_fp16 = add(x = pretrained_out_337_cast_fp16, y = lora_out_337_cast_fp16)[name = tensor("query_57_cast_fp16")]; + tensor pretrained_out_339_pad_type_0 = const()[name = tensor("pretrained_out_339_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_339_strides_0 = const()[name = tensor("pretrained_out_339_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_339_pad_0 = const()[name = tensor("pretrained_out_339_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_339_dilations_0 = const()[name = tensor("pretrained_out_339_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_339_groups_0 = const()[name = tensor("pretrained_out_339_groups_0"), val = tensor(1)]; + tensor layers_28_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(312398592))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(313217856))), name = tensor("layers_28_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_339_cast_fp16 = conv(dilations = pretrained_out_339_dilations_0, groups = pretrained_out_339_groups_0, pad = pretrained_out_339_pad_0, pad_type = pretrained_out_339_pad_type_0, strides = pretrained_out_339_strides_0, weight = layers_28_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_113_cast_fp16)[name = tensor("pretrained_out_339_cast_fp16")]; + tensor input_563_pad_type_0 = const()[name = tensor("input_563_pad_type_0"), val = tensor("valid")]; + tensor input_563_strides_0 = const()[name = tensor("input_563_strides_0"), val = tensor([1, 1])]; + tensor input_563_pad_0 = const()[name = tensor("input_563_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_563_dilations_0 = const()[name = tensor("input_563_dilations_0"), val = tensor([1, 1])]; + tensor input_563_groups_0 = const()[name = tensor("input_563_groups_0"), val = tensor(1)]; + tensor layers_28_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_28_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(313217984)))]; + tensor input_563_cast_fp16 = conv(dilations = input_563_dilations_0, groups = input_563_groups_0, pad = input_563_pad_0, pad_type = input_563_pad_type_0, strides = input_563_strides_0, weight = layers_28_self_attn_k_proj_loraA_weight_to_fp16, x = obj_113_cast_fp16)[name = tensor("input_563_cast_fp16")]; + tensor lora_out_339_pad_type_0 = const()[name = tensor("lora_out_339_pad_type_0"), val = tensor("valid")]; + tensor lora_out_339_strides_0 = const()[name = tensor("lora_out_339_strides_0"), val = tensor([1, 1])]; + tensor lora_out_339_pad_0 = const()[name = tensor("lora_out_339_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_339_dilations_0 = const()[name = tensor("lora_out_339_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_339_groups_0 = const()[name = tensor("lora_out_339_groups_0"), val = tensor(1)]; + tensor layers_28_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_28_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(313259008)))]; + tensor lora_out_339_cast_fp16 = conv(dilations = lora_out_339_dilations_0, groups = lora_out_339_groups_0, pad = lora_out_339_pad_0, pad_type = lora_out_339_pad_type_0, strides = lora_out_339_strides_0, weight = layers_28_self_attn_k_proj_loraB_weight_to_fp16, x = input_563_cast_fp16)[name = tensor("lora_out_339_cast_fp16")]; + tensor key_57_cast_fp16 = add(x = pretrained_out_339_cast_fp16, y = lora_out_339_cast_fp16)[name = tensor("key_57_cast_fp16")]; + tensor pretrained_out_341_pad_type_0 = const()[name = tensor("pretrained_out_341_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_341_strides_0 = const()[name = tensor("pretrained_out_341_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_341_pad_0 = const()[name = tensor("pretrained_out_341_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_341_dilations_0 = const()[name = tensor("pretrained_out_341_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_341_groups_0 = const()[name = tensor("pretrained_out_341_groups_0"), val = tensor(1)]; + tensor layers_28_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(313300032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(314119296))), name = tensor("layers_28_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_28_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_28_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(314119424)))]; + tensor pretrained_out_341_cast_fp16 = conv(bias = layers_28_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_341_dilations_0, groups = pretrained_out_341_groups_0, pad = pretrained_out_341_pad_0, pad_type = pretrained_out_341_pad_type_0, strides = pretrained_out_341_strides_0, weight = layers_28_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_113_cast_fp16)[name = tensor("pretrained_out_341_cast_fp16")]; + tensor input_565_pad_type_0 = const()[name = tensor("input_565_pad_type_0"), val = tensor("valid")]; + tensor input_565_strides_0 = const()[name = tensor("input_565_strides_0"), val = tensor([1, 1])]; + tensor input_565_pad_0 = const()[name = tensor("input_565_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_565_dilations_0 = const()[name = tensor("input_565_dilations_0"), val = tensor([1, 1])]; + tensor input_565_groups_0 = const()[name = tensor("input_565_groups_0"), val = tensor(1)]; + tensor layers_28_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_28_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(314122048)))]; + tensor input_565_cast_fp16 = conv(dilations = input_565_dilations_0, groups = input_565_groups_0, pad = input_565_pad_0, pad_type = input_565_pad_type_0, strides = input_565_strides_0, weight = layers_28_self_attn_v_proj_loraA_weight_to_fp16, x = obj_113_cast_fp16)[name = tensor("input_565_cast_fp16")]; + tensor lora_out_341_pad_type_0 = const()[name = tensor("lora_out_341_pad_type_0"), val = tensor("valid")]; + tensor lora_out_341_strides_0 = const()[name = tensor("lora_out_341_strides_0"), val = tensor([1, 1])]; + tensor lora_out_341_pad_0 = const()[name = tensor("lora_out_341_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_341_dilations_0 = const()[name = tensor("lora_out_341_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_341_groups_0 = const()[name = tensor("lora_out_341_groups_0"), val = tensor(1)]; + tensor layers_28_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_28_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(314163072)))]; + tensor lora_out_341_cast_fp16 = conv(dilations = lora_out_341_dilations_0, groups = lora_out_341_groups_0, pad = lora_out_341_pad_0, pad_type = lora_out_341_pad_type_0, strides = lora_out_341_strides_0, weight = layers_28_self_attn_v_proj_loraB_weight_to_fp16, x = input_565_cast_fp16)[name = tensor("lora_out_341_cast_fp16")]; + tensor value_57_cast_fp16 = add(x = pretrained_out_341_cast_fp16, y = lora_out_341_cast_fp16)[name = tensor("value_57_cast_fp16")]; + tensor var_6272 = const()[name = tensor("op_6272"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_57_cast_fp16 = reshape(shape = var_6272, x = query_57_cast_fp16)[name = tensor("mh_q_57_cast_fp16")]; + tensor var_6274_to_fp16 = const()[name = tensor("op_6274_to_fp16"), val = tensor(0x1p-3)]; + tensor var_6275_cast_fp16 = mul(x = mh_q_57_cast_fp16, y = var_6274_to_fp16)[name = tensor("op_6275_cast_fp16")]; + tensor var_6276 = const()[name = tensor("op_6276"), val = tensor([1, 20, 64, -1])]; + tensor var_6277_cast_fp16 = reshape(shape = var_6276, x = key_57_cast_fp16)[name = tensor("op_6277_cast_fp16")]; + tensor mh_w_57_transpose_x_0 = const()[name = tensor("mh_w_57_transpose_x_0"), val = tensor(true)]; + tensor mh_w_57_transpose_y_0 = const()[name = tensor("mh_w_57_transpose_y_0"), val = tensor(false)]; + tensor mh_w_57_cast_fp16 = matmul(transpose_x = mh_w_57_transpose_x_0, transpose_y = mh_w_57_transpose_y_0, x = var_6275_cast_fp16, y = var_6277_cast_fp16)[name = tensor("mh_w_57_cast_fp16")]; + tensor var_6280_cast_fp16 = softmax(axis = var_6170, x = mh_w_57_cast_fp16)[name = tensor("op_6280_cast_fp16")]; + tensor var_6281 = const()[name = tensor("op_6281"), val = tensor([1, 20, 64, -1])]; + tensor var_6282_cast_fp16 = reshape(shape = var_6281, x = value_57_cast_fp16)[name = tensor("op_6282_cast_fp16")]; + tensor attn_57_transpose_x_0 = const()[name = tensor("attn_57_transpose_x_0"), val = tensor(false)]; + tensor attn_57_transpose_y_0 = const()[name = tensor("attn_57_transpose_y_0"), val = tensor(true)]; + tensor attn_57_cast_fp16 = matmul(transpose_x = attn_57_transpose_x_0, transpose_y = attn_57_transpose_y_0, x = var_6282_cast_fp16, y = var_6280_cast_fp16)[name = tensor("attn_57_cast_fp16")]; + tensor var_6285 = const()[name = tensor("op_6285"), val = tensor([1, 1280, 1, -1])]; + tensor input_567_cast_fp16 = reshape(shape = var_6285, x = attn_57_cast_fp16)[name = tensor("input_567_cast_fp16")]; + tensor pretrained_out_343_pad_type_0 = const()[name = tensor("pretrained_out_343_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_343_strides_0 = const()[name = tensor("pretrained_out_343_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_343_pad_0 = const()[name = tensor("pretrained_out_343_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_343_dilations_0 = const()[name = tensor("pretrained_out_343_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_343_groups_0 = const()[name = tensor("pretrained_out_343_groups_0"), val = tensor(1)]; + tensor layers_28_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(314204096))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(315023360))), name = tensor("layers_28_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_28_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_28_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(315023488)))]; + tensor pretrained_out_343_cast_fp16 = conv(bias = layers_28_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_343_dilations_0, groups = pretrained_out_343_groups_0, pad = pretrained_out_343_pad_0, pad_type = pretrained_out_343_pad_type_0, strides = pretrained_out_343_strides_0, weight = layers_28_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_567_cast_fp16)[name = tensor("pretrained_out_343_cast_fp16")]; + tensor input_569_pad_type_0 = const()[name = tensor("input_569_pad_type_0"), val = tensor("valid")]; + tensor input_569_strides_0 = const()[name = tensor("input_569_strides_0"), val = tensor([1, 1])]; + tensor input_569_pad_0 = const()[name = tensor("input_569_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_569_dilations_0 = const()[name = tensor("input_569_dilations_0"), val = tensor([1, 1])]; + tensor input_569_groups_0 = const()[name = tensor("input_569_groups_0"), val = tensor(1)]; + tensor layers_28_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_28_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(315026112)))]; + tensor input_569_cast_fp16 = conv(dilations = input_569_dilations_0, groups = input_569_groups_0, pad = input_569_pad_0, pad_type = input_569_pad_type_0, strides = input_569_strides_0, weight = layers_28_self_attn_o_proj_loraA_weight_to_fp16, x = input_567_cast_fp16)[name = tensor("input_569_cast_fp16")]; + tensor lora_out_343_pad_type_0 = const()[name = tensor("lora_out_343_pad_type_0"), val = tensor("valid")]; + tensor lora_out_343_strides_0 = const()[name = tensor("lora_out_343_strides_0"), val = tensor([1, 1])]; + tensor lora_out_343_pad_0 = const()[name = tensor("lora_out_343_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_343_dilations_0 = const()[name = tensor("lora_out_343_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_343_groups_0 = const()[name = tensor("lora_out_343_groups_0"), val = tensor(1)]; + tensor layers_28_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_28_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(315067136)))]; + tensor lora_out_343_cast_fp16 = conv(dilations = lora_out_343_dilations_0, groups = lora_out_343_groups_0, pad = lora_out_343_pad_0, pad_type = lora_out_343_pad_type_0, strides = lora_out_343_strides_0, weight = layers_28_self_attn_o_proj_loraB_weight_to_fp16, x = input_569_cast_fp16)[name = tensor("lora_out_343_cast_fp16")]; + tensor obj_115_cast_fp16 = add(x = pretrained_out_343_cast_fp16, y = lora_out_343_cast_fp16)[name = tensor("obj_115_cast_fp16")]; + tensor inputs_115_cast_fp16 = add(x = inputs_113_cast_fp16, y = obj_115_cast_fp16)[name = tensor("inputs_115_cast_fp16")]; + tensor out_115_axes_0 = const()[name = tensor("out_115_axes_0"), val = tensor([1])]; + tensor var_6319_to_fp16 = const()[name = tensor("op_6319_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_115_cast_fp16 = layer_norm(axes = out_115_axes_0, epsilon = var_6319_to_fp16, x = inputs_115_cast_fp16)[name = tensor("out_115_cast_fp16")]; + tensor input_571_gamma_0_to_fp16 = const()[name = tensor("input_571_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(315108160)))]; + tensor input_571_beta_0_to_fp16 = const()[name = tensor("input_571_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(315110784)))]; + tensor input_571_epsilon_0_to_fp16 = const()[name = tensor("input_571_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_571_cast_fp16 = batch_norm(beta = input_571_beta_0_to_fp16, epsilon = input_571_epsilon_0_to_fp16, gamma = input_571_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_115_cast_fp16)[name = tensor("input_571_cast_fp16")]; + tensor pretrained_out_345_pad_type_0 = const()[name = tensor("pretrained_out_345_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_345_strides_0 = const()[name = tensor("pretrained_out_345_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_345_pad_0 = const()[name = tensor("pretrained_out_345_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_345_dilations_0 = const()[name = tensor("pretrained_out_345_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_345_groups_0 = const()[name = tensor("pretrained_out_345_groups_0"), val = tensor(1)]; + tensor layers_28_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(315113408))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(318390272))), name = tensor("layers_28_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_28_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_28_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(318390400)))]; + tensor pretrained_out_345_cast_fp16 = conv(bias = layers_28_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_345_dilations_0, groups = pretrained_out_345_groups_0, pad = pretrained_out_345_pad_0, pad_type = pretrained_out_345_pad_type_0, strides = pretrained_out_345_strides_0, weight = layers_28_fc1_pretrained_weight_to_fp16_palettized, x = input_571_cast_fp16)[name = tensor("pretrained_out_345_cast_fp16")]; + tensor input_573_pad_type_0 = const()[name = tensor("input_573_pad_type_0"), val = tensor("valid")]; + tensor input_573_strides_0 = const()[name = tensor("input_573_strides_0"), val = tensor([1, 1])]; + tensor input_573_pad_0 = const()[name = tensor("input_573_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_573_dilations_0 = const()[name = tensor("input_573_dilations_0"), val = tensor([1, 1])]; + tensor input_573_groups_0 = const()[name = tensor("input_573_groups_0"), val = tensor(1)]; + tensor layers_28_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_28_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(318400704)))]; + tensor input_573_cast_fp16 = conv(dilations = input_573_dilations_0, groups = input_573_groups_0, pad = input_573_pad_0, pad_type = input_573_pad_type_0, strides = input_573_strides_0, weight = layers_28_fc1_loraA_weight_to_fp16, x = input_571_cast_fp16)[name = tensor("input_573_cast_fp16")]; + tensor lora_out_345_pad_type_0 = const()[name = tensor("lora_out_345_pad_type_0"), val = tensor("valid")]; + tensor lora_out_345_strides_0 = const()[name = tensor("lora_out_345_strides_0"), val = tensor([1, 1])]; + tensor lora_out_345_pad_0 = const()[name = tensor("lora_out_345_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_345_dilations_0 = const()[name = tensor("lora_out_345_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_345_groups_0 = const()[name = tensor("lora_out_345_groups_0"), val = tensor(1)]; + tensor layers_28_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_28_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(318441728)))]; + tensor lora_out_345_cast_fp16 = conv(dilations = lora_out_345_dilations_0, groups = lora_out_345_groups_0, pad = lora_out_345_pad_0, pad_type = lora_out_345_pad_type_0, strides = lora_out_345_strides_0, weight = layers_28_fc1_loraB_weight_to_fp16, x = input_573_cast_fp16)[name = tensor("lora_out_345_cast_fp16")]; + tensor input_575_cast_fp16 = add(x = pretrained_out_345_cast_fp16, y = lora_out_345_cast_fp16)[name = tensor("input_575_cast_fp16")]; + tensor input_577_mode_0 = const()[name = tensor("input_577_mode_0"), val = tensor("EXACT")]; + tensor input_577_cast_fp16 = gelu(mode = input_577_mode_0, x = input_575_cast_fp16)[name = tensor("input_577_cast_fp16")]; + tensor pretrained_out_347_pad_type_0 = const()[name = tensor("pretrained_out_347_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_347_strides_0 = const()[name = tensor("pretrained_out_347_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_347_pad_0 = const()[name = tensor("pretrained_out_347_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_347_dilations_0 = const()[name = tensor("pretrained_out_347_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_347_groups_0 = const()[name = tensor("pretrained_out_347_groups_0"), val = tensor(1)]; + tensor layers_28_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(318605632))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(321882496))), name = tensor("layers_28_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_28_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_28_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(321882624)))]; + tensor pretrained_out_347_cast_fp16 = conv(bias = layers_28_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_347_dilations_0, groups = pretrained_out_347_groups_0, pad = pretrained_out_347_pad_0, pad_type = pretrained_out_347_pad_type_0, strides = pretrained_out_347_strides_0, weight = layers_28_fc2_pretrained_weight_to_fp16_palettized, x = input_577_cast_fp16)[name = tensor("pretrained_out_347_cast_fp16")]; + tensor input_579_pad_type_0 = const()[name = tensor("input_579_pad_type_0"), val = tensor("valid")]; + tensor input_579_strides_0 = const()[name = tensor("input_579_strides_0"), val = tensor([1, 1])]; + tensor input_579_pad_0 = const()[name = tensor("input_579_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_579_dilations_0 = const()[name = tensor("input_579_dilations_0"), val = tensor([1, 1])]; + tensor input_579_groups_0 = const()[name = tensor("input_579_groups_0"), val = tensor(1)]; + tensor layers_28_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_28_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(321885248)))]; + tensor input_579_cast_fp16 = conv(dilations = input_579_dilations_0, groups = input_579_groups_0, pad = input_579_pad_0, pad_type = input_579_pad_type_0, strides = input_579_strides_0, weight = layers_28_fc2_loraA_weight_to_fp16, x = input_577_cast_fp16)[name = tensor("input_579_cast_fp16")]; + tensor lora_out_347_pad_type_0 = const()[name = tensor("lora_out_347_pad_type_0"), val = tensor("valid")]; + tensor lora_out_347_strides_0 = const()[name = tensor("lora_out_347_strides_0"), val = tensor([1, 1])]; + tensor lora_out_347_pad_0 = const()[name = tensor("lora_out_347_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_347_dilations_0 = const()[name = tensor("lora_out_347_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_347_groups_0 = const()[name = tensor("lora_out_347_groups_0"), val = tensor(1)]; + tensor layers_28_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_28_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(322049152)))]; + tensor lora_out_347_cast_fp16 = conv(dilations = lora_out_347_dilations_0, groups = lora_out_347_groups_0, pad = lora_out_347_pad_0, pad_type = lora_out_347_pad_type_0, strides = lora_out_347_strides_0, weight = layers_28_fc2_loraB_weight_to_fp16, x = input_579_cast_fp16)[name = tensor("lora_out_347_cast_fp16")]; + tensor hidden_states_61_cast_fp16 = add(x = pretrained_out_347_cast_fp16, y = lora_out_347_cast_fp16)[name = tensor("hidden_states_61_cast_fp16")]; + tensor inputs_117_cast_fp16 = add(x = inputs_115_cast_fp16, y = hidden_states_61_cast_fp16)[name = tensor("inputs_117_cast_fp16")]; + tensor var_6384 = const()[name = tensor("op_6384"), val = tensor(3)]; + tensor out_117_axes_0 = const()[name = tensor("out_117_axes_0"), val = tensor([1])]; + tensor var_6403_to_fp16 = const()[name = tensor("op_6403_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_117_cast_fp16 = layer_norm(axes = out_117_axes_0, epsilon = var_6403_to_fp16, x = inputs_117_cast_fp16)[name = tensor("out_117_cast_fp16")]; + tensor obj_117_gamma_0_to_fp16 = const()[name = tensor("obj_117_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(322090176)))]; + tensor obj_117_beta_0_to_fp16 = const()[name = tensor("obj_117_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(322092800)))]; + tensor obj_117_epsilon_0_to_fp16 = const()[name = tensor("obj_117_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_117_cast_fp16 = batch_norm(beta = obj_117_beta_0_to_fp16, epsilon = obj_117_epsilon_0_to_fp16, gamma = obj_117_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_117_cast_fp16)[name = tensor("obj_117_cast_fp16")]; + tensor pretrained_out_349_pad_type_0 = const()[name = tensor("pretrained_out_349_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_349_strides_0 = const()[name = tensor("pretrained_out_349_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_349_pad_0 = const()[name = tensor("pretrained_out_349_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_349_dilations_0 = const()[name = tensor("pretrained_out_349_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_349_groups_0 = const()[name = tensor("pretrained_out_349_groups_0"), val = tensor(1)]; + tensor layers_29_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(322095424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(322914688))), name = tensor("layers_29_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_29_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_29_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(322914816)))]; + tensor pretrained_out_349_cast_fp16 = conv(bias = layers_29_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_349_dilations_0, groups = pretrained_out_349_groups_0, pad = pretrained_out_349_pad_0, pad_type = pretrained_out_349_pad_type_0, strides = pretrained_out_349_strides_0, weight = layers_29_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_117_cast_fp16)[name = tensor("pretrained_out_349_cast_fp16")]; + tensor input_581_pad_type_0 = const()[name = tensor("input_581_pad_type_0"), val = tensor("valid")]; + tensor input_581_strides_0 = const()[name = tensor("input_581_strides_0"), val = tensor([1, 1])]; + tensor input_581_pad_0 = const()[name = tensor("input_581_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_581_dilations_0 = const()[name = tensor("input_581_dilations_0"), val = tensor([1, 1])]; + tensor input_581_groups_0 = const()[name = tensor("input_581_groups_0"), val = tensor(1)]; + tensor layers_29_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_29_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(322917440)))]; + tensor input_581_cast_fp16 = conv(dilations = input_581_dilations_0, groups = input_581_groups_0, pad = input_581_pad_0, pad_type = input_581_pad_type_0, strides = input_581_strides_0, weight = layers_29_self_attn_q_proj_loraA_weight_to_fp16, x = obj_117_cast_fp16)[name = tensor("input_581_cast_fp16")]; + tensor lora_out_349_pad_type_0 = const()[name = tensor("lora_out_349_pad_type_0"), val = tensor("valid")]; + tensor lora_out_349_strides_0 = const()[name = tensor("lora_out_349_strides_0"), val = tensor([1, 1])]; + tensor lora_out_349_pad_0 = const()[name = tensor("lora_out_349_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_349_dilations_0 = const()[name = tensor("lora_out_349_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_349_groups_0 = const()[name = tensor("lora_out_349_groups_0"), val = tensor(1)]; + tensor layers_29_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_29_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(322958464)))]; + tensor lora_out_349_cast_fp16 = conv(dilations = lora_out_349_dilations_0, groups = lora_out_349_groups_0, pad = lora_out_349_pad_0, pad_type = lora_out_349_pad_type_0, strides = lora_out_349_strides_0, weight = layers_29_self_attn_q_proj_loraB_weight_to_fp16, x = input_581_cast_fp16)[name = tensor("lora_out_349_cast_fp16")]; + tensor query_59_cast_fp16 = add(x = pretrained_out_349_cast_fp16, y = lora_out_349_cast_fp16)[name = tensor("query_59_cast_fp16")]; + tensor pretrained_out_351_pad_type_0 = const()[name = tensor("pretrained_out_351_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_351_strides_0 = const()[name = tensor("pretrained_out_351_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_351_pad_0 = const()[name = tensor("pretrained_out_351_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_351_dilations_0 = const()[name = tensor("pretrained_out_351_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_351_groups_0 = const()[name = tensor("pretrained_out_351_groups_0"), val = tensor(1)]; + tensor layers_29_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(322999488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(323818752))), name = tensor("layers_29_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_351_cast_fp16 = conv(dilations = pretrained_out_351_dilations_0, groups = pretrained_out_351_groups_0, pad = pretrained_out_351_pad_0, pad_type = pretrained_out_351_pad_type_0, strides = pretrained_out_351_strides_0, weight = layers_29_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_117_cast_fp16)[name = tensor("pretrained_out_351_cast_fp16")]; + tensor input_583_pad_type_0 = const()[name = tensor("input_583_pad_type_0"), val = tensor("valid")]; + tensor input_583_strides_0 = const()[name = tensor("input_583_strides_0"), val = tensor([1, 1])]; + tensor input_583_pad_0 = const()[name = tensor("input_583_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_583_dilations_0 = const()[name = tensor("input_583_dilations_0"), val = tensor([1, 1])]; + tensor input_583_groups_0 = const()[name = tensor("input_583_groups_0"), val = tensor(1)]; + tensor layers_29_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_29_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(323818880)))]; + tensor input_583_cast_fp16 = conv(dilations = input_583_dilations_0, groups = input_583_groups_0, pad = input_583_pad_0, pad_type = input_583_pad_type_0, strides = input_583_strides_0, weight = layers_29_self_attn_k_proj_loraA_weight_to_fp16, x = obj_117_cast_fp16)[name = tensor("input_583_cast_fp16")]; + tensor lora_out_351_pad_type_0 = const()[name = tensor("lora_out_351_pad_type_0"), val = tensor("valid")]; + tensor lora_out_351_strides_0 = const()[name = tensor("lora_out_351_strides_0"), val = tensor([1, 1])]; + tensor lora_out_351_pad_0 = const()[name = tensor("lora_out_351_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_351_dilations_0 = const()[name = tensor("lora_out_351_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_351_groups_0 = const()[name = tensor("lora_out_351_groups_0"), val = tensor(1)]; + tensor layers_29_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_29_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(323859904)))]; + tensor lora_out_351_cast_fp16 = conv(dilations = lora_out_351_dilations_0, groups = lora_out_351_groups_0, pad = lora_out_351_pad_0, pad_type = lora_out_351_pad_type_0, strides = lora_out_351_strides_0, weight = layers_29_self_attn_k_proj_loraB_weight_to_fp16, x = input_583_cast_fp16)[name = tensor("lora_out_351_cast_fp16")]; + tensor key_59_cast_fp16 = add(x = pretrained_out_351_cast_fp16, y = lora_out_351_cast_fp16)[name = tensor("key_59_cast_fp16")]; + tensor pretrained_out_353_pad_type_0 = const()[name = tensor("pretrained_out_353_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_353_strides_0 = const()[name = tensor("pretrained_out_353_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_353_pad_0 = const()[name = tensor("pretrained_out_353_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_353_dilations_0 = const()[name = tensor("pretrained_out_353_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_353_groups_0 = const()[name = tensor("pretrained_out_353_groups_0"), val = tensor(1)]; + tensor layers_29_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(323900928))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(324720192))), name = tensor("layers_29_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_29_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_29_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(324720320)))]; + tensor pretrained_out_353_cast_fp16 = conv(bias = layers_29_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_353_dilations_0, groups = pretrained_out_353_groups_0, pad = pretrained_out_353_pad_0, pad_type = pretrained_out_353_pad_type_0, strides = pretrained_out_353_strides_0, weight = layers_29_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_117_cast_fp16)[name = tensor("pretrained_out_353_cast_fp16")]; + tensor input_585_pad_type_0 = const()[name = tensor("input_585_pad_type_0"), val = tensor("valid")]; + tensor input_585_strides_0 = const()[name = tensor("input_585_strides_0"), val = tensor([1, 1])]; + tensor input_585_pad_0 = const()[name = tensor("input_585_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_585_dilations_0 = const()[name = tensor("input_585_dilations_0"), val = tensor([1, 1])]; + tensor input_585_groups_0 = const()[name = tensor("input_585_groups_0"), val = tensor(1)]; + tensor layers_29_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_29_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(324722944)))]; + tensor input_585_cast_fp16 = conv(dilations = input_585_dilations_0, groups = input_585_groups_0, pad = input_585_pad_0, pad_type = input_585_pad_type_0, strides = input_585_strides_0, weight = layers_29_self_attn_v_proj_loraA_weight_to_fp16, x = obj_117_cast_fp16)[name = tensor("input_585_cast_fp16")]; + tensor lora_out_353_pad_type_0 = const()[name = tensor("lora_out_353_pad_type_0"), val = tensor("valid")]; + tensor lora_out_353_strides_0 = const()[name = tensor("lora_out_353_strides_0"), val = tensor([1, 1])]; + tensor lora_out_353_pad_0 = const()[name = tensor("lora_out_353_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_353_dilations_0 = const()[name = tensor("lora_out_353_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_353_groups_0 = const()[name = tensor("lora_out_353_groups_0"), val = tensor(1)]; + tensor layers_29_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_29_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(324763968)))]; + tensor lora_out_353_cast_fp16 = conv(dilations = lora_out_353_dilations_0, groups = lora_out_353_groups_0, pad = lora_out_353_pad_0, pad_type = lora_out_353_pad_type_0, strides = lora_out_353_strides_0, weight = layers_29_self_attn_v_proj_loraB_weight_to_fp16, x = input_585_cast_fp16)[name = tensor("lora_out_353_cast_fp16")]; + tensor value_59_cast_fp16 = add(x = pretrained_out_353_cast_fp16, y = lora_out_353_cast_fp16)[name = tensor("value_59_cast_fp16")]; + tensor var_6486 = const()[name = tensor("op_6486"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_59_cast_fp16 = reshape(shape = var_6486, x = query_59_cast_fp16)[name = tensor("mh_q_59_cast_fp16")]; + tensor var_6488_to_fp16 = const()[name = tensor("op_6488_to_fp16"), val = tensor(0x1p-3)]; + tensor var_6489_cast_fp16 = mul(x = mh_q_59_cast_fp16, y = var_6488_to_fp16)[name = tensor("op_6489_cast_fp16")]; + tensor var_6490 = const()[name = tensor("op_6490"), val = tensor([1, 20, 64, -1])]; + tensor var_6491_cast_fp16 = reshape(shape = var_6490, x = key_59_cast_fp16)[name = tensor("op_6491_cast_fp16")]; + tensor mh_w_59_transpose_x_0 = const()[name = tensor("mh_w_59_transpose_x_0"), val = tensor(true)]; + tensor mh_w_59_transpose_y_0 = const()[name = tensor("mh_w_59_transpose_y_0"), val = tensor(false)]; + tensor mh_w_59_cast_fp16 = matmul(transpose_x = mh_w_59_transpose_x_0, transpose_y = mh_w_59_transpose_y_0, x = var_6489_cast_fp16, y = var_6491_cast_fp16)[name = tensor("mh_w_59_cast_fp16")]; + tensor var_6494_cast_fp16 = softmax(axis = var_6384, x = mh_w_59_cast_fp16)[name = tensor("op_6494_cast_fp16")]; + tensor var_6495 = const()[name = tensor("op_6495"), val = tensor([1, 20, 64, -1])]; + tensor var_6496_cast_fp16 = reshape(shape = var_6495, x = value_59_cast_fp16)[name = tensor("op_6496_cast_fp16")]; + tensor attn_59_transpose_x_0 = const()[name = tensor("attn_59_transpose_x_0"), val = tensor(false)]; + tensor attn_59_transpose_y_0 = const()[name = tensor("attn_59_transpose_y_0"), val = tensor(true)]; + tensor attn_59_cast_fp16 = matmul(transpose_x = attn_59_transpose_x_0, transpose_y = attn_59_transpose_y_0, x = var_6496_cast_fp16, y = var_6494_cast_fp16)[name = tensor("attn_59_cast_fp16")]; + tensor var_6499 = const()[name = tensor("op_6499"), val = tensor([1, 1280, 1, -1])]; + tensor input_587_cast_fp16 = reshape(shape = var_6499, x = attn_59_cast_fp16)[name = tensor("input_587_cast_fp16")]; + tensor pretrained_out_355_pad_type_0 = const()[name = tensor("pretrained_out_355_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_355_strides_0 = const()[name = tensor("pretrained_out_355_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_355_pad_0 = const()[name = tensor("pretrained_out_355_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_355_dilations_0 = const()[name = tensor("pretrained_out_355_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_355_groups_0 = const()[name = tensor("pretrained_out_355_groups_0"), val = tensor(1)]; + tensor layers_29_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(324804992))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(325624256))), name = tensor("layers_29_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_29_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_29_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(325624384)))]; + tensor pretrained_out_355_cast_fp16 = conv(bias = layers_29_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_355_dilations_0, groups = pretrained_out_355_groups_0, pad = pretrained_out_355_pad_0, pad_type = pretrained_out_355_pad_type_0, strides = pretrained_out_355_strides_0, weight = layers_29_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_587_cast_fp16)[name = tensor("pretrained_out_355_cast_fp16")]; + tensor input_589_pad_type_0 = const()[name = tensor("input_589_pad_type_0"), val = tensor("valid")]; + tensor input_589_strides_0 = const()[name = tensor("input_589_strides_0"), val = tensor([1, 1])]; + tensor input_589_pad_0 = const()[name = tensor("input_589_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_589_dilations_0 = const()[name = tensor("input_589_dilations_0"), val = tensor([1, 1])]; + tensor input_589_groups_0 = const()[name = tensor("input_589_groups_0"), val = tensor(1)]; + tensor layers_29_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_29_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(325627008)))]; + tensor input_589_cast_fp16 = conv(dilations = input_589_dilations_0, groups = input_589_groups_0, pad = input_589_pad_0, pad_type = input_589_pad_type_0, strides = input_589_strides_0, weight = layers_29_self_attn_o_proj_loraA_weight_to_fp16, x = input_587_cast_fp16)[name = tensor("input_589_cast_fp16")]; + tensor lora_out_355_pad_type_0 = const()[name = tensor("lora_out_355_pad_type_0"), val = tensor("valid")]; + tensor lora_out_355_strides_0 = const()[name = tensor("lora_out_355_strides_0"), val = tensor([1, 1])]; + tensor lora_out_355_pad_0 = const()[name = tensor("lora_out_355_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_355_dilations_0 = const()[name = tensor("lora_out_355_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_355_groups_0 = const()[name = tensor("lora_out_355_groups_0"), val = tensor(1)]; + tensor layers_29_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_29_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(325668032)))]; + tensor lora_out_355_cast_fp16 = conv(dilations = lora_out_355_dilations_0, groups = lora_out_355_groups_0, pad = lora_out_355_pad_0, pad_type = lora_out_355_pad_type_0, strides = lora_out_355_strides_0, weight = layers_29_self_attn_o_proj_loraB_weight_to_fp16, x = input_589_cast_fp16)[name = tensor("lora_out_355_cast_fp16")]; + tensor obj_119_cast_fp16 = add(x = pretrained_out_355_cast_fp16, y = lora_out_355_cast_fp16)[name = tensor("obj_119_cast_fp16")]; + tensor inputs_119_cast_fp16 = add(x = inputs_117_cast_fp16, y = obj_119_cast_fp16)[name = tensor("inputs_119_cast_fp16")]; + tensor out_119_axes_0 = const()[name = tensor("out_119_axes_0"), val = tensor([1])]; + tensor var_6533_to_fp16 = const()[name = tensor("op_6533_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_119_cast_fp16 = layer_norm(axes = out_119_axes_0, epsilon = var_6533_to_fp16, x = inputs_119_cast_fp16)[name = tensor("out_119_cast_fp16")]; + tensor input_591_gamma_0_to_fp16 = const()[name = tensor("input_591_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(325709056)))]; + tensor input_591_beta_0_to_fp16 = const()[name = tensor("input_591_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(325711680)))]; + tensor input_591_epsilon_0_to_fp16 = const()[name = tensor("input_591_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_591_cast_fp16 = batch_norm(beta = input_591_beta_0_to_fp16, epsilon = input_591_epsilon_0_to_fp16, gamma = input_591_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_119_cast_fp16)[name = tensor("input_591_cast_fp16")]; + tensor pretrained_out_357_pad_type_0 = const()[name = tensor("pretrained_out_357_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_357_strides_0 = const()[name = tensor("pretrained_out_357_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_357_pad_0 = const()[name = tensor("pretrained_out_357_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_357_dilations_0 = const()[name = tensor("pretrained_out_357_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_357_groups_0 = const()[name = tensor("pretrained_out_357_groups_0"), val = tensor(1)]; + tensor layers_29_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(325714304))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(328991168))), name = tensor("layers_29_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_29_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_29_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(328991296)))]; + tensor pretrained_out_357_cast_fp16 = conv(bias = layers_29_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_357_dilations_0, groups = pretrained_out_357_groups_0, pad = pretrained_out_357_pad_0, pad_type = pretrained_out_357_pad_type_0, strides = pretrained_out_357_strides_0, weight = layers_29_fc1_pretrained_weight_to_fp16_palettized, x = input_591_cast_fp16)[name = tensor("pretrained_out_357_cast_fp16")]; + tensor input_593_pad_type_0 = const()[name = tensor("input_593_pad_type_0"), val = tensor("valid")]; + tensor input_593_strides_0 = const()[name = tensor("input_593_strides_0"), val = tensor([1, 1])]; + tensor input_593_pad_0 = const()[name = tensor("input_593_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_593_dilations_0 = const()[name = tensor("input_593_dilations_0"), val = tensor([1, 1])]; + tensor input_593_groups_0 = const()[name = tensor("input_593_groups_0"), val = tensor(1)]; + tensor layers_29_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_29_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(329001600)))]; + tensor input_593_cast_fp16 = conv(dilations = input_593_dilations_0, groups = input_593_groups_0, pad = input_593_pad_0, pad_type = input_593_pad_type_0, strides = input_593_strides_0, weight = layers_29_fc1_loraA_weight_to_fp16, x = input_591_cast_fp16)[name = tensor("input_593_cast_fp16")]; + tensor lora_out_357_pad_type_0 = const()[name = tensor("lora_out_357_pad_type_0"), val = tensor("valid")]; + tensor lora_out_357_strides_0 = const()[name = tensor("lora_out_357_strides_0"), val = tensor([1, 1])]; + tensor lora_out_357_pad_0 = const()[name = tensor("lora_out_357_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_357_dilations_0 = const()[name = tensor("lora_out_357_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_357_groups_0 = const()[name = tensor("lora_out_357_groups_0"), val = tensor(1)]; + tensor layers_29_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_29_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(329042624)))]; + tensor lora_out_357_cast_fp16 = conv(dilations = lora_out_357_dilations_0, groups = lora_out_357_groups_0, pad = lora_out_357_pad_0, pad_type = lora_out_357_pad_type_0, strides = lora_out_357_strides_0, weight = layers_29_fc1_loraB_weight_to_fp16, x = input_593_cast_fp16)[name = tensor("lora_out_357_cast_fp16")]; + tensor input_595_cast_fp16 = add(x = pretrained_out_357_cast_fp16, y = lora_out_357_cast_fp16)[name = tensor("input_595_cast_fp16")]; + tensor input_597_mode_0 = const()[name = tensor("input_597_mode_0"), val = tensor("EXACT")]; + tensor input_597_cast_fp16 = gelu(mode = input_597_mode_0, x = input_595_cast_fp16)[name = tensor("input_597_cast_fp16")]; + tensor pretrained_out_359_pad_type_0 = const()[name = tensor("pretrained_out_359_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_359_strides_0 = const()[name = tensor("pretrained_out_359_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_359_pad_0 = const()[name = tensor("pretrained_out_359_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_359_dilations_0 = const()[name = tensor("pretrained_out_359_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_359_groups_0 = const()[name = tensor("pretrained_out_359_groups_0"), val = tensor(1)]; + tensor layers_29_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(329206528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(332483392))), name = tensor("layers_29_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_29_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_29_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(332483520)))]; + tensor pretrained_out_359_cast_fp16 = conv(bias = layers_29_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_359_dilations_0, groups = pretrained_out_359_groups_0, pad = pretrained_out_359_pad_0, pad_type = pretrained_out_359_pad_type_0, strides = pretrained_out_359_strides_0, weight = layers_29_fc2_pretrained_weight_to_fp16_palettized, x = input_597_cast_fp16)[name = tensor("pretrained_out_359_cast_fp16")]; + tensor input_599_pad_type_0 = const()[name = tensor("input_599_pad_type_0"), val = tensor("valid")]; + tensor input_599_strides_0 = const()[name = tensor("input_599_strides_0"), val = tensor([1, 1])]; + tensor input_599_pad_0 = const()[name = tensor("input_599_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_599_dilations_0 = const()[name = tensor("input_599_dilations_0"), val = tensor([1, 1])]; + tensor input_599_groups_0 = const()[name = tensor("input_599_groups_0"), val = tensor(1)]; + tensor layers_29_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_29_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(332486144)))]; + tensor input_599_cast_fp16 = conv(dilations = input_599_dilations_0, groups = input_599_groups_0, pad = input_599_pad_0, pad_type = input_599_pad_type_0, strides = input_599_strides_0, weight = layers_29_fc2_loraA_weight_to_fp16, x = input_597_cast_fp16)[name = tensor("input_599_cast_fp16")]; + tensor lora_out_359_pad_type_0 = const()[name = tensor("lora_out_359_pad_type_0"), val = tensor("valid")]; + tensor lora_out_359_strides_0 = const()[name = tensor("lora_out_359_strides_0"), val = tensor([1, 1])]; + tensor lora_out_359_pad_0 = const()[name = tensor("lora_out_359_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_359_dilations_0 = const()[name = tensor("lora_out_359_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_359_groups_0 = const()[name = tensor("lora_out_359_groups_0"), val = tensor(1)]; + tensor layers_29_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_29_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(332650048)))]; + tensor lora_out_359_cast_fp16 = conv(dilations = lora_out_359_dilations_0, groups = lora_out_359_groups_0, pad = lora_out_359_pad_0, pad_type = lora_out_359_pad_type_0, strides = lora_out_359_strides_0, weight = layers_29_fc2_loraB_weight_to_fp16, x = input_599_cast_fp16)[name = tensor("lora_out_359_cast_fp16")]; + tensor hidden_states_63_cast_fp16 = add(x = pretrained_out_359_cast_fp16, y = lora_out_359_cast_fp16)[name = tensor("hidden_states_63_cast_fp16")]; + tensor inputs_121_cast_fp16 = add(x = inputs_119_cast_fp16, y = hidden_states_63_cast_fp16)[name = tensor("inputs_121_cast_fp16")]; + tensor var_6598 = const()[name = tensor("op_6598"), val = tensor(3)]; + tensor out_121_axes_0 = const()[name = tensor("out_121_axes_0"), val = tensor([1])]; + tensor var_6617_to_fp16 = const()[name = tensor("op_6617_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_121_cast_fp16 = layer_norm(axes = out_121_axes_0, epsilon = var_6617_to_fp16, x = inputs_121_cast_fp16)[name = tensor("out_121_cast_fp16")]; + tensor obj_121_gamma_0_to_fp16 = const()[name = tensor("obj_121_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(332691072)))]; + tensor obj_121_beta_0_to_fp16 = const()[name = tensor("obj_121_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(332693696)))]; + tensor obj_121_epsilon_0_to_fp16 = const()[name = tensor("obj_121_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_121_cast_fp16 = batch_norm(beta = obj_121_beta_0_to_fp16, epsilon = obj_121_epsilon_0_to_fp16, gamma = obj_121_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_121_cast_fp16)[name = tensor("obj_121_cast_fp16")]; + tensor pretrained_out_361_pad_type_0 = const()[name = tensor("pretrained_out_361_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_361_strides_0 = const()[name = tensor("pretrained_out_361_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_361_pad_0 = const()[name = tensor("pretrained_out_361_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_361_dilations_0 = const()[name = tensor("pretrained_out_361_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_361_groups_0 = const()[name = tensor("pretrained_out_361_groups_0"), val = tensor(1)]; + tensor layers_30_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(332696320))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(333515584))), name = tensor("layers_30_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_30_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_30_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(333515712)))]; + tensor pretrained_out_361_cast_fp16 = conv(bias = layers_30_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_361_dilations_0, groups = pretrained_out_361_groups_0, pad = pretrained_out_361_pad_0, pad_type = pretrained_out_361_pad_type_0, strides = pretrained_out_361_strides_0, weight = layers_30_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_121_cast_fp16)[name = tensor("pretrained_out_361_cast_fp16")]; + tensor input_601_pad_type_0 = const()[name = tensor("input_601_pad_type_0"), val = tensor("valid")]; + tensor input_601_strides_0 = const()[name = tensor("input_601_strides_0"), val = tensor([1, 1])]; + tensor input_601_pad_0 = const()[name = tensor("input_601_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_601_dilations_0 = const()[name = tensor("input_601_dilations_0"), val = tensor([1, 1])]; + tensor input_601_groups_0 = const()[name = tensor("input_601_groups_0"), val = tensor(1)]; + tensor layers_30_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_30_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(333518336)))]; + tensor input_601_cast_fp16 = conv(dilations = input_601_dilations_0, groups = input_601_groups_0, pad = input_601_pad_0, pad_type = input_601_pad_type_0, strides = input_601_strides_0, weight = layers_30_self_attn_q_proj_loraA_weight_to_fp16, x = obj_121_cast_fp16)[name = tensor("input_601_cast_fp16")]; + tensor lora_out_361_pad_type_0 = const()[name = tensor("lora_out_361_pad_type_0"), val = tensor("valid")]; + tensor lora_out_361_strides_0 = const()[name = tensor("lora_out_361_strides_0"), val = tensor([1, 1])]; + tensor lora_out_361_pad_0 = const()[name = tensor("lora_out_361_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_361_dilations_0 = const()[name = tensor("lora_out_361_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_361_groups_0 = const()[name = tensor("lora_out_361_groups_0"), val = tensor(1)]; + tensor layers_30_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_30_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(333559360)))]; + tensor lora_out_361_cast_fp16 = conv(dilations = lora_out_361_dilations_0, groups = lora_out_361_groups_0, pad = lora_out_361_pad_0, pad_type = lora_out_361_pad_type_0, strides = lora_out_361_strides_0, weight = layers_30_self_attn_q_proj_loraB_weight_to_fp16, x = input_601_cast_fp16)[name = tensor("lora_out_361_cast_fp16")]; + tensor query_61_cast_fp16 = add(x = pretrained_out_361_cast_fp16, y = lora_out_361_cast_fp16)[name = tensor("query_61_cast_fp16")]; + tensor pretrained_out_363_pad_type_0 = const()[name = tensor("pretrained_out_363_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_363_strides_0 = const()[name = tensor("pretrained_out_363_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_363_pad_0 = const()[name = tensor("pretrained_out_363_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_363_dilations_0 = const()[name = tensor("pretrained_out_363_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_363_groups_0 = const()[name = tensor("pretrained_out_363_groups_0"), val = tensor(1)]; + tensor layers_30_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(333600384))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(334419648))), name = tensor("layers_30_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_363_cast_fp16 = conv(dilations = pretrained_out_363_dilations_0, groups = pretrained_out_363_groups_0, pad = pretrained_out_363_pad_0, pad_type = pretrained_out_363_pad_type_0, strides = pretrained_out_363_strides_0, weight = layers_30_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_121_cast_fp16)[name = tensor("pretrained_out_363_cast_fp16")]; + tensor input_603_pad_type_0 = const()[name = tensor("input_603_pad_type_0"), val = tensor("valid")]; + tensor input_603_strides_0 = const()[name = tensor("input_603_strides_0"), val = tensor([1, 1])]; + tensor input_603_pad_0 = const()[name = tensor("input_603_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_603_dilations_0 = const()[name = tensor("input_603_dilations_0"), val = tensor([1, 1])]; + tensor input_603_groups_0 = const()[name = tensor("input_603_groups_0"), val = tensor(1)]; + tensor layers_30_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_30_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(334419776)))]; + tensor input_603_cast_fp16 = conv(dilations = input_603_dilations_0, groups = input_603_groups_0, pad = input_603_pad_0, pad_type = input_603_pad_type_0, strides = input_603_strides_0, weight = layers_30_self_attn_k_proj_loraA_weight_to_fp16, x = obj_121_cast_fp16)[name = tensor("input_603_cast_fp16")]; + tensor lora_out_363_pad_type_0 = const()[name = tensor("lora_out_363_pad_type_0"), val = tensor("valid")]; + tensor lora_out_363_strides_0 = const()[name = tensor("lora_out_363_strides_0"), val = tensor([1, 1])]; + tensor lora_out_363_pad_0 = const()[name = tensor("lora_out_363_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_363_dilations_0 = const()[name = tensor("lora_out_363_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_363_groups_0 = const()[name = tensor("lora_out_363_groups_0"), val = tensor(1)]; + tensor layers_30_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_30_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(334460800)))]; + tensor lora_out_363_cast_fp16 = conv(dilations = lora_out_363_dilations_0, groups = lora_out_363_groups_0, pad = lora_out_363_pad_0, pad_type = lora_out_363_pad_type_0, strides = lora_out_363_strides_0, weight = layers_30_self_attn_k_proj_loraB_weight_to_fp16, x = input_603_cast_fp16)[name = tensor("lora_out_363_cast_fp16")]; + tensor key_61_cast_fp16 = add(x = pretrained_out_363_cast_fp16, y = lora_out_363_cast_fp16)[name = tensor("key_61_cast_fp16")]; + tensor pretrained_out_365_pad_type_0 = const()[name = tensor("pretrained_out_365_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_365_strides_0 = const()[name = tensor("pretrained_out_365_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_365_pad_0 = const()[name = tensor("pretrained_out_365_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_365_dilations_0 = const()[name = tensor("pretrained_out_365_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_365_groups_0 = const()[name = tensor("pretrained_out_365_groups_0"), val = tensor(1)]; + tensor layers_30_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(334501824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(335321088))), name = tensor("layers_30_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_30_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_30_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(335321216)))]; + tensor pretrained_out_365_cast_fp16 = conv(bias = layers_30_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_365_dilations_0, groups = pretrained_out_365_groups_0, pad = pretrained_out_365_pad_0, pad_type = pretrained_out_365_pad_type_0, strides = pretrained_out_365_strides_0, weight = layers_30_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_121_cast_fp16)[name = tensor("pretrained_out_365_cast_fp16")]; + tensor input_605_pad_type_0 = const()[name = tensor("input_605_pad_type_0"), val = tensor("valid")]; + tensor input_605_strides_0 = const()[name = tensor("input_605_strides_0"), val = tensor([1, 1])]; + tensor input_605_pad_0 = const()[name = tensor("input_605_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_605_dilations_0 = const()[name = tensor("input_605_dilations_0"), val = tensor([1, 1])]; + tensor input_605_groups_0 = const()[name = tensor("input_605_groups_0"), val = tensor(1)]; + tensor layers_30_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_30_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(335323840)))]; + tensor input_605_cast_fp16 = conv(dilations = input_605_dilations_0, groups = input_605_groups_0, pad = input_605_pad_0, pad_type = input_605_pad_type_0, strides = input_605_strides_0, weight = layers_30_self_attn_v_proj_loraA_weight_to_fp16, x = obj_121_cast_fp16)[name = tensor("input_605_cast_fp16")]; + tensor lora_out_365_pad_type_0 = const()[name = tensor("lora_out_365_pad_type_0"), val = tensor("valid")]; + tensor lora_out_365_strides_0 = const()[name = tensor("lora_out_365_strides_0"), val = tensor([1, 1])]; + tensor lora_out_365_pad_0 = const()[name = tensor("lora_out_365_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_365_dilations_0 = const()[name = tensor("lora_out_365_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_365_groups_0 = const()[name = tensor("lora_out_365_groups_0"), val = tensor(1)]; + tensor layers_30_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_30_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(335364864)))]; + tensor lora_out_365_cast_fp16 = conv(dilations = lora_out_365_dilations_0, groups = lora_out_365_groups_0, pad = lora_out_365_pad_0, pad_type = lora_out_365_pad_type_0, strides = lora_out_365_strides_0, weight = layers_30_self_attn_v_proj_loraB_weight_to_fp16, x = input_605_cast_fp16)[name = tensor("lora_out_365_cast_fp16")]; + tensor value_61_cast_fp16 = add(x = pretrained_out_365_cast_fp16, y = lora_out_365_cast_fp16)[name = tensor("value_61_cast_fp16")]; + tensor var_6700 = const()[name = tensor("op_6700"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_61_cast_fp16 = reshape(shape = var_6700, x = query_61_cast_fp16)[name = tensor("mh_q_61_cast_fp16")]; + tensor var_6702_to_fp16 = const()[name = tensor("op_6702_to_fp16"), val = tensor(0x1p-3)]; + tensor var_6703_cast_fp16 = mul(x = mh_q_61_cast_fp16, y = var_6702_to_fp16)[name = tensor("op_6703_cast_fp16")]; + tensor var_6704 = const()[name = tensor("op_6704"), val = tensor([1, 20, 64, -1])]; + tensor var_6705_cast_fp16 = reshape(shape = var_6704, x = key_61_cast_fp16)[name = tensor("op_6705_cast_fp16")]; + tensor mh_w_61_transpose_x_0 = const()[name = tensor("mh_w_61_transpose_x_0"), val = tensor(true)]; + tensor mh_w_61_transpose_y_0 = const()[name = tensor("mh_w_61_transpose_y_0"), val = tensor(false)]; + tensor mh_w_61_cast_fp16 = matmul(transpose_x = mh_w_61_transpose_x_0, transpose_y = mh_w_61_transpose_y_0, x = var_6703_cast_fp16, y = var_6705_cast_fp16)[name = tensor("mh_w_61_cast_fp16")]; + tensor var_6708_cast_fp16 = softmax(axis = var_6598, x = mh_w_61_cast_fp16)[name = tensor("op_6708_cast_fp16")]; + tensor var_6709 = const()[name = tensor("op_6709"), val = tensor([1, 20, 64, -1])]; + tensor var_6710_cast_fp16 = reshape(shape = var_6709, x = value_61_cast_fp16)[name = tensor("op_6710_cast_fp16")]; + tensor attn_61_transpose_x_0 = const()[name = tensor("attn_61_transpose_x_0"), val = tensor(false)]; + tensor attn_61_transpose_y_0 = const()[name = tensor("attn_61_transpose_y_0"), val = tensor(true)]; + tensor attn_61_cast_fp16 = matmul(transpose_x = attn_61_transpose_x_0, transpose_y = attn_61_transpose_y_0, x = var_6710_cast_fp16, y = var_6708_cast_fp16)[name = tensor("attn_61_cast_fp16")]; + tensor var_6713 = const()[name = tensor("op_6713"), val = tensor([1, 1280, 1, -1])]; + tensor input_607_cast_fp16 = reshape(shape = var_6713, x = attn_61_cast_fp16)[name = tensor("input_607_cast_fp16")]; + tensor pretrained_out_367_pad_type_0 = const()[name = tensor("pretrained_out_367_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_367_strides_0 = const()[name = tensor("pretrained_out_367_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_367_pad_0 = const()[name = tensor("pretrained_out_367_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_367_dilations_0 = const()[name = tensor("pretrained_out_367_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_367_groups_0 = const()[name = tensor("pretrained_out_367_groups_0"), val = tensor(1)]; + tensor layers_30_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(335405888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(336225152))), name = tensor("layers_30_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_30_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_30_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(336225280)))]; + tensor pretrained_out_367_cast_fp16 = conv(bias = layers_30_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_367_dilations_0, groups = pretrained_out_367_groups_0, pad = pretrained_out_367_pad_0, pad_type = pretrained_out_367_pad_type_0, strides = pretrained_out_367_strides_0, weight = layers_30_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_607_cast_fp16)[name = tensor("pretrained_out_367_cast_fp16")]; + tensor input_609_pad_type_0 = const()[name = tensor("input_609_pad_type_0"), val = tensor("valid")]; + tensor input_609_strides_0 = const()[name = tensor("input_609_strides_0"), val = tensor([1, 1])]; + tensor input_609_pad_0 = const()[name = tensor("input_609_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_609_dilations_0 = const()[name = tensor("input_609_dilations_0"), val = tensor([1, 1])]; + tensor input_609_groups_0 = const()[name = tensor("input_609_groups_0"), val = tensor(1)]; + tensor layers_30_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_30_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(336227904)))]; + tensor input_609_cast_fp16 = conv(dilations = input_609_dilations_0, groups = input_609_groups_0, pad = input_609_pad_0, pad_type = input_609_pad_type_0, strides = input_609_strides_0, weight = layers_30_self_attn_o_proj_loraA_weight_to_fp16, x = input_607_cast_fp16)[name = tensor("input_609_cast_fp16")]; + tensor lora_out_367_pad_type_0 = const()[name = tensor("lora_out_367_pad_type_0"), val = tensor("valid")]; + tensor lora_out_367_strides_0 = const()[name = tensor("lora_out_367_strides_0"), val = tensor([1, 1])]; + tensor lora_out_367_pad_0 = const()[name = tensor("lora_out_367_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_367_dilations_0 = const()[name = tensor("lora_out_367_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_367_groups_0 = const()[name = tensor("lora_out_367_groups_0"), val = tensor(1)]; + tensor layers_30_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_30_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(336268928)))]; + tensor lora_out_367_cast_fp16 = conv(dilations = lora_out_367_dilations_0, groups = lora_out_367_groups_0, pad = lora_out_367_pad_0, pad_type = lora_out_367_pad_type_0, strides = lora_out_367_strides_0, weight = layers_30_self_attn_o_proj_loraB_weight_to_fp16, x = input_609_cast_fp16)[name = tensor("lora_out_367_cast_fp16")]; + tensor obj_123_cast_fp16 = add(x = pretrained_out_367_cast_fp16, y = lora_out_367_cast_fp16)[name = tensor("obj_123_cast_fp16")]; + tensor inputs_123_cast_fp16 = add(x = inputs_121_cast_fp16, y = obj_123_cast_fp16)[name = tensor("inputs_123_cast_fp16")]; + tensor out_123_axes_0 = const()[name = tensor("out_123_axes_0"), val = tensor([1])]; + tensor var_6747_to_fp16 = const()[name = tensor("op_6747_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_123_cast_fp16 = layer_norm(axes = out_123_axes_0, epsilon = var_6747_to_fp16, x = inputs_123_cast_fp16)[name = tensor("out_123_cast_fp16")]; + tensor input_611_gamma_0_to_fp16 = const()[name = tensor("input_611_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(336309952)))]; + tensor input_611_beta_0_to_fp16 = const()[name = tensor("input_611_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(336312576)))]; + tensor input_611_epsilon_0_to_fp16 = const()[name = tensor("input_611_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_611_cast_fp16 = batch_norm(beta = input_611_beta_0_to_fp16, epsilon = input_611_epsilon_0_to_fp16, gamma = input_611_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_123_cast_fp16)[name = tensor("input_611_cast_fp16")]; + tensor pretrained_out_369_pad_type_0 = const()[name = tensor("pretrained_out_369_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_369_strides_0 = const()[name = tensor("pretrained_out_369_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_369_pad_0 = const()[name = tensor("pretrained_out_369_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_369_dilations_0 = const()[name = tensor("pretrained_out_369_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_369_groups_0 = const()[name = tensor("pretrained_out_369_groups_0"), val = tensor(1)]; + tensor layers_30_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(336315200))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(339592064))), name = tensor("layers_30_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_30_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_30_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(339592192)))]; + tensor pretrained_out_369_cast_fp16 = conv(bias = layers_30_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_369_dilations_0, groups = pretrained_out_369_groups_0, pad = pretrained_out_369_pad_0, pad_type = pretrained_out_369_pad_type_0, strides = pretrained_out_369_strides_0, weight = layers_30_fc1_pretrained_weight_to_fp16_palettized, x = input_611_cast_fp16)[name = tensor("pretrained_out_369_cast_fp16")]; + tensor input_613_pad_type_0 = const()[name = tensor("input_613_pad_type_0"), val = tensor("valid")]; + tensor input_613_strides_0 = const()[name = tensor("input_613_strides_0"), val = tensor([1, 1])]; + tensor input_613_pad_0 = const()[name = tensor("input_613_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_613_dilations_0 = const()[name = tensor("input_613_dilations_0"), val = tensor([1, 1])]; + tensor input_613_groups_0 = const()[name = tensor("input_613_groups_0"), val = tensor(1)]; + tensor layers_30_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_30_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(339602496)))]; + tensor input_613_cast_fp16 = conv(dilations = input_613_dilations_0, groups = input_613_groups_0, pad = input_613_pad_0, pad_type = input_613_pad_type_0, strides = input_613_strides_0, weight = layers_30_fc1_loraA_weight_to_fp16, x = input_611_cast_fp16)[name = tensor("input_613_cast_fp16")]; + tensor lora_out_369_pad_type_0 = const()[name = tensor("lora_out_369_pad_type_0"), val = tensor("valid")]; + tensor lora_out_369_strides_0 = const()[name = tensor("lora_out_369_strides_0"), val = tensor([1, 1])]; + tensor lora_out_369_pad_0 = const()[name = tensor("lora_out_369_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_369_dilations_0 = const()[name = tensor("lora_out_369_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_369_groups_0 = const()[name = tensor("lora_out_369_groups_0"), val = tensor(1)]; + tensor layers_30_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_30_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(339643520)))]; + tensor lora_out_369_cast_fp16 = conv(dilations = lora_out_369_dilations_0, groups = lora_out_369_groups_0, pad = lora_out_369_pad_0, pad_type = lora_out_369_pad_type_0, strides = lora_out_369_strides_0, weight = layers_30_fc1_loraB_weight_to_fp16, x = input_613_cast_fp16)[name = tensor("lora_out_369_cast_fp16")]; + tensor input_615_cast_fp16 = add(x = pretrained_out_369_cast_fp16, y = lora_out_369_cast_fp16)[name = tensor("input_615_cast_fp16")]; + tensor input_617_mode_0 = const()[name = tensor("input_617_mode_0"), val = tensor("EXACT")]; + tensor input_617_cast_fp16 = gelu(mode = input_617_mode_0, x = input_615_cast_fp16)[name = tensor("input_617_cast_fp16")]; + tensor pretrained_out_371_pad_type_0 = const()[name = tensor("pretrained_out_371_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_371_strides_0 = const()[name = tensor("pretrained_out_371_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_371_pad_0 = const()[name = tensor("pretrained_out_371_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_371_dilations_0 = const()[name = tensor("pretrained_out_371_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_371_groups_0 = const()[name = tensor("pretrained_out_371_groups_0"), val = tensor(1)]; + tensor layers_30_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(339807424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(343084288))), name = tensor("layers_30_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_30_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_30_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(343084416)))]; + tensor pretrained_out_371_cast_fp16 = conv(bias = layers_30_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_371_dilations_0, groups = pretrained_out_371_groups_0, pad = pretrained_out_371_pad_0, pad_type = pretrained_out_371_pad_type_0, strides = pretrained_out_371_strides_0, weight = layers_30_fc2_pretrained_weight_to_fp16_palettized, x = input_617_cast_fp16)[name = tensor("pretrained_out_371_cast_fp16")]; + tensor input_619_pad_type_0 = const()[name = tensor("input_619_pad_type_0"), val = tensor("valid")]; + tensor input_619_strides_0 = const()[name = tensor("input_619_strides_0"), val = tensor([1, 1])]; + tensor input_619_pad_0 = const()[name = tensor("input_619_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_619_dilations_0 = const()[name = tensor("input_619_dilations_0"), val = tensor([1, 1])]; + tensor input_619_groups_0 = const()[name = tensor("input_619_groups_0"), val = tensor(1)]; + tensor layers_30_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_30_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(343087040)))]; + tensor input_619_cast_fp16 = conv(dilations = input_619_dilations_0, groups = input_619_groups_0, pad = input_619_pad_0, pad_type = input_619_pad_type_0, strides = input_619_strides_0, weight = layers_30_fc2_loraA_weight_to_fp16, x = input_617_cast_fp16)[name = tensor("input_619_cast_fp16")]; + tensor lora_out_371_pad_type_0 = const()[name = tensor("lora_out_371_pad_type_0"), val = tensor("valid")]; + tensor lora_out_371_strides_0 = const()[name = tensor("lora_out_371_strides_0"), val = tensor([1, 1])]; + tensor lora_out_371_pad_0 = const()[name = tensor("lora_out_371_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_371_dilations_0 = const()[name = tensor("lora_out_371_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_371_groups_0 = const()[name = tensor("lora_out_371_groups_0"), val = tensor(1)]; + tensor layers_30_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_30_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(343250944)))]; + tensor lora_out_371_cast_fp16 = conv(dilations = lora_out_371_dilations_0, groups = lora_out_371_groups_0, pad = lora_out_371_pad_0, pad_type = lora_out_371_pad_type_0, strides = lora_out_371_strides_0, weight = layers_30_fc2_loraB_weight_to_fp16, x = input_619_cast_fp16)[name = tensor("lora_out_371_cast_fp16")]; + tensor hidden_states_65_cast_fp16 = add(x = pretrained_out_371_cast_fp16, y = lora_out_371_cast_fp16)[name = tensor("hidden_states_65_cast_fp16")]; + tensor inputs_125_cast_fp16 = add(x = inputs_123_cast_fp16, y = hidden_states_65_cast_fp16)[name = tensor("inputs_125_cast_fp16")]; + tensor var_6812 = const()[name = tensor("op_6812"), val = tensor(3)]; + tensor out_125_axes_0 = const()[name = tensor("out_125_axes_0"), val = tensor([1])]; + tensor var_6831_to_fp16 = const()[name = tensor("op_6831_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_125_cast_fp16 = layer_norm(axes = out_125_axes_0, epsilon = var_6831_to_fp16, x = inputs_125_cast_fp16)[name = tensor("out_125_cast_fp16")]; + tensor obj_125_gamma_0_to_fp16 = const()[name = tensor("obj_125_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(343291968)))]; + tensor obj_125_beta_0_to_fp16 = const()[name = tensor("obj_125_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(343294592)))]; + tensor obj_125_epsilon_0_to_fp16 = const()[name = tensor("obj_125_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor obj_125_cast_fp16 = batch_norm(beta = obj_125_beta_0_to_fp16, epsilon = obj_125_epsilon_0_to_fp16, gamma = obj_125_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_125_cast_fp16)[name = tensor("obj_125_cast_fp16")]; + tensor pretrained_out_373_pad_type_0 = const()[name = tensor("pretrained_out_373_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_373_strides_0 = const()[name = tensor("pretrained_out_373_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_373_pad_0 = const()[name = tensor("pretrained_out_373_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_373_dilations_0 = const()[name = tensor("pretrained_out_373_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_373_groups_0 = const()[name = tensor("pretrained_out_373_groups_0"), val = tensor(1)]; + tensor layers_31_self_attn_q_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(343297216))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(344116480))), name = tensor("layers_31_self_attn_q_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_31_self_attn_q_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_31_self_attn_q_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(344116608)))]; + tensor pretrained_out_373_cast_fp16 = conv(bias = layers_31_self_attn_q_proj_pretrained_bias_to_fp16, dilations = pretrained_out_373_dilations_0, groups = pretrained_out_373_groups_0, pad = pretrained_out_373_pad_0, pad_type = pretrained_out_373_pad_type_0, strides = pretrained_out_373_strides_0, weight = layers_31_self_attn_q_proj_pretrained_weight_to_fp16_palettized, x = obj_125_cast_fp16)[name = tensor("pretrained_out_373_cast_fp16")]; + tensor input_621_pad_type_0 = const()[name = tensor("input_621_pad_type_0"), val = tensor("valid")]; + tensor input_621_strides_0 = const()[name = tensor("input_621_strides_0"), val = tensor([1, 1])]; + tensor input_621_pad_0 = const()[name = tensor("input_621_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_621_dilations_0 = const()[name = tensor("input_621_dilations_0"), val = tensor([1, 1])]; + tensor input_621_groups_0 = const()[name = tensor("input_621_groups_0"), val = tensor(1)]; + tensor layers_31_self_attn_q_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_31_self_attn_q_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(344119232)))]; + tensor input_621_cast_fp16 = conv(dilations = input_621_dilations_0, groups = input_621_groups_0, pad = input_621_pad_0, pad_type = input_621_pad_type_0, strides = input_621_strides_0, weight = layers_31_self_attn_q_proj_loraA_weight_to_fp16, x = obj_125_cast_fp16)[name = tensor("input_621_cast_fp16")]; + tensor lora_out_373_pad_type_0 = const()[name = tensor("lora_out_373_pad_type_0"), val = tensor("valid")]; + tensor lora_out_373_strides_0 = const()[name = tensor("lora_out_373_strides_0"), val = tensor([1, 1])]; + tensor lora_out_373_pad_0 = const()[name = tensor("lora_out_373_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_373_dilations_0 = const()[name = tensor("lora_out_373_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_373_groups_0 = const()[name = tensor("lora_out_373_groups_0"), val = tensor(1)]; + tensor layers_31_self_attn_q_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_31_self_attn_q_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(344160256)))]; + tensor lora_out_373_cast_fp16 = conv(dilations = lora_out_373_dilations_0, groups = lora_out_373_groups_0, pad = lora_out_373_pad_0, pad_type = lora_out_373_pad_type_0, strides = lora_out_373_strides_0, weight = layers_31_self_attn_q_proj_loraB_weight_to_fp16, x = input_621_cast_fp16)[name = tensor("lora_out_373_cast_fp16")]; + tensor query_cast_fp16 = add(x = pretrained_out_373_cast_fp16, y = lora_out_373_cast_fp16)[name = tensor("query_cast_fp16")]; + tensor pretrained_out_375_pad_type_0 = const()[name = tensor("pretrained_out_375_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_375_strides_0 = const()[name = tensor("pretrained_out_375_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_375_pad_0 = const()[name = tensor("pretrained_out_375_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_375_dilations_0 = const()[name = tensor("pretrained_out_375_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_375_groups_0 = const()[name = tensor("pretrained_out_375_groups_0"), val = tensor(1)]; + tensor layers_31_self_attn_k_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(344201280))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(345020544))), name = tensor("layers_31_self_attn_k_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor pretrained_out_375_cast_fp16 = conv(dilations = pretrained_out_375_dilations_0, groups = pretrained_out_375_groups_0, pad = pretrained_out_375_pad_0, pad_type = pretrained_out_375_pad_type_0, strides = pretrained_out_375_strides_0, weight = layers_31_self_attn_k_proj_pretrained_weight_to_fp16_palettized, x = obj_125_cast_fp16)[name = tensor("pretrained_out_375_cast_fp16")]; + tensor input_623_pad_type_0 = const()[name = tensor("input_623_pad_type_0"), val = tensor("valid")]; + tensor input_623_strides_0 = const()[name = tensor("input_623_strides_0"), val = tensor([1, 1])]; + tensor input_623_pad_0 = const()[name = tensor("input_623_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_623_dilations_0 = const()[name = tensor("input_623_dilations_0"), val = tensor([1, 1])]; + tensor input_623_groups_0 = const()[name = tensor("input_623_groups_0"), val = tensor(1)]; + tensor layers_31_self_attn_k_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_31_self_attn_k_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(345020672)))]; + tensor input_623_cast_fp16 = conv(dilations = input_623_dilations_0, groups = input_623_groups_0, pad = input_623_pad_0, pad_type = input_623_pad_type_0, strides = input_623_strides_0, weight = layers_31_self_attn_k_proj_loraA_weight_to_fp16, x = obj_125_cast_fp16)[name = tensor("input_623_cast_fp16")]; + tensor lora_out_375_pad_type_0 = const()[name = tensor("lora_out_375_pad_type_0"), val = tensor("valid")]; + tensor lora_out_375_strides_0 = const()[name = tensor("lora_out_375_strides_0"), val = tensor([1, 1])]; + tensor lora_out_375_pad_0 = const()[name = tensor("lora_out_375_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_375_dilations_0 = const()[name = tensor("lora_out_375_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_375_groups_0 = const()[name = tensor("lora_out_375_groups_0"), val = tensor(1)]; + tensor layers_31_self_attn_k_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_31_self_attn_k_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(345061696)))]; + tensor lora_out_375_cast_fp16 = conv(dilations = lora_out_375_dilations_0, groups = lora_out_375_groups_0, pad = lora_out_375_pad_0, pad_type = lora_out_375_pad_type_0, strides = lora_out_375_strides_0, weight = layers_31_self_attn_k_proj_loraB_weight_to_fp16, x = input_623_cast_fp16)[name = tensor("lora_out_375_cast_fp16")]; + tensor key_cast_fp16 = add(x = pretrained_out_375_cast_fp16, y = lora_out_375_cast_fp16)[name = tensor("key_cast_fp16")]; + tensor pretrained_out_377_pad_type_0 = const()[name = tensor("pretrained_out_377_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_377_strides_0 = const()[name = tensor("pretrained_out_377_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_377_pad_0 = const()[name = tensor("pretrained_out_377_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_377_dilations_0 = const()[name = tensor("pretrained_out_377_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_377_groups_0 = const()[name = tensor("pretrained_out_377_groups_0"), val = tensor(1)]; + tensor layers_31_self_attn_v_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(345102720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(345921984))), name = tensor("layers_31_self_attn_v_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_31_self_attn_v_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_31_self_attn_v_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(345922112)))]; + tensor pretrained_out_377_cast_fp16 = conv(bias = layers_31_self_attn_v_proj_pretrained_bias_to_fp16, dilations = pretrained_out_377_dilations_0, groups = pretrained_out_377_groups_0, pad = pretrained_out_377_pad_0, pad_type = pretrained_out_377_pad_type_0, strides = pretrained_out_377_strides_0, weight = layers_31_self_attn_v_proj_pretrained_weight_to_fp16_palettized, x = obj_125_cast_fp16)[name = tensor("pretrained_out_377_cast_fp16")]; + tensor input_625_pad_type_0 = const()[name = tensor("input_625_pad_type_0"), val = tensor("valid")]; + tensor input_625_strides_0 = const()[name = tensor("input_625_strides_0"), val = tensor([1, 1])]; + tensor input_625_pad_0 = const()[name = tensor("input_625_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_625_dilations_0 = const()[name = tensor("input_625_dilations_0"), val = tensor([1, 1])]; + tensor input_625_groups_0 = const()[name = tensor("input_625_groups_0"), val = tensor(1)]; + tensor layers_31_self_attn_v_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_31_self_attn_v_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(345924736)))]; + tensor input_625_cast_fp16 = conv(dilations = input_625_dilations_0, groups = input_625_groups_0, pad = input_625_pad_0, pad_type = input_625_pad_type_0, strides = input_625_strides_0, weight = layers_31_self_attn_v_proj_loraA_weight_to_fp16, x = obj_125_cast_fp16)[name = tensor("input_625_cast_fp16")]; + tensor lora_out_377_pad_type_0 = const()[name = tensor("lora_out_377_pad_type_0"), val = tensor("valid")]; + tensor lora_out_377_strides_0 = const()[name = tensor("lora_out_377_strides_0"), val = tensor([1, 1])]; + tensor lora_out_377_pad_0 = const()[name = tensor("lora_out_377_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_377_dilations_0 = const()[name = tensor("lora_out_377_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_377_groups_0 = const()[name = tensor("lora_out_377_groups_0"), val = tensor(1)]; + tensor layers_31_self_attn_v_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_31_self_attn_v_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(345965760)))]; + tensor lora_out_377_cast_fp16 = conv(dilations = lora_out_377_dilations_0, groups = lora_out_377_groups_0, pad = lora_out_377_pad_0, pad_type = lora_out_377_pad_type_0, strides = lora_out_377_strides_0, weight = layers_31_self_attn_v_proj_loraB_weight_to_fp16, x = input_625_cast_fp16)[name = tensor("lora_out_377_cast_fp16")]; + tensor value_cast_fp16 = add(x = pretrained_out_377_cast_fp16, y = lora_out_377_cast_fp16)[name = tensor("value_cast_fp16")]; + tensor var_6914 = const()[name = tensor("op_6914"), val = tensor([1, 20, 64, -1])]; + tensor mh_q_cast_fp16 = reshape(shape = var_6914, x = query_cast_fp16)[name = tensor("mh_q_cast_fp16")]; + tensor var_6916_to_fp16 = const()[name = tensor("op_6916_to_fp16"), val = tensor(0x1p-3)]; + tensor var_6917_cast_fp16 = mul(x = mh_q_cast_fp16, y = var_6916_to_fp16)[name = tensor("op_6917_cast_fp16")]; + tensor var_6918 = const()[name = tensor("op_6918"), val = tensor([1, 20, 64, -1])]; + tensor var_6919_cast_fp16 = reshape(shape = var_6918, x = key_cast_fp16)[name = tensor("op_6919_cast_fp16")]; + tensor mh_w_transpose_x_0 = const()[name = tensor("mh_w_transpose_x_0"), val = tensor(true)]; + tensor mh_w_transpose_y_0 = const()[name = tensor("mh_w_transpose_y_0"), val = tensor(false)]; + tensor mh_w_cast_fp16 = matmul(transpose_x = mh_w_transpose_x_0, transpose_y = mh_w_transpose_y_0, x = var_6917_cast_fp16, y = var_6919_cast_fp16)[name = tensor("mh_w_cast_fp16")]; + tensor var_6922_cast_fp16 = softmax(axis = var_6812, x = mh_w_cast_fp16)[name = tensor("op_6922_cast_fp16")]; + tensor var_6923 = const()[name = tensor("op_6923"), val = tensor([1, 20, 64, -1])]; + tensor var_6924_cast_fp16 = reshape(shape = var_6923, x = value_cast_fp16)[name = tensor("op_6924_cast_fp16")]; + tensor attn_transpose_x_0 = const()[name = tensor("attn_transpose_x_0"), val = tensor(false)]; + tensor attn_transpose_y_0 = const()[name = tensor("attn_transpose_y_0"), val = tensor(true)]; + tensor attn_cast_fp16 = matmul(transpose_x = attn_transpose_x_0, transpose_y = attn_transpose_y_0, x = var_6924_cast_fp16, y = var_6922_cast_fp16)[name = tensor("attn_cast_fp16")]; + tensor var_6927 = const()[name = tensor("op_6927"), val = tensor([1, 1280, 1, -1])]; + tensor input_627_cast_fp16 = reshape(shape = var_6927, x = attn_cast_fp16)[name = tensor("input_627_cast_fp16")]; + tensor pretrained_out_379_pad_type_0 = const()[name = tensor("pretrained_out_379_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_379_strides_0 = const()[name = tensor("pretrained_out_379_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_379_pad_0 = const()[name = tensor("pretrained_out_379_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_379_dilations_0 = const()[name = tensor("pretrained_out_379_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_379_groups_0 = const()[name = tensor("pretrained_out_379_groups_0"), val = tensor(1)]; + tensor layers_31_self_attn_o_proj_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(346006784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(346826048))), name = tensor("layers_31_self_attn_o_proj_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor layers_31_self_attn_o_proj_pretrained_bias_to_fp16 = const()[name = tensor("layers_31_self_attn_o_proj_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(346826176)))]; + tensor pretrained_out_379_cast_fp16 = conv(bias = layers_31_self_attn_o_proj_pretrained_bias_to_fp16, dilations = pretrained_out_379_dilations_0, groups = pretrained_out_379_groups_0, pad = pretrained_out_379_pad_0, pad_type = pretrained_out_379_pad_type_0, strides = pretrained_out_379_strides_0, weight = layers_31_self_attn_o_proj_pretrained_weight_to_fp16_palettized, x = input_627_cast_fp16)[name = tensor("pretrained_out_379_cast_fp16")]; + tensor input_629_pad_type_0 = const()[name = tensor("input_629_pad_type_0"), val = tensor("valid")]; + tensor input_629_strides_0 = const()[name = tensor("input_629_strides_0"), val = tensor([1, 1])]; + tensor input_629_pad_0 = const()[name = tensor("input_629_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_629_dilations_0 = const()[name = tensor("input_629_dilations_0"), val = tensor([1, 1])]; + tensor input_629_groups_0 = const()[name = tensor("input_629_groups_0"), val = tensor(1)]; + tensor layers_31_self_attn_o_proj_loraA_weight_to_fp16 = const()[name = tensor("layers_31_self_attn_o_proj_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(346828800)))]; + tensor input_629_cast_fp16 = conv(dilations = input_629_dilations_0, groups = input_629_groups_0, pad = input_629_pad_0, pad_type = input_629_pad_type_0, strides = input_629_strides_0, weight = layers_31_self_attn_o_proj_loraA_weight_to_fp16, x = input_627_cast_fp16)[name = tensor("input_629_cast_fp16")]; + tensor lora_out_379_pad_type_0 = const()[name = tensor("lora_out_379_pad_type_0"), val = tensor("valid")]; + tensor lora_out_379_strides_0 = const()[name = tensor("lora_out_379_strides_0"), val = tensor([1, 1])]; + tensor lora_out_379_pad_0 = const()[name = tensor("lora_out_379_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_379_dilations_0 = const()[name = tensor("lora_out_379_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_379_groups_0 = const()[name = tensor("lora_out_379_groups_0"), val = tensor(1)]; + tensor layers_31_self_attn_o_proj_loraB_weight_to_fp16 = const()[name = tensor("layers_31_self_attn_o_proj_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(346869824)))]; + tensor lora_out_379_cast_fp16 = conv(dilations = lora_out_379_dilations_0, groups = lora_out_379_groups_0, pad = lora_out_379_pad_0, pad_type = lora_out_379_pad_type_0, strides = lora_out_379_strides_0, weight = layers_31_self_attn_o_proj_loraB_weight_to_fp16, x = input_629_cast_fp16)[name = tensor("lora_out_379_cast_fp16")]; + tensor obj_cast_fp16 = add(x = pretrained_out_379_cast_fp16, y = lora_out_379_cast_fp16)[name = tensor("obj_cast_fp16")]; + tensor inputs_127_cast_fp16 = add(x = inputs_125_cast_fp16, y = obj_cast_fp16)[name = tensor("inputs_127_cast_fp16")]; + tensor out_127_axes_0 = const()[name = tensor("out_127_axes_0"), val = tensor([1])]; + tensor var_6961_to_fp16 = const()[name = tensor("op_6961_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_127_cast_fp16 = layer_norm(axes = out_127_axes_0, epsilon = var_6961_to_fp16, x = inputs_127_cast_fp16)[name = tensor("out_127_cast_fp16")]; + tensor input_631_gamma_0_to_fp16 = const()[name = tensor("input_631_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(346910848)))]; + tensor input_631_beta_0_to_fp16 = const()[name = tensor("input_631_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(346913472)))]; + tensor input_631_epsilon_0_to_fp16 = const()[name = tensor("input_631_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_631_cast_fp16 = batch_norm(beta = input_631_beta_0_to_fp16, epsilon = input_631_epsilon_0_to_fp16, gamma = input_631_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_127_cast_fp16)[name = tensor("input_631_cast_fp16")]; + tensor pretrained_out_381_pad_type_0 = const()[name = tensor("pretrained_out_381_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_381_strides_0 = const()[name = tensor("pretrained_out_381_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_381_pad_0 = const()[name = tensor("pretrained_out_381_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_381_dilations_0 = const()[name = tensor("pretrained_out_381_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_381_groups_0 = const()[name = tensor("pretrained_out_381_groups_0"), val = tensor(1)]; + tensor layers_31_fc1_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(346916096))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(350192960))), name = tensor("layers_31_fc1_pretrained_weight_to_fp16_palettized"), shape = tensor([5120, 1280, 1, 1])]; + tensor layers_31_fc1_pretrained_bias_to_fp16 = const()[name = tensor("layers_31_fc1_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(350193088)))]; + tensor pretrained_out_381_cast_fp16 = conv(bias = layers_31_fc1_pretrained_bias_to_fp16, dilations = pretrained_out_381_dilations_0, groups = pretrained_out_381_groups_0, pad = pretrained_out_381_pad_0, pad_type = pretrained_out_381_pad_type_0, strides = pretrained_out_381_strides_0, weight = layers_31_fc1_pretrained_weight_to_fp16_palettized, x = input_631_cast_fp16)[name = tensor("pretrained_out_381_cast_fp16")]; + tensor input_633_pad_type_0 = const()[name = tensor("input_633_pad_type_0"), val = tensor("valid")]; + tensor input_633_strides_0 = const()[name = tensor("input_633_strides_0"), val = tensor([1, 1])]; + tensor input_633_pad_0 = const()[name = tensor("input_633_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_633_dilations_0 = const()[name = tensor("input_633_dilations_0"), val = tensor([1, 1])]; + tensor input_633_groups_0 = const()[name = tensor("input_633_groups_0"), val = tensor(1)]; + tensor layers_31_fc1_loraA_weight_to_fp16 = const()[name = tensor("layers_31_fc1_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(350203392)))]; + tensor input_633_cast_fp16 = conv(dilations = input_633_dilations_0, groups = input_633_groups_0, pad = input_633_pad_0, pad_type = input_633_pad_type_0, strides = input_633_strides_0, weight = layers_31_fc1_loraA_weight_to_fp16, x = input_631_cast_fp16)[name = tensor("input_633_cast_fp16")]; + tensor lora_out_381_pad_type_0 = const()[name = tensor("lora_out_381_pad_type_0"), val = tensor("valid")]; + tensor lora_out_381_strides_0 = const()[name = tensor("lora_out_381_strides_0"), val = tensor([1, 1])]; + tensor lora_out_381_pad_0 = const()[name = tensor("lora_out_381_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_381_dilations_0 = const()[name = tensor("lora_out_381_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_381_groups_0 = const()[name = tensor("lora_out_381_groups_0"), val = tensor(1)]; + tensor layers_31_fc1_loraB_weight_to_fp16 = const()[name = tensor("layers_31_fc1_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(350244416)))]; + tensor lora_out_381_cast_fp16 = conv(dilations = lora_out_381_dilations_0, groups = lora_out_381_groups_0, pad = lora_out_381_pad_0, pad_type = lora_out_381_pad_type_0, strides = lora_out_381_strides_0, weight = layers_31_fc1_loraB_weight_to_fp16, x = input_633_cast_fp16)[name = tensor("lora_out_381_cast_fp16")]; + tensor input_635_cast_fp16 = add(x = pretrained_out_381_cast_fp16, y = lora_out_381_cast_fp16)[name = tensor("input_635_cast_fp16")]; + tensor input_637_mode_0 = const()[name = tensor("input_637_mode_0"), val = tensor("EXACT")]; + tensor input_637_cast_fp16 = gelu(mode = input_637_mode_0, x = input_635_cast_fp16)[name = tensor("input_637_cast_fp16")]; + tensor pretrained_out_pad_type_0 = const()[name = tensor("pretrained_out_pad_type_0"), val = tensor("valid")]; + tensor pretrained_out_strides_0 = const()[name = tensor("pretrained_out_strides_0"), val = tensor([1, 1])]; + tensor pretrained_out_pad_0 = const()[name = tensor("pretrained_out_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor pretrained_out_dilations_0 = const()[name = tensor("pretrained_out_dilations_0"), val = tensor([1, 1])]; + tensor pretrained_out_groups_0 = const()[name = tensor("pretrained_out_groups_0"), val = tensor(1)]; + tensor layers_31_fc2_pretrained_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(350408320))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353685184))), name = tensor("layers_31_fc2_pretrained_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor layers_31_fc2_pretrained_bias_to_fp16 = const()[name = tensor("layers_31_fc2_pretrained_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353685312)))]; + tensor pretrained_out_cast_fp16 = conv(bias = layers_31_fc2_pretrained_bias_to_fp16, dilations = pretrained_out_dilations_0, groups = pretrained_out_groups_0, pad = pretrained_out_pad_0, pad_type = pretrained_out_pad_type_0, strides = pretrained_out_strides_0, weight = layers_31_fc2_pretrained_weight_to_fp16_palettized, x = input_637_cast_fp16)[name = tensor("pretrained_out_cast_fp16")]; + tensor input_pad_type_0 = const()[name = tensor("input_pad_type_0"), val = tensor("valid")]; + tensor input_strides_0 = const()[name = tensor("input_strides_0"), val = tensor([1, 1])]; + tensor input_pad_0 = const()[name = tensor("input_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_dilations_0 = const()[name = tensor("input_dilations_0"), val = tensor([1, 1])]; + tensor input_groups_0 = const()[name = tensor("input_groups_0"), val = tensor(1)]; + tensor layers_31_fc2_loraA_weight_to_fp16 = const()[name = tensor("layers_31_fc2_loraA_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353687936)))]; + tensor input_cast_fp16 = conv(dilations = input_dilations_0, groups = input_groups_0, pad = input_pad_0, pad_type = input_pad_type_0, strides = input_strides_0, weight = layers_31_fc2_loraA_weight_to_fp16, x = input_637_cast_fp16)[name = tensor("input_cast_fp16")]; + tensor lora_out_pad_type_0 = const()[name = tensor("lora_out_pad_type_0"), val = tensor("valid")]; + tensor lora_out_strides_0 = const()[name = tensor("lora_out_strides_0"), val = tensor([1, 1])]; + tensor lora_out_pad_0 = const()[name = tensor("lora_out_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor lora_out_dilations_0 = const()[name = tensor("lora_out_dilations_0"), val = tensor([1, 1])]; + tensor lora_out_groups_0 = const()[name = tensor("lora_out_groups_0"), val = tensor(1)]; + tensor layers_31_fc2_loraB_weight_to_fp16 = const()[name = tensor("layers_31_fc2_loraB_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353851840)))]; + tensor lora_out_cast_fp16 = conv(dilations = lora_out_dilations_0, groups = lora_out_groups_0, pad = lora_out_pad_0, pad_type = lora_out_pad_type_0, strides = lora_out_strides_0, weight = layers_31_fc2_loraB_weight_to_fp16, x = input_cast_fp16)[name = tensor("lora_out_cast_fp16")]; + tensor hidden_states_cast_fp16 = add(x = pretrained_out_cast_fp16, y = lora_out_cast_fp16)[name = tensor("hidden_states_cast_fp16")]; + tensor inputs_cast_fp16 = add(x = inputs_127_cast_fp16, y = hidden_states_cast_fp16)[name = tensor("inputs_cast_fp16")]; + tensor out_axes_0 = const()[name = tensor("out_axes_0"), val = tensor([1])]; + tensor var_7031_to_fp16 = const()[name = tensor("op_7031_to_fp16"), val = tensor(0x1.5p-17)]; + tensor out_cast_fp16 = layer_norm(axes = out_axes_0, epsilon = var_7031_to_fp16, x = inputs_cast_fp16)[name = tensor("out_cast_fp16")]; + tensor encoder_output_embeds_type_fp32_gamma_0_to_fp16 = const()[name = tensor("encoder_output_embeds_type_fp32_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353892864)))]; + tensor encoder_output_embeds_type_fp32_beta_0_to_fp16 = const()[name = tensor("encoder_output_embeds_type_fp32_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353895488)))]; + tensor encoder_output_embeds_type_fp32_epsilon_0_to_fp16 = const()[name = tensor("encoder_output_embeds_type_fp32_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor encoder_output_embeds = batch_norm(beta = encoder_output_embeds_type_fp32_beta_0_to_fp16, epsilon = encoder_output_embeds_type_fp32_epsilon_0_to_fp16, gamma = encoder_output_embeds_type_fp32_gamma_0_to_fp16, mean = obj_1_mean_0_to_fp16, variance = obj_1_variance_0_to_fp16, x = out_cast_fp16)[name = tensor("encoder_output_embeds_type_fp32_cast_fp16")]; + } -> (encoder_output_embeds); +} \ No newline at end of file