diff --git "a/DeepSeek_FFN_PF_lut6_chunk_07of08.mlmodelc/model.mil" "b/DeepSeek_FFN_PF_lut6_chunk_07of08.mlmodelc/model.mil" new file mode 100644--- /dev/null +++ "b/DeepSeek_FFN_PF_lut6_chunk_07of08.mlmodelc/model.mil" @@ -0,0 +1,1687 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3404.16.1"}, {"coremlc-version", "3404.23.1"}})] +{ + func infer(tensor causal_mask, tensor current_pos, tensor hidden_states, state> model_model_kv_cache_0, tensor position_ids) { + tensor model_model_layers_24_self_attn_q_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12583040))))[name = string("model_model_layers_24_self_attn_q_proj_weight_palettized")]; + tensor model_model_layers_24_self_attn_k_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12648640))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15794432))))[name = string("model_model_layers_24_self_attn_k_proj_weight_palettized")]; + tensor model_model_layers_24_self_attn_v_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15810880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18956672))))[name = string("model_model_layers_24_self_attn_v_proj_weight_palettized")]; + tensor model_model_layers_24_mlp_gate_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18973120))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63013376))))[name = string("model_model_layers_24_mlp_gate_proj_weight_palettized")]; + tensor model_model_layers_24_mlp_up_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63242816))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107283072))))[name = string("model_model_layers_24_mlp_up_proj_weight_palettized")]; + tensor model_model_layers_24_mlp_down_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107512512))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151552768))))[name = string("model_model_layers_24_mlp_down_proj_weight_palettized")]; + tensor model_model_layers_25_self_attn_q_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151618368))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164201344))))[name = string("model_model_layers_25_self_attn_q_proj_weight_palettized")]; + tensor model_model_layers_25_self_attn_k_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164266944))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167412736))))[name = string("model_model_layers_25_self_attn_k_proj_weight_palettized")]; + tensor model_model_layers_25_self_attn_v_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167429184))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170574976))))[name = string("model_model_layers_25_self_attn_v_proj_weight_palettized")]; + tensor model_model_layers_25_mlp_gate_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170591424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214631680))))[name = string("model_model_layers_25_mlp_gate_proj_weight_palettized")]; + tensor model_model_layers_25_mlp_up_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214861120))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258901376))))[name = string("model_model_layers_25_mlp_up_proj_weight_palettized")]; + tensor model_model_layers_25_mlp_down_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259130816))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303171072))))[name = string("model_model_layers_25_mlp_down_proj_weight_palettized")]; + tensor model_model_layers_26_self_attn_q_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303236672))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315819648))))[name = string("model_model_layers_26_self_attn_q_proj_weight_palettized")]; + tensor model_model_layers_26_self_attn_k_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315885248))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(319031040))))[name = string("model_model_layers_26_self_attn_k_proj_weight_palettized")]; + tensor model_model_layers_26_self_attn_v_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(319047488))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(322193280))))[name = string("model_model_layers_26_self_attn_v_proj_weight_palettized")]; + tensor model_model_layers_26_mlp_gate_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(322209728))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(366249984))))[name = string("model_model_layers_26_mlp_gate_proj_weight_palettized")]; + tensor model_model_layers_26_mlp_up_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(366479424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(410519680))))[name = string("model_model_layers_26_mlp_up_proj_weight_palettized")]; + tensor model_model_layers_26_mlp_down_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(410749120))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454789376))))[name = string("model_model_layers_26_mlp_down_proj_weight_palettized")]; + tensor model_model_layers_27_self_attn_q_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454854976))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(467437952))))[name = string("model_model_layers_27_self_attn_q_proj_weight_palettized")]; + tensor model_model_layers_27_self_attn_k_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(467503552))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(470649344))))[name = string("model_model_layers_27_self_attn_k_proj_weight_palettized")]; + tensor model_model_layers_27_self_attn_v_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(470665792))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(473811584))))[name = string("model_model_layers_27_self_attn_v_proj_weight_palettized")]; + tensor model_model_layers_27_mlp_gate_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(473828032))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(517868288))))[name = string("model_model_layers_27_mlp_gate_proj_weight_palettized")]; + tensor model_model_layers_27_mlp_up_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(518097728))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(562137984))))[name = string("model_model_layers_27_mlp_up_proj_weight_palettized")]; + tensor model_model_layers_27_mlp_down_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(562367424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(606407680))))[name = string("model_model_layers_27_mlp_down_proj_weight_palettized")]; + int32 var_41 = const()[name = string("op_41"), val = int32(-1)]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor greater_equal_0 = greater_equal(x = current_pos, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(131072)]; + tensor add_0 = add(x = current_pos, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = current_pos, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + int32 var_150_axis_0 = const()[name = string("op_150_axis_0"), val = int32(1)]; + int32 var_150_batch_dims_0 = const()[name = string("op_150_batch_dims_0"), val = int32(0)]; + bool var_150_validate_indices_0 = const()[name = string("op_150_validate_indices_0"), val = bool(false)]; + tensor var_46_to_fp16 = const()[name = string("op_46_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(606473280)))]; + tensor var_150_cast_fp16 = gather(axis = var_150_axis_0, batch_dims = var_150_batch_dims_0, indices = select_0, validate_indices = var_150_validate_indices_0, x = var_46_to_fp16)[name = string("op_150_cast_fp16")]; + tensor var_151 = const()[name = string("op_151"), val = tensor([1, 1, 1, -1])]; + tensor sin_1_cast_fp16 = reshape(shape = var_151, x = var_150_cast_fp16)[name = string("sin_1_cast_fp16")]; + int32 var_155_axis_0 = const()[name = string("op_155_axis_0"), val = int32(1)]; + int32 var_155_batch_dims_0 = const()[name = string("op_155_batch_dims_0"), val = int32(0)]; + bool var_155_validate_indices_0 = const()[name = string("op_155_validate_indices_0"), val = bool(false)]; + tensor var_40_to_fp16 = const()[name = string("op_40_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(640027776)))]; + tensor var_155_cast_fp16 = gather(axis = var_155_axis_0, batch_dims = var_155_batch_dims_0, indices = select_0, validate_indices = var_155_validate_indices_0, x = var_40_to_fp16)[name = string("op_155_cast_fp16")]; + tensor var_156 = const()[name = string("op_156"), val = tensor([1, 1, 1, -1])]; + tensor cos_1_cast_fp16 = reshape(shape = var_156, x = var_155_cast_fp16)[name = string("cos_1_cast_fp16")]; + tensor mean_1_axes_0 = const()[name = string("mean_1_axes_0"), val = tensor([-1])]; + bool mean_1_keep_dims_0 = const()[name = string("mean_1_keep_dims_0"), val = bool(true)]; + tensor mean_1_cast_fp16 = reduce_mean(axes = mean_1_axes_0, keep_dims = mean_1_keep_dims_0, x = hidden_states)[name = string("mean_1_cast_fp16")]; + tensor input_1_cast_fp16 = sub(x = hidden_states, y = mean_1_cast_fp16)[name = string("input_1_cast_fp16")]; + tensor var_164_axes_0 = const()[name = string("op_164_axes_0"), val = tensor([-1])]; + tensor model_model_layers_24_input_layernorm_weight_to_fp16 = const()[name = string("model_model_layers_24_input_layernorm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(673582272)))]; + fp16 var_36_to_fp16 = const()[name = string("op_36_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_164_cast_fp16 = layer_norm(axes = var_164_axes_0, epsilon = var_36_to_fp16, gamma = model_model_layers_24_input_layernorm_weight_to_fp16, x = input_1_cast_fp16)[name = string("op_164_cast_fp16")]; + tensor var_167 = const()[name = string("op_167"), val = tensor([0, 2, 1])]; + tensor var_169_axes_0 = const()[name = string("op_169_axes_0"), val = tensor([2])]; + tensor var_168 = transpose(perm = var_167, x = var_164_cast_fp16)[name = string("transpose_15")]; + tensor var_169 = expand_dims(axes = var_169_axes_0, x = var_168)[name = string("op_169")]; + string var_176_pad_type_0 = const()[name = string("op_176_pad_type_0"), val = string("valid")]; + tensor var_176_strides_0 = const()[name = string("op_176_strides_0"), val = tensor([1, 1])]; + tensor var_176_pad_0 = const()[name = string("op_176_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor var_176_dilations_0 = const()[name = string("op_176_dilations_0"), val = tensor([1, 1])]; + int32 var_176_groups_0 = const()[name = string("op_176_groups_0"), val = int32(1)]; + tensor var_176 = conv(dilations = var_176_dilations_0, groups = var_176_groups_0, pad = var_176_pad_0, pad_type = var_176_pad_type_0, strides = var_176_strides_0, weight = model_model_layers_24_self_attn_q_proj_weight_palettized, x = var_169)[name = string("op_176")]; + tensor var_177 = const()[name = string("op_177"), val = tensor([1, 32, 1, 128])]; + tensor var_178 = reshape(shape = var_177, x = var_176)[name = string("op_178")]; + string var_185_pad_type_0 = const()[name = string("op_185_pad_type_0"), val = string("valid")]; + tensor var_185_strides_0 = const()[name = string("op_185_strides_0"), val = tensor([1, 1])]; + tensor var_185_pad_0 = const()[name = string("op_185_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor var_185_dilations_0 = const()[name = string("op_185_dilations_0"), val = tensor([1, 1])]; + int32 var_185_groups_0 = const()[name = string("op_185_groups_0"), val = int32(1)]; + tensor var_185 = conv(dilations = var_185_dilations_0, groups = var_185_groups_0, pad = var_185_pad_0, pad_type = var_185_pad_type_0, strides = var_185_strides_0, weight = model_model_layers_24_self_attn_k_proj_weight_palettized, x = var_169)[name = string("op_185")]; + tensor var_186 = const()[name = string("op_186"), val = tensor([1, 8, 1, 128])]; + tensor var_187 = reshape(shape = var_186, x = var_185)[name = string("op_187")]; + string var_194_pad_type_0 = const()[name = string("op_194_pad_type_0"), val = string("valid")]; + tensor var_194_strides_0 = const()[name = string("op_194_strides_0"), val = tensor([1, 1])]; + tensor var_194_pad_0 = const()[name = string("op_194_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor var_194_dilations_0 = const()[name = string("op_194_dilations_0"), val = tensor([1, 1])]; + int32 var_194_groups_0 = const()[name = string("op_194_groups_0"), val = int32(1)]; + tensor var_194 = conv(dilations = var_194_dilations_0, groups = var_194_groups_0, pad = var_194_pad_0, pad_type = var_194_pad_type_0, strides = var_194_strides_0, weight = model_model_layers_24_self_attn_v_proj_weight_palettized, x = var_169)[name = string("op_194")]; + tensor var_195 = const()[name = string("op_195"), val = tensor([1, 8, 1, 128])]; + tensor var_196 = reshape(shape = var_195, x = var_194)[name = string("op_196")]; + tensor x1_1_begin_0 = const()[name = string("x1_1_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x1_1_end_0 = const()[name = string("x1_1_end_0"), val = tensor([1, 32, 1, 64])]; + tensor x1_1_end_mask_0 = const()[name = string("x1_1_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x1_1 = slice_by_index(begin = x1_1_begin_0, end = x1_1_end_0, end_mask = x1_1_end_mask_0, x = var_178)[name = string("x1_1")]; + tensor x2_1_begin_0 = const()[name = string("x2_1_begin_0"), val = tensor([0, 0, 0, 64])]; + tensor x2_1_end_0 = const()[name = string("x2_1_end_0"), val = tensor([1, 32, 1, 128])]; + tensor x2_1_end_mask_0 = const()[name = string("x2_1_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x2_1 = slice_by_index(begin = x2_1_begin_0, end = x2_1_end_0, end_mask = x2_1_end_mask_0, x = var_178)[name = string("x2_1")]; + tensor cos_3_begin_0 = const()[name = string("cos_3_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor cos_3_end_0 = const()[name = string("cos_3_end_0"), val = tensor([1, 1, 1, 64])]; + tensor cos_3_end_mask_0 = const()[name = string("cos_3_end_mask_0"), val = tensor([true, true, true, false])]; + tensor cos_3_cast_fp16 = slice_by_index(begin = cos_3_begin_0, end = cos_3_end_0, end_mask = cos_3_end_mask_0, x = cos_1_cast_fp16)[name = string("cos_3_cast_fp16")]; + tensor sin_3_begin_0 = const()[name = string("sin_3_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor sin_3_end_0 = const()[name = string("sin_3_end_0"), val = tensor([1, 1, 1, 64])]; + tensor sin_3_end_mask_0 = const()[name = string("sin_3_end_mask_0"), val = tensor([true, true, true, false])]; + tensor sin_3_cast_fp16 = slice_by_index(begin = sin_3_begin_0, end = sin_3_end_0, end_mask = sin_3_end_mask_0, x = sin_1_cast_fp16)[name = string("sin_3_cast_fp16")]; + tensor var_210_cast_fp16 = mul(x = x1_1, y = cos_3_cast_fp16)[name = string("op_210_cast_fp16")]; + tensor var_211_cast_fp16 = mul(x = x2_1, y = sin_3_cast_fp16)[name = string("op_211_cast_fp16")]; + tensor var_212_cast_fp16 = sub(x = var_210_cast_fp16, y = var_211_cast_fp16)[name = string("op_212_cast_fp16")]; + tensor var_213_cast_fp16 = mul(x = x2_1, y = cos_3_cast_fp16)[name = string("op_213_cast_fp16")]; + tensor var_214_cast_fp16 = mul(x = x1_1, y = sin_3_cast_fp16)[name = string("op_214_cast_fp16")]; + tensor var_215_cast_fp16 = add(x = var_213_cast_fp16, y = var_214_cast_fp16)[name = string("op_215_cast_fp16")]; + bool rotated_1_interleave_0 = const()[name = string("rotated_1_interleave_0"), val = bool(false)]; + tensor rotated_1_cast_fp16 = concat(axis = var_41, interleave = rotated_1_interleave_0, values = (var_212_cast_fp16, var_215_cast_fp16))[name = string("rotated_1_cast_fp16")]; + tensor x1_3_begin_0 = const()[name = string("x1_3_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x1_3_end_0 = const()[name = string("x1_3_end_0"), val = tensor([1, 8, 1, 64])]; + tensor x1_3_end_mask_0 = const()[name = string("x1_3_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x1_3 = slice_by_index(begin = x1_3_begin_0, end = x1_3_end_0, end_mask = x1_3_end_mask_0, x = var_187)[name = string("x1_3")]; + tensor x2_3_begin_0 = const()[name = string("x2_3_begin_0"), val = tensor([0, 0, 0, 64])]; + tensor x2_3_end_0 = const()[name = string("x2_3_end_0"), val = tensor([1, 8, 1, 128])]; + tensor x2_3_end_mask_0 = const()[name = string("x2_3_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x2_3 = slice_by_index(begin = x2_3_begin_0, end = x2_3_end_0, end_mask = x2_3_end_mask_0, x = var_187)[name = string("x2_3")]; + tensor var_231_cast_fp16 = mul(x = x1_3, y = cos_3_cast_fp16)[name = string("op_231_cast_fp16")]; + tensor var_232_cast_fp16 = mul(x = x2_3, y = sin_3_cast_fp16)[name = string("op_232_cast_fp16")]; + tensor var_233_cast_fp16 = sub(x = var_231_cast_fp16, y = var_232_cast_fp16)[name = string("op_233_cast_fp16")]; + tensor var_234_cast_fp16 = mul(x = x2_3, y = cos_3_cast_fp16)[name = string("op_234_cast_fp16")]; + tensor var_235_cast_fp16 = mul(x = x1_3, y = sin_3_cast_fp16)[name = string("op_235_cast_fp16")]; + tensor var_236_cast_fp16 = add(x = var_234_cast_fp16, y = var_235_cast_fp16)[name = string("op_236_cast_fp16")]; + bool rotated_3_interleave_0 = const()[name = string("rotated_3_interleave_0"), val = bool(false)]; + tensor rotated_3_cast_fp16 = concat(axis = var_41, interleave = rotated_3_interleave_0, values = (var_233_cast_fp16, var_236_cast_fp16))[name = string("rotated_3_cast_fp16")]; + int32 var_240 = const()[name = string("op_240"), val = int32(1)]; + tensor var_241 = add(x = current_pos, y = var_240)[name = string("op_241")]; + tensor read_state_0 = read_state(input = model_model_kv_cache_0)[name = string("read_state_0")]; + tensor expand_dims_0 = const()[name = string("expand_dims_0"), val = tensor([24])]; + tensor expand_dims_1 = const()[name = string("expand_dims_1"), val = tensor([0])]; + tensor expand_dims_3 = const()[name = string("expand_dims_3"), val = tensor([0])]; + tensor expand_dims_4 = const()[name = string("expand_dims_4"), val = tensor([25])]; + int32 concat_2_axis_0 = const()[name = string("concat_2_axis_0"), val = int32(0)]; + bool concat_2_interleave_0 = const()[name = string("concat_2_interleave_0"), val = bool(false)]; + tensor concat_2 = concat(axis = concat_2_axis_0, interleave = concat_2_interleave_0, values = (expand_dims_0, expand_dims_1, current_pos, expand_dims_3))[name = string("concat_2")]; + tensor concat_3_values1_0 = const()[name = string("concat_3_values1_0"), val = tensor([0])]; + tensor concat_3_values3_0 = const()[name = string("concat_3_values3_0"), val = tensor([0])]; + int32 concat_3_axis_0 = const()[name = string("concat_3_axis_0"), val = int32(0)]; + bool concat_3_interleave_0 = const()[name = string("concat_3_interleave_0"), val = bool(false)]; + tensor concat_3 = concat(axis = concat_3_axis_0, interleave = concat_3_interleave_0, values = (expand_dims_4, concat_3_values1_0, var_241, concat_3_values3_0))[name = string("concat_3")]; + tensor model_model_kv_cache_0_internal_tensor_assign_1_stride_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1])]; + tensor model_model_kv_cache_0_internal_tensor_assign_1_begin_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, false, false, false])]; + tensor model_model_kv_cache_0_internal_tensor_assign_1_end_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, false, true])]; + tensor model_model_kv_cache_0_internal_tensor_assign_1_squeeze_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([false, false, false, false])]; + tensor model_model_kv_cache_0_internal_tensor_assign_1_cast_fp16 = slice_update(begin = concat_2, begin_mask = model_model_kv_cache_0_internal_tensor_assign_1_begin_mask_0, end = concat_3, end_mask = model_model_kv_cache_0_internal_tensor_assign_1_end_mask_0, squeeze_mask = model_model_kv_cache_0_internal_tensor_assign_1_squeeze_mask_0, stride = model_model_kv_cache_0_internal_tensor_assign_1_stride_0, update = rotated_3_cast_fp16, x = read_state_0)[name = string("model_model_kv_cache_0_internal_tensor_assign_1_cast_fp16")]; + write_state(data = model_model_kv_cache_0_internal_tensor_assign_1_cast_fp16, input = model_model_kv_cache_0)[name = string("coreml_update_state_0_write_state")]; + tensor coreml_update_state_8 = read_state(input = model_model_kv_cache_0)[name = string("coreml_update_state_0")]; + tensor expand_dims_6 = const()[name = string("expand_dims_6"), val = tensor([56])]; + tensor expand_dims_7 = const()[name = string("expand_dims_7"), val = tensor([0])]; + tensor expand_dims_9 = const()[name = string("expand_dims_9"), val = tensor([0])]; + tensor expand_dims_10 = const()[name = string("expand_dims_10"), val = tensor([57])]; + int32 concat_6_axis_0 = const()[name = string("concat_6_axis_0"), val = int32(0)]; + bool concat_6_interleave_0 = const()[name = string("concat_6_interleave_0"), val = bool(false)]; + tensor concat_6 = concat(axis = concat_6_axis_0, interleave = concat_6_interleave_0, values = (expand_dims_6, expand_dims_7, current_pos, expand_dims_9))[name = string("concat_6")]; + tensor concat_7_values1_0 = const()[name = string("concat_7_values1_0"), val = tensor([0])]; + tensor concat_7_values3_0 = const()[name = string("concat_7_values3_0"), val = tensor([0])]; + int32 concat_7_axis_0 = const()[name = string("concat_7_axis_0"), val = int32(0)]; + bool concat_7_interleave_0 = const()[name = string("concat_7_interleave_0"), val = bool(false)]; + tensor concat_7 = concat(axis = concat_7_axis_0, interleave = concat_7_interleave_0, values = (expand_dims_10, concat_7_values1_0, var_241, concat_7_values3_0))[name = string("concat_7")]; + tensor model_model_kv_cache_0_internal_tensor_assign_2_stride_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1])]; + tensor model_model_kv_cache_0_internal_tensor_assign_2_begin_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, false, false, false])]; + tensor model_model_kv_cache_0_internal_tensor_assign_2_end_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, false, true])]; + tensor model_model_kv_cache_0_internal_tensor_assign_2_squeeze_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([false, false, false, false])]; + tensor model_model_kv_cache_0_internal_tensor_assign_2_cast_fp16 = slice_update(begin = concat_6, begin_mask = model_model_kv_cache_0_internal_tensor_assign_2_begin_mask_0, end = concat_7, end_mask = model_model_kv_cache_0_internal_tensor_assign_2_end_mask_0, squeeze_mask = model_model_kv_cache_0_internal_tensor_assign_2_squeeze_mask_0, stride = model_model_kv_cache_0_internal_tensor_assign_2_stride_0, update = var_196, x = coreml_update_state_8)[name = string("model_model_kv_cache_0_internal_tensor_assign_2_cast_fp16")]; + write_state(data = model_model_kv_cache_0_internal_tensor_assign_2_cast_fp16, input = model_model_kv_cache_0)[name = string("coreml_update_state_1_write_state")]; + tensor coreml_update_state_9 = read_state(input = model_model_kv_cache_0)[name = string("coreml_update_state_1")]; + tensor var_256_begin_0 = const()[name = string("op_256_begin_0"), val = tensor([24, 0, 0, 0])]; + tensor var_256_end_0 = const()[name = string("op_256_end_0"), val = tensor([25, 8, 1024, 128])]; + tensor var_256_end_mask_0 = const()[name = string("op_256_end_mask_0"), val = tensor([false, true, true, true])]; + tensor var_256_cast_fp16 = slice_by_index(begin = var_256_begin_0, end = var_256_end_0, end_mask = var_256_end_mask_0, x = coreml_update_state_9)[name = string("op_256_cast_fp16")]; + tensor K_layer_cache_1_axes_0 = const()[name = string("K_layer_cache_1_axes_0"), val = tensor([0])]; + tensor K_layer_cache_1_cast_fp16 = squeeze(axes = K_layer_cache_1_axes_0, x = var_256_cast_fp16)[name = string("K_layer_cache_1_cast_fp16")]; + tensor var_258_begin_0 = const()[name = string("op_258_begin_0"), val = tensor([56, 0, 0, 0])]; + tensor var_258_end_0 = const()[name = string("op_258_end_0"), val = tensor([57, 8, 1024, 128])]; + tensor var_258_end_mask_0 = const()[name = string("op_258_end_mask_0"), val = tensor([false, true, true, true])]; + tensor var_258_cast_fp16 = slice_by_index(begin = var_258_begin_0, end = var_258_end_0, end_mask = var_258_end_mask_0, x = coreml_update_state_9)[name = string("op_258_cast_fp16")]; + tensor V_layer_cache_1_axes_0 = const()[name = string("V_layer_cache_1_axes_0"), val = tensor([0])]; + tensor V_layer_cache_1_cast_fp16 = squeeze(axes = V_layer_cache_1_axes_0, x = var_258_cast_fp16)[name = string("V_layer_cache_1_cast_fp16")]; + tensor x_11_axes_0 = const()[name = string("x_11_axes_0"), val = tensor([1])]; + tensor x_11_cast_fp16 = expand_dims(axes = x_11_axes_0, x = K_layer_cache_1_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor var_267 = const()[name = string("op_267"), val = tensor([1, 4, 1, 1])]; + tensor x_13_cast_fp16 = tile(reps = var_267, x = x_11_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_271 = const()[name = string("op_271"), val = tensor([1, -1, 1024, 128])]; + tensor key_states_3_cast_fp16 = reshape(shape = var_271, x = x_13_cast_fp16)[name = string("key_states_3_cast_fp16")]; + tensor x_17_axes_0 = const()[name = string("x_17_axes_0"), val = tensor([1])]; + tensor x_17_cast_fp16 = expand_dims(axes = x_17_axes_0, x = V_layer_cache_1_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor var_274 = const()[name = string("op_274"), val = tensor([1, 4, 1, 1])]; + tensor x_19_cast_fp16 = tile(reps = var_274, x = x_17_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_278 = const()[name = string("op_278"), val = tensor([1, -1, 1024, 128])]; + tensor value_states_3_cast_fp16 = reshape(shape = var_278, x = x_19_cast_fp16)[name = string("value_states_3_cast_fp16")]; + bool var_281_transpose_x_1 = const()[name = string("op_281_transpose_x_1"), val = bool(false)]; + bool var_281_transpose_y_1 = const()[name = string("op_281_transpose_y_1"), val = bool(true)]; + tensor var_281_cast_fp16 = matmul(transpose_x = var_281_transpose_x_1, transpose_y = var_281_transpose_y_1, x = rotated_1_cast_fp16, y = key_states_3_cast_fp16)[name = string("op_281_cast_fp16")]; + fp16 var_282_to_fp16 = const()[name = string("op_282_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor attn_weights_1_cast_fp16 = mul(x = var_281_cast_fp16, y = var_282_to_fp16)[name = string("attn_weights_1_cast_fp16")]; + tensor x_21_cast_fp16 = add(x = attn_weights_1_cast_fp16, y = causal_mask)[name = string("x_21_cast_fp16")]; + tensor reduce_max_0_axes_0 = const()[name = string("reduce_max_0_axes_0"), val = tensor([-1])]; + bool reduce_max_0_keep_dims_0 = const()[name = string("reduce_max_0_keep_dims_0"), val = bool(true)]; + tensor reduce_max_0_cast_fp16 = reduce_max(axes = reduce_max_0_axes_0, keep_dims = reduce_max_0_keep_dims_0, x = x_21_cast_fp16)[name = string("reduce_max_0_cast_fp16")]; + tensor x_23_cast_fp16 = sub(x = x_21_cast_fp16, y = reduce_max_0_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor exp_x_1_cast_fp16 = exp(x = x_23_cast_fp16)[name = string("exp_x_1_cast_fp16")]; + tensor var_293_axes_0 = const()[name = string("op_293_axes_0"), val = tensor([-1])]; + bool var_293_keep_dims_0 = const()[name = string("op_293_keep_dims_0"), val = bool(true)]; + tensor var_293_cast_fp16 = reduce_sum(axes = var_293_axes_0, keep_dims = var_293_keep_dims_0, x = exp_x_1_cast_fp16)[name = string("op_293_cast_fp16")]; + tensor attn_weights_3_cast_fp16 = real_div(x = exp_x_1_cast_fp16, y = var_293_cast_fp16)[name = string("attn_weights_3_cast_fp16")]; + bool attn_output_1_transpose_x_0 = const()[name = string("attn_output_1_transpose_x_0"), val = bool(false)]; + bool attn_output_1_transpose_y_0 = const()[name = string("attn_output_1_transpose_y_0"), val = bool(false)]; + tensor attn_output_1_cast_fp16 = matmul(transpose_x = attn_output_1_transpose_x_0, transpose_y = attn_output_1_transpose_y_0, x = attn_weights_3_cast_fp16, y = value_states_3_cast_fp16)[name = string("attn_output_1_cast_fp16")]; + tensor var_296_perm_0 = const()[name = string("op_296_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_298 = const()[name = string("op_298"), val = tensor([1, 1, 4096])]; + tensor var_296_cast_fp16 = transpose(perm = var_296_perm_0, x = attn_output_1_cast_fp16)[name = string("transpose_14")]; + tensor input_5_cast_fp16 = reshape(shape = var_298, x = var_296_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor model_model_layers_24_self_attn_o_proj_weight_promoted_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(673590528))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686173504))))[name = string("model_model_layers_24_self_attn_o_proj_weight_promoted_to_fp16_palettized")]; + tensor linear_0_bias_0_to_fp16 = const()[name = string("linear_0_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686239104)))]; + tensor linear_0_cast_fp16 = linear(bias = linear_0_bias_0_to_fp16, weight = model_model_layers_24_self_attn_o_proj_weight_promoted_to_fp16_palettized, x = input_5_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor hidden_states_5_cast_fp16 = add(x = hidden_states, y = linear_0_cast_fp16)[name = string("hidden_states_5_cast_fp16")]; + tensor mean_3_axes_0 = const()[name = string("mean_3_axes_0"), val = tensor([-1])]; + bool mean_3_keep_dims_0 = const()[name = string("mean_3_keep_dims_0"), val = bool(true)]; + tensor mean_3_cast_fp16 = reduce_mean(axes = mean_3_axes_0, keep_dims = mean_3_keep_dims_0, x = hidden_states_5_cast_fp16)[name = string("mean_3_cast_fp16")]; + tensor input_7_cast_fp16 = sub(x = hidden_states_5_cast_fp16, y = mean_3_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor var_309_axes_0 = const()[name = string("op_309_axes_0"), val = tensor([-1])]; + tensor model_model_layers_24_post_attention_layernorm_weight_to_fp16 = const()[name = string("model_model_layers_24_post_attention_layernorm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686247360)))]; + tensor var_309_cast_fp16 = layer_norm(axes = var_309_axes_0, epsilon = var_36_to_fp16, gamma = model_model_layers_24_post_attention_layernorm_weight_to_fp16, x = input_7_cast_fp16)[name = string("op_309_cast_fp16")]; + tensor var_316 = const()[name = string("op_316"), val = tensor([0, 2, 1])]; + tensor input_9_axes_0 = const()[name = string("input_9_axes_0"), val = tensor([2])]; + tensor var_317 = transpose(perm = var_316, x = var_309_cast_fp16)[name = string("transpose_13")]; + tensor input_9 = expand_dims(axes = input_9_axes_0, x = var_317)[name = string("input_9")]; + string input_11_pad_type_0 = const()[name = string("input_11_pad_type_0"), val = string("valid")]; + tensor input_11_strides_0 = const()[name = string("input_11_strides_0"), val = tensor([1, 1])]; + tensor input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_11_dilations_0 = const()[name = string("input_11_dilations_0"), val = tensor([1, 1])]; + int32 input_11_groups_0 = const()[name = string("input_11_groups_0"), val = int32(1)]; + tensor input_11 = conv(dilations = input_11_dilations_0, groups = input_11_groups_0, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = input_11_strides_0, weight = model_model_layers_24_mlp_gate_proj_weight_palettized, x = input_9)[name = string("input_11")]; + string up_states_1_pad_type_0 = const()[name = string("up_states_1_pad_type_0"), val = string("valid")]; + tensor up_states_1_strides_0 = const()[name = string("up_states_1_strides_0"), val = tensor([1, 1])]; + tensor up_states_1_pad_0 = const()[name = string("up_states_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_states_1_dilations_0 = const()[name = string("up_states_1_dilations_0"), val = tensor([1, 1])]; + int32 up_states_1_groups_0 = const()[name = string("up_states_1_groups_0"), val = int32(1)]; + tensor up_states_1 = conv(dilations = up_states_1_dilations_0, groups = up_states_1_groups_0, pad = up_states_1_pad_0, pad_type = up_states_1_pad_type_0, strides = up_states_1_strides_0, weight = model_model_layers_24_mlp_up_proj_weight_palettized, x = input_9)[name = string("up_states_1")]; + tensor gate_states_1 = silu(x = input_11)[name = string("gate_states_1")]; + tensor input_13 = mul(x = gate_states_1, y = up_states_1)[name = string("input_13")]; + string hidden_states_7_pad_type_0 = const()[name = string("hidden_states_7_pad_type_0"), val = string("valid")]; + tensor hidden_states_7_strides_0 = const()[name = string("hidden_states_7_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_7_pad_0 = const()[name = string("hidden_states_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_7_dilations_0 = const()[name = string("hidden_states_7_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_7_groups_0 = const()[name = string("hidden_states_7_groups_0"), val = int32(1)]; + tensor hidden_states_7 = conv(dilations = hidden_states_7_dilations_0, groups = hidden_states_7_groups_0, pad = hidden_states_7_pad_0, pad_type = hidden_states_7_pad_type_0, strides = hidden_states_7_strides_0, weight = model_model_layers_24_mlp_down_proj_weight_palettized, x = input_13)[name = string("hidden_states_7")]; + tensor var_339_axes_0 = const()[name = string("op_339_axes_0"), val = tensor([2])]; + tensor var_339 = squeeze(axes = var_339_axes_0, x = hidden_states_7)[name = string("op_339")]; + tensor var_340 = const()[name = string("op_340"), val = tensor([0, 2, 1])]; + tensor var_341 = transpose(perm = var_340, x = var_339)[name = string("transpose_12")]; + tensor hidden_states_9_cast_fp16 = add(x = hidden_states_5_cast_fp16, y = var_341)[name = string("hidden_states_9_cast_fp16")]; + tensor mean_5_axes_0 = const()[name = string("mean_5_axes_0"), val = tensor([-1])]; + bool mean_5_keep_dims_0 = const()[name = string("mean_5_keep_dims_0"), val = bool(true)]; + tensor mean_5_cast_fp16 = reduce_mean(axes = mean_5_axes_0, keep_dims = mean_5_keep_dims_0, x = hidden_states_9_cast_fp16)[name = string("mean_5_cast_fp16")]; + tensor input_15_cast_fp16 = sub(x = hidden_states_9_cast_fp16, y = mean_5_cast_fp16)[name = string("input_15_cast_fp16")]; + tensor var_349_axes_0 = const()[name = string("op_349_axes_0"), val = tensor([-1])]; + tensor model_model_layers_25_input_layernorm_weight_to_fp16 = const()[name = string("model_model_layers_25_input_layernorm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686255616)))]; + tensor var_349_cast_fp16 = layer_norm(axes = var_349_axes_0, epsilon = var_36_to_fp16, gamma = model_model_layers_25_input_layernorm_weight_to_fp16, x = input_15_cast_fp16)[name = string("op_349_cast_fp16")]; + tensor var_352 = const()[name = string("op_352"), val = tensor([0, 2, 1])]; + tensor var_354_axes_0 = const()[name = string("op_354_axes_0"), val = tensor([2])]; + tensor var_353 = transpose(perm = var_352, x = var_349_cast_fp16)[name = string("transpose_11")]; + tensor var_354 = expand_dims(axes = var_354_axes_0, x = var_353)[name = string("op_354")]; + string var_361_pad_type_0 = const()[name = string("op_361_pad_type_0"), val = string("valid")]; + tensor var_361_strides_0 = const()[name = string("op_361_strides_0"), val = tensor([1, 1])]; + tensor var_361_pad_0 = const()[name = string("op_361_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor var_361_dilations_0 = const()[name = string("op_361_dilations_0"), val = tensor([1, 1])]; + int32 var_361_groups_0 = const()[name = string("op_361_groups_0"), val = int32(1)]; + tensor var_361 = conv(dilations = var_361_dilations_0, groups = var_361_groups_0, pad = var_361_pad_0, pad_type = var_361_pad_type_0, strides = var_361_strides_0, weight = model_model_layers_25_self_attn_q_proj_weight_palettized, x = var_354)[name = string("op_361")]; + tensor var_362 = const()[name = string("op_362"), val = tensor([1, 32, 1, 128])]; + tensor var_363 = reshape(shape = var_362, x = var_361)[name = string("op_363")]; + string var_370_pad_type_0 = const()[name = string("op_370_pad_type_0"), val = string("valid")]; + tensor var_370_strides_0 = const()[name = string("op_370_strides_0"), val = tensor([1, 1])]; + tensor var_370_pad_0 = const()[name = string("op_370_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor var_370_dilations_0 = const()[name = string("op_370_dilations_0"), val = tensor([1, 1])]; + int32 var_370_groups_0 = const()[name = string("op_370_groups_0"), val = int32(1)]; + tensor var_370 = conv(dilations = var_370_dilations_0, groups = var_370_groups_0, pad = var_370_pad_0, pad_type = var_370_pad_type_0, strides = var_370_strides_0, weight = model_model_layers_25_self_attn_k_proj_weight_palettized, x = var_354)[name = string("op_370")]; + tensor var_371 = const()[name = string("op_371"), val = tensor([1, 8, 1, 128])]; + tensor var_372 = reshape(shape = var_371, x = var_370)[name = string("op_372")]; + string var_379_pad_type_0 = const()[name = string("op_379_pad_type_0"), val = string("valid")]; + tensor var_379_strides_0 = const()[name = string("op_379_strides_0"), val = tensor([1, 1])]; + tensor var_379_pad_0 = const()[name = string("op_379_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor var_379_dilations_0 = const()[name = string("op_379_dilations_0"), val = tensor([1, 1])]; + int32 var_379_groups_0 = const()[name = string("op_379_groups_0"), val = int32(1)]; + tensor var_379 = conv(dilations = var_379_dilations_0, groups = var_379_groups_0, pad = var_379_pad_0, pad_type = var_379_pad_type_0, strides = var_379_strides_0, weight = model_model_layers_25_self_attn_v_proj_weight_palettized, x = var_354)[name = string("op_379")]; + tensor var_380 = const()[name = string("op_380"), val = tensor([1, 8, 1, 128])]; + tensor var_381 = reshape(shape = var_380, x = var_379)[name = string("op_381")]; + tensor x1_5_begin_0 = const()[name = string("x1_5_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x1_5_end_0 = const()[name = string("x1_5_end_0"), val = tensor([1, 32, 1, 64])]; + tensor x1_5_end_mask_0 = const()[name = string("x1_5_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x1_5 = slice_by_index(begin = x1_5_begin_0, end = x1_5_end_0, end_mask = x1_5_end_mask_0, x = var_363)[name = string("x1_5")]; + tensor x2_5_begin_0 = const()[name = string("x2_5_begin_0"), val = tensor([0, 0, 0, 64])]; + tensor x2_5_end_0 = const()[name = string("x2_5_end_0"), val = tensor([1, 32, 1, 128])]; + tensor x2_5_end_mask_0 = const()[name = string("x2_5_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x2_5 = slice_by_index(begin = x2_5_begin_0, end = x2_5_end_0, end_mask = x2_5_end_mask_0, x = var_363)[name = string("x2_5")]; + tensor var_395_cast_fp16 = mul(x = x1_5, y = cos_3_cast_fp16)[name = string("op_395_cast_fp16")]; + tensor var_396_cast_fp16 = mul(x = x2_5, y = sin_3_cast_fp16)[name = string("op_396_cast_fp16")]; + tensor var_397_cast_fp16 = sub(x = var_395_cast_fp16, y = var_396_cast_fp16)[name = string("op_397_cast_fp16")]; + tensor var_398_cast_fp16 = mul(x = x2_5, y = cos_3_cast_fp16)[name = string("op_398_cast_fp16")]; + tensor var_399_cast_fp16 = mul(x = x1_5, y = sin_3_cast_fp16)[name = string("op_399_cast_fp16")]; + tensor var_400_cast_fp16 = add(x = var_398_cast_fp16, y = var_399_cast_fp16)[name = string("op_400_cast_fp16")]; + bool rotated_5_interleave_0 = const()[name = string("rotated_5_interleave_0"), val = bool(false)]; + tensor rotated_5_cast_fp16 = concat(axis = var_41, interleave = rotated_5_interleave_0, values = (var_397_cast_fp16, var_400_cast_fp16))[name = string("rotated_5_cast_fp16")]; + tensor x1_7_begin_0 = const()[name = string("x1_7_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x1_7_end_0 = const()[name = string("x1_7_end_0"), val = tensor([1, 8, 1, 64])]; + tensor x1_7_end_mask_0 = const()[name = string("x1_7_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x1_7 = slice_by_index(begin = x1_7_begin_0, end = x1_7_end_0, end_mask = x1_7_end_mask_0, x = var_372)[name = string("x1_7")]; + tensor x2_7_begin_0 = const()[name = string("x2_7_begin_0"), val = tensor([0, 0, 0, 64])]; + tensor x2_7_end_0 = const()[name = string("x2_7_end_0"), val = tensor([1, 8, 1, 128])]; + tensor x2_7_end_mask_0 = const()[name = string("x2_7_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x2_7 = slice_by_index(begin = x2_7_begin_0, end = x2_7_end_0, end_mask = x2_7_end_mask_0, x = var_372)[name = string("x2_7")]; + tensor var_416_cast_fp16 = mul(x = x1_7, y = cos_3_cast_fp16)[name = string("op_416_cast_fp16")]; + tensor var_417_cast_fp16 = mul(x = x2_7, y = sin_3_cast_fp16)[name = string("op_417_cast_fp16")]; + tensor var_418_cast_fp16 = sub(x = var_416_cast_fp16, y = var_417_cast_fp16)[name = string("op_418_cast_fp16")]; + tensor var_419_cast_fp16 = mul(x = x2_7, y = cos_3_cast_fp16)[name = string("op_419_cast_fp16")]; + tensor var_420_cast_fp16 = mul(x = x1_7, y = sin_3_cast_fp16)[name = string("op_420_cast_fp16")]; + tensor var_421_cast_fp16 = add(x = var_419_cast_fp16, y = var_420_cast_fp16)[name = string("op_421_cast_fp16")]; + bool rotated_7_interleave_0 = const()[name = string("rotated_7_interleave_0"), val = bool(false)]; + tensor rotated_7_cast_fp16 = concat(axis = var_41, interleave = rotated_7_interleave_0, values = (var_418_cast_fp16, var_421_cast_fp16))[name = string("rotated_7_cast_fp16")]; + tensor expand_dims_12 = const()[name = string("expand_dims_12"), val = tensor([25])]; + tensor expand_dims_13 = const()[name = string("expand_dims_13"), val = tensor([0])]; + tensor expand_dims_15 = const()[name = string("expand_dims_15"), val = tensor([0])]; + tensor expand_dims_16 = const()[name = string("expand_dims_16"), val = tensor([26])]; + int32 concat_10_axis_0 = const()[name = string("concat_10_axis_0"), val = int32(0)]; + bool concat_10_interleave_0 = const()[name = string("concat_10_interleave_0"), val = bool(false)]; + tensor concat_10 = concat(axis = concat_10_axis_0, interleave = concat_10_interleave_0, values = (expand_dims_12, expand_dims_13, current_pos, expand_dims_15))[name = string("concat_10")]; + tensor concat_11_values1_0 = const()[name = string("concat_11_values1_0"), val = tensor([0])]; + tensor concat_11_values3_0 = const()[name = string("concat_11_values3_0"), val = tensor([0])]; + int32 concat_11_axis_0 = const()[name = string("concat_11_axis_0"), val = int32(0)]; + bool concat_11_interleave_0 = const()[name = string("concat_11_interleave_0"), val = bool(false)]; + tensor concat_11 = concat(axis = concat_11_axis_0, interleave = concat_11_interleave_0, values = (expand_dims_16, concat_11_values1_0, var_241, concat_11_values3_0))[name = string("concat_11")]; + tensor model_model_kv_cache_0_internal_tensor_assign_3_stride_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_3_stride_0"), val = tensor([1, 1, 1, 1])]; + tensor model_model_kv_cache_0_internal_tensor_assign_3_begin_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_3_begin_mask_0"), val = tensor([false, false, false, false])]; + tensor model_model_kv_cache_0_internal_tensor_assign_3_end_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_3_end_mask_0"), val = tensor([false, true, false, true])]; + tensor model_model_kv_cache_0_internal_tensor_assign_3_squeeze_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_3_squeeze_mask_0"), val = tensor([false, false, false, false])]; + tensor model_model_kv_cache_0_internal_tensor_assign_3_cast_fp16 = slice_update(begin = concat_10, begin_mask = model_model_kv_cache_0_internal_tensor_assign_3_begin_mask_0, end = concat_11, end_mask = model_model_kv_cache_0_internal_tensor_assign_3_end_mask_0, squeeze_mask = model_model_kv_cache_0_internal_tensor_assign_3_squeeze_mask_0, stride = model_model_kv_cache_0_internal_tensor_assign_3_stride_0, update = rotated_7_cast_fp16, x = coreml_update_state_9)[name = string("model_model_kv_cache_0_internal_tensor_assign_3_cast_fp16")]; + write_state(data = model_model_kv_cache_0_internal_tensor_assign_3_cast_fp16, input = model_model_kv_cache_0)[name = string("coreml_update_state_2_write_state")]; + tensor coreml_update_state_10 = read_state(input = model_model_kv_cache_0)[name = string("coreml_update_state_2")]; + tensor expand_dims_18 = const()[name = string("expand_dims_18"), val = tensor([57])]; + tensor expand_dims_19 = const()[name = string("expand_dims_19"), val = tensor([0])]; + tensor expand_dims_21 = const()[name = string("expand_dims_21"), val = tensor([0])]; + tensor expand_dims_22 = const()[name = string("expand_dims_22"), val = tensor([58])]; + int32 concat_14_axis_0 = const()[name = string("concat_14_axis_0"), val = int32(0)]; + bool concat_14_interleave_0 = const()[name = string("concat_14_interleave_0"), val = bool(false)]; + tensor concat_14 = concat(axis = concat_14_axis_0, interleave = concat_14_interleave_0, values = (expand_dims_18, expand_dims_19, current_pos, expand_dims_21))[name = string("concat_14")]; + tensor concat_15_values1_0 = const()[name = string("concat_15_values1_0"), val = tensor([0])]; + tensor concat_15_values3_0 = const()[name = string("concat_15_values3_0"), val = tensor([0])]; + int32 concat_15_axis_0 = const()[name = string("concat_15_axis_0"), val = int32(0)]; + bool concat_15_interleave_0 = const()[name = string("concat_15_interleave_0"), val = bool(false)]; + tensor concat_15 = concat(axis = concat_15_axis_0, interleave = concat_15_interleave_0, values = (expand_dims_22, concat_15_values1_0, var_241, concat_15_values3_0))[name = string("concat_15")]; + tensor model_model_kv_cache_0_internal_tensor_assign_4_stride_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_4_stride_0"), val = tensor([1, 1, 1, 1])]; + tensor model_model_kv_cache_0_internal_tensor_assign_4_begin_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_4_begin_mask_0"), val = tensor([false, false, false, false])]; + tensor model_model_kv_cache_0_internal_tensor_assign_4_end_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_4_end_mask_0"), val = tensor([false, true, false, true])]; + tensor model_model_kv_cache_0_internal_tensor_assign_4_squeeze_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_4_squeeze_mask_0"), val = tensor([false, false, false, false])]; + tensor model_model_kv_cache_0_internal_tensor_assign_4_cast_fp16 = slice_update(begin = concat_14, begin_mask = model_model_kv_cache_0_internal_tensor_assign_4_begin_mask_0, end = concat_15, end_mask = model_model_kv_cache_0_internal_tensor_assign_4_end_mask_0, squeeze_mask = model_model_kv_cache_0_internal_tensor_assign_4_squeeze_mask_0, stride = model_model_kv_cache_0_internal_tensor_assign_4_stride_0, update = var_381, x = coreml_update_state_10)[name = string("model_model_kv_cache_0_internal_tensor_assign_4_cast_fp16")]; + write_state(data = model_model_kv_cache_0_internal_tensor_assign_4_cast_fp16, input = model_model_kv_cache_0)[name = string("coreml_update_state_3_write_state")]; + tensor coreml_update_state_11 = read_state(input = model_model_kv_cache_0)[name = string("coreml_update_state_3")]; + tensor var_441_begin_0 = const()[name = string("op_441_begin_0"), val = tensor([25, 0, 0, 0])]; + tensor var_441_end_0 = const()[name = string("op_441_end_0"), val = tensor([26, 8, 1024, 128])]; + tensor var_441_end_mask_0 = const()[name = string("op_441_end_mask_0"), val = tensor([false, true, true, true])]; + tensor var_441_cast_fp16 = slice_by_index(begin = var_441_begin_0, end = var_441_end_0, end_mask = var_441_end_mask_0, x = coreml_update_state_11)[name = string("op_441_cast_fp16")]; + tensor K_layer_cache_3_axes_0 = const()[name = string("K_layer_cache_3_axes_0"), val = tensor([0])]; + tensor K_layer_cache_3_cast_fp16 = squeeze(axes = K_layer_cache_3_axes_0, x = var_441_cast_fp16)[name = string("K_layer_cache_3_cast_fp16")]; + tensor var_443_begin_0 = const()[name = string("op_443_begin_0"), val = tensor([57, 0, 0, 0])]; + tensor var_443_end_0 = const()[name = string("op_443_end_0"), val = tensor([58, 8, 1024, 128])]; + tensor var_443_end_mask_0 = const()[name = string("op_443_end_mask_0"), val = tensor([false, true, true, true])]; + tensor var_443_cast_fp16 = slice_by_index(begin = var_443_begin_0, end = var_443_end_0, end_mask = var_443_end_mask_0, x = coreml_update_state_11)[name = string("op_443_cast_fp16")]; + tensor V_layer_cache_3_axes_0 = const()[name = string("V_layer_cache_3_axes_0"), val = tensor([0])]; + tensor V_layer_cache_3_cast_fp16 = squeeze(axes = V_layer_cache_3_axes_0, x = var_443_cast_fp16)[name = string("V_layer_cache_3_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([1])]; + tensor x_39_cast_fp16 = expand_dims(axes = x_39_axes_0, x = K_layer_cache_3_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_452 = const()[name = string("op_452"), val = tensor([1, 4, 1, 1])]; + tensor x_41_cast_fp16 = tile(reps = var_452, x = x_39_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor var_456 = const()[name = string("op_456"), val = tensor([1, -1, 1024, 128])]; + tensor key_states_7_cast_fp16 = reshape(shape = var_456, x = x_41_cast_fp16)[name = string("key_states_7_cast_fp16")]; + tensor x_45_axes_0 = const()[name = string("x_45_axes_0"), val = tensor([1])]; + tensor x_45_cast_fp16 = expand_dims(axes = x_45_axes_0, x = V_layer_cache_3_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_459 = const()[name = string("op_459"), val = tensor([1, 4, 1, 1])]; + tensor x_47_cast_fp16 = tile(reps = var_459, x = x_45_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor var_463 = const()[name = string("op_463"), val = tensor([1, -1, 1024, 128])]; + tensor value_states_7_cast_fp16 = reshape(shape = var_463, x = x_47_cast_fp16)[name = string("value_states_7_cast_fp16")]; + bool var_466_transpose_x_1 = const()[name = string("op_466_transpose_x_1"), val = bool(false)]; + bool var_466_transpose_y_1 = const()[name = string("op_466_transpose_y_1"), val = bool(true)]; + tensor var_466_cast_fp16 = matmul(transpose_x = var_466_transpose_x_1, transpose_y = var_466_transpose_y_1, x = rotated_5_cast_fp16, y = key_states_7_cast_fp16)[name = string("op_466_cast_fp16")]; + fp16 var_467_to_fp16 = const()[name = string("op_467_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor attn_weights_5_cast_fp16 = mul(x = var_466_cast_fp16, y = var_467_to_fp16)[name = string("attn_weights_5_cast_fp16")]; + tensor x_49_cast_fp16 = add(x = attn_weights_5_cast_fp16, y = causal_mask)[name = string("x_49_cast_fp16")]; + tensor reduce_max_1_axes_0 = const()[name = string("reduce_max_1_axes_0"), val = tensor([-1])]; + bool reduce_max_1_keep_dims_0 = const()[name = string("reduce_max_1_keep_dims_0"), val = bool(true)]; + tensor reduce_max_1_cast_fp16 = reduce_max(axes = reduce_max_1_axes_0, keep_dims = reduce_max_1_keep_dims_0, x = x_49_cast_fp16)[name = string("reduce_max_1_cast_fp16")]; + tensor x_51_cast_fp16 = sub(x = x_49_cast_fp16, y = reduce_max_1_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor exp_x_3_cast_fp16 = exp(x = x_51_cast_fp16)[name = string("exp_x_3_cast_fp16")]; + tensor var_478_axes_0 = const()[name = string("op_478_axes_0"), val = tensor([-1])]; + bool var_478_keep_dims_0 = const()[name = string("op_478_keep_dims_0"), val = bool(true)]; + tensor var_478_cast_fp16 = reduce_sum(axes = var_478_axes_0, keep_dims = var_478_keep_dims_0, x = exp_x_3_cast_fp16)[name = string("op_478_cast_fp16")]; + tensor attn_weights_7_cast_fp16 = real_div(x = exp_x_3_cast_fp16, y = var_478_cast_fp16)[name = string("attn_weights_7_cast_fp16")]; + bool attn_output_7_transpose_x_0 = const()[name = string("attn_output_7_transpose_x_0"), val = bool(false)]; + bool attn_output_7_transpose_y_0 = const()[name = string("attn_output_7_transpose_y_0"), val = bool(false)]; + tensor attn_output_7_cast_fp16 = matmul(transpose_x = attn_output_7_transpose_x_0, transpose_y = attn_output_7_transpose_y_0, x = attn_weights_7_cast_fp16, y = value_states_7_cast_fp16)[name = string("attn_output_7_cast_fp16")]; + tensor var_481_perm_0 = const()[name = string("op_481_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_483 = const()[name = string("op_483"), val = tensor([1, 1, 4096])]; + tensor var_481_cast_fp16 = transpose(perm = var_481_perm_0, x = attn_output_7_cast_fp16)[name = string("transpose_10")]; + tensor input_19_cast_fp16 = reshape(shape = var_483, x = var_481_cast_fp16)[name = string("input_19_cast_fp16")]; + tensor model_model_layers_25_self_attn_o_proj_weight_promoted_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686263872))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(698846848))))[name = string("model_model_layers_25_self_attn_o_proj_weight_promoted_to_fp16_palettized")]; + tensor linear_1_cast_fp16 = linear(bias = linear_0_bias_0_to_fp16, weight = model_model_layers_25_self_attn_o_proj_weight_promoted_to_fp16_palettized, x = input_19_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor hidden_states_13_cast_fp16 = add(x = hidden_states_9_cast_fp16, y = linear_1_cast_fp16)[name = string("hidden_states_13_cast_fp16")]; + tensor mean_7_axes_0 = const()[name = string("mean_7_axes_0"), val = tensor([-1])]; + bool mean_7_keep_dims_0 = const()[name = string("mean_7_keep_dims_0"), val = bool(true)]; + tensor mean_7_cast_fp16 = reduce_mean(axes = mean_7_axes_0, keep_dims = mean_7_keep_dims_0, x = hidden_states_13_cast_fp16)[name = string("mean_7_cast_fp16")]; + tensor input_21_cast_fp16 = sub(x = hidden_states_13_cast_fp16, y = mean_7_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor var_494_axes_0 = const()[name = string("op_494_axes_0"), val = tensor([-1])]; + tensor model_model_layers_25_post_attention_layernorm_weight_to_fp16 = const()[name = string("model_model_layers_25_post_attention_layernorm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(698912448)))]; + tensor var_494_cast_fp16 = layer_norm(axes = var_494_axes_0, epsilon = var_36_to_fp16, gamma = model_model_layers_25_post_attention_layernorm_weight_to_fp16, x = input_21_cast_fp16)[name = string("op_494_cast_fp16")]; + tensor var_501 = const()[name = string("op_501"), val = tensor([0, 2, 1])]; + tensor input_23_axes_0 = const()[name = string("input_23_axes_0"), val = tensor([2])]; + tensor var_502 = transpose(perm = var_501, x = var_494_cast_fp16)[name = string("transpose_9")]; + tensor input_23 = expand_dims(axes = input_23_axes_0, x = var_502)[name = string("input_23")]; + string input_25_pad_type_0 = const()[name = string("input_25_pad_type_0"), val = string("valid")]; + tensor input_25_strides_0 = const()[name = string("input_25_strides_0"), val = tensor([1, 1])]; + tensor input_25_pad_0 = const()[name = string("input_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_25_dilations_0 = const()[name = string("input_25_dilations_0"), val = tensor([1, 1])]; + int32 input_25_groups_0 = const()[name = string("input_25_groups_0"), val = int32(1)]; + tensor input_25 = 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 = model_model_layers_25_mlp_gate_proj_weight_palettized, x = input_23)[name = string("input_25")]; + string up_states_3_pad_type_0 = const()[name = string("up_states_3_pad_type_0"), val = string("valid")]; + tensor up_states_3_strides_0 = const()[name = string("up_states_3_strides_0"), val = tensor([1, 1])]; + tensor up_states_3_pad_0 = const()[name = string("up_states_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_states_3_dilations_0 = const()[name = string("up_states_3_dilations_0"), val = tensor([1, 1])]; + int32 up_states_3_groups_0 = const()[name = string("up_states_3_groups_0"), val = int32(1)]; + tensor up_states_3 = conv(dilations = up_states_3_dilations_0, groups = up_states_3_groups_0, pad = up_states_3_pad_0, pad_type = up_states_3_pad_type_0, strides = up_states_3_strides_0, weight = model_model_layers_25_mlp_up_proj_weight_palettized, x = input_23)[name = string("up_states_3")]; + tensor gate_states_3 = silu(x = input_25)[name = string("gate_states_3")]; + tensor input_27 = mul(x = gate_states_3, y = up_states_3)[name = string("input_27")]; + string hidden_states_15_pad_type_0 = const()[name = string("hidden_states_15_pad_type_0"), val = string("valid")]; + tensor hidden_states_15_strides_0 = const()[name = string("hidden_states_15_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_15_pad_0 = const()[name = string("hidden_states_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_15_dilations_0 = const()[name = string("hidden_states_15_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_15_groups_0 = const()[name = string("hidden_states_15_groups_0"), val = int32(1)]; + tensor hidden_states_15 = conv(dilations = hidden_states_15_dilations_0, groups = hidden_states_15_groups_0, pad = hidden_states_15_pad_0, pad_type = hidden_states_15_pad_type_0, strides = hidden_states_15_strides_0, weight = model_model_layers_25_mlp_down_proj_weight_palettized, x = input_27)[name = string("hidden_states_15")]; + tensor var_524_axes_0 = const()[name = string("op_524_axes_0"), val = tensor([2])]; + tensor var_524 = squeeze(axes = var_524_axes_0, x = hidden_states_15)[name = string("op_524")]; + tensor var_525 = const()[name = string("op_525"), val = tensor([0, 2, 1])]; + tensor var_526 = transpose(perm = var_525, x = var_524)[name = string("transpose_8")]; + tensor hidden_states_17_cast_fp16 = add(x = hidden_states_13_cast_fp16, y = var_526)[name = string("hidden_states_17_cast_fp16")]; + tensor mean_9_axes_0 = const()[name = string("mean_9_axes_0"), val = tensor([-1])]; + bool mean_9_keep_dims_0 = const()[name = string("mean_9_keep_dims_0"), val = bool(true)]; + tensor mean_9_cast_fp16 = reduce_mean(axes = mean_9_axes_0, keep_dims = mean_9_keep_dims_0, x = hidden_states_17_cast_fp16)[name = string("mean_9_cast_fp16")]; + tensor input_29_cast_fp16 = sub(x = hidden_states_17_cast_fp16, y = mean_9_cast_fp16)[name = string("input_29_cast_fp16")]; + tensor var_534_axes_0 = const()[name = string("op_534_axes_0"), val = tensor([-1])]; + tensor model_model_layers_26_input_layernorm_weight_to_fp16 = const()[name = string("model_model_layers_26_input_layernorm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(698920704)))]; + tensor var_534_cast_fp16 = layer_norm(axes = var_534_axes_0, epsilon = var_36_to_fp16, gamma = model_model_layers_26_input_layernorm_weight_to_fp16, x = input_29_cast_fp16)[name = string("op_534_cast_fp16")]; + tensor var_537 = const()[name = string("op_537"), val = tensor([0, 2, 1])]; + tensor var_539_axes_0 = const()[name = string("op_539_axes_0"), val = tensor([2])]; + tensor var_538 = transpose(perm = var_537, x = var_534_cast_fp16)[name = string("transpose_7")]; + tensor var_539 = expand_dims(axes = var_539_axes_0, x = var_538)[name = string("op_539")]; + string var_546_pad_type_0 = const()[name = string("op_546_pad_type_0"), val = string("valid")]; + tensor var_546_strides_0 = const()[name = string("op_546_strides_0"), val = tensor([1, 1])]; + tensor var_546_pad_0 = const()[name = string("op_546_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor var_546_dilations_0 = const()[name = string("op_546_dilations_0"), val = tensor([1, 1])]; + int32 var_546_groups_0 = const()[name = string("op_546_groups_0"), val = int32(1)]; + tensor var_546 = conv(dilations = var_546_dilations_0, groups = var_546_groups_0, pad = var_546_pad_0, pad_type = var_546_pad_type_0, strides = var_546_strides_0, weight = model_model_layers_26_self_attn_q_proj_weight_palettized, x = var_539)[name = string("op_546")]; + tensor var_547 = const()[name = string("op_547"), val = tensor([1, 32, 1, 128])]; + tensor var_548 = reshape(shape = var_547, x = var_546)[name = string("op_548")]; + string var_555_pad_type_0 = const()[name = string("op_555_pad_type_0"), val = string("valid")]; + tensor var_555_strides_0 = const()[name = string("op_555_strides_0"), val = tensor([1, 1])]; + tensor var_555_pad_0 = const()[name = string("op_555_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor var_555_dilations_0 = const()[name = string("op_555_dilations_0"), val = tensor([1, 1])]; + int32 var_555_groups_0 = const()[name = string("op_555_groups_0"), val = int32(1)]; + tensor var_555 = conv(dilations = var_555_dilations_0, groups = var_555_groups_0, pad = var_555_pad_0, pad_type = var_555_pad_type_0, strides = var_555_strides_0, weight = model_model_layers_26_self_attn_k_proj_weight_palettized, x = var_539)[name = string("op_555")]; + tensor var_556 = const()[name = string("op_556"), val = tensor([1, 8, 1, 128])]; + tensor var_557 = reshape(shape = var_556, x = var_555)[name = string("op_557")]; + string var_564_pad_type_0 = const()[name = string("op_564_pad_type_0"), val = string("valid")]; + tensor var_564_strides_0 = const()[name = string("op_564_strides_0"), val = tensor([1, 1])]; + tensor var_564_pad_0 = const()[name = string("op_564_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor var_564_dilations_0 = const()[name = string("op_564_dilations_0"), val = tensor([1, 1])]; + int32 var_564_groups_0 = const()[name = string("op_564_groups_0"), val = int32(1)]; + tensor var_564 = conv(dilations = var_564_dilations_0, groups = var_564_groups_0, pad = var_564_pad_0, pad_type = var_564_pad_type_0, strides = var_564_strides_0, weight = model_model_layers_26_self_attn_v_proj_weight_palettized, x = var_539)[name = string("op_564")]; + tensor var_565 = const()[name = string("op_565"), val = tensor([1, 8, 1, 128])]; + tensor var_566 = reshape(shape = var_565, x = var_564)[name = string("op_566")]; + tensor x1_9_begin_0 = const()[name = string("x1_9_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x1_9_end_0 = const()[name = string("x1_9_end_0"), val = tensor([1, 32, 1, 64])]; + tensor x1_9_end_mask_0 = const()[name = string("x1_9_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x1_9 = slice_by_index(begin = x1_9_begin_0, end = x1_9_end_0, end_mask = x1_9_end_mask_0, x = var_548)[name = string("x1_9")]; + tensor x2_9_begin_0 = const()[name = string("x2_9_begin_0"), val = tensor([0, 0, 0, 64])]; + tensor x2_9_end_0 = const()[name = string("x2_9_end_0"), val = tensor([1, 32, 1, 128])]; + tensor x2_9_end_mask_0 = const()[name = string("x2_9_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x2_9 = slice_by_index(begin = x2_9_begin_0, end = x2_9_end_0, end_mask = x2_9_end_mask_0, x = var_548)[name = string("x2_9")]; + tensor var_580_cast_fp16 = mul(x = x1_9, y = cos_3_cast_fp16)[name = string("op_580_cast_fp16")]; + tensor var_581_cast_fp16 = mul(x = x2_9, y = sin_3_cast_fp16)[name = string("op_581_cast_fp16")]; + tensor var_582_cast_fp16 = sub(x = var_580_cast_fp16, y = var_581_cast_fp16)[name = string("op_582_cast_fp16")]; + tensor var_583_cast_fp16 = mul(x = x2_9, y = cos_3_cast_fp16)[name = string("op_583_cast_fp16")]; + tensor var_584_cast_fp16 = mul(x = x1_9, y = sin_3_cast_fp16)[name = string("op_584_cast_fp16")]; + tensor var_585_cast_fp16 = add(x = var_583_cast_fp16, y = var_584_cast_fp16)[name = string("op_585_cast_fp16")]; + bool rotated_9_interleave_0 = const()[name = string("rotated_9_interleave_0"), val = bool(false)]; + tensor rotated_9_cast_fp16 = concat(axis = var_41, interleave = rotated_9_interleave_0, values = (var_582_cast_fp16, var_585_cast_fp16))[name = string("rotated_9_cast_fp16")]; + tensor x1_11_begin_0 = const()[name = string("x1_11_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x1_11_end_0 = const()[name = string("x1_11_end_0"), val = tensor([1, 8, 1, 64])]; + tensor x1_11_end_mask_0 = const()[name = string("x1_11_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x1_11 = slice_by_index(begin = x1_11_begin_0, end = x1_11_end_0, end_mask = x1_11_end_mask_0, x = var_557)[name = string("x1_11")]; + tensor x2_11_begin_0 = const()[name = string("x2_11_begin_0"), val = tensor([0, 0, 0, 64])]; + tensor x2_11_end_0 = const()[name = string("x2_11_end_0"), val = tensor([1, 8, 1, 128])]; + tensor x2_11_end_mask_0 = const()[name = string("x2_11_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x2_11 = slice_by_index(begin = x2_11_begin_0, end = x2_11_end_0, end_mask = x2_11_end_mask_0, x = var_557)[name = string("x2_11")]; + tensor var_601_cast_fp16 = mul(x = x1_11, y = cos_3_cast_fp16)[name = string("op_601_cast_fp16")]; + tensor var_602_cast_fp16 = mul(x = x2_11, y = sin_3_cast_fp16)[name = string("op_602_cast_fp16")]; + tensor var_603_cast_fp16 = sub(x = var_601_cast_fp16, y = var_602_cast_fp16)[name = string("op_603_cast_fp16")]; + tensor var_604_cast_fp16 = mul(x = x2_11, y = cos_3_cast_fp16)[name = string("op_604_cast_fp16")]; + tensor var_605_cast_fp16 = mul(x = x1_11, y = sin_3_cast_fp16)[name = string("op_605_cast_fp16")]; + tensor var_606_cast_fp16 = add(x = var_604_cast_fp16, y = var_605_cast_fp16)[name = string("op_606_cast_fp16")]; + bool rotated_11_interleave_0 = const()[name = string("rotated_11_interleave_0"), val = bool(false)]; + tensor rotated_11_cast_fp16 = concat(axis = var_41, interleave = rotated_11_interleave_0, values = (var_603_cast_fp16, var_606_cast_fp16))[name = string("rotated_11_cast_fp16")]; + tensor expand_dims_24 = const()[name = string("expand_dims_24"), val = tensor([26])]; + tensor expand_dims_25 = const()[name = string("expand_dims_25"), val = tensor([0])]; + tensor expand_dims_27 = const()[name = string("expand_dims_27"), val = tensor([0])]; + tensor expand_dims_28 = const()[name = string("expand_dims_28"), val = tensor([27])]; + int32 concat_18_axis_0 = const()[name = string("concat_18_axis_0"), val = int32(0)]; + bool concat_18_interleave_0 = const()[name = string("concat_18_interleave_0"), val = bool(false)]; + tensor concat_18 = concat(axis = concat_18_axis_0, interleave = concat_18_interleave_0, values = (expand_dims_24, expand_dims_25, current_pos, expand_dims_27))[name = string("concat_18")]; + tensor concat_19_values1_0 = const()[name = string("concat_19_values1_0"), val = tensor([0])]; + tensor concat_19_values3_0 = const()[name = string("concat_19_values3_0"), val = tensor([0])]; + int32 concat_19_axis_0 = const()[name = string("concat_19_axis_0"), val = int32(0)]; + bool concat_19_interleave_0 = const()[name = string("concat_19_interleave_0"), val = bool(false)]; + tensor concat_19 = concat(axis = concat_19_axis_0, interleave = concat_19_interleave_0, values = (expand_dims_28, concat_19_values1_0, var_241, concat_19_values3_0))[name = string("concat_19")]; + tensor model_model_kv_cache_0_internal_tensor_assign_5_stride_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_5_stride_0"), val = tensor([1, 1, 1, 1])]; + tensor model_model_kv_cache_0_internal_tensor_assign_5_begin_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_5_begin_mask_0"), val = tensor([false, false, false, false])]; + tensor model_model_kv_cache_0_internal_tensor_assign_5_end_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_5_end_mask_0"), val = tensor([false, true, false, true])]; + tensor model_model_kv_cache_0_internal_tensor_assign_5_squeeze_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_5_squeeze_mask_0"), val = tensor([false, false, false, false])]; + tensor model_model_kv_cache_0_internal_tensor_assign_5_cast_fp16 = slice_update(begin = concat_18, begin_mask = model_model_kv_cache_0_internal_tensor_assign_5_begin_mask_0, end = concat_19, end_mask = model_model_kv_cache_0_internal_tensor_assign_5_end_mask_0, squeeze_mask = model_model_kv_cache_0_internal_tensor_assign_5_squeeze_mask_0, stride = model_model_kv_cache_0_internal_tensor_assign_5_stride_0, update = rotated_11_cast_fp16, x = coreml_update_state_11)[name = string("model_model_kv_cache_0_internal_tensor_assign_5_cast_fp16")]; + write_state(data = model_model_kv_cache_0_internal_tensor_assign_5_cast_fp16, input = model_model_kv_cache_0)[name = string("coreml_update_state_4_write_state")]; + tensor coreml_update_state_12 = read_state(input = model_model_kv_cache_0)[name = string("coreml_update_state_4")]; + tensor expand_dims_30 = const()[name = string("expand_dims_30"), val = tensor([58])]; + tensor expand_dims_31 = const()[name = string("expand_dims_31"), val = tensor([0])]; + tensor expand_dims_33 = const()[name = string("expand_dims_33"), val = tensor([0])]; + tensor expand_dims_34 = const()[name = string("expand_dims_34"), val = tensor([59])]; + int32 concat_22_axis_0 = const()[name = string("concat_22_axis_0"), val = int32(0)]; + bool concat_22_interleave_0 = const()[name = string("concat_22_interleave_0"), val = bool(false)]; + tensor concat_22 = concat(axis = concat_22_axis_0, interleave = concat_22_interleave_0, values = (expand_dims_30, expand_dims_31, current_pos, expand_dims_33))[name = string("concat_22")]; + tensor concat_23_values1_0 = const()[name = string("concat_23_values1_0"), val = tensor([0])]; + tensor concat_23_values3_0 = const()[name = string("concat_23_values3_0"), val = tensor([0])]; + int32 concat_23_axis_0 = const()[name = string("concat_23_axis_0"), val = int32(0)]; + bool concat_23_interleave_0 = const()[name = string("concat_23_interleave_0"), val = bool(false)]; + tensor concat_23 = concat(axis = concat_23_axis_0, interleave = concat_23_interleave_0, values = (expand_dims_34, concat_23_values1_0, var_241, concat_23_values3_0))[name = string("concat_23")]; + tensor model_model_kv_cache_0_internal_tensor_assign_6_stride_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_6_stride_0"), val = tensor([1, 1, 1, 1])]; + tensor model_model_kv_cache_0_internal_tensor_assign_6_begin_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_6_begin_mask_0"), val = tensor([false, false, false, false])]; + tensor model_model_kv_cache_0_internal_tensor_assign_6_end_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_6_end_mask_0"), val = tensor([false, true, false, true])]; + tensor model_model_kv_cache_0_internal_tensor_assign_6_squeeze_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_6_squeeze_mask_0"), val = tensor([false, false, false, false])]; + tensor model_model_kv_cache_0_internal_tensor_assign_6_cast_fp16 = slice_update(begin = concat_22, begin_mask = model_model_kv_cache_0_internal_tensor_assign_6_begin_mask_0, end = concat_23, end_mask = model_model_kv_cache_0_internal_tensor_assign_6_end_mask_0, squeeze_mask = model_model_kv_cache_0_internal_tensor_assign_6_squeeze_mask_0, stride = model_model_kv_cache_0_internal_tensor_assign_6_stride_0, update = var_566, x = coreml_update_state_12)[name = string("model_model_kv_cache_0_internal_tensor_assign_6_cast_fp16")]; + write_state(data = model_model_kv_cache_0_internal_tensor_assign_6_cast_fp16, input = model_model_kv_cache_0)[name = string("coreml_update_state_5_write_state")]; + tensor coreml_update_state_13 = read_state(input = model_model_kv_cache_0)[name = string("coreml_update_state_5")]; + tensor var_626_begin_0 = const()[name = string("op_626_begin_0"), val = tensor([26, 0, 0, 0])]; + tensor var_626_end_0 = const()[name = string("op_626_end_0"), val = tensor([27, 8, 1024, 128])]; + tensor var_626_end_mask_0 = const()[name = string("op_626_end_mask_0"), val = tensor([false, true, true, true])]; + tensor var_626_cast_fp16 = slice_by_index(begin = var_626_begin_0, end = var_626_end_0, end_mask = var_626_end_mask_0, x = coreml_update_state_13)[name = string("op_626_cast_fp16")]; + tensor K_layer_cache_5_axes_0 = const()[name = string("K_layer_cache_5_axes_0"), val = tensor([0])]; + tensor K_layer_cache_5_cast_fp16 = squeeze(axes = K_layer_cache_5_axes_0, x = var_626_cast_fp16)[name = string("K_layer_cache_5_cast_fp16")]; + tensor var_628_begin_0 = const()[name = string("op_628_begin_0"), val = tensor([58, 0, 0, 0])]; + tensor var_628_end_0 = const()[name = string("op_628_end_0"), val = tensor([59, 8, 1024, 128])]; + tensor var_628_end_mask_0 = const()[name = string("op_628_end_mask_0"), val = tensor([false, true, true, true])]; + tensor var_628_cast_fp16 = slice_by_index(begin = var_628_begin_0, end = var_628_end_0, end_mask = var_628_end_mask_0, x = coreml_update_state_13)[name = string("op_628_cast_fp16")]; + tensor V_layer_cache_5_axes_0 = const()[name = string("V_layer_cache_5_axes_0"), val = tensor([0])]; + tensor V_layer_cache_5_cast_fp16 = squeeze(axes = V_layer_cache_5_axes_0, x = var_628_cast_fp16)[name = string("V_layer_cache_5_cast_fp16")]; + tensor x_67_axes_0 = const()[name = string("x_67_axes_0"), val = tensor([1])]; + tensor x_67_cast_fp16 = expand_dims(axes = x_67_axes_0, x = K_layer_cache_5_cast_fp16)[name = string("x_67_cast_fp16")]; + tensor var_637 = const()[name = string("op_637"), val = tensor([1, 4, 1, 1])]; + tensor x_69_cast_fp16 = tile(reps = var_637, x = x_67_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor var_641 = const()[name = string("op_641"), val = tensor([1, -1, 1024, 128])]; + tensor key_states_11_cast_fp16 = reshape(shape = var_641, x = x_69_cast_fp16)[name = string("key_states_11_cast_fp16")]; + tensor x_73_axes_0 = const()[name = string("x_73_axes_0"), val = tensor([1])]; + tensor x_73_cast_fp16 = expand_dims(axes = x_73_axes_0, x = V_layer_cache_5_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_644 = const()[name = string("op_644"), val = tensor([1, 4, 1, 1])]; + tensor x_75_cast_fp16 = tile(reps = var_644, x = x_73_cast_fp16)[name = string("x_75_cast_fp16")]; + tensor var_648 = const()[name = string("op_648"), val = tensor([1, -1, 1024, 128])]; + tensor value_states_11_cast_fp16 = reshape(shape = var_648, x = x_75_cast_fp16)[name = string("value_states_11_cast_fp16")]; + bool var_651_transpose_x_1 = const()[name = string("op_651_transpose_x_1"), val = bool(false)]; + bool var_651_transpose_y_1 = const()[name = string("op_651_transpose_y_1"), val = bool(true)]; + tensor var_651_cast_fp16 = matmul(transpose_x = var_651_transpose_x_1, transpose_y = var_651_transpose_y_1, x = rotated_9_cast_fp16, y = key_states_11_cast_fp16)[name = string("op_651_cast_fp16")]; + fp16 var_652_to_fp16 = const()[name = string("op_652_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor attn_weights_9_cast_fp16 = mul(x = var_651_cast_fp16, y = var_652_to_fp16)[name = string("attn_weights_9_cast_fp16")]; + tensor x_77_cast_fp16 = add(x = attn_weights_9_cast_fp16, y = causal_mask)[name = string("x_77_cast_fp16")]; + tensor reduce_max_2_axes_0 = const()[name = string("reduce_max_2_axes_0"), val = tensor([-1])]; + bool reduce_max_2_keep_dims_0 = const()[name = string("reduce_max_2_keep_dims_0"), val = bool(true)]; + tensor reduce_max_2_cast_fp16 = reduce_max(axes = reduce_max_2_axes_0, keep_dims = reduce_max_2_keep_dims_0, x = x_77_cast_fp16)[name = string("reduce_max_2_cast_fp16")]; + tensor x_79_cast_fp16 = sub(x = x_77_cast_fp16, y = reduce_max_2_cast_fp16)[name = string("x_79_cast_fp16")]; + tensor exp_x_5_cast_fp16 = exp(x = x_79_cast_fp16)[name = string("exp_x_5_cast_fp16")]; + tensor var_663_axes_0 = const()[name = string("op_663_axes_0"), val = tensor([-1])]; + bool var_663_keep_dims_0 = const()[name = string("op_663_keep_dims_0"), val = bool(true)]; + tensor var_663_cast_fp16 = reduce_sum(axes = var_663_axes_0, keep_dims = var_663_keep_dims_0, x = exp_x_5_cast_fp16)[name = string("op_663_cast_fp16")]; + tensor attn_weights_11_cast_fp16 = real_div(x = exp_x_5_cast_fp16, y = var_663_cast_fp16)[name = string("attn_weights_11_cast_fp16")]; + bool attn_output_13_transpose_x_0 = const()[name = string("attn_output_13_transpose_x_0"), val = bool(false)]; + bool attn_output_13_transpose_y_0 = const()[name = string("attn_output_13_transpose_y_0"), val = bool(false)]; + tensor attn_output_13_cast_fp16 = matmul(transpose_x = attn_output_13_transpose_x_0, transpose_y = attn_output_13_transpose_y_0, x = attn_weights_11_cast_fp16, y = value_states_11_cast_fp16)[name = string("attn_output_13_cast_fp16")]; + tensor var_666_perm_0 = const()[name = string("op_666_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_668 = const()[name = string("op_668"), val = tensor([1, 1, 4096])]; + tensor var_666_cast_fp16 = transpose(perm = var_666_perm_0, x = attn_output_13_cast_fp16)[name = string("transpose_6")]; + tensor input_33_cast_fp16 = reshape(shape = var_668, x = var_666_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor model_model_layers_26_self_attn_o_proj_weight_promoted_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(698928960))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(711511936))))[name = string("model_model_layers_26_self_attn_o_proj_weight_promoted_to_fp16_palettized")]; + tensor linear_2_cast_fp16 = linear(bias = linear_0_bias_0_to_fp16, weight = model_model_layers_26_self_attn_o_proj_weight_promoted_to_fp16_palettized, x = input_33_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor hidden_states_21_cast_fp16 = add(x = hidden_states_17_cast_fp16, y = linear_2_cast_fp16)[name = string("hidden_states_21_cast_fp16")]; + tensor mean_11_axes_0 = const()[name = string("mean_11_axes_0"), val = tensor([-1])]; + bool mean_11_keep_dims_0 = const()[name = string("mean_11_keep_dims_0"), val = bool(true)]; + tensor mean_11_cast_fp16 = reduce_mean(axes = mean_11_axes_0, keep_dims = mean_11_keep_dims_0, x = hidden_states_21_cast_fp16)[name = string("mean_11_cast_fp16")]; + tensor input_35_cast_fp16 = sub(x = hidden_states_21_cast_fp16, y = mean_11_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor var_679_axes_0 = const()[name = string("op_679_axes_0"), val = tensor([-1])]; + tensor model_model_layers_26_post_attention_layernorm_weight_to_fp16 = const()[name = string("model_model_layers_26_post_attention_layernorm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(711577536)))]; + tensor var_679_cast_fp16 = layer_norm(axes = var_679_axes_0, epsilon = var_36_to_fp16, gamma = model_model_layers_26_post_attention_layernorm_weight_to_fp16, x = input_35_cast_fp16)[name = string("op_679_cast_fp16")]; + tensor var_686 = const()[name = string("op_686"), val = tensor([0, 2, 1])]; + tensor input_37_axes_0 = const()[name = string("input_37_axes_0"), val = tensor([2])]; + tensor var_687 = transpose(perm = var_686, x = var_679_cast_fp16)[name = string("transpose_5")]; + tensor input_37 = expand_dims(axes = input_37_axes_0, x = var_687)[name = string("input_37")]; + string input_39_pad_type_0 = const()[name = string("input_39_pad_type_0"), val = string("valid")]; + tensor input_39_strides_0 = const()[name = string("input_39_strides_0"), val = tensor([1, 1])]; + tensor input_39_pad_0 = const()[name = string("input_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_39_dilations_0 = const()[name = string("input_39_dilations_0"), val = tensor([1, 1])]; + int32 input_39_groups_0 = const()[name = string("input_39_groups_0"), val = int32(1)]; + tensor input_39 = 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 = model_model_layers_26_mlp_gate_proj_weight_palettized, x = input_37)[name = string("input_39")]; + string up_states_5_pad_type_0 = const()[name = string("up_states_5_pad_type_0"), val = string("valid")]; + tensor up_states_5_strides_0 = const()[name = string("up_states_5_strides_0"), val = tensor([1, 1])]; + tensor up_states_5_pad_0 = const()[name = string("up_states_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_states_5_dilations_0 = const()[name = string("up_states_5_dilations_0"), val = tensor([1, 1])]; + int32 up_states_5_groups_0 = const()[name = string("up_states_5_groups_0"), val = int32(1)]; + tensor up_states_5 = conv(dilations = up_states_5_dilations_0, groups = up_states_5_groups_0, pad = up_states_5_pad_0, pad_type = up_states_5_pad_type_0, strides = up_states_5_strides_0, weight = model_model_layers_26_mlp_up_proj_weight_palettized, x = input_37)[name = string("up_states_5")]; + tensor gate_states_5 = silu(x = input_39)[name = string("gate_states_5")]; + tensor input_41 = mul(x = gate_states_5, y = up_states_5)[name = string("input_41")]; + string hidden_states_23_pad_type_0 = const()[name = string("hidden_states_23_pad_type_0"), val = string("valid")]; + tensor hidden_states_23_strides_0 = const()[name = string("hidden_states_23_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_23_pad_0 = const()[name = string("hidden_states_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_23_dilations_0 = const()[name = string("hidden_states_23_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_23_groups_0 = const()[name = string("hidden_states_23_groups_0"), val = int32(1)]; + tensor hidden_states_23 = conv(dilations = hidden_states_23_dilations_0, groups = hidden_states_23_groups_0, pad = hidden_states_23_pad_0, pad_type = hidden_states_23_pad_type_0, strides = hidden_states_23_strides_0, weight = model_model_layers_26_mlp_down_proj_weight_palettized, x = input_41)[name = string("hidden_states_23")]; + tensor var_709_axes_0 = const()[name = string("op_709_axes_0"), val = tensor([2])]; + tensor var_709 = squeeze(axes = var_709_axes_0, x = hidden_states_23)[name = string("op_709")]; + tensor var_710 = const()[name = string("op_710"), val = tensor([0, 2, 1])]; + tensor var_711 = transpose(perm = var_710, x = var_709)[name = string("transpose_4")]; + tensor hidden_states_25_cast_fp16 = add(x = hidden_states_21_cast_fp16, y = var_711)[name = string("hidden_states_25_cast_fp16")]; + tensor mean_13_axes_0 = const()[name = string("mean_13_axes_0"), val = tensor([-1])]; + bool mean_13_keep_dims_0 = const()[name = string("mean_13_keep_dims_0"), val = bool(true)]; + tensor mean_13_cast_fp16 = reduce_mean(axes = mean_13_axes_0, keep_dims = mean_13_keep_dims_0, x = hidden_states_25_cast_fp16)[name = string("mean_13_cast_fp16")]; + tensor input_43_cast_fp16 = sub(x = hidden_states_25_cast_fp16, y = mean_13_cast_fp16)[name = string("input_43_cast_fp16")]; + tensor var_719_axes_0 = const()[name = string("op_719_axes_0"), val = tensor([-1])]; + tensor model_model_layers_27_input_layernorm_weight_to_fp16 = const()[name = string("model_model_layers_27_input_layernorm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(711585792)))]; + tensor var_719_cast_fp16 = layer_norm(axes = var_719_axes_0, epsilon = var_36_to_fp16, gamma = model_model_layers_27_input_layernorm_weight_to_fp16, x = input_43_cast_fp16)[name = string("op_719_cast_fp16")]; + tensor var_722 = const()[name = string("op_722"), val = tensor([0, 2, 1])]; + tensor var_724_axes_0 = const()[name = string("op_724_axes_0"), val = tensor([2])]; + tensor var_723 = transpose(perm = var_722, x = var_719_cast_fp16)[name = string("transpose_3")]; + tensor var_724 = expand_dims(axes = var_724_axes_0, x = var_723)[name = string("op_724")]; + string var_731_pad_type_0 = const()[name = string("op_731_pad_type_0"), val = string("valid")]; + tensor var_731_strides_0 = const()[name = string("op_731_strides_0"), val = tensor([1, 1])]; + tensor var_731_pad_0 = const()[name = string("op_731_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor var_731_dilations_0 = const()[name = string("op_731_dilations_0"), val = tensor([1, 1])]; + int32 var_731_groups_0 = const()[name = string("op_731_groups_0"), val = int32(1)]; + tensor var_731 = conv(dilations = var_731_dilations_0, groups = var_731_groups_0, pad = var_731_pad_0, pad_type = var_731_pad_type_0, strides = var_731_strides_0, weight = model_model_layers_27_self_attn_q_proj_weight_palettized, x = var_724)[name = string("op_731")]; + tensor var_732 = const()[name = string("op_732"), val = tensor([1, 32, 1, 128])]; + tensor var_733 = reshape(shape = var_732, x = var_731)[name = string("op_733")]; + string var_740_pad_type_0 = const()[name = string("op_740_pad_type_0"), val = string("valid")]; + tensor var_740_strides_0 = const()[name = string("op_740_strides_0"), val = tensor([1, 1])]; + tensor var_740_pad_0 = const()[name = string("op_740_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor var_740_dilations_0 = const()[name = string("op_740_dilations_0"), val = tensor([1, 1])]; + int32 var_740_groups_0 = const()[name = string("op_740_groups_0"), val = int32(1)]; + tensor var_740 = conv(dilations = var_740_dilations_0, groups = var_740_groups_0, pad = var_740_pad_0, pad_type = var_740_pad_type_0, strides = var_740_strides_0, weight = model_model_layers_27_self_attn_k_proj_weight_palettized, x = var_724)[name = string("op_740")]; + tensor var_741 = const()[name = string("op_741"), val = tensor([1, 8, 1, 128])]; + tensor var_742 = reshape(shape = var_741, x = var_740)[name = string("op_742")]; + string var_749_pad_type_0 = const()[name = string("op_749_pad_type_0"), val = string("valid")]; + tensor var_749_strides_0 = const()[name = string("op_749_strides_0"), val = tensor([1, 1])]; + tensor var_749_pad_0 = const()[name = string("op_749_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor var_749_dilations_0 = const()[name = string("op_749_dilations_0"), val = tensor([1, 1])]; + int32 var_749_groups_0 = const()[name = string("op_749_groups_0"), val = int32(1)]; + tensor var_749 = conv(dilations = var_749_dilations_0, groups = var_749_groups_0, pad = var_749_pad_0, pad_type = var_749_pad_type_0, strides = var_749_strides_0, weight = model_model_layers_27_self_attn_v_proj_weight_palettized, x = var_724)[name = string("op_749")]; + tensor var_750 = const()[name = string("op_750"), val = tensor([1, 8, 1, 128])]; + tensor var_751 = reshape(shape = var_750, x = var_749)[name = string("op_751")]; + tensor x1_13_begin_0 = const()[name = string("x1_13_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x1_13_end_0 = const()[name = string("x1_13_end_0"), val = tensor([1, 32, 1, 64])]; + tensor x1_13_end_mask_0 = const()[name = string("x1_13_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x1_13 = slice_by_index(begin = x1_13_begin_0, end = x1_13_end_0, end_mask = x1_13_end_mask_0, x = var_733)[name = string("x1_13")]; + tensor x2_13_begin_0 = const()[name = string("x2_13_begin_0"), val = tensor([0, 0, 0, 64])]; + tensor x2_13_end_0 = const()[name = string("x2_13_end_0"), val = tensor([1, 32, 1, 128])]; + tensor x2_13_end_mask_0 = const()[name = string("x2_13_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x2_13 = slice_by_index(begin = x2_13_begin_0, end = x2_13_end_0, end_mask = x2_13_end_mask_0, x = var_733)[name = string("x2_13")]; + tensor var_765_cast_fp16 = mul(x = x1_13, y = cos_3_cast_fp16)[name = string("op_765_cast_fp16")]; + tensor var_766_cast_fp16 = mul(x = x2_13, y = sin_3_cast_fp16)[name = string("op_766_cast_fp16")]; + tensor var_767_cast_fp16 = sub(x = var_765_cast_fp16, y = var_766_cast_fp16)[name = string("op_767_cast_fp16")]; + tensor var_768_cast_fp16 = mul(x = x2_13, y = cos_3_cast_fp16)[name = string("op_768_cast_fp16")]; + tensor var_769_cast_fp16 = mul(x = x1_13, y = sin_3_cast_fp16)[name = string("op_769_cast_fp16")]; + tensor var_770_cast_fp16 = add(x = var_768_cast_fp16, y = var_769_cast_fp16)[name = string("op_770_cast_fp16")]; + bool rotated_13_interleave_0 = const()[name = string("rotated_13_interleave_0"), val = bool(false)]; + tensor rotated_13_cast_fp16 = concat(axis = var_41, interleave = rotated_13_interleave_0, values = (var_767_cast_fp16, var_770_cast_fp16))[name = string("rotated_13_cast_fp16")]; + tensor x1_begin_0 = const()[name = string("x1_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x1_end_0 = const()[name = string("x1_end_0"), val = tensor([1, 8, 1, 64])]; + tensor x1_end_mask_0 = const()[name = string("x1_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x1 = slice_by_index(begin = x1_begin_0, end = x1_end_0, end_mask = x1_end_mask_0, x = var_742)[name = string("x1")]; + tensor x2_begin_0 = const()[name = string("x2_begin_0"), val = tensor([0, 0, 0, 64])]; + tensor x2_end_0 = const()[name = string("x2_end_0"), val = tensor([1, 8, 1, 128])]; + tensor x2_end_mask_0 = const()[name = string("x2_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x2 = slice_by_index(begin = x2_begin_0, end = x2_end_0, end_mask = x2_end_mask_0, x = var_742)[name = string("x2")]; + tensor var_786_cast_fp16 = mul(x = x1, y = cos_3_cast_fp16)[name = string("op_786_cast_fp16")]; + tensor var_787_cast_fp16 = mul(x = x2, y = sin_3_cast_fp16)[name = string("op_787_cast_fp16")]; + tensor var_788_cast_fp16 = sub(x = var_786_cast_fp16, y = var_787_cast_fp16)[name = string("op_788_cast_fp16")]; + tensor var_789_cast_fp16 = mul(x = x2, y = cos_3_cast_fp16)[name = string("op_789_cast_fp16")]; + tensor var_790_cast_fp16 = mul(x = x1, y = sin_3_cast_fp16)[name = string("op_790_cast_fp16")]; + tensor var_791_cast_fp16 = add(x = var_789_cast_fp16, y = var_790_cast_fp16)[name = string("op_791_cast_fp16")]; + bool rotated_interleave_0 = const()[name = string("rotated_interleave_0"), val = bool(false)]; + tensor rotated_cast_fp16 = concat(axis = var_41, interleave = rotated_interleave_0, values = (var_788_cast_fp16, var_791_cast_fp16))[name = string("rotated_cast_fp16")]; + tensor expand_dims_36 = const()[name = string("expand_dims_36"), val = tensor([27])]; + tensor expand_dims_37 = const()[name = string("expand_dims_37"), val = tensor([0])]; + tensor expand_dims_39 = const()[name = string("expand_dims_39"), val = tensor([0])]; + tensor expand_dims_40 = const()[name = string("expand_dims_40"), val = tensor([28])]; + int32 concat_26_axis_0 = const()[name = string("concat_26_axis_0"), val = int32(0)]; + bool concat_26_interleave_0 = const()[name = string("concat_26_interleave_0"), val = bool(false)]; + tensor concat_26 = concat(axis = concat_26_axis_0, interleave = concat_26_interleave_0, values = (expand_dims_36, expand_dims_37, current_pos, expand_dims_39))[name = string("concat_26")]; + tensor concat_27_values1_0 = const()[name = string("concat_27_values1_0"), val = tensor([0])]; + tensor concat_27_values3_0 = const()[name = string("concat_27_values3_0"), val = tensor([0])]; + int32 concat_27_axis_0 = const()[name = string("concat_27_axis_0"), val = int32(0)]; + bool concat_27_interleave_0 = const()[name = string("concat_27_interleave_0"), val = bool(false)]; + tensor concat_27 = concat(axis = concat_27_axis_0, interleave = concat_27_interleave_0, values = (expand_dims_40, concat_27_values1_0, var_241, concat_27_values3_0))[name = string("concat_27")]; + tensor model_model_kv_cache_0_internal_tensor_assign_7_stride_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_7_stride_0"), val = tensor([1, 1, 1, 1])]; + tensor model_model_kv_cache_0_internal_tensor_assign_7_begin_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_7_begin_mask_0"), val = tensor([false, false, false, false])]; + tensor model_model_kv_cache_0_internal_tensor_assign_7_end_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_7_end_mask_0"), val = tensor([false, true, false, true])]; + tensor model_model_kv_cache_0_internal_tensor_assign_7_squeeze_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_7_squeeze_mask_0"), val = tensor([false, false, false, false])]; + tensor model_model_kv_cache_0_internal_tensor_assign_7_cast_fp16 = slice_update(begin = concat_26, begin_mask = model_model_kv_cache_0_internal_tensor_assign_7_begin_mask_0, end = concat_27, end_mask = model_model_kv_cache_0_internal_tensor_assign_7_end_mask_0, squeeze_mask = model_model_kv_cache_0_internal_tensor_assign_7_squeeze_mask_0, stride = model_model_kv_cache_0_internal_tensor_assign_7_stride_0, update = rotated_cast_fp16, x = coreml_update_state_13)[name = string("model_model_kv_cache_0_internal_tensor_assign_7_cast_fp16")]; + write_state(data = model_model_kv_cache_0_internal_tensor_assign_7_cast_fp16, input = model_model_kv_cache_0)[name = string("coreml_update_state_6_write_state")]; + tensor coreml_update_state_14 = read_state(input = model_model_kv_cache_0)[name = string("coreml_update_state_6")]; + tensor expand_dims_42 = const()[name = string("expand_dims_42"), val = tensor([59])]; + tensor expand_dims_43 = const()[name = string("expand_dims_43"), val = tensor([0])]; + tensor expand_dims_45 = const()[name = string("expand_dims_45"), val = tensor([0])]; + tensor expand_dims_46 = const()[name = string("expand_dims_46"), val = tensor([60])]; + int32 concat_30_axis_0 = const()[name = string("concat_30_axis_0"), val = int32(0)]; + bool concat_30_interleave_0 = const()[name = string("concat_30_interleave_0"), val = bool(false)]; + tensor concat_30 = concat(axis = concat_30_axis_0, interleave = concat_30_interleave_0, values = (expand_dims_42, expand_dims_43, current_pos, expand_dims_45))[name = string("concat_30")]; + tensor concat_31_values1_0 = const()[name = string("concat_31_values1_0"), val = tensor([0])]; + tensor concat_31_values3_0 = const()[name = string("concat_31_values3_0"), val = tensor([0])]; + int32 concat_31_axis_0 = const()[name = string("concat_31_axis_0"), val = int32(0)]; + bool concat_31_interleave_0 = const()[name = string("concat_31_interleave_0"), val = bool(false)]; + tensor concat_31 = concat(axis = concat_31_axis_0, interleave = concat_31_interleave_0, values = (expand_dims_46, concat_31_values1_0, var_241, concat_31_values3_0))[name = string("concat_31")]; + tensor model_model_kv_cache_0_internal_tensor_assign_8_stride_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_8_stride_0"), val = tensor([1, 1, 1, 1])]; + tensor model_model_kv_cache_0_internal_tensor_assign_8_begin_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_8_begin_mask_0"), val = tensor([false, false, false, false])]; + tensor model_model_kv_cache_0_internal_tensor_assign_8_end_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_8_end_mask_0"), val = tensor([false, true, false, true])]; + tensor model_model_kv_cache_0_internal_tensor_assign_8_squeeze_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_8_squeeze_mask_0"), val = tensor([false, false, false, false])]; + tensor model_model_kv_cache_0_internal_tensor_assign_8_cast_fp16 = slice_update(begin = concat_30, begin_mask = model_model_kv_cache_0_internal_tensor_assign_8_begin_mask_0, end = concat_31, end_mask = model_model_kv_cache_0_internal_tensor_assign_8_end_mask_0, squeeze_mask = model_model_kv_cache_0_internal_tensor_assign_8_squeeze_mask_0, stride = model_model_kv_cache_0_internal_tensor_assign_8_stride_0, update = var_751, x = coreml_update_state_14)[name = string("model_model_kv_cache_0_internal_tensor_assign_8_cast_fp16")]; + write_state(data = model_model_kv_cache_0_internal_tensor_assign_8_cast_fp16, input = model_model_kv_cache_0)[name = string("coreml_update_state_7_write_state")]; + tensor coreml_update_state_15 = read_state(input = model_model_kv_cache_0)[name = string("coreml_update_state_7")]; + tensor var_811_begin_0 = const()[name = string("op_811_begin_0"), val = tensor([27, 0, 0, 0])]; + tensor var_811_end_0 = const()[name = string("op_811_end_0"), val = tensor([28, 8, 1024, 128])]; + tensor var_811_end_mask_0 = const()[name = string("op_811_end_mask_0"), val = tensor([false, true, true, true])]; + tensor var_811_cast_fp16 = slice_by_index(begin = var_811_begin_0, end = var_811_end_0, end_mask = var_811_end_mask_0, x = coreml_update_state_15)[name = string("op_811_cast_fp16")]; + tensor K_layer_cache_axes_0 = const()[name = string("K_layer_cache_axes_0"), val = tensor([0])]; + tensor K_layer_cache_cast_fp16 = squeeze(axes = K_layer_cache_axes_0, x = var_811_cast_fp16)[name = string("K_layer_cache_cast_fp16")]; + tensor var_813_begin_0 = const()[name = string("op_813_begin_0"), val = tensor([59, 0, 0, 0])]; + tensor var_813_end_0 = const()[name = string("op_813_end_0"), val = tensor([60, 8, 1024, 128])]; + tensor var_813_end_mask_0 = const()[name = string("op_813_end_mask_0"), val = tensor([false, true, true, true])]; + tensor var_813_cast_fp16 = slice_by_index(begin = var_813_begin_0, end = var_813_end_0, end_mask = var_813_end_mask_0, x = coreml_update_state_15)[name = string("op_813_cast_fp16")]; + tensor V_layer_cache_axes_0 = const()[name = string("V_layer_cache_axes_0"), val = tensor([0])]; + tensor V_layer_cache_cast_fp16 = squeeze(axes = V_layer_cache_axes_0, x = var_813_cast_fp16)[name = string("V_layer_cache_cast_fp16")]; + tensor x_95_axes_0 = const()[name = string("x_95_axes_0"), val = tensor([1])]; + tensor x_95_cast_fp16 = expand_dims(axes = x_95_axes_0, x = K_layer_cache_cast_fp16)[name = string("x_95_cast_fp16")]; + tensor var_822 = const()[name = string("op_822"), val = tensor([1, 4, 1, 1])]; + tensor x_97_cast_fp16 = tile(reps = var_822, x = x_95_cast_fp16)[name = string("x_97_cast_fp16")]; + tensor var_826 = const()[name = string("op_826"), val = tensor([1, -1, 1024, 128])]; + tensor key_states_cast_fp16 = reshape(shape = var_826, x = x_97_cast_fp16)[name = string("key_states_cast_fp16")]; + tensor x_101_axes_0 = const()[name = string("x_101_axes_0"), val = tensor([1])]; + tensor x_101_cast_fp16 = expand_dims(axes = x_101_axes_0, x = V_layer_cache_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor var_829 = const()[name = string("op_829"), val = tensor([1, 4, 1, 1])]; + tensor x_103_cast_fp16 = tile(reps = var_829, x = x_101_cast_fp16)[name = string("x_103_cast_fp16")]; + tensor var_833 = const()[name = string("op_833"), val = tensor([1, -1, 1024, 128])]; + tensor value_states_cast_fp16 = reshape(shape = var_833, x = x_103_cast_fp16)[name = string("value_states_cast_fp16")]; + bool var_836_transpose_x_1 = const()[name = string("op_836_transpose_x_1"), val = bool(false)]; + bool var_836_transpose_y_1 = const()[name = string("op_836_transpose_y_1"), val = bool(true)]; + tensor var_836_cast_fp16 = matmul(transpose_x = var_836_transpose_x_1, transpose_y = var_836_transpose_y_1, x = rotated_13_cast_fp16, y = key_states_cast_fp16)[name = string("op_836_cast_fp16")]; + fp16 var_837_to_fp16 = const()[name = string("op_837_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor attn_weights_13_cast_fp16 = mul(x = var_836_cast_fp16, y = var_837_to_fp16)[name = string("attn_weights_13_cast_fp16")]; + tensor x_105_cast_fp16 = add(x = attn_weights_13_cast_fp16, y = causal_mask)[name = string("x_105_cast_fp16")]; + tensor reduce_max_3_axes_0 = const()[name = string("reduce_max_3_axes_0"), val = tensor([-1])]; + bool reduce_max_3_keep_dims_0 = const()[name = string("reduce_max_3_keep_dims_0"), val = bool(true)]; + tensor reduce_max_3_cast_fp16 = reduce_max(axes = reduce_max_3_axes_0, keep_dims = reduce_max_3_keep_dims_0, x = x_105_cast_fp16)[name = string("reduce_max_3_cast_fp16")]; + tensor x_107_cast_fp16 = sub(x = x_105_cast_fp16, y = reduce_max_3_cast_fp16)[name = string("x_107_cast_fp16")]; + tensor exp_x_cast_fp16 = exp(x = x_107_cast_fp16)[name = string("exp_x_cast_fp16")]; + tensor var_848_axes_0 = const()[name = string("op_848_axes_0"), val = tensor([-1])]; + bool var_848_keep_dims_0 = const()[name = string("op_848_keep_dims_0"), val = bool(true)]; + tensor var_848_cast_fp16 = reduce_sum(axes = var_848_axes_0, keep_dims = var_848_keep_dims_0, x = exp_x_cast_fp16)[name = string("op_848_cast_fp16")]; + tensor attn_weights_cast_fp16 = real_div(x = exp_x_cast_fp16, y = var_848_cast_fp16)[name = string("attn_weights_cast_fp16")]; + bool attn_output_19_transpose_x_0 = const()[name = string("attn_output_19_transpose_x_0"), val = bool(false)]; + bool attn_output_19_transpose_y_0 = const()[name = string("attn_output_19_transpose_y_0"), val = bool(false)]; + tensor attn_output_19_cast_fp16 = matmul(transpose_x = attn_output_19_transpose_x_0, transpose_y = attn_output_19_transpose_y_0, x = attn_weights_cast_fp16, y = value_states_cast_fp16)[name = string("attn_output_19_cast_fp16")]; + tensor var_851_perm_0 = const()[name = string("op_851_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_853 = const()[name = string("op_853"), val = tensor([1, 1, 4096])]; + tensor var_851_cast_fp16 = transpose(perm = var_851_perm_0, x = attn_output_19_cast_fp16)[name = string("transpose_2")]; + tensor input_47_cast_fp16 = reshape(shape = var_853, x = var_851_cast_fp16)[name = string("input_47_cast_fp16")]; + tensor model_model_layers_27_self_attn_o_proj_weight_promoted_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(711594048))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(724177024))))[name = string("model_model_layers_27_self_attn_o_proj_weight_promoted_to_fp16_palettized")]; + tensor linear_3_cast_fp16 = linear(bias = linear_0_bias_0_to_fp16, weight = model_model_layers_27_self_attn_o_proj_weight_promoted_to_fp16_palettized, x = input_47_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor hidden_states_29_cast_fp16 = add(x = hidden_states_25_cast_fp16, y = linear_3_cast_fp16)[name = string("hidden_states_29_cast_fp16")]; + tensor mean_axes_0 = const()[name = string("mean_axes_0"), val = tensor([-1])]; + bool mean_keep_dims_0 = const()[name = string("mean_keep_dims_0"), val = bool(true)]; + tensor mean_cast_fp16 = reduce_mean(axes = mean_axes_0, keep_dims = mean_keep_dims_0, x = hidden_states_29_cast_fp16)[name = string("mean_cast_fp16")]; + tensor input_49_cast_fp16 = sub(x = hidden_states_29_cast_fp16, y = mean_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor var_864_axes_0 = const()[name = string("op_864_axes_0"), val = tensor([-1])]; + tensor model_model_layers_27_post_attention_layernorm_weight_to_fp16 = const()[name = string("model_model_layers_27_post_attention_layernorm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(724242624)))]; + tensor var_864_cast_fp16 = layer_norm(axes = var_864_axes_0, epsilon = var_36_to_fp16, gamma = model_model_layers_27_post_attention_layernorm_weight_to_fp16, x = input_49_cast_fp16)[name = string("op_864_cast_fp16")]; + tensor var_871 = const()[name = string("op_871"), val = tensor([0, 2, 1])]; + tensor input_51_axes_0 = const()[name = string("input_51_axes_0"), val = tensor([2])]; + tensor var_872 = transpose(perm = var_871, x = var_864_cast_fp16)[name = string("transpose_1")]; + tensor input_51 = expand_dims(axes = input_51_axes_0, x = var_872)[name = string("input_51")]; + string input_53_pad_type_0 = const()[name = string("input_53_pad_type_0"), val = string("valid")]; + tensor input_53_strides_0 = const()[name = string("input_53_strides_0"), val = tensor([1, 1])]; + tensor input_53_pad_0 = const()[name = string("input_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_53_dilations_0 = const()[name = string("input_53_dilations_0"), val = tensor([1, 1])]; + int32 input_53_groups_0 = const()[name = string("input_53_groups_0"), val = int32(1)]; + tensor input_53 = 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 = model_model_layers_27_mlp_gate_proj_weight_palettized, x = input_51)[name = string("input_53")]; + string up_states_pad_type_0 = const()[name = string("up_states_pad_type_0"), val = string("valid")]; + tensor up_states_strides_0 = const()[name = string("up_states_strides_0"), val = tensor([1, 1])]; + tensor up_states_pad_0 = const()[name = string("up_states_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_states_dilations_0 = const()[name = string("up_states_dilations_0"), val = tensor([1, 1])]; + int32 up_states_groups_0 = const()[name = string("up_states_groups_0"), val = int32(1)]; + tensor up_states = conv(dilations = up_states_dilations_0, groups = up_states_groups_0, pad = up_states_pad_0, pad_type = up_states_pad_type_0, strides = up_states_strides_0, weight = model_model_layers_27_mlp_up_proj_weight_palettized, x = input_51)[name = string("up_states")]; + tensor gate_states = silu(x = input_53)[name = string("gate_states")]; + tensor input = mul(x = gate_states, y = up_states)[name = string("input")]; + string hidden_states_pad_type_0 = const()[name = string("hidden_states_pad_type_0"), val = string("valid")]; + tensor hidden_states_strides_0 = const()[name = string("hidden_states_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_pad_0 = const()[name = string("hidden_states_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_dilations_0 = const()[name = string("hidden_states_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_groups_0 = const()[name = string("hidden_states_groups_0"), val = int32(1)]; + tensor hidden_states_1 = conv(dilations = hidden_states_dilations_0, groups = hidden_states_groups_0, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = hidden_states_strides_0, weight = model_model_layers_27_mlp_down_proj_weight_palettized, x = input)[name = string("hidden_states")]; + tensor var_894_axes_0 = const()[name = string("op_894_axes_0"), val = tensor([2])]; + tensor var_894 = squeeze(axes = var_894_axes_0, x = hidden_states_1)[name = string("op_894")]; + tensor var_895 = const()[name = string("op_895"), val = tensor([0, 2, 1])]; + tensor var_896 = transpose(perm = var_895, x = var_894)[name = string("transpose_0")]; + tensor output_hidden_states = add(x = hidden_states_29_cast_fp16, y = var_896)[name = string("op_897_cast_fp16")]; + tensor position_ids_tmp = identity(x = position_ids)[name = string("position_ids_tmp")]; + } -> (output_hidden_states); + func prefill(tensor causal_mask, tensor current_pos, tensor hidden_states, state> model_model_kv_cache_0, tensor position_ids) { + tensor model_model_layers_24_self_attn_q_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12583040))))[name = string("model_model_layers_24_self_attn_q_proj_weight_palettized")]; + tensor model_model_layers_24_self_attn_k_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12648640))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15794432))))[name = string("model_model_layers_24_self_attn_k_proj_weight_palettized")]; + tensor model_model_layers_24_self_attn_v_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15810880))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18956672))))[name = string("model_model_layers_24_self_attn_v_proj_weight_palettized")]; + tensor model_model_layers_24_mlp_gate_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18973120))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63013376))))[name = string("model_model_layers_24_mlp_gate_proj_weight_palettized")]; + tensor model_model_layers_24_mlp_up_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63242816))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107283072))))[name = string("model_model_layers_24_mlp_up_proj_weight_palettized")]; + tensor model_model_layers_24_mlp_down_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(107512512))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151552768))))[name = string("model_model_layers_24_mlp_down_proj_weight_palettized")]; + tensor model_model_layers_25_self_attn_q_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(151618368))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164201344))))[name = string("model_model_layers_25_self_attn_q_proj_weight_palettized")]; + tensor model_model_layers_25_self_attn_k_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164266944))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167412736))))[name = string("model_model_layers_25_self_attn_k_proj_weight_palettized")]; + tensor model_model_layers_25_self_attn_v_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167429184))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170574976))))[name = string("model_model_layers_25_self_attn_v_proj_weight_palettized")]; + tensor model_model_layers_25_mlp_gate_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(170591424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214631680))))[name = string("model_model_layers_25_mlp_gate_proj_weight_palettized")]; + tensor model_model_layers_25_mlp_up_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(214861120))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258901376))))[name = string("model_model_layers_25_mlp_up_proj_weight_palettized")]; + tensor model_model_layers_25_mlp_down_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259130816))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303171072))))[name = string("model_model_layers_25_mlp_down_proj_weight_palettized")]; + tensor model_model_layers_26_self_attn_q_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303236672))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315819648))))[name = string("model_model_layers_26_self_attn_q_proj_weight_palettized")]; + tensor model_model_layers_26_self_attn_k_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(315885248))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(319031040))))[name = string("model_model_layers_26_self_attn_k_proj_weight_palettized")]; + tensor model_model_layers_26_self_attn_v_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(319047488))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(322193280))))[name = string("model_model_layers_26_self_attn_v_proj_weight_palettized")]; + tensor model_model_layers_26_mlp_gate_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(322209728))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(366249984))))[name = string("model_model_layers_26_mlp_gate_proj_weight_palettized")]; + tensor model_model_layers_26_mlp_up_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(366479424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(410519680))))[name = string("model_model_layers_26_mlp_up_proj_weight_palettized")]; + tensor model_model_layers_26_mlp_down_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(410749120))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454789376))))[name = string("model_model_layers_26_mlp_down_proj_weight_palettized")]; + tensor model_model_layers_27_self_attn_q_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454854976))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(467437952))))[name = string("model_model_layers_27_self_attn_q_proj_weight_palettized")]; + tensor model_model_layers_27_self_attn_k_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(467503552))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(470649344))))[name = string("model_model_layers_27_self_attn_k_proj_weight_palettized")]; + tensor model_model_layers_27_self_attn_v_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(470665792))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(473811584))))[name = string("model_model_layers_27_self_attn_v_proj_weight_palettized")]; + tensor model_model_layers_27_mlp_gate_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(473828032))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(517868288))))[name = string("model_model_layers_27_mlp_gate_proj_weight_palettized")]; + tensor model_model_layers_27_mlp_up_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(518097728))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(562137984))))[name = string("model_model_layers_27_mlp_up_proj_weight_palettized")]; + tensor model_model_layers_27_mlp_down_proj_weight_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(562367424))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(606407680))))[name = string("model_model_layers_27_mlp_down_proj_weight_palettized")]; + int32 var_36 = const()[name = string("op_36"), val = int32(-1)]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor greater_equal_0 = greater_equal(x = position_ids, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(131072)]; + tensor add_0 = add(x = position_ids, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = position_ids, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + int32 var_153_axis_0 = const()[name = string("op_153_axis_0"), val = int32(1)]; + int32 var_153_batch_dims_0 = const()[name = string("op_153_batch_dims_0"), val = int32(0)]; + bool var_153_validate_indices_0 = const()[name = string("op_153_validate_indices_0"), val = bool(false)]; + tensor var_47_to_fp16 = const()[name = string("op_47_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(640027776)))]; + tensor var_153_cast_fp16 = gather(axis = var_153_axis_0, batch_dims = var_153_batch_dims_0, indices = select_0, validate_indices = var_153_validate_indices_0, x = var_47_to_fp16)[name = string("op_153_cast_fp16")]; + tensor var_154 = const()[name = string("op_154"), val = tensor([1, 256, 1, 128])]; + tensor cos_1_cast_fp16 = reshape(shape = var_154, x = var_153_cast_fp16)[name = string("cos_1_cast_fp16")]; + int32 var_158_axis_0 = const()[name = string("op_158_axis_0"), val = int32(1)]; + int32 var_158_batch_dims_0 = const()[name = string("op_158_batch_dims_0"), val = int32(0)]; + bool var_158_validate_indices_0 = const()[name = string("op_158_validate_indices_0"), val = bool(false)]; + tensor var_42_to_fp16 = const()[name = string("op_42_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(606473280)))]; + tensor var_158_cast_fp16 = gather(axis = var_158_axis_0, batch_dims = var_158_batch_dims_0, indices = select_0, validate_indices = var_158_validate_indices_0, x = var_42_to_fp16)[name = string("op_158_cast_fp16")]; + tensor var_159 = const()[name = string("op_159"), val = tensor([1, 256, 1, 128])]; + tensor sin_1_cast_fp16 = reshape(shape = var_159, x = var_158_cast_fp16)[name = string("sin_1_cast_fp16")]; + tensor mean_1_axes_0 = const()[name = string("mean_1_axes_0"), val = tensor([-1])]; + bool mean_1_keep_dims_0 = const()[name = string("mean_1_keep_dims_0"), val = bool(true)]; + tensor mean_1_cast_fp16 = reduce_mean(axes = mean_1_axes_0, keep_dims = mean_1_keep_dims_0, x = hidden_states)[name = string("mean_1_cast_fp16")]; + tensor input_1_cast_fp16 = sub(x = hidden_states, y = mean_1_cast_fp16)[name = string("input_1_cast_fp16")]; + tensor var_169_axes_0 = const()[name = string("op_169_axes_0"), val = tensor([-1])]; + tensor model_model_layers_24_input_layernorm_weight_to_fp16 = const()[name = string("model_model_layers_24_input_layernorm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(673582272)))]; + fp16 var_38_to_fp16 = const()[name = string("op_38_to_fp16"), val = fp16(0x1.5p-17)]; + tensor var_169_cast_fp16 = layer_norm(axes = var_169_axes_0, epsilon = var_38_to_fp16, gamma = model_model_layers_24_input_layernorm_weight_to_fp16, x = input_1_cast_fp16)[name = string("op_169_cast_fp16")]; + tensor var_173 = const()[name = string("op_173"), val = tensor([0, 2, 1])]; + tensor var_175_axes_0 = const()[name = string("op_175_axes_0"), val = tensor([2])]; + tensor var_174 = transpose(perm = var_173, x = var_169_cast_fp16)[name = string("transpose_29")]; + tensor var_175 = expand_dims(axes = var_175_axes_0, x = var_174)[name = string("op_175")]; + string query_states_1_pad_type_0 = const()[name = string("query_states_1_pad_type_0"), val = string("valid")]; + tensor query_states_1_strides_0 = const()[name = string("query_states_1_strides_0"), val = tensor([1, 1])]; + tensor query_states_1_pad_0 = const()[name = string("query_states_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_states_1_dilations_0 = const()[name = string("query_states_1_dilations_0"), val = tensor([1, 1])]; + int32 query_states_1_groups_0 = const()[name = string("query_states_1_groups_0"), val = int32(1)]; + tensor query_states_1 = conv(dilations = query_states_1_dilations_0, groups = query_states_1_groups_0, pad = query_states_1_pad_0, pad_type = query_states_1_pad_type_0, strides = query_states_1_strides_0, weight = model_model_layers_24_self_attn_q_proj_weight_palettized, x = var_175)[name = string("query_states_1")]; + string key_states_1_pad_type_0 = const()[name = string("key_states_1_pad_type_0"), val = string("valid")]; + tensor key_states_1_strides_0 = const()[name = string("key_states_1_strides_0"), val = tensor([1, 1])]; + tensor key_states_1_pad_0 = const()[name = string("key_states_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_states_1_dilations_0 = const()[name = string("key_states_1_dilations_0"), val = tensor([1, 1])]; + int32 key_states_1_groups_0 = const()[name = string("key_states_1_groups_0"), val = int32(1)]; + tensor key_states_1 = conv(dilations = key_states_1_dilations_0, groups = key_states_1_groups_0, pad = key_states_1_pad_0, pad_type = key_states_1_pad_type_0, strides = key_states_1_strides_0, weight = model_model_layers_24_self_attn_k_proj_weight_palettized, x = var_175)[name = string("key_states_1")]; + string value_states_1_pad_type_0 = const()[name = string("value_states_1_pad_type_0"), val = string("valid")]; + tensor value_states_1_strides_0 = const()[name = string("value_states_1_strides_0"), val = tensor([1, 1])]; + tensor value_states_1_pad_0 = const()[name = string("value_states_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor value_states_1_dilations_0 = const()[name = string("value_states_1_dilations_0"), val = tensor([1, 1])]; + int32 value_states_1_groups_0 = const()[name = string("value_states_1_groups_0"), val = int32(1)]; + tensor value_states_1 = conv(dilations = value_states_1_dilations_0, groups = value_states_1_groups_0, pad = value_states_1_pad_0, pad_type = value_states_1_pad_type_0, strides = value_states_1_strides_0, weight = model_model_layers_24_self_attn_v_proj_weight_palettized, x = var_175)[name = string("value_states_1")]; + tensor var_195 = const()[name = string("op_195"), val = tensor([1, 32, 128, 256])]; + tensor var_196 = reshape(shape = var_195, x = query_states_1)[name = string("op_196")]; + tensor var_197 = const()[name = string("op_197"), val = tensor([0, 1, 3, 2])]; + tensor var_199 = const()[name = string("op_199"), val = tensor([1, 8, 128, 256])]; + tensor var_200 = reshape(shape = var_199, x = key_states_1)[name = string("op_200")]; + tensor var_201 = const()[name = string("op_201"), val = tensor([0, 1, 3, 2])]; + tensor var_203 = const()[name = string("op_203"), val = tensor([1, 8, 128, 256])]; + tensor var_204 = reshape(shape = var_203, x = value_states_1)[name = string("op_204")]; + tensor var_205 = const()[name = string("op_205"), val = tensor([0, 1, 3, 2])]; + tensor var_207 = const()[name = string("op_207"), val = tensor([0, 2, 1, 3])]; + tensor var_209 = const()[name = string("op_209"), val = tensor([0, 2, 1, 3])]; + tensor x1_1_begin_0 = const()[name = string("x1_1_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x1_1_end_0 = const()[name = string("x1_1_end_0"), val = tensor([1, 32, 256, 64])]; + tensor x1_1_end_mask_0 = const()[name = string("x1_1_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_1 = transpose(perm = var_197, x = var_196)[name = string("transpose_28")]; + tensor x1_1 = slice_by_index(begin = x1_1_begin_0, end = x1_1_end_0, end_mask = x1_1_end_mask_0, x = x_1)[name = string("x1_1")]; + tensor x2_1_begin_0 = const()[name = string("x2_1_begin_0"), val = tensor([0, 0, 0, 64])]; + tensor x2_1_end_0 = const()[name = string("x2_1_end_0"), val = tensor([1, 32, 256, 128])]; + tensor x2_1_end_mask_0 = const()[name = string("x2_1_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x2_1 = slice_by_index(begin = x2_1_begin_0, end = x2_1_end_0, end_mask = x2_1_end_mask_0, x = x_1)[name = string("x2_1")]; + tensor cos_7_begin_0 = const()[name = string("cos_7_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor cos_7_end_0 = const()[name = string("cos_7_end_0"), val = tensor([1, 1, 256, 64])]; + tensor cos_7_end_mask_0 = const()[name = string("cos_7_end_mask_0"), val = tensor([true, true, true, false])]; + tensor cos_5 = transpose(perm = var_207, x = cos_1_cast_fp16)[name = string("transpose_27")]; + tensor cos_7 = slice_by_index(begin = cos_7_begin_0, end = cos_7_end_0, end_mask = cos_7_end_mask_0, x = cos_5)[name = string("cos_7")]; + tensor sin_7_begin_0 = const()[name = string("sin_7_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor sin_7_end_0 = const()[name = string("sin_7_end_0"), val = tensor([1, 1, 256, 64])]; + tensor sin_7_end_mask_0 = const()[name = string("sin_7_end_mask_0"), val = tensor([true, true, true, false])]; + tensor sin_5 = transpose(perm = var_209, x = sin_1_cast_fp16)[name = string("transpose_26")]; + tensor sin_7 = slice_by_index(begin = sin_7_begin_0, end = sin_7_end_0, end_mask = sin_7_end_mask_0, x = sin_5)[name = string("sin_7")]; + tensor var_223 = mul(x = x1_1, y = cos_7)[name = string("op_223")]; + tensor var_224 = mul(x = x2_1, y = sin_7)[name = string("op_224")]; + tensor var_225 = sub(x = var_223, y = var_224)[name = string("op_225")]; + tensor var_226 = mul(x = x2_1, y = cos_7)[name = string("op_226")]; + tensor var_227 = mul(x = x1_1, y = sin_7)[name = string("op_227")]; + tensor var_228 = add(x = var_226, y = var_227)[name = string("op_228")]; + bool rotated_1_interleave_0 = const()[name = string("rotated_1_interleave_0"), val = bool(false)]; + tensor rotated_1 = concat(axis = var_36, interleave = rotated_1_interleave_0, values = (var_225, var_228))[name = string("rotated_1")]; + tensor x1_3_begin_0 = const()[name = string("x1_3_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x1_3_end_0 = const()[name = string("x1_3_end_0"), val = tensor([1, 8, 256, 64])]; + tensor x1_3_end_mask_0 = const()[name = string("x1_3_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_5 = transpose(perm = var_201, x = var_200)[name = string("transpose_25")]; + tensor x1_3 = slice_by_index(begin = x1_3_begin_0, end = x1_3_end_0, end_mask = x1_3_end_mask_0, x = x_5)[name = string("x1_3")]; + tensor x2_3_begin_0 = const()[name = string("x2_3_begin_0"), val = tensor([0, 0, 0, 64])]; + tensor x2_3_end_0 = const()[name = string("x2_3_end_0"), val = tensor([1, 8, 256, 128])]; + tensor x2_3_end_mask_0 = const()[name = string("x2_3_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x2_3 = slice_by_index(begin = x2_3_begin_0, end = x2_3_end_0, end_mask = x2_3_end_mask_0, x = x_5)[name = string("x2_3")]; + tensor var_244 = mul(x = x1_3, y = cos_7)[name = string("op_244")]; + tensor var_245 = mul(x = x2_3, y = sin_7)[name = string("op_245")]; + tensor var_246 = sub(x = var_244, y = var_245)[name = string("op_246")]; + tensor var_247 = mul(x = x2_3, y = cos_7)[name = string("op_247")]; + tensor var_248 = mul(x = x1_3, y = sin_7)[name = string("op_248")]; + tensor var_249 = add(x = var_247, y = var_248)[name = string("op_249")]; + bool rotated_3_interleave_0 = const()[name = string("rotated_3_interleave_0"), val = bool(false)]; + tensor rotated_3 = concat(axis = var_36, interleave = rotated_3_interleave_0, values = (var_246, var_249))[name = string("rotated_3")]; + tensor seq_length_1 = const()[name = string("seq_length_1"), val = tensor([256])]; + tensor var_258 = add(x = current_pos, y = seq_length_1)[name = string("op_258")]; + tensor read_state_0 = read_state(input = model_model_kv_cache_0)[name = string("read_state_0")]; + tensor expand_dims_0 = const()[name = string("expand_dims_0"), val = tensor([24])]; + tensor expand_dims_1 = const()[name = string("expand_dims_1"), val = tensor([0])]; + tensor expand_dims_3 = const()[name = string("expand_dims_3"), val = tensor([0])]; + tensor expand_dims_4 = const()[name = string("expand_dims_4"), val = tensor([25])]; + int32 concat_2_axis_0 = const()[name = string("concat_2_axis_0"), val = int32(0)]; + bool concat_2_interleave_0 = const()[name = string("concat_2_interleave_0"), val = bool(false)]; + tensor concat_2 = concat(axis = concat_2_axis_0, interleave = concat_2_interleave_0, values = (expand_dims_0, expand_dims_1, current_pos, expand_dims_3))[name = string("concat_2")]; + tensor concat_3_values1_0 = const()[name = string("concat_3_values1_0"), val = tensor([0])]; + tensor concat_3_values3_0 = const()[name = string("concat_3_values3_0"), val = tensor([0])]; + int32 concat_3_axis_0 = const()[name = string("concat_3_axis_0"), val = int32(0)]; + bool concat_3_interleave_0 = const()[name = string("concat_3_interleave_0"), val = bool(false)]; + tensor concat_3 = concat(axis = concat_3_axis_0, interleave = concat_3_interleave_0, values = (expand_dims_4, concat_3_values1_0, var_258, concat_3_values3_0))[name = string("concat_3")]; + tensor model_model_kv_cache_0_internal_tensor_assign_1_stride_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_1_stride_0"), val = tensor([1, 1, 1, 1])]; + tensor model_model_kv_cache_0_internal_tensor_assign_1_begin_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_1_begin_mask_0"), val = tensor([false, false, false, false])]; + tensor model_model_kv_cache_0_internal_tensor_assign_1_end_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_1_end_mask_0"), val = tensor([false, true, false, true])]; + tensor model_model_kv_cache_0_internal_tensor_assign_1_squeeze_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_1_squeeze_mask_0"), val = tensor([false, false, false, false])]; + tensor model_model_kv_cache_0_internal_tensor_assign_1_cast_fp16 = slice_update(begin = concat_2, begin_mask = model_model_kv_cache_0_internal_tensor_assign_1_begin_mask_0, end = concat_3, end_mask = model_model_kv_cache_0_internal_tensor_assign_1_end_mask_0, squeeze_mask = model_model_kv_cache_0_internal_tensor_assign_1_squeeze_mask_0, stride = model_model_kv_cache_0_internal_tensor_assign_1_stride_0, update = rotated_3, x = read_state_0)[name = string("model_model_kv_cache_0_internal_tensor_assign_1_cast_fp16")]; + write_state(data = model_model_kv_cache_0_internal_tensor_assign_1_cast_fp16, input = model_model_kv_cache_0)[name = string("coreml_update_state_8_write_state")]; + tensor coreml_update_state_8 = read_state(input = model_model_kv_cache_0)[name = string("coreml_update_state_8")]; + tensor expand_dims_6 = const()[name = string("expand_dims_6"), val = tensor([56])]; + tensor expand_dims_7 = const()[name = string("expand_dims_7"), val = tensor([0])]; + tensor expand_dims_9 = const()[name = string("expand_dims_9"), val = tensor([0])]; + tensor expand_dims_10 = const()[name = string("expand_dims_10"), val = tensor([57])]; + int32 concat_6_axis_0 = const()[name = string("concat_6_axis_0"), val = int32(0)]; + bool concat_6_interleave_0 = const()[name = string("concat_6_interleave_0"), val = bool(false)]; + tensor concat_6 = concat(axis = concat_6_axis_0, interleave = concat_6_interleave_0, values = (expand_dims_6, expand_dims_7, current_pos, expand_dims_9))[name = string("concat_6")]; + tensor concat_7_values1_0 = const()[name = string("concat_7_values1_0"), val = tensor([0])]; + tensor concat_7_values3_0 = const()[name = string("concat_7_values3_0"), val = tensor([0])]; + int32 concat_7_axis_0 = const()[name = string("concat_7_axis_0"), val = int32(0)]; + bool concat_7_interleave_0 = const()[name = string("concat_7_interleave_0"), val = bool(false)]; + tensor concat_7 = concat(axis = concat_7_axis_0, interleave = concat_7_interleave_0, values = (expand_dims_10, concat_7_values1_0, var_258, concat_7_values3_0))[name = string("concat_7")]; + tensor model_model_kv_cache_0_internal_tensor_assign_2_stride_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_2_stride_0"), val = tensor([1, 1, 1, 1])]; + tensor model_model_kv_cache_0_internal_tensor_assign_2_begin_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_2_begin_mask_0"), val = tensor([false, false, false, false])]; + tensor model_model_kv_cache_0_internal_tensor_assign_2_end_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_2_end_mask_0"), val = tensor([false, true, false, true])]; + tensor model_model_kv_cache_0_internal_tensor_assign_2_squeeze_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_2_squeeze_mask_0"), val = tensor([false, false, false, false])]; + tensor value_states_3 = transpose(perm = var_205, x = var_204)[name = string("transpose_24")]; + tensor model_model_kv_cache_0_internal_tensor_assign_2_cast_fp16 = slice_update(begin = concat_6, begin_mask = model_model_kv_cache_0_internal_tensor_assign_2_begin_mask_0, end = concat_7, end_mask = model_model_kv_cache_0_internal_tensor_assign_2_end_mask_0, squeeze_mask = model_model_kv_cache_0_internal_tensor_assign_2_squeeze_mask_0, stride = model_model_kv_cache_0_internal_tensor_assign_2_stride_0, update = value_states_3, x = coreml_update_state_8)[name = string("model_model_kv_cache_0_internal_tensor_assign_2_cast_fp16")]; + write_state(data = model_model_kv_cache_0_internal_tensor_assign_2_cast_fp16, input = model_model_kv_cache_0)[name = string("coreml_update_state_9_write_state")]; + tensor coreml_update_state_9 = read_state(input = model_model_kv_cache_0)[name = string("coreml_update_state_9")]; + tensor var_272_begin_0 = const()[name = string("op_272_begin_0"), val = tensor([24, 0, 0, 0])]; + tensor var_272_end_0 = const()[name = string("op_272_end_0"), val = tensor([25, 8, 1024, 128])]; + tensor var_272_end_mask_0 = const()[name = string("op_272_end_mask_0"), val = tensor([false, true, true, true])]; + tensor var_272_cast_fp16 = slice_by_index(begin = var_272_begin_0, end = var_272_end_0, end_mask = var_272_end_mask_0, x = coreml_update_state_9)[name = string("op_272_cast_fp16")]; + tensor K_layer_cache_1_axes_0 = const()[name = string("K_layer_cache_1_axes_0"), val = tensor([0])]; + tensor K_layer_cache_1_cast_fp16 = squeeze(axes = K_layer_cache_1_axes_0, x = var_272_cast_fp16)[name = string("K_layer_cache_1_cast_fp16")]; + tensor var_274_begin_0 = const()[name = string("op_274_begin_0"), val = tensor([56, 0, 0, 0])]; + tensor var_274_end_0 = const()[name = string("op_274_end_0"), val = tensor([57, 8, 1024, 128])]; + tensor var_274_end_mask_0 = const()[name = string("op_274_end_mask_0"), val = tensor([false, true, true, true])]; + tensor var_274_cast_fp16 = slice_by_index(begin = var_274_begin_0, end = var_274_end_0, end_mask = var_274_end_mask_0, x = coreml_update_state_9)[name = string("op_274_cast_fp16")]; + tensor V_layer_cache_1_axes_0 = const()[name = string("V_layer_cache_1_axes_0"), val = tensor([0])]; + tensor V_layer_cache_1_cast_fp16 = squeeze(axes = V_layer_cache_1_axes_0, x = var_274_cast_fp16)[name = string("V_layer_cache_1_cast_fp16")]; + tensor x_11_axes_0 = const()[name = string("x_11_axes_0"), val = tensor([1])]; + tensor x_11_cast_fp16 = expand_dims(axes = x_11_axes_0, x = K_layer_cache_1_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor var_283 = const()[name = string("op_283"), val = tensor([1, 4, 1, 1])]; + tensor x_13_cast_fp16 = tile(reps = var_283, x = x_11_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_287 = const()[name = string("op_287"), val = tensor([1, -1, 1024, 128])]; + tensor var_288_cast_fp16 = reshape(shape = var_287, x = x_13_cast_fp16)[name = string("op_288_cast_fp16")]; + tensor x_17_axes_0 = const()[name = string("x_17_axes_0"), val = tensor([1])]; + tensor x_17_cast_fp16 = expand_dims(axes = x_17_axes_0, x = V_layer_cache_1_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor var_290 = const()[name = string("op_290"), val = tensor([1, 4, 1, 1])]; + tensor x_19_cast_fp16 = tile(reps = var_290, x = x_17_cast_fp16)[name = string("x_19_cast_fp16")]; + bool var_297_transpose_x_0 = const()[name = string("op_297_transpose_x_0"), val = bool(false)]; + bool var_297_transpose_y_0 = const()[name = string("op_297_transpose_y_0"), val = bool(true)]; + tensor var_297_cast_fp16 = matmul(transpose_x = var_297_transpose_x_0, transpose_y = var_297_transpose_y_0, x = rotated_1, y = var_288_cast_fp16)[name = string("op_297_cast_fp16")]; + fp16 var_298_to_fp16 = const()[name = string("op_298_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor attn_weights_1_cast_fp16 = mul(x = var_297_cast_fp16, y = var_298_to_fp16)[name = string("attn_weights_1_cast_fp16")]; + tensor x_21_cast_fp16 = add(x = attn_weights_1_cast_fp16, y = causal_mask)[name = string("x_21_cast_fp16")]; + tensor reduce_max_0_axes_0 = const()[name = string("reduce_max_0_axes_0"), val = tensor([-1])]; + bool reduce_max_0_keep_dims_0 = const()[name = string("reduce_max_0_keep_dims_0"), val = bool(true)]; + tensor reduce_max_0_cast_fp16 = reduce_max(axes = reduce_max_0_axes_0, keep_dims = reduce_max_0_keep_dims_0, x = x_21_cast_fp16)[name = string("reduce_max_0_cast_fp16")]; + tensor x_23_cast_fp16 = sub(x = x_21_cast_fp16, y = reduce_max_0_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor exp_x_1_cast_fp16 = exp(x = x_23_cast_fp16)[name = string("exp_x_1_cast_fp16")]; + tensor var_309_axes_0 = const()[name = string("op_309_axes_0"), val = tensor([-1])]; + bool var_309_keep_dims_0 = const()[name = string("op_309_keep_dims_0"), val = bool(true)]; + tensor var_309_cast_fp16 = reduce_sum(axes = var_309_axes_0, keep_dims = var_309_keep_dims_0, x = exp_x_1_cast_fp16)[name = string("op_309_cast_fp16")]; + tensor var_310_cast_fp16 = real_div(x = exp_x_1_cast_fp16, y = var_309_cast_fp16)[name = string("op_310_cast_fp16")]; + tensor concat_12 = const()[name = string("concat_12"), val = tensor([32, 256, 1024])]; + tensor reshape_0_cast_fp16 = reshape(shape = concat_12, x = var_310_cast_fp16)[name = string("reshape_0_cast_fp16")]; + tensor concat_13 = const()[name = string("concat_13"), val = tensor([32, 1024, 128])]; + tensor reshape_1_cast_fp16 = reshape(shape = concat_13, x = x_19_cast_fp16)[name = string("reshape_1_cast_fp16")]; + bool matmul_0_transpose_x_0 = const()[name = string("matmul_0_transpose_x_0"), val = bool(false)]; + bool matmul_0_transpose_y_0 = const()[name = string("matmul_0_transpose_y_0"), val = bool(false)]; + tensor matmul_0_cast_fp16 = matmul(transpose_x = matmul_0_transpose_x_0, transpose_y = matmul_0_transpose_y_0, x = reshape_0_cast_fp16, y = reshape_1_cast_fp16)[name = string("matmul_0_cast_fp16")]; + tensor concat_17 = const()[name = string("concat_17"), val = tensor([1, 32, 256, 128])]; + tensor reshape_2_cast_fp16 = reshape(shape = concat_17, x = matmul_0_cast_fp16)[name = string("reshape_2_cast_fp16")]; + tensor var_313_perm_0 = const()[name = string("op_313_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_315 = const()[name = string("op_315"), val = tensor([1, 256, 4096])]; + tensor var_313_cast_fp16 = transpose(perm = var_313_perm_0, x = reshape_2_cast_fp16)[name = string("transpose_23")]; + tensor input_5_cast_fp16 = reshape(shape = var_315, x = var_313_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor model_model_layers_24_self_attn_o_proj_weight_promoted_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(673590528))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686173504))))[name = string("model_model_layers_24_self_attn_o_proj_weight_promoted_to_fp16_palettized")]; + tensor linear_0_bias_0_to_fp16 = const()[name = string("linear_0_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686239104)))]; + tensor linear_0_cast_fp16 = linear(bias = linear_0_bias_0_to_fp16, weight = model_model_layers_24_self_attn_o_proj_weight_promoted_to_fp16_palettized, x = input_5_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor hidden_states_5_cast_fp16 = add(x = hidden_states, y = linear_0_cast_fp16)[name = string("hidden_states_5_cast_fp16")]; + tensor mean_3_axes_0 = const()[name = string("mean_3_axes_0"), val = tensor([-1])]; + bool mean_3_keep_dims_0 = const()[name = string("mean_3_keep_dims_0"), val = bool(true)]; + tensor mean_3_cast_fp16 = reduce_mean(axes = mean_3_axes_0, keep_dims = mean_3_keep_dims_0, x = hidden_states_5_cast_fp16)[name = string("mean_3_cast_fp16")]; + tensor input_7_cast_fp16 = sub(x = hidden_states_5_cast_fp16, y = mean_3_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor var_326_axes_0 = const()[name = string("op_326_axes_0"), val = tensor([-1])]; + tensor model_model_layers_24_post_attention_layernorm_weight_to_fp16 = const()[name = string("model_model_layers_24_post_attention_layernorm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686247360)))]; + tensor var_326_cast_fp16 = layer_norm(axes = var_326_axes_0, epsilon = var_38_to_fp16, gamma = model_model_layers_24_post_attention_layernorm_weight_to_fp16, x = input_7_cast_fp16)[name = string("op_326_cast_fp16")]; + tensor var_333 = const()[name = string("op_333"), val = tensor([0, 2, 1])]; + tensor input_9_axes_0 = const()[name = string("input_9_axes_0"), val = tensor([2])]; + tensor var_334 = transpose(perm = var_333, x = var_326_cast_fp16)[name = string("transpose_22")]; + tensor input_9 = expand_dims(axes = input_9_axes_0, x = var_334)[name = string("input_9")]; + string input_11_pad_type_0 = const()[name = string("input_11_pad_type_0"), val = string("valid")]; + tensor input_11_strides_0 = const()[name = string("input_11_strides_0"), val = tensor([1, 1])]; + tensor input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_11_dilations_0 = const()[name = string("input_11_dilations_0"), val = tensor([1, 1])]; + int32 input_11_groups_0 = const()[name = string("input_11_groups_0"), val = int32(1)]; + tensor input_11 = conv(dilations = input_11_dilations_0, groups = input_11_groups_0, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = input_11_strides_0, weight = model_model_layers_24_mlp_gate_proj_weight_palettized, x = input_9)[name = string("input_11")]; + string up_states_1_pad_type_0 = const()[name = string("up_states_1_pad_type_0"), val = string("valid")]; + tensor up_states_1_strides_0 = const()[name = string("up_states_1_strides_0"), val = tensor([1, 1])]; + tensor up_states_1_pad_0 = const()[name = string("up_states_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_states_1_dilations_0 = const()[name = string("up_states_1_dilations_0"), val = tensor([1, 1])]; + int32 up_states_1_groups_0 = const()[name = string("up_states_1_groups_0"), val = int32(1)]; + tensor up_states_1 = conv(dilations = up_states_1_dilations_0, groups = up_states_1_groups_0, pad = up_states_1_pad_0, pad_type = up_states_1_pad_type_0, strides = up_states_1_strides_0, weight = model_model_layers_24_mlp_up_proj_weight_palettized, x = input_9)[name = string("up_states_1")]; + tensor gate_states_1 = silu(x = input_11)[name = string("gate_states_1")]; + tensor input_13 = mul(x = gate_states_1, y = up_states_1)[name = string("input_13")]; + string hidden_states_7_pad_type_0 = const()[name = string("hidden_states_7_pad_type_0"), val = string("valid")]; + tensor hidden_states_7_strides_0 = const()[name = string("hidden_states_7_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_7_pad_0 = const()[name = string("hidden_states_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_7_dilations_0 = const()[name = string("hidden_states_7_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_7_groups_0 = const()[name = string("hidden_states_7_groups_0"), val = int32(1)]; + tensor hidden_states_7 = conv(dilations = hidden_states_7_dilations_0, groups = hidden_states_7_groups_0, pad = hidden_states_7_pad_0, pad_type = hidden_states_7_pad_type_0, strides = hidden_states_7_strides_0, weight = model_model_layers_24_mlp_down_proj_weight_palettized, x = input_13)[name = string("hidden_states_7")]; + tensor var_356_axes_0 = const()[name = string("op_356_axes_0"), val = tensor([2])]; + tensor var_356 = squeeze(axes = var_356_axes_0, x = hidden_states_7)[name = string("op_356")]; + tensor var_357 = const()[name = string("op_357"), val = tensor([0, 2, 1])]; + tensor var_358 = transpose(perm = var_357, x = var_356)[name = string("transpose_21")]; + tensor hidden_states_9_cast_fp16 = add(x = hidden_states_5_cast_fp16, y = var_358)[name = string("hidden_states_9_cast_fp16")]; + tensor mean_5_axes_0 = const()[name = string("mean_5_axes_0"), val = tensor([-1])]; + bool mean_5_keep_dims_0 = const()[name = string("mean_5_keep_dims_0"), val = bool(true)]; + tensor mean_5_cast_fp16 = reduce_mean(axes = mean_5_axes_0, keep_dims = mean_5_keep_dims_0, x = hidden_states_9_cast_fp16)[name = string("mean_5_cast_fp16")]; + tensor input_15_cast_fp16 = sub(x = hidden_states_9_cast_fp16, y = mean_5_cast_fp16)[name = string("input_15_cast_fp16")]; + tensor var_366_axes_0 = const()[name = string("op_366_axes_0"), val = tensor([-1])]; + tensor model_model_layers_25_input_layernorm_weight_to_fp16 = const()[name = string("model_model_layers_25_input_layernorm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686255616)))]; + tensor var_366_cast_fp16 = layer_norm(axes = var_366_axes_0, epsilon = var_38_to_fp16, gamma = model_model_layers_25_input_layernorm_weight_to_fp16, x = input_15_cast_fp16)[name = string("op_366_cast_fp16")]; + tensor var_370 = const()[name = string("op_370"), val = tensor([0, 2, 1])]; + tensor var_372_axes_0 = const()[name = string("op_372_axes_0"), val = tensor([2])]; + tensor var_371 = transpose(perm = var_370, x = var_366_cast_fp16)[name = string("transpose_20")]; + tensor var_372 = expand_dims(axes = var_372_axes_0, x = var_371)[name = string("op_372")]; + string query_states_5_pad_type_0 = const()[name = string("query_states_5_pad_type_0"), val = string("valid")]; + tensor query_states_5_strides_0 = const()[name = string("query_states_5_strides_0"), val = tensor([1, 1])]; + tensor query_states_5_pad_0 = const()[name = string("query_states_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_states_5_dilations_0 = const()[name = string("query_states_5_dilations_0"), val = tensor([1, 1])]; + int32 query_states_5_groups_0 = const()[name = string("query_states_5_groups_0"), val = int32(1)]; + tensor query_states_5 = conv(dilations = query_states_5_dilations_0, groups = query_states_5_groups_0, pad = query_states_5_pad_0, pad_type = query_states_5_pad_type_0, strides = query_states_5_strides_0, weight = model_model_layers_25_self_attn_q_proj_weight_palettized, x = var_372)[name = string("query_states_5")]; + string key_states_7_pad_type_0 = const()[name = string("key_states_7_pad_type_0"), val = string("valid")]; + tensor key_states_7_strides_0 = const()[name = string("key_states_7_strides_0"), val = tensor([1, 1])]; + tensor key_states_7_pad_0 = const()[name = string("key_states_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_states_7_dilations_0 = const()[name = string("key_states_7_dilations_0"), val = tensor([1, 1])]; + int32 key_states_7_groups_0 = const()[name = string("key_states_7_groups_0"), val = int32(1)]; + tensor key_states_7 = conv(dilations = key_states_7_dilations_0, groups = key_states_7_groups_0, pad = key_states_7_pad_0, pad_type = key_states_7_pad_type_0, strides = key_states_7_strides_0, weight = model_model_layers_25_self_attn_k_proj_weight_palettized, x = var_372)[name = string("key_states_7")]; + string value_states_7_pad_type_0 = const()[name = string("value_states_7_pad_type_0"), val = string("valid")]; + tensor value_states_7_strides_0 = const()[name = string("value_states_7_strides_0"), val = tensor([1, 1])]; + tensor value_states_7_pad_0 = const()[name = string("value_states_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor value_states_7_dilations_0 = const()[name = string("value_states_7_dilations_0"), val = tensor([1, 1])]; + int32 value_states_7_groups_0 = const()[name = string("value_states_7_groups_0"), val = int32(1)]; + tensor value_states_7 = conv(dilations = value_states_7_dilations_0, groups = value_states_7_groups_0, pad = value_states_7_pad_0, pad_type = value_states_7_pad_type_0, strides = value_states_7_strides_0, weight = model_model_layers_25_self_attn_v_proj_weight_palettized, x = var_372)[name = string("value_states_7")]; + tensor var_392 = const()[name = string("op_392"), val = tensor([1, 32, 128, 256])]; + tensor var_393 = reshape(shape = var_392, x = query_states_5)[name = string("op_393")]; + tensor var_394 = const()[name = string("op_394"), val = tensor([0, 1, 3, 2])]; + tensor var_396 = const()[name = string("op_396"), val = tensor([1, 8, 128, 256])]; + tensor var_397 = reshape(shape = var_396, x = key_states_7)[name = string("op_397")]; + tensor var_398 = const()[name = string("op_398"), val = tensor([0, 1, 3, 2])]; + tensor var_400 = const()[name = string("op_400"), val = tensor([1, 8, 128, 256])]; + tensor var_401 = reshape(shape = var_400, x = value_states_7)[name = string("op_401")]; + tensor var_402 = const()[name = string("op_402"), val = tensor([0, 1, 3, 2])]; + tensor x1_5_begin_0 = const()[name = string("x1_5_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x1_5_end_0 = const()[name = string("x1_5_end_0"), val = tensor([1, 32, 256, 64])]; + tensor x1_5_end_mask_0 = const()[name = string("x1_5_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_29 = transpose(perm = var_394, x = var_393)[name = string("transpose_19")]; + tensor x1_5 = slice_by_index(begin = x1_5_begin_0, end = x1_5_end_0, end_mask = x1_5_end_mask_0, x = x_29)[name = string("x1_5")]; + tensor x2_5_begin_0 = const()[name = string("x2_5_begin_0"), val = tensor([0, 0, 0, 64])]; + tensor x2_5_end_0 = const()[name = string("x2_5_end_0"), val = tensor([1, 32, 256, 128])]; + tensor x2_5_end_mask_0 = const()[name = string("x2_5_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x2_5 = slice_by_index(begin = x2_5_begin_0, end = x2_5_end_0, end_mask = x2_5_end_mask_0, x = x_29)[name = string("x2_5")]; + tensor var_420 = mul(x = x1_5, y = cos_7)[name = string("op_420")]; + tensor var_421 = mul(x = x2_5, y = sin_7)[name = string("op_421")]; + tensor var_422 = sub(x = var_420, y = var_421)[name = string("op_422")]; + tensor var_423 = mul(x = x2_5, y = cos_7)[name = string("op_423")]; + tensor var_424 = mul(x = x1_5, y = sin_7)[name = string("op_424")]; + tensor var_425 = add(x = var_423, y = var_424)[name = string("op_425")]; + bool rotated_5_interleave_0 = const()[name = string("rotated_5_interleave_0"), val = bool(false)]; + tensor rotated_5 = concat(axis = var_36, interleave = rotated_5_interleave_0, values = (var_422, var_425))[name = string("rotated_5")]; + tensor x1_7_begin_0 = const()[name = string("x1_7_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x1_7_end_0 = const()[name = string("x1_7_end_0"), val = tensor([1, 8, 256, 64])]; + tensor x1_7_end_mask_0 = const()[name = string("x1_7_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_33 = transpose(perm = var_398, x = var_397)[name = string("transpose_18")]; + tensor x1_7 = slice_by_index(begin = x1_7_begin_0, end = x1_7_end_0, end_mask = x1_7_end_mask_0, x = x_33)[name = string("x1_7")]; + tensor x2_7_begin_0 = const()[name = string("x2_7_begin_0"), val = tensor([0, 0, 0, 64])]; + tensor x2_7_end_0 = const()[name = string("x2_7_end_0"), val = tensor([1, 8, 256, 128])]; + tensor x2_7_end_mask_0 = const()[name = string("x2_7_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x2_7 = slice_by_index(begin = x2_7_begin_0, end = x2_7_end_0, end_mask = x2_7_end_mask_0, x = x_33)[name = string("x2_7")]; + tensor var_441 = mul(x = x1_7, y = cos_7)[name = string("op_441")]; + tensor var_442 = mul(x = x2_7, y = sin_7)[name = string("op_442")]; + tensor var_443 = sub(x = var_441, y = var_442)[name = string("op_443")]; + tensor var_444 = mul(x = x2_7, y = cos_7)[name = string("op_444")]; + tensor var_445 = mul(x = x1_7, y = sin_7)[name = string("op_445")]; + tensor var_446 = add(x = var_444, y = var_445)[name = string("op_446")]; + bool rotated_7_interleave_0 = const()[name = string("rotated_7_interleave_0"), val = bool(false)]; + tensor rotated_7 = concat(axis = var_36, interleave = rotated_7_interleave_0, values = (var_443, var_446))[name = string("rotated_7")]; + tensor expand_dims_12 = const()[name = string("expand_dims_12"), val = tensor([25])]; + tensor expand_dims_13 = const()[name = string("expand_dims_13"), val = tensor([0])]; + tensor expand_dims_15 = const()[name = string("expand_dims_15"), val = tensor([0])]; + tensor expand_dims_16 = const()[name = string("expand_dims_16"), val = tensor([26])]; + int32 concat_20_axis_0 = const()[name = string("concat_20_axis_0"), val = int32(0)]; + bool concat_20_interleave_0 = const()[name = string("concat_20_interleave_0"), val = bool(false)]; + tensor concat_20 = concat(axis = concat_20_axis_0, interleave = concat_20_interleave_0, values = (expand_dims_12, expand_dims_13, current_pos, expand_dims_15))[name = string("concat_20")]; + tensor concat_21_values1_0 = const()[name = string("concat_21_values1_0"), val = tensor([0])]; + tensor concat_21_values3_0 = const()[name = string("concat_21_values3_0"), val = tensor([0])]; + int32 concat_21_axis_0 = const()[name = string("concat_21_axis_0"), val = int32(0)]; + bool concat_21_interleave_0 = const()[name = string("concat_21_interleave_0"), val = bool(false)]; + tensor concat_21 = concat(axis = concat_21_axis_0, interleave = concat_21_interleave_0, values = (expand_dims_16, concat_21_values1_0, var_258, concat_21_values3_0))[name = string("concat_21")]; + tensor model_model_kv_cache_0_internal_tensor_assign_3_stride_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_3_stride_0"), val = tensor([1, 1, 1, 1])]; + tensor model_model_kv_cache_0_internal_tensor_assign_3_begin_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_3_begin_mask_0"), val = tensor([false, false, false, false])]; + tensor model_model_kv_cache_0_internal_tensor_assign_3_end_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_3_end_mask_0"), val = tensor([false, true, false, true])]; + tensor model_model_kv_cache_0_internal_tensor_assign_3_squeeze_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_3_squeeze_mask_0"), val = tensor([false, false, false, false])]; + tensor model_model_kv_cache_0_internal_tensor_assign_3_cast_fp16 = slice_update(begin = concat_20, begin_mask = model_model_kv_cache_0_internal_tensor_assign_3_begin_mask_0, end = concat_21, end_mask = model_model_kv_cache_0_internal_tensor_assign_3_end_mask_0, squeeze_mask = model_model_kv_cache_0_internal_tensor_assign_3_squeeze_mask_0, stride = model_model_kv_cache_0_internal_tensor_assign_3_stride_0, update = rotated_7, x = coreml_update_state_9)[name = string("model_model_kv_cache_0_internal_tensor_assign_3_cast_fp16")]; + write_state(data = model_model_kv_cache_0_internal_tensor_assign_3_cast_fp16, input = model_model_kv_cache_0)[name = string("coreml_update_state_10_write_state")]; + tensor coreml_update_state_10 = read_state(input = model_model_kv_cache_0)[name = string("coreml_update_state_10")]; + tensor expand_dims_18 = const()[name = string("expand_dims_18"), val = tensor([57])]; + tensor expand_dims_19 = const()[name = string("expand_dims_19"), val = tensor([0])]; + tensor expand_dims_21 = const()[name = string("expand_dims_21"), val = tensor([0])]; + tensor expand_dims_22 = const()[name = string("expand_dims_22"), val = tensor([58])]; + int32 concat_24_axis_0 = const()[name = string("concat_24_axis_0"), val = int32(0)]; + bool concat_24_interleave_0 = const()[name = string("concat_24_interleave_0"), val = bool(false)]; + tensor concat_24 = concat(axis = concat_24_axis_0, interleave = concat_24_interleave_0, values = (expand_dims_18, expand_dims_19, current_pos, expand_dims_21))[name = string("concat_24")]; + tensor concat_25_values1_0 = const()[name = string("concat_25_values1_0"), val = tensor([0])]; + tensor concat_25_values3_0 = const()[name = string("concat_25_values3_0"), val = tensor([0])]; + int32 concat_25_axis_0 = const()[name = string("concat_25_axis_0"), val = int32(0)]; + bool concat_25_interleave_0 = const()[name = string("concat_25_interleave_0"), val = bool(false)]; + tensor concat_25 = concat(axis = concat_25_axis_0, interleave = concat_25_interleave_0, values = (expand_dims_22, concat_25_values1_0, var_258, concat_25_values3_0))[name = string("concat_25")]; + tensor model_model_kv_cache_0_internal_tensor_assign_4_stride_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_4_stride_0"), val = tensor([1, 1, 1, 1])]; + tensor model_model_kv_cache_0_internal_tensor_assign_4_begin_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_4_begin_mask_0"), val = tensor([false, false, false, false])]; + tensor model_model_kv_cache_0_internal_tensor_assign_4_end_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_4_end_mask_0"), val = tensor([false, true, false, true])]; + tensor model_model_kv_cache_0_internal_tensor_assign_4_squeeze_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_4_squeeze_mask_0"), val = tensor([false, false, false, false])]; + tensor value_states_9 = transpose(perm = var_402, x = var_401)[name = string("transpose_17")]; + tensor model_model_kv_cache_0_internal_tensor_assign_4_cast_fp16 = slice_update(begin = concat_24, begin_mask = model_model_kv_cache_0_internal_tensor_assign_4_begin_mask_0, end = concat_25, end_mask = model_model_kv_cache_0_internal_tensor_assign_4_end_mask_0, squeeze_mask = model_model_kv_cache_0_internal_tensor_assign_4_squeeze_mask_0, stride = model_model_kv_cache_0_internal_tensor_assign_4_stride_0, update = value_states_9, x = coreml_update_state_10)[name = string("model_model_kv_cache_0_internal_tensor_assign_4_cast_fp16")]; + write_state(data = model_model_kv_cache_0_internal_tensor_assign_4_cast_fp16, input = model_model_kv_cache_0)[name = string("coreml_update_state_11_write_state")]; + tensor coreml_update_state_11 = read_state(input = model_model_kv_cache_0)[name = string("coreml_update_state_11")]; + tensor var_469_begin_0 = const()[name = string("op_469_begin_0"), val = tensor([25, 0, 0, 0])]; + tensor var_469_end_0 = const()[name = string("op_469_end_0"), val = tensor([26, 8, 1024, 128])]; + tensor var_469_end_mask_0 = const()[name = string("op_469_end_mask_0"), val = tensor([false, true, true, true])]; + tensor var_469_cast_fp16 = slice_by_index(begin = var_469_begin_0, end = var_469_end_0, end_mask = var_469_end_mask_0, x = coreml_update_state_11)[name = string("op_469_cast_fp16")]; + tensor K_layer_cache_3_axes_0 = const()[name = string("K_layer_cache_3_axes_0"), val = tensor([0])]; + tensor K_layer_cache_3_cast_fp16 = squeeze(axes = K_layer_cache_3_axes_0, x = var_469_cast_fp16)[name = string("K_layer_cache_3_cast_fp16")]; + tensor var_471_begin_0 = const()[name = string("op_471_begin_0"), val = tensor([57, 0, 0, 0])]; + tensor var_471_end_0 = const()[name = string("op_471_end_0"), val = tensor([58, 8, 1024, 128])]; + tensor var_471_end_mask_0 = const()[name = string("op_471_end_mask_0"), val = tensor([false, true, true, true])]; + tensor var_471_cast_fp16 = slice_by_index(begin = var_471_begin_0, end = var_471_end_0, end_mask = var_471_end_mask_0, x = coreml_update_state_11)[name = string("op_471_cast_fp16")]; + tensor V_layer_cache_3_axes_0 = const()[name = string("V_layer_cache_3_axes_0"), val = tensor([0])]; + tensor V_layer_cache_3_cast_fp16 = squeeze(axes = V_layer_cache_3_axes_0, x = var_471_cast_fp16)[name = string("V_layer_cache_3_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([1])]; + tensor x_39_cast_fp16 = expand_dims(axes = x_39_axes_0, x = K_layer_cache_3_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_480 = const()[name = string("op_480"), val = tensor([1, 4, 1, 1])]; + tensor x_41_cast_fp16 = tile(reps = var_480, x = x_39_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor var_484 = const()[name = string("op_484"), val = tensor([1, -1, 1024, 128])]; + tensor var_485_cast_fp16 = reshape(shape = var_484, x = x_41_cast_fp16)[name = string("op_485_cast_fp16")]; + tensor x_45_axes_0 = const()[name = string("x_45_axes_0"), val = tensor([1])]; + tensor x_45_cast_fp16 = expand_dims(axes = x_45_axes_0, x = V_layer_cache_3_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_487 = const()[name = string("op_487"), val = tensor([1, 4, 1, 1])]; + tensor x_47_cast_fp16 = tile(reps = var_487, x = x_45_cast_fp16)[name = string("x_47_cast_fp16")]; + bool var_494_transpose_x_0 = const()[name = string("op_494_transpose_x_0"), val = bool(false)]; + bool var_494_transpose_y_0 = const()[name = string("op_494_transpose_y_0"), val = bool(true)]; + tensor var_494_cast_fp16 = matmul(transpose_x = var_494_transpose_x_0, transpose_y = var_494_transpose_y_0, x = rotated_5, y = var_485_cast_fp16)[name = string("op_494_cast_fp16")]; + fp16 var_495_to_fp16 = const()[name = string("op_495_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor attn_weights_3_cast_fp16 = mul(x = var_494_cast_fp16, y = var_495_to_fp16)[name = string("attn_weights_3_cast_fp16")]; + tensor x_49_cast_fp16 = add(x = attn_weights_3_cast_fp16, y = causal_mask)[name = string("x_49_cast_fp16")]; + tensor reduce_max_1_axes_0 = const()[name = string("reduce_max_1_axes_0"), val = tensor([-1])]; + bool reduce_max_1_keep_dims_0 = const()[name = string("reduce_max_1_keep_dims_0"), val = bool(true)]; + tensor reduce_max_1_cast_fp16 = reduce_max(axes = reduce_max_1_axes_0, keep_dims = reduce_max_1_keep_dims_0, x = x_49_cast_fp16)[name = string("reduce_max_1_cast_fp16")]; + tensor x_51_cast_fp16 = sub(x = x_49_cast_fp16, y = reduce_max_1_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor exp_x_3_cast_fp16 = exp(x = x_51_cast_fp16)[name = string("exp_x_3_cast_fp16")]; + tensor var_506_axes_0 = const()[name = string("op_506_axes_0"), val = tensor([-1])]; + bool var_506_keep_dims_0 = const()[name = string("op_506_keep_dims_0"), val = bool(true)]; + tensor var_506_cast_fp16 = reduce_sum(axes = var_506_axes_0, keep_dims = var_506_keep_dims_0, x = exp_x_3_cast_fp16)[name = string("op_506_cast_fp16")]; + tensor var_507_cast_fp16 = real_div(x = exp_x_3_cast_fp16, y = var_506_cast_fp16)[name = string("op_507_cast_fp16")]; + tensor concat_30 = const()[name = string("concat_30"), val = tensor([32, 256, 1024])]; + tensor reshape_3_cast_fp16 = reshape(shape = concat_30, x = var_507_cast_fp16)[name = string("reshape_3_cast_fp16")]; + tensor concat_31 = const()[name = string("concat_31"), val = tensor([32, 1024, 128])]; + tensor reshape_4_cast_fp16 = reshape(shape = concat_31, x = x_47_cast_fp16)[name = string("reshape_4_cast_fp16")]; + bool matmul_1_transpose_x_0 = const()[name = string("matmul_1_transpose_x_0"), val = bool(false)]; + bool matmul_1_transpose_y_0 = const()[name = string("matmul_1_transpose_y_0"), val = bool(false)]; + tensor matmul_1_cast_fp16 = matmul(transpose_x = matmul_1_transpose_x_0, transpose_y = matmul_1_transpose_y_0, x = reshape_3_cast_fp16, y = reshape_4_cast_fp16)[name = string("matmul_1_cast_fp16")]; + tensor concat_35 = const()[name = string("concat_35"), val = tensor([1, 32, 256, 128])]; + tensor reshape_5_cast_fp16 = reshape(shape = concat_35, x = matmul_1_cast_fp16)[name = string("reshape_5_cast_fp16")]; + tensor var_510_perm_0 = const()[name = string("op_510_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_512 = const()[name = string("op_512"), val = tensor([1, 256, 4096])]; + tensor var_510_cast_fp16 = transpose(perm = var_510_perm_0, x = reshape_5_cast_fp16)[name = string("transpose_16")]; + tensor input_19_cast_fp16 = reshape(shape = var_512, x = var_510_cast_fp16)[name = string("input_19_cast_fp16")]; + tensor model_model_layers_25_self_attn_o_proj_weight_promoted_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(686263872))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(698846848))))[name = string("model_model_layers_25_self_attn_o_proj_weight_promoted_to_fp16_palettized")]; + tensor linear_1_cast_fp16 = linear(bias = linear_0_bias_0_to_fp16, weight = model_model_layers_25_self_attn_o_proj_weight_promoted_to_fp16_palettized, x = input_19_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor hidden_states_13_cast_fp16 = add(x = hidden_states_9_cast_fp16, y = linear_1_cast_fp16)[name = string("hidden_states_13_cast_fp16")]; + tensor mean_7_axes_0 = const()[name = string("mean_7_axes_0"), val = tensor([-1])]; + bool mean_7_keep_dims_0 = const()[name = string("mean_7_keep_dims_0"), val = bool(true)]; + tensor mean_7_cast_fp16 = reduce_mean(axes = mean_7_axes_0, keep_dims = mean_7_keep_dims_0, x = hidden_states_13_cast_fp16)[name = string("mean_7_cast_fp16")]; + tensor input_21_cast_fp16 = sub(x = hidden_states_13_cast_fp16, y = mean_7_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor var_523_axes_0 = const()[name = string("op_523_axes_0"), val = tensor([-1])]; + tensor model_model_layers_25_post_attention_layernorm_weight_to_fp16 = const()[name = string("model_model_layers_25_post_attention_layernorm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(698912448)))]; + tensor var_523_cast_fp16 = layer_norm(axes = var_523_axes_0, epsilon = var_38_to_fp16, gamma = model_model_layers_25_post_attention_layernorm_weight_to_fp16, x = input_21_cast_fp16)[name = string("op_523_cast_fp16")]; + tensor var_530 = const()[name = string("op_530"), val = tensor([0, 2, 1])]; + tensor input_23_axes_0 = const()[name = string("input_23_axes_0"), val = tensor([2])]; + tensor var_531 = transpose(perm = var_530, x = var_523_cast_fp16)[name = string("transpose_15")]; + tensor input_23 = expand_dims(axes = input_23_axes_0, x = var_531)[name = string("input_23")]; + string input_25_pad_type_0 = const()[name = string("input_25_pad_type_0"), val = string("valid")]; + tensor input_25_strides_0 = const()[name = string("input_25_strides_0"), val = tensor([1, 1])]; + tensor input_25_pad_0 = const()[name = string("input_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_25_dilations_0 = const()[name = string("input_25_dilations_0"), val = tensor([1, 1])]; + int32 input_25_groups_0 = const()[name = string("input_25_groups_0"), val = int32(1)]; + tensor input_25 = 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 = model_model_layers_25_mlp_gate_proj_weight_palettized, x = input_23)[name = string("input_25")]; + string up_states_3_pad_type_0 = const()[name = string("up_states_3_pad_type_0"), val = string("valid")]; + tensor up_states_3_strides_0 = const()[name = string("up_states_3_strides_0"), val = tensor([1, 1])]; + tensor up_states_3_pad_0 = const()[name = string("up_states_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_states_3_dilations_0 = const()[name = string("up_states_3_dilations_0"), val = tensor([1, 1])]; + int32 up_states_3_groups_0 = const()[name = string("up_states_3_groups_0"), val = int32(1)]; + tensor up_states_3 = conv(dilations = up_states_3_dilations_0, groups = up_states_3_groups_0, pad = up_states_3_pad_0, pad_type = up_states_3_pad_type_0, strides = up_states_3_strides_0, weight = model_model_layers_25_mlp_up_proj_weight_palettized, x = input_23)[name = string("up_states_3")]; + tensor gate_states_3 = silu(x = input_25)[name = string("gate_states_3")]; + tensor input_27 = mul(x = gate_states_3, y = up_states_3)[name = string("input_27")]; + string hidden_states_15_pad_type_0 = const()[name = string("hidden_states_15_pad_type_0"), val = string("valid")]; + tensor hidden_states_15_strides_0 = const()[name = string("hidden_states_15_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_15_pad_0 = const()[name = string("hidden_states_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_15_dilations_0 = const()[name = string("hidden_states_15_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_15_groups_0 = const()[name = string("hidden_states_15_groups_0"), val = int32(1)]; + tensor hidden_states_15 = conv(dilations = hidden_states_15_dilations_0, groups = hidden_states_15_groups_0, pad = hidden_states_15_pad_0, pad_type = hidden_states_15_pad_type_0, strides = hidden_states_15_strides_0, weight = model_model_layers_25_mlp_down_proj_weight_palettized, x = input_27)[name = string("hidden_states_15")]; + tensor var_553_axes_0 = const()[name = string("op_553_axes_0"), val = tensor([2])]; + tensor var_553 = squeeze(axes = var_553_axes_0, x = hidden_states_15)[name = string("op_553")]; + tensor var_554 = const()[name = string("op_554"), val = tensor([0, 2, 1])]; + tensor var_555 = transpose(perm = var_554, x = var_553)[name = string("transpose_14")]; + tensor hidden_states_17_cast_fp16 = add(x = hidden_states_13_cast_fp16, y = var_555)[name = string("hidden_states_17_cast_fp16")]; + tensor mean_9_axes_0 = const()[name = string("mean_9_axes_0"), val = tensor([-1])]; + bool mean_9_keep_dims_0 = const()[name = string("mean_9_keep_dims_0"), val = bool(true)]; + tensor mean_9_cast_fp16 = reduce_mean(axes = mean_9_axes_0, keep_dims = mean_9_keep_dims_0, x = hidden_states_17_cast_fp16)[name = string("mean_9_cast_fp16")]; + tensor input_29_cast_fp16 = sub(x = hidden_states_17_cast_fp16, y = mean_9_cast_fp16)[name = string("input_29_cast_fp16")]; + tensor var_563_axes_0 = const()[name = string("op_563_axes_0"), val = tensor([-1])]; + tensor model_model_layers_26_input_layernorm_weight_to_fp16 = const()[name = string("model_model_layers_26_input_layernorm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(698920704)))]; + tensor var_563_cast_fp16 = layer_norm(axes = var_563_axes_0, epsilon = var_38_to_fp16, gamma = model_model_layers_26_input_layernorm_weight_to_fp16, x = input_29_cast_fp16)[name = string("op_563_cast_fp16")]; + tensor var_567 = const()[name = string("op_567"), val = tensor([0, 2, 1])]; + tensor var_569_axes_0 = const()[name = string("op_569_axes_0"), val = tensor([2])]; + tensor var_568 = transpose(perm = var_567, x = var_563_cast_fp16)[name = string("transpose_13")]; + tensor var_569 = expand_dims(axes = var_569_axes_0, x = var_568)[name = string("op_569")]; + string query_states_9_pad_type_0 = const()[name = string("query_states_9_pad_type_0"), val = string("valid")]; + tensor query_states_9_strides_0 = const()[name = string("query_states_9_strides_0"), val = tensor([1, 1])]; + tensor query_states_9_pad_0 = const()[name = string("query_states_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_states_9_dilations_0 = const()[name = string("query_states_9_dilations_0"), val = tensor([1, 1])]; + int32 query_states_9_groups_0 = const()[name = string("query_states_9_groups_0"), val = int32(1)]; + tensor query_states_9 = conv(dilations = query_states_9_dilations_0, groups = query_states_9_groups_0, pad = query_states_9_pad_0, pad_type = query_states_9_pad_type_0, strides = query_states_9_strides_0, weight = model_model_layers_26_self_attn_q_proj_weight_palettized, x = var_569)[name = string("query_states_9")]; + string key_states_13_pad_type_0 = const()[name = string("key_states_13_pad_type_0"), val = string("valid")]; + tensor key_states_13_strides_0 = const()[name = string("key_states_13_strides_0"), val = tensor([1, 1])]; + tensor key_states_13_pad_0 = const()[name = string("key_states_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_states_13_dilations_0 = const()[name = string("key_states_13_dilations_0"), val = tensor([1, 1])]; + int32 key_states_13_groups_0 = const()[name = string("key_states_13_groups_0"), val = int32(1)]; + tensor key_states_13 = conv(dilations = key_states_13_dilations_0, groups = key_states_13_groups_0, pad = key_states_13_pad_0, pad_type = key_states_13_pad_type_0, strides = key_states_13_strides_0, weight = model_model_layers_26_self_attn_k_proj_weight_palettized, x = var_569)[name = string("key_states_13")]; + string value_states_13_pad_type_0 = const()[name = string("value_states_13_pad_type_0"), val = string("valid")]; + tensor value_states_13_strides_0 = const()[name = string("value_states_13_strides_0"), val = tensor([1, 1])]; + tensor value_states_13_pad_0 = const()[name = string("value_states_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor value_states_13_dilations_0 = const()[name = string("value_states_13_dilations_0"), val = tensor([1, 1])]; + int32 value_states_13_groups_0 = const()[name = string("value_states_13_groups_0"), val = int32(1)]; + tensor value_states_13 = conv(dilations = value_states_13_dilations_0, groups = value_states_13_groups_0, pad = value_states_13_pad_0, pad_type = value_states_13_pad_type_0, strides = value_states_13_strides_0, weight = model_model_layers_26_self_attn_v_proj_weight_palettized, x = var_569)[name = string("value_states_13")]; + tensor var_589 = const()[name = string("op_589"), val = tensor([1, 32, 128, 256])]; + tensor var_590 = reshape(shape = var_589, x = query_states_9)[name = string("op_590")]; + tensor var_591 = const()[name = string("op_591"), val = tensor([0, 1, 3, 2])]; + tensor var_593 = const()[name = string("op_593"), val = tensor([1, 8, 128, 256])]; + tensor var_594 = reshape(shape = var_593, x = key_states_13)[name = string("op_594")]; + tensor var_595 = const()[name = string("op_595"), val = tensor([0, 1, 3, 2])]; + tensor var_597 = const()[name = string("op_597"), val = tensor([1, 8, 128, 256])]; + tensor var_598 = reshape(shape = var_597, x = value_states_13)[name = string("op_598")]; + tensor var_599 = const()[name = string("op_599"), val = tensor([0, 1, 3, 2])]; + tensor x1_9_begin_0 = const()[name = string("x1_9_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x1_9_end_0 = const()[name = string("x1_9_end_0"), val = tensor([1, 32, 256, 64])]; + tensor x1_9_end_mask_0 = const()[name = string("x1_9_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_57 = transpose(perm = var_591, x = var_590)[name = string("transpose_12")]; + tensor x1_9 = slice_by_index(begin = x1_9_begin_0, end = x1_9_end_0, end_mask = x1_9_end_mask_0, x = x_57)[name = string("x1_9")]; + tensor x2_9_begin_0 = const()[name = string("x2_9_begin_0"), val = tensor([0, 0, 0, 64])]; + tensor x2_9_end_0 = const()[name = string("x2_9_end_0"), val = tensor([1, 32, 256, 128])]; + tensor x2_9_end_mask_0 = const()[name = string("x2_9_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x2_9 = slice_by_index(begin = x2_9_begin_0, end = x2_9_end_0, end_mask = x2_9_end_mask_0, x = x_57)[name = string("x2_9")]; + tensor var_617 = mul(x = x1_9, y = cos_7)[name = string("op_617")]; + tensor var_618 = mul(x = x2_9, y = sin_7)[name = string("op_618")]; + tensor var_619 = sub(x = var_617, y = var_618)[name = string("op_619")]; + tensor var_620 = mul(x = x2_9, y = cos_7)[name = string("op_620")]; + tensor var_621 = mul(x = x1_9, y = sin_7)[name = string("op_621")]; + tensor var_622 = add(x = var_620, y = var_621)[name = string("op_622")]; + bool rotated_9_interleave_0 = const()[name = string("rotated_9_interleave_0"), val = bool(false)]; + tensor rotated_9 = concat(axis = var_36, interleave = rotated_9_interleave_0, values = (var_619, var_622))[name = string("rotated_9")]; + tensor x1_11_begin_0 = const()[name = string("x1_11_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x1_11_end_0 = const()[name = string("x1_11_end_0"), val = tensor([1, 8, 256, 64])]; + tensor x1_11_end_mask_0 = const()[name = string("x1_11_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_61 = transpose(perm = var_595, x = var_594)[name = string("transpose_11")]; + tensor x1_11 = slice_by_index(begin = x1_11_begin_0, end = x1_11_end_0, end_mask = x1_11_end_mask_0, x = x_61)[name = string("x1_11")]; + tensor x2_11_begin_0 = const()[name = string("x2_11_begin_0"), val = tensor([0, 0, 0, 64])]; + tensor x2_11_end_0 = const()[name = string("x2_11_end_0"), val = tensor([1, 8, 256, 128])]; + tensor x2_11_end_mask_0 = const()[name = string("x2_11_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x2_11 = slice_by_index(begin = x2_11_begin_0, end = x2_11_end_0, end_mask = x2_11_end_mask_0, x = x_61)[name = string("x2_11")]; + tensor var_638 = mul(x = x1_11, y = cos_7)[name = string("op_638")]; + tensor var_639 = mul(x = x2_11, y = sin_7)[name = string("op_639")]; + tensor var_640 = sub(x = var_638, y = var_639)[name = string("op_640")]; + tensor var_641 = mul(x = x2_11, y = cos_7)[name = string("op_641")]; + tensor var_642 = mul(x = x1_11, y = sin_7)[name = string("op_642")]; + tensor var_643 = add(x = var_641, y = var_642)[name = string("op_643")]; + bool rotated_11_interleave_0 = const()[name = string("rotated_11_interleave_0"), val = bool(false)]; + tensor rotated_11 = concat(axis = var_36, interleave = rotated_11_interleave_0, values = (var_640, var_643))[name = string("rotated_11")]; + tensor expand_dims_24 = const()[name = string("expand_dims_24"), val = tensor([26])]; + tensor expand_dims_25 = const()[name = string("expand_dims_25"), val = tensor([0])]; + tensor expand_dims_27 = const()[name = string("expand_dims_27"), val = tensor([0])]; + tensor expand_dims_28 = const()[name = string("expand_dims_28"), val = tensor([27])]; + int32 concat_38_axis_0 = const()[name = string("concat_38_axis_0"), val = int32(0)]; + bool concat_38_interleave_0 = const()[name = string("concat_38_interleave_0"), val = bool(false)]; + tensor concat_38 = concat(axis = concat_38_axis_0, interleave = concat_38_interleave_0, values = (expand_dims_24, expand_dims_25, current_pos, expand_dims_27))[name = string("concat_38")]; + tensor concat_39_values1_0 = const()[name = string("concat_39_values1_0"), val = tensor([0])]; + tensor concat_39_values3_0 = const()[name = string("concat_39_values3_0"), val = tensor([0])]; + int32 concat_39_axis_0 = const()[name = string("concat_39_axis_0"), val = int32(0)]; + bool concat_39_interleave_0 = const()[name = string("concat_39_interleave_0"), val = bool(false)]; + tensor concat_39 = concat(axis = concat_39_axis_0, interleave = concat_39_interleave_0, values = (expand_dims_28, concat_39_values1_0, var_258, concat_39_values3_0))[name = string("concat_39")]; + tensor model_model_kv_cache_0_internal_tensor_assign_5_stride_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_5_stride_0"), val = tensor([1, 1, 1, 1])]; + tensor model_model_kv_cache_0_internal_tensor_assign_5_begin_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_5_begin_mask_0"), val = tensor([false, false, false, false])]; + tensor model_model_kv_cache_0_internal_tensor_assign_5_end_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_5_end_mask_0"), val = tensor([false, true, false, true])]; + tensor model_model_kv_cache_0_internal_tensor_assign_5_squeeze_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_5_squeeze_mask_0"), val = tensor([false, false, false, false])]; + tensor model_model_kv_cache_0_internal_tensor_assign_5_cast_fp16 = slice_update(begin = concat_38, begin_mask = model_model_kv_cache_0_internal_tensor_assign_5_begin_mask_0, end = concat_39, end_mask = model_model_kv_cache_0_internal_tensor_assign_5_end_mask_0, squeeze_mask = model_model_kv_cache_0_internal_tensor_assign_5_squeeze_mask_0, stride = model_model_kv_cache_0_internal_tensor_assign_5_stride_0, update = rotated_11, x = coreml_update_state_11)[name = string("model_model_kv_cache_0_internal_tensor_assign_5_cast_fp16")]; + write_state(data = model_model_kv_cache_0_internal_tensor_assign_5_cast_fp16, input = model_model_kv_cache_0)[name = string("coreml_update_state_12_write_state")]; + tensor coreml_update_state_12 = read_state(input = model_model_kv_cache_0)[name = string("coreml_update_state_12")]; + tensor expand_dims_30 = const()[name = string("expand_dims_30"), val = tensor([58])]; + tensor expand_dims_31 = const()[name = string("expand_dims_31"), val = tensor([0])]; + tensor expand_dims_33 = const()[name = string("expand_dims_33"), val = tensor([0])]; + tensor expand_dims_34 = const()[name = string("expand_dims_34"), val = tensor([59])]; + int32 concat_42_axis_0 = const()[name = string("concat_42_axis_0"), val = int32(0)]; + bool concat_42_interleave_0 = const()[name = string("concat_42_interleave_0"), val = bool(false)]; + tensor concat_42 = concat(axis = concat_42_axis_0, interleave = concat_42_interleave_0, values = (expand_dims_30, expand_dims_31, current_pos, expand_dims_33))[name = string("concat_42")]; + tensor concat_43_values1_0 = const()[name = string("concat_43_values1_0"), val = tensor([0])]; + tensor concat_43_values3_0 = const()[name = string("concat_43_values3_0"), val = tensor([0])]; + int32 concat_43_axis_0 = const()[name = string("concat_43_axis_0"), val = int32(0)]; + bool concat_43_interleave_0 = const()[name = string("concat_43_interleave_0"), val = bool(false)]; + tensor concat_43 = concat(axis = concat_43_axis_0, interleave = concat_43_interleave_0, values = (expand_dims_34, concat_43_values1_0, var_258, concat_43_values3_0))[name = string("concat_43")]; + tensor model_model_kv_cache_0_internal_tensor_assign_6_stride_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_6_stride_0"), val = tensor([1, 1, 1, 1])]; + tensor model_model_kv_cache_0_internal_tensor_assign_6_begin_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_6_begin_mask_0"), val = tensor([false, false, false, false])]; + tensor model_model_kv_cache_0_internal_tensor_assign_6_end_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_6_end_mask_0"), val = tensor([false, true, false, true])]; + tensor model_model_kv_cache_0_internal_tensor_assign_6_squeeze_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_6_squeeze_mask_0"), val = tensor([false, false, false, false])]; + tensor value_states_15 = transpose(perm = var_599, x = var_598)[name = string("transpose_10")]; + tensor model_model_kv_cache_0_internal_tensor_assign_6_cast_fp16 = slice_update(begin = concat_42, begin_mask = model_model_kv_cache_0_internal_tensor_assign_6_begin_mask_0, end = concat_43, end_mask = model_model_kv_cache_0_internal_tensor_assign_6_end_mask_0, squeeze_mask = model_model_kv_cache_0_internal_tensor_assign_6_squeeze_mask_0, stride = model_model_kv_cache_0_internal_tensor_assign_6_stride_0, update = value_states_15, x = coreml_update_state_12)[name = string("model_model_kv_cache_0_internal_tensor_assign_6_cast_fp16")]; + write_state(data = model_model_kv_cache_0_internal_tensor_assign_6_cast_fp16, input = model_model_kv_cache_0)[name = string("coreml_update_state_13_write_state")]; + tensor coreml_update_state_13 = read_state(input = model_model_kv_cache_0)[name = string("coreml_update_state_13")]; + tensor var_666_begin_0 = const()[name = string("op_666_begin_0"), val = tensor([26, 0, 0, 0])]; + tensor var_666_end_0 = const()[name = string("op_666_end_0"), val = tensor([27, 8, 1024, 128])]; + tensor var_666_end_mask_0 = const()[name = string("op_666_end_mask_0"), val = tensor([false, true, true, true])]; + tensor var_666_cast_fp16 = slice_by_index(begin = var_666_begin_0, end = var_666_end_0, end_mask = var_666_end_mask_0, x = coreml_update_state_13)[name = string("op_666_cast_fp16")]; + tensor K_layer_cache_5_axes_0 = const()[name = string("K_layer_cache_5_axes_0"), val = tensor([0])]; + tensor K_layer_cache_5_cast_fp16 = squeeze(axes = K_layer_cache_5_axes_0, x = var_666_cast_fp16)[name = string("K_layer_cache_5_cast_fp16")]; + tensor var_668_begin_0 = const()[name = string("op_668_begin_0"), val = tensor([58, 0, 0, 0])]; + tensor var_668_end_0 = const()[name = string("op_668_end_0"), val = tensor([59, 8, 1024, 128])]; + tensor var_668_end_mask_0 = const()[name = string("op_668_end_mask_0"), val = tensor([false, true, true, true])]; + tensor var_668_cast_fp16 = slice_by_index(begin = var_668_begin_0, end = var_668_end_0, end_mask = var_668_end_mask_0, x = coreml_update_state_13)[name = string("op_668_cast_fp16")]; + tensor V_layer_cache_5_axes_0 = const()[name = string("V_layer_cache_5_axes_0"), val = tensor([0])]; + tensor V_layer_cache_5_cast_fp16 = squeeze(axes = V_layer_cache_5_axes_0, x = var_668_cast_fp16)[name = string("V_layer_cache_5_cast_fp16")]; + tensor x_67_axes_0 = const()[name = string("x_67_axes_0"), val = tensor([1])]; + tensor x_67_cast_fp16 = expand_dims(axes = x_67_axes_0, x = K_layer_cache_5_cast_fp16)[name = string("x_67_cast_fp16")]; + tensor var_677 = const()[name = string("op_677"), val = tensor([1, 4, 1, 1])]; + tensor x_69_cast_fp16 = tile(reps = var_677, x = x_67_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor var_681 = const()[name = string("op_681"), val = tensor([1, -1, 1024, 128])]; + tensor var_682_cast_fp16 = reshape(shape = var_681, x = x_69_cast_fp16)[name = string("op_682_cast_fp16")]; + tensor x_73_axes_0 = const()[name = string("x_73_axes_0"), val = tensor([1])]; + tensor x_73_cast_fp16 = expand_dims(axes = x_73_axes_0, x = V_layer_cache_5_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_684 = const()[name = string("op_684"), val = tensor([1, 4, 1, 1])]; + tensor x_75_cast_fp16 = tile(reps = var_684, x = x_73_cast_fp16)[name = string("x_75_cast_fp16")]; + bool var_691_transpose_x_0 = const()[name = string("op_691_transpose_x_0"), val = bool(false)]; + bool var_691_transpose_y_0 = const()[name = string("op_691_transpose_y_0"), val = bool(true)]; + tensor var_691_cast_fp16 = matmul(transpose_x = var_691_transpose_x_0, transpose_y = var_691_transpose_y_0, x = rotated_9, y = var_682_cast_fp16)[name = string("op_691_cast_fp16")]; + fp16 var_692_to_fp16 = const()[name = string("op_692_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor attn_weights_5_cast_fp16 = mul(x = var_691_cast_fp16, y = var_692_to_fp16)[name = string("attn_weights_5_cast_fp16")]; + tensor x_77_cast_fp16 = add(x = attn_weights_5_cast_fp16, y = causal_mask)[name = string("x_77_cast_fp16")]; + tensor reduce_max_2_axes_0 = const()[name = string("reduce_max_2_axes_0"), val = tensor([-1])]; + bool reduce_max_2_keep_dims_0 = const()[name = string("reduce_max_2_keep_dims_0"), val = bool(true)]; + tensor reduce_max_2_cast_fp16 = reduce_max(axes = reduce_max_2_axes_0, keep_dims = reduce_max_2_keep_dims_0, x = x_77_cast_fp16)[name = string("reduce_max_2_cast_fp16")]; + tensor x_79_cast_fp16 = sub(x = x_77_cast_fp16, y = reduce_max_2_cast_fp16)[name = string("x_79_cast_fp16")]; + tensor exp_x_5_cast_fp16 = exp(x = x_79_cast_fp16)[name = string("exp_x_5_cast_fp16")]; + tensor var_703_axes_0 = const()[name = string("op_703_axes_0"), val = tensor([-1])]; + bool var_703_keep_dims_0 = const()[name = string("op_703_keep_dims_0"), val = bool(true)]; + tensor var_703_cast_fp16 = reduce_sum(axes = var_703_axes_0, keep_dims = var_703_keep_dims_0, x = exp_x_5_cast_fp16)[name = string("op_703_cast_fp16")]; + tensor var_704_cast_fp16 = real_div(x = exp_x_5_cast_fp16, y = var_703_cast_fp16)[name = string("op_704_cast_fp16")]; + tensor concat_48 = const()[name = string("concat_48"), val = tensor([32, 256, 1024])]; + tensor reshape_6_cast_fp16 = reshape(shape = concat_48, x = var_704_cast_fp16)[name = string("reshape_6_cast_fp16")]; + tensor concat_49 = const()[name = string("concat_49"), val = tensor([32, 1024, 128])]; + tensor reshape_7_cast_fp16 = reshape(shape = concat_49, x = x_75_cast_fp16)[name = string("reshape_7_cast_fp16")]; + bool matmul_2_transpose_x_0 = const()[name = string("matmul_2_transpose_x_0"), val = bool(false)]; + bool matmul_2_transpose_y_0 = const()[name = string("matmul_2_transpose_y_0"), val = bool(false)]; + tensor matmul_2_cast_fp16 = matmul(transpose_x = matmul_2_transpose_x_0, transpose_y = matmul_2_transpose_y_0, x = reshape_6_cast_fp16, y = reshape_7_cast_fp16)[name = string("matmul_2_cast_fp16")]; + tensor concat_53 = const()[name = string("concat_53"), val = tensor([1, 32, 256, 128])]; + tensor reshape_8_cast_fp16 = reshape(shape = concat_53, x = matmul_2_cast_fp16)[name = string("reshape_8_cast_fp16")]; + tensor var_707_perm_0 = const()[name = string("op_707_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_709 = const()[name = string("op_709"), val = tensor([1, 256, 4096])]; + tensor var_707_cast_fp16 = transpose(perm = var_707_perm_0, x = reshape_8_cast_fp16)[name = string("transpose_9")]; + tensor input_33_cast_fp16 = reshape(shape = var_709, x = var_707_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor model_model_layers_26_self_attn_o_proj_weight_promoted_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(698928960))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(711511936))))[name = string("model_model_layers_26_self_attn_o_proj_weight_promoted_to_fp16_palettized")]; + tensor linear_2_cast_fp16 = linear(bias = linear_0_bias_0_to_fp16, weight = model_model_layers_26_self_attn_o_proj_weight_promoted_to_fp16_palettized, x = input_33_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor hidden_states_21_cast_fp16 = add(x = hidden_states_17_cast_fp16, y = linear_2_cast_fp16)[name = string("hidden_states_21_cast_fp16")]; + tensor mean_11_axes_0 = const()[name = string("mean_11_axes_0"), val = tensor([-1])]; + bool mean_11_keep_dims_0 = const()[name = string("mean_11_keep_dims_0"), val = bool(true)]; + tensor mean_11_cast_fp16 = reduce_mean(axes = mean_11_axes_0, keep_dims = mean_11_keep_dims_0, x = hidden_states_21_cast_fp16)[name = string("mean_11_cast_fp16")]; + tensor input_35_cast_fp16 = sub(x = hidden_states_21_cast_fp16, y = mean_11_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor var_720_axes_0 = const()[name = string("op_720_axes_0"), val = tensor([-1])]; + tensor model_model_layers_26_post_attention_layernorm_weight_to_fp16 = const()[name = string("model_model_layers_26_post_attention_layernorm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(711577536)))]; + tensor var_720_cast_fp16 = layer_norm(axes = var_720_axes_0, epsilon = var_38_to_fp16, gamma = model_model_layers_26_post_attention_layernorm_weight_to_fp16, x = input_35_cast_fp16)[name = string("op_720_cast_fp16")]; + tensor var_727 = const()[name = string("op_727"), val = tensor([0, 2, 1])]; + tensor input_37_axes_0 = const()[name = string("input_37_axes_0"), val = tensor([2])]; + tensor var_728 = transpose(perm = var_727, x = var_720_cast_fp16)[name = string("transpose_8")]; + tensor input_37 = expand_dims(axes = input_37_axes_0, x = var_728)[name = string("input_37")]; + string input_39_pad_type_0 = const()[name = string("input_39_pad_type_0"), val = string("valid")]; + tensor input_39_strides_0 = const()[name = string("input_39_strides_0"), val = tensor([1, 1])]; + tensor input_39_pad_0 = const()[name = string("input_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_39_dilations_0 = const()[name = string("input_39_dilations_0"), val = tensor([1, 1])]; + int32 input_39_groups_0 = const()[name = string("input_39_groups_0"), val = int32(1)]; + tensor input_39 = 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 = model_model_layers_26_mlp_gate_proj_weight_palettized, x = input_37)[name = string("input_39")]; + string up_states_5_pad_type_0 = const()[name = string("up_states_5_pad_type_0"), val = string("valid")]; + tensor up_states_5_strides_0 = const()[name = string("up_states_5_strides_0"), val = tensor([1, 1])]; + tensor up_states_5_pad_0 = const()[name = string("up_states_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_states_5_dilations_0 = const()[name = string("up_states_5_dilations_0"), val = tensor([1, 1])]; + int32 up_states_5_groups_0 = const()[name = string("up_states_5_groups_0"), val = int32(1)]; + tensor up_states_5 = conv(dilations = up_states_5_dilations_0, groups = up_states_5_groups_0, pad = up_states_5_pad_0, pad_type = up_states_5_pad_type_0, strides = up_states_5_strides_0, weight = model_model_layers_26_mlp_up_proj_weight_palettized, x = input_37)[name = string("up_states_5")]; + tensor gate_states_5 = silu(x = input_39)[name = string("gate_states_5")]; + tensor input_41 = mul(x = gate_states_5, y = up_states_5)[name = string("input_41")]; + string hidden_states_23_pad_type_0 = const()[name = string("hidden_states_23_pad_type_0"), val = string("valid")]; + tensor hidden_states_23_strides_0 = const()[name = string("hidden_states_23_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_23_pad_0 = const()[name = string("hidden_states_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_23_dilations_0 = const()[name = string("hidden_states_23_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_23_groups_0 = const()[name = string("hidden_states_23_groups_0"), val = int32(1)]; + tensor hidden_states_23 = conv(dilations = hidden_states_23_dilations_0, groups = hidden_states_23_groups_0, pad = hidden_states_23_pad_0, pad_type = hidden_states_23_pad_type_0, strides = hidden_states_23_strides_0, weight = model_model_layers_26_mlp_down_proj_weight_palettized, x = input_41)[name = string("hidden_states_23")]; + tensor var_750_axes_0 = const()[name = string("op_750_axes_0"), val = tensor([2])]; + tensor var_750 = squeeze(axes = var_750_axes_0, x = hidden_states_23)[name = string("op_750")]; + tensor var_751 = const()[name = string("op_751"), val = tensor([0, 2, 1])]; + tensor var_752 = transpose(perm = var_751, x = var_750)[name = string("transpose_7")]; + tensor hidden_states_25_cast_fp16 = add(x = hidden_states_21_cast_fp16, y = var_752)[name = string("hidden_states_25_cast_fp16")]; + tensor mean_13_axes_0 = const()[name = string("mean_13_axes_0"), val = tensor([-1])]; + bool mean_13_keep_dims_0 = const()[name = string("mean_13_keep_dims_0"), val = bool(true)]; + tensor mean_13_cast_fp16 = reduce_mean(axes = mean_13_axes_0, keep_dims = mean_13_keep_dims_0, x = hidden_states_25_cast_fp16)[name = string("mean_13_cast_fp16")]; + tensor input_43_cast_fp16 = sub(x = hidden_states_25_cast_fp16, y = mean_13_cast_fp16)[name = string("input_43_cast_fp16")]; + tensor var_760_axes_0 = const()[name = string("op_760_axes_0"), val = tensor([-1])]; + tensor model_model_layers_27_input_layernorm_weight_to_fp16 = const()[name = string("model_model_layers_27_input_layernorm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(711585792)))]; + tensor var_760_cast_fp16 = layer_norm(axes = var_760_axes_0, epsilon = var_38_to_fp16, gamma = model_model_layers_27_input_layernorm_weight_to_fp16, x = input_43_cast_fp16)[name = string("op_760_cast_fp16")]; + tensor var_764 = const()[name = string("op_764"), val = tensor([0, 2, 1])]; + tensor var_766_axes_0 = const()[name = string("op_766_axes_0"), val = tensor([2])]; + tensor var_765 = transpose(perm = var_764, x = var_760_cast_fp16)[name = string("transpose_6")]; + tensor var_766 = expand_dims(axes = var_766_axes_0, x = var_765)[name = string("op_766")]; + string query_states_13_pad_type_0 = const()[name = string("query_states_13_pad_type_0"), val = string("valid")]; + tensor query_states_13_strides_0 = const()[name = string("query_states_13_strides_0"), val = tensor([1, 1])]; + tensor query_states_13_pad_0 = const()[name = string("query_states_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor query_states_13_dilations_0 = const()[name = string("query_states_13_dilations_0"), val = tensor([1, 1])]; + int32 query_states_13_groups_0 = const()[name = string("query_states_13_groups_0"), val = int32(1)]; + tensor query_states_13 = conv(dilations = query_states_13_dilations_0, groups = query_states_13_groups_0, pad = query_states_13_pad_0, pad_type = query_states_13_pad_type_0, strides = query_states_13_strides_0, weight = model_model_layers_27_self_attn_q_proj_weight_palettized, x = var_766)[name = string("query_states_13")]; + string key_states_19_pad_type_0 = const()[name = string("key_states_19_pad_type_0"), val = string("valid")]; + tensor key_states_19_strides_0 = const()[name = string("key_states_19_strides_0"), val = tensor([1, 1])]; + tensor key_states_19_pad_0 = const()[name = string("key_states_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor key_states_19_dilations_0 = const()[name = string("key_states_19_dilations_0"), val = tensor([1, 1])]; + int32 key_states_19_groups_0 = const()[name = string("key_states_19_groups_0"), val = int32(1)]; + tensor key_states_19 = conv(dilations = key_states_19_dilations_0, groups = key_states_19_groups_0, pad = key_states_19_pad_0, pad_type = key_states_19_pad_type_0, strides = key_states_19_strides_0, weight = model_model_layers_27_self_attn_k_proj_weight_palettized, x = var_766)[name = string("key_states_19")]; + string value_states_19_pad_type_0 = const()[name = string("value_states_19_pad_type_0"), val = string("valid")]; + tensor value_states_19_strides_0 = const()[name = string("value_states_19_strides_0"), val = tensor([1, 1])]; + tensor value_states_19_pad_0 = const()[name = string("value_states_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor value_states_19_dilations_0 = const()[name = string("value_states_19_dilations_0"), val = tensor([1, 1])]; + int32 value_states_19_groups_0 = const()[name = string("value_states_19_groups_0"), val = int32(1)]; + tensor value_states_19 = conv(dilations = value_states_19_dilations_0, groups = value_states_19_groups_0, pad = value_states_19_pad_0, pad_type = value_states_19_pad_type_0, strides = value_states_19_strides_0, weight = model_model_layers_27_self_attn_v_proj_weight_palettized, x = var_766)[name = string("value_states_19")]; + tensor var_786 = const()[name = string("op_786"), val = tensor([1, 32, 128, 256])]; + tensor var_787 = reshape(shape = var_786, x = query_states_13)[name = string("op_787")]; + tensor var_788 = const()[name = string("op_788"), val = tensor([0, 1, 3, 2])]; + tensor var_790 = const()[name = string("op_790"), val = tensor([1, 8, 128, 256])]; + tensor var_791 = reshape(shape = var_790, x = key_states_19)[name = string("op_791")]; + tensor var_792 = const()[name = string("op_792"), val = tensor([0, 1, 3, 2])]; + tensor var_794 = const()[name = string("op_794"), val = tensor([1, 8, 128, 256])]; + tensor var_795 = reshape(shape = var_794, x = value_states_19)[name = string("op_795")]; + tensor var_796 = const()[name = string("op_796"), val = tensor([0, 1, 3, 2])]; + tensor x1_13_begin_0 = const()[name = string("x1_13_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x1_13_end_0 = const()[name = string("x1_13_end_0"), val = tensor([1, 32, 256, 64])]; + tensor x1_13_end_mask_0 = const()[name = string("x1_13_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_85 = transpose(perm = var_788, x = var_787)[name = string("transpose_5")]; + tensor x1_13 = slice_by_index(begin = x1_13_begin_0, end = x1_13_end_0, end_mask = x1_13_end_mask_0, x = x_85)[name = string("x1_13")]; + tensor x2_13_begin_0 = const()[name = string("x2_13_begin_0"), val = tensor([0, 0, 0, 64])]; + tensor x2_13_end_0 = const()[name = string("x2_13_end_0"), val = tensor([1, 32, 256, 128])]; + tensor x2_13_end_mask_0 = const()[name = string("x2_13_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x2_13 = slice_by_index(begin = x2_13_begin_0, end = x2_13_end_0, end_mask = x2_13_end_mask_0, x = x_85)[name = string("x2_13")]; + tensor var_814 = mul(x = x1_13, y = cos_7)[name = string("op_814")]; + tensor var_815 = mul(x = x2_13, y = sin_7)[name = string("op_815")]; + tensor var_816 = sub(x = var_814, y = var_815)[name = string("op_816")]; + tensor var_817 = mul(x = x2_13, y = cos_7)[name = string("op_817")]; + tensor var_818 = mul(x = x1_13, y = sin_7)[name = string("op_818")]; + tensor var_819 = add(x = var_817, y = var_818)[name = string("op_819")]; + bool rotated_13_interleave_0 = const()[name = string("rotated_13_interleave_0"), val = bool(false)]; + tensor rotated_13 = concat(axis = var_36, interleave = rotated_13_interleave_0, values = (var_816, var_819))[name = string("rotated_13")]; + tensor x1_begin_0 = const()[name = string("x1_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor x1_end_0 = const()[name = string("x1_end_0"), val = tensor([1, 8, 256, 64])]; + tensor x1_end_mask_0 = const()[name = string("x1_end_mask_0"), val = tensor([true, true, true, false])]; + tensor x_89 = transpose(perm = var_792, x = var_791)[name = string("transpose_4")]; + tensor x1 = slice_by_index(begin = x1_begin_0, end = x1_end_0, end_mask = x1_end_mask_0, x = x_89)[name = string("x1")]; + tensor x2_begin_0 = const()[name = string("x2_begin_0"), val = tensor([0, 0, 0, 64])]; + tensor x2_end_0 = const()[name = string("x2_end_0"), val = tensor([1, 8, 256, 128])]; + tensor x2_end_mask_0 = const()[name = string("x2_end_mask_0"), val = tensor([true, true, true, true])]; + tensor x2 = slice_by_index(begin = x2_begin_0, end = x2_end_0, end_mask = x2_end_mask_0, x = x_89)[name = string("x2")]; + tensor var_835 = mul(x = x1, y = cos_7)[name = string("op_835")]; + tensor var_836 = mul(x = x2, y = sin_7)[name = string("op_836")]; + tensor var_837 = sub(x = var_835, y = var_836)[name = string("op_837")]; + tensor var_838 = mul(x = x2, y = cos_7)[name = string("op_838")]; + tensor var_839 = mul(x = x1, y = sin_7)[name = string("op_839")]; + tensor var_840 = add(x = var_838, y = var_839)[name = string("op_840")]; + bool rotated_interleave_0 = const()[name = string("rotated_interleave_0"), val = bool(false)]; + tensor rotated = concat(axis = var_36, interleave = rotated_interleave_0, values = (var_837, var_840))[name = string("rotated")]; + tensor expand_dims_36 = const()[name = string("expand_dims_36"), val = tensor([27])]; + tensor expand_dims_37 = const()[name = string("expand_dims_37"), val = tensor([0])]; + tensor expand_dims_39 = const()[name = string("expand_dims_39"), val = tensor([0])]; + tensor expand_dims_40 = const()[name = string("expand_dims_40"), val = tensor([28])]; + int32 concat_56_axis_0 = const()[name = string("concat_56_axis_0"), val = int32(0)]; + bool concat_56_interleave_0 = const()[name = string("concat_56_interleave_0"), val = bool(false)]; + tensor concat_56 = concat(axis = concat_56_axis_0, interleave = concat_56_interleave_0, values = (expand_dims_36, expand_dims_37, current_pos, expand_dims_39))[name = string("concat_56")]; + tensor concat_57_values1_0 = const()[name = string("concat_57_values1_0"), val = tensor([0])]; + tensor concat_57_values3_0 = const()[name = string("concat_57_values3_0"), val = tensor([0])]; + int32 concat_57_axis_0 = const()[name = string("concat_57_axis_0"), val = int32(0)]; + bool concat_57_interleave_0 = const()[name = string("concat_57_interleave_0"), val = bool(false)]; + tensor concat_57 = concat(axis = concat_57_axis_0, interleave = concat_57_interleave_0, values = (expand_dims_40, concat_57_values1_0, var_258, concat_57_values3_0))[name = string("concat_57")]; + tensor model_model_kv_cache_0_internal_tensor_assign_7_stride_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_7_stride_0"), val = tensor([1, 1, 1, 1])]; + tensor model_model_kv_cache_0_internal_tensor_assign_7_begin_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_7_begin_mask_0"), val = tensor([false, false, false, false])]; + tensor model_model_kv_cache_0_internal_tensor_assign_7_end_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_7_end_mask_0"), val = tensor([false, true, false, true])]; + tensor model_model_kv_cache_0_internal_tensor_assign_7_squeeze_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_7_squeeze_mask_0"), val = tensor([false, false, false, false])]; + tensor model_model_kv_cache_0_internal_tensor_assign_7_cast_fp16 = slice_update(begin = concat_56, begin_mask = model_model_kv_cache_0_internal_tensor_assign_7_begin_mask_0, end = concat_57, end_mask = model_model_kv_cache_0_internal_tensor_assign_7_end_mask_0, squeeze_mask = model_model_kv_cache_0_internal_tensor_assign_7_squeeze_mask_0, stride = model_model_kv_cache_0_internal_tensor_assign_7_stride_0, update = rotated, x = coreml_update_state_13)[name = string("model_model_kv_cache_0_internal_tensor_assign_7_cast_fp16")]; + write_state(data = model_model_kv_cache_0_internal_tensor_assign_7_cast_fp16, input = model_model_kv_cache_0)[name = string("coreml_update_state_14_write_state")]; + tensor coreml_update_state_14 = read_state(input = model_model_kv_cache_0)[name = string("coreml_update_state_14")]; + tensor expand_dims_42 = const()[name = string("expand_dims_42"), val = tensor([59])]; + tensor expand_dims_43 = const()[name = string("expand_dims_43"), val = tensor([0])]; + tensor expand_dims_45 = const()[name = string("expand_dims_45"), val = tensor([0])]; + tensor expand_dims_46 = const()[name = string("expand_dims_46"), val = tensor([60])]; + int32 concat_60_axis_0 = const()[name = string("concat_60_axis_0"), val = int32(0)]; + bool concat_60_interleave_0 = const()[name = string("concat_60_interleave_0"), val = bool(false)]; + tensor concat_60 = concat(axis = concat_60_axis_0, interleave = concat_60_interleave_0, values = (expand_dims_42, expand_dims_43, current_pos, expand_dims_45))[name = string("concat_60")]; + tensor concat_61_values1_0 = const()[name = string("concat_61_values1_0"), val = tensor([0])]; + tensor concat_61_values3_0 = const()[name = string("concat_61_values3_0"), val = tensor([0])]; + int32 concat_61_axis_0 = const()[name = string("concat_61_axis_0"), val = int32(0)]; + bool concat_61_interleave_0 = const()[name = string("concat_61_interleave_0"), val = bool(false)]; + tensor concat_61 = concat(axis = concat_61_axis_0, interleave = concat_61_interleave_0, values = (expand_dims_46, concat_61_values1_0, var_258, concat_61_values3_0))[name = string("concat_61")]; + tensor model_model_kv_cache_0_internal_tensor_assign_8_stride_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_8_stride_0"), val = tensor([1, 1, 1, 1])]; + tensor model_model_kv_cache_0_internal_tensor_assign_8_begin_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_8_begin_mask_0"), val = tensor([false, false, false, false])]; + tensor model_model_kv_cache_0_internal_tensor_assign_8_end_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_8_end_mask_0"), val = tensor([false, true, false, true])]; + tensor model_model_kv_cache_0_internal_tensor_assign_8_squeeze_mask_0 = const()[name = string("model_model_kv_cache_0_internal_tensor_assign_8_squeeze_mask_0"), val = tensor([false, false, false, false])]; + tensor value_states_21 = transpose(perm = var_796, x = var_795)[name = string("transpose_3")]; + tensor model_model_kv_cache_0_internal_tensor_assign_8_cast_fp16 = slice_update(begin = concat_60, begin_mask = model_model_kv_cache_0_internal_tensor_assign_8_begin_mask_0, end = concat_61, end_mask = model_model_kv_cache_0_internal_tensor_assign_8_end_mask_0, squeeze_mask = model_model_kv_cache_0_internal_tensor_assign_8_squeeze_mask_0, stride = model_model_kv_cache_0_internal_tensor_assign_8_stride_0, update = value_states_21, x = coreml_update_state_14)[name = string("model_model_kv_cache_0_internal_tensor_assign_8_cast_fp16")]; + write_state(data = model_model_kv_cache_0_internal_tensor_assign_8_cast_fp16, input = model_model_kv_cache_0)[name = string("coreml_update_state_15_write_state")]; + tensor coreml_update_state_15 = read_state(input = model_model_kv_cache_0)[name = string("coreml_update_state_15")]; + tensor var_863_begin_0 = const()[name = string("op_863_begin_0"), val = tensor([27, 0, 0, 0])]; + tensor var_863_end_0 = const()[name = string("op_863_end_0"), val = tensor([28, 8, 1024, 128])]; + tensor var_863_end_mask_0 = const()[name = string("op_863_end_mask_0"), val = tensor([false, true, true, true])]; + tensor var_863_cast_fp16 = slice_by_index(begin = var_863_begin_0, end = var_863_end_0, end_mask = var_863_end_mask_0, x = coreml_update_state_15)[name = string("op_863_cast_fp16")]; + tensor K_layer_cache_axes_0 = const()[name = string("K_layer_cache_axes_0"), val = tensor([0])]; + tensor K_layer_cache_cast_fp16 = squeeze(axes = K_layer_cache_axes_0, x = var_863_cast_fp16)[name = string("K_layer_cache_cast_fp16")]; + tensor var_865_begin_0 = const()[name = string("op_865_begin_0"), val = tensor([59, 0, 0, 0])]; + tensor var_865_end_0 = const()[name = string("op_865_end_0"), val = tensor([60, 8, 1024, 128])]; + tensor var_865_end_mask_0 = const()[name = string("op_865_end_mask_0"), val = tensor([false, true, true, true])]; + tensor var_865_cast_fp16 = slice_by_index(begin = var_865_begin_0, end = var_865_end_0, end_mask = var_865_end_mask_0, x = coreml_update_state_15)[name = string("op_865_cast_fp16")]; + tensor V_layer_cache_axes_0 = const()[name = string("V_layer_cache_axes_0"), val = tensor([0])]; + tensor V_layer_cache_cast_fp16 = squeeze(axes = V_layer_cache_axes_0, x = var_865_cast_fp16)[name = string("V_layer_cache_cast_fp16")]; + tensor x_95_axes_0 = const()[name = string("x_95_axes_0"), val = tensor([1])]; + tensor x_95_cast_fp16 = expand_dims(axes = x_95_axes_0, x = K_layer_cache_cast_fp16)[name = string("x_95_cast_fp16")]; + tensor var_874 = const()[name = string("op_874"), val = tensor([1, 4, 1, 1])]; + tensor x_97_cast_fp16 = tile(reps = var_874, x = x_95_cast_fp16)[name = string("x_97_cast_fp16")]; + tensor var_878 = const()[name = string("op_878"), val = tensor([1, -1, 1024, 128])]; + tensor var_879_cast_fp16 = reshape(shape = var_878, x = x_97_cast_fp16)[name = string("op_879_cast_fp16")]; + tensor x_101_axes_0 = const()[name = string("x_101_axes_0"), val = tensor([1])]; + tensor x_101_cast_fp16 = expand_dims(axes = x_101_axes_0, x = V_layer_cache_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor var_881 = const()[name = string("op_881"), val = tensor([1, 4, 1, 1])]; + tensor x_103_cast_fp16 = tile(reps = var_881, x = x_101_cast_fp16)[name = string("x_103_cast_fp16")]; + bool var_888_transpose_x_0 = const()[name = string("op_888_transpose_x_0"), val = bool(false)]; + bool var_888_transpose_y_0 = const()[name = string("op_888_transpose_y_0"), val = bool(true)]; + tensor var_888_cast_fp16 = matmul(transpose_x = var_888_transpose_x_0, transpose_y = var_888_transpose_y_0, x = rotated_13, y = var_879_cast_fp16)[name = string("op_888_cast_fp16")]; + fp16 var_889_to_fp16 = const()[name = string("op_889_to_fp16"), val = fp16(0x1.6ap-4)]; + tensor attn_weights_cast_fp16 = mul(x = var_888_cast_fp16, y = var_889_to_fp16)[name = string("attn_weights_cast_fp16")]; + tensor x_105_cast_fp16 = add(x = attn_weights_cast_fp16, y = causal_mask)[name = string("x_105_cast_fp16")]; + tensor reduce_max_3_axes_0 = const()[name = string("reduce_max_3_axes_0"), val = tensor([-1])]; + bool reduce_max_3_keep_dims_0 = const()[name = string("reduce_max_3_keep_dims_0"), val = bool(true)]; + tensor reduce_max_3_cast_fp16 = reduce_max(axes = reduce_max_3_axes_0, keep_dims = reduce_max_3_keep_dims_0, x = x_105_cast_fp16)[name = string("reduce_max_3_cast_fp16")]; + tensor x_107_cast_fp16 = sub(x = x_105_cast_fp16, y = reduce_max_3_cast_fp16)[name = string("x_107_cast_fp16")]; + tensor exp_x_cast_fp16 = exp(x = x_107_cast_fp16)[name = string("exp_x_cast_fp16")]; + tensor var_900_axes_0 = const()[name = string("op_900_axes_0"), val = tensor([-1])]; + bool var_900_keep_dims_0 = const()[name = string("op_900_keep_dims_0"), val = bool(true)]; + tensor var_900_cast_fp16 = reduce_sum(axes = var_900_axes_0, keep_dims = var_900_keep_dims_0, x = exp_x_cast_fp16)[name = string("op_900_cast_fp16")]; + tensor var_901_cast_fp16 = real_div(x = exp_x_cast_fp16, y = var_900_cast_fp16)[name = string("op_901_cast_fp16")]; + tensor concat_66 = const()[name = string("concat_66"), val = tensor([32, 256, 1024])]; + tensor reshape_9_cast_fp16 = reshape(shape = concat_66, x = var_901_cast_fp16)[name = string("reshape_9_cast_fp16")]; + tensor concat_67 = const()[name = string("concat_67"), val = tensor([32, 1024, 128])]; + tensor reshape_10_cast_fp16 = reshape(shape = concat_67, x = x_103_cast_fp16)[name = string("reshape_10_cast_fp16")]; + bool matmul_3_transpose_x_0 = const()[name = string("matmul_3_transpose_x_0"), val = bool(false)]; + bool matmul_3_transpose_y_0 = const()[name = string("matmul_3_transpose_y_0"), val = bool(false)]; + tensor matmul_3_cast_fp16 = matmul(transpose_x = matmul_3_transpose_x_0, transpose_y = matmul_3_transpose_y_0, x = reshape_9_cast_fp16, y = reshape_10_cast_fp16)[name = string("matmul_3_cast_fp16")]; + tensor concat_71 = const()[name = string("concat_71"), val = tensor([1, 32, 256, 128])]; + tensor reshape_11_cast_fp16 = reshape(shape = concat_71, x = matmul_3_cast_fp16)[name = string("reshape_11_cast_fp16")]; + tensor var_904_perm_0 = const()[name = string("op_904_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_906 = const()[name = string("op_906"), val = tensor([1, 256, 4096])]; + tensor var_904_cast_fp16 = transpose(perm = var_904_perm_0, x = reshape_11_cast_fp16)[name = string("transpose_2")]; + tensor input_47_cast_fp16 = reshape(shape = var_906, x = var_904_cast_fp16)[name = string("input_47_cast_fp16")]; + tensor model_model_layers_27_self_attn_o_proj_weight_promoted_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(711594048))), lut = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(724177024))))[name = string("model_model_layers_27_self_attn_o_proj_weight_promoted_to_fp16_palettized")]; + tensor linear_3_cast_fp16 = linear(bias = linear_0_bias_0_to_fp16, weight = model_model_layers_27_self_attn_o_proj_weight_promoted_to_fp16_palettized, x = input_47_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor hidden_states_29_cast_fp16 = add(x = hidden_states_25_cast_fp16, y = linear_3_cast_fp16)[name = string("hidden_states_29_cast_fp16")]; + tensor mean_axes_0 = const()[name = string("mean_axes_0"), val = tensor([-1])]; + bool mean_keep_dims_0 = const()[name = string("mean_keep_dims_0"), val = bool(true)]; + tensor mean_cast_fp16 = reduce_mean(axes = mean_axes_0, keep_dims = mean_keep_dims_0, x = hidden_states_29_cast_fp16)[name = string("mean_cast_fp16")]; + tensor input_49_cast_fp16 = sub(x = hidden_states_29_cast_fp16, y = mean_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor var_917_axes_0 = const()[name = string("op_917_axes_0"), val = tensor([-1])]; + tensor model_model_layers_27_post_attention_layernorm_weight_to_fp16 = const()[name = string("model_model_layers_27_post_attention_layernorm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(724242624)))]; + tensor var_917_cast_fp16 = layer_norm(axes = var_917_axes_0, epsilon = var_38_to_fp16, gamma = model_model_layers_27_post_attention_layernorm_weight_to_fp16, x = input_49_cast_fp16)[name = string("op_917_cast_fp16")]; + tensor var_924 = const()[name = string("op_924"), val = tensor([0, 2, 1])]; + tensor input_51_axes_0 = const()[name = string("input_51_axes_0"), val = tensor([2])]; + tensor var_925 = transpose(perm = var_924, x = var_917_cast_fp16)[name = string("transpose_1")]; + tensor input_51 = expand_dims(axes = input_51_axes_0, x = var_925)[name = string("input_51")]; + string input_53_pad_type_0 = const()[name = string("input_53_pad_type_0"), val = string("valid")]; + tensor input_53_strides_0 = const()[name = string("input_53_strides_0"), val = tensor([1, 1])]; + tensor input_53_pad_0 = const()[name = string("input_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_53_dilations_0 = const()[name = string("input_53_dilations_0"), val = tensor([1, 1])]; + int32 input_53_groups_0 = const()[name = string("input_53_groups_0"), val = int32(1)]; + tensor input_53 = 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 = model_model_layers_27_mlp_gate_proj_weight_palettized, x = input_51)[name = string("input_53")]; + string up_states_pad_type_0 = const()[name = string("up_states_pad_type_0"), val = string("valid")]; + tensor up_states_strides_0 = const()[name = string("up_states_strides_0"), val = tensor([1, 1])]; + tensor up_states_pad_0 = const()[name = string("up_states_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_states_dilations_0 = const()[name = string("up_states_dilations_0"), val = tensor([1, 1])]; + int32 up_states_groups_0 = const()[name = string("up_states_groups_0"), val = int32(1)]; + tensor up_states = conv(dilations = up_states_dilations_0, groups = up_states_groups_0, pad = up_states_pad_0, pad_type = up_states_pad_type_0, strides = up_states_strides_0, weight = model_model_layers_27_mlp_up_proj_weight_palettized, x = input_51)[name = string("up_states")]; + tensor gate_states = silu(x = input_53)[name = string("gate_states")]; + tensor input = mul(x = gate_states, y = up_states)[name = string("input")]; + string hidden_states_pad_type_0 = const()[name = string("hidden_states_pad_type_0"), val = string("valid")]; + tensor hidden_states_strides_0 = const()[name = string("hidden_states_strides_0"), val = tensor([1, 1])]; + tensor hidden_states_pad_0 = const()[name = string("hidden_states_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor hidden_states_dilations_0 = const()[name = string("hidden_states_dilations_0"), val = tensor([1, 1])]; + int32 hidden_states_groups_0 = const()[name = string("hidden_states_groups_0"), val = int32(1)]; + tensor hidden_states_1 = conv(dilations = hidden_states_dilations_0, groups = hidden_states_groups_0, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = hidden_states_strides_0, weight = model_model_layers_27_mlp_down_proj_weight_palettized, x = input)[name = string("hidden_states")]; + tensor var_947_axes_0 = const()[name = string("op_947_axes_0"), val = tensor([2])]; + tensor var_947 = squeeze(axes = var_947_axes_0, x = hidden_states_1)[name = string("op_947")]; + tensor var_948 = const()[name = string("op_948"), val = tensor([0, 2, 1])]; + tensor var_949 = transpose(perm = var_948, x = var_947)[name = string("transpose_0")]; + tensor output_hidden_states = add(x = hidden_states_29_cast_fp16, y = var_949)[name = string("op_950_cast_fp16")]; + } -> (output_hidden_states); +} \ No newline at end of file