program(1.0) [buildInfo = dict, tensor>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.9.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] { func main(tensor attn0_cache, tensor attn0_offset, tensor attn1_cache, tensor attn1_offset, tensor conv0_first, tensor conv0_prev, tensor conv_final_first, tensor conv_final_prev, tensor convtr0_partial, tensor convtr1_partial, tensor convtr2_partial, tensor latent, tensor res0_conv0_first, tensor res0_conv0_prev, tensor res0_conv1_first, tensor res0_conv1_prev, tensor res1_conv0_first, tensor res1_conv0_prev, tensor res1_conv1_first, tensor res1_conv1_prev, tensor res2_conv0_first, tensor res2_conv0_prev, tensor res2_conv1_first, tensor res2_conv1_prev, tensor upsample_partial) { tensor emb_mean = const()[name = tensor("emb_mean"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; tensor emb_std = const()[name = tensor("emb_std"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(256)))]; tensor mimi_quantizer_output_proj_weight = const()[name = tensor("mimi_quantizer_output_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(448)))]; tensor mimi_upsample_convtr_convtr_weight = const()[name = tensor("mimi_upsample_convtr_convtr_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(66048)))]; tensor mimi_decoder_transformer_transformer_layers_0_norm1_bias = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_norm1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131648)))]; tensor mimi_decoder_transformer_transformer_layers_0_norm1_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_norm1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133760)))]; tensor mimi_decoder_transformer_transformer_layers_0_self_attn_in_proj_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_self_attn_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135872)))]; tensor mimi_decoder_transformer_transformer_layers_0_self_attn_out_proj_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_self_attn_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3281664)))]; tensor mimi_decoder_transformer_transformer_layers_0_layer_scale_1_scale = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_layer_scale_1_scale"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4330304)))]; tensor mimi_decoder_transformer_transformer_layers_0_norm2_bias = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_norm2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4332416)))]; tensor mimi_decoder_transformer_transformer_layers_0_norm2_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_norm2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4334528)))]; tensor mimi_decoder_transformer_transformer_layers_0_linear1_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4336640)))]; tensor mimi_decoder_transformer_transformer_layers_0_linear2_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8531008)))]; tensor mimi_decoder_transformer_transformer_layers_0_layer_scale_2_scale = const()[name = tensor("mimi_decoder_transformer_transformer_layers_0_layer_scale_2_scale"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12725376)))]; tensor mimi_decoder_transformer_transformer_layers_1_norm1_bias = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_norm1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12727488)))]; tensor mimi_decoder_transformer_transformer_layers_1_norm1_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_norm1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12729600)))]; tensor mimi_decoder_transformer_transformer_layers_1_self_attn_in_proj_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_self_attn_in_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(12731712)))]; tensor mimi_decoder_transformer_transformer_layers_1_self_attn_out_proj_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_self_attn_out_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15877504)))]; tensor mimi_decoder_transformer_transformer_layers_1_layer_scale_1_scale = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_layer_scale_1_scale"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16926144)))]; tensor mimi_decoder_transformer_transformer_layers_1_norm2_bias = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_norm2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16928256)))]; tensor mimi_decoder_transformer_transformer_layers_1_norm2_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_norm2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16930368)))]; tensor mimi_decoder_transformer_transformer_layers_1_linear1_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_linear1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16932480)))]; tensor mimi_decoder_transformer_transformer_layers_1_linear2_weight = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_linear2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21126848)))]; tensor mimi_decoder_transformer_transformer_layers_1_layer_scale_2_scale = const()[name = tensor("mimi_decoder_transformer_transformer_layers_1_layer_scale_2_scale"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25321216)))]; tensor mimi_decoder_model_0_conv_bias = const()[name = tensor("mimi_decoder_model_0_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25323328)))]; tensor mimi_decoder_model_0_conv_weight = const()[name = tensor("mimi_decoder_model_0_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25325440)))]; tensor mimi_decoder_model_2_convtr_bias = const()[name = tensor("mimi_decoder_model_2_convtr_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32665536)))]; tensor mimi_decoder_model_2_convtr_weight = const()[name = tensor("mimi_decoder_model_2_convtr_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32666624)))]; tensor mimi_decoder_model_3_block_1_conv_bias = const()[name = tensor("mimi_decoder_model_3_block_1_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38958144)))]; tensor mimi_decoder_model_3_block_1_conv_weight = const()[name = tensor("mimi_decoder_model_3_block_1_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38958720)))]; tensor mimi_decoder_model_3_block_3_conv_bias = const()[name = tensor("mimi_decoder_model_3_block_3_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39352000)))]; tensor mimi_decoder_model_3_block_3_conv_weight = const()[name = tensor("mimi_decoder_model_3_block_3_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39353088)))]; tensor mimi_decoder_model_5_convtr_bias = const()[name = tensor("mimi_decoder_model_5_convtr_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39484224)))]; tensor mimi_decoder_model_5_convtr_weight = const()[name = tensor("mimi_decoder_model_5_convtr_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39484800)))]; tensor mimi_decoder_model_6_block_1_conv_bias = const()[name = tensor("mimi_decoder_model_6_block_1_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40795584)))]; tensor mimi_decoder_model_6_block_1_conv_weight = const()[name = tensor("mimi_decoder_model_6_block_1_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40795904)))]; tensor mimi_decoder_model_6_block_3_conv_bias = const()[name = tensor("mimi_decoder_model_6_block_3_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40894272)))]; tensor mimi_decoder_model_6_block_3_conv_weight = const()[name = tensor("mimi_decoder_model_6_block_3_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40894848)))]; tensor mimi_decoder_model_8_convtr_bias = const()[name = tensor("mimi_decoder_model_8_convtr_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40927680)))]; tensor mimi_decoder_model_8_convtr_weight = const()[name = tensor("mimi_decoder_model_8_convtr_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40928000)))]; tensor mimi_decoder_model_9_block_1_conv_bias = const()[name = tensor("mimi_decoder_model_9_block_1_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41190208)))]; tensor mimi_decoder_model_9_block_1_conv_weight = const()[name = tensor("mimi_decoder_model_9_block_1_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41190400)))]; tensor mimi_decoder_model_9_block_3_conv_bias = const()[name = tensor("mimi_decoder_model_9_block_3_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41215040)))]; tensor mimi_decoder_model_9_block_3_conv_weight = const()[name = tensor("mimi_decoder_model_9_block_3_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41215360)))]; tensor mimi_decoder_model_11_conv_bias = const()[name = tensor("mimi_decoder_model_11_conv_bias"), val = tensor([-0x1.8p-13])]; tensor mimi_decoder_model_11_conv_weight = const()[name = tensor("mimi_decoder_model_11_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41223616)))]; tensor var_38 = mul(x = latent, y = emb_std)[name = tensor("op_38")]; tensor denorm = add(x = var_38, y = emb_mean)[name = tensor("denorm")]; tensor input_1_axes_0 = const()[name = tensor("input_1_axes_0"), val = tensor([-1])]; tensor input_1 = expand_dims(axes = input_1_axes_0, x = denorm)[name = tensor("input_1")]; tensor x_1_pad_type_0 = const()[name = tensor("x_1_pad_type_0"), val = tensor("valid")]; tensor x_1_strides_0 = const()[name = tensor("x_1_strides_0"), val = tensor([1])]; tensor x_1_pad_0 = const()[name = tensor("x_1_pad_0"), val = tensor([0, 0])]; tensor x_1_dilations_0 = const()[name = tensor("x_1_dilations_0"), val = tensor([1])]; tensor x_1_groups_0 = const()[name = tensor("x_1_groups_0"), val = tensor(1)]; tensor x_1 = conv(dilations = x_1_dilations_0, groups = x_1_groups_0, pad = x_1_pad_0, pad_type = x_1_pad_type_0, strides = x_1_strides_0, weight = mimi_quantizer_output_proj_weight, x = input_1)[name = tensor("x_1")]; tensor var_62 = const()[name = tensor("op_62"), val = tensor(-1)]; tensor y_1_pad_type_0 = const()[name = tensor("y_1_pad_type_0"), val = tensor("valid")]; tensor y_1_strides_0 = const()[name = tensor("y_1_strides_0"), val = tensor([16])]; tensor y_1_groups_0 = const()[name = tensor("y_1_groups_0"), val = tensor(512)]; tensor y_1_pad_0 = const()[name = tensor("y_1_pad_0"), val = tensor([0, 0])]; tensor y_1_dilations_0 = const()[name = tensor("y_1_dilations_0"), val = tensor([1])]; tensor y_1_has_output_shape_output_shape_0 = const()[name = tensor("y_1_has_output_shape_output_shape_0"), val = tensor([1, 512, 32])]; tensor y_1_has_output_shape = conv_transpose(dilations = y_1_dilations_0, groups = y_1_groups_0, output_shape = y_1_has_output_shape_output_shape_0, pad = y_1_pad_0, pad_type = y_1_pad_type_0, strides = y_1_strides_0, weight = mimi_upsample_convtr_convtr_weight, x = x_1)[name = tensor("y_1_has_output_shape")]; tensor var_72_begin_0 = const()[name = tensor("op_72_begin_0"), val = tensor([0, 0, 0])]; tensor var_72_end_0 = const()[name = tensor("op_72_end_0"), val = tensor([1, 512, 16])]; tensor var_72_end_mask_0 = const()[name = tensor("op_72_end_mask_0"), val = tensor([true, true, false])]; tensor var_72 = slice_by_index(begin = var_72_begin_0, end = var_72_end_0, end_mask = var_72_end_mask_0, x = y_1_has_output_shape)[name = tensor("op_72")]; tensor var_73 = add(x = var_72, y = upsample_partial)[name = tensor("op_73")]; tensor var_74_begin_0 = const()[name = tensor("op_74_begin_0"), val = tensor([0, 0, 16])]; tensor var_74_end_0 = const()[name = tensor("op_74_end_0"), val = tensor([1, 512, 32])]; tensor var_74_end_mask_0 = const()[name = tensor("op_74_end_mask_0"), val = tensor([true, true, true])]; tensor var_74 = slice_by_index(begin = var_74_begin_0, end = var_74_end_0, end_mask = var_74_end_mask_0, x = y_1_has_output_shape)[name = tensor("op_74")]; tensor y_3_interleave_0 = const()[name = tensor("y_3_interleave_0"), val = tensor(false)]; tensor y_3 = concat(axis = var_62, interleave = y_3_interleave_0, values = (var_73, var_74))[name = tensor("y_3")]; tensor var_77_begin_0 = const()[name = tensor("op_77_begin_0"), val = tensor([0, 0, 16])]; tensor var_77_end_0 = const()[name = tensor("op_77_end_0"), val = tensor([1, 512, 32])]; tensor var_77_end_mask_0 = const()[name = tensor("op_77_end_mask_0"), val = tensor([true, true, true])]; tensor var_77 = slice_by_index(begin = var_77_begin_0, end = var_77_end_0, end_mask = var_77_end_mask_0, x = y_3)[name = tensor("op_77")]; tensor x_3_begin_0 = const()[name = tensor("x_3_begin_0"), val = tensor([0, 0, 0])]; tensor x_3_end_0 = const()[name = tensor("x_3_end_0"), val = tensor([1, 512, 16])]; tensor x_3_end_mask_0 = const()[name = tensor("x_3_end_mask_0"), val = tensor([true, true, false])]; tensor x_3 = slice_by_index(begin = x_3_begin_0, end = x_3_end_0, end_mask = x_3_end_mask_0, x = y_3)[name = tensor("x_3")]; tensor var_86 = const()[name = tensor("op_86"), val = tensor(0)]; tensor var_91 = const()[name = tensor("op_91"), val = tensor(-1)]; tensor var_100 = const()[name = tensor("op_100"), val = tensor(-0x1.ff933cp+127)]; tensor var_102 = const()[name = tensor("op_102"), val = tensor(0x1.4f8b58p-17)]; tensor input_3_perm_0 = const()[name = tensor("input_3_perm_0"), val = tensor([0, 2, 1])]; tensor query_1_axes_0 = const()[name = tensor("query_1_axes_0"), val = tensor([-1])]; tensor input_3 = transpose(perm = input_3_perm_0, x = x_3)[name = tensor("transpose_19")]; tensor query_1 = layer_norm(axes = query_1_axes_0, beta = mimi_decoder_transformer_transformer_layers_0_norm1_bias, epsilon = var_102, gamma = mimi_decoder_transformer_transformer_layers_0_norm1_weight, x = input_3)[name = tensor("query_1")]; tensor linear_0_bias_0 = const()[name = tensor("linear_0_bias_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41224448)))]; tensor projected_1 = linear(bias = linear_0_bias_0, weight = mimi_decoder_transformer_transformer_layers_0_self_attn_in_proj_weight, x = query_1)[name = tensor("linear_0")]; tensor var_130 = const()[name = tensor("op_130"), val = tensor([1, 16, 3, 8, 64])]; tensor packed_1 = reshape(shape = var_130, x = projected_1)[name = tensor("packed_1")]; tensor var_132_split_sizes_0 = const()[name = tensor("op_132_split_sizes_0"), val = tensor([1, 1, 1])]; tensor var_132_axis_0 = const()[name = tensor("op_132_axis_0"), val = tensor(2)]; tensor var_132_0, tensor var_132_1, tensor var_132_2 = split(axis = var_132_axis_0, split_sizes = var_132_split_sizes_0, x = packed_1)[name = tensor("op_132")]; tensor squeeze_0_axes_0 = const()[name = tensor("squeeze_0_axes_0"), val = tensor([2])]; tensor squeeze_0 = squeeze(axes = squeeze_0_axes_0, x = var_132_0)[name = tensor("squeeze_0")]; tensor squeeze_1_axes_0 = const()[name = tensor("squeeze_1_axes_0"), val = tensor([2])]; tensor squeeze_1 = squeeze(axes = squeeze_1_axes_0, x = var_132_1)[name = tensor("squeeze_1")]; tensor squeeze_2_axes_0 = const()[name = tensor("squeeze_2_axes_0"), val = tensor([2])]; tensor squeeze_2 = squeeze(axes = squeeze_2_axes_0, x = var_132_2)[name = tensor("squeeze_2")]; tensor offset_3_begin_0 = const()[name = tensor("offset_3_begin_0"), val = tensor([0])]; tensor offset_3_end_0 = const()[name = tensor("offset_3_end_0"), val = tensor([1])]; tensor offset_3_end_mask_0 = const()[name = tensor("offset_3_end_mask_0"), val = tensor([false])]; tensor offset_3_squeeze_mask_0 = const()[name = tensor("offset_3_squeeze_mask_0"), val = tensor([true])]; tensor offset_3 = slice_by_index(begin = offset_3_begin_0, end = offset_3_end_0, end_mask = offset_3_end_mask_0, squeeze_mask = offset_3_squeeze_mask_0, x = attn0_offset)[name = tensor("offset_3")]; tensor freqs_1 = const()[name = tensor("freqs_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41230656)))]; tensor ts_1_promoted = const()[name = tensor("ts_1_promoted"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41230848)))]; tensor ts_3 = add(x = ts_1_promoted, y = offset_3)[name = tensor("ts_3")]; tensor var_148 = const()[name = tensor("op_148"), val = tensor([-1, 1, 1])]; tensor ts_5 = reshape(shape = var_148, x = ts_3)[name = tensor("ts_5")]; tensor var_150 = const()[name = tensor("op_150"), val = tensor([1, 16, 8, 32, 2])]; tensor q_3 = reshape(shape = var_150, x = squeeze_0)[name = tensor("q_3")]; tensor var_152 = const()[name = tensor("op_152"), val = tensor([1, 16, 8, 32, 2])]; tensor k_3 = reshape(shape = var_152, x = squeeze_1)[name = tensor("k_3")]; tensor var_154_begin_0 = const()[name = tensor("op_154_begin_0"), val = tensor([0, 0, 0, 0, 0])]; tensor var_154_end_0 = const()[name = tensor("op_154_end_0"), val = tensor([1, 16, 8, 32, 1])]; tensor var_154_end_mask_0 = const()[name = tensor("op_154_end_mask_0"), val = tensor([true, true, true, true, false])]; tensor var_154_squeeze_mask_0 = const()[name = tensor("op_154_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; tensor var_154 = slice_by_index(begin = var_154_begin_0, end = var_154_end_0, end_mask = var_154_end_mask_0, squeeze_mask = var_154_squeeze_mask_0, x = q_3)[name = tensor("op_154")]; tensor var_156_begin_0 = const()[name = tensor("op_156_begin_0"), val = tensor([0, 0, 0, 0, 1])]; tensor var_156_end_0 = const()[name = tensor("op_156_end_0"), val = tensor([1, 16, 8, 32, 2])]; tensor var_156_end_mask_0 = const()[name = tensor("op_156_end_mask_0"), val = tensor([true, true, true, true, false])]; tensor var_156_squeeze_mask_0 = const()[name = tensor("op_156_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; tensor var_156 = slice_by_index(begin = var_156_begin_0, end = var_156_end_0, end_mask = var_156_end_mask_0, squeeze_mask = var_156_squeeze_mask_0, x = q_3)[name = tensor("op_156")]; tensor var_158_begin_0 = const()[name = tensor("op_158_begin_0"), val = tensor([0, 0, 0, 0, 0])]; tensor var_158_end_0 = const()[name = tensor("op_158_end_0"), val = tensor([1, 16, 8, 32, 1])]; tensor var_158_end_mask_0 = const()[name = tensor("op_158_end_mask_0"), val = tensor([true, true, true, true, false])]; tensor var_158_squeeze_mask_0 = const()[name = tensor("op_158_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; tensor var_158 = slice_by_index(begin = var_158_begin_0, end = var_158_end_0, end_mask = var_158_end_mask_0, squeeze_mask = var_158_squeeze_mask_0, x = k_3)[name = tensor("op_158")]; tensor var_160_begin_0 = const()[name = tensor("op_160_begin_0"), val = tensor([0, 0, 0, 0, 1])]; tensor var_160_end_0 = const()[name = tensor("op_160_end_0"), val = tensor([1, 16, 8, 32, 2])]; tensor var_160_end_mask_0 = const()[name = tensor("op_160_end_mask_0"), val = tensor([true, true, true, true, false])]; tensor var_160_squeeze_mask_0 = const()[name = tensor("op_160_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; tensor var_160 = slice_by_index(begin = var_160_begin_0, end = var_160_end_0, end_mask = var_160_end_mask_0, squeeze_mask = var_160_squeeze_mask_0, x = k_3)[name = tensor("op_160")]; tensor var_162 = mul(x = freqs_1, y = ts_5)[name = tensor("op_162")]; tensor rotr_1 = cos(x = var_162)[name = tensor("rotr_1")]; tensor roti_1 = sin(x = var_162)[name = tensor("roti_1")]; tensor var_166 = mul(x = var_154, y = rotr_1)[name = tensor("op_166")]; tensor var_167 = mul(x = var_156, y = roti_1)[name = tensor("op_167")]; tensor qor_1 = sub(x = var_166, y = var_167)[name = tensor("qor_1")]; tensor var_169 = mul(x = var_154, y = roti_1)[name = tensor("op_169")]; tensor var_170 = mul(x = var_156, y = rotr_1)[name = tensor("op_170")]; tensor qoi_1 = add(x = var_169, y = var_170)[name = tensor("qoi_1")]; tensor var_172 = mul(x = var_158, y = rotr_1)[name = tensor("op_172")]; tensor var_173 = mul(x = var_160, y = roti_1)[name = tensor("op_173")]; tensor kor_1 = sub(x = var_172, y = var_173)[name = tensor("kor_1")]; tensor var_175 = mul(x = var_158, y = roti_1)[name = tensor("op_175")]; tensor var_176 = mul(x = var_160, y = rotr_1)[name = tensor("op_176")]; tensor koi_1 = add(x = var_175, y = var_176)[name = tensor("koi_1")]; tensor qo_1_axis_0 = const()[name = tensor("qo_1_axis_0"), val = tensor(-1)]; tensor qo_1 = stack(axis = qo_1_axis_0, values = (qor_1, qoi_1))[name = tensor("qo_1")]; tensor ko_1_axis_0 = const()[name = tensor("ko_1_axis_0"), val = tensor(-1)]; tensor ko_1 = stack(axis = ko_1_axis_0, values = (kor_1, koi_1))[name = tensor("ko_1")]; tensor var_186 = const()[name = tensor("op_186"), val = tensor([1, 16, 8, 64])]; tensor q_5 = reshape(shape = var_186, x = qo_1)[name = tensor("q_5")]; tensor var_188 = const()[name = tensor("op_188"), val = tensor([1, 16, 8, 64])]; tensor k_5 = reshape(shape = var_188, x = ko_1)[name = tensor("k_5")]; tensor capacity_1 = const()[name = tensor("capacity_1"), val = tensor([256])]; tensor var_193_dtype_0 = const()[name = tensor("op_193_dtype_0"), val = tensor("int32")]; tensor var_194 = const()[name = tensor("op_194"), val = tensor([1, 1])]; tensor var_193 = cast(dtype = var_193_dtype_0, x = attn0_offset)[name = tensor("cast_49")]; tensor write_base_1 = reshape(shape = var_194, x = var_193)[name = tensor("write_base_1")]; tensor write_range_1 = const()[name = tensor("write_range_1"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]])]; tensor abs_idx_1 = add(x = write_base_1, y = write_range_1)[name = tensor("abs_idx_1")]; tensor wrapped_1_div = floor_div(x = abs_idx_1, y = capacity_1)[name = tensor("wrapped_1_div")]; tensor wrapped_1_div_scaled = mul(x = wrapped_1_div, y = capacity_1)[name = tensor("wrapped_1_div_scaled")]; tensor wrapped_1 = sub(x = abs_idx_1, y = wrapped_1_div_scaled)[name = tensor("wrapped_1")]; tensor var_201 = const()[name = tensor("op_201"), val = tensor([1, 16, 1, 1])]; tensor var_202 = reshape(shape = var_201, x = wrapped_1)[name = tensor("op_202")]; tensor write_indexes_1_reps_0 = const()[name = tensor("write_indexes_1_reps_0"), val = tensor([1, 1, 8, 64])]; tensor write_indexes_1 = tile(reps = write_indexes_1_reps_0, x = var_202)[name = tensor("write_indexes_1")]; tensor var_205_begin_0 = const()[name = tensor("op_205_begin_0"), val = tensor([0, 0, 0, 0, 0])]; tensor var_205_end_0 = const()[name = tensor("op_205_end_0"), val = tensor([1, 1, 256, 8, 64])]; tensor var_205_end_mask_0 = const()[name = tensor("op_205_end_mask_0"), val = tensor([false, true, true, true, true])]; tensor var_205_squeeze_mask_0 = const()[name = tensor("op_205_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; tensor var_205 = slice_by_index(begin = var_205_begin_0, end = var_205_end_0, end_mask = var_205_end_mask_0, squeeze_mask = var_205_squeeze_mask_0, x = attn0_cache)[name = tensor("op_205")]; tensor new_k_cache_1_axis_0 = const()[name = tensor("new_k_cache_1_axis_0"), val = tensor(1)]; tensor new_k_cache_1_mode_0 = const()[name = tensor("new_k_cache_1_mode_0"), val = tensor("update")]; tensor new_k_cache_1_validate_indices_0 = const()[name = tensor("new_k_cache_1_validate_indices_0"), val = tensor(false)]; tensor new_k_cache_1 = scatter_along_axis(axis = new_k_cache_1_axis_0, data = var_205, indices = write_indexes_1, mode = new_k_cache_1_mode_0, updates = k_5, validate_indices = new_k_cache_1_validate_indices_0)[name = tensor("new_k_cache_1")]; tensor var_207_begin_0 = const()[name = tensor("op_207_begin_0"), val = tensor([1, 0, 0, 0, 0])]; tensor var_207_end_0 = const()[name = tensor("op_207_end_0"), val = tensor([2, 1, 256, 8, 64])]; tensor var_207_end_mask_0 = const()[name = tensor("op_207_end_mask_0"), val = tensor([false, true, true, true, true])]; tensor var_207_squeeze_mask_0 = const()[name = tensor("op_207_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; tensor var_207 = slice_by_index(begin = var_207_begin_0, end = var_207_end_0, end_mask = var_207_end_mask_0, squeeze_mask = var_207_squeeze_mask_0, x = attn0_cache)[name = tensor("op_207")]; tensor new_v_cache_1_axis_0 = const()[name = tensor("new_v_cache_1_axis_0"), val = tensor(1)]; tensor new_v_cache_1_mode_0 = const()[name = tensor("new_v_cache_1_mode_0"), val = tensor("update")]; tensor new_v_cache_1_validate_indices_0 = const()[name = tensor("new_v_cache_1_validate_indices_0"), val = tensor(false)]; tensor new_v_cache_1 = scatter_along_axis(axis = new_v_cache_1_axis_0, data = var_207, indices = write_indexes_1, mode = new_v_cache_1_mode_0, updates = squeeze_2, validate_indices = new_v_cache_1_validate_indices_0)[name = tensor("new_v_cache_1")]; tensor var_210_axis_0 = const()[name = tensor("op_210_axis_0"), val = tensor(0)]; tensor var_210 = stack(axis = var_210_axis_0, values = (new_k_cache_1, new_v_cache_1))[name = tensor("op_210")]; tensor var_211 = not_equal(x = new_k_cache_1, y = new_k_cache_1)[name = tensor("op_211")]; tensor var_212 = const()[name = tensor("op_212"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41230976)))]; tensor new_k_cache_3 = select(a = var_212, b = new_k_cache_1, cond = var_211)[name = tensor("new_k_cache_3")]; tensor var_214 = not_equal(x = new_v_cache_1, y = new_v_cache_1)[name = tensor("op_214")]; tensor new_v_cache_3 = select(a = var_212, b = new_v_cache_1, cond = var_214)[name = tensor("new_v_cache_3")]; tensor var_219 = const()[name = tensor("op_219"), val = tensor([0, 2, 1, 3])]; tensor var_221 = const()[name = tensor("op_221"), val = tensor([1, 1])]; tensor var_222 = reshape(shape = var_221, x = attn0_offset)[name = tensor("op_222")]; tensor var_224_promoted = const()[name = tensor("op_224_promoted"), val = tensor([0x1.ep+3])]; tensor var_225 = add(x = var_222, y = var_224_promoted)[name = tensor("op_225")]; tensor last_pos_1_dtype_0 = const()[name = tensor("last_pos_1_dtype_0"), val = tensor("int32")]; tensor slot_idx_1 = const()[name = tensor("slot_idx_1"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255]])]; tensor last_pos_1 = cast(dtype = last_pos_1_dtype_0, x = var_225)[name = tensor("cast_48")]; tensor diff_1 = sub(x = last_pos_1, y = slot_idx_1)[name = tensor("diff_1")]; tensor var_231_div = floor_div(x = diff_1, y = capacity_1)[name = tensor("op_231_div")]; tensor var_231_div_scaled = mul(x = var_231_div, y = capacity_1)[name = tensor("op_231_div_scaled")]; tensor var_231 = sub(x = diff_1, y = var_231_div_scaled)[name = tensor("op_231")]; tensor pos_k_1 = sub(x = last_pos_1, y = var_231)[name = tensor("pos_k_1")]; tensor var_237_promoted = const()[name = tensor("op_237_promoted"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41755328)))]; tensor pos_q_1 = add(x = var_222, y = var_237_promoted)[name = tensor("pos_q_1")]; tensor var_241_axes_0 = const()[name = tensor("op_241_axes_0"), val = tensor([2])]; tensor var_241 = expand_dims(axes = var_241_axes_0, x = pos_q_1)[name = tensor("op_241")]; tensor var_243_axes_0 = const()[name = tensor("op_243_axes_0"), val = tensor([1])]; tensor var_243 = expand_dims(axes = var_243_axes_0, x = pos_k_1)[name = tensor("op_243")]; tensor var_244_promoted_dtype_0 = const()[name = tensor("op_244_promoted_dtype_0"), val = tensor("fp32")]; tensor var_244_promoted = cast(dtype = var_244_promoted_dtype_0, x = var_243)[name = tensor("cast_47")]; tensor delta_1 = sub(x = var_241, y = var_244_promoted)[name = tensor("delta_1")]; tensor valid_1 = greater_equal(x = var_243, y = var_86)[name = tensor("valid_1")]; tensor var_253 = const()[name = tensor("op_253"), val = tensor([1, 1, 1])]; tensor var_254 = reshape(shape = var_253, x = attn0_offset)[name = tensor("op_254")]; tensor var_256_promoted = const()[name = tensor("op_256_promoted"), val = tensor([0x1.ep+3])]; tensor var_257 = add(x = var_254, y = var_256_promoted)[name = tensor("op_257")]; tensor var_258 = less_equal(x = var_244_promoted, y = var_257)[name = tensor("op_258")]; tensor valid_3 = logical_and(x = valid_1, y = var_258)[name = tensor("valid_3")]; tensor var_86_promoted = const()[name = tensor("op_86_promoted"), val = tensor(0x0p+0)]; tensor var_260 = greater_equal(x = delta_1, y = var_86_promoted)[name = tensor("op_260")]; tensor attn_mask_1 = logical_and(x = valid_3, y = var_260)[name = tensor("attn_mask_1")]; tensor var_98_promoted = const()[name = tensor("op_98_promoted"), val = tensor(0x1.f4p+7)]; tensor var_262 = less(x = delta_1, y = var_98_promoted)[name = tensor("op_262")]; tensor attn_mask_3 = logical_and(x = attn_mask_1, y = var_262)[name = tensor("attn_mask_3")]; tensor attn_mask_5_axes_0 = const()[name = tensor("attn_mask_5_axes_0"), val = tensor([1])]; tensor attn_mask_5 = expand_dims(axes = attn_mask_5_axes_0, x = attn_mask_3)[name = tensor("attn_mask_5")]; tensor var_267_transpose_x_0 = const()[name = tensor("op_267_transpose_x_0"), val = tensor(false)]; tensor var_267_transpose_y_0 = const()[name = tensor("op_267_transpose_y_0"), val = tensor(false)]; tensor transpose_6_perm_0 = const()[name = tensor("transpose_6_perm_0"), val = tensor([0, 2, -3, -1])]; tensor transpose_7_perm_0 = const()[name = tensor("transpose_7_perm_0"), val = tensor([0, 2, -1, -3])]; tensor transpose_7 = transpose(perm = transpose_7_perm_0, x = new_k_cache_3)[name = tensor("transpose_16")]; tensor transpose_6 = transpose(perm = transpose_6_perm_0, x = q_5)[name = tensor("transpose_17")]; tensor var_267 = matmul(transpose_x = var_267_transpose_x_0, transpose_y = var_267_transpose_y_0, x = transpose_6, y = transpose_7)[name = tensor("op_267")]; tensor var_268 = const()[name = tensor("op_268"), val = tensor(0x1p-3)]; tensor attn_1 = mul(x = var_267, y = var_268)[name = tensor("attn_1")]; tensor var_270 = logical_not(x = attn_mask_5)[name = tensor("op_270")]; tensor attn_3 = select(a = var_100, b = attn_1, cond = var_270)[name = tensor("attn_3")]; tensor attn_5 = softmax(axis = var_91, x = attn_3)[name = tensor("attn_5")]; tensor x_5_transpose_x_0 = const()[name = tensor("x_5_transpose_x_0"), val = tensor(false)]; tensor x_5_transpose_y_0 = const()[name = tensor("x_5_transpose_y_0"), val = tensor(false)]; tensor v_attn_1 = transpose(perm = var_219, x = new_v_cache_3)[name = tensor("transpose_18")]; tensor x_5 = matmul(transpose_x = x_5_transpose_x_0, transpose_y = x_5_transpose_y_0, x = attn_5, y = v_attn_1)[name = tensor("x_5")]; tensor var_274_perm_0 = const()[name = tensor("op_274_perm_0"), val = tensor([0, 2, 1, 3])]; tensor var_275 = const()[name = tensor("op_275"), val = tensor([1, 16, 512])]; tensor var_274 = transpose(perm = var_274_perm_0, x = x_5)[name = tensor("transpose_15")]; tensor input_5 = reshape(shape = var_275, x = var_274)[name = tensor("input_5")]; tensor linear_1_bias_0 = const()[name = tensor("linear_1_bias_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41755456)))]; tensor x_7 = linear(bias = linear_1_bias_0, weight = mimi_decoder_transformer_transformer_layers_0_self_attn_out_proj_weight, x = input_5)[name = tensor("linear_1")]; tensor var_284 = mul(x = mimi_decoder_transformer_transformer_layers_0_layer_scale_1_scale, y = x_7)[name = tensor("op_284")]; tensor input_7 = add(x = input_3, y = var_284)[name = tensor("input_7")]; tensor input_9_axes_0 = const()[name = tensor("input_9_axes_0"), val = tensor([-1])]; tensor input_9 = layer_norm(axes = input_9_axes_0, beta = mimi_decoder_transformer_transformer_layers_0_norm2_bias, epsilon = var_102, gamma = mimi_decoder_transformer_transformer_layers_0_norm2_weight, x = input_7)[name = tensor("input_9")]; tensor linear_2_bias_0 = const()[name = tensor("linear_2_bias_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41757568)))]; tensor var_291 = linear(bias = linear_2_bias_0, weight = mimi_decoder_transformer_transformer_layers_0_linear1_weight, x = input_9)[name = tensor("linear_2")]; tensor input_11_mode_0 = const()[name = tensor("input_11_mode_0"), val = tensor("EXACT")]; tensor input_11 = gelu(mode = input_11_mode_0, x = var_291)[name = tensor("input_11")]; tensor x_9 = linear(bias = linear_1_bias_0, weight = mimi_decoder_transformer_transformer_layers_0_linear2_weight, x = input_11)[name = tensor("linear_3")]; tensor var_297 = mul(x = mimi_decoder_transformer_transformer_layers_0_layer_scale_2_scale, y = x_9)[name = tensor("op_297")]; tensor input_13 = add(x = input_7, y = var_297)[name = tensor("input_13")]; tensor query_axes_0 = const()[name = tensor("query_axes_0"), val = tensor([-1])]; tensor query = layer_norm(axes = query_axes_0, beta = mimi_decoder_transformer_transformer_layers_1_norm1_bias, epsilon = var_102, gamma = mimi_decoder_transformer_transformer_layers_1_norm1_weight, x = input_13)[name = tensor("query")]; tensor projected = linear(bias = linear_0_bias_0, weight = mimi_decoder_transformer_transformer_layers_1_self_attn_in_proj_weight, x = query)[name = tensor("linear_4")]; tensor var_320 = const()[name = tensor("op_320"), val = tensor([1, 16, 3, 8, 64])]; tensor packed = reshape(shape = var_320, x = projected)[name = tensor("packed")]; tensor var_322_split_sizes_0 = const()[name = tensor("op_322_split_sizes_0"), val = tensor([1, 1, 1])]; tensor var_322_axis_0 = const()[name = tensor("op_322_axis_0"), val = tensor(2)]; tensor var_322_0, tensor var_322_1, tensor var_322_2 = split(axis = var_322_axis_0, split_sizes = var_322_split_sizes_0, x = packed)[name = tensor("op_322")]; tensor squeeze_3_axes_0 = const()[name = tensor("squeeze_3_axes_0"), val = tensor([2])]; tensor squeeze_3 = squeeze(axes = squeeze_3_axes_0, x = var_322_0)[name = tensor("squeeze_3")]; tensor squeeze_4_axes_0 = const()[name = tensor("squeeze_4_axes_0"), val = tensor([2])]; tensor squeeze_4 = squeeze(axes = squeeze_4_axes_0, x = var_322_1)[name = tensor("squeeze_4")]; tensor squeeze_5_axes_0 = const()[name = tensor("squeeze_5_axes_0"), val = tensor([2])]; tensor squeeze_5 = squeeze(axes = squeeze_5_axes_0, x = var_322_2)[name = tensor("squeeze_5")]; tensor offset_begin_0 = const()[name = tensor("offset_begin_0"), val = tensor([0])]; tensor offset_end_0 = const()[name = tensor("offset_end_0"), val = tensor([1])]; tensor offset_end_mask_0 = const()[name = tensor("offset_end_mask_0"), val = tensor([false])]; tensor offset_squeeze_mask_0 = const()[name = tensor("offset_squeeze_mask_0"), val = tensor([true])]; tensor offset = slice_by_index(begin = offset_begin_0, end = offset_end_0, end_mask = offset_end_mask_0, squeeze_mask = offset_squeeze_mask_0, x = attn1_offset)[name = tensor("offset")]; tensor freqs = const()[name = tensor("freqs"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41765824)))]; tensor ts_7_promoted = const()[name = tensor("ts_7_promoted"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41766016)))]; tensor ts_9 = add(x = ts_7_promoted, y = offset)[name = tensor("ts_9")]; tensor var_338 = const()[name = tensor("op_338"), val = tensor([-1, 1, 1])]; tensor ts = reshape(shape = var_338, x = ts_9)[name = tensor("ts")]; tensor var_340 = const()[name = tensor("op_340"), val = tensor([1, 16, 8, 32, 2])]; tensor q_9 = reshape(shape = var_340, x = squeeze_3)[name = tensor("q_9")]; tensor var_342 = const()[name = tensor("op_342"), val = tensor([1, 16, 8, 32, 2])]; tensor k_9 = reshape(shape = var_342, x = squeeze_4)[name = tensor("k_9")]; tensor var_344_begin_0 = const()[name = tensor("op_344_begin_0"), val = tensor([0, 0, 0, 0, 0])]; tensor var_344_end_0 = const()[name = tensor("op_344_end_0"), val = tensor([1, 16, 8, 32, 1])]; tensor var_344_end_mask_0 = const()[name = tensor("op_344_end_mask_0"), val = tensor([true, true, true, true, false])]; tensor var_344_squeeze_mask_0 = const()[name = tensor("op_344_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; tensor var_344 = slice_by_index(begin = var_344_begin_0, end = var_344_end_0, end_mask = var_344_end_mask_0, squeeze_mask = var_344_squeeze_mask_0, x = q_9)[name = tensor("op_344")]; tensor var_346_begin_0 = const()[name = tensor("op_346_begin_0"), val = tensor([0, 0, 0, 0, 1])]; tensor var_346_end_0 = const()[name = tensor("op_346_end_0"), val = tensor([1, 16, 8, 32, 2])]; tensor var_346_end_mask_0 = const()[name = tensor("op_346_end_mask_0"), val = tensor([true, true, true, true, false])]; tensor var_346_squeeze_mask_0 = const()[name = tensor("op_346_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; tensor var_346 = slice_by_index(begin = var_346_begin_0, end = var_346_end_0, end_mask = var_346_end_mask_0, squeeze_mask = var_346_squeeze_mask_0, x = q_9)[name = tensor("op_346")]; tensor var_348_begin_0 = const()[name = tensor("op_348_begin_0"), val = tensor([0, 0, 0, 0, 0])]; tensor var_348_end_0 = const()[name = tensor("op_348_end_0"), val = tensor([1, 16, 8, 32, 1])]; tensor var_348_end_mask_0 = const()[name = tensor("op_348_end_mask_0"), val = tensor([true, true, true, true, false])]; tensor var_348_squeeze_mask_0 = const()[name = tensor("op_348_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; tensor var_348 = slice_by_index(begin = var_348_begin_0, end = var_348_end_0, end_mask = var_348_end_mask_0, squeeze_mask = var_348_squeeze_mask_0, x = k_9)[name = tensor("op_348")]; tensor var_350_begin_0 = const()[name = tensor("op_350_begin_0"), val = tensor([0, 0, 0, 0, 1])]; tensor var_350_end_0 = const()[name = tensor("op_350_end_0"), val = tensor([1, 16, 8, 32, 2])]; tensor var_350_end_mask_0 = const()[name = tensor("op_350_end_mask_0"), val = tensor([true, true, true, true, false])]; tensor var_350_squeeze_mask_0 = const()[name = tensor("op_350_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; tensor var_350 = slice_by_index(begin = var_350_begin_0, end = var_350_end_0, end_mask = var_350_end_mask_0, squeeze_mask = var_350_squeeze_mask_0, x = k_9)[name = tensor("op_350")]; tensor var_352 = mul(x = freqs, y = ts)[name = tensor("op_352")]; tensor rotr = cos(x = var_352)[name = tensor("rotr")]; tensor roti = sin(x = var_352)[name = tensor("roti")]; tensor var_356 = mul(x = var_344, y = rotr)[name = tensor("op_356")]; tensor var_357 = mul(x = var_346, y = roti)[name = tensor("op_357")]; tensor qor_5 = sub(x = var_356, y = var_357)[name = tensor("qor_5")]; tensor var_359 = mul(x = var_344, y = roti)[name = tensor("op_359")]; tensor var_360 = mul(x = var_346, y = rotr)[name = tensor("op_360")]; tensor qoi_5 = add(x = var_359, y = var_360)[name = tensor("qoi_5")]; tensor var_362 = mul(x = var_348, y = rotr)[name = tensor("op_362")]; tensor var_363 = mul(x = var_350, y = roti)[name = tensor("op_363")]; tensor kor_5 = sub(x = var_362, y = var_363)[name = tensor("kor_5")]; tensor var_365 = mul(x = var_348, y = roti)[name = tensor("op_365")]; tensor var_366 = mul(x = var_350, y = rotr)[name = tensor("op_366")]; tensor koi_5 = add(x = var_365, y = var_366)[name = tensor("koi_5")]; tensor qo_axis_0 = const()[name = tensor("qo_axis_0"), val = tensor(-1)]; tensor qo = stack(axis = qo_axis_0, values = (qor_5, qoi_5))[name = tensor("qo")]; tensor ko_axis_0 = const()[name = tensor("ko_axis_0"), val = tensor(-1)]; tensor ko = stack(axis = ko_axis_0, values = (kor_5, koi_5))[name = tensor("ko")]; tensor var_376 = const()[name = tensor("op_376"), val = tensor([1, 16, 8, 64])]; tensor q = reshape(shape = var_376, x = qo)[name = tensor("q")]; tensor var_378 = const()[name = tensor("op_378"), val = tensor([1, 16, 8, 64])]; tensor k = reshape(shape = var_378, x = ko)[name = tensor("k")]; tensor capacity = const()[name = tensor("capacity"), val = tensor([256])]; tensor var_383_dtype_0 = const()[name = tensor("op_383_dtype_0"), val = tensor("int32")]; tensor var_384 = const()[name = tensor("op_384"), val = tensor([1, 1])]; tensor var_383 = cast(dtype = var_383_dtype_0, x = attn1_offset)[name = tensor("cast_46")]; tensor write_base = reshape(shape = var_384, x = var_383)[name = tensor("write_base")]; tensor write_range = const()[name = tensor("write_range"), val = tensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]])]; tensor abs_idx = add(x = write_base, y = write_range)[name = tensor("abs_idx")]; tensor wrapped_div = floor_div(x = abs_idx, y = capacity)[name = tensor("wrapped_div")]; tensor wrapped_div_scaled = mul(x = wrapped_div, y = capacity)[name = tensor("wrapped_div_scaled")]; tensor wrapped = sub(x = abs_idx, y = wrapped_div_scaled)[name = tensor("wrapped")]; tensor var_391 = const()[name = tensor("op_391"), val = tensor([1, 16, 1, 1])]; tensor var_392 = reshape(shape = var_391, x = wrapped)[name = tensor("op_392")]; tensor write_indexes_reps_0 = const()[name = tensor("write_indexes_reps_0"), val = tensor([1, 1, 8, 64])]; tensor write_indexes = tile(reps = write_indexes_reps_0, x = var_392)[name = tensor("write_indexes")]; tensor var_395_begin_0 = const()[name = tensor("op_395_begin_0"), val = tensor([0, 0, 0, 0, 0])]; tensor var_395_end_0 = const()[name = tensor("op_395_end_0"), val = tensor([1, 1, 256, 8, 64])]; tensor var_395_end_mask_0 = const()[name = tensor("op_395_end_mask_0"), val = tensor([false, true, true, true, true])]; tensor var_395_squeeze_mask_0 = const()[name = tensor("op_395_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; tensor var_395 = slice_by_index(begin = var_395_begin_0, end = var_395_end_0, end_mask = var_395_end_mask_0, squeeze_mask = var_395_squeeze_mask_0, x = attn1_cache)[name = tensor("op_395")]; tensor new_k_cache_5_axis_0 = const()[name = tensor("new_k_cache_5_axis_0"), val = tensor(1)]; tensor new_k_cache_5_mode_0 = const()[name = tensor("new_k_cache_5_mode_0"), val = tensor("update")]; tensor new_k_cache_5_validate_indices_0 = const()[name = tensor("new_k_cache_5_validate_indices_0"), val = tensor(false)]; tensor new_k_cache_5 = scatter_along_axis(axis = new_k_cache_5_axis_0, data = var_395, indices = write_indexes, mode = new_k_cache_5_mode_0, updates = k, validate_indices = new_k_cache_5_validate_indices_0)[name = tensor("new_k_cache_5")]; tensor var_397_begin_0 = const()[name = tensor("op_397_begin_0"), val = tensor([1, 0, 0, 0, 0])]; tensor var_397_end_0 = const()[name = tensor("op_397_end_0"), val = tensor([2, 1, 256, 8, 64])]; tensor var_397_end_mask_0 = const()[name = tensor("op_397_end_mask_0"), val = tensor([false, true, true, true, true])]; tensor var_397_squeeze_mask_0 = const()[name = tensor("op_397_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; tensor var_397 = slice_by_index(begin = var_397_begin_0, end = var_397_end_0, end_mask = var_397_end_mask_0, squeeze_mask = var_397_squeeze_mask_0, x = attn1_cache)[name = tensor("op_397")]; tensor new_v_cache_5_axis_0 = const()[name = tensor("new_v_cache_5_axis_0"), val = tensor(1)]; tensor new_v_cache_5_mode_0 = const()[name = tensor("new_v_cache_5_mode_0"), val = tensor("update")]; tensor new_v_cache_5_validate_indices_0 = const()[name = tensor("new_v_cache_5_validate_indices_0"), val = tensor(false)]; tensor new_v_cache_5 = scatter_along_axis(axis = new_v_cache_5_axis_0, data = var_397, indices = write_indexes, mode = new_v_cache_5_mode_0, updates = squeeze_5, validate_indices = new_v_cache_5_validate_indices_0)[name = tensor("new_v_cache_5")]; tensor var_400_axis_0 = const()[name = tensor("op_400_axis_0"), val = tensor(0)]; tensor var_400 = stack(axis = var_400_axis_0, values = (new_k_cache_5, new_v_cache_5))[name = tensor("op_400")]; tensor var_401 = not_equal(x = new_k_cache_5, y = new_k_cache_5)[name = tensor("op_401")]; tensor new_k_cache = select(a = var_212, b = new_k_cache_5, cond = var_401)[name = tensor("new_k_cache")]; tensor var_404 = not_equal(x = new_v_cache_5, y = new_v_cache_5)[name = tensor("op_404")]; tensor new_v_cache = select(a = var_212, b = new_v_cache_5, cond = var_404)[name = tensor("new_v_cache")]; tensor var_409 = const()[name = tensor("op_409"), val = tensor([0, 2, 1, 3])]; tensor var_411 = const()[name = tensor("op_411"), val = tensor([1, 1])]; tensor var_412 = reshape(shape = var_411, x = attn1_offset)[name = tensor("op_412")]; tensor var_414_promoted = const()[name = tensor("op_414_promoted"), val = tensor([0x1.ep+3])]; tensor var_415 = add(x = var_412, y = var_414_promoted)[name = tensor("op_415")]; tensor last_pos_dtype_0 = const()[name = tensor("last_pos_dtype_0"), val = tensor("int32")]; tensor last_pos = cast(dtype = last_pos_dtype_0, x = var_415)[name = tensor("cast_45")]; tensor diff = sub(x = last_pos, y = slot_idx_1)[name = tensor("diff")]; tensor var_421_div = floor_div(x = diff, y = capacity)[name = tensor("op_421_div")]; tensor var_421_div_scaled = mul(x = var_421_div, y = capacity)[name = tensor("op_421_div_scaled")]; tensor var_421 = sub(x = diff, y = var_421_div_scaled)[name = tensor("op_421")]; tensor pos_k = sub(x = last_pos, y = var_421)[name = tensor("pos_k")]; tensor var_427_promoted = const()[name = tensor("op_427_promoted"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41766144)))]; tensor pos_q = add(x = var_412, y = var_427_promoted)[name = tensor("pos_q")]; tensor var_431_axes_0 = const()[name = tensor("op_431_axes_0"), val = tensor([2])]; tensor var_431 = expand_dims(axes = var_431_axes_0, x = pos_q)[name = tensor("op_431")]; tensor var_433_axes_0 = const()[name = tensor("op_433_axes_0"), val = tensor([1])]; tensor var_433 = expand_dims(axes = var_433_axes_0, x = pos_k)[name = tensor("op_433")]; tensor var_434_promoted_dtype_0 = const()[name = tensor("op_434_promoted_dtype_0"), val = tensor("fp32")]; tensor var_434_promoted = cast(dtype = var_434_promoted_dtype_0, x = var_433)[name = tensor("cast_44")]; tensor delta = sub(x = var_431, y = var_434_promoted)[name = tensor("delta")]; tensor valid_5 = greater_equal(x = var_433, y = var_86)[name = tensor("valid_5")]; tensor var_443 = const()[name = tensor("op_443"), val = tensor([1, 1, 1])]; tensor var_444 = reshape(shape = var_443, x = attn1_offset)[name = tensor("op_444")]; tensor var_446_promoted = const()[name = tensor("op_446_promoted"), val = tensor([0x1.ep+3])]; tensor var_447 = add(x = var_444, y = var_446_promoted)[name = tensor("op_447")]; tensor var_448 = less_equal(x = var_434_promoted, y = var_447)[name = tensor("op_448")]; tensor valid = logical_and(x = valid_5, y = var_448)[name = tensor("valid")]; tensor var_86_promoted_1 = const()[name = tensor("op_86_promoted_1"), val = tensor(0x0p+0)]; tensor var_450 = greater_equal(x = delta, y = var_86_promoted_1)[name = tensor("op_450")]; tensor attn_mask_7 = logical_and(x = valid, y = var_450)[name = tensor("attn_mask_7")]; tensor var_98_promoted_1 = const()[name = tensor("op_98_promoted_1"), val = tensor(0x1.f4p+7)]; tensor var_452 = less(x = delta, y = var_98_promoted_1)[name = tensor("op_452")]; tensor attn_mask_9 = logical_and(x = attn_mask_7, y = var_452)[name = tensor("attn_mask_9")]; tensor attn_mask_axes_0 = const()[name = tensor("attn_mask_axes_0"), val = tensor([1])]; tensor attn_mask = expand_dims(axes = attn_mask_axes_0, x = attn_mask_9)[name = tensor("attn_mask")]; tensor var_457_transpose_x_0 = const()[name = tensor("op_457_transpose_x_0"), val = tensor(false)]; tensor var_457_transpose_y_0 = const()[name = tensor("op_457_transpose_y_0"), val = tensor(false)]; tensor transpose_8_perm_0 = const()[name = tensor("transpose_8_perm_0"), val = tensor([0, 2, -3, -1])]; tensor transpose_9_perm_0 = const()[name = tensor("transpose_9_perm_0"), val = tensor([0, 2, -1, -3])]; tensor transpose_9 = transpose(perm = transpose_9_perm_0, x = new_k_cache)[name = tensor("transpose_12")]; tensor transpose_8 = transpose(perm = transpose_8_perm_0, x = q)[name = tensor("transpose_13")]; tensor var_457 = matmul(transpose_x = var_457_transpose_x_0, transpose_y = var_457_transpose_y_0, x = transpose_8, y = transpose_9)[name = tensor("op_457")]; tensor var_458 = const()[name = tensor("op_458"), val = tensor(0x1p-3)]; tensor attn_7 = mul(x = var_457, y = var_458)[name = tensor("attn_7")]; tensor var_460 = logical_not(x = attn_mask)[name = tensor("op_460")]; tensor attn_9 = select(a = var_100, b = attn_7, cond = var_460)[name = tensor("attn_9")]; tensor attn = softmax(axis = var_91, x = attn_9)[name = tensor("attn")]; tensor x_11_transpose_x_0 = const()[name = tensor("x_11_transpose_x_0"), val = tensor(false)]; tensor x_11_transpose_y_0 = const()[name = tensor("x_11_transpose_y_0"), val = tensor(false)]; tensor v_attn = transpose(perm = var_409, x = new_v_cache)[name = tensor("transpose_14")]; tensor x_11 = matmul(transpose_x = x_11_transpose_x_0, transpose_y = x_11_transpose_y_0, x = attn, y = v_attn)[name = tensor("x_11")]; tensor var_464_perm_0 = const()[name = tensor("op_464_perm_0"), val = tensor([0, 2, 1, 3])]; tensor var_465 = const()[name = tensor("op_465"), val = tensor([1, 16, 512])]; tensor var_464 = transpose(perm = var_464_perm_0, x = x_11)[name = tensor("transpose_11")]; tensor input_15 = reshape(shape = var_465, x = var_464)[name = tensor("input_15")]; tensor x_13 = linear(bias = linear_1_bias_0, weight = mimi_decoder_transformer_transformer_layers_1_self_attn_out_proj_weight, x = input_15)[name = tensor("linear_5")]; tensor var_474 = mul(x = mimi_decoder_transformer_transformer_layers_1_layer_scale_1_scale, y = x_13)[name = tensor("op_474")]; tensor input_17 = add(x = input_13, y = var_474)[name = tensor("input_17")]; tensor input_19_axes_0 = const()[name = tensor("input_19_axes_0"), val = tensor([-1])]; tensor input_19 = layer_norm(axes = input_19_axes_0, beta = mimi_decoder_transformer_transformer_layers_1_norm2_bias, epsilon = var_102, gamma = mimi_decoder_transformer_transformer_layers_1_norm2_weight, x = input_17)[name = tensor("input_19")]; tensor var_481 = linear(bias = linear_2_bias_0, weight = mimi_decoder_transformer_transformer_layers_1_linear1_weight, x = input_19)[name = tensor("linear_6")]; tensor input_21_mode_0 = const()[name = tensor("input_21_mode_0"), val = tensor("EXACT")]; tensor input_21 = gelu(mode = input_21_mode_0, x = var_481)[name = tensor("input_21")]; tensor x_15 = linear(bias = linear_1_bias_0, weight = mimi_decoder_transformer_transformer_layers_1_linear2_weight, x = input_21)[name = tensor("linear_7")]; tensor var_487 = mul(x = mimi_decoder_transformer_transformer_layers_1_layer_scale_2_scale, y = x_15)[name = tensor("op_487")]; tensor z = add(x = input_17, y = var_487)[name = tensor("z")]; tensor x_17_perm_0 = const()[name = tensor("x_17_perm_0"), val = tensor([0, 2, 1])]; tensor var_507 = const()[name = tensor("op_507"), val = tensor(0x1p+0)]; tensor var_508 = const()[name = tensor("op_508"), val = tensor(-1)]; tensor input_23_interleave_0 = const()[name = tensor("input_23_interleave_0"), val = tensor(false)]; tensor x_17 = transpose(perm = x_17_perm_0, x = z)[name = tensor("transpose_10")]; tensor input_23 = concat(axis = var_508, interleave = input_23_interleave_0, values = (conv0_prev, x_17))[name = tensor("input_23")]; tensor input_25_pad_type_0 = const()[name = tensor("input_25_pad_type_0"), val = tensor("valid")]; tensor input_25_strides_0 = const()[name = tensor("input_25_strides_0"), val = tensor([1])]; tensor input_25_pad_0 = const()[name = tensor("input_25_pad_0"), val = tensor([0, 0])]; tensor input_25_dilations_0 = const()[name = tensor("input_25_dilations_0"), val = tensor([1])]; tensor input_25_groups_0 = const()[name = tensor("input_25_groups_0"), val = tensor(1)]; tensor input_25 = conv(bias = mimi_decoder_model_0_conv_bias, 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 = mimi_decoder_model_0_conv_weight, x = input_23)[name = tensor("input_25")]; tensor var_542_begin_0 = const()[name = tensor("op_542_begin_0"), val = tensor([0, 0, 16])]; tensor var_542_end_0 = const()[name = tensor("op_542_end_0"), val = tensor([1, 512, 22])]; tensor var_542_end_mask_0 = const()[name = tensor("op_542_end_mask_0"), val = tensor([true, true, true])]; tensor var_542 = slice_by_index(begin = var_542_begin_0, end = var_542_end_0, end_mask = var_542_end_mask_0, x = input_23)[name = tensor("op_542")]; tensor input_27 = elu(alpha = var_507, x = input_25)[name = tensor("input_27")]; tensor y_5_pad_type_0 = const()[name = tensor("y_5_pad_type_0"), val = tensor("valid")]; tensor y_5_strides_0 = const()[name = tensor("y_5_strides_0"), val = tensor([6])]; tensor y_5_pad_0 = const()[name = tensor("y_5_pad_0"), val = tensor([0, 0])]; tensor y_5_dilations_0 = const()[name = tensor("y_5_dilations_0"), val = tensor([1])]; tensor y_5_groups_0 = const()[name = tensor("y_5_groups_0"), val = tensor(1)]; tensor y_5_has_output_shape_output_shape_0 = const()[name = tensor("y_5_has_output_shape_output_shape_0"), val = tensor([1, 256, 102])]; tensor y_5_has_output_shape = conv_transpose(bias = mimi_decoder_model_2_convtr_bias, dilations = y_5_dilations_0, groups = y_5_groups_0, output_shape = y_5_has_output_shape_output_shape_0, pad = y_5_pad_0, pad_type = y_5_pad_type_0, strides = y_5_strides_0, weight = mimi_decoder_model_2_convtr_weight, x = input_27)[name = tensor("y_5_has_output_shape")]; tensor var_557_begin_0 = const()[name = tensor("op_557_begin_0"), val = tensor([0, 0, 0])]; tensor var_557_end_0 = const()[name = tensor("op_557_end_0"), val = tensor([1, 256, 6])]; tensor var_557_end_mask_0 = const()[name = tensor("op_557_end_mask_0"), val = tensor([true, true, false])]; tensor var_557 = slice_by_index(begin = var_557_begin_0, end = var_557_end_0, end_mask = var_557_end_mask_0, x = y_5_has_output_shape)[name = tensor("op_557")]; tensor var_558 = add(x = var_557, y = convtr0_partial)[name = tensor("op_558")]; tensor var_559_begin_0 = const()[name = tensor("op_559_begin_0"), val = tensor([0, 0, 6])]; tensor var_559_end_0 = const()[name = tensor("op_559_end_0"), val = tensor([1, 256, 102])]; tensor var_559_end_mask_0 = const()[name = tensor("op_559_end_mask_0"), val = tensor([true, true, true])]; tensor var_559 = slice_by_index(begin = var_559_begin_0, end = var_559_end_0, end_mask = var_559_end_mask_0, x = y_5_has_output_shape)[name = tensor("op_559")]; tensor y_7_interleave_0 = const()[name = tensor("y_7_interleave_0"), val = tensor(false)]; tensor y_7 = concat(axis = var_508, interleave = y_7_interleave_0, values = (var_558, var_559))[name = tensor("y_7")]; tensor new_partial_1_begin_0 = const()[name = tensor("new_partial_1_begin_0"), val = tensor([0, 0, 96])]; tensor new_partial_1_end_0 = const()[name = tensor("new_partial_1_end_0"), val = tensor([1, 256, 102])]; tensor new_partial_1_end_mask_0 = const()[name = tensor("new_partial_1_end_mask_0"), val = tensor([true, true, true])]; tensor new_partial_1 = slice_by_index(begin = new_partial_1_begin_0, end = new_partial_1_end_0, end_mask = new_partial_1_end_mask_0, x = y_7)[name = tensor("new_partial_1")]; tensor var_564 = const()[name = tensor("op_564"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41766272)))]; tensor var_565 = sub(x = new_partial_1, y = var_564)[name = tensor("op_565")]; tensor input_29_begin_0 = const()[name = tensor("input_29_begin_0"), val = tensor([0, 0, 0])]; tensor input_29_end_0 = const()[name = tensor("input_29_end_0"), val = tensor([1, 256, 96])]; tensor input_29_end_mask_0 = const()[name = tensor("input_29_end_mask_0"), val = tensor([true, true, false])]; tensor input_29 = slice_by_index(begin = input_29_begin_0, end = input_29_end_0, end_mask = input_29_end_mask_0, x = y_7)[name = tensor("input_29")]; tensor x_19 = elu(alpha = var_507, x = input_29)[name = tensor("x_19")]; tensor input_31_interleave_0 = const()[name = tensor("input_31_interleave_0"), val = tensor(false)]; tensor input_31 = concat(axis = var_508, interleave = input_31_interleave_0, values = (res0_conv0_prev, x_19))[name = tensor("input_31")]; tensor input_33_pad_type_0 = const()[name = tensor("input_33_pad_type_0"), val = tensor("valid")]; tensor input_33_strides_0 = const()[name = tensor("input_33_strides_0"), val = tensor([1])]; tensor input_33_pad_0 = const()[name = tensor("input_33_pad_0"), val = tensor([0, 0])]; tensor input_33_dilations_0 = const()[name = tensor("input_33_dilations_0"), val = tensor([1])]; tensor input_33_groups_0 = const()[name = tensor("input_33_groups_0"), val = tensor(1)]; tensor input_33 = conv(bias = mimi_decoder_model_3_block_1_conv_bias, dilations = input_33_dilations_0, groups = input_33_groups_0, pad = input_33_pad_0, pad_type = input_33_pad_type_0, strides = input_33_strides_0, weight = mimi_decoder_model_3_block_1_conv_weight, x = input_31)[name = tensor("input_33")]; tensor var_585_begin_0 = const()[name = tensor("op_585_begin_0"), val = tensor([0, 0, 96])]; tensor var_585_end_0 = const()[name = tensor("op_585_end_0"), val = tensor([1, 256, 98])]; tensor var_585_end_mask_0 = const()[name = tensor("op_585_end_mask_0"), val = tensor([true, true, true])]; tensor var_585 = slice_by_index(begin = var_585_begin_0, end = var_585_end_0, end_mask = var_585_end_mask_0, x = input_31)[name = tensor("op_585")]; tensor x_21 = elu(alpha = var_507, x = input_33)[name = tensor("x_21")]; tensor v_5_pad_type_0 = const()[name = tensor("v_5_pad_type_0"), val = tensor("valid")]; tensor v_5_strides_0 = const()[name = tensor("v_5_strides_0"), val = tensor([1])]; tensor v_5_pad_0 = const()[name = tensor("v_5_pad_0"), val = tensor([0, 0])]; tensor v_5_dilations_0 = const()[name = tensor("v_5_dilations_0"), val = tensor([1])]; tensor v_5_groups_0 = const()[name = tensor("v_5_groups_0"), val = tensor(1)]; tensor v_5 = conv(bias = mimi_decoder_model_3_block_3_conv_bias, dilations = v_5_dilations_0, groups = v_5_groups_0, pad = v_5_pad_0, pad_type = v_5_pad_type_0, strides = v_5_strides_0, weight = mimi_decoder_model_3_block_3_conv_weight, x = x_21)[name = tensor("v_5")]; tensor input_35 = add(x = input_29, y = v_5)[name = tensor("input_35")]; tensor input_37 = elu(alpha = var_507, x = input_35)[name = tensor("input_37")]; tensor y_9_pad_type_0 = const()[name = tensor("y_9_pad_type_0"), val = tensor("valid")]; tensor y_9_strides_0 = const()[name = tensor("y_9_strides_0"), val = tensor([5])]; tensor y_9_pad_0 = const()[name = tensor("y_9_pad_0"), val = tensor([0, 0])]; tensor y_9_dilations_0 = const()[name = tensor("y_9_dilations_0"), val = tensor([1])]; tensor y_9_groups_0 = const()[name = tensor("y_9_groups_0"), val = tensor(1)]; tensor y_9_has_output_shape_output_shape_0 = const()[name = tensor("y_9_has_output_shape_output_shape_0"), val = tensor([1, 128, 485])]; tensor y_9_has_output_shape = conv_transpose(bias = mimi_decoder_model_5_convtr_bias, dilations = y_9_dilations_0, groups = y_9_groups_0, output_shape = y_9_has_output_shape_output_shape_0, pad = y_9_pad_0, pad_type = y_9_pad_type_0, strides = y_9_strides_0, weight = mimi_decoder_model_5_convtr_weight, x = input_37)[name = tensor("y_9_has_output_shape")]; tensor var_613_begin_0 = const()[name = tensor("op_613_begin_0"), val = tensor([0, 0, 0])]; tensor var_613_end_0 = const()[name = tensor("op_613_end_0"), val = tensor([1, 128, 5])]; tensor var_613_end_mask_0 = const()[name = tensor("op_613_end_mask_0"), val = tensor([true, true, false])]; tensor var_613 = slice_by_index(begin = var_613_begin_0, end = var_613_end_0, end_mask = var_613_end_mask_0, x = y_9_has_output_shape)[name = tensor("op_613")]; tensor var_614 = add(x = var_613, y = convtr1_partial)[name = tensor("op_614")]; tensor var_615_begin_0 = const()[name = tensor("op_615_begin_0"), val = tensor([0, 0, 5])]; tensor var_615_end_0 = const()[name = tensor("op_615_end_0"), val = tensor([1, 128, 485])]; tensor var_615_end_mask_0 = const()[name = tensor("op_615_end_mask_0"), val = tensor([true, true, true])]; tensor var_615 = slice_by_index(begin = var_615_begin_0, end = var_615_end_0, end_mask = var_615_end_mask_0, x = y_9_has_output_shape)[name = tensor("op_615")]; tensor y_11_interleave_0 = const()[name = tensor("y_11_interleave_0"), val = tensor(false)]; tensor y_11 = concat(axis = var_508, interleave = y_11_interleave_0, values = (var_614, var_615))[name = tensor("y_11")]; tensor new_partial_3_begin_0 = const()[name = tensor("new_partial_3_begin_0"), val = tensor([0, 0, 480])]; tensor new_partial_3_end_0 = const()[name = tensor("new_partial_3_end_0"), val = tensor([1, 128, 485])]; tensor new_partial_3_end_mask_0 = const()[name = tensor("new_partial_3_end_mask_0"), val = tensor([true, true, true])]; tensor new_partial_3 = slice_by_index(begin = new_partial_3_begin_0, end = new_partial_3_end_0, end_mask = new_partial_3_end_mask_0, x = y_11)[name = tensor("new_partial_3")]; tensor var_620 = const()[name = tensor("op_620"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41767360)))]; tensor var_621 = sub(x = new_partial_3, y = var_620)[name = tensor("op_621")]; tensor input_39_begin_0 = const()[name = tensor("input_39_begin_0"), val = tensor([0, 0, 0])]; tensor input_39_end_0 = const()[name = tensor("input_39_end_0"), val = tensor([1, 128, 480])]; tensor input_39_end_mask_0 = const()[name = tensor("input_39_end_mask_0"), val = tensor([true, true, false])]; tensor input_39 = slice_by_index(begin = input_39_begin_0, end = input_39_end_0, end_mask = input_39_end_mask_0, x = y_11)[name = tensor("input_39")]; tensor x_23 = elu(alpha = var_507, x = input_39)[name = tensor("x_23")]; tensor input_41_interleave_0 = const()[name = tensor("input_41_interleave_0"), val = tensor(false)]; tensor input_41 = concat(axis = var_508, interleave = input_41_interleave_0, values = (res1_conv0_prev, x_23))[name = tensor("input_41")]; tensor input_43_pad_type_0 = const()[name = tensor("input_43_pad_type_0"), val = tensor("valid")]; tensor input_43_strides_0 = const()[name = tensor("input_43_strides_0"), val = tensor([1])]; tensor input_43_pad_0 = const()[name = tensor("input_43_pad_0"), val = tensor([0, 0])]; tensor input_43_dilations_0 = const()[name = tensor("input_43_dilations_0"), val = tensor([1])]; tensor input_43_groups_0 = const()[name = tensor("input_43_groups_0"), val = tensor(1)]; tensor input_43 = conv(bias = mimi_decoder_model_6_block_1_conv_bias, dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = mimi_decoder_model_6_block_1_conv_weight, x = input_41)[name = tensor("input_43")]; tensor var_641_begin_0 = const()[name = tensor("op_641_begin_0"), val = tensor([0, 0, 480])]; tensor var_641_end_0 = const()[name = tensor("op_641_end_0"), val = tensor([1, 128, 482])]; tensor var_641_end_mask_0 = const()[name = tensor("op_641_end_mask_0"), val = tensor([true, true, true])]; tensor var_641 = slice_by_index(begin = var_641_begin_0, end = var_641_end_0, end_mask = var_641_end_mask_0, x = input_41)[name = tensor("op_641")]; tensor x_25 = elu(alpha = var_507, x = input_43)[name = tensor("x_25")]; tensor v_7_pad_type_0 = const()[name = tensor("v_7_pad_type_0"), val = tensor("valid")]; tensor v_7_strides_0 = const()[name = tensor("v_7_strides_0"), val = tensor([1])]; tensor v_7_pad_0 = const()[name = tensor("v_7_pad_0"), val = tensor([0, 0])]; tensor v_7_dilations_0 = const()[name = tensor("v_7_dilations_0"), val = tensor([1])]; tensor v_7_groups_0 = const()[name = tensor("v_7_groups_0"), val = tensor(1)]; tensor v_7 = conv(bias = mimi_decoder_model_6_block_3_conv_bias, dilations = v_7_dilations_0, groups = v_7_groups_0, pad = v_7_pad_0, pad_type = v_7_pad_type_0, strides = v_7_strides_0, weight = mimi_decoder_model_6_block_3_conv_weight, x = x_25)[name = tensor("v_7")]; tensor input_45 = add(x = input_39, y = v_7)[name = tensor("input_45")]; tensor input_47 = elu(alpha = var_507, x = input_45)[name = tensor("input_47")]; tensor y_13_pad_type_0 = const()[name = tensor("y_13_pad_type_0"), val = tensor("valid")]; tensor y_13_strides_0 = const()[name = tensor("y_13_strides_0"), val = tensor([4])]; tensor y_13_pad_0 = const()[name = tensor("y_13_pad_0"), val = tensor([0, 0])]; tensor y_13_dilations_0 = const()[name = tensor("y_13_dilations_0"), val = tensor([1])]; tensor y_13_groups_0 = const()[name = tensor("y_13_groups_0"), val = tensor(1)]; tensor y_13_has_output_shape_output_shape_0 = const()[name = tensor("y_13_has_output_shape_output_shape_0"), val = tensor([1, 64, 1924])]; tensor y_13_has_output_shape = conv_transpose(bias = mimi_decoder_model_8_convtr_bias, dilations = y_13_dilations_0, groups = y_13_groups_0, output_shape = y_13_has_output_shape_output_shape_0, pad = y_13_pad_0, pad_type = y_13_pad_type_0, strides = y_13_strides_0, weight = mimi_decoder_model_8_convtr_weight, x = input_47)[name = tensor("y_13_has_output_shape")]; tensor var_669_begin_0 = const()[name = tensor("op_669_begin_0"), val = tensor([0, 0, 0])]; tensor var_669_end_0 = const()[name = tensor("op_669_end_0"), val = tensor([1, 64, 4])]; tensor var_669_end_mask_0 = const()[name = tensor("op_669_end_mask_0"), val = tensor([true, true, false])]; tensor var_669 = slice_by_index(begin = var_669_begin_0, end = var_669_end_0, end_mask = var_669_end_mask_0, x = y_13_has_output_shape)[name = tensor("op_669")]; tensor var_670 = add(x = var_669, y = convtr2_partial)[name = tensor("op_670")]; tensor var_671_begin_0 = const()[name = tensor("op_671_begin_0"), val = tensor([0, 0, 4])]; tensor var_671_end_0 = const()[name = tensor("op_671_end_0"), val = tensor([1, 64, 1924])]; tensor var_671_end_mask_0 = const()[name = tensor("op_671_end_mask_0"), val = tensor([true, true, true])]; tensor var_671 = slice_by_index(begin = var_671_begin_0, end = var_671_end_0, end_mask = var_671_end_mask_0, x = y_13_has_output_shape)[name = tensor("op_671")]; tensor y_interleave_0 = const()[name = tensor("y_interleave_0"), val = tensor(false)]; tensor y = concat(axis = var_508, interleave = y_interleave_0, values = (var_670, var_671))[name = tensor("y")]; tensor new_partial_begin_0 = const()[name = tensor("new_partial_begin_0"), val = tensor([0, 0, 1920])]; tensor new_partial_end_0 = const()[name = tensor("new_partial_end_0"), val = tensor([1, 64, 1924])]; tensor new_partial_end_mask_0 = const()[name = tensor("new_partial_end_mask_0"), val = tensor([true, true, true])]; tensor new_partial = slice_by_index(begin = new_partial_begin_0, end = new_partial_end_0, end_mask = new_partial_end_mask_0, x = y)[name = tensor("new_partial")]; tensor var_676 = const()[name = tensor("op_676"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41767936)))]; tensor var_677 = sub(x = new_partial, y = var_676)[name = tensor("op_677")]; tensor input_49_begin_0 = const()[name = tensor("input_49_begin_0"), val = tensor([0, 0, 0])]; tensor input_49_end_0 = const()[name = tensor("input_49_end_0"), val = tensor([1, 64, 1920])]; tensor input_49_end_mask_0 = const()[name = tensor("input_49_end_mask_0"), val = tensor([true, true, false])]; tensor input_49 = slice_by_index(begin = input_49_begin_0, end = input_49_end_0, end_mask = input_49_end_mask_0, x = y)[name = tensor("input_49")]; tensor x_27 = elu(alpha = var_507, x = input_49)[name = tensor("x_27")]; tensor input_51_interleave_0 = const()[name = tensor("input_51_interleave_0"), val = tensor(false)]; tensor input_51 = concat(axis = var_508, interleave = input_51_interleave_0, values = (res2_conv0_prev, x_27))[name = tensor("input_51")]; tensor input_53_pad_type_0 = const()[name = tensor("input_53_pad_type_0"), val = tensor("valid")]; tensor input_53_strides_0 = const()[name = tensor("input_53_strides_0"), val = tensor([1])]; tensor input_53_pad_0 = const()[name = tensor("input_53_pad_0"), val = tensor([0, 0])]; tensor input_53_dilations_0 = const()[name = tensor("input_53_dilations_0"), val = tensor([1])]; tensor input_53_groups_0 = const()[name = tensor("input_53_groups_0"), val = tensor(1)]; tensor input_53 = conv(bias = mimi_decoder_model_9_block_1_conv_bias, 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 = mimi_decoder_model_9_block_1_conv_weight, x = input_51)[name = tensor("input_53")]; tensor var_697_begin_0 = const()[name = tensor("op_697_begin_0"), val = tensor([0, 0, 1920])]; tensor var_697_end_0 = const()[name = tensor("op_697_end_0"), val = tensor([1, 64, 1922])]; tensor var_697_end_mask_0 = const()[name = tensor("op_697_end_mask_0"), val = tensor([true, true, true])]; tensor var_697 = slice_by_index(begin = var_697_begin_0, end = var_697_end_0, end_mask = var_697_end_mask_0, x = input_51)[name = tensor("op_697")]; tensor x_29 = elu(alpha = var_507, x = input_53)[name = tensor("x_29")]; tensor v_pad_type_0 = const()[name = tensor("v_pad_type_0"), val = tensor("valid")]; tensor v_strides_0 = const()[name = tensor("v_strides_0"), val = tensor([1])]; tensor v_pad_0 = const()[name = tensor("v_pad_0"), val = tensor([0, 0])]; tensor v_dilations_0 = const()[name = tensor("v_dilations_0"), val = tensor([1])]; tensor v_groups_0 = const()[name = tensor("v_groups_0"), val = tensor(1)]; tensor v = conv(bias = mimi_decoder_model_9_block_3_conv_bias, dilations = v_dilations_0, groups = v_groups_0, pad = v_pad_0, pad_type = v_pad_type_0, strides = v_strides_0, weight = mimi_decoder_model_9_block_3_conv_weight, x = x_29)[name = tensor("v")]; tensor input_55 = add(x = input_49, y = v)[name = tensor("input_55")]; tensor x = elu(alpha = var_507, x = input_55)[name = tensor("x")]; tensor input_interleave_0 = const()[name = tensor("input_interleave_0"), val = tensor(false)]; tensor input = concat(axis = var_508, interleave = input_interleave_0, values = (conv_final_prev, x))[name = tensor("input")]; tensor var_724_pad_type_0 = const()[name = tensor("op_724_pad_type_0"), val = tensor("valid")]; tensor var_724_strides_0 = const()[name = tensor("op_724_strides_0"), val = tensor([1])]; tensor var_724_pad_0 = const()[name = tensor("op_724_pad_0"), val = tensor([0, 0])]; tensor var_724_dilations_0 = const()[name = tensor("op_724_dilations_0"), val = tensor([1])]; tensor var_724_groups_0 = const()[name = tensor("op_724_groups_0"), val = tensor(1)]; tensor var_724 = conv(bias = mimi_decoder_model_11_conv_bias, dilations = var_724_dilations_0, groups = var_724_groups_0, pad = var_724_pad_0, pad_type = var_724_pad_type_0, strides = var_724_strides_0, weight = mimi_decoder_model_11_conv_weight, x = input)[name = tensor("op_724")]; tensor var_725_begin_0 = const()[name = tensor("op_725_begin_0"), val = tensor([0, 0, 1920])]; tensor var_725_end_0 = const()[name = tensor("op_725_end_0"), val = tensor([1, 64, 1922])]; tensor var_725_end_mask_0 = const()[name = tensor("op_725_end_mask_0"), val = tensor([true, true, true])]; tensor var_725 = slice_by_index(begin = var_725_begin_0, end = var_725_end_0, end_mask = var_725_end_mask_0, x = input)[name = tensor("op_725")]; tensor var_740_promoted = const()[name = tensor("op_740_promoted"), val = tensor(0x1p+4)]; tensor var_741 = add(x = attn0_offset, y = var_740_promoted)[name = tensor("op_741")]; tensor var_743_promoted = const()[name = tensor("op_743_promoted"), val = tensor(0x1p+4)]; tensor var_744 = add(x = attn1_offset, y = var_743_promoted)[name = tensor("op_744")]; tensor conv0_first_tmp = identity(x = conv0_first)[name = tensor("conv0_first_tmp")]; tensor res0_conv0_first_tmp = identity(x = res0_conv0_first)[name = tensor("res0_conv0_first_tmp")]; tensor res0_conv1_prev_tmp = identity(x = res0_conv1_prev)[name = tensor("res0_conv1_prev_tmp")]; tensor res0_conv1_first_tmp = identity(x = res0_conv1_first)[name = tensor("res0_conv1_first_tmp")]; tensor res1_conv0_first_tmp = identity(x = res1_conv0_first)[name = tensor("res1_conv0_first_tmp")]; tensor res1_conv1_prev_tmp = identity(x = res1_conv1_prev)[name = tensor("res1_conv1_prev_tmp")]; tensor res1_conv1_first_tmp = identity(x = res1_conv1_first)[name = tensor("res1_conv1_first_tmp")]; tensor res2_conv0_first_tmp = identity(x = res2_conv0_first)[name = tensor("res2_conv0_first_tmp")]; tensor res2_conv1_prev_tmp = identity(x = res2_conv1_prev)[name = tensor("res2_conv1_prev_tmp")]; tensor res2_conv1_first_tmp = identity(x = res2_conv1_first)[name = tensor("res2_conv1_first_tmp")]; tensor conv_final_first_tmp = identity(x = conv_final_first)[name = tensor("conv_final_first_tmp")]; } -> (var_724, var_77, var_210, var_741, var_400, var_744, var_542, conv0_first, var_565, var_585, res0_conv0_first, res0_conv1_prev, res0_conv1_first, var_621, var_641, res1_conv0_first, res1_conv1_prev, res1_conv1_first, var_677, var_697, res2_conv0_first, res2_conv1_prev, res2_conv1_first, var_725, conv_final_first); }