diff --git "a/mimi_decoder_v2.mlmodelc/mimi_decoder.mlmodelc/model.mil" "b/mimi_decoder_v2.mlmodelc/mimi_decoder.mlmodelc/model.mil" new file mode 100644--- /dev/null +++ "b/mimi_decoder_v2.mlmodelc/mimi_decoder.mlmodelc/model.mil" @@ -0,0 +1,652 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.9.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.3.0"}})] +{ + func main(tensor attn0_cache, tensor attn0_end_offset, tensor attn0_offset, tensor attn1_cache, tensor attn1_end_offset, 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([0x1.38p-15])]; + 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_40 = mul(x = latent, y = emb_std)[name = tensor("op_40")]; + tensor denorm = add(x = var_40, 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_64 = const()[name = tensor("op_64"), 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_75_begin_0 = const()[name = tensor("op_75_begin_0"), val = tensor([0, 0, 0])]; + tensor var_75_end_0 = const()[name = tensor("op_75_end_0"), val = tensor([1, 512, 16])]; + tensor var_75_end_mask_0 = const()[name = tensor("op_75_end_mask_0"), val = tensor([true, true, false])]; + tensor var_75 = slice_by_index(begin = var_75_begin_0, end = var_75_end_0, end_mask = var_75_end_mask_0, x = y_1_has_output_shape)[name = tensor("op_75")]; + tensor var_76 = add(x = var_75, y = upsample_partial)[name = tensor("op_76")]; + 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_1_has_output_shape)[name = tensor("op_77")]; + tensor y_3_interleave_0 = const()[name = tensor("y_3_interleave_0"), val = tensor(false)]; + tensor y_3 = concat(axis = var_64, interleave = y_3_interleave_0, values = (var_76, var_77))[name = tensor("y_3")]; + tensor var_82_begin_0 = const()[name = tensor("op_82_begin_0"), val = tensor([0, 0, 16])]; + tensor var_82_end_0 = const()[name = tensor("op_82_end_0"), val = tensor([1, 512, 32])]; + tensor var_82_end_mask_0 = const()[name = tensor("op_82_end_mask_0"), val = tensor([true, true, true])]; + tensor var_82 = slice_by_index(begin = var_82_begin_0, end = var_82_end_0, end_mask = var_82_end_mask_0, x = y_3)[name = tensor("op_82")]; + 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_99 = const()[name = tensor("op_99"), val = tensor(-1)]; + tensor var_107 = const()[name = tensor("op_107"), val = tensor(-0x1.ff933cp+127)]; + tensor var_109 = const()[name = tensor("op_109"), 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_17")]; + tensor query_1 = layer_norm(axes = query_1_axes_0, beta = mimi_decoder_transformer_transformer_layers_0_norm1_bias, epsilon = var_109, 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 x_5 = 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_141 = const()[name = tensor("op_141"), val = tensor([1, 16, 3, 8, 64])]; + tensor x_7 = reshape(shape = var_141, x = x_5)[name = tensor("x_7")]; + tensor var_143 = const()[name = tensor("op_143"), val = tensor([2, 0, 3, 1, 4])]; + tensor var_145_split_sizes_0 = const()[name = tensor("op_145_split_sizes_0"), val = tensor([1, 1, 1])]; + tensor var_145_axis_0 = const()[name = tensor("op_145_axis_0"), val = tensor(0)]; + tensor var_144 = transpose(perm = var_143, x = x_7)[name = tensor("transpose_16")]; + tensor var_145_0, tensor var_145_1, tensor var_145_2 = split(axis = var_145_axis_0, split_sizes = var_145_split_sizes_0, x = var_144)[name = tensor("op_145")]; + tensor squeeze_0_axes_0 = const()[name = tensor("squeeze_0_axes_0"), val = tensor([0])]; + tensor squeeze_0 = squeeze(axes = squeeze_0_axes_0, x = var_145_0)[name = tensor("squeeze_0")]; + tensor squeeze_1_axes_0 = const()[name = tensor("squeeze_1_axes_0"), val = tensor([0])]; + tensor squeeze_1 = squeeze(axes = squeeze_1_axes_0, x = var_145_1)[name = tensor("squeeze_1")]; + tensor squeeze_2_axes_0 = const()[name = tensor("squeeze_2_axes_0"), val = tensor([0])]; + tensor squeeze_2 = squeeze(axes = squeeze_2_axes_0, x = var_145_2)[name = tensor("squeeze_2")]; + tensor var_149 = const()[name = tensor("op_149"), val = tensor([0, 2, 1, 3])]; + tensor var_151 = const()[name = tensor("op_151"), val = tensor([0, 2, 1, 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 = attn0_offset)[name = tensor("ts_3")]; + tensor var_167 = const()[name = tensor("op_167"), val = tensor([-1, 1, 1])]; + tensor ts_5 = reshape(shape = var_167, x = ts_3)[name = tensor("ts_5")]; + tensor var_171 = const()[name = tensor("op_171"), val = tensor([1, 16, 8, 32, 2])]; + tensor q_3 = transpose(perm = var_149, x = squeeze_0)[name = tensor("transpose_15")]; + tensor q_5 = reshape(shape = var_171, x = q_3)[name = tensor("q_5")]; + tensor var_175 = const()[name = tensor("op_175"), val = tensor([1, 16, 8, 32, 2])]; + tensor k_3 = transpose(perm = var_151, x = squeeze_1)[name = tensor("transpose_14")]; + tensor k_5 = reshape(shape = var_175, x = k_3)[name = tensor("k_5")]; + tensor var_177_begin_0 = const()[name = tensor("op_177_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_177_end_0 = const()[name = tensor("op_177_end_0"), val = tensor([1, 16, 8, 32, 1])]; + tensor var_177_end_mask_0 = const()[name = tensor("op_177_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_177_squeeze_mask_0 = const()[name = tensor("op_177_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_177 = slice_by_index(begin = var_177_begin_0, end = var_177_end_0, end_mask = var_177_end_mask_0, squeeze_mask = var_177_squeeze_mask_0, x = q_5)[name = tensor("op_177")]; + tensor var_179_begin_0 = const()[name = tensor("op_179_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_179_end_0 = const()[name = tensor("op_179_end_0"), val = tensor([1, 16, 8, 32, 2])]; + tensor var_179_end_mask_0 = const()[name = tensor("op_179_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_179_squeeze_mask_0 = const()[name = tensor("op_179_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_179 = slice_by_index(begin = var_179_begin_0, end = var_179_end_0, end_mask = var_179_end_mask_0, squeeze_mask = var_179_squeeze_mask_0, x = q_5)[name = tensor("op_179")]; + tensor var_181_begin_0 = const()[name = tensor("op_181_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_181_end_0 = const()[name = tensor("op_181_end_0"), val = tensor([1, 16, 8, 32, 1])]; + tensor var_181_end_mask_0 = const()[name = tensor("op_181_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_181_squeeze_mask_0 = const()[name = tensor("op_181_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_181 = slice_by_index(begin = var_181_begin_0, end = var_181_end_0, end_mask = var_181_end_mask_0, squeeze_mask = var_181_squeeze_mask_0, x = k_5)[name = tensor("op_181")]; + tensor var_183_begin_0 = const()[name = tensor("op_183_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_183_end_0 = const()[name = tensor("op_183_end_0"), val = tensor([1, 16, 8, 32, 2])]; + tensor var_183_end_mask_0 = const()[name = tensor("op_183_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_183_squeeze_mask_0 = const()[name = tensor("op_183_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_183 = slice_by_index(begin = var_183_begin_0, end = var_183_end_0, end_mask = var_183_end_mask_0, squeeze_mask = var_183_squeeze_mask_0, x = k_5)[name = tensor("op_183")]; + tensor var_185 = mul(x = freqs_1, y = ts_5)[name = tensor("op_185")]; + tensor rotr_1 = cos(x = var_185)[name = tensor("rotr_1")]; + tensor roti_1 = sin(x = var_185)[name = tensor("roti_1")]; + tensor var_189 = mul(x = var_177, y = rotr_1)[name = tensor("op_189")]; + tensor var_190 = mul(x = var_179, y = roti_1)[name = tensor("op_190")]; + tensor qor_1 = sub(x = var_189, y = var_190)[name = tensor("qor_1")]; + tensor var_192 = mul(x = var_177, y = roti_1)[name = tensor("op_192")]; + tensor var_193 = mul(x = var_179, y = rotr_1)[name = tensor("op_193")]; + tensor qoi_1 = add(x = var_192, y = var_193)[name = tensor("qoi_1")]; + tensor var_195 = mul(x = var_181, y = rotr_1)[name = tensor("op_195")]; + tensor var_196 = mul(x = var_183, y = roti_1)[name = tensor("op_196")]; + tensor kor_1 = sub(x = var_195, y = var_196)[name = tensor("kor_1")]; + tensor var_198 = mul(x = var_181, y = roti_1)[name = tensor("op_198")]; + tensor var_199 = mul(x = var_183, y = rotr_1)[name = tensor("op_199")]; + tensor koi_1 = add(x = var_198, y = var_199)[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_209 = const()[name = tensor("op_209"), val = tensor([1, 16, 8, 64])]; + tensor q_7 = reshape(shape = var_209, x = qo_1)[name = tensor("q_7")]; + tensor var_211 = const()[name = tensor("op_211"), val = tensor([1, 16, 8, 64])]; + tensor k_7 = reshape(shape = var_211, x = ko_1)[name = tensor("k_7")]; + tensor var_218 = const()[name = tensor("op_218"), val = tensor([0, 2, 1, 3])]; + tensor capacity_1 = const()[name = tensor("capacity_1"), val = tensor([256])]; + tensor indexes_1 = const()[name = tensor("indexes_1"), val = tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])]; + tensor cast_14_dtype_0 = const()[name = tensor("cast_14_dtype_0"), val = tensor("int32")]; + tensor var_229 = const()[name = tensor("op_229"), val = tensor([-1, 1])]; + tensor cast_14 = cast(dtype = cast_14_dtype_0, x = attn0_end_offset)[name = tensor("cast_61")]; + tensor var_230 = reshape(shape = var_229, x = cast_14)[name = tensor("op_230")]; + tensor indexes_3 = add(x = indexes_1, y = var_230)[name = tensor("indexes_3")]; + tensor indexes_5_div = floor_div(x = indexes_3, y = capacity_1)[name = tensor("indexes_5_div")]; + tensor indexes_5_div_scaled = mul(x = indexes_5_div, y = capacity_1)[name = tensor("indexes_5_div_scaled")]; + tensor indexes_5 = sub(x = indexes_3, y = indexes_5_div_scaled)[name = tensor("indexes_5")]; + tensor var_233 = const()[name = tensor("op_233"), val = tensor([1, 1, 16, 1])]; + tensor var_234 = reshape(shape = var_233, x = indexes_5)[name = tensor("op_234")]; + tensor var_236_reps_0 = const()[name = tensor("op_236_reps_0"), val = tensor([1, 8, 1, 64])]; + tensor var_236 = tile(reps = var_236_reps_0, x = var_234)[name = tensor("op_236")]; + tensor var_238_begin_0 = const()[name = tensor("op_238_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_238_end_0 = const()[name = tensor("op_238_end_0"), val = tensor([1, 1, 8, 256, 64])]; + tensor var_238_end_mask_0 = const()[name = tensor("op_238_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_238_squeeze_mask_0 = const()[name = tensor("op_238_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_238 = slice_by_index(begin = var_238_begin_0, end = var_238_end_0, end_mask = var_238_end_mask_0, squeeze_mask = var_238_squeeze_mask_0, x = attn0_cache)[name = tensor("op_238")]; + tensor new_k_1_axis_0 = const()[name = tensor("new_k_1_axis_0"), val = tensor(2)]; + tensor new_k_1_mode_0 = const()[name = tensor("new_k_1_mode_0"), val = tensor("update")]; + tensor new_k_1_validate_indices_0 = const()[name = tensor("new_k_1_validate_indices_0"), val = tensor(false)]; + tensor k_9 = transpose(perm = var_218, x = k_7)[name = tensor("transpose_13")]; + tensor new_k_1 = scatter_along_axis(axis = new_k_1_axis_0, data = var_238, indices = var_236, mode = new_k_1_mode_0, updates = k_9, validate_indices = new_k_1_validate_indices_0)[name = tensor("new_k_1")]; + tensor var_240_begin_0 = const()[name = tensor("op_240_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_240_end_0 = const()[name = tensor("op_240_end_0"), val = tensor([2, 1, 8, 256, 64])]; + tensor var_240_end_mask_0 = const()[name = tensor("op_240_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_240_squeeze_mask_0 = const()[name = tensor("op_240_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_240 = slice_by_index(begin = var_240_begin_0, end = var_240_end_0, end_mask = var_240_end_mask_0, squeeze_mask = var_240_squeeze_mask_0, x = attn0_cache)[name = tensor("op_240")]; + tensor new_v_1_axis_0 = const()[name = tensor("new_v_1_axis_0"), val = tensor(2)]; + tensor new_v_1_mode_0 = const()[name = tensor("new_v_1_mode_0"), val = tensor("update")]; + tensor new_v_1_validate_indices_0 = const()[name = tensor("new_v_1_validate_indices_0"), val = tensor(false)]; + tensor new_v_1 = scatter_along_axis(axis = new_v_1_axis_0, data = var_240, indices = var_236, mode = new_v_1_mode_0, updates = squeeze_2, validate_indices = new_v_1_validate_indices_0)[name = tensor("new_v_1")]; + tensor var_243 = const()[name = tensor("op_243"), val = tensor([-1, 1])]; + tensor var_244 = reshape(shape = var_243, x = attn0_end_offset)[name = tensor("op_244")]; + tensor T_3_promoted = const()[name = tensor("T_3_promoted"), val = tensor([0x1p+4])]; + tensor var_245 = add(x = var_244, y = T_3_promoted)[name = tensor("op_245")]; + tensor var_246_promoted = const()[name = tensor("op_246_promoted"), val = tensor(0x1p+0)]; + tensor last_offset_1 = sub(x = var_245, y = var_246_promoted)[name = tensor("last_offset_1")]; + tensor capacity_1_promoted = const()[name = tensor("capacity_1_promoted"), val = tensor([0x1p+8])]; + tensor end_index_1_div = floor_div(x = last_offset_1, y = capacity_1_promoted)[name = tensor("end_index_1_div")]; + tensor end_index_1_div_scaled = mul(x = end_index_1_div, y = capacity_1_promoted)[name = tensor("end_index_1_div_scaled")]; + tensor end_index_1 = sub(x = last_offset_1, y = end_index_1_div_scaled)[name = tensor("end_index_1")]; + tensor indexes_7_promoted = const()[name = tensor("indexes_7_promoted"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41230976)))]; + tensor delta_1 = sub(x = indexes_7_promoted, y = end_index_1)[name = tensor("delta_1")]; + tensor var_94_promoted = const()[name = tensor("op_94_promoted"), val = tensor(0x0p+0)]; + tensor var_250 = less_equal(x = delta_1, y = var_94_promoted)[name = tensor("op_250")]; + tensor var_251 = add(x = last_offset_1, y = delta_1)[name = tensor("op_251")]; + tensor capacity_1_promoted_1 = const()[name = tensor("capacity_1_promoted_1"), val = tensor([0x1p+8])]; + tensor var_253 = sub(x = var_251, y = capacity_1_promoted_1)[name = tensor("op_253")]; + tensor positions_1 = select(a = var_251, b = var_253, cond = var_250)[name = tensor("positions_1")]; + tensor T_3_promoted_1 = const()[name = tensor("T_3_promoted_1"), val = tensor([0x1p+4])]; + tensor new_end_offset_1 = add(x = attn0_end_offset, y = T_3_promoted_1)[name = tensor("new_end_offset_1")]; + tensor var_256 = const()[name = tensor("op_256"), val = tensor([-1, 1])]; + tensor var_257 = reshape(shape = var_256, x = new_end_offset_1)[name = tensor("op_257")]; + tensor invalid_1 = greater_equal(x = indexes_7_promoted, y = var_257)[name = tensor("invalid_1")]; + tensor var_259_value_0 = const()[name = tensor("op_259_value_0"), val = tensor(-1)]; + tensor var_259 = fill_like(ref_tensor = positions_1, value = var_259_value_0)[name = tensor("op_259")]; + tensor var_259_promoted_dtype_0 = const()[name = tensor("op_259_promoted_dtype_0"), val = tensor("fp32")]; + tensor var_259_promoted = cast(dtype = var_259_promoted_dtype_0, x = var_259)[name = tensor("cast_60")]; + tensor pos_k_1 = select(a = var_259_promoted, b = positions_1, cond = invalid_1)[name = tensor("pos_k_1")]; + tensor var_262_axis_0 = const()[name = tensor("op_262_axis_0"), val = tensor(0)]; + tensor var_262 = stack(axis = var_262_axis_0, values = (new_k_1, new_v_1))[name = tensor("op_262")]; + tensor pos_k_3_axes_0 = const()[name = tensor("pos_k_3_axes_0"), val = tensor([1])]; + tensor pos_k_3 = expand_dims(axes = pos_k_3_axes_0, x = pos_k_1)[name = tensor("pos_k_3")]; + tensor var_265 = const()[name = tensor("op_265"), val = tensor([-1, 1, 1])]; + tensor var_266 = reshape(shape = var_265, x = attn0_offset)[name = tensor("op_266")]; + tensor var_269_promoted = const()[name = tensor("op_269_promoted"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41232064)))]; + tensor pos_q_1 = add(x = var_266, y = var_269_promoted)[name = tensor("pos_q_1")]; + tensor delta_3 = sub(x = pos_q_1, y = pos_k_3)[name = tensor("delta_3")]; + tensor var_94_promoted_1 = const()[name = tensor("op_94_promoted_1"), val = tensor(0x0p+0)]; + tensor var_272 = greater_equal(x = pos_k_3, y = var_94_promoted_1)[name = tensor("op_272")]; + tensor var_94_promoted_2 = const()[name = tensor("op_94_promoted_2"), val = tensor(0x0p+0)]; + tensor var_273 = greater_equal(x = delta_3, y = var_94_promoted_2)[name = tensor("op_273")]; + tensor attn_bias_1 = logical_and(x = var_272, y = var_273)[name = tensor("attn_bias_1")]; + tensor var_105_promoted = const()[name = tensor("op_105_promoted"), val = tensor(0x1.f4p+7)]; + tensor var_275 = less(x = delta_3, y = var_105_promoted)[name = tensor("op_275")]; + tensor attn_bias_3 = logical_and(x = attn_bias_1, y = var_275)[name = tensor("attn_bias_3")]; + tensor attn_bias_5_axes_0 = const()[name = tensor("attn_bias_5_axes_0"), val = tensor([1])]; + tensor attn_bias_5 = expand_dims(axes = attn_bias_5_axes_0, x = attn_bias_3)[name = tensor("attn_bias_5")]; + tensor transpose_0_perm_0 = const()[name = tensor("transpose_0_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_280_transpose_x_1 = const()[name = tensor("op_280_transpose_x_1"), val = tensor(false)]; + tensor var_280_transpose_y_1 = const()[name = tensor("op_280_transpose_y_1"), val = tensor(true)]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = q_7)[name = tensor("transpose_12")]; + tensor var_280 = matmul(transpose_x = var_280_transpose_x_1, transpose_y = var_280_transpose_y_1, x = transpose_0, y = new_k_1)[name = tensor("op_280")]; + tensor var_281 = const()[name = tensor("op_281"), val = tensor(0x1p-3)]; + tensor attn_1 = mul(x = var_280, y = var_281)[name = tensor("attn_1")]; + tensor var_283 = logical_not(x = attn_bias_5)[name = tensor("op_283")]; + tensor attn_3 = select(a = var_107, b = attn_1, cond = var_283)[name = tensor("attn_3")]; + tensor attn_5 = softmax(axis = var_99, x = attn_3)[name = tensor("attn_5")]; + tensor x_9_transpose_x_0 = const()[name = tensor("x_9_transpose_x_0"), val = tensor(false)]; + tensor x_9_transpose_y_0 = const()[name = tensor("x_9_transpose_y_0"), val = tensor(false)]; + tensor x_9 = matmul(transpose_x = x_9_transpose_x_0, transpose_y = x_9_transpose_y_0, x = attn_5, y = new_v_1)[name = tensor("x_9")]; + tensor var_305 = const()[name = tensor("op_305"), val = tensor([0, 2, 1, 3])]; + tensor var_307 = const()[name = tensor("op_307"), val = tensor([1, 16, 512])]; + tensor x_11 = transpose(perm = var_305, x = x_9)[name = tensor("transpose_11")]; + tensor input_5 = reshape(shape = var_307, x = x_11)[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(41232192)))]; + tensor x_13 = 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_317 = mul(x = mimi_decoder_transformer_transformer_layers_0_layer_scale_1_scale, y = x_13)[name = tensor("op_317")]; + tensor input_7 = add(x = input_3, y = var_317)[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_109, 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(41234304)))]; + tensor var_324 = 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_324)[name = tensor("input_11")]; + tensor x_15 = linear(bias = linear_1_bias_0, weight = mimi_decoder_transformer_transformer_layers_0_linear2_weight, x = input_11)[name = tensor("linear_3")]; + tensor var_330 = mul(x = mimi_decoder_transformer_transformer_layers_0_layer_scale_2_scale, y = x_15)[name = tensor("op_330")]; + tensor input_13 = add(x = input_7, y = var_330)[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_109, gamma = mimi_decoder_transformer_transformer_layers_1_norm1_weight, x = input_13)[name = tensor("query")]; + tensor x_17 = 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_358 = const()[name = tensor("op_358"), val = tensor([1, 16, 3, 8, 64])]; + tensor x_19 = reshape(shape = var_358, x = x_17)[name = tensor("x_19")]; + tensor var_360 = const()[name = tensor("op_360"), val = tensor([2, 0, 3, 1, 4])]; + tensor var_362_split_sizes_0 = const()[name = tensor("op_362_split_sizes_0"), val = tensor([1, 1, 1])]; + tensor var_362_axis_0 = const()[name = tensor("op_362_axis_0"), val = tensor(0)]; + tensor var_361 = transpose(perm = var_360, x = x_19)[name = tensor("transpose_10")]; + tensor var_362_0, tensor var_362_1, tensor var_362_2 = split(axis = var_362_axis_0, split_sizes = var_362_split_sizes_0, x = var_361)[name = tensor("op_362")]; + tensor squeeze_3_axes_0 = const()[name = tensor("squeeze_3_axes_0"), val = tensor([0])]; + tensor squeeze_3 = squeeze(axes = squeeze_3_axes_0, x = var_362_0)[name = tensor("squeeze_3")]; + tensor squeeze_4_axes_0 = const()[name = tensor("squeeze_4_axes_0"), val = tensor([0])]; + tensor squeeze_4 = squeeze(axes = squeeze_4_axes_0, x = var_362_1)[name = tensor("squeeze_4")]; + tensor squeeze_5_axes_0 = const()[name = tensor("squeeze_5_axes_0"), val = tensor([0])]; + tensor squeeze_5 = squeeze(axes = squeeze_5_axes_0, x = var_362_2)[name = tensor("squeeze_5")]; + tensor var_366 = const()[name = tensor("op_366"), val = tensor([0, 2, 1, 3])]; + tensor var_368 = const()[name = tensor("op_368"), val = tensor([0, 2, 1, 3])]; + tensor freqs = const()[name = tensor("freqs"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41242560)))]; + tensor ts_7_promoted = const()[name = tensor("ts_7_promoted"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41242752)))]; + tensor ts_9 = add(x = ts_7_promoted, y = attn1_offset)[name = tensor("ts_9")]; + tensor var_384 = const()[name = tensor("op_384"), val = tensor([-1, 1, 1])]; + tensor ts = reshape(shape = var_384, x = ts_9)[name = tensor("ts")]; + tensor var_388 = const()[name = tensor("op_388"), val = tensor([1, 16, 8, 32, 2])]; + tensor q_13 = transpose(perm = var_366, x = squeeze_3)[name = tensor("transpose_9")]; + tensor q_15 = reshape(shape = var_388, x = q_13)[name = tensor("q_15")]; + tensor var_392 = const()[name = tensor("op_392"), val = tensor([1, 16, 8, 32, 2])]; + tensor k_13 = transpose(perm = var_368, x = squeeze_4)[name = tensor("transpose_8")]; + tensor k_15 = reshape(shape = var_392, x = k_13)[name = tensor("k_15")]; + tensor var_394_begin_0 = const()[name = tensor("op_394_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_394_end_0 = const()[name = tensor("op_394_end_0"), val = tensor([1, 16, 8, 32, 1])]; + tensor var_394_end_mask_0 = const()[name = tensor("op_394_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_394_squeeze_mask_0 = const()[name = tensor("op_394_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_394 = slice_by_index(begin = var_394_begin_0, end = var_394_end_0, end_mask = var_394_end_mask_0, squeeze_mask = var_394_squeeze_mask_0, x = q_15)[name = tensor("op_394")]; + tensor var_396_begin_0 = const()[name = tensor("op_396_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_396_end_0 = const()[name = tensor("op_396_end_0"), val = tensor([1, 16, 8, 32, 2])]; + tensor var_396_end_mask_0 = const()[name = tensor("op_396_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_396_squeeze_mask_0 = const()[name = tensor("op_396_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_396 = slice_by_index(begin = var_396_begin_0, end = var_396_end_0, end_mask = var_396_end_mask_0, squeeze_mask = var_396_squeeze_mask_0, x = q_15)[name = tensor("op_396")]; + tensor var_398_begin_0 = const()[name = tensor("op_398_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_398_end_0 = const()[name = tensor("op_398_end_0"), val = tensor([1, 16, 8, 32, 1])]; + tensor var_398_end_mask_0 = const()[name = tensor("op_398_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_398_squeeze_mask_0 = const()[name = tensor("op_398_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_398 = slice_by_index(begin = var_398_begin_0, end = var_398_end_0, end_mask = var_398_end_mask_0, squeeze_mask = var_398_squeeze_mask_0, x = k_15)[name = tensor("op_398")]; + tensor var_400_begin_0 = const()[name = tensor("op_400_begin_0"), val = tensor([0, 0, 0, 0, 1])]; + tensor var_400_end_0 = const()[name = tensor("op_400_end_0"), val = tensor([1, 16, 8, 32, 2])]; + tensor var_400_end_mask_0 = const()[name = tensor("op_400_end_mask_0"), val = tensor([true, true, true, true, false])]; + tensor var_400_squeeze_mask_0 = const()[name = tensor("op_400_squeeze_mask_0"), val = tensor([false, false, false, false, true])]; + tensor var_400 = slice_by_index(begin = var_400_begin_0, end = var_400_end_0, end_mask = var_400_end_mask_0, squeeze_mask = var_400_squeeze_mask_0, x = k_15)[name = tensor("op_400")]; + tensor var_402 = mul(x = freqs, y = ts)[name = tensor("op_402")]; + tensor rotr = cos(x = var_402)[name = tensor("rotr")]; + tensor roti = sin(x = var_402)[name = tensor("roti")]; + tensor var_406 = mul(x = var_394, y = rotr)[name = tensor("op_406")]; + tensor var_407 = mul(x = var_396, y = roti)[name = tensor("op_407")]; + tensor qor_5 = sub(x = var_406, y = var_407)[name = tensor("qor_5")]; + tensor var_409 = mul(x = var_394, y = roti)[name = tensor("op_409")]; + tensor var_410 = mul(x = var_396, y = rotr)[name = tensor("op_410")]; + tensor qoi_5 = add(x = var_409, y = var_410)[name = tensor("qoi_5")]; + tensor var_412 = mul(x = var_398, y = rotr)[name = tensor("op_412")]; + tensor var_413 = mul(x = var_400, y = roti)[name = tensor("op_413")]; + tensor kor_5 = sub(x = var_412, y = var_413)[name = tensor("kor_5")]; + tensor var_415 = mul(x = var_398, y = roti)[name = tensor("op_415")]; + tensor var_416 = mul(x = var_400, y = rotr)[name = tensor("op_416")]; + tensor koi_5 = add(x = var_415, y = var_416)[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_426 = const()[name = tensor("op_426"), val = tensor([1, 16, 8, 64])]; + tensor q_17 = reshape(shape = var_426, x = qo)[name = tensor("q_17")]; + tensor var_428 = const()[name = tensor("op_428"), val = tensor([1, 16, 8, 64])]; + tensor k_17 = reshape(shape = var_428, x = ko)[name = tensor("k_17")]; + tensor var_435 = const()[name = tensor("op_435"), val = tensor([0, 2, 1, 3])]; + tensor capacity = const()[name = tensor("capacity"), val = tensor([256])]; + tensor indexes_9 = const()[name = tensor("indexes_9"), val = tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])]; + tensor cast_43_dtype_0 = const()[name = tensor("cast_43_dtype_0"), val = tensor("int32")]; + tensor var_446 = const()[name = tensor("op_446"), val = tensor([-1, 1])]; + tensor cast_43 = cast(dtype = cast_43_dtype_0, x = attn1_end_offset)[name = tensor("cast_59")]; + tensor var_447 = reshape(shape = var_446, x = cast_43)[name = tensor("op_447")]; + tensor indexes_11 = add(x = indexes_9, y = var_447)[name = tensor("indexes_11")]; + tensor indexes_13_div = floor_div(x = indexes_11, y = capacity)[name = tensor("indexes_13_div")]; + tensor indexes_13_div_scaled = mul(x = indexes_13_div, y = capacity)[name = tensor("indexes_13_div_scaled")]; + tensor indexes_13 = sub(x = indexes_11, y = indexes_13_div_scaled)[name = tensor("indexes_13")]; + tensor var_450 = const()[name = tensor("op_450"), val = tensor([1, 1, 16, 1])]; + tensor var_451 = reshape(shape = var_450, x = indexes_13)[name = tensor("op_451")]; + tensor var_453_reps_0 = const()[name = tensor("op_453_reps_0"), val = tensor([1, 8, 1, 64])]; + tensor var_453 = tile(reps = var_453_reps_0, x = var_451)[name = tensor("op_453")]; + tensor var_455_begin_0 = const()[name = tensor("op_455_begin_0"), val = tensor([0, 0, 0, 0, 0])]; + tensor var_455_end_0 = const()[name = tensor("op_455_end_0"), val = tensor([1, 1, 8, 256, 64])]; + tensor var_455_end_mask_0 = const()[name = tensor("op_455_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_455_squeeze_mask_0 = const()[name = tensor("op_455_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_455 = slice_by_index(begin = var_455_begin_0, end = var_455_end_0, end_mask = var_455_end_mask_0, squeeze_mask = var_455_squeeze_mask_0, x = attn1_cache)[name = tensor("op_455")]; + tensor new_k_axis_0 = const()[name = tensor("new_k_axis_0"), val = tensor(2)]; + tensor new_k_mode_0 = const()[name = tensor("new_k_mode_0"), val = tensor("update")]; + tensor new_k_validate_indices_0 = const()[name = tensor("new_k_validate_indices_0"), val = tensor(false)]; + tensor k = transpose(perm = var_435, x = k_17)[name = tensor("transpose_7")]; + tensor new_k = scatter_along_axis(axis = new_k_axis_0, data = var_455, indices = var_453, mode = new_k_mode_0, updates = k, validate_indices = new_k_validate_indices_0)[name = tensor("new_k")]; + tensor var_457_begin_0 = const()[name = tensor("op_457_begin_0"), val = tensor([1, 0, 0, 0, 0])]; + tensor var_457_end_0 = const()[name = tensor("op_457_end_0"), val = tensor([2, 1, 8, 256, 64])]; + tensor var_457_end_mask_0 = const()[name = tensor("op_457_end_mask_0"), val = tensor([false, true, true, true, true])]; + tensor var_457_squeeze_mask_0 = const()[name = tensor("op_457_squeeze_mask_0"), val = tensor([true, false, false, false, false])]; + tensor var_457 = slice_by_index(begin = var_457_begin_0, end = var_457_end_0, end_mask = var_457_end_mask_0, squeeze_mask = var_457_squeeze_mask_0, x = attn1_cache)[name = tensor("op_457")]; + tensor new_v_axis_0 = const()[name = tensor("new_v_axis_0"), val = tensor(2)]; + tensor new_v_mode_0 = const()[name = tensor("new_v_mode_0"), val = tensor("update")]; + tensor new_v_validate_indices_0 = const()[name = tensor("new_v_validate_indices_0"), val = tensor(false)]; + tensor new_v = scatter_along_axis(axis = new_v_axis_0, data = var_457, indices = var_453, mode = new_v_mode_0, updates = squeeze_5, validate_indices = new_v_validate_indices_0)[name = tensor("new_v")]; + tensor var_460 = const()[name = tensor("op_460"), val = tensor([-1, 1])]; + tensor var_461 = reshape(shape = var_460, x = attn1_end_offset)[name = tensor("op_461")]; + tensor T_9_promoted = const()[name = tensor("T_9_promoted"), val = tensor([0x1p+4])]; + tensor var_462 = add(x = var_461, y = T_9_promoted)[name = tensor("op_462")]; + tensor var_463_promoted = const()[name = tensor("op_463_promoted"), val = tensor(0x1p+0)]; + tensor last_offset = sub(x = var_462, y = var_463_promoted)[name = tensor("last_offset")]; + tensor capacity_promoted = const()[name = tensor("capacity_promoted"), val = tensor([0x1p+8])]; + tensor end_index_div = floor_div(x = last_offset, y = capacity_promoted)[name = tensor("end_index_div")]; + tensor end_index_div_scaled = mul(x = end_index_div, y = capacity_promoted)[name = tensor("end_index_div_scaled")]; + tensor end_index = sub(x = last_offset, y = end_index_div_scaled)[name = tensor("end_index")]; + tensor delta_5 = sub(x = indexes_7_promoted, y = end_index)[name = tensor("delta_5")]; + tensor var_94_promoted_3 = const()[name = tensor("op_94_promoted_3"), val = tensor(0x0p+0)]; + tensor var_467 = less_equal(x = delta_5, y = var_94_promoted_3)[name = tensor("op_467")]; + tensor var_468 = add(x = last_offset, y = delta_5)[name = tensor("op_468")]; + tensor capacity_promoted_1 = const()[name = tensor("capacity_promoted_1"), val = tensor([0x1p+8])]; + tensor var_470 = sub(x = var_468, y = capacity_promoted_1)[name = tensor("op_470")]; + tensor positions = select(a = var_468, b = var_470, cond = var_467)[name = tensor("positions")]; + tensor T_9_promoted_1 = const()[name = tensor("T_9_promoted_1"), val = tensor([0x1p+4])]; + tensor new_end_offset = add(x = attn1_end_offset, y = T_9_promoted_1)[name = tensor("new_end_offset")]; + tensor var_473 = const()[name = tensor("op_473"), val = tensor([-1, 1])]; + tensor var_474 = reshape(shape = var_473, x = new_end_offset)[name = tensor("op_474")]; + tensor invalid = greater_equal(x = indexes_7_promoted, y = var_474)[name = tensor("invalid")]; + tensor var_476_value_0 = const()[name = tensor("op_476_value_0"), val = tensor(-1)]; + tensor var_476 = fill_like(ref_tensor = positions, value = var_476_value_0)[name = tensor("op_476")]; + tensor var_476_promoted_dtype_0 = const()[name = tensor("op_476_promoted_dtype_0"), val = tensor("fp32")]; + tensor var_476_promoted = cast(dtype = var_476_promoted_dtype_0, x = var_476)[name = tensor("cast_58")]; + tensor pos_k_5 = select(a = var_476_promoted, b = positions, cond = invalid)[name = tensor("pos_k_5")]; + tensor var_479_axis_0 = const()[name = tensor("op_479_axis_0"), val = tensor(0)]; + tensor var_479 = stack(axis = var_479_axis_0, values = (new_k, new_v))[name = tensor("op_479")]; + tensor pos_k_axes_0 = const()[name = tensor("pos_k_axes_0"), val = tensor([1])]; + tensor pos_k = expand_dims(axes = pos_k_axes_0, x = pos_k_5)[name = tensor("pos_k")]; + tensor var_482 = const()[name = tensor("op_482"), val = tensor([-1, 1, 1])]; + tensor var_483 = reshape(shape = var_482, x = attn1_offset)[name = tensor("op_483")]; + tensor var_486_promoted = const()[name = tensor("op_486_promoted"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41242880)))]; + tensor pos_q = add(x = var_483, y = var_486_promoted)[name = tensor("pos_q")]; + tensor delta = sub(x = pos_q, y = pos_k)[name = tensor("delta")]; + tensor var_94_promoted_4 = const()[name = tensor("op_94_promoted_4"), val = tensor(0x0p+0)]; + tensor var_489 = greater_equal(x = pos_k, y = var_94_promoted_4)[name = tensor("op_489")]; + tensor var_94_promoted_5 = const()[name = tensor("op_94_promoted_5"), val = tensor(0x0p+0)]; + tensor var_490 = greater_equal(x = delta, y = var_94_promoted_5)[name = tensor("op_490")]; + tensor attn_bias_7 = logical_and(x = var_489, y = var_490)[name = tensor("attn_bias_7")]; + tensor var_105_promoted_1 = const()[name = tensor("op_105_promoted_1"), val = tensor(0x1.f4p+7)]; + tensor var_492 = less(x = delta, y = var_105_promoted_1)[name = tensor("op_492")]; + tensor attn_bias_9 = logical_and(x = attn_bias_7, y = var_492)[name = tensor("attn_bias_9")]; + tensor attn_bias_axes_0 = const()[name = tensor("attn_bias_axes_0"), val = tensor([1])]; + tensor attn_bias = expand_dims(axes = attn_bias_axes_0, x = attn_bias_9)[name = tensor("attn_bias")]; + tensor transpose_2_perm_0 = const()[name = tensor("transpose_2_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_497_transpose_x_1 = const()[name = tensor("op_497_transpose_x_1"), val = tensor(false)]; + tensor var_497_transpose_y_1 = const()[name = tensor("op_497_transpose_y_1"), val = tensor(true)]; + tensor transpose_2 = transpose(perm = transpose_2_perm_0, x = q_17)[name = tensor("transpose_6")]; + tensor var_497 = matmul(transpose_x = var_497_transpose_x_1, transpose_y = var_497_transpose_y_1, x = transpose_2, y = new_k)[name = tensor("op_497")]; + tensor var_498 = const()[name = tensor("op_498"), val = tensor(0x1p-3)]; + tensor attn_7 = mul(x = var_497, y = var_498)[name = tensor("attn_7")]; + tensor var_500 = logical_not(x = attn_bias)[name = tensor("op_500")]; + tensor attn_9 = select(a = var_107, b = attn_7, cond = var_500)[name = tensor("attn_9")]; + tensor attn = softmax(axis = var_99, x = attn_9)[name = tensor("attn")]; + tensor x_21_transpose_x_0 = const()[name = tensor("x_21_transpose_x_0"), val = tensor(false)]; + tensor x_21_transpose_y_0 = const()[name = tensor("x_21_transpose_y_0"), val = tensor(false)]; + tensor x_21 = matmul(transpose_x = x_21_transpose_x_0, transpose_y = x_21_transpose_y_0, x = attn, y = new_v)[name = tensor("x_21")]; + tensor var_522 = const()[name = tensor("op_522"), val = tensor([0, 2, 1, 3])]; + tensor var_524 = const()[name = tensor("op_524"), val = tensor([1, 16, 512])]; + tensor x_23 = transpose(perm = var_522, x = x_21)[name = tensor("transpose_5")]; + tensor input_15 = reshape(shape = var_524, x = x_23)[name = tensor("input_15")]; + tensor x_25 = 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_534 = mul(x = mimi_decoder_transformer_transformer_layers_1_layer_scale_1_scale, y = x_25)[name = tensor("op_534")]; + tensor input_17 = add(x = input_13, y = var_534)[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_109, gamma = mimi_decoder_transformer_transformer_layers_1_norm2_weight, x = input_17)[name = tensor("input_19")]; + tensor var_541 = 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_541)[name = tensor("input_21")]; + tensor x_27 = linear(bias = linear_1_bias_0, weight = mimi_decoder_transformer_transformer_layers_1_linear2_weight, x = input_21)[name = tensor("linear_7")]; + tensor var_547 = mul(x = mimi_decoder_transformer_transformer_layers_1_layer_scale_2_scale, y = x_27)[name = tensor("op_547")]; + tensor z = add(x = input_17, y = var_547)[name = tensor("z")]; + tensor x_29_perm_0 = const()[name = tensor("x_29_perm_0"), val = tensor([0, 2, 1])]; + tensor var_569 = const()[name = tensor("op_569"), val = tensor(0x1p+0)]; + tensor var_571 = const()[name = tensor("op_571"), val = tensor(-1)]; + tensor input_23_interleave_0 = const()[name = tensor("input_23_interleave_0"), val = tensor(false)]; + tensor x_29 = transpose(perm = x_29_perm_0, x = z)[name = tensor("transpose_4")]; + tensor input_23 = concat(axis = var_571, interleave = input_23_interleave_0, values = (conv0_prev, x_29))[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_607_begin_0 = const()[name = tensor("op_607_begin_0"), val = tensor([0, 0, 16])]; + tensor var_607_end_0 = const()[name = tensor("op_607_end_0"), val = tensor([1, 512, 22])]; + tensor var_607_end_mask_0 = const()[name = tensor("op_607_end_mask_0"), val = tensor([true, true, true])]; + tensor var_607 = slice_by_index(begin = var_607_begin_0, end = var_607_end_0, end_mask = var_607_end_mask_0, x = input_23)[name = tensor("op_607")]; + tensor input_27 = elu(alpha = var_569, 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_624_begin_0 = const()[name = tensor("op_624_begin_0"), val = tensor([0, 0, 0])]; + tensor var_624_end_0 = const()[name = tensor("op_624_end_0"), val = tensor([1, 256, 6])]; + tensor var_624_end_mask_0 = const()[name = tensor("op_624_end_mask_0"), val = tensor([true, true, false])]; + tensor var_624 = slice_by_index(begin = var_624_begin_0, end = var_624_end_0, end_mask = var_624_end_mask_0, x = y_5_has_output_shape)[name = tensor("op_624")]; + tensor var_625 = add(x = var_624, y = convtr0_partial)[name = tensor("op_625")]; + tensor var_626_begin_0 = const()[name = tensor("op_626_begin_0"), val = tensor([0, 0, 6])]; + tensor var_626_end_0 = const()[name = tensor("op_626_end_0"), val = tensor([1, 256, 102])]; + tensor var_626_end_mask_0 = const()[name = tensor("op_626_end_mask_0"), val = tensor([true, true, true])]; + tensor var_626 = slice_by_index(begin = var_626_begin_0, end = var_626_end_0, end_mask = var_626_end_mask_0, x = y_5_has_output_shape)[name = tensor("op_626")]; + tensor y_7_interleave_0 = const()[name = tensor("y_7_interleave_0"), val = tensor(false)]; + tensor y_7 = concat(axis = var_571, interleave = y_7_interleave_0, values = (var_625, var_626))[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_633 = const()[name = tensor("op_633"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41243008)))]; + tensor var_634 = sub(x = new_partial_1, y = var_633)[name = tensor("op_634")]; + 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_31 = elu(alpha = var_569, x = input_29)[name = tensor("x_31")]; + tensor input_31_interleave_0 = const()[name = tensor("input_31_interleave_0"), val = tensor(false)]; + tensor input_31 = concat(axis = var_571, interleave = input_31_interleave_0, values = (res0_conv0_prev, x_31))[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_660_begin_0 = const()[name = tensor("op_660_begin_0"), val = tensor([0, 0, 96])]; + tensor var_660_end_0 = const()[name = tensor("op_660_end_0"), val = tensor([1, 256, 98])]; + tensor var_660_end_mask_0 = const()[name = tensor("op_660_end_mask_0"), val = tensor([true, true, true])]; + tensor var_660 = slice_by_index(begin = var_660_begin_0, end = var_660_end_0, end_mask = var_660_end_mask_0, x = input_31)[name = tensor("op_660")]; + tensor x_33 = elu(alpha = var_569, x = input_33)[name = tensor("x_33")]; + 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_33)[name = tensor("v_5")]; + tensor input_35 = add(x = input_29, y = v_5)[name = tensor("input_35")]; + tensor input_37 = elu(alpha = var_569, 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_690_begin_0 = const()[name = tensor("op_690_begin_0"), val = tensor([0, 0, 0])]; + tensor var_690_end_0 = const()[name = tensor("op_690_end_0"), val = tensor([1, 128, 5])]; + tensor var_690_end_mask_0 = const()[name = tensor("op_690_end_mask_0"), val = tensor([true, true, false])]; + tensor var_690 = slice_by_index(begin = var_690_begin_0, end = var_690_end_0, end_mask = var_690_end_mask_0, x = y_9_has_output_shape)[name = tensor("op_690")]; + tensor var_691 = add(x = var_690, y = convtr1_partial)[name = tensor("op_691")]; + tensor var_692_begin_0 = const()[name = tensor("op_692_begin_0"), val = tensor([0, 0, 5])]; + tensor var_692_end_0 = const()[name = tensor("op_692_end_0"), val = tensor([1, 128, 485])]; + tensor var_692_end_mask_0 = const()[name = tensor("op_692_end_mask_0"), val = tensor([true, true, true])]; + tensor var_692 = slice_by_index(begin = var_692_begin_0, end = var_692_end_0, end_mask = var_692_end_mask_0, x = y_9_has_output_shape)[name = tensor("op_692")]; + tensor y_11_interleave_0 = const()[name = tensor("y_11_interleave_0"), val = tensor(false)]; + tensor y_11 = concat(axis = var_571, interleave = y_11_interleave_0, values = (var_691, var_692))[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_699 = const()[name = tensor("op_699"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41244096)))]; + tensor var_700 = sub(x = new_partial_3, y = var_699)[name = tensor("op_700")]; + 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_35 = elu(alpha = var_569, x = input_39)[name = tensor("x_35")]; + tensor input_41_interleave_0 = const()[name = tensor("input_41_interleave_0"), val = tensor(false)]; + tensor input_41 = concat(axis = var_571, interleave = input_41_interleave_0, values = (res1_conv0_prev, x_35))[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_726_begin_0 = const()[name = tensor("op_726_begin_0"), val = tensor([0, 0, 480])]; + tensor var_726_end_0 = const()[name = tensor("op_726_end_0"), val = tensor([1, 128, 482])]; + tensor var_726_end_mask_0 = const()[name = tensor("op_726_end_mask_0"), val = tensor([true, true, true])]; + tensor var_726 = slice_by_index(begin = var_726_begin_0, end = var_726_end_0, end_mask = var_726_end_mask_0, x = input_41)[name = tensor("op_726")]; + tensor x_37 = elu(alpha = var_569, x = input_43)[name = tensor("x_37")]; + 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_37)[name = tensor("v_7")]; + tensor input_45 = add(x = input_39, y = v_7)[name = tensor("input_45")]; + tensor input_47 = elu(alpha = var_569, 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_756_begin_0 = const()[name = tensor("op_756_begin_0"), val = tensor([0, 0, 0])]; + tensor var_756_end_0 = const()[name = tensor("op_756_end_0"), val = tensor([1, 64, 4])]; + tensor var_756_end_mask_0 = const()[name = tensor("op_756_end_mask_0"), val = tensor([true, true, false])]; + tensor var_756 = slice_by_index(begin = var_756_begin_0, end = var_756_end_0, end_mask = var_756_end_mask_0, x = y_13_has_output_shape)[name = tensor("op_756")]; + tensor var_757 = add(x = var_756, y = convtr2_partial)[name = tensor("op_757")]; + tensor var_758_begin_0 = const()[name = tensor("op_758_begin_0"), val = tensor([0, 0, 4])]; + tensor var_758_end_0 = const()[name = tensor("op_758_end_0"), val = tensor([1, 64, 1924])]; + tensor var_758_end_mask_0 = const()[name = tensor("op_758_end_mask_0"), val = tensor([true, true, true])]; + tensor var_758 = slice_by_index(begin = var_758_begin_0, end = var_758_end_0, end_mask = var_758_end_mask_0, x = y_13_has_output_shape)[name = tensor("op_758")]; + tensor y_interleave_0 = const()[name = tensor("y_interleave_0"), val = tensor(false)]; + tensor y = concat(axis = var_571, interleave = y_interleave_0, values = (var_757, var_758))[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_765 = const()[name = tensor("op_765"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41244672)))]; + tensor var_766 = sub(x = new_partial, y = var_765)[name = tensor("op_766")]; + 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_39 = elu(alpha = var_569, x = input_49)[name = tensor("x_39")]; + tensor input_51_interleave_0 = const()[name = tensor("input_51_interleave_0"), val = tensor(false)]; + tensor input_51 = concat(axis = var_571, interleave = input_51_interleave_0, values = (res2_conv0_prev, x_39))[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_792_begin_0 = const()[name = tensor("op_792_begin_0"), val = tensor([0, 0, 1920])]; + tensor var_792_end_0 = const()[name = tensor("op_792_end_0"), val = tensor([1, 64, 1922])]; + tensor var_792_end_mask_0 = const()[name = tensor("op_792_end_mask_0"), val = tensor([true, true, true])]; + tensor var_792 = slice_by_index(begin = var_792_begin_0, end = var_792_end_0, end_mask = var_792_end_mask_0, x = input_51)[name = tensor("op_792")]; + tensor x_41 = elu(alpha = var_569, x = input_53)[name = tensor("x_41")]; + 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_41)[name = tensor("v")]; + tensor input_55 = add(x = input_49, y = v)[name = tensor("input_55")]; + tensor x = elu(alpha = var_569, x = input_55)[name = tensor("x")]; + tensor input_interleave_0 = const()[name = tensor("input_interleave_0"), val = tensor(false)]; + tensor input = concat(axis = var_571, interleave = input_interleave_0, values = (conv_final_prev, x))[name = tensor("input")]; + tensor var_821_pad_type_0 = const()[name = tensor("op_821_pad_type_0"), val = tensor("valid")]; + tensor var_821_strides_0 = const()[name = tensor("op_821_strides_0"), val = tensor([1])]; + tensor var_821_pad_0 = const()[name = tensor("op_821_pad_0"), val = tensor([0, 0])]; + tensor var_821_dilations_0 = const()[name = tensor("op_821_dilations_0"), val = tensor([1])]; + tensor var_821_groups_0 = const()[name = tensor("op_821_groups_0"), val = tensor(1)]; + tensor var_821 = conv(bias = mimi_decoder_model_11_conv_bias, dilations = var_821_dilations_0, groups = var_821_groups_0, pad = var_821_pad_0, pad_type = var_821_pad_type_0, strides = var_821_strides_0, weight = mimi_decoder_model_11_conv_weight, x = input)[name = tensor("op_821")]; + tensor var_824_begin_0 = const()[name = tensor("op_824_begin_0"), val = tensor([0, 0, 1920])]; + tensor var_824_end_0 = const()[name = tensor("op_824_end_0"), val = tensor([1, 64, 1922])]; + tensor var_824_end_mask_0 = const()[name = tensor("op_824_end_mask_0"), val = tensor([true, true, true])]; + tensor var_824 = slice_by_index(begin = var_824_begin_0, end = var_824_end_0, end_mask = var_824_end_mask_0, x = input)[name = tensor("op_824")]; + tensor var_839_promoted = const()[name = tensor("op_839_promoted"), val = tensor(0x1p+4)]; + tensor var_840 = add(x = attn0_offset, y = var_839_promoted)[name = tensor("op_840")]; + tensor var_842_promoted = const()[name = tensor("op_842_promoted"), val = tensor(0x1p+4)]; + tensor var_843 = add(x = attn1_offset, y = var_842_promoted)[name = tensor("op_843")]; + 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_821, var_82, var_262, var_840, new_end_offset_1, var_479, var_843, new_end_offset, var_607, conv0_first, var_634, var_660, res0_conv0_first, res0_conv1_prev, res0_conv1_first, var_700, var_726, res1_conv0_first, res1_conv1_prev, res1_conv1_first, var_766, var_792, res2_conv0_first, res2_conv1_prev, res2_conv1_first, var_824, conv_final_first); +} \ No newline at end of file