| program(1.0) |
| [buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"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<ios17>(tensor<fp32, [2, 1, 256, 8, 64]> attn0_cache, tensor<fp32, [1]> attn0_offset, tensor<fp32, [2, 1, 256, 8, 64]> attn1_cache, tensor<fp32, [1]> attn1_offset, tensor<fp32, [1]> conv0_first, tensor<fp32, [1, 512, 6]> conv0_prev, tensor<fp32, [1]> conv_final_first, tensor<fp32, [1, 64, 2]> conv_final_prev, tensor<fp32, [1, 256, 6]> convtr0_partial, tensor<fp32, [1, 128, 5]> convtr1_partial, tensor<fp32, [1, 64, 4]> convtr2_partial, tensor<fp32, [1, 32]> latent, tensor<fp32, [1]> res0_conv0_first, tensor<fp32, [1, 256, 2]> res0_conv0_prev, tensor<fp32, [1]> res0_conv1_first, tensor<fp32, [1, 128, 0]> res0_conv1_prev, tensor<fp32, [1]> res1_conv0_first, tensor<fp32, [1, 128, 2]> res1_conv0_prev, tensor<fp32, [1]> res1_conv1_first, tensor<fp32, [1, 64, 0]> res1_conv1_prev, tensor<fp32, [1]> res2_conv0_first, tensor<fp32, [1, 64, 2]> res2_conv0_prev, tensor<fp32, [1]> res2_conv1_first, tensor<fp32, [1, 32, 0]> res2_conv1_prev, tensor<fp32, [1, 512, 16]> upsample_partial) { |
| tensor<fp32, [32]> emb_mean = const()[name = tensor<string, []>("emb_mean"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))]; |
| tensor<fp32, [32]> emb_std = const()[name = tensor<string, []>("emb_std"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(256)))]; |
| tensor<fp32, [512, 32, 1]> mimi_quantizer_output_proj_weight = const()[name = tensor<string, []>("mimi_quantizer_output_proj_weight"), val = tensor<fp32, [512, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448)))]; |
| tensor<fp32, [512, 1, 32]> mimi_upsample_convtr_convtr_weight = const()[name = tensor<string, []>("mimi_upsample_convtr_convtr_weight"), val = tensor<fp32, [512, 1, 32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(66048)))]; |
| tensor<fp32, [512]> mimi_decoder_transformer_transformer_layers_0_norm1_bias = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_0_norm1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(131648)))]; |
| tensor<fp32, [512]> mimi_decoder_transformer_transformer_layers_0_norm1_weight = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_0_norm1_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(133760)))]; |
| tensor<fp32, [1536, 512]> mimi_decoder_transformer_transformer_layers_0_self_attn_in_proj_weight = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_0_self_attn_in_proj_weight"), val = tensor<fp32, [1536, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(135872)))]; |
| tensor<fp32, [512, 512]> mimi_decoder_transformer_transformer_layers_0_self_attn_out_proj_weight = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_0_self_attn_out_proj_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3281664)))]; |
| tensor<fp32, [512]> mimi_decoder_transformer_transformer_layers_0_layer_scale_1_scale = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_0_layer_scale_1_scale"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4330304)))]; |
| tensor<fp32, [512]> mimi_decoder_transformer_transformer_layers_0_norm2_bias = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_0_norm2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4332416)))]; |
| tensor<fp32, [512]> mimi_decoder_transformer_transformer_layers_0_norm2_weight = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_0_norm2_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4334528)))]; |
| tensor<fp32, [2048, 512]> mimi_decoder_transformer_transformer_layers_0_linear1_weight = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_0_linear1_weight"), val = tensor<fp32, [2048, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4336640)))]; |
| tensor<fp32, [512, 2048]> mimi_decoder_transformer_transformer_layers_0_linear2_weight = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_0_linear2_weight"), val = tensor<fp32, [512, 2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8531008)))]; |
| tensor<fp32, [512]> mimi_decoder_transformer_transformer_layers_0_layer_scale_2_scale = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_0_layer_scale_2_scale"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12725376)))]; |
| tensor<fp32, [512]> mimi_decoder_transformer_transformer_layers_1_norm1_bias = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_1_norm1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12727488)))]; |
| tensor<fp32, [512]> mimi_decoder_transformer_transformer_layers_1_norm1_weight = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_1_norm1_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12729600)))]; |
| tensor<fp32, [1536, 512]> mimi_decoder_transformer_transformer_layers_1_self_attn_in_proj_weight = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_1_self_attn_in_proj_weight"), val = tensor<fp32, [1536, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12731712)))]; |
| tensor<fp32, [512, 512]> mimi_decoder_transformer_transformer_layers_1_self_attn_out_proj_weight = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_1_self_attn_out_proj_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15877504)))]; |
| tensor<fp32, [512]> mimi_decoder_transformer_transformer_layers_1_layer_scale_1_scale = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_1_layer_scale_1_scale"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16926144)))]; |
| tensor<fp32, [512]> mimi_decoder_transformer_transformer_layers_1_norm2_bias = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_1_norm2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16928256)))]; |
| tensor<fp32, [512]> mimi_decoder_transformer_transformer_layers_1_norm2_weight = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_1_norm2_weight"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16930368)))]; |
| tensor<fp32, [2048, 512]> mimi_decoder_transformer_transformer_layers_1_linear1_weight = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_1_linear1_weight"), val = tensor<fp32, [2048, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16932480)))]; |
| tensor<fp32, [512, 2048]> mimi_decoder_transformer_transformer_layers_1_linear2_weight = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_1_linear2_weight"), val = tensor<fp32, [512, 2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(21126848)))]; |
| tensor<fp32, [512]> mimi_decoder_transformer_transformer_layers_1_layer_scale_2_scale = const()[name = tensor<string, []>("mimi_decoder_transformer_transformer_layers_1_layer_scale_2_scale"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25321216)))]; |
| tensor<fp32, [512]> mimi_decoder_model_0_conv_bias = const()[name = tensor<string, []>("mimi_decoder_model_0_conv_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25323328)))]; |
| tensor<fp32, [512, 512, 7]> mimi_decoder_model_0_conv_weight = const()[name = tensor<string, []>("mimi_decoder_model_0_conv_weight"), val = tensor<fp32, [512, 512, 7]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25325440)))]; |
| tensor<fp32, [256]> mimi_decoder_model_2_convtr_bias = const()[name = tensor<string, []>("mimi_decoder_model_2_convtr_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32665536)))]; |
| tensor<fp32, [512, 256, 12]> mimi_decoder_model_2_convtr_weight = const()[name = tensor<string, []>("mimi_decoder_model_2_convtr_weight"), val = tensor<fp32, [512, 256, 12]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32666624)))]; |
| tensor<fp32, [128]> mimi_decoder_model_3_block_1_conv_bias = const()[name = tensor<string, []>("mimi_decoder_model_3_block_1_conv_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38958144)))]; |
| tensor<fp32, [128, 256, 3]> mimi_decoder_model_3_block_1_conv_weight = const()[name = tensor<string, []>("mimi_decoder_model_3_block_1_conv_weight"), val = tensor<fp32, [128, 256, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38958720)))]; |
| tensor<fp32, [256]> mimi_decoder_model_3_block_3_conv_bias = const()[name = tensor<string, []>("mimi_decoder_model_3_block_3_conv_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39352000)))]; |
| tensor<fp32, [256, 128, 1]> mimi_decoder_model_3_block_3_conv_weight = const()[name = tensor<string, []>("mimi_decoder_model_3_block_3_conv_weight"), val = tensor<fp32, [256, 128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39353088)))]; |
| tensor<fp32, [128]> mimi_decoder_model_5_convtr_bias = const()[name = tensor<string, []>("mimi_decoder_model_5_convtr_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39484224)))]; |
| tensor<fp32, [256, 128, 10]> mimi_decoder_model_5_convtr_weight = const()[name = tensor<string, []>("mimi_decoder_model_5_convtr_weight"), val = tensor<fp32, [256, 128, 10]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39484800)))]; |
| tensor<fp32, [64]> mimi_decoder_model_6_block_1_conv_bias = const()[name = tensor<string, []>("mimi_decoder_model_6_block_1_conv_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40795584)))]; |
| tensor<fp32, [64, 128, 3]> mimi_decoder_model_6_block_1_conv_weight = const()[name = tensor<string, []>("mimi_decoder_model_6_block_1_conv_weight"), val = tensor<fp32, [64, 128, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40795904)))]; |
| tensor<fp32, [128]> mimi_decoder_model_6_block_3_conv_bias = const()[name = tensor<string, []>("mimi_decoder_model_6_block_3_conv_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40894272)))]; |
| tensor<fp32, [128, 64, 1]> mimi_decoder_model_6_block_3_conv_weight = const()[name = tensor<string, []>("mimi_decoder_model_6_block_3_conv_weight"), val = tensor<fp32, [128, 64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40894848)))]; |
| tensor<fp32, [64]> mimi_decoder_model_8_convtr_bias = const()[name = tensor<string, []>("mimi_decoder_model_8_convtr_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40927680)))]; |
| tensor<fp32, [128, 64, 8]> mimi_decoder_model_8_convtr_weight = const()[name = tensor<string, []>("mimi_decoder_model_8_convtr_weight"), val = tensor<fp32, [128, 64, 8]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40928000)))]; |
| tensor<fp32, [32]> mimi_decoder_model_9_block_1_conv_bias = const()[name = tensor<string, []>("mimi_decoder_model_9_block_1_conv_bias"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41190208)))]; |
| tensor<fp32, [32, 64, 3]> mimi_decoder_model_9_block_1_conv_weight = const()[name = tensor<string, []>("mimi_decoder_model_9_block_1_conv_weight"), val = tensor<fp32, [32, 64, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41190400)))]; |
| tensor<fp32, [64]> mimi_decoder_model_9_block_3_conv_bias = const()[name = tensor<string, []>("mimi_decoder_model_9_block_3_conv_bias"), val = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41215040)))]; |
| tensor<fp32, [64, 32, 1]> mimi_decoder_model_9_block_3_conv_weight = const()[name = tensor<string, []>("mimi_decoder_model_9_block_3_conv_weight"), val = tensor<fp32, [64, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41215360)))]; |
| tensor<fp32, [1]> mimi_decoder_model_11_conv_bias = const()[name = tensor<string, []>("mimi_decoder_model_11_conv_bias"), val = tensor<fp32, [1]>([-0x1.8p-13])]; |
| tensor<fp32, [1, 64, 3]> mimi_decoder_model_11_conv_weight = const()[name = tensor<string, []>("mimi_decoder_model_11_conv_weight"), val = tensor<fp32, [1, 64, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41223616)))]; |
| tensor<fp32, [1, 32]> var_38 = mul(x = latent, y = emb_std)[name = tensor<string, []>("op_38")]; |
| tensor<fp32, [1, 32]> denorm = add(x = var_38, y = emb_mean)[name = tensor<string, []>("denorm")]; |
| tensor<int32, [1]> input_1_axes_0 = const()[name = tensor<string, []>("input_1_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp32, [1, 32, 1]> input_1 = expand_dims(axes = input_1_axes_0, x = denorm)[name = tensor<string, []>("input_1")]; |
| tensor<string, []> x_1_pad_type_0 = const()[name = tensor<string, []>("x_1_pad_type_0"), val = tensor<string, []>("valid")]; |
| tensor<int32, [1]> x_1_strides_0 = const()[name = tensor<string, []>("x_1_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> x_1_pad_0 = const()[name = tensor<string, []>("x_1_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> x_1_dilations_0 = const()[name = tensor<string, []>("x_1_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> x_1_groups_0 = const()[name = tensor<string, []>("x_1_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 512, 1]> 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<string, []>("x_1")]; |
| tensor<int32, []> var_62 = const()[name = tensor<string, []>("op_62"), val = tensor<int32, []>(-1)]; |
| tensor<string, []> y_1_pad_type_0 = const()[name = tensor<string, []>("y_1_pad_type_0"), val = tensor<string, []>("valid")]; |
| tensor<int32, [1]> y_1_strides_0 = const()[name = tensor<string, []>("y_1_strides_0"), val = tensor<int32, [1]>([16])]; |
| tensor<int32, []> y_1_groups_0 = const()[name = tensor<string, []>("y_1_groups_0"), val = tensor<int32, []>(512)]; |
| tensor<int32, [2]> y_1_pad_0 = const()[name = tensor<string, []>("y_1_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> y_1_dilations_0 = const()[name = tensor<string, []>("y_1_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [3]> y_1_has_output_shape_output_shape_0 = const()[name = tensor<string, []>("y_1_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 512, 32])]; |
| tensor<fp32, [1, 512, 32]> 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<string, []>("y_1_has_output_shape")]; |
| tensor<int32, [3]> var_72_begin_0 = const()[name = tensor<string, []>("op_72_begin_0"), val = tensor<int32, [3]>([0, 0, 0])]; |
| tensor<int32, [3]> var_72_end_0 = const()[name = tensor<string, []>("op_72_end_0"), val = tensor<int32, [3]>([1, 512, 16])]; |
| tensor<bool, [3]> var_72_end_mask_0 = const()[name = tensor<string, []>("op_72_end_mask_0"), val = tensor<bool, [3]>([true, true, false])]; |
| tensor<fp32, [1, 512, 16]> 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<string, []>("op_72")]; |
| tensor<fp32, [1, 512, 16]> var_73 = add(x = var_72, y = upsample_partial)[name = tensor<string, []>("op_73")]; |
| tensor<int32, [3]> var_74_begin_0 = const()[name = tensor<string, []>("op_74_begin_0"), val = tensor<int32, [3]>([0, 0, 16])]; |
| tensor<int32, [3]> var_74_end_0 = const()[name = tensor<string, []>("op_74_end_0"), val = tensor<int32, [3]>([1, 512, 32])]; |
| tensor<bool, [3]> var_74_end_mask_0 = const()[name = tensor<string, []>("op_74_end_mask_0"), val = tensor<bool, [3]>([true, true, true])]; |
| tensor<fp32, [1, 512, 16]> 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<string, []>("op_74")]; |
| tensor<bool, []> y_3_interleave_0 = const()[name = tensor<string, []>("y_3_interleave_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [1, 512, 32]> y_3 = concat(axis = var_62, interleave = y_3_interleave_0, values = (var_73, var_74))[name = tensor<string, []>("y_3")]; |
| tensor<int32, [3]> var_77_begin_0 = const()[name = tensor<string, []>("op_77_begin_0"), val = tensor<int32, [3]>([0, 0, 16])]; |
| tensor<int32, [3]> var_77_end_0 = const()[name = tensor<string, []>("op_77_end_0"), val = tensor<int32, [3]>([1, 512, 32])]; |
| tensor<bool, [3]> var_77_end_mask_0 = const()[name = tensor<string, []>("op_77_end_mask_0"), val = tensor<bool, [3]>([true, true, true])]; |
| tensor<fp32, [1, 512, 16]> 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<string, []>("op_77")]; |
| tensor<int32, [3]> x_3_begin_0 = const()[name = tensor<string, []>("x_3_begin_0"), val = tensor<int32, [3]>([0, 0, 0])]; |
| tensor<int32, [3]> x_3_end_0 = const()[name = tensor<string, []>("x_3_end_0"), val = tensor<int32, [3]>([1, 512, 16])]; |
| tensor<bool, [3]> x_3_end_mask_0 = const()[name = tensor<string, []>("x_3_end_mask_0"), val = tensor<bool, [3]>([true, true, false])]; |
| tensor<fp32, [1, 512, 16]> 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<string, []>("x_3")]; |
| tensor<int32, []> var_86 = const()[name = tensor<string, []>("op_86"), val = tensor<int32, []>(0)]; |
| tensor<int32, []> var_91 = const()[name = tensor<string, []>("op_91"), val = tensor<int32, []>(-1)]; |
| tensor<fp32, []> var_100 = const()[name = tensor<string, []>("op_100"), val = tensor<fp32, []>(-0x1.ff933cp+127)]; |
| tensor<fp32, []> var_102 = const()[name = tensor<string, []>("op_102"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<int32, [3]> input_3_perm_0 = const()[name = tensor<string, []>("input_3_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<int32, [1]> query_1_axes_0 = const()[name = tensor<string, []>("query_1_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp32, [1, 16, 512]> input_3 = transpose(perm = input_3_perm_0, x = x_3)[name = tensor<string, []>("transpose_19")]; |
| tensor<fp32, [1, 16, 512]> 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<string, []>("query_1")]; |
| tensor<fp32, [1536]> linear_0_bias_0 = const()[name = tensor<string, []>("linear_0_bias_0"), val = tensor<fp32, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41224448)))]; |
| tensor<fp32, [1, 16, 1536]> 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<string, []>("linear_0")]; |
| tensor<int32, [5]> var_130 = const()[name = tensor<string, []>("op_130"), val = tensor<int32, [5]>([1, 16, 3, 8, 64])]; |
| tensor<fp32, [1, 16, 3, 8, 64]> packed_1 = reshape(shape = var_130, x = projected_1)[name = tensor<string, []>("packed_1")]; |
| tensor<int32, [3]> var_132_split_sizes_0 = const()[name = tensor<string, []>("op_132_split_sizes_0"), val = tensor<int32, [3]>([1, 1, 1])]; |
| tensor<int32, []> var_132_axis_0 = const()[name = tensor<string, []>("op_132_axis_0"), val = tensor<int32, []>(2)]; |
| tensor<fp32, [1, 16, 1, 8, 64]> var_132_0, tensor<fp32, [1, 16, 1, 8, 64]> var_132_1, tensor<fp32, [1, 16, 1, 8, 64]> var_132_2 = split(axis = var_132_axis_0, split_sizes = var_132_split_sizes_0, x = packed_1)[name = tensor<string, []>("op_132")]; |
| tensor<int32, [1]> squeeze_0_axes_0 = const()[name = tensor<string, []>("squeeze_0_axes_0"), val = tensor<int32, [1]>([2])]; |
| tensor<fp32, [1, 16, 8, 64]> squeeze_0 = squeeze(axes = squeeze_0_axes_0, x = var_132_0)[name = tensor<string, []>("squeeze_0")]; |
| tensor<int32, [1]> squeeze_1_axes_0 = const()[name = tensor<string, []>("squeeze_1_axes_0"), val = tensor<int32, [1]>([2])]; |
| tensor<fp32, [1, 16, 8, 64]> squeeze_1 = squeeze(axes = squeeze_1_axes_0, x = var_132_1)[name = tensor<string, []>("squeeze_1")]; |
| tensor<int32, [1]> squeeze_2_axes_0 = const()[name = tensor<string, []>("squeeze_2_axes_0"), val = tensor<int32, [1]>([2])]; |
| tensor<fp32, [1, 16, 8, 64]> squeeze_2 = squeeze(axes = squeeze_2_axes_0, x = var_132_2)[name = tensor<string, []>("squeeze_2")]; |
| tensor<int32, [1]> offset_3_begin_0 = const()[name = tensor<string, []>("offset_3_begin_0"), val = tensor<int32, [1]>([0])]; |
| tensor<int32, [1]> offset_3_end_0 = const()[name = tensor<string, []>("offset_3_end_0"), val = tensor<int32, [1]>([1])]; |
| tensor<bool, [1]> offset_3_end_mask_0 = const()[name = tensor<string, []>("offset_3_end_mask_0"), val = tensor<bool, [1]>([false])]; |
| tensor<bool, [1]> offset_3_squeeze_mask_0 = const()[name = tensor<string, []>("offset_3_squeeze_mask_0"), val = tensor<bool, [1]>([true])]; |
| tensor<fp32, []> 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<string, []>("offset_3")]; |
| tensor<fp32, [32]> freqs_1 = const()[name = tensor<string, []>("freqs_1"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41230656)))]; |
| tensor<fp32, [16]> ts_1_promoted = const()[name = tensor<string, []>("ts_1_promoted"), val = tensor<fp32, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41230848)))]; |
| tensor<fp32, [16]> ts_3 = add(x = ts_1_promoted, y = offset_3)[name = tensor<string, []>("ts_3")]; |
| tensor<int32, [3]> var_148 = const()[name = tensor<string, []>("op_148"), val = tensor<int32, [3]>([-1, 1, 1])]; |
| tensor<fp32, [16, 1, 1]> ts_5 = reshape(shape = var_148, x = ts_3)[name = tensor<string, []>("ts_5")]; |
| tensor<int32, [5]> var_150 = const()[name = tensor<string, []>("op_150"), val = tensor<int32, [5]>([1, 16, 8, 32, 2])]; |
| tensor<fp32, [1, 16, 8, 32, 2]> q_3 = reshape(shape = var_150, x = squeeze_0)[name = tensor<string, []>("q_3")]; |
| tensor<int32, [5]> var_152 = const()[name = tensor<string, []>("op_152"), val = tensor<int32, [5]>([1, 16, 8, 32, 2])]; |
| tensor<fp32, [1, 16, 8, 32, 2]> k_3 = reshape(shape = var_152, x = squeeze_1)[name = tensor<string, []>("k_3")]; |
| tensor<int32, [5]> var_154_begin_0 = const()[name = tensor<string, []>("op_154_begin_0"), val = tensor<int32, [5]>([0, 0, 0, 0, 0])]; |
| tensor<int32, [5]> var_154_end_0 = const()[name = tensor<string, []>("op_154_end_0"), val = tensor<int32, [5]>([1, 16, 8, 32, 1])]; |
| tensor<bool, [5]> var_154_end_mask_0 = const()[name = tensor<string, []>("op_154_end_mask_0"), val = tensor<bool, [5]>([true, true, true, true, false])]; |
| tensor<bool, [5]> var_154_squeeze_mask_0 = const()[name = tensor<string, []>("op_154_squeeze_mask_0"), val = tensor<bool, [5]>([false, false, false, false, true])]; |
| tensor<fp32, [1, 16, 8, 32]> 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<string, []>("op_154")]; |
| tensor<int32, [5]> var_156_begin_0 = const()[name = tensor<string, []>("op_156_begin_0"), val = tensor<int32, [5]>([0, 0, 0, 0, 1])]; |
| tensor<int32, [5]> var_156_end_0 = const()[name = tensor<string, []>("op_156_end_0"), val = tensor<int32, [5]>([1, 16, 8, 32, 2])]; |
| tensor<bool, [5]> var_156_end_mask_0 = const()[name = tensor<string, []>("op_156_end_mask_0"), val = tensor<bool, [5]>([true, true, true, true, false])]; |
| tensor<bool, [5]> var_156_squeeze_mask_0 = const()[name = tensor<string, []>("op_156_squeeze_mask_0"), val = tensor<bool, [5]>([false, false, false, false, true])]; |
| tensor<fp32, [1, 16, 8, 32]> 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<string, []>("op_156")]; |
| tensor<int32, [5]> var_158_begin_0 = const()[name = tensor<string, []>("op_158_begin_0"), val = tensor<int32, [5]>([0, 0, 0, 0, 0])]; |
| tensor<int32, [5]> var_158_end_0 = const()[name = tensor<string, []>("op_158_end_0"), val = tensor<int32, [5]>([1, 16, 8, 32, 1])]; |
| tensor<bool, [5]> var_158_end_mask_0 = const()[name = tensor<string, []>("op_158_end_mask_0"), val = tensor<bool, [5]>([true, true, true, true, false])]; |
| tensor<bool, [5]> var_158_squeeze_mask_0 = const()[name = tensor<string, []>("op_158_squeeze_mask_0"), val = tensor<bool, [5]>([false, false, false, false, true])]; |
| tensor<fp32, [1, 16, 8, 32]> 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<string, []>("op_158")]; |
| tensor<int32, [5]> var_160_begin_0 = const()[name = tensor<string, []>("op_160_begin_0"), val = tensor<int32, [5]>([0, 0, 0, 0, 1])]; |
| tensor<int32, [5]> var_160_end_0 = const()[name = tensor<string, []>("op_160_end_0"), val = tensor<int32, [5]>([1, 16, 8, 32, 2])]; |
| tensor<bool, [5]> var_160_end_mask_0 = const()[name = tensor<string, []>("op_160_end_mask_0"), val = tensor<bool, [5]>([true, true, true, true, false])]; |
| tensor<bool, [5]> var_160_squeeze_mask_0 = const()[name = tensor<string, []>("op_160_squeeze_mask_0"), val = tensor<bool, [5]>([false, false, false, false, true])]; |
| tensor<fp32, [1, 16, 8, 32]> 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<string, []>("op_160")]; |
| tensor<fp32, [16, 1, 32]> var_162 = mul(x = freqs_1, y = ts_5)[name = tensor<string, []>("op_162")]; |
| tensor<fp32, [16, 1, 32]> rotr_1 = cos(x = var_162)[name = tensor<string, []>("rotr_1")]; |
| tensor<fp32, [16, 1, 32]> roti_1 = sin(x = var_162)[name = tensor<string, []>("roti_1")]; |
| tensor<fp32, [1, 16, 8, 32]> var_166 = mul(x = var_154, y = rotr_1)[name = tensor<string, []>("op_166")]; |
| tensor<fp32, [1, 16, 8, 32]> var_167 = mul(x = var_156, y = roti_1)[name = tensor<string, []>("op_167")]; |
| tensor<fp32, [1, 16, 8, 32]> qor_1 = sub(x = var_166, y = var_167)[name = tensor<string, []>("qor_1")]; |
| tensor<fp32, [1, 16, 8, 32]> var_169 = mul(x = var_154, y = roti_1)[name = tensor<string, []>("op_169")]; |
| tensor<fp32, [1, 16, 8, 32]> var_170 = mul(x = var_156, y = rotr_1)[name = tensor<string, []>("op_170")]; |
| tensor<fp32, [1, 16, 8, 32]> qoi_1 = add(x = var_169, y = var_170)[name = tensor<string, []>("qoi_1")]; |
| tensor<fp32, [1, 16, 8, 32]> var_172 = mul(x = var_158, y = rotr_1)[name = tensor<string, []>("op_172")]; |
| tensor<fp32, [1, 16, 8, 32]> var_173 = mul(x = var_160, y = roti_1)[name = tensor<string, []>("op_173")]; |
| tensor<fp32, [1, 16, 8, 32]> kor_1 = sub(x = var_172, y = var_173)[name = tensor<string, []>("kor_1")]; |
| tensor<fp32, [1, 16, 8, 32]> var_175 = mul(x = var_158, y = roti_1)[name = tensor<string, []>("op_175")]; |
| tensor<fp32, [1, 16, 8, 32]> var_176 = mul(x = var_160, y = rotr_1)[name = tensor<string, []>("op_176")]; |
| tensor<fp32, [1, 16, 8, 32]> koi_1 = add(x = var_175, y = var_176)[name = tensor<string, []>("koi_1")]; |
| tensor<int32, []> qo_1_axis_0 = const()[name = tensor<string, []>("qo_1_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp32, [1, 16, 8, 32, 2]> qo_1 = stack(axis = qo_1_axis_0, values = (qor_1, qoi_1))[name = tensor<string, []>("qo_1")]; |
| tensor<int32, []> ko_1_axis_0 = const()[name = tensor<string, []>("ko_1_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp32, [1, 16, 8, 32, 2]> ko_1 = stack(axis = ko_1_axis_0, values = (kor_1, koi_1))[name = tensor<string, []>("ko_1")]; |
| tensor<int32, [4]> var_186 = const()[name = tensor<string, []>("op_186"), val = tensor<int32, [4]>([1, 16, 8, 64])]; |
| tensor<fp32, [1, 16, 8, 64]> q_5 = reshape(shape = var_186, x = qo_1)[name = tensor<string, []>("q_5")]; |
| tensor<int32, [4]> var_188 = const()[name = tensor<string, []>("op_188"), val = tensor<int32, [4]>([1, 16, 8, 64])]; |
| tensor<fp32, [1, 16, 8, 64]> k_5 = reshape(shape = var_188, x = ko_1)[name = tensor<string, []>("k_5")]; |
| tensor<int32, [1]> capacity_1 = const()[name = tensor<string, []>("capacity_1"), val = tensor<int32, [1]>([256])]; |
| tensor<string, []> var_193_dtype_0 = const()[name = tensor<string, []>("op_193_dtype_0"), val = tensor<string, []>("int32")]; |
| tensor<int32, [2]> var_194 = const()[name = tensor<string, []>("op_194"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [1]> var_193 = cast(dtype = var_193_dtype_0, x = attn0_offset)[name = tensor<string, []>("cast_49")]; |
| tensor<int32, [1, 1]> write_base_1 = reshape(shape = var_194, x = var_193)[name = tensor<string, []>("write_base_1")]; |
| tensor<int32, [1, 16]> write_range_1 = const()[name = tensor<string, []>("write_range_1"), val = tensor<int32, [1, 16]>([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]])]; |
| tensor<int32, [1, 16]> abs_idx_1 = add(x = write_base_1, y = write_range_1)[name = tensor<string, []>("abs_idx_1")]; |
| tensor<int32, [1, 16]> wrapped_1_div = floor_div(x = abs_idx_1, y = capacity_1)[name = tensor<string, []>("wrapped_1_div")]; |
| tensor<int32, [1, 16]> wrapped_1_div_scaled = mul(x = wrapped_1_div, y = capacity_1)[name = tensor<string, []>("wrapped_1_div_scaled")]; |
| tensor<int32, [1, 16]> wrapped_1 = sub(x = abs_idx_1, y = wrapped_1_div_scaled)[name = tensor<string, []>("wrapped_1")]; |
| tensor<int32, [4]> var_201 = const()[name = tensor<string, []>("op_201"), val = tensor<int32, [4]>([1, 16, 1, 1])]; |
| tensor<int32, [1, 16, 1, 1]> var_202 = reshape(shape = var_201, x = wrapped_1)[name = tensor<string, []>("op_202")]; |
| tensor<int32, [4]> write_indexes_1_reps_0 = const()[name = tensor<string, []>("write_indexes_1_reps_0"), val = tensor<int32, [4]>([1, 1, 8, 64])]; |
| tensor<int32, [1, 16, 8, 64]> write_indexes_1 = tile(reps = write_indexes_1_reps_0, x = var_202)[name = tensor<string, []>("write_indexes_1")]; |
| tensor<int32, [5]> var_205_begin_0 = const()[name = tensor<string, []>("op_205_begin_0"), val = tensor<int32, [5]>([0, 0, 0, 0, 0])]; |
| tensor<int32, [5]> var_205_end_0 = const()[name = tensor<string, []>("op_205_end_0"), val = tensor<int32, [5]>([1, 1, 256, 8, 64])]; |
| tensor<bool, [5]> var_205_end_mask_0 = const()[name = tensor<string, []>("op_205_end_mask_0"), val = tensor<bool, [5]>([false, true, true, true, true])]; |
| tensor<bool, [5]> var_205_squeeze_mask_0 = const()[name = tensor<string, []>("op_205_squeeze_mask_0"), val = tensor<bool, [5]>([true, false, false, false, false])]; |
| tensor<fp32, [1, 256, 8, 64]> 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<string, []>("op_205")]; |
| tensor<int32, []> new_k_cache_1_axis_0 = const()[name = tensor<string, []>("new_k_cache_1_axis_0"), val = tensor<int32, []>(1)]; |
| tensor<string, []> new_k_cache_1_mode_0 = const()[name = tensor<string, []>("new_k_cache_1_mode_0"), val = tensor<string, []>("update")]; |
| tensor<bool, []> new_k_cache_1_validate_indices_0 = const()[name = tensor<string, []>("new_k_cache_1_validate_indices_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [1, 256, 8, 64]> 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<string, []>("new_k_cache_1")]; |
| tensor<int32, [5]> var_207_begin_0 = const()[name = tensor<string, []>("op_207_begin_0"), val = tensor<int32, [5]>([1, 0, 0, 0, 0])]; |
| tensor<int32, [5]> var_207_end_0 = const()[name = tensor<string, []>("op_207_end_0"), val = tensor<int32, [5]>([2, 1, 256, 8, 64])]; |
| tensor<bool, [5]> var_207_end_mask_0 = const()[name = tensor<string, []>("op_207_end_mask_0"), val = tensor<bool, [5]>([false, true, true, true, true])]; |
| tensor<bool, [5]> var_207_squeeze_mask_0 = const()[name = tensor<string, []>("op_207_squeeze_mask_0"), val = tensor<bool, [5]>([true, false, false, false, false])]; |
| tensor<fp32, [1, 256, 8, 64]> 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<string, []>("op_207")]; |
| tensor<int32, []> new_v_cache_1_axis_0 = const()[name = tensor<string, []>("new_v_cache_1_axis_0"), val = tensor<int32, []>(1)]; |
| tensor<string, []> new_v_cache_1_mode_0 = const()[name = tensor<string, []>("new_v_cache_1_mode_0"), val = tensor<string, []>("update")]; |
| tensor<bool, []> new_v_cache_1_validate_indices_0 = const()[name = tensor<string, []>("new_v_cache_1_validate_indices_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [1, 256, 8, 64]> 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<string, []>("new_v_cache_1")]; |
| tensor<int32, []> var_210_axis_0 = const()[name = tensor<string, []>("op_210_axis_0"), val = tensor<int32, []>(0)]; |
| tensor<fp32, [2, 1, 256, 8, 64]> var_210 = stack(axis = var_210_axis_0, values = (new_k_cache_1, new_v_cache_1))[name = tensor<string, []>("op_210")]; |
| tensor<bool, [1, 256, 8, 64]> var_211 = not_equal(x = new_k_cache_1, y = new_k_cache_1)[name = tensor<string, []>("op_211")]; |
| tensor<fp32, [1, 256, 8, 64]> var_212 = const()[name = tensor<string, []>("op_212"), val = tensor<fp32, [1, 256, 8, 64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41230976)))]; |
| tensor<fp32, [1, 256, 8, 64]> new_k_cache_3 = select(a = var_212, b = new_k_cache_1, cond = var_211)[name = tensor<string, []>("new_k_cache_3")]; |
| tensor<bool, [1, 256, 8, 64]> var_214 = not_equal(x = new_v_cache_1, y = new_v_cache_1)[name = tensor<string, []>("op_214")]; |
| tensor<fp32, [1, 256, 8, 64]> new_v_cache_3 = select(a = var_212, b = new_v_cache_1, cond = var_214)[name = tensor<string, []>("new_v_cache_3")]; |
| tensor<int32, [4]> var_219 = const()[name = tensor<string, []>("op_219"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [2]> var_221 = const()[name = tensor<string, []>("op_221"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<fp32, [1, 1]> var_222 = reshape(shape = var_221, x = attn0_offset)[name = tensor<string, []>("op_222")]; |
| tensor<fp32, [1]> var_224_promoted = const()[name = tensor<string, []>("op_224_promoted"), val = tensor<fp32, [1]>([0x1.ep+3])]; |
| tensor<fp32, [1, 1]> var_225 = add(x = var_222, y = var_224_promoted)[name = tensor<string, []>("op_225")]; |
| tensor<string, []> last_pos_1_dtype_0 = const()[name = tensor<string, []>("last_pos_1_dtype_0"), val = tensor<string, []>("int32")]; |
| tensor<int32, [1, 256]> slot_idx_1 = const()[name = tensor<string, []>("slot_idx_1"), val = tensor<int32, [1, 256]>([[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<int32, [1, 1]> last_pos_1 = cast(dtype = last_pos_1_dtype_0, x = var_225)[name = tensor<string, []>("cast_48")]; |
| tensor<int32, [1, 256]> diff_1 = sub(x = last_pos_1, y = slot_idx_1)[name = tensor<string, []>("diff_1")]; |
| tensor<int32, [1, 256]> var_231_div = floor_div(x = diff_1, y = capacity_1)[name = tensor<string, []>("op_231_div")]; |
| tensor<int32, [1, 256]> var_231_div_scaled = mul(x = var_231_div, y = capacity_1)[name = tensor<string, []>("op_231_div_scaled")]; |
| tensor<int32, [1, 256]> var_231 = sub(x = diff_1, y = var_231_div_scaled)[name = tensor<string, []>("op_231")]; |
| tensor<int32, [1, 256]> pos_k_1 = sub(x = last_pos_1, y = var_231)[name = tensor<string, []>("pos_k_1")]; |
| tensor<fp32, [1, 16]> var_237_promoted = const()[name = tensor<string, []>("op_237_promoted"), val = tensor<fp32, [1, 16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41755328)))]; |
| tensor<fp32, [1, 16]> pos_q_1 = add(x = var_222, y = var_237_promoted)[name = tensor<string, []>("pos_q_1")]; |
| tensor<int32, [1]> var_241_axes_0 = const()[name = tensor<string, []>("op_241_axes_0"), val = tensor<int32, [1]>([2])]; |
| tensor<fp32, [1, 16, 1]> var_241 = expand_dims(axes = var_241_axes_0, x = pos_q_1)[name = tensor<string, []>("op_241")]; |
| tensor<int32, [1]> var_243_axes_0 = const()[name = tensor<string, []>("op_243_axes_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [1, 1, 256]> var_243 = expand_dims(axes = var_243_axes_0, x = pos_k_1)[name = tensor<string, []>("op_243")]; |
| tensor<string, []> var_244_promoted_dtype_0 = const()[name = tensor<string, []>("op_244_promoted_dtype_0"), val = tensor<string, []>("fp32")]; |
| tensor<fp32, [1, 1, 256]> var_244_promoted = cast(dtype = var_244_promoted_dtype_0, x = var_243)[name = tensor<string, []>("cast_47")]; |
| tensor<fp32, [1, 16, 256]> delta_1 = sub(x = var_241, y = var_244_promoted)[name = tensor<string, []>("delta_1")]; |
| tensor<bool, [1, 1, 256]> valid_1 = greater_equal(x = var_243, y = var_86)[name = tensor<string, []>("valid_1")]; |
| tensor<int32, [3]> var_253 = const()[name = tensor<string, []>("op_253"), val = tensor<int32, [3]>([1, 1, 1])]; |
| tensor<fp32, [1, 1, 1]> var_254 = reshape(shape = var_253, x = attn0_offset)[name = tensor<string, []>("op_254")]; |
| tensor<fp32, [1]> var_256_promoted = const()[name = tensor<string, []>("op_256_promoted"), val = tensor<fp32, [1]>([0x1.ep+3])]; |
| tensor<fp32, [1, 1, 1]> var_257 = add(x = var_254, y = var_256_promoted)[name = tensor<string, []>("op_257")]; |
| tensor<bool, [1, 1, 256]> var_258 = less_equal(x = var_244_promoted, y = var_257)[name = tensor<string, []>("op_258")]; |
| tensor<bool, [1, 1, 256]> valid_3 = logical_and(x = valid_1, y = var_258)[name = tensor<string, []>("valid_3")]; |
| tensor<fp32, []> var_86_promoted = const()[name = tensor<string, []>("op_86_promoted"), val = tensor<fp32, []>(0x0p+0)]; |
| tensor<bool, [1, 16, 256]> var_260 = greater_equal(x = delta_1, y = var_86_promoted)[name = tensor<string, []>("op_260")]; |
| tensor<bool, [1, 16, 256]> attn_mask_1 = logical_and(x = valid_3, y = var_260)[name = tensor<string, []>("attn_mask_1")]; |
| tensor<fp32, []> var_98_promoted = const()[name = tensor<string, []>("op_98_promoted"), val = tensor<fp32, []>(0x1.f4p+7)]; |
| tensor<bool, [1, 16, 256]> var_262 = less(x = delta_1, y = var_98_promoted)[name = tensor<string, []>("op_262")]; |
| tensor<bool, [1, 16, 256]> attn_mask_3 = logical_and(x = attn_mask_1, y = var_262)[name = tensor<string, []>("attn_mask_3")]; |
| tensor<int32, [1]> attn_mask_5_axes_0 = const()[name = tensor<string, []>("attn_mask_5_axes_0"), val = tensor<int32, [1]>([1])]; |
| tensor<bool, [1, 1, 16, 256]> attn_mask_5 = expand_dims(axes = attn_mask_5_axes_0, x = attn_mask_3)[name = tensor<string, []>("attn_mask_5")]; |
| tensor<bool, []> var_267_transpose_x_0 = const()[name = tensor<string, []>("op_267_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> var_267_transpose_y_0 = const()[name = tensor<string, []>("op_267_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_6_perm_0 = const()[name = tensor<string, []>("transpose_6_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_7_perm_0 = const()[name = tensor<string, []>("transpose_7_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])]; |
| tensor<fp32, [1, 8, 64, 256]> transpose_7 = transpose(perm = transpose_7_perm_0, x = new_k_cache_3)[name = tensor<string, []>("transpose_16")]; |
| tensor<fp32, [1, 8, 16, 64]> transpose_6 = transpose(perm = transpose_6_perm_0, x = q_5)[name = tensor<string, []>("transpose_17")]; |
| tensor<fp32, [1, 8, 16, 256]> 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<string, []>("op_267")]; |
| tensor<fp32, []> var_268 = const()[name = tensor<string, []>("op_268"), val = tensor<fp32, []>(0x1p-3)]; |
| tensor<fp32, [1, 8, 16, 256]> attn_1 = mul(x = var_267, y = var_268)[name = tensor<string, []>("attn_1")]; |
| tensor<bool, [1, 1, 16, 256]> var_270 = logical_not(x = attn_mask_5)[name = tensor<string, []>("op_270")]; |
| tensor<fp32, [1, 8, 16, 256]> attn_3 = select(a = var_100, b = attn_1, cond = var_270)[name = tensor<string, []>("attn_3")]; |
| tensor<fp32, [1, 8, 16, 256]> attn_5 = softmax(axis = var_91, x = attn_3)[name = tensor<string, []>("attn_5")]; |
| tensor<bool, []> x_5_transpose_x_0 = const()[name = tensor<string, []>("x_5_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> x_5_transpose_y_0 = const()[name = tensor<string, []>("x_5_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [1, 8, 256, 64]> v_attn_1 = transpose(perm = var_219, x = new_v_cache_3)[name = tensor<string, []>("transpose_18")]; |
| tensor<fp32, [1, 8, 16, 64]> 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<string, []>("x_5")]; |
| tensor<int32, [4]> var_274_perm_0 = const()[name = tensor<string, []>("op_274_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_275 = const()[name = tensor<string, []>("op_275"), val = tensor<int32, [3]>([1, 16, 512])]; |
| tensor<fp32, [1, 16, 8, 64]> var_274 = transpose(perm = var_274_perm_0, x = x_5)[name = tensor<string, []>("transpose_15")]; |
| tensor<fp32, [1, 16, 512]> input_5 = reshape(shape = var_275, x = var_274)[name = tensor<string, []>("input_5")]; |
| tensor<fp32, [512]> linear_1_bias_0 = const()[name = tensor<string, []>("linear_1_bias_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41755456)))]; |
| tensor<fp32, [1, 16, 512]> 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<string, []>("linear_1")]; |
| tensor<fp32, [1, 16, 512]> var_284 = mul(x = mimi_decoder_transformer_transformer_layers_0_layer_scale_1_scale, y = x_7)[name = tensor<string, []>("op_284")]; |
| tensor<fp32, [1, 16, 512]> input_7 = add(x = input_3, y = var_284)[name = tensor<string, []>("input_7")]; |
| tensor<int32, [1]> input_9_axes_0 = const()[name = tensor<string, []>("input_9_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp32, [1, 16, 512]> 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<string, []>("input_9")]; |
| tensor<fp32, [2048]> linear_2_bias_0 = const()[name = tensor<string, []>("linear_2_bias_0"), val = tensor<fp32, [2048]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41757568)))]; |
| tensor<fp32, [1, 16, 2048]> var_291 = linear(bias = linear_2_bias_0, weight = mimi_decoder_transformer_transformer_layers_0_linear1_weight, x = input_9)[name = tensor<string, []>("linear_2")]; |
| tensor<string, []> input_11_mode_0 = const()[name = tensor<string, []>("input_11_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp32, [1, 16, 2048]> input_11 = gelu(mode = input_11_mode_0, x = var_291)[name = tensor<string, []>("input_11")]; |
| tensor<fp32, [1, 16, 512]> x_9 = linear(bias = linear_1_bias_0, weight = mimi_decoder_transformer_transformer_layers_0_linear2_weight, x = input_11)[name = tensor<string, []>("linear_3")]; |
| tensor<fp32, [1, 16, 512]> var_297 = mul(x = mimi_decoder_transformer_transformer_layers_0_layer_scale_2_scale, y = x_9)[name = tensor<string, []>("op_297")]; |
| tensor<fp32, [1, 16, 512]> input_13 = add(x = input_7, y = var_297)[name = tensor<string, []>("input_13")]; |
| tensor<int32, [1]> query_axes_0 = const()[name = tensor<string, []>("query_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp32, [1, 16, 512]> 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<string, []>("query")]; |
| tensor<fp32, [1, 16, 1536]> projected = linear(bias = linear_0_bias_0, weight = mimi_decoder_transformer_transformer_layers_1_self_attn_in_proj_weight, x = query)[name = tensor<string, []>("linear_4")]; |
| tensor<int32, [5]> var_320 = const()[name = tensor<string, []>("op_320"), val = tensor<int32, [5]>([1, 16, 3, 8, 64])]; |
| tensor<fp32, [1, 16, 3, 8, 64]> packed = reshape(shape = var_320, x = projected)[name = tensor<string, []>("packed")]; |
| tensor<int32, [3]> var_322_split_sizes_0 = const()[name = tensor<string, []>("op_322_split_sizes_0"), val = tensor<int32, [3]>([1, 1, 1])]; |
| tensor<int32, []> var_322_axis_0 = const()[name = tensor<string, []>("op_322_axis_0"), val = tensor<int32, []>(2)]; |
| tensor<fp32, [1, 16, 1, 8, 64]> var_322_0, tensor<fp32, [1, 16, 1, 8, 64]> var_322_1, tensor<fp32, [1, 16, 1, 8, 64]> var_322_2 = split(axis = var_322_axis_0, split_sizes = var_322_split_sizes_0, x = packed)[name = tensor<string, []>("op_322")]; |
| tensor<int32, [1]> squeeze_3_axes_0 = const()[name = tensor<string, []>("squeeze_3_axes_0"), val = tensor<int32, [1]>([2])]; |
| tensor<fp32, [1, 16, 8, 64]> squeeze_3 = squeeze(axes = squeeze_3_axes_0, x = var_322_0)[name = tensor<string, []>("squeeze_3")]; |
| tensor<int32, [1]> squeeze_4_axes_0 = const()[name = tensor<string, []>("squeeze_4_axes_0"), val = tensor<int32, [1]>([2])]; |
| tensor<fp32, [1, 16, 8, 64]> squeeze_4 = squeeze(axes = squeeze_4_axes_0, x = var_322_1)[name = tensor<string, []>("squeeze_4")]; |
| tensor<int32, [1]> squeeze_5_axes_0 = const()[name = tensor<string, []>("squeeze_5_axes_0"), val = tensor<int32, [1]>([2])]; |
| tensor<fp32, [1, 16, 8, 64]> squeeze_5 = squeeze(axes = squeeze_5_axes_0, x = var_322_2)[name = tensor<string, []>("squeeze_5")]; |
| tensor<int32, [1]> offset_begin_0 = const()[name = tensor<string, []>("offset_begin_0"), val = tensor<int32, [1]>([0])]; |
| tensor<int32, [1]> offset_end_0 = const()[name = tensor<string, []>("offset_end_0"), val = tensor<int32, [1]>([1])]; |
| tensor<bool, [1]> offset_end_mask_0 = const()[name = tensor<string, []>("offset_end_mask_0"), val = tensor<bool, [1]>([false])]; |
| tensor<bool, [1]> offset_squeeze_mask_0 = const()[name = tensor<string, []>("offset_squeeze_mask_0"), val = tensor<bool, [1]>([true])]; |
| tensor<fp32, []> 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<string, []>("offset")]; |
| tensor<fp32, [32]> freqs = const()[name = tensor<string, []>("freqs"), val = tensor<fp32, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41765824)))]; |
| tensor<fp32, [16]> ts_7_promoted = const()[name = tensor<string, []>("ts_7_promoted"), val = tensor<fp32, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41766016)))]; |
| tensor<fp32, [16]> ts_9 = add(x = ts_7_promoted, y = offset)[name = tensor<string, []>("ts_9")]; |
| tensor<int32, [3]> var_338 = const()[name = tensor<string, []>("op_338"), val = tensor<int32, [3]>([-1, 1, 1])]; |
| tensor<fp32, [16, 1, 1]> ts = reshape(shape = var_338, x = ts_9)[name = tensor<string, []>("ts")]; |
| tensor<int32, [5]> var_340 = const()[name = tensor<string, []>("op_340"), val = tensor<int32, [5]>([1, 16, 8, 32, 2])]; |
| tensor<fp32, [1, 16, 8, 32, 2]> q_9 = reshape(shape = var_340, x = squeeze_3)[name = tensor<string, []>("q_9")]; |
| tensor<int32, [5]> var_342 = const()[name = tensor<string, []>("op_342"), val = tensor<int32, [5]>([1, 16, 8, 32, 2])]; |
| tensor<fp32, [1, 16, 8, 32, 2]> k_9 = reshape(shape = var_342, x = squeeze_4)[name = tensor<string, []>("k_9")]; |
| tensor<int32, [5]> var_344_begin_0 = const()[name = tensor<string, []>("op_344_begin_0"), val = tensor<int32, [5]>([0, 0, 0, 0, 0])]; |
| tensor<int32, [5]> var_344_end_0 = const()[name = tensor<string, []>("op_344_end_0"), val = tensor<int32, [5]>([1, 16, 8, 32, 1])]; |
| tensor<bool, [5]> var_344_end_mask_0 = const()[name = tensor<string, []>("op_344_end_mask_0"), val = tensor<bool, [5]>([true, true, true, true, false])]; |
| tensor<bool, [5]> var_344_squeeze_mask_0 = const()[name = tensor<string, []>("op_344_squeeze_mask_0"), val = tensor<bool, [5]>([false, false, false, false, true])]; |
| tensor<fp32, [1, 16, 8, 32]> 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<string, []>("op_344")]; |
| tensor<int32, [5]> var_346_begin_0 = const()[name = tensor<string, []>("op_346_begin_0"), val = tensor<int32, [5]>([0, 0, 0, 0, 1])]; |
| tensor<int32, [5]> var_346_end_0 = const()[name = tensor<string, []>("op_346_end_0"), val = tensor<int32, [5]>([1, 16, 8, 32, 2])]; |
| tensor<bool, [5]> var_346_end_mask_0 = const()[name = tensor<string, []>("op_346_end_mask_0"), val = tensor<bool, [5]>([true, true, true, true, false])]; |
| tensor<bool, [5]> var_346_squeeze_mask_0 = const()[name = tensor<string, []>("op_346_squeeze_mask_0"), val = tensor<bool, [5]>([false, false, false, false, true])]; |
| tensor<fp32, [1, 16, 8, 32]> 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<string, []>("op_346")]; |
| tensor<int32, [5]> var_348_begin_0 = const()[name = tensor<string, []>("op_348_begin_0"), val = tensor<int32, [5]>([0, 0, 0, 0, 0])]; |
| tensor<int32, [5]> var_348_end_0 = const()[name = tensor<string, []>("op_348_end_0"), val = tensor<int32, [5]>([1, 16, 8, 32, 1])]; |
| tensor<bool, [5]> var_348_end_mask_0 = const()[name = tensor<string, []>("op_348_end_mask_0"), val = tensor<bool, [5]>([true, true, true, true, false])]; |
| tensor<bool, [5]> var_348_squeeze_mask_0 = const()[name = tensor<string, []>("op_348_squeeze_mask_0"), val = tensor<bool, [5]>([false, false, false, false, true])]; |
| tensor<fp32, [1, 16, 8, 32]> 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<string, []>("op_348")]; |
| tensor<int32, [5]> var_350_begin_0 = const()[name = tensor<string, []>("op_350_begin_0"), val = tensor<int32, [5]>([0, 0, 0, 0, 1])]; |
| tensor<int32, [5]> var_350_end_0 = const()[name = tensor<string, []>("op_350_end_0"), val = tensor<int32, [5]>([1, 16, 8, 32, 2])]; |
| tensor<bool, [5]> var_350_end_mask_0 = const()[name = tensor<string, []>("op_350_end_mask_0"), val = tensor<bool, [5]>([true, true, true, true, false])]; |
| tensor<bool, [5]> var_350_squeeze_mask_0 = const()[name = tensor<string, []>("op_350_squeeze_mask_0"), val = tensor<bool, [5]>([false, false, false, false, true])]; |
| tensor<fp32, [1, 16, 8, 32]> 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<string, []>("op_350")]; |
| tensor<fp32, [16, 1, 32]> var_352 = mul(x = freqs, y = ts)[name = tensor<string, []>("op_352")]; |
| tensor<fp32, [16, 1, 32]> rotr = cos(x = var_352)[name = tensor<string, []>("rotr")]; |
| tensor<fp32, [16, 1, 32]> roti = sin(x = var_352)[name = tensor<string, []>("roti")]; |
| tensor<fp32, [1, 16, 8, 32]> var_356 = mul(x = var_344, y = rotr)[name = tensor<string, []>("op_356")]; |
| tensor<fp32, [1, 16, 8, 32]> var_357 = mul(x = var_346, y = roti)[name = tensor<string, []>("op_357")]; |
| tensor<fp32, [1, 16, 8, 32]> qor_5 = sub(x = var_356, y = var_357)[name = tensor<string, []>("qor_5")]; |
| tensor<fp32, [1, 16, 8, 32]> var_359 = mul(x = var_344, y = roti)[name = tensor<string, []>("op_359")]; |
| tensor<fp32, [1, 16, 8, 32]> var_360 = mul(x = var_346, y = rotr)[name = tensor<string, []>("op_360")]; |
| tensor<fp32, [1, 16, 8, 32]> qoi_5 = add(x = var_359, y = var_360)[name = tensor<string, []>("qoi_5")]; |
| tensor<fp32, [1, 16, 8, 32]> var_362 = mul(x = var_348, y = rotr)[name = tensor<string, []>("op_362")]; |
| tensor<fp32, [1, 16, 8, 32]> var_363 = mul(x = var_350, y = roti)[name = tensor<string, []>("op_363")]; |
| tensor<fp32, [1, 16, 8, 32]> kor_5 = sub(x = var_362, y = var_363)[name = tensor<string, []>("kor_5")]; |
| tensor<fp32, [1, 16, 8, 32]> var_365 = mul(x = var_348, y = roti)[name = tensor<string, []>("op_365")]; |
| tensor<fp32, [1, 16, 8, 32]> var_366 = mul(x = var_350, y = rotr)[name = tensor<string, []>("op_366")]; |
| tensor<fp32, [1, 16, 8, 32]> koi_5 = add(x = var_365, y = var_366)[name = tensor<string, []>("koi_5")]; |
| tensor<int32, []> qo_axis_0 = const()[name = tensor<string, []>("qo_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp32, [1, 16, 8, 32, 2]> qo = stack(axis = qo_axis_0, values = (qor_5, qoi_5))[name = tensor<string, []>("qo")]; |
| tensor<int32, []> ko_axis_0 = const()[name = tensor<string, []>("ko_axis_0"), val = tensor<int32, []>(-1)]; |
| tensor<fp32, [1, 16, 8, 32, 2]> ko = stack(axis = ko_axis_0, values = (kor_5, koi_5))[name = tensor<string, []>("ko")]; |
| tensor<int32, [4]> var_376 = const()[name = tensor<string, []>("op_376"), val = tensor<int32, [4]>([1, 16, 8, 64])]; |
| tensor<fp32, [1, 16, 8, 64]> q = reshape(shape = var_376, x = qo)[name = tensor<string, []>("q")]; |
| tensor<int32, [4]> var_378 = const()[name = tensor<string, []>("op_378"), val = tensor<int32, [4]>([1, 16, 8, 64])]; |
| tensor<fp32, [1, 16, 8, 64]> k = reshape(shape = var_378, x = ko)[name = tensor<string, []>("k")]; |
| tensor<int32, [1]> capacity = const()[name = tensor<string, []>("capacity"), val = tensor<int32, [1]>([256])]; |
| tensor<string, []> var_383_dtype_0 = const()[name = tensor<string, []>("op_383_dtype_0"), val = tensor<string, []>("int32")]; |
| tensor<int32, [2]> var_384 = const()[name = tensor<string, []>("op_384"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [1]> var_383 = cast(dtype = var_383_dtype_0, x = attn1_offset)[name = tensor<string, []>("cast_46")]; |
| tensor<int32, [1, 1]> write_base = reshape(shape = var_384, x = var_383)[name = tensor<string, []>("write_base")]; |
| tensor<int32, [1, 16]> write_range = const()[name = tensor<string, []>("write_range"), val = tensor<int32, [1, 16]>([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]])]; |
| tensor<int32, [1, 16]> abs_idx = add(x = write_base, y = write_range)[name = tensor<string, []>("abs_idx")]; |
| tensor<int32, [1, 16]> wrapped_div = floor_div(x = abs_idx, y = capacity)[name = tensor<string, []>("wrapped_div")]; |
| tensor<int32, [1, 16]> wrapped_div_scaled = mul(x = wrapped_div, y = capacity)[name = tensor<string, []>("wrapped_div_scaled")]; |
| tensor<int32, [1, 16]> wrapped = sub(x = abs_idx, y = wrapped_div_scaled)[name = tensor<string, []>("wrapped")]; |
| tensor<int32, [4]> var_391 = const()[name = tensor<string, []>("op_391"), val = tensor<int32, [4]>([1, 16, 1, 1])]; |
| tensor<int32, [1, 16, 1, 1]> var_392 = reshape(shape = var_391, x = wrapped)[name = tensor<string, []>("op_392")]; |
| tensor<int32, [4]> write_indexes_reps_0 = const()[name = tensor<string, []>("write_indexes_reps_0"), val = tensor<int32, [4]>([1, 1, 8, 64])]; |
| tensor<int32, [1, 16, 8, 64]> write_indexes = tile(reps = write_indexes_reps_0, x = var_392)[name = tensor<string, []>("write_indexes")]; |
| tensor<int32, [5]> var_395_begin_0 = const()[name = tensor<string, []>("op_395_begin_0"), val = tensor<int32, [5]>([0, 0, 0, 0, 0])]; |
| tensor<int32, [5]> var_395_end_0 = const()[name = tensor<string, []>("op_395_end_0"), val = tensor<int32, [5]>([1, 1, 256, 8, 64])]; |
| tensor<bool, [5]> var_395_end_mask_0 = const()[name = tensor<string, []>("op_395_end_mask_0"), val = tensor<bool, [5]>([false, true, true, true, true])]; |
| tensor<bool, [5]> var_395_squeeze_mask_0 = const()[name = tensor<string, []>("op_395_squeeze_mask_0"), val = tensor<bool, [5]>([true, false, false, false, false])]; |
| tensor<fp32, [1, 256, 8, 64]> 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<string, []>("op_395")]; |
| tensor<int32, []> new_k_cache_5_axis_0 = const()[name = tensor<string, []>("new_k_cache_5_axis_0"), val = tensor<int32, []>(1)]; |
| tensor<string, []> new_k_cache_5_mode_0 = const()[name = tensor<string, []>("new_k_cache_5_mode_0"), val = tensor<string, []>("update")]; |
| tensor<bool, []> new_k_cache_5_validate_indices_0 = const()[name = tensor<string, []>("new_k_cache_5_validate_indices_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [1, 256, 8, 64]> 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<string, []>("new_k_cache_5")]; |
| tensor<int32, [5]> var_397_begin_0 = const()[name = tensor<string, []>("op_397_begin_0"), val = tensor<int32, [5]>([1, 0, 0, 0, 0])]; |
| tensor<int32, [5]> var_397_end_0 = const()[name = tensor<string, []>("op_397_end_0"), val = tensor<int32, [5]>([2, 1, 256, 8, 64])]; |
| tensor<bool, [5]> var_397_end_mask_0 = const()[name = tensor<string, []>("op_397_end_mask_0"), val = tensor<bool, [5]>([false, true, true, true, true])]; |
| tensor<bool, [5]> var_397_squeeze_mask_0 = const()[name = tensor<string, []>("op_397_squeeze_mask_0"), val = tensor<bool, [5]>([true, false, false, false, false])]; |
| tensor<fp32, [1, 256, 8, 64]> 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<string, []>("op_397")]; |
| tensor<int32, []> new_v_cache_5_axis_0 = const()[name = tensor<string, []>("new_v_cache_5_axis_0"), val = tensor<int32, []>(1)]; |
| tensor<string, []> new_v_cache_5_mode_0 = const()[name = tensor<string, []>("new_v_cache_5_mode_0"), val = tensor<string, []>("update")]; |
| tensor<bool, []> new_v_cache_5_validate_indices_0 = const()[name = tensor<string, []>("new_v_cache_5_validate_indices_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [1, 256, 8, 64]> 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<string, []>("new_v_cache_5")]; |
| tensor<int32, []> var_400_axis_0 = const()[name = tensor<string, []>("op_400_axis_0"), val = tensor<int32, []>(0)]; |
| tensor<fp32, [2, 1, 256, 8, 64]> var_400 = stack(axis = var_400_axis_0, values = (new_k_cache_5, new_v_cache_5))[name = tensor<string, []>("op_400")]; |
| tensor<bool, [1, 256, 8, 64]> var_401 = not_equal(x = new_k_cache_5, y = new_k_cache_5)[name = tensor<string, []>("op_401")]; |
| tensor<fp32, [1, 256, 8, 64]> new_k_cache = select(a = var_212, b = new_k_cache_5, cond = var_401)[name = tensor<string, []>("new_k_cache")]; |
| tensor<bool, [1, 256, 8, 64]> var_404 = not_equal(x = new_v_cache_5, y = new_v_cache_5)[name = tensor<string, []>("op_404")]; |
| tensor<fp32, [1, 256, 8, 64]> new_v_cache = select(a = var_212, b = new_v_cache_5, cond = var_404)[name = tensor<string, []>("new_v_cache")]; |
| tensor<int32, [4]> var_409 = const()[name = tensor<string, []>("op_409"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [2]> var_411 = const()[name = tensor<string, []>("op_411"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<fp32, [1, 1]> var_412 = reshape(shape = var_411, x = attn1_offset)[name = tensor<string, []>("op_412")]; |
| tensor<fp32, [1]> var_414_promoted = const()[name = tensor<string, []>("op_414_promoted"), val = tensor<fp32, [1]>([0x1.ep+3])]; |
| tensor<fp32, [1, 1]> var_415 = add(x = var_412, y = var_414_promoted)[name = tensor<string, []>("op_415")]; |
| tensor<string, []> last_pos_dtype_0 = const()[name = tensor<string, []>("last_pos_dtype_0"), val = tensor<string, []>("int32")]; |
| tensor<int32, [1, 1]> last_pos = cast(dtype = last_pos_dtype_0, x = var_415)[name = tensor<string, []>("cast_45")]; |
| tensor<int32, [1, 256]> diff = sub(x = last_pos, y = slot_idx_1)[name = tensor<string, []>("diff")]; |
| tensor<int32, [1, 256]> var_421_div = floor_div(x = diff, y = capacity)[name = tensor<string, []>("op_421_div")]; |
| tensor<int32, [1, 256]> var_421_div_scaled = mul(x = var_421_div, y = capacity)[name = tensor<string, []>("op_421_div_scaled")]; |
| tensor<int32, [1, 256]> var_421 = sub(x = diff, y = var_421_div_scaled)[name = tensor<string, []>("op_421")]; |
| tensor<int32, [1, 256]> pos_k = sub(x = last_pos, y = var_421)[name = tensor<string, []>("pos_k")]; |
| tensor<fp32, [1, 16]> var_427_promoted = const()[name = tensor<string, []>("op_427_promoted"), val = tensor<fp32, [1, 16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41766144)))]; |
| tensor<fp32, [1, 16]> pos_q = add(x = var_412, y = var_427_promoted)[name = tensor<string, []>("pos_q")]; |
| tensor<int32, [1]> var_431_axes_0 = const()[name = tensor<string, []>("op_431_axes_0"), val = tensor<int32, [1]>([2])]; |
| tensor<fp32, [1, 16, 1]> var_431 = expand_dims(axes = var_431_axes_0, x = pos_q)[name = tensor<string, []>("op_431")]; |
| tensor<int32, [1]> var_433_axes_0 = const()[name = tensor<string, []>("op_433_axes_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [1, 1, 256]> var_433 = expand_dims(axes = var_433_axes_0, x = pos_k)[name = tensor<string, []>("op_433")]; |
| tensor<string, []> var_434_promoted_dtype_0 = const()[name = tensor<string, []>("op_434_promoted_dtype_0"), val = tensor<string, []>("fp32")]; |
| tensor<fp32, [1, 1, 256]> var_434_promoted = cast(dtype = var_434_promoted_dtype_0, x = var_433)[name = tensor<string, []>("cast_44")]; |
| tensor<fp32, [1, 16, 256]> delta = sub(x = var_431, y = var_434_promoted)[name = tensor<string, []>("delta")]; |
| tensor<bool, [1, 1, 256]> valid_5 = greater_equal(x = var_433, y = var_86)[name = tensor<string, []>("valid_5")]; |
| tensor<int32, [3]> var_443 = const()[name = tensor<string, []>("op_443"), val = tensor<int32, [3]>([1, 1, 1])]; |
| tensor<fp32, [1, 1, 1]> var_444 = reshape(shape = var_443, x = attn1_offset)[name = tensor<string, []>("op_444")]; |
| tensor<fp32, [1]> var_446_promoted = const()[name = tensor<string, []>("op_446_promoted"), val = tensor<fp32, [1]>([0x1.ep+3])]; |
| tensor<fp32, [1, 1, 1]> var_447 = add(x = var_444, y = var_446_promoted)[name = tensor<string, []>("op_447")]; |
| tensor<bool, [1, 1, 256]> var_448 = less_equal(x = var_434_promoted, y = var_447)[name = tensor<string, []>("op_448")]; |
| tensor<bool, [1, 1, 256]> valid = logical_and(x = valid_5, y = var_448)[name = tensor<string, []>("valid")]; |
| tensor<fp32, []> var_86_promoted_1 = const()[name = tensor<string, []>("op_86_promoted_1"), val = tensor<fp32, []>(0x0p+0)]; |
| tensor<bool, [1, 16, 256]> var_450 = greater_equal(x = delta, y = var_86_promoted_1)[name = tensor<string, []>("op_450")]; |
| tensor<bool, [1, 16, 256]> attn_mask_7 = logical_and(x = valid, y = var_450)[name = tensor<string, []>("attn_mask_7")]; |
| tensor<fp32, []> var_98_promoted_1 = const()[name = tensor<string, []>("op_98_promoted_1"), val = tensor<fp32, []>(0x1.f4p+7)]; |
| tensor<bool, [1, 16, 256]> var_452 = less(x = delta, y = var_98_promoted_1)[name = tensor<string, []>("op_452")]; |
| tensor<bool, [1, 16, 256]> attn_mask_9 = logical_and(x = attn_mask_7, y = var_452)[name = tensor<string, []>("attn_mask_9")]; |
| tensor<int32, [1]> attn_mask_axes_0 = const()[name = tensor<string, []>("attn_mask_axes_0"), val = tensor<int32, [1]>([1])]; |
| tensor<bool, [1, 1, 16, 256]> attn_mask = expand_dims(axes = attn_mask_axes_0, x = attn_mask_9)[name = tensor<string, []>("attn_mask")]; |
| tensor<bool, []> var_457_transpose_x_0 = const()[name = tensor<string, []>("op_457_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> var_457_transpose_y_0 = const()[name = tensor<string, []>("op_457_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_8_perm_0 = const()[name = tensor<string, []>("transpose_8_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_9_perm_0 = const()[name = tensor<string, []>("transpose_9_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])]; |
| tensor<fp32, [1, 8, 64, 256]> transpose_9 = transpose(perm = transpose_9_perm_0, x = new_k_cache)[name = tensor<string, []>("transpose_12")]; |
| tensor<fp32, [1, 8, 16, 64]> transpose_8 = transpose(perm = transpose_8_perm_0, x = q)[name = tensor<string, []>("transpose_13")]; |
| tensor<fp32, [1, 8, 16, 256]> 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<string, []>("op_457")]; |
| tensor<fp32, []> var_458 = const()[name = tensor<string, []>("op_458"), val = tensor<fp32, []>(0x1p-3)]; |
| tensor<fp32, [1, 8, 16, 256]> attn_7 = mul(x = var_457, y = var_458)[name = tensor<string, []>("attn_7")]; |
| tensor<bool, [1, 1, 16, 256]> var_460 = logical_not(x = attn_mask)[name = tensor<string, []>("op_460")]; |
| tensor<fp32, [1, 8, 16, 256]> attn_9 = select(a = var_100, b = attn_7, cond = var_460)[name = tensor<string, []>("attn_9")]; |
| tensor<fp32, [1, 8, 16, 256]> attn = softmax(axis = var_91, x = attn_9)[name = tensor<string, []>("attn")]; |
| tensor<bool, []> x_11_transpose_x_0 = const()[name = tensor<string, []>("x_11_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> x_11_transpose_y_0 = const()[name = tensor<string, []>("x_11_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [1, 8, 256, 64]> v_attn = transpose(perm = var_409, x = new_v_cache)[name = tensor<string, []>("transpose_14")]; |
| tensor<fp32, [1, 8, 16, 64]> x_11 = matmul(transpose_x = x_11_transpose_x_0, transpose_y = x_11_transpose_y_0, x = attn, y = v_attn)[name = tensor<string, []>("x_11")]; |
| tensor<int32, [4]> var_464_perm_0 = const()[name = tensor<string, []>("op_464_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> var_465 = const()[name = tensor<string, []>("op_465"), val = tensor<int32, [3]>([1, 16, 512])]; |
| tensor<fp32, [1, 16, 8, 64]> var_464 = transpose(perm = var_464_perm_0, x = x_11)[name = tensor<string, []>("transpose_11")]; |
| tensor<fp32, [1, 16, 512]> input_15 = reshape(shape = var_465, x = var_464)[name = tensor<string, []>("input_15")]; |
| tensor<fp32, [1, 16, 512]> 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<string, []>("linear_5")]; |
| tensor<fp32, [1, 16, 512]> var_474 = mul(x = mimi_decoder_transformer_transformer_layers_1_layer_scale_1_scale, y = x_13)[name = tensor<string, []>("op_474")]; |
| tensor<fp32, [1, 16, 512]> input_17 = add(x = input_13, y = var_474)[name = tensor<string, []>("input_17")]; |
| tensor<int32, [1]> input_19_axes_0 = const()[name = tensor<string, []>("input_19_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp32, [1, 16, 512]> 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<string, []>("input_19")]; |
| tensor<fp32, [1, 16, 2048]> var_481 = linear(bias = linear_2_bias_0, weight = mimi_decoder_transformer_transformer_layers_1_linear1_weight, x = input_19)[name = tensor<string, []>("linear_6")]; |
| tensor<string, []> input_21_mode_0 = const()[name = tensor<string, []>("input_21_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp32, [1, 16, 2048]> input_21 = gelu(mode = input_21_mode_0, x = var_481)[name = tensor<string, []>("input_21")]; |
| tensor<fp32, [1, 16, 512]> x_15 = linear(bias = linear_1_bias_0, weight = mimi_decoder_transformer_transformer_layers_1_linear2_weight, x = input_21)[name = tensor<string, []>("linear_7")]; |
| tensor<fp32, [1, 16, 512]> var_487 = mul(x = mimi_decoder_transformer_transformer_layers_1_layer_scale_2_scale, y = x_15)[name = tensor<string, []>("op_487")]; |
| tensor<fp32, [1, 16, 512]> z = add(x = input_17, y = var_487)[name = tensor<string, []>("z")]; |
| tensor<int32, [3]> x_17_perm_0 = const()[name = tensor<string, []>("x_17_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<fp32, []> var_507 = const()[name = tensor<string, []>("op_507"), val = tensor<fp32, []>(0x1p+0)]; |
| tensor<int32, []> var_508 = const()[name = tensor<string, []>("op_508"), val = tensor<int32, []>(-1)]; |
| tensor<bool, []> input_23_interleave_0 = const()[name = tensor<string, []>("input_23_interleave_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [1, 512, 16]> x_17 = transpose(perm = x_17_perm_0, x = z)[name = tensor<string, []>("transpose_10")]; |
| tensor<fp32, [1, 512, 22]> input_23 = concat(axis = var_508, interleave = input_23_interleave_0, values = (conv0_prev, x_17))[name = tensor<string, []>("input_23")]; |
| tensor<string, []> input_25_pad_type_0 = const()[name = tensor<string, []>("input_25_pad_type_0"), val = tensor<string, []>("valid")]; |
| tensor<int32, [1]> input_25_strides_0 = const()[name = tensor<string, []>("input_25_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> input_25_pad_0 = const()[name = tensor<string, []>("input_25_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> input_25_dilations_0 = const()[name = tensor<string, []>("input_25_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> input_25_groups_0 = const()[name = tensor<string, []>("input_25_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 512, 16]> 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<string, []>("input_25")]; |
| tensor<int32, [3]> var_542_begin_0 = const()[name = tensor<string, []>("op_542_begin_0"), val = tensor<int32, [3]>([0, 0, 16])]; |
| tensor<int32, [3]> var_542_end_0 = const()[name = tensor<string, []>("op_542_end_0"), val = tensor<int32, [3]>([1, 512, 22])]; |
| tensor<bool, [3]> var_542_end_mask_0 = const()[name = tensor<string, []>("op_542_end_mask_0"), val = tensor<bool, [3]>([true, true, true])]; |
| tensor<fp32, [1, 512, 6]> 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<string, []>("op_542")]; |
| tensor<fp32, [1, 512, 16]> input_27 = elu(alpha = var_507, x = input_25)[name = tensor<string, []>("input_27")]; |
| tensor<string, []> y_5_pad_type_0 = const()[name = tensor<string, []>("y_5_pad_type_0"), val = tensor<string, []>("valid")]; |
| tensor<int32, [1]> y_5_strides_0 = const()[name = tensor<string, []>("y_5_strides_0"), val = tensor<int32, [1]>([6])]; |
| tensor<int32, [2]> y_5_pad_0 = const()[name = tensor<string, []>("y_5_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> y_5_dilations_0 = const()[name = tensor<string, []>("y_5_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> y_5_groups_0 = const()[name = tensor<string, []>("y_5_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<int32, [3]> y_5_has_output_shape_output_shape_0 = const()[name = tensor<string, []>("y_5_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 256, 102])]; |
| tensor<fp32, [1, 256, 102]> 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<string, []>("y_5_has_output_shape")]; |
| tensor<int32, [3]> var_557_begin_0 = const()[name = tensor<string, []>("op_557_begin_0"), val = tensor<int32, [3]>([0, 0, 0])]; |
| tensor<int32, [3]> var_557_end_0 = const()[name = tensor<string, []>("op_557_end_0"), val = tensor<int32, [3]>([1, 256, 6])]; |
| tensor<bool, [3]> var_557_end_mask_0 = const()[name = tensor<string, []>("op_557_end_mask_0"), val = tensor<bool, [3]>([true, true, false])]; |
| tensor<fp32, [1, 256, 6]> 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<string, []>("op_557")]; |
| tensor<fp32, [1, 256, 6]> var_558 = add(x = var_557, y = convtr0_partial)[name = tensor<string, []>("op_558")]; |
| tensor<int32, [3]> var_559_begin_0 = const()[name = tensor<string, []>("op_559_begin_0"), val = tensor<int32, [3]>([0, 0, 6])]; |
| tensor<int32, [3]> var_559_end_0 = const()[name = tensor<string, []>("op_559_end_0"), val = tensor<int32, [3]>([1, 256, 102])]; |
| tensor<bool, [3]> var_559_end_mask_0 = const()[name = tensor<string, []>("op_559_end_mask_0"), val = tensor<bool, [3]>([true, true, true])]; |
| tensor<fp32, [1, 256, 96]> 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<string, []>("op_559")]; |
| tensor<bool, []> y_7_interleave_0 = const()[name = tensor<string, []>("y_7_interleave_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [1, 256, 102]> y_7 = concat(axis = var_508, interleave = y_7_interleave_0, values = (var_558, var_559))[name = tensor<string, []>("y_7")]; |
| tensor<int32, [3]> new_partial_1_begin_0 = const()[name = tensor<string, []>("new_partial_1_begin_0"), val = tensor<int32, [3]>([0, 0, 96])]; |
| tensor<int32, [3]> new_partial_1_end_0 = const()[name = tensor<string, []>("new_partial_1_end_0"), val = tensor<int32, [3]>([1, 256, 102])]; |
| tensor<bool, [3]> new_partial_1_end_mask_0 = const()[name = tensor<string, []>("new_partial_1_end_mask_0"), val = tensor<bool, [3]>([true, true, true])]; |
| tensor<fp32, [1, 256, 6]> 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<string, []>("new_partial_1")]; |
| tensor<fp32, [256, 1]> var_564 = const()[name = tensor<string, []>("op_564"), val = tensor<fp32, [256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41766272)))]; |
| tensor<fp32, [1, 256, 6]> var_565 = sub(x = new_partial_1, y = var_564)[name = tensor<string, []>("op_565")]; |
| tensor<int32, [3]> input_29_begin_0 = const()[name = tensor<string, []>("input_29_begin_0"), val = tensor<int32, [3]>([0, 0, 0])]; |
| tensor<int32, [3]> input_29_end_0 = const()[name = tensor<string, []>("input_29_end_0"), val = tensor<int32, [3]>([1, 256, 96])]; |
| tensor<bool, [3]> input_29_end_mask_0 = const()[name = tensor<string, []>("input_29_end_mask_0"), val = tensor<bool, [3]>([true, true, false])]; |
| tensor<fp32, [1, 256, 96]> 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<string, []>("input_29")]; |
| tensor<fp32, [1, 256, 96]> x_19 = elu(alpha = var_507, x = input_29)[name = tensor<string, []>("x_19")]; |
| tensor<bool, []> input_31_interleave_0 = const()[name = tensor<string, []>("input_31_interleave_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [1, 256, 98]> input_31 = concat(axis = var_508, interleave = input_31_interleave_0, values = (res0_conv0_prev, x_19))[name = tensor<string, []>("input_31")]; |
| tensor<string, []> input_33_pad_type_0 = const()[name = tensor<string, []>("input_33_pad_type_0"), val = tensor<string, []>("valid")]; |
| tensor<int32, [1]> input_33_strides_0 = const()[name = tensor<string, []>("input_33_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> input_33_pad_0 = const()[name = tensor<string, []>("input_33_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> input_33_dilations_0 = const()[name = tensor<string, []>("input_33_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> input_33_groups_0 = const()[name = tensor<string, []>("input_33_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 128, 96]> 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<string, []>("input_33")]; |
| tensor<int32, [3]> var_585_begin_0 = const()[name = tensor<string, []>("op_585_begin_0"), val = tensor<int32, [3]>([0, 0, 96])]; |
| tensor<int32, [3]> var_585_end_0 = const()[name = tensor<string, []>("op_585_end_0"), val = tensor<int32, [3]>([1, 256, 98])]; |
| tensor<bool, [3]> var_585_end_mask_0 = const()[name = tensor<string, []>("op_585_end_mask_0"), val = tensor<bool, [3]>([true, true, true])]; |
| tensor<fp32, [1, 256, 2]> 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<string, []>("op_585")]; |
| tensor<fp32, [1, 128, 96]> x_21 = elu(alpha = var_507, x = input_33)[name = tensor<string, []>("x_21")]; |
| tensor<string, []> v_5_pad_type_0 = const()[name = tensor<string, []>("v_5_pad_type_0"), val = tensor<string, []>("valid")]; |
| tensor<int32, [1]> v_5_strides_0 = const()[name = tensor<string, []>("v_5_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> v_5_pad_0 = const()[name = tensor<string, []>("v_5_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> v_5_dilations_0 = const()[name = tensor<string, []>("v_5_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> v_5_groups_0 = const()[name = tensor<string, []>("v_5_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 256, 96]> 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<string, []>("v_5")]; |
| tensor<fp32, [1, 256, 96]> input_35 = add(x = input_29, y = v_5)[name = tensor<string, []>("input_35")]; |
| tensor<fp32, [1, 256, 96]> input_37 = elu(alpha = var_507, x = input_35)[name = tensor<string, []>("input_37")]; |
| tensor<string, []> y_9_pad_type_0 = const()[name = tensor<string, []>("y_9_pad_type_0"), val = tensor<string, []>("valid")]; |
| tensor<int32, [1]> y_9_strides_0 = const()[name = tensor<string, []>("y_9_strides_0"), val = tensor<int32, [1]>([5])]; |
| tensor<int32, [2]> y_9_pad_0 = const()[name = tensor<string, []>("y_9_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> y_9_dilations_0 = const()[name = tensor<string, []>("y_9_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> y_9_groups_0 = const()[name = tensor<string, []>("y_9_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<int32, [3]> y_9_has_output_shape_output_shape_0 = const()[name = tensor<string, []>("y_9_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 128, 485])]; |
| tensor<fp32, [1, 128, 485]> 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<string, []>("y_9_has_output_shape")]; |
| tensor<int32, [3]> var_613_begin_0 = const()[name = tensor<string, []>("op_613_begin_0"), val = tensor<int32, [3]>([0, 0, 0])]; |
| tensor<int32, [3]> var_613_end_0 = const()[name = tensor<string, []>("op_613_end_0"), val = tensor<int32, [3]>([1, 128, 5])]; |
| tensor<bool, [3]> var_613_end_mask_0 = const()[name = tensor<string, []>("op_613_end_mask_0"), val = tensor<bool, [3]>([true, true, false])]; |
| tensor<fp32, [1, 128, 5]> 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<string, []>("op_613")]; |
| tensor<fp32, [1, 128, 5]> var_614 = add(x = var_613, y = convtr1_partial)[name = tensor<string, []>("op_614")]; |
| tensor<int32, [3]> var_615_begin_0 = const()[name = tensor<string, []>("op_615_begin_0"), val = tensor<int32, [3]>([0, 0, 5])]; |
| tensor<int32, [3]> var_615_end_0 = const()[name = tensor<string, []>("op_615_end_0"), val = tensor<int32, [3]>([1, 128, 485])]; |
| tensor<bool, [3]> var_615_end_mask_0 = const()[name = tensor<string, []>("op_615_end_mask_0"), val = tensor<bool, [3]>([true, true, true])]; |
| tensor<fp32, [1, 128, 480]> 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<string, []>("op_615")]; |
| tensor<bool, []> y_11_interleave_0 = const()[name = tensor<string, []>("y_11_interleave_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [1, 128, 485]> y_11 = concat(axis = var_508, interleave = y_11_interleave_0, values = (var_614, var_615))[name = tensor<string, []>("y_11")]; |
| tensor<int32, [3]> new_partial_3_begin_0 = const()[name = tensor<string, []>("new_partial_3_begin_0"), val = tensor<int32, [3]>([0, 0, 480])]; |
| tensor<int32, [3]> new_partial_3_end_0 = const()[name = tensor<string, []>("new_partial_3_end_0"), val = tensor<int32, [3]>([1, 128, 485])]; |
| tensor<bool, [3]> new_partial_3_end_mask_0 = const()[name = tensor<string, []>("new_partial_3_end_mask_0"), val = tensor<bool, [3]>([true, true, true])]; |
| tensor<fp32, [1, 128, 5]> 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<string, []>("new_partial_3")]; |
| tensor<fp32, [128, 1]> var_620 = const()[name = tensor<string, []>("op_620"), val = tensor<fp32, [128, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41767360)))]; |
| tensor<fp32, [1, 128, 5]> var_621 = sub(x = new_partial_3, y = var_620)[name = tensor<string, []>("op_621")]; |
| tensor<int32, [3]> input_39_begin_0 = const()[name = tensor<string, []>("input_39_begin_0"), val = tensor<int32, [3]>([0, 0, 0])]; |
| tensor<int32, [3]> input_39_end_0 = const()[name = tensor<string, []>("input_39_end_0"), val = tensor<int32, [3]>([1, 128, 480])]; |
| tensor<bool, [3]> input_39_end_mask_0 = const()[name = tensor<string, []>("input_39_end_mask_0"), val = tensor<bool, [3]>([true, true, false])]; |
| tensor<fp32, [1, 128, 480]> 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<string, []>("input_39")]; |
| tensor<fp32, [1, 128, 480]> x_23 = elu(alpha = var_507, x = input_39)[name = tensor<string, []>("x_23")]; |
| tensor<bool, []> input_41_interleave_0 = const()[name = tensor<string, []>("input_41_interleave_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [1, 128, 482]> input_41 = concat(axis = var_508, interleave = input_41_interleave_0, values = (res1_conv0_prev, x_23))[name = tensor<string, []>("input_41")]; |
| tensor<string, []> input_43_pad_type_0 = const()[name = tensor<string, []>("input_43_pad_type_0"), val = tensor<string, []>("valid")]; |
| tensor<int32, [1]> input_43_strides_0 = const()[name = tensor<string, []>("input_43_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> input_43_pad_0 = const()[name = tensor<string, []>("input_43_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> input_43_dilations_0 = const()[name = tensor<string, []>("input_43_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> input_43_groups_0 = const()[name = tensor<string, []>("input_43_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 64, 480]> 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<string, []>("input_43")]; |
| tensor<int32, [3]> var_641_begin_0 = const()[name = tensor<string, []>("op_641_begin_0"), val = tensor<int32, [3]>([0, 0, 480])]; |
| tensor<int32, [3]> var_641_end_0 = const()[name = tensor<string, []>("op_641_end_0"), val = tensor<int32, [3]>([1, 128, 482])]; |
| tensor<bool, [3]> var_641_end_mask_0 = const()[name = tensor<string, []>("op_641_end_mask_0"), val = tensor<bool, [3]>([true, true, true])]; |
| tensor<fp32, [1, 128, 2]> 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<string, []>("op_641")]; |
| tensor<fp32, [1, 64, 480]> x_25 = elu(alpha = var_507, x = input_43)[name = tensor<string, []>("x_25")]; |
| tensor<string, []> v_7_pad_type_0 = const()[name = tensor<string, []>("v_7_pad_type_0"), val = tensor<string, []>("valid")]; |
| tensor<int32, [1]> v_7_strides_0 = const()[name = tensor<string, []>("v_7_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> v_7_pad_0 = const()[name = tensor<string, []>("v_7_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> v_7_dilations_0 = const()[name = tensor<string, []>("v_7_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> v_7_groups_0 = const()[name = tensor<string, []>("v_7_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 128, 480]> 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<string, []>("v_7")]; |
| tensor<fp32, [1, 128, 480]> input_45 = add(x = input_39, y = v_7)[name = tensor<string, []>("input_45")]; |
| tensor<fp32, [1, 128, 480]> input_47 = elu(alpha = var_507, x = input_45)[name = tensor<string, []>("input_47")]; |
| tensor<string, []> y_13_pad_type_0 = const()[name = tensor<string, []>("y_13_pad_type_0"), val = tensor<string, []>("valid")]; |
| tensor<int32, [1]> y_13_strides_0 = const()[name = tensor<string, []>("y_13_strides_0"), val = tensor<int32, [1]>([4])]; |
| tensor<int32, [2]> y_13_pad_0 = const()[name = tensor<string, []>("y_13_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> y_13_dilations_0 = const()[name = tensor<string, []>("y_13_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> y_13_groups_0 = const()[name = tensor<string, []>("y_13_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<int32, [3]> y_13_has_output_shape_output_shape_0 = const()[name = tensor<string, []>("y_13_has_output_shape_output_shape_0"), val = tensor<int32, [3]>([1, 64, 1924])]; |
| tensor<fp32, [1, 64, 1924]> 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<string, []>("y_13_has_output_shape")]; |
| tensor<int32, [3]> var_669_begin_0 = const()[name = tensor<string, []>("op_669_begin_0"), val = tensor<int32, [3]>([0, 0, 0])]; |
| tensor<int32, [3]> var_669_end_0 = const()[name = tensor<string, []>("op_669_end_0"), val = tensor<int32, [3]>([1, 64, 4])]; |
| tensor<bool, [3]> var_669_end_mask_0 = const()[name = tensor<string, []>("op_669_end_mask_0"), val = tensor<bool, [3]>([true, true, false])]; |
| tensor<fp32, [1, 64, 4]> 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<string, []>("op_669")]; |
| tensor<fp32, [1, 64, 4]> var_670 = add(x = var_669, y = convtr2_partial)[name = tensor<string, []>("op_670")]; |
| tensor<int32, [3]> var_671_begin_0 = const()[name = tensor<string, []>("op_671_begin_0"), val = tensor<int32, [3]>([0, 0, 4])]; |
| tensor<int32, [3]> var_671_end_0 = const()[name = tensor<string, []>("op_671_end_0"), val = tensor<int32, [3]>([1, 64, 1924])]; |
| tensor<bool, [3]> var_671_end_mask_0 = const()[name = tensor<string, []>("op_671_end_mask_0"), val = tensor<bool, [3]>([true, true, true])]; |
| tensor<fp32, [1, 64, 1920]> 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<string, []>("op_671")]; |
| tensor<bool, []> y_interleave_0 = const()[name = tensor<string, []>("y_interleave_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [1, 64, 1924]> y = concat(axis = var_508, interleave = y_interleave_0, values = (var_670, var_671))[name = tensor<string, []>("y")]; |
| tensor<int32, [3]> new_partial_begin_0 = const()[name = tensor<string, []>("new_partial_begin_0"), val = tensor<int32, [3]>([0, 0, 1920])]; |
| tensor<int32, [3]> new_partial_end_0 = const()[name = tensor<string, []>("new_partial_end_0"), val = tensor<int32, [3]>([1, 64, 1924])]; |
| tensor<bool, [3]> new_partial_end_mask_0 = const()[name = tensor<string, []>("new_partial_end_mask_0"), val = tensor<bool, [3]>([true, true, true])]; |
| tensor<fp32, [1, 64, 4]> 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<string, []>("new_partial")]; |
| tensor<fp32, [64, 1]> var_676 = const()[name = tensor<string, []>("op_676"), val = tensor<fp32, [64, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(41767936)))]; |
| tensor<fp32, [1, 64, 4]> var_677 = sub(x = new_partial, y = var_676)[name = tensor<string, []>("op_677")]; |
| tensor<int32, [3]> input_49_begin_0 = const()[name = tensor<string, []>("input_49_begin_0"), val = tensor<int32, [3]>([0, 0, 0])]; |
| tensor<int32, [3]> input_49_end_0 = const()[name = tensor<string, []>("input_49_end_0"), val = tensor<int32, [3]>([1, 64, 1920])]; |
| tensor<bool, [3]> input_49_end_mask_0 = const()[name = tensor<string, []>("input_49_end_mask_0"), val = tensor<bool, [3]>([true, true, false])]; |
| tensor<fp32, [1, 64, 1920]> 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<string, []>("input_49")]; |
| tensor<fp32, [1, 64, 1920]> x_27 = elu(alpha = var_507, x = input_49)[name = tensor<string, []>("x_27")]; |
| tensor<bool, []> input_51_interleave_0 = const()[name = tensor<string, []>("input_51_interleave_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [1, 64, 1922]> input_51 = concat(axis = var_508, interleave = input_51_interleave_0, values = (res2_conv0_prev, x_27))[name = tensor<string, []>("input_51")]; |
| tensor<string, []> input_53_pad_type_0 = const()[name = tensor<string, []>("input_53_pad_type_0"), val = tensor<string, []>("valid")]; |
| tensor<int32, [1]> input_53_strides_0 = const()[name = tensor<string, []>("input_53_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> input_53_pad_0 = const()[name = tensor<string, []>("input_53_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> input_53_dilations_0 = const()[name = tensor<string, []>("input_53_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> input_53_groups_0 = const()[name = tensor<string, []>("input_53_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 32, 1920]> 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<string, []>("input_53")]; |
| tensor<int32, [3]> var_697_begin_0 = const()[name = tensor<string, []>("op_697_begin_0"), val = tensor<int32, [3]>([0, 0, 1920])]; |
| tensor<int32, [3]> var_697_end_0 = const()[name = tensor<string, []>("op_697_end_0"), val = tensor<int32, [3]>([1, 64, 1922])]; |
| tensor<bool, [3]> var_697_end_mask_0 = const()[name = tensor<string, []>("op_697_end_mask_0"), val = tensor<bool, [3]>([true, true, true])]; |
| tensor<fp32, [1, 64, 2]> 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<string, []>("op_697")]; |
| tensor<fp32, [1, 32, 1920]> x_29 = elu(alpha = var_507, x = input_53)[name = tensor<string, []>("x_29")]; |
| tensor<string, []> v_pad_type_0 = const()[name = tensor<string, []>("v_pad_type_0"), val = tensor<string, []>("valid")]; |
| tensor<int32, [1]> v_strides_0 = const()[name = tensor<string, []>("v_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> v_pad_0 = const()[name = tensor<string, []>("v_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> v_dilations_0 = const()[name = tensor<string, []>("v_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> v_groups_0 = const()[name = tensor<string, []>("v_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 64, 1920]> 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<string, []>("v")]; |
| tensor<fp32, [1, 64, 1920]> input_55 = add(x = input_49, y = v)[name = tensor<string, []>("input_55")]; |
| tensor<fp32, [1, 64, 1920]> x = elu(alpha = var_507, x = input_55)[name = tensor<string, []>("x")]; |
| tensor<bool, []> input_interleave_0 = const()[name = tensor<string, []>("input_interleave_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [1, 64, 1922]> input = concat(axis = var_508, interleave = input_interleave_0, values = (conv_final_prev, x))[name = tensor<string, []>("input")]; |
| tensor<string, []> var_724_pad_type_0 = const()[name = tensor<string, []>("op_724_pad_type_0"), val = tensor<string, []>("valid")]; |
| tensor<int32, [1]> var_724_strides_0 = const()[name = tensor<string, []>("op_724_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> var_724_pad_0 = const()[name = tensor<string, []>("op_724_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> var_724_dilations_0 = const()[name = tensor<string, []>("op_724_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> var_724_groups_0 = const()[name = tensor<string, []>("op_724_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 1, 1920]> 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<string, []>("op_724")]; |
| tensor<int32, [3]> var_725_begin_0 = const()[name = tensor<string, []>("op_725_begin_0"), val = tensor<int32, [3]>([0, 0, 1920])]; |
| tensor<int32, [3]> var_725_end_0 = const()[name = tensor<string, []>("op_725_end_0"), val = tensor<int32, [3]>([1, 64, 1922])]; |
| tensor<bool, [3]> var_725_end_mask_0 = const()[name = tensor<string, []>("op_725_end_mask_0"), val = tensor<bool, [3]>([true, true, true])]; |
| tensor<fp32, [1, 64, 2]> 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<string, []>("op_725")]; |
| tensor<fp32, []> var_740_promoted = const()[name = tensor<string, []>("op_740_promoted"), val = tensor<fp32, []>(0x1p+4)]; |
| tensor<fp32, [1]> var_741 = add(x = attn0_offset, y = var_740_promoted)[name = tensor<string, []>("op_741")]; |
| tensor<fp32, []> var_743_promoted = const()[name = tensor<string, []>("op_743_promoted"), val = tensor<fp32, []>(0x1p+4)]; |
| tensor<fp32, [1]> var_744 = add(x = attn1_offset, y = var_743_promoted)[name = tensor<string, []>("op_744")]; |
| tensor<fp32, [1]> conv0_first_tmp = identity(x = conv0_first)[name = tensor<string, []>("conv0_first_tmp")]; |
| tensor<fp32, [1]> res0_conv0_first_tmp = identity(x = res0_conv0_first)[name = tensor<string, []>("res0_conv0_first_tmp")]; |
| tensor<fp32, [1, 128, 0]> res0_conv1_prev_tmp = identity(x = res0_conv1_prev)[name = tensor<string, []>("res0_conv1_prev_tmp")]; |
| tensor<fp32, [1]> res0_conv1_first_tmp = identity(x = res0_conv1_first)[name = tensor<string, []>("res0_conv1_first_tmp")]; |
| tensor<fp32, [1]> res1_conv0_first_tmp = identity(x = res1_conv0_first)[name = tensor<string, []>("res1_conv0_first_tmp")]; |
| tensor<fp32, [1, 64, 0]> res1_conv1_prev_tmp = identity(x = res1_conv1_prev)[name = tensor<string, []>("res1_conv1_prev_tmp")]; |
| tensor<fp32, [1]> res1_conv1_first_tmp = identity(x = res1_conv1_first)[name = tensor<string, []>("res1_conv1_first_tmp")]; |
| tensor<fp32, [1]> res2_conv0_first_tmp = identity(x = res2_conv0_first)[name = tensor<string, []>("res2_conv0_first_tmp")]; |
| tensor<fp32, [1, 32, 0]> res2_conv1_prev_tmp = identity(x = res2_conv1_prev)[name = tensor<string, []>("res2_conv1_prev_tmp")]; |
| tensor<fp32, [1]> res2_conv1_first_tmp = identity(x = res2_conv1_first)[name = tensor<string, []>("res2_conv1_first_tmp")]; |
| tensor<fp32, [1]> conv_final_first_tmp = identity(x = conv_final_first)[name = tensor<string, []>("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); |
| } |
