| program(1.0) |
| [buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.8.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0b1"}})] |
| { |
| func main<ios17>(tensor<fp32, [?, 1, 160000]> audio) [FlexibleShapeInformation = tuple<tuple<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>, tuple<tensor<string, []>, dict<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>>>((("DefaultShapes", {{"audio", [32, 1, 160000]}}), ("EnumeratedShapes", {{"audio_1_1_10_1_160000_", {{"audio", [10, 1, 160000]}}}, {"audio_1_1_11_1_160000_", {{"audio", [11, 1, 160000]}}}, {"audio_1_1_12_1_160000_", {{"audio", [12, 1, 160000]}}}, {"audio_1_1_13_1_160000_", {{"audio", [13, 1, 160000]}}}, {"audio_1_1_14_1_160000_", {{"audio", [14, 1, 160000]}}}, {"audio_1_1_15_1_160000_", {{"audio", [15, 1, 160000]}}}, {"audio_1_1_16_1_160000_", {{"audio", [16, 1, 160000]}}}, {"audio_1_1_17_1_160000_", {{"audio", [17, 1, 160000]}}}, {"audio_1_1_18_1_160000_", {{"audio", [18, 1, 160000]}}}, {"audio_1_1_19_1_160000_", {{"audio", [19, 1, 160000]}}}, {"audio_1_1_1_1_160000_", {{"audio", [1, 1, 160000]}}}, {"audio_1_1_20_1_160000_", {{"audio", [20, 1, 160000]}}}, {"audio_1_1_21_1_160000_", {{"audio", [21, 1, 160000]}}}, {"audio_1_1_22_1_160000_", {{"audio", [22, 1, 160000]}}}, {"audio_1_1_23_1_160000_", {{"audio", [23, 1, 160000]}}}, {"audio_1_1_24_1_160000_", {{"audio", [24, 1, 160000]}}}, {"audio_1_1_25_1_160000_", {{"audio", [25, 1, 160000]}}}, {"audio_1_1_26_1_160000_", {{"audio", [26, 1, 160000]}}}, {"audio_1_1_27_1_160000_", {{"audio", [27, 1, 160000]}}}, {"audio_1_1_28_1_160000_", {{"audio", [28, 1, 160000]}}}, {"audio_1_1_29_1_160000_", {{"audio", [29, 1, 160000]}}}, {"audio_1_1_2_1_160000_", {{"audio", [2, 1, 160000]}}}, {"audio_1_1_30_1_160000_", {{"audio", [30, 1, 160000]}}}, {"audio_1_1_31_1_160000_", {{"audio", [31, 1, 160000]}}}, {"audio_1_1_32_1_160000_", {{"audio", [32, 1, 160000]}}}, {"audio_1_1_3_1_160000_", {{"audio", [3, 1, 160000]}}}, {"audio_1_1_4_1_160000_", {{"audio", [4, 1, 160000]}}}, {"audio_1_1_5_1_160000_", {{"audio", [5, 1, 160000]}}}, {"audio_1_1_6_1_160000_", {{"audio", [6, 1, 160000]}}}, {"audio_1_1_7_1_160000_", {{"audio", [7, 1, 160000]}}}, {"audio_1_1_8_1_160000_", {{"audio", [8, 1, 160000]}}}, {"audio_1_1_9_1_160000_", {{"audio", [9, 1, 160000]}}}})))] { |
| tensor<fp32, []> var_9 = const()[name = tensor<string, []>("op_9"), val = tensor<fp32, []>(0x1.47ae14p-7)]; |
| tensor<string, []> audio_to_fp16_dtype_0 = const()[name = tensor<string, []>("audio_to_fp16_dtype_0"), val = tensor<string, []>("fp16")]; |
| tensor<fp16, [1]> sincnet_wav_norm1d_weight_to_fp16 = const()[name = tensor<string, []>("sincnet_wav_norm1d_weight_to_fp16"), val = tensor<fp16, [1]>([0x1.44p-7])]; |
| tensor<fp16, [1]> sincnet_wav_norm1d_bias_to_fp16 = const()[name = tensor<string, []>("sincnet_wav_norm1d_bias_to_fp16"), val = tensor<fp16, [1]>([0x1.734p-5])]; |
| tensor<fp16, []> var_24_to_fp16 = const()[name = tensor<string, []>("op_24_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)]; |
| tensor<fp16, [?, 1, 160000]> audio_to_fp16 = cast(dtype = audio_to_fp16_dtype_0, x = audio)[name = tensor<string, []>("cast_19")]; |
| tensor<fp16, [?, 1, 160000]> waveform_cast_fp16 = instance_norm(beta = sincnet_wav_norm1d_bias_to_fp16, epsilon = var_24_to_fp16, gamma = sincnet_wav_norm1d_weight_to_fp16, x = audio_to_fp16)[name = tensor<string, []>("waveform_cast_fp16")]; |
| tensor<string, []> outputs_pad_type_0 = const()[name = tensor<string, []>("outputs_pad_type_0"), val = tensor<string, []>("valid")]; |
| tensor<int32, [1]> outputs_strides_0 = const()[name = tensor<string, []>("outputs_strides_0"), val = tensor<int32, [1]>([10])]; |
| tensor<int32, [2]> outputs_pad_0 = const()[name = tensor<string, []>("outputs_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> outputs_dilations_0 = const()[name = tensor<string, []>("outputs_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> outputs_groups_0 = const()[name = tensor<string, []>("outputs_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp16, [80, 1, 251]> filters_to_fp16 = const()[name = tensor<string, []>("filters_to_fp16"), val = tensor<fp16, [80, 1, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))]; |
| tensor<fp16, [?, 80, 15975]> outputs_cast_fp16 = conv(dilations = outputs_dilations_0, groups = outputs_groups_0, pad = outputs_pad_0, pad_type = outputs_pad_type_0, strides = outputs_strides_0, weight = filters_to_fp16, x = waveform_cast_fp16)[name = tensor<string, []>("outputs_cast_fp16")]; |
| tensor<fp16, [?, 80, 15975]> input_1_cast_fp16 = abs(x = outputs_cast_fp16)[name = tensor<string, []>("input_1_cast_fp16")]; |
| tensor<int32, [1]> var_119 = const()[name = tensor<string, []>("op_119"), val = tensor<int32, [1]>([3])]; |
| tensor<int32, [1]> var_120 = const()[name = tensor<string, []>("op_120"), val = tensor<int32, [1]>([3])]; |
| tensor<string, []> input_3_pad_type_0 = const()[name = tensor<string, []>("input_3_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [2]> input_3_pad_0 = const()[name = tensor<string, []>("input_3_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<bool, []> input_3_ceil_mode_0 = const()[name = tensor<string, []>("input_3_ceil_mode_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [?, 80, 5325]> input_3_cast_fp16 = max_pool(ceil_mode = input_3_ceil_mode_0, kernel_sizes = var_119, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_120, x = input_1_cast_fp16)[name = tensor<string, []>("input_3_cast_fp16")]; |
| tensor<fp16, [80]> sincnet_norm1d_0_weight_to_fp16 = const()[name = tensor<string, []>("sincnet_norm1d_0_weight_to_fp16"), val = tensor<fp16, [80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40320)))]; |
| tensor<fp16, [80]> sincnet_norm1d_0_bias_to_fp16 = const()[name = tensor<string, []>("sincnet_norm1d_0_bias_to_fp16"), val = tensor<fp16, [80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40576)))]; |
| tensor<fp16, [?, 80, 5325]> input_5_cast_fp16 = instance_norm(beta = sincnet_norm1d_0_bias_to_fp16, epsilon = var_24_to_fp16, gamma = sincnet_norm1d_0_weight_to_fp16, x = input_3_cast_fp16)[name = tensor<string, []>("input_5_cast_fp16")]; |
| tensor<fp16, [?, 80, 5325]> input_7_cast_fp16 = leaky_relu(alpha = var_9, x = input_5_cast_fp16)[name = tensor<string, []>("input_7_cast_fp16")]; |
| tensor<string, []> input_9_pad_type_0 = const()[name = tensor<string, []>("input_9_pad_type_0"), val = tensor<string, []>("valid")]; |
| tensor<int32, [1]> input_9_strides_0 = const()[name = tensor<string, []>("input_9_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> input_9_pad_0 = const()[name = tensor<string, []>("input_9_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> input_9_dilations_0 = const()[name = tensor<string, []>("input_9_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> input_9_groups_0 = const()[name = tensor<string, []>("input_9_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp16, [60, 80, 5]> sincnet_conv1d_1_weight_to_fp16 = const()[name = tensor<string, []>("sincnet_conv1d_1_weight_to_fp16"), val = tensor<fp16, [60, 80, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(40832)))]; |
| tensor<fp16, [60]> sincnet_conv1d_1_bias_to_fp16 = const()[name = tensor<string, []>("sincnet_conv1d_1_bias_to_fp16"), val = tensor<fp16, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(88896)))]; |
| tensor<fp16, [?, 60, 5321]> input_9_cast_fp16 = conv(bias = sincnet_conv1d_1_bias_to_fp16, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = sincnet_conv1d_1_weight_to_fp16, x = input_7_cast_fp16)[name = tensor<string, []>("input_9_cast_fp16")]; |
| tensor<int32, [1]> var_135 = const()[name = tensor<string, []>("op_135"), val = tensor<int32, [1]>([3])]; |
| tensor<int32, [1]> var_136 = const()[name = tensor<string, []>("op_136"), val = tensor<int32, [1]>([3])]; |
| tensor<string, []> input_11_pad_type_0 = const()[name = tensor<string, []>("input_11_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [2]> input_11_pad_0 = const()[name = tensor<string, []>("input_11_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<bool, []> input_11_ceil_mode_0 = const()[name = tensor<string, []>("input_11_ceil_mode_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [?, 60, 1773]> input_11_cast_fp16 = max_pool(ceil_mode = input_11_ceil_mode_0, kernel_sizes = var_135, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = var_136, x = input_9_cast_fp16)[name = tensor<string, []>("input_11_cast_fp16")]; |
| tensor<fp16, [60]> sincnet_norm1d_1_weight_to_fp16 = const()[name = tensor<string, []>("sincnet_norm1d_1_weight_to_fp16"), val = tensor<fp16, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89088)))]; |
| tensor<fp16, [60]> sincnet_norm1d_1_bias_to_fp16 = const()[name = tensor<string, []>("sincnet_norm1d_1_bias_to_fp16"), val = tensor<fp16, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89280)))]; |
| tensor<fp16, [?, 60, 1773]> input_13_cast_fp16 = instance_norm(beta = sincnet_norm1d_1_bias_to_fp16, epsilon = var_24_to_fp16, gamma = sincnet_norm1d_1_weight_to_fp16, x = input_11_cast_fp16)[name = tensor<string, []>("input_13_cast_fp16")]; |
| tensor<fp16, [?, 60, 1773]> input_15_cast_fp16 = leaky_relu(alpha = var_9, x = input_13_cast_fp16)[name = tensor<string, []>("input_15_cast_fp16")]; |
| tensor<string, []> input_17_pad_type_0 = const()[name = tensor<string, []>("input_17_pad_type_0"), val = tensor<string, []>("valid")]; |
| tensor<int32, [1]> input_17_strides_0 = const()[name = tensor<string, []>("input_17_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [2]> input_17_pad_0 = const()[name = tensor<string, []>("input_17_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<int32, [1]> input_17_dilations_0 = const()[name = tensor<string, []>("input_17_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> input_17_groups_0 = const()[name = tensor<string, []>("input_17_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp16, [60, 60, 5]> sincnet_conv1d_2_weight_to_fp16 = const()[name = tensor<string, []>("sincnet_conv1d_2_weight_to_fp16"), val = tensor<fp16, [60, 60, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89472)))]; |
| tensor<fp16, [60]> sincnet_conv1d_2_bias_to_fp16 = const()[name = tensor<string, []>("sincnet_conv1d_2_bias_to_fp16"), val = tensor<fp16, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(125568)))]; |
| tensor<fp16, [?, 60, 1769]> input_17_cast_fp16 = conv(bias = sincnet_conv1d_2_bias_to_fp16, dilations = input_17_dilations_0, groups = input_17_groups_0, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = input_17_strides_0, weight = sincnet_conv1d_2_weight_to_fp16, x = input_15_cast_fp16)[name = tensor<string, []>("input_17_cast_fp16")]; |
| tensor<int32, [1]> var_151 = const()[name = tensor<string, []>("op_151"), val = tensor<int32, [1]>([3])]; |
| tensor<int32, [1]> var_152 = const()[name = tensor<string, []>("op_152"), val = tensor<int32, [1]>([3])]; |
| tensor<string, []> input_19_pad_type_0 = const()[name = tensor<string, []>("input_19_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [2]> input_19_pad_0 = const()[name = tensor<string, []>("input_19_pad_0"), val = tensor<int32, [2]>([0, 0])]; |
| tensor<bool, []> input_19_ceil_mode_0 = const()[name = tensor<string, []>("input_19_ceil_mode_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [?, 60, 589]> input_19_cast_fp16 = max_pool(ceil_mode = input_19_ceil_mode_0, kernel_sizes = var_151, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = var_152, x = input_17_cast_fp16)[name = tensor<string, []>("input_19_cast_fp16")]; |
| tensor<fp16, [60]> sincnet_norm1d_2_weight_to_fp16 = const()[name = tensor<string, []>("sincnet_norm1d_2_weight_to_fp16"), val = tensor<fp16, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(125760)))]; |
| tensor<fp16, [60]> sincnet_norm1d_2_bias_to_fp16 = const()[name = tensor<string, []>("sincnet_norm1d_2_bias_to_fp16"), val = tensor<fp16, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(125952)))]; |
| tensor<fp16, [?, 60, 589]> input_21_cast_fp16 = instance_norm(beta = sincnet_norm1d_2_bias_to_fp16, epsilon = var_24_to_fp16, gamma = sincnet_norm1d_2_weight_to_fp16, x = input_19_cast_fp16)[name = tensor<string, []>("input_21_cast_fp16")]; |
| tensor<fp16, [?, 60, 589]> x_cast_fp16 = leaky_relu(alpha = var_9, x = input_21_cast_fp16)[name = tensor<string, []>("x_cast_fp16")]; |
| tensor<int32, [3]> var_163 = const()[name = tensor<string, []>("op_163"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<int32, []> var_172 = const()[name = tensor<string, []>("op_172"), val = tensor<int32, []>(128)]; |
| tensor<int32, []> var_173 = const()[name = tensor<string, []>("op_173"), val = tensor<int32, []>(8)]; |
| tensor<fp16, [?, 589, 60]> input_23_cast_fp16 = transpose(perm = var_163, x = x_cast_fp16)[name = tensor<string, []>("transpose_6")]; |
| tensor<int32, [3]> var_207_shape_cast_fp16 = shape(x = input_23_cast_fp16)[name = tensor<string, []>("op_207_shape_cast_fp16")]; |
| tensor<int32, []> gather_0_axis_0 = const()[name = tensor<string, []>("gather_0_axis_0"), val = tensor<int32, []>(0)]; |
| tensor<int32, []> gather_0_batch_dims_0 = const()[name = tensor<string, []>("gather_0_batch_dims_0"), val = tensor<int32, []>(0)]; |
| tensor<bool, []> gather_0_validate_indices_0 = const()[name = tensor<string, []>("gather_0_validate_indices_0"), val = tensor<bool, []>(false)]; |
| tensor<string, []> var_207_shape_cast_fp16_to_int16_dtype_0 = const()[name = tensor<string, []>("op_207_shape_cast_fp16_to_int16_dtype_0"), val = tensor<string, []>("int16")]; |
| tensor<uint16, []> gather_0_indices_0_to_uint16 = const()[name = tensor<string, []>("gather_0_indices_0_to_uint16"), val = tensor<uint16, []>(0)]; |
| tensor<int16, [3]> var_207_shape_cast_fp16_to_int16 = cast(dtype = var_207_shape_cast_fp16_to_int16_dtype_0, x = var_207_shape_cast_fp16)[name = tensor<string, []>("cast_18")]; |
| tensor<int16, []> gather_0_cast_uint16 = gather(axis = gather_0_axis_0, batch_dims = gather_0_batch_dims_0, indices = gather_0_indices_0_to_uint16, validate_indices = gather_0_validate_indices_0, x = var_207_shape_cast_fp16_to_int16)[name = tensor<string, []>("gather_0_cast_uint16")]; |
| tensor<string, []> gather_0_cast_uint16_to_int32_dtype_0 = const()[name = tensor<string, []>("gather_0_cast_uint16_to_int32_dtype_0"), val = tensor<string, []>("int32")]; |
| tensor<int32, []> concat_0_axis_0 = const()[name = tensor<string, []>("concat_0_axis_0"), val = tensor<int32, []>(0)]; |
| tensor<bool, []> concat_0_interleave_0 = const()[name = tensor<string, []>("concat_0_interleave_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, []> gather_0_cast_uint16_to_int32 = cast(dtype = gather_0_cast_uint16_to_int32_dtype_0, x = gather_0_cast_uint16)[name = tensor<string, []>("cast_17")]; |
| tensor<int32, [3]> concat_0 = concat(axis = concat_0_axis_0, interleave = concat_0_interleave_0, values = (var_173, gather_0_cast_uint16_to_int32, var_172))[name = tensor<string, []>("concat_0")]; |
| tensor<fp16, []> hx_1_value_0_to_fp16 = const()[name = tensor<string, []>("hx_1_value_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)]; |
| tensor<fp16, [8, ?, 128]> hx_1_cast_fp16 = fill(shape = concat_0, value = hx_1_value_0_to_fp16)[name = tensor<string, []>("hx_1_cast_fp16")]; |
| tensor<int32, [3]> input_23_batch_first_transpose_perm_0 = const()[name = tensor<string, []>("input_23_batch_first_transpose_perm_0"), val = tensor<int32, [3]>([1, 0, 2])]; |
| tensor<int32, []> split_0_num_splits_0 = const()[name = tensor<string, []>("split_0_num_splits_0"), val = tensor<int32, []>(4)]; |
| tensor<int32, []> split_0_axis_0 = const()[name = tensor<string, []>("split_0_axis_0"), val = tensor<int32, []>(0)]; |
| tensor<fp16, [2, ?, 128]> split_0_cast_fp16_0, tensor<fp16, [2, ?, 128]> split_0_cast_fp16_1, tensor<fp16, [2, ?, 128]> split_0_cast_fp16_2, tensor<fp16, [2, ?, 128]> split_0_cast_fp16_3 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = hx_1_cast_fp16)[name = tensor<string, []>("split_0_cast_fp16")]; |
| tensor<int32, []> split_1_num_splits_0 = const()[name = tensor<string, []>("split_1_num_splits_0"), val = tensor<int32, []>(4)]; |
| tensor<int32, []> split_1_axis_0 = const()[name = tensor<string, []>("split_1_axis_0"), val = tensor<int32, []>(0)]; |
| tensor<fp16, [2, ?, 128]> split_1_cast_fp16_0, tensor<fp16, [2, ?, 128]> split_1_cast_fp16_1, tensor<fp16, [2, ?, 128]> split_1_cast_fp16_2, tensor<fp16, [2, ?, 128]> split_1_cast_fp16_3 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = hx_1_cast_fp16)[name = tensor<string, []>("split_1_cast_fp16")]; |
| tensor<int32, [2]> split_10_split_sizes_0 = const()[name = tensor<string, []>("split_10_split_sizes_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> split_10_axis_0 = const()[name = tensor<string, []>("split_10_axis_0"), val = tensor<int32, []>(0)]; |
| tensor<fp16, [1, ?, 128]> split_10_cast_fp16_0, tensor<fp16, [1, ?, 128]> split_10_cast_fp16_1 = split(axis = split_10_axis_0, split_sizes = split_10_split_sizes_0, x = split_0_cast_fp16_0)[name = tensor<string, []>("split_10_cast_fp16")]; |
| tensor<int32, []> concat_10_axis_0 = const()[name = tensor<string, []>("concat_10_axis_0"), val = tensor<int32, []>(2)]; |
| tensor<bool, []> concat_10_interleave_0 = const()[name = tensor<string, []>("concat_10_interleave_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [1, ?, 256]> concat_10_cast_fp16 = concat(axis = concat_10_axis_0, interleave = concat_10_interleave_0, values = (split_10_cast_fp16_0, split_10_cast_fp16_1))[name = tensor<string, []>("concat_10_cast_fp16")]; |
| tensor<int32, [1]> input_25_lstm_layer_0_lstm_h0_reshaped_axes_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_lstm_h0_reshaped_axes_0"), val = tensor<int32, [1]>([0])]; |
| tensor<fp16, [?, 256]> input_25_lstm_layer_0_lstm_h0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_0_lstm_h0_reshaped_axes_0, x = concat_10_cast_fp16)[name = tensor<string, []>("input_25_lstm_layer_0_lstm_h0_reshaped_cast_fp16")]; |
| tensor<int32, [2]> split_11_split_sizes_0 = const()[name = tensor<string, []>("split_11_split_sizes_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> split_11_axis_0 = const()[name = tensor<string, []>("split_11_axis_0"), val = tensor<int32, []>(0)]; |
| tensor<fp16, [1, ?, 128]> split_11_cast_fp16_0, tensor<fp16, [1, ?, 128]> split_11_cast_fp16_1 = split(axis = split_11_axis_0, split_sizes = split_11_split_sizes_0, x = split_1_cast_fp16_0)[name = tensor<string, []>("split_11_cast_fp16")]; |
| tensor<int32, []> concat_11_axis_0 = const()[name = tensor<string, []>("concat_11_axis_0"), val = tensor<int32, []>(2)]; |
| tensor<bool, []> concat_11_interleave_0 = const()[name = tensor<string, []>("concat_11_interleave_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [1, ?, 256]> concat_11_cast_fp16 = concat(axis = concat_11_axis_0, interleave = concat_11_interleave_0, values = (split_11_cast_fp16_0, split_11_cast_fp16_1))[name = tensor<string, []>("concat_11_cast_fp16")]; |
| tensor<int32, [1]> input_25_lstm_layer_0_lstm_c0_reshaped_axes_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_lstm_c0_reshaped_axes_0"), val = tensor<int32, [1]>([0])]; |
| tensor<fp16, [?, 256]> input_25_lstm_layer_0_lstm_c0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_0_lstm_c0_reshaped_axes_0, x = concat_11_cast_fp16)[name = tensor<string, []>("input_25_lstm_layer_0_lstm_c0_reshaped_cast_fp16")]; |
| tensor<string, []> input_25_lstm_layer_0_direction_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_direction_0"), val = tensor<string, []>("bidirectional")]; |
| tensor<bool, []> input_25_lstm_layer_0_output_sequence_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_output_sequence_0"), val = tensor<bool, []>(true)]; |
| tensor<string, []> input_25_lstm_layer_0_recurrent_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_recurrent_activation_0"), val = tensor<string, []>("sigmoid")]; |
| tensor<string, []> input_25_lstm_layer_0_cell_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_cell_activation_0"), val = tensor<string, []>("tanh")]; |
| tensor<string, []> input_25_lstm_layer_0_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_activation_0"), val = tensor<string, []>("tanh")]; |
| tensor<fp16, [512, 60]> concat_6_to_fp16 = const()[name = tensor<string, []>("concat_6_to_fp16"), val = tensor<fp16, [512, 60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126144)))]; |
| tensor<fp16, [512, 128]> concat_7_to_fp16 = const()[name = tensor<string, []>("concat_7_to_fp16"), val = tensor<fp16, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(187648)))]; |
| tensor<fp16, [512]> add_0_to_fp16 = const()[name = tensor<string, []>("add_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(318784)))]; |
| tensor<fp16, [512, 60]> concat_8_to_fp16 = const()[name = tensor<string, []>("concat_8_to_fp16"), val = tensor<fp16, [512, 60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(319872)))]; |
| tensor<fp16, [512, 128]> concat_9_to_fp16 = const()[name = tensor<string, []>("concat_9_to_fp16"), val = tensor<fp16, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(381376)))]; |
| tensor<fp16, [512]> add_1_to_fp16 = const()[name = tensor<string, []>("add_1_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(512512)))]; |
| tensor<fp16, [589, ?, 60]> input_23_batch_first_transpose_cast_fp16 = transpose(perm = input_23_batch_first_transpose_perm_0, x = input_23_cast_fp16)[name = tensor<string, []>("transpose_5")]; |
| tensor<fp16, [589, ?, 256]> input_25_lstm_layer_0_cast_fp16_0, tensor<fp16, [?, 256]> input_25_lstm_layer_0_cast_fp16_1, tensor<fp16, [?, 256]> input_25_lstm_layer_0_cast_fp16_2 = lstm(activation = input_25_lstm_layer_0_activation_0, bias = add_0_to_fp16, bias_back = add_1_to_fp16, cell_activation = input_25_lstm_layer_0_cell_activation_0, direction = input_25_lstm_layer_0_direction_0, initial_c = input_25_lstm_layer_0_lstm_c0_reshaped_cast_fp16, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped_cast_fp16, output_sequence = input_25_lstm_layer_0_output_sequence_0, recurrent_activation = input_25_lstm_layer_0_recurrent_activation_0, weight_hh = concat_7_to_fp16, weight_hh_back = concat_9_to_fp16, weight_ih = concat_6_to_fp16, weight_ih_back = concat_8_to_fp16, x = input_23_batch_first_transpose_cast_fp16)[name = tensor<string, []>("input_25_lstm_layer_0_cast_fp16")]; |
| tensor<int32, [2]> split_20_split_sizes_0 = const()[name = tensor<string, []>("split_20_split_sizes_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> split_20_axis_0 = const()[name = tensor<string, []>("split_20_axis_0"), val = tensor<int32, []>(0)]; |
| tensor<fp16, [1, ?, 128]> split_20_cast_fp16_0, tensor<fp16, [1, ?, 128]> split_20_cast_fp16_1 = split(axis = split_20_axis_0, split_sizes = split_20_split_sizes_0, x = split_0_cast_fp16_1)[name = tensor<string, []>("split_20_cast_fp16")]; |
| tensor<int32, []> concat_20_axis_0 = const()[name = tensor<string, []>("concat_20_axis_0"), val = tensor<int32, []>(2)]; |
| tensor<bool, []> concat_20_interleave_0 = const()[name = tensor<string, []>("concat_20_interleave_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [1, ?, 256]> concat_20_cast_fp16 = concat(axis = concat_20_axis_0, interleave = concat_20_interleave_0, values = (split_20_cast_fp16_0, split_20_cast_fp16_1))[name = tensor<string, []>("concat_20_cast_fp16")]; |
| tensor<int32, [1]> input_25_lstm_layer_1_lstm_h0_reshaped_axes_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_lstm_h0_reshaped_axes_0"), val = tensor<int32, [1]>([0])]; |
| tensor<fp16, [?, 256]> input_25_lstm_layer_1_lstm_h0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_1_lstm_h0_reshaped_axes_0, x = concat_20_cast_fp16)[name = tensor<string, []>("input_25_lstm_layer_1_lstm_h0_reshaped_cast_fp16")]; |
| tensor<int32, [2]> split_21_split_sizes_0 = const()[name = tensor<string, []>("split_21_split_sizes_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> split_21_axis_0 = const()[name = tensor<string, []>("split_21_axis_0"), val = tensor<int32, []>(0)]; |
| tensor<fp16, [1, ?, 128]> split_21_cast_fp16_0, tensor<fp16, [1, ?, 128]> split_21_cast_fp16_1 = split(axis = split_21_axis_0, split_sizes = split_21_split_sizes_0, x = split_1_cast_fp16_1)[name = tensor<string, []>("split_21_cast_fp16")]; |
| tensor<int32, []> concat_21_axis_0 = const()[name = tensor<string, []>("concat_21_axis_0"), val = tensor<int32, []>(2)]; |
| tensor<bool, []> concat_21_interleave_0 = const()[name = tensor<string, []>("concat_21_interleave_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [1, ?, 256]> concat_21_cast_fp16 = concat(axis = concat_21_axis_0, interleave = concat_21_interleave_0, values = (split_21_cast_fp16_0, split_21_cast_fp16_1))[name = tensor<string, []>("concat_21_cast_fp16")]; |
| tensor<int32, [1]> input_25_lstm_layer_1_lstm_c0_reshaped_axes_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_lstm_c0_reshaped_axes_0"), val = tensor<int32, [1]>([0])]; |
| tensor<fp16, [?, 256]> input_25_lstm_layer_1_lstm_c0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_1_lstm_c0_reshaped_axes_0, x = concat_21_cast_fp16)[name = tensor<string, []>("input_25_lstm_layer_1_lstm_c0_reshaped_cast_fp16")]; |
| tensor<string, []> input_25_lstm_layer_1_direction_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_direction_0"), val = tensor<string, []>("bidirectional")]; |
| tensor<bool, []> input_25_lstm_layer_1_output_sequence_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_output_sequence_0"), val = tensor<bool, []>(true)]; |
| tensor<string, []> input_25_lstm_layer_1_recurrent_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_recurrent_activation_0"), val = tensor<string, []>("sigmoid")]; |
| tensor<string, []> input_25_lstm_layer_1_cell_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_cell_activation_0"), val = tensor<string, []>("tanh")]; |
| tensor<string, []> input_25_lstm_layer_1_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_activation_0"), val = tensor<string, []>("tanh")]; |
| tensor<fp16, [512, 256]> concat_16_to_fp16 = const()[name = tensor<string, []>("concat_16_to_fp16"), val = tensor<fp16, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(513600)))]; |
| tensor<fp16, [512, 128]> concat_17_to_fp16 = const()[name = tensor<string, []>("concat_17_to_fp16"), val = tensor<fp16, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(775808)))]; |
| tensor<fp16, [512]> add_2_to_fp16 = const()[name = tensor<string, []>("add_2_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(906944)))]; |
| tensor<fp16, [512, 256]> concat_18_to_fp16 = const()[name = tensor<string, []>("concat_18_to_fp16"), val = tensor<fp16, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(908032)))]; |
| tensor<fp16, [512, 128]> concat_19_to_fp16 = const()[name = tensor<string, []>("concat_19_to_fp16"), val = tensor<fp16, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1170240)))]; |
| tensor<fp16, [512]> add_3_to_fp16 = const()[name = tensor<string, []>("add_3_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1301376)))]; |
| tensor<fp16, [589, ?, 256]> input_25_lstm_layer_1_cast_fp16_0, tensor<fp16, [?, 256]> input_25_lstm_layer_1_cast_fp16_1, tensor<fp16, [?, 256]> input_25_lstm_layer_1_cast_fp16_2 = lstm(activation = input_25_lstm_layer_1_activation_0, bias = add_2_to_fp16, bias_back = add_3_to_fp16, cell_activation = input_25_lstm_layer_1_cell_activation_0, direction = input_25_lstm_layer_1_direction_0, initial_c = input_25_lstm_layer_1_lstm_c0_reshaped_cast_fp16, initial_h = input_25_lstm_layer_1_lstm_h0_reshaped_cast_fp16, output_sequence = input_25_lstm_layer_1_output_sequence_0, recurrent_activation = input_25_lstm_layer_1_recurrent_activation_0, weight_hh = concat_17_to_fp16, weight_hh_back = concat_19_to_fp16, weight_ih = concat_16_to_fp16, weight_ih_back = concat_18_to_fp16, x = input_25_lstm_layer_0_cast_fp16_0)[name = tensor<string, []>("input_25_lstm_layer_1_cast_fp16")]; |
| tensor<int32, [2]> split_30_split_sizes_0 = const()[name = tensor<string, []>("split_30_split_sizes_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> split_30_axis_0 = const()[name = tensor<string, []>("split_30_axis_0"), val = tensor<int32, []>(0)]; |
| tensor<fp16, [1, ?, 128]> split_30_cast_fp16_0, tensor<fp16, [1, ?, 128]> split_30_cast_fp16_1 = split(axis = split_30_axis_0, split_sizes = split_30_split_sizes_0, x = split_0_cast_fp16_2)[name = tensor<string, []>("split_30_cast_fp16")]; |
| tensor<int32, []> concat_30_axis_0 = const()[name = tensor<string, []>("concat_30_axis_0"), val = tensor<int32, []>(2)]; |
| tensor<bool, []> concat_30_interleave_0 = const()[name = tensor<string, []>("concat_30_interleave_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [1, ?, 256]> concat_30_cast_fp16 = concat(axis = concat_30_axis_0, interleave = concat_30_interleave_0, values = (split_30_cast_fp16_0, split_30_cast_fp16_1))[name = tensor<string, []>("concat_30_cast_fp16")]; |
| tensor<int32, [1]> input_25_lstm_layer_2_lstm_h0_reshaped_axes_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_lstm_h0_reshaped_axes_0"), val = tensor<int32, [1]>([0])]; |
| tensor<fp16, [?, 256]> input_25_lstm_layer_2_lstm_h0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_2_lstm_h0_reshaped_axes_0, x = concat_30_cast_fp16)[name = tensor<string, []>("input_25_lstm_layer_2_lstm_h0_reshaped_cast_fp16")]; |
| tensor<int32, [2]> split_31_split_sizes_0 = const()[name = tensor<string, []>("split_31_split_sizes_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> split_31_axis_0 = const()[name = tensor<string, []>("split_31_axis_0"), val = tensor<int32, []>(0)]; |
| tensor<fp16, [1, ?, 128]> split_31_cast_fp16_0, tensor<fp16, [1, ?, 128]> split_31_cast_fp16_1 = split(axis = split_31_axis_0, split_sizes = split_31_split_sizes_0, x = split_1_cast_fp16_2)[name = tensor<string, []>("split_31_cast_fp16")]; |
| tensor<int32, []> concat_31_axis_0 = const()[name = tensor<string, []>("concat_31_axis_0"), val = tensor<int32, []>(2)]; |
| tensor<bool, []> concat_31_interleave_0 = const()[name = tensor<string, []>("concat_31_interleave_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [1, ?, 256]> concat_31_cast_fp16 = concat(axis = concat_31_axis_0, interleave = concat_31_interleave_0, values = (split_31_cast_fp16_0, split_31_cast_fp16_1))[name = tensor<string, []>("concat_31_cast_fp16")]; |
| tensor<int32, [1]> input_25_lstm_layer_2_lstm_c0_reshaped_axes_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_lstm_c0_reshaped_axes_0"), val = tensor<int32, [1]>([0])]; |
| tensor<fp16, [?, 256]> input_25_lstm_layer_2_lstm_c0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_2_lstm_c0_reshaped_axes_0, x = concat_31_cast_fp16)[name = tensor<string, []>("input_25_lstm_layer_2_lstm_c0_reshaped_cast_fp16")]; |
| tensor<string, []> input_25_lstm_layer_2_direction_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_direction_0"), val = tensor<string, []>("bidirectional")]; |
| tensor<bool, []> input_25_lstm_layer_2_output_sequence_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_output_sequence_0"), val = tensor<bool, []>(true)]; |
| tensor<string, []> input_25_lstm_layer_2_recurrent_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_recurrent_activation_0"), val = tensor<string, []>("sigmoid")]; |
| tensor<string, []> input_25_lstm_layer_2_cell_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_cell_activation_0"), val = tensor<string, []>("tanh")]; |
| tensor<string, []> input_25_lstm_layer_2_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_activation_0"), val = tensor<string, []>("tanh")]; |
| tensor<fp16, [512, 256]> concat_26_to_fp16 = const()[name = tensor<string, []>("concat_26_to_fp16"), val = tensor<fp16, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1302464)))]; |
| tensor<fp16, [512, 128]> concat_27_to_fp16 = const()[name = tensor<string, []>("concat_27_to_fp16"), val = tensor<fp16, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1564672)))]; |
| tensor<fp16, [512]> add_4_to_fp16 = const()[name = tensor<string, []>("add_4_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1695808)))]; |
| tensor<fp16, [512, 256]> concat_28_to_fp16 = const()[name = tensor<string, []>("concat_28_to_fp16"), val = tensor<fp16, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1696896)))]; |
| tensor<fp16, [512, 128]> concat_29_to_fp16 = const()[name = tensor<string, []>("concat_29_to_fp16"), val = tensor<fp16, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1959104)))]; |
| tensor<fp16, [512]> add_5_to_fp16 = const()[name = tensor<string, []>("add_5_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2090240)))]; |
| tensor<fp16, [589, ?, 256]> input_25_lstm_layer_2_cast_fp16_0, tensor<fp16, [?, 256]> input_25_lstm_layer_2_cast_fp16_1, tensor<fp16, [?, 256]> input_25_lstm_layer_2_cast_fp16_2 = lstm(activation = input_25_lstm_layer_2_activation_0, bias = add_4_to_fp16, bias_back = add_5_to_fp16, cell_activation = input_25_lstm_layer_2_cell_activation_0, direction = input_25_lstm_layer_2_direction_0, initial_c = input_25_lstm_layer_2_lstm_c0_reshaped_cast_fp16, initial_h = input_25_lstm_layer_2_lstm_h0_reshaped_cast_fp16, output_sequence = input_25_lstm_layer_2_output_sequence_0, recurrent_activation = input_25_lstm_layer_2_recurrent_activation_0, weight_hh = concat_27_to_fp16, weight_hh_back = concat_29_to_fp16, weight_ih = concat_26_to_fp16, weight_ih_back = concat_28_to_fp16, x = input_25_lstm_layer_1_cast_fp16_0)[name = tensor<string, []>("input_25_lstm_layer_2_cast_fp16")]; |
| tensor<int32, [2]> split_40_split_sizes_0 = const()[name = tensor<string, []>("split_40_split_sizes_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> split_40_axis_0 = const()[name = tensor<string, []>("split_40_axis_0"), val = tensor<int32, []>(0)]; |
| tensor<fp16, [1, ?, 128]> split_40_cast_fp16_0, tensor<fp16, [1, ?, 128]> split_40_cast_fp16_1 = split(axis = split_40_axis_0, split_sizes = split_40_split_sizes_0, x = split_0_cast_fp16_3)[name = tensor<string, []>("split_40_cast_fp16")]; |
| tensor<int32, []> concat_40_axis_0 = const()[name = tensor<string, []>("concat_40_axis_0"), val = tensor<int32, []>(2)]; |
| tensor<bool, []> concat_40_interleave_0 = const()[name = tensor<string, []>("concat_40_interleave_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [1, ?, 256]> concat_40_cast_fp16 = concat(axis = concat_40_axis_0, interleave = concat_40_interleave_0, values = (split_40_cast_fp16_0, split_40_cast_fp16_1))[name = tensor<string, []>("concat_40_cast_fp16")]; |
| tensor<int32, [1]> input_25_batch_first_lstm_h0_reshaped_axes_0 = const()[name = tensor<string, []>("input_25_batch_first_lstm_h0_reshaped_axes_0"), val = tensor<int32, [1]>([0])]; |
| tensor<fp16, [?, 256]> input_25_batch_first_lstm_h0_reshaped_cast_fp16 = squeeze(axes = input_25_batch_first_lstm_h0_reshaped_axes_0, x = concat_40_cast_fp16)[name = tensor<string, []>("input_25_batch_first_lstm_h0_reshaped_cast_fp16")]; |
| tensor<int32, [2]> split_41_split_sizes_0 = const()[name = tensor<string, []>("split_41_split_sizes_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, []> split_41_axis_0 = const()[name = tensor<string, []>("split_41_axis_0"), val = tensor<int32, []>(0)]; |
| tensor<fp16, [1, ?, 128]> split_41_cast_fp16_0, tensor<fp16, [1, ?, 128]> split_41_cast_fp16_1 = split(axis = split_41_axis_0, split_sizes = split_41_split_sizes_0, x = split_1_cast_fp16_3)[name = tensor<string, []>("split_41_cast_fp16")]; |
| tensor<int32, []> concat_41_axis_0 = const()[name = tensor<string, []>("concat_41_axis_0"), val = tensor<int32, []>(2)]; |
| tensor<bool, []> concat_41_interleave_0 = const()[name = tensor<string, []>("concat_41_interleave_0"), val = tensor<bool, []>(false)]; |
| tensor<fp16, [1, ?, 256]> concat_41_cast_fp16 = concat(axis = concat_41_axis_0, interleave = concat_41_interleave_0, values = (split_41_cast_fp16_0, split_41_cast_fp16_1))[name = tensor<string, []>("concat_41_cast_fp16")]; |
| tensor<int32, [1]> input_25_batch_first_lstm_c0_reshaped_axes_0 = const()[name = tensor<string, []>("input_25_batch_first_lstm_c0_reshaped_axes_0"), val = tensor<int32, [1]>([0])]; |
| tensor<fp16, [?, 256]> input_25_batch_first_lstm_c0_reshaped_cast_fp16 = squeeze(axes = input_25_batch_first_lstm_c0_reshaped_axes_0, x = concat_41_cast_fp16)[name = tensor<string, []>("input_25_batch_first_lstm_c0_reshaped_cast_fp16")]; |
| tensor<string, []> input_25_batch_first_direction_0 = const()[name = tensor<string, []>("input_25_batch_first_direction_0"), val = tensor<string, []>("bidirectional")]; |
| tensor<bool, []> input_25_batch_first_output_sequence_0 = const()[name = tensor<string, []>("input_25_batch_first_output_sequence_0"), val = tensor<bool, []>(true)]; |
| tensor<string, []> input_25_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("input_25_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")]; |
| tensor<string, []> input_25_batch_first_cell_activation_0 = const()[name = tensor<string, []>("input_25_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")]; |
| tensor<string, []> input_25_batch_first_activation_0 = const()[name = tensor<string, []>("input_25_batch_first_activation_0"), val = tensor<string, []>("tanh")]; |
| tensor<fp16, [512, 256]> concat_36_to_fp16 = const()[name = tensor<string, []>("concat_36_to_fp16"), val = tensor<fp16, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2091328)))]; |
| tensor<fp16, [512, 128]> concat_37_to_fp16 = const()[name = tensor<string, []>("concat_37_to_fp16"), val = tensor<fp16, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2353536)))]; |
| tensor<fp16, [512]> add_6_to_fp16 = const()[name = tensor<string, []>("add_6_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2484672)))]; |
| tensor<fp16, [512, 256]> concat_38_to_fp16 = const()[name = tensor<string, []>("concat_38_to_fp16"), val = tensor<fp16, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2485760)))]; |
| tensor<fp16, [512, 128]> concat_39_to_fp16 = const()[name = tensor<string, []>("concat_39_to_fp16"), val = tensor<fp16, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2747968)))]; |
| tensor<fp16, [512]> add_7_to_fp16 = const()[name = tensor<string, []>("add_7_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2879104)))]; |
| tensor<fp16, [589, ?, 256]> input_25_batch_first_cast_fp16_0, tensor<fp16, [?, 256]> input_25_batch_first_cast_fp16_1, tensor<fp16, [?, 256]> input_25_batch_first_cast_fp16_2 = lstm(activation = input_25_batch_first_activation_0, bias = add_6_to_fp16, bias_back = add_7_to_fp16, cell_activation = input_25_batch_first_cell_activation_0, direction = input_25_batch_first_direction_0, initial_c = input_25_batch_first_lstm_c0_reshaped_cast_fp16, initial_h = input_25_batch_first_lstm_h0_reshaped_cast_fp16, output_sequence = input_25_batch_first_output_sequence_0, recurrent_activation = input_25_batch_first_recurrent_activation_0, weight_hh = concat_37_to_fp16, weight_hh_back = concat_39_to_fp16, weight_ih = concat_36_to_fp16, weight_ih_back = concat_38_to_fp16, x = input_25_lstm_layer_2_cast_fp16_0)[name = tensor<string, []>("input_25_batch_first_cast_fp16")]; |
| tensor<int32, [3]> input_25_perm_0 = const()[name = tensor<string, []>("input_25_perm_0"), val = tensor<int32, [3]>([1, 0, 2])]; |
| tensor<fp16, [128, 256]> linear_0_weight_to_fp16 = const()[name = tensor<string, []>("linear_0_weight_to_fp16"), val = tensor<fp16, [128, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2880192)))]; |
| tensor<fp16, [128]> linear_0_bias_to_fp16 = const()[name = tensor<string, []>("linear_0_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2945792)))]; |
| tensor<fp16, [?, 589, 256]> input_25_cast_fp16 = transpose(perm = input_25_perm_0, x = input_25_batch_first_cast_fp16_0)[name = tensor<string, []>("transpose_4")]; |
| tensor<fp16, [?, 589, 128]> linear_0_cast_fp16 = linear(bias = linear_0_bias_to_fp16, weight = linear_0_weight_to_fp16, x = input_25_cast_fp16)[name = tensor<string, []>("linear_0_cast_fp16")]; |
| tensor<fp32, []> var_220 = const()[name = tensor<string, []>("op_220"), val = tensor<fp32, []>(0x1.47ae14p-7)]; |
| tensor<fp16, [?, 589, 128]> input_29_cast_fp16 = leaky_relu(alpha = var_220, x = linear_0_cast_fp16)[name = tensor<string, []>("input_29_cast_fp16")]; |
| tensor<fp16, [128, 128]> linear_1_weight_to_fp16 = const()[name = tensor<string, []>("linear_1_weight_to_fp16"), val = tensor<fp16, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2946112)))]; |
| tensor<fp16, [128]> linear_1_bias_to_fp16 = const()[name = tensor<string, []>("linear_1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2978944)))]; |
| tensor<fp16, [?, 589, 128]> linear_1_cast_fp16 = linear(bias = linear_1_bias_to_fp16, weight = linear_1_weight_to_fp16, x = input_29_cast_fp16)[name = tensor<string, []>("linear_1_cast_fp16")]; |
| tensor<fp32, []> var_225 = const()[name = tensor<string, []>("op_225"), val = tensor<fp32, []>(0x1.47ae14p-7)]; |
| tensor<fp16, [?, 589, 128]> input_33_cast_fp16 = leaky_relu(alpha = var_225, x = linear_1_cast_fp16)[name = tensor<string, []>("input_33_cast_fp16")]; |
| tensor<fp16, [7, 128]> classifier_weight_to_fp16 = const()[name = tensor<string, []>("classifier_weight_to_fp16"), val = tensor<fp16, [7, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2979264)))]; |
| tensor<fp16, [7]> classifier_bias_to_fp16 = const()[name = tensor<string, []>("classifier_bias_to_fp16"), val = tensor<fp16, [7]>([-0x1.01p+0, 0x1.67cp-2, 0x1.3d8p-1, 0x1.c8cp-2, -0x1.444p-2, -0x1.59p-1, -0x1.8fcp-2])]; |
| tensor<fp16, [?, 589, 7]> linear_2_cast_fp16 = linear(bias = classifier_bias_to_fp16, weight = classifier_weight_to_fp16, x = input_33_cast_fp16)[name = tensor<string, []>("linear_2_cast_fp16")]; |
| tensor<string, []> linear_2_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("linear_2_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")]; |
| tensor<int32, []> var_231 = const()[name = tensor<string, []>("op_231"), val = tensor<int32, []>(-1)]; |
| tensor<fp32, [?, 589, 7]> linear_2_cast_fp16_to_fp32 = cast(dtype = linear_2_cast_fp16_to_fp32_dtype_0, x = linear_2_cast_fp16)[name = tensor<string, []>("cast_16")]; |
| tensor<fp32, [?, 589, 7]> var_232_softmax = softmax(axis = var_231, x = linear_2_cast_fp16_to_fp32)[name = tensor<string, []>("op_232_softmax")]; |
| tensor<fp32, []> var_232_epsilon_0 = const()[name = tensor<string, []>("op_232_epsilon_0"), val = tensor<fp32, []>(0x1p-149)]; |
| tensor<fp32, [?, 589, 7]> log_probs = log(epsilon = var_232_epsilon_0, x = var_232_softmax)[name = tensor<string, []>("op_232")]; |
| } -> (log_probs); |
| } |
