Add compiled micro .mlmodelc bundles (encoder + 8 buckets)
Browse files- micro/encoder.mlmodelc/analytics/coremldata.bin +3 -0
- micro/encoder.mlmodelc/coremldata.bin +3 -0
- micro/encoder.mlmodelc/model.mil +538 -0
- micro/encoder.mlmodelc/weights/weight.bin +3 -0
- micro/synthesizer_f1024.mlmodelc/analytics/coremldata.bin +3 -0
- micro/synthesizer_f1024.mlmodelc/coremldata.bin +3 -0
- micro/synthesizer_f1024.mlmodelc/model.mil +0 -0
- micro/synthesizer_f1024.mlmodelc/weights/weight.bin +3 -0
- micro/synthesizer_f2048.mlmodelc/analytics/coremldata.bin +3 -0
- micro/synthesizer_f2048.mlmodelc/coremldata.bin +3 -0
- micro/synthesizer_f2048.mlmodelc/model.mil +0 -0
- micro/synthesizer_f2048.mlmodelc/weights/weight.bin +3 -0
- micro/synthesizer_f256.mlmodelc/analytics/coremldata.bin +3 -0
- micro/synthesizer_f256.mlmodelc/coremldata.bin +3 -0
- micro/synthesizer_f256.mlmodelc/model.mil +0 -0
- micro/synthesizer_f256.mlmodelc/weights/weight.bin +3 -0
- micro/synthesizer_f384.mlmodelc/analytics/coremldata.bin +3 -0
- micro/synthesizer_f384.mlmodelc/coremldata.bin +3 -0
- micro/synthesizer_f384.mlmodelc/model.mil +0 -0
- micro/synthesizer_f384.mlmodelc/weights/weight.bin +3 -0
- micro/synthesizer_f512.mlmodelc/analytics/coremldata.bin +3 -0
- micro/synthesizer_f512.mlmodelc/coremldata.bin +3 -0
- micro/synthesizer_f512.mlmodelc/model.mil +0 -0
- micro/synthesizer_f512.mlmodelc/weights/weight.bin +3 -0
- micro/synthesizer_f640.mlmodelc/analytics/coremldata.bin +3 -0
- micro/synthesizer_f640.mlmodelc/coremldata.bin +3 -0
- micro/synthesizer_f640.mlmodelc/model.mil +0 -0
- micro/synthesizer_f640.mlmodelc/weights/weight.bin +3 -0
- micro/synthesizer_f768.mlmodelc/analytics/coremldata.bin +3 -0
- micro/synthesizer_f768.mlmodelc/coremldata.bin +3 -0
- micro/synthesizer_f768.mlmodelc/model.mil +0 -0
- micro/synthesizer_f768.mlmodelc/weights/weight.bin +3 -0
- micro/synthesizer_f896.mlmodelc/analytics/coremldata.bin +3 -0
- micro/synthesizer_f896.mlmodelc/coremldata.bin +3 -0
- micro/synthesizer_f896.mlmodelc/model.mil +0 -0
- micro/synthesizer_f896.mlmodelc/weights/weight.bin +3 -0
micro/encoder.mlmodelc/analytics/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:e7d0e9a973f74102c0444dc579754e44b9047d4f648e25fbc1724a221eede83a
|
| 3 |
+
size 243
|
micro/encoder.mlmodelc/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c252d6cac963d85ab0c608906db46ea1ca0dcb84297addf764d599be6ba4c3c9
|
| 3 |
+
size 441
|
micro/encoder.mlmodelc/model.mil
ADDED
|
@@ -0,0 +1,538 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
program(1.0)
|
| 2 |
+
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.7.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
|
| 3 |
+
{
|
| 4 |
+
func main<ios17>(tensor<int32, [1, 512]> tokens, tensor<fp32, [1, 1, 512]> x_mask) {
|
| 5 |
+
tensor<int32, []> var_13_batch_dims_0 = const()[name = tensor<string, []>("op_13_batch_dims_0"), val = tensor<int32, []>(0)];
|
| 6 |
+
tensor<bool, []> var_13_validate_indices_0 = const()[name = tensor<string, []>("op_13_validate_indices_0"), val = tensor<bool, []>(false)];
|
| 7 |
+
tensor<fp16, [178, 96]> enc_p_emb_weight_to_fp16 = const()[name = tensor<string, []>("enc_p_emb_weight_to_fp16"), val = tensor<fp16, [178, 96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
|
| 8 |
+
tensor<string, []> tokens_to_int16_dtype_0 = const()[name = tensor<string, []>("tokens_to_int16_dtype_0"), val = tensor<string, []>("int16")];
|
| 9 |
+
tensor<string, []> cast_40_dtype_0 = const()[name = tensor<string, []>("cast_40_dtype_0"), val = tensor<string, []>("int32")];
|
| 10 |
+
tensor<int32, []> greater_equal_0_y_0 = const()[name = tensor<string, []>("greater_equal_0_y_0"), val = tensor<int32, []>(0)];
|
| 11 |
+
tensor<int16, [1, 512]> tokens_to_int16 = cast(dtype = tokens_to_int16_dtype_0, x = tokens)[name = tensor<string, []>("cast_44")];
|
| 12 |
+
tensor<int32, [1, 512]> cast_40 = cast(dtype = cast_40_dtype_0, x = tokens_to_int16)[name = tensor<string, []>("cast_43")];
|
| 13 |
+
tensor<bool, [1, 512]> greater_equal_0 = greater_equal(x = cast_40, y = greater_equal_0_y_0)[name = tensor<string, []>("greater_equal_0")];
|
| 14 |
+
tensor<int32, []> slice_by_index_0 = const()[name = tensor<string, []>("slice_by_index_0"), val = tensor<int32, []>(178)];
|
| 15 |
+
tensor<int32, [1, 512]> add_0 = add(x = cast_40, y = slice_by_index_0)[name = tensor<string, []>("add_0")];
|
| 16 |
+
tensor<int32, [1, 512]> select_0 = select(a = cast_40, b = add_0, cond = greater_equal_0)[name = tensor<string, []>("select_0")];
|
| 17 |
+
tensor<int32, []> var_13_cast_fp16_cast_uint16_axis_0 = const()[name = tensor<string, []>("op_13_cast_fp16_cast_uint16_axis_0"), val = tensor<int32, []>(0)];
|
| 18 |
+
tensor<string, []> select_0_to_int16_dtype_0 = const()[name = tensor<string, []>("select_0_to_int16_dtype_0"), val = tensor<string, []>("int16")];
|
| 19 |
+
tensor<int16, [1, 512]> select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = tensor<string, []>("cast_42")];
|
| 20 |
+
tensor<fp16, [1, 512, 96]> var_13_cast_fp16_cast_uint16_cast_uint16 = gather(axis = var_13_cast_fp16_cast_uint16_axis_0, batch_dims = var_13_batch_dims_0, indices = select_0_to_int16, validate_indices = var_13_validate_indices_0, x = enc_p_emb_weight_to_fp16)[name = tensor<string, []>("op_13_cast_fp16_cast_uint16_cast_uint16")];
|
| 21 |
+
tensor<fp16, []> var_14_to_fp16 = const()[name = tensor<string, []>("op_14_to_fp16"), val = tensor<fp16, []>(0x1.398p+3)];
|
| 22 |
+
tensor<fp16, [1, 512, 96]> x_1_cast_fp16 = mul(x = var_13_cast_fp16_cast_uint16_cast_uint16, y = var_14_to_fp16)[name = tensor<string, []>("x_1_cast_fp16")];
|
| 23 |
+
tensor<int32, [3]> x_3_perm_0 = const()[name = tensor<string, []>("x_3_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
|
| 24 |
+
tensor<string, []> x_mask_to_fp16_dtype_0 = const()[name = tensor<string, []>("x_mask_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
|
| 25 |
+
tensor<fp16, [1, 1, 512]> x_mask_to_fp16 = cast(dtype = x_mask_to_fp16_dtype_0, x = x_mask)[name = tensor<string, []>("cast_41")];
|
| 26 |
+
tensor<fp16, [1, 96, 512]> x_3_cast_fp16 = transpose(perm = x_3_perm_0, x = x_1_cast_fp16)[name = tensor<string, []>("transpose_22")];
|
| 27 |
+
tensor<fp16, [1, 96, 512]> x_5_cast_fp16 = mul(x = x_3_cast_fp16, y = x_mask_to_fp16)[name = tensor<string, []>("x_5_cast_fp16")];
|
| 28 |
+
tensor<int32, []> var_42 = const()[name = tensor<string, []>("op_42"), val = tensor<int32, []>(-1)];
|
| 29 |
+
tensor<int32, [1]> var_68_axes_0 = const()[name = tensor<string, []>("op_68_axes_0"), val = tensor<int32, [1]>([2])];
|
| 30 |
+
tensor<fp16, [1, 1, 1, 512]> var_68_cast_fp16 = expand_dims(axes = var_68_axes_0, x = x_mask_to_fp16)[name = tensor<string, []>("op_68_cast_fp16")];
|
| 31 |
+
tensor<int32, [1]> var_69_axes_0 = const()[name = tensor<string, []>("op_69_axes_0"), val = tensor<int32, [1]>([-1])];
|
| 32 |
+
tensor<fp16, [1, 1, 512, 1]> var_69_cast_fp16 = expand_dims(axes = var_69_axes_0, x = x_mask_to_fp16)[name = tensor<string, []>("op_69_cast_fp16")];
|
| 33 |
+
tensor<fp16, [1, 1, 512, 512]> mask_cast_fp16 = mul(x = var_68_cast_fp16, y = var_69_cast_fp16)[name = tensor<string, []>("mask_cast_fp16")];
|
| 34 |
+
tensor<fp16, [1, 96, 512]> input_1_cast_fp16 = mul(x = x_5_cast_fp16, y = x_mask_to_fp16)[name = tensor<string, []>("input_1_cast_fp16")];
|
| 35 |
+
tensor<string, []> query_1_pad_type_0 = const()[name = tensor<string, []>("query_1_pad_type_0"), val = tensor<string, []>("valid")];
|
| 36 |
+
tensor<int32, [1]> query_1_strides_0 = const()[name = tensor<string, []>("query_1_strides_0"), val = tensor<int32, [1]>([1])];
|
| 37 |
+
tensor<int32, [2]> query_1_pad_0 = const()[name = tensor<string, []>("query_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
|
| 38 |
+
tensor<int32, [1]> query_1_dilations_0 = const()[name = tensor<string, []>("query_1_dilations_0"), val = tensor<int32, [1]>([1])];
|
| 39 |
+
tensor<int32, []> query_1_groups_0 = const()[name = tensor<string, []>("query_1_groups_0"), val = tensor<int32, []>(1)];
|
| 40 |
+
tensor<fp16, [96, 96, 1]> enc_p_encoder_attn_layers_0_conv_q_weight_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_attn_layers_0_conv_q_weight_to_fp16"), val = tensor<fp16, [96, 96, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(34304)))];
|
| 41 |
+
tensor<fp16, [96]> enc_p_encoder_attn_layers_0_conv_q_bias_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_attn_layers_0_conv_q_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(52800)))];
|
| 42 |
+
tensor<fp16, [1, 96, 512]> query_1_cast_fp16 = conv(bias = enc_p_encoder_attn_layers_0_conv_q_bias_to_fp16, dilations = query_1_dilations_0, groups = query_1_groups_0, pad = query_1_pad_0, pad_type = query_1_pad_type_0, strides = query_1_strides_0, weight = enc_p_encoder_attn_layers_0_conv_q_weight_to_fp16, x = input_1_cast_fp16)[name = tensor<string, []>("query_1_cast_fp16")];
|
| 43 |
+
tensor<string, []> key_1_pad_type_0 = const()[name = tensor<string, []>("key_1_pad_type_0"), val = tensor<string, []>("valid")];
|
| 44 |
+
tensor<int32, [1]> key_1_strides_0 = const()[name = tensor<string, []>("key_1_strides_0"), val = tensor<int32, [1]>([1])];
|
| 45 |
+
tensor<int32, [2]> key_1_pad_0 = const()[name = tensor<string, []>("key_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
|
| 46 |
+
tensor<int32, [1]> key_1_dilations_0 = const()[name = tensor<string, []>("key_1_dilations_0"), val = tensor<int32, [1]>([1])];
|
| 47 |
+
tensor<int32, []> key_1_groups_0 = const()[name = tensor<string, []>("key_1_groups_0"), val = tensor<int32, []>(1)];
|
| 48 |
+
tensor<fp16, [96, 96, 1]> enc_p_encoder_attn_layers_0_conv_k_weight_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_attn_layers_0_conv_k_weight_to_fp16"), val = tensor<fp16, [96, 96, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(53056)))];
|
| 49 |
+
tensor<fp16, [96]> enc_p_encoder_attn_layers_0_conv_k_bias_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_attn_layers_0_conv_k_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(71552)))];
|
| 50 |
+
tensor<fp16, [1, 96, 512]> key_1_cast_fp16 = conv(bias = enc_p_encoder_attn_layers_0_conv_k_bias_to_fp16, dilations = key_1_dilations_0, groups = key_1_groups_0, pad = key_1_pad_0, pad_type = key_1_pad_type_0, strides = key_1_strides_0, weight = enc_p_encoder_attn_layers_0_conv_k_weight_to_fp16, x = input_1_cast_fp16)[name = tensor<string, []>("key_1_cast_fp16")];
|
| 51 |
+
tensor<string, []> value_1_pad_type_0 = const()[name = tensor<string, []>("value_1_pad_type_0"), val = tensor<string, []>("valid")];
|
| 52 |
+
tensor<int32, [1]> value_1_strides_0 = const()[name = tensor<string, []>("value_1_strides_0"), val = tensor<int32, [1]>([1])];
|
| 53 |
+
tensor<int32, [2]> value_1_pad_0 = const()[name = tensor<string, []>("value_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
|
| 54 |
+
tensor<int32, [1]> value_1_dilations_0 = const()[name = tensor<string, []>("value_1_dilations_0"), val = tensor<int32, [1]>([1])];
|
| 55 |
+
tensor<int32, []> value_1_groups_0 = const()[name = tensor<string, []>("value_1_groups_0"), val = tensor<int32, []>(1)];
|
| 56 |
+
tensor<fp16, [96, 96, 1]> enc_p_encoder_attn_layers_0_conv_v_weight_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_attn_layers_0_conv_v_weight_to_fp16"), val = tensor<fp16, [96, 96, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(71808)))];
|
| 57 |
+
tensor<fp16, [96]> enc_p_encoder_attn_layers_0_conv_v_bias_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_attn_layers_0_conv_v_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(90304)))];
|
| 58 |
+
tensor<fp16, [1, 96, 512]> value_1_cast_fp16 = conv(bias = enc_p_encoder_attn_layers_0_conv_v_bias_to_fp16, dilations = value_1_dilations_0, groups = value_1_groups_0, pad = value_1_pad_0, pad_type = value_1_pad_type_0, strides = value_1_strides_0, weight = enc_p_encoder_attn_layers_0_conv_v_weight_to_fp16, x = input_1_cast_fp16)[name = tensor<string, []>("value_1_cast_fp16")];
|
| 59 |
+
tensor<int32, [4]> var_99 = const()[name = tensor<string, []>("op_99"), val = tensor<int32, [4]>([1, 2, 48, 512])];
|
| 60 |
+
tensor<fp16, [1, 2, 48, 512]> var_100_cast_fp16 = reshape(shape = var_99, x = query_1_cast_fp16)[name = tensor<string, []>("op_100_cast_fp16")];
|
| 61 |
+
tensor<int32, [4]> query_3_perm_0 = const()[name = tensor<string, []>("query_3_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
|
| 62 |
+
tensor<int32, [4]> var_102 = const()[name = tensor<string, []>("op_102"), val = tensor<int32, [4]>([1, 2, 48, 512])];
|
| 63 |
+
tensor<fp16, [1, 2, 48, 512]> var_103_cast_fp16 = reshape(shape = var_102, x = key_1_cast_fp16)[name = tensor<string, []>("op_103_cast_fp16")];
|
| 64 |
+
tensor<int32, [4]> var_105 = const()[name = tensor<string, []>("op_105"), val = tensor<int32, [4]>([1, 2, 48, 512])];
|
| 65 |
+
tensor<fp16, [1, 2, 48, 512]> var_106_cast_fp16 = reshape(shape = var_105, x = value_1_cast_fp16)[name = tensor<string, []>("op_106_cast_fp16")];
|
| 66 |
+
tensor<fp16, []> _inversed_109_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_109_y_0_to_fp16"), val = tensor<fp16, []>(0x1.278p-3)];
|
| 67 |
+
tensor<fp16, [1, 2, 512, 48]> query_3_cast_fp16 = transpose(perm = query_3_perm_0, x = var_100_cast_fp16)[name = tensor<string, []>("transpose_21")];
|
| 68 |
+
tensor<fp16, [1, 2, 512, 48]> _inversed_109_cast_fp16 = mul(x = query_3_cast_fp16, y = _inversed_109_y_0_to_fp16)[name = tensor<string, []>("_inversed_109_cast_fp16")];
|
| 69 |
+
tensor<bool, []> scores_1_transpose_x_0 = const()[name = tensor<string, []>("scores_1_transpose_x_0"), val = tensor<bool, []>(false)];
|
| 70 |
+
tensor<bool, []> scores_1_transpose_y_0 = const()[name = tensor<string, []>("scores_1_transpose_y_0"), val = tensor<bool, []>(false)];
|
| 71 |
+
tensor<fp16, [1, 2, 512, 512]> scores_1_cast_fp16 = matmul(transpose_x = scores_1_transpose_x_0, transpose_y = scores_1_transpose_y_0, x = _inversed_109_cast_fp16, y = var_103_cast_fp16)[name = tensor<string, []>("scores_1_cast_fp16")];
|
| 72 |
+
tensor<bool, []> x_9_transpose_x_0 = const()[name = tensor<string, []>("x_9_transpose_x_0"), val = tensor<bool, []>(false)];
|
| 73 |
+
tensor<bool, []> x_9_transpose_y_0 = const()[name = tensor<string, []>("x_9_transpose_y_0"), val = tensor<bool, []>(false)];
|
| 74 |
+
tensor<fp16, [1, 1, 48, 1023]> var_119_to_fp16 = const()[name = tensor<string, []>("op_119_to_fp16"), val = tensor<fp16, [1, 1, 48, 1023]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(90560)))];
|
| 75 |
+
tensor<fp16, [1, 2, 512, 1023]> x_9_cast_fp16 = matmul(transpose_x = x_9_transpose_x_0, transpose_y = x_9_transpose_y_0, x = _inversed_109_cast_fp16, y = var_119_to_fp16)[name = tensor<string, []>("x_9_cast_fp16")];
|
| 76 |
+
tensor<int32, [8]> x_11_pad_0 = const()[name = tensor<string, []>("x_11_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 0, 1])];
|
| 77 |
+
tensor<string, []> x_11_mode_0 = const()[name = tensor<string, []>("x_11_mode_0"), val = tensor<string, []>("constant")];
|
| 78 |
+
tensor<fp16, []> const_1_to_fp16 = const()[name = tensor<string, []>("const_1_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
|
| 79 |
+
tensor<fp16, [1, 2, 512, 1024]> x_11_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = x_11_mode_0, pad = x_11_pad_0, x = x_9_cast_fp16)[name = tensor<string, []>("x_11_cast_fp16")];
|
| 80 |
+
tensor<int32, [3]> var_123 = const()[name = tensor<string, []>("op_123"), val = tensor<int32, [3]>([1, 2, 524288])];
|
| 81 |
+
tensor<fp16, [1, 2, 524288]> input_5_cast_fp16 = reshape(shape = var_123, x = x_11_cast_fp16)[name = tensor<string, []>("input_5_cast_fp16")];
|
| 82 |
+
tensor<int32, [6]> x_flat_1_pad_0 = const()[name = tensor<string, []>("x_flat_1_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 0, 511])];
|
| 83 |
+
tensor<string, []> x_flat_1_mode_0 = const()[name = tensor<string, []>("x_flat_1_mode_0"), val = tensor<string, []>("constant")];
|
| 84 |
+
tensor<fp16, []> const_2_to_fp16 = const()[name = tensor<string, []>("const_2_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
|
| 85 |
+
tensor<fp16, [1, 2, 524799]> x_flat_1_cast_fp16 = pad(constant_val = const_2_to_fp16, mode = x_flat_1_mode_0, pad = x_flat_1_pad_0, x = input_5_cast_fp16)[name = tensor<string, []>("x_flat_1_cast_fp16")];
|
| 86 |
+
tensor<int32, [4]> var_127 = const()[name = tensor<string, []>("op_127"), val = tensor<int32, [4]>([1, 2, 513, 1023])];
|
| 87 |
+
tensor<fp16, [1, 2, 513, 1023]> var_128_cast_fp16 = reshape(shape = var_127, x = x_flat_1_cast_fp16)[name = tensor<string, []>("op_128_cast_fp16")];
|
| 88 |
+
tensor<int32, [4]> var_131_begin_0 = const()[name = tensor<string, []>("op_131_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
|
| 89 |
+
tensor<int32, [4]> var_131_end_0 = const()[name = tensor<string, []>("op_131_end_0"), val = tensor<int32, [4]>([1, 2, 512, 1023])];
|
| 90 |
+
tensor<bool, [4]> var_131_end_mask_0 = const()[name = tensor<string, []>("op_131_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
|
| 91 |
+
tensor<fp16, [1, 2, 512, 1023]> var_131_cast_fp16 = slice_by_index(begin = var_131_begin_0, end = var_131_end_0, end_mask = var_131_end_mask_0, x = var_128_cast_fp16)[name = tensor<string, []>("op_131_cast_fp16")];
|
| 92 |
+
tensor<int32, [4]> var_132_begin_0 = const()[name = tensor<string, []>("op_132_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 511])];
|
| 93 |
+
tensor<int32, [4]> var_132_end_0 = const()[name = tensor<string, []>("op_132_end_0"), val = tensor<int32, [4]>([1, 2, 512, 1023])];
|
| 94 |
+
tensor<bool, [4]> var_132_end_mask_0 = const()[name = tensor<string, []>("op_132_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
|
| 95 |
+
tensor<fp16, [1, 2, 512, 512]> var_132_cast_fp16 = slice_by_index(begin = var_132_begin_0, end = var_132_end_0, end_mask = var_132_end_mask_0, x = var_131_cast_fp16)[name = tensor<string, []>("op_132_cast_fp16")];
|
| 96 |
+
tensor<fp16, [1, 2, 512, 512]> scores_3_cast_fp16 = add(x = scores_1_cast_fp16, y = var_132_cast_fp16)[name = tensor<string, []>("scores_3_cast_fp16")];
|
| 97 |
+
tensor<fp16, []> var_24_promoted_to_fp16 = const()[name = tensor<string, []>("op_24_promoted_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
|
| 98 |
+
tensor<bool, [1, 1, 512, 512]> var_134_cast_fp16 = equal(x = mask_cast_fp16, y = var_24_promoted_to_fp16)[name = tensor<string, []>("op_134_cast_fp16")];
|
| 99 |
+
tensor<fp16, []> var_38_to_fp16 = const()[name = tensor<string, []>("op_38_to_fp16"), val = tensor<fp16, []>(-0x1.388p+13)];
|
| 100 |
+
tensor<fp16, [1, 2, 512, 512]> scores_5_cast_fp16 = select(a = var_38_to_fp16, b = scores_3_cast_fp16, cond = var_134_cast_fp16)[name = tensor<string, []>("scores_5_cast_fp16")];
|
| 101 |
+
tensor<fp16, [1, 2, 512, 512]> p_attn_1_cast_fp16 = softmax(axis = var_42, x = scores_5_cast_fp16)[name = tensor<string, []>("p_attn_1_cast_fp16")];
|
| 102 |
+
tensor<bool, []> output_1_transpose_x_1 = const()[name = tensor<string, []>("output_1_transpose_x_1"), val = tensor<bool, []>(false)];
|
| 103 |
+
tensor<bool, []> output_1_transpose_y_1 = const()[name = tensor<string, []>("output_1_transpose_y_1"), val = tensor<bool, []>(true)];
|
| 104 |
+
tensor<fp16, [1, 2, 512, 48]> output_1_cast_fp16 = matmul(transpose_x = output_1_transpose_x_1, transpose_y = output_1_transpose_y_1, x = p_attn_1_cast_fp16, y = var_106_cast_fp16)[name = tensor<string, []>("output_1_cast_fp16")];
|
| 105 |
+
tensor<int32, [8]> x_13_pad_0 = const()[name = tensor<string, []>("x_13_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 0, 511])];
|
| 106 |
+
tensor<string, []> x_13_mode_0 = const()[name = tensor<string, []>("x_13_mode_0"), val = tensor<string, []>("constant")];
|
| 107 |
+
tensor<fp16, []> const_3_to_fp16 = const()[name = tensor<string, []>("const_3_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
|
| 108 |
+
tensor<fp16, [1, 2, 512, 1023]> x_13_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = x_13_mode_0, pad = x_13_pad_0, x = p_attn_1_cast_fp16)[name = tensor<string, []>("x_13_cast_fp16")];
|
| 109 |
+
tensor<int32, [3]> var_140 = const()[name = tensor<string, []>("op_140"), val = tensor<int32, [3]>([1, 2, 523776])];
|
| 110 |
+
tensor<fp16, [1, 2, 523776]> input_7_cast_fp16 = reshape(shape = var_140, x = x_13_cast_fp16)[name = tensor<string, []>("input_7_cast_fp16")];
|
| 111 |
+
tensor<int32, [6]> x_flat_3_pad_0 = const()[name = tensor<string, []>("x_flat_3_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 512, 0])];
|
| 112 |
+
tensor<string, []> x_flat_3_mode_0 = const()[name = tensor<string, []>("x_flat_3_mode_0"), val = tensor<string, []>("constant")];
|
| 113 |
+
tensor<fp16, []> const_4_to_fp16 = const()[name = tensor<string, []>("const_4_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
|
| 114 |
+
tensor<fp16, [1, 2, 524288]> x_flat_3_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = x_flat_3_mode_0, pad = x_flat_3_pad_0, x = input_7_cast_fp16)[name = tensor<string, []>("x_flat_3_cast_fp16")];
|
| 115 |
+
tensor<int32, [4]> var_144 = const()[name = tensor<string, []>("op_144"), val = tensor<int32, [4]>([1, 2, 512, 1024])];
|
| 116 |
+
tensor<fp16, [1, 2, 512, 1024]> var_145_cast_fp16 = reshape(shape = var_144, x = x_flat_3_cast_fp16)[name = tensor<string, []>("op_145_cast_fp16")];
|
| 117 |
+
tensor<int32, [4]> x_15_begin_0 = const()[name = tensor<string, []>("x_15_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 1])];
|
| 118 |
+
tensor<int32, [4]> x_15_end_0 = const()[name = tensor<string, []>("x_15_end_0"), val = tensor<int32, [4]>([1, 2, 512, 1024])];
|
| 119 |
+
tensor<bool, [4]> x_15_end_mask_0 = const()[name = tensor<string, []>("x_15_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
|
| 120 |
+
tensor<fp16, [1, 2, 512, 1023]> x_15_cast_fp16 = slice_by_index(begin = x_15_begin_0, end = x_15_end_0, end_mask = x_15_end_mask_0, x = var_145_cast_fp16)[name = tensor<string, []>("x_15_cast_fp16")];
|
| 121 |
+
tensor<bool, []> var_155_transpose_x_0 = const()[name = tensor<string, []>("op_155_transpose_x_0"), val = tensor<bool, []>(false)];
|
| 122 |
+
tensor<bool, []> var_155_transpose_y_0 = const()[name = tensor<string, []>("op_155_transpose_y_0"), val = tensor<bool, []>(false)];
|
| 123 |
+
tensor<fp16, [1, 1, 1023, 48]> var_154_to_fp16 = const()[name = tensor<string, []>("op_154_to_fp16"), val = tensor<fp16, [1, 1, 1023, 48]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(188864)))];
|
| 124 |
+
tensor<fp16, [1, 2, 512, 48]> var_155_cast_fp16 = matmul(transpose_x = var_155_transpose_x_0, transpose_y = var_155_transpose_y_0, x = x_15_cast_fp16, y = var_154_to_fp16)[name = tensor<string, []>("op_155_cast_fp16")];
|
| 125 |
+
tensor<fp16, [1, 2, 512, 48]> output_3_cast_fp16 = add(x = output_1_cast_fp16, y = var_155_cast_fp16)[name = tensor<string, []>("output_3_cast_fp16")];
|
| 126 |
+
tensor<int32, [4]> var_157_perm_0 = const()[name = tensor<string, []>("op_157_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
|
| 127 |
+
tensor<int32, [3]> var_159 = const()[name = tensor<string, []>("op_159"), val = tensor<int32, [3]>([1, 96, 512])];
|
| 128 |
+
tensor<fp16, [1, 2, 48, 512]> var_157_cast_fp16 = transpose(perm = var_157_perm_0, x = output_3_cast_fp16)[name = tensor<string, []>("transpose_20")];
|
| 129 |
+
tensor<fp16, [1, 96, 512]> input_11_cast_fp16 = reshape(shape = var_159, x = var_157_cast_fp16)[name = tensor<string, []>("input_11_cast_fp16")];
|
| 130 |
+
tensor<string, []> input_13_pad_type_0 = const()[name = tensor<string, []>("input_13_pad_type_0"), val = tensor<string, []>("valid")];
|
| 131 |
+
tensor<int32, [1]> input_13_strides_0 = const()[name = tensor<string, []>("input_13_strides_0"), val = tensor<int32, [1]>([1])];
|
| 132 |
+
tensor<int32, [2]> input_13_pad_0 = const()[name = tensor<string, []>("input_13_pad_0"), val = tensor<int32, [2]>([0, 0])];
|
| 133 |
+
tensor<int32, [1]> input_13_dilations_0 = const()[name = tensor<string, []>("input_13_dilations_0"), val = tensor<int32, [1]>([1])];
|
| 134 |
+
tensor<int32, []> input_13_groups_0 = const()[name = tensor<string, []>("input_13_groups_0"), val = tensor<int32, []>(1)];
|
| 135 |
+
tensor<fp16, [96, 96, 1]> enc_p_encoder_attn_layers_0_conv_o_weight_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_attn_layers_0_conv_o_weight_to_fp16"), val = tensor<fp16, [96, 96, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(287168)))];
|
| 136 |
+
tensor<fp16, [96]> enc_p_encoder_attn_layers_0_conv_o_bias_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_attn_layers_0_conv_o_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(305664)))];
|
| 137 |
+
tensor<fp16, [1, 96, 512]> input_13_cast_fp16 = conv(bias = enc_p_encoder_attn_layers_0_conv_o_bias_to_fp16, dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = enc_p_encoder_attn_layers_0_conv_o_weight_to_fp16, x = input_11_cast_fp16)[name = tensor<string, []>("input_13_cast_fp16")];
|
| 138 |
+
tensor<fp16, [1, 96, 512]> x_17_cast_fp16 = add(x = input_1_cast_fp16, y = input_13_cast_fp16)[name = tensor<string, []>("x_17_cast_fp16")];
|
| 139 |
+
tensor<int32, [3]> input_15_perm_0 = const()[name = tensor<string, []>("input_15_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
|
| 140 |
+
tensor<int32, [1]> x_19_axes_0 = const()[name = tensor<string, []>("x_19_axes_0"), val = tensor<int32, [1]>([-1])];
|
| 141 |
+
tensor<fp16, [96]> enc_p_encoder_norm_layers_1_0_gamma_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_norm_layers_1_0_gamma_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(305920)))];
|
| 142 |
+
tensor<fp16, [96]> enc_p_encoder_norm_layers_1_0_beta_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_norm_layers_1_0_beta_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(306176)))];
|
| 143 |
+
tensor<fp16, []> var_20_to_fp16 = const()[name = tensor<string, []>("op_20_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
|
| 144 |
+
tensor<fp16, [1, 512, 96]> input_15_cast_fp16 = transpose(perm = input_15_perm_0, x = x_17_cast_fp16)[name = tensor<string, []>("transpose_19")];
|
| 145 |
+
tensor<fp16, [1, 512, 96]> x_19_cast_fp16 = layer_norm(axes = x_19_axes_0, beta = enc_p_encoder_norm_layers_1_0_beta_to_fp16, epsilon = var_20_to_fp16, gamma = enc_p_encoder_norm_layers_1_0_gamma_to_fp16, x = input_15_cast_fp16)[name = tensor<string, []>("x_19_cast_fp16")];
|
| 146 |
+
tensor<int32, [3]> x_21_perm_0 = const()[name = tensor<string, []>("x_21_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
|
| 147 |
+
tensor<fp16, [1, 96, 512]> x_21_cast_fp16 = transpose(perm = x_21_perm_0, x = x_19_cast_fp16)[name = tensor<string, []>("transpose_18")];
|
| 148 |
+
tensor<fp16, [1, 96, 512]> input_17_cast_fp16 = mul(x = x_21_cast_fp16, y = x_mask_to_fp16)[name = tensor<string, []>("input_17_cast_fp16")];
|
| 149 |
+
tensor<int32, [6]> input_19_pad_0 = const()[name = tensor<string, []>("input_19_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 1, 1])];
|
| 150 |
+
tensor<string, []> input_19_mode_0 = const()[name = tensor<string, []>("input_19_mode_0"), val = tensor<string, []>("constant")];
|
| 151 |
+
tensor<fp16, []> const_6_to_fp16 = const()[name = tensor<string, []>("const_6_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
|
| 152 |
+
tensor<fp16, [1, 96, 514]> input_19_cast_fp16 = pad(constant_val = const_6_to_fp16, mode = input_19_mode_0, pad = input_19_pad_0, x = input_17_cast_fp16)[name = tensor<string, []>("input_19_cast_fp16")];
|
| 153 |
+
tensor<string, []> x_23_pad_type_0 = const()[name = tensor<string, []>("x_23_pad_type_0"), val = tensor<string, []>("valid")];
|
| 154 |
+
tensor<int32, [1]> x_23_strides_0 = const()[name = tensor<string, []>("x_23_strides_0"), val = tensor<int32, [1]>([1])];
|
| 155 |
+
tensor<int32, [2]> x_23_pad_0 = const()[name = tensor<string, []>("x_23_pad_0"), val = tensor<int32, [2]>([0, 0])];
|
| 156 |
+
tensor<int32, [1]> x_23_dilations_0 = const()[name = tensor<string, []>("x_23_dilations_0"), val = tensor<int32, [1]>([1])];
|
| 157 |
+
tensor<int32, []> x_23_groups_0 = const()[name = tensor<string, []>("x_23_groups_0"), val = tensor<int32, []>(1)];
|
| 158 |
+
tensor<fp16, [768, 96, 3]> enc_p_encoder_ffn_layers_0_conv_1_weight_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_ffn_layers_0_conv_1_weight_to_fp16"), val = tensor<fp16, [768, 96, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(306432)))];
|
| 159 |
+
tensor<fp16, [768]> enc_p_encoder_ffn_layers_0_conv_1_bias_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_ffn_layers_0_conv_1_bias_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(748864)))];
|
| 160 |
+
tensor<fp16, [1, 768, 512]> x_23_cast_fp16 = conv(bias = enc_p_encoder_ffn_layers_0_conv_1_bias_to_fp16, dilations = x_23_dilations_0, groups = x_23_groups_0, pad = x_23_pad_0, pad_type = x_23_pad_type_0, strides = x_23_strides_0, weight = enc_p_encoder_ffn_layers_0_conv_1_weight_to_fp16, x = input_19_cast_fp16)[name = tensor<string, []>("x_23_cast_fp16")];
|
| 161 |
+
tensor<fp16, [1, 768, 512]> input_21_cast_fp16 = relu(x = x_23_cast_fp16)[name = tensor<string, []>("input_21_cast_fp16")];
|
| 162 |
+
tensor<fp16, [1, 768, 512]> input_23_cast_fp16 = mul(x = input_21_cast_fp16, y = x_mask_to_fp16)[name = tensor<string, []>("input_23_cast_fp16")];
|
| 163 |
+
tensor<int32, [6]> input_25_pad_0 = const()[name = tensor<string, []>("input_25_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 1, 1])];
|
| 164 |
+
tensor<string, []> input_25_mode_0 = const()[name = tensor<string, []>("input_25_mode_0"), val = tensor<string, []>("constant")];
|
| 165 |
+
tensor<fp16, []> const_7_to_fp16 = const()[name = tensor<string, []>("const_7_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
|
| 166 |
+
tensor<fp16, [1, 768, 514]> input_25_cast_fp16 = pad(constant_val = const_7_to_fp16, mode = input_25_mode_0, pad = input_25_pad_0, x = input_23_cast_fp16)[name = tensor<string, []>("input_25_cast_fp16")];
|
| 167 |
+
tensor<string, []> x_27_pad_type_0 = const()[name = tensor<string, []>("x_27_pad_type_0"), val = tensor<string, []>("valid")];
|
| 168 |
+
tensor<int32, [1]> x_27_strides_0 = const()[name = tensor<string, []>("x_27_strides_0"), val = tensor<int32, [1]>([1])];
|
| 169 |
+
tensor<int32, [2]> x_27_pad_0 = const()[name = tensor<string, []>("x_27_pad_0"), val = tensor<int32, [2]>([0, 0])];
|
| 170 |
+
tensor<int32, [1]> x_27_dilations_0 = const()[name = tensor<string, []>("x_27_dilations_0"), val = tensor<int32, [1]>([1])];
|
| 171 |
+
tensor<int32, []> x_27_groups_0 = const()[name = tensor<string, []>("x_27_groups_0"), val = tensor<int32, []>(1)];
|
| 172 |
+
tensor<fp16, [96, 768, 3]> enc_p_encoder_ffn_layers_0_conv_2_weight_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_ffn_layers_0_conv_2_weight_to_fp16"), val = tensor<fp16, [96, 768, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(750464)))];
|
| 173 |
+
tensor<fp16, [96]> enc_p_encoder_ffn_layers_0_conv_2_bias_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_ffn_layers_0_conv_2_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1192896)))];
|
| 174 |
+
tensor<fp16, [1, 96, 512]> x_27_cast_fp16 = conv(bias = enc_p_encoder_ffn_layers_0_conv_2_bias_to_fp16, dilations = x_27_dilations_0, groups = x_27_groups_0, pad = x_27_pad_0, pad_type = x_27_pad_type_0, strides = x_27_strides_0, weight = enc_p_encoder_ffn_layers_0_conv_2_weight_to_fp16, x = input_25_cast_fp16)[name = tensor<string, []>("x_27_cast_fp16")];
|
| 175 |
+
tensor<fp16, [1, 96, 512]> input_27_cast_fp16 = mul(x = x_27_cast_fp16, y = x_mask_to_fp16)[name = tensor<string, []>("input_27_cast_fp16")];
|
| 176 |
+
tensor<fp16, [1, 96, 512]> x_29_cast_fp16 = add(x = x_21_cast_fp16, y = input_27_cast_fp16)[name = tensor<string, []>("x_29_cast_fp16")];
|
| 177 |
+
tensor<int32, [3]> input_29_perm_0 = const()[name = tensor<string, []>("input_29_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
|
| 178 |
+
tensor<int32, [1]> x_31_axes_0 = const()[name = tensor<string, []>("x_31_axes_0"), val = tensor<int32, [1]>([-1])];
|
| 179 |
+
tensor<fp16, [96]> enc_p_encoder_norm_layers_2_0_gamma_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_norm_layers_2_0_gamma_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1193152)))];
|
| 180 |
+
tensor<fp16, [96]> enc_p_encoder_norm_layers_2_0_beta_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_norm_layers_2_0_beta_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1193408)))];
|
| 181 |
+
tensor<fp16, [1, 512, 96]> input_29_cast_fp16 = transpose(perm = input_29_perm_0, x = x_29_cast_fp16)[name = tensor<string, []>("transpose_17")];
|
| 182 |
+
tensor<fp16, [1, 512, 96]> x_31_cast_fp16 = layer_norm(axes = x_31_axes_0, beta = enc_p_encoder_norm_layers_2_0_beta_to_fp16, epsilon = var_20_to_fp16, gamma = enc_p_encoder_norm_layers_2_0_gamma_to_fp16, x = input_29_cast_fp16)[name = tensor<string, []>("x_31_cast_fp16")];
|
| 183 |
+
tensor<int32, [3]> input_31_perm_0 = const()[name = tensor<string, []>("input_31_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
|
| 184 |
+
tensor<string, []> query_5_pad_type_0 = const()[name = tensor<string, []>("query_5_pad_type_0"), val = tensor<string, []>("valid")];
|
| 185 |
+
tensor<int32, [1]> query_5_strides_0 = const()[name = tensor<string, []>("query_5_strides_0"), val = tensor<int32, [1]>([1])];
|
| 186 |
+
tensor<int32, [2]> query_5_pad_0 = const()[name = tensor<string, []>("query_5_pad_0"), val = tensor<int32, [2]>([0, 0])];
|
| 187 |
+
tensor<int32, [1]> query_5_dilations_0 = const()[name = tensor<string, []>("query_5_dilations_0"), val = tensor<int32, [1]>([1])];
|
| 188 |
+
tensor<int32, []> query_5_groups_0 = const()[name = tensor<string, []>("query_5_groups_0"), val = tensor<int32, []>(1)];
|
| 189 |
+
tensor<fp16, [96, 96, 1]> enc_p_encoder_attn_layers_1_conv_q_weight_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_attn_layers_1_conv_q_weight_to_fp16"), val = tensor<fp16, [96, 96, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1193664)))];
|
| 190 |
+
tensor<fp16, [96]> enc_p_encoder_attn_layers_1_conv_q_bias_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_attn_layers_1_conv_q_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1212160)))];
|
| 191 |
+
tensor<fp16, [1, 96, 512]> input_31_cast_fp16 = transpose(perm = input_31_perm_0, x = x_31_cast_fp16)[name = tensor<string, []>("transpose_16")];
|
| 192 |
+
tensor<fp16, [1, 96, 512]> query_5_cast_fp16 = conv(bias = enc_p_encoder_attn_layers_1_conv_q_bias_to_fp16, dilations = query_5_dilations_0, groups = query_5_groups_0, pad = query_5_pad_0, pad_type = query_5_pad_type_0, strides = query_5_strides_0, weight = enc_p_encoder_attn_layers_1_conv_q_weight_to_fp16, x = input_31_cast_fp16)[name = tensor<string, []>("query_5_cast_fp16")];
|
| 193 |
+
tensor<string, []> key_5_pad_type_0 = const()[name = tensor<string, []>("key_5_pad_type_0"), val = tensor<string, []>("valid")];
|
| 194 |
+
tensor<int32, [1]> key_5_strides_0 = const()[name = tensor<string, []>("key_5_strides_0"), val = tensor<int32, [1]>([1])];
|
| 195 |
+
tensor<int32, [2]> key_5_pad_0 = const()[name = tensor<string, []>("key_5_pad_0"), val = tensor<int32, [2]>([0, 0])];
|
| 196 |
+
tensor<int32, [1]> key_5_dilations_0 = const()[name = tensor<string, []>("key_5_dilations_0"), val = tensor<int32, [1]>([1])];
|
| 197 |
+
tensor<int32, []> key_5_groups_0 = const()[name = tensor<string, []>("key_5_groups_0"), val = tensor<int32, []>(1)];
|
| 198 |
+
tensor<fp16, [96, 96, 1]> enc_p_encoder_attn_layers_1_conv_k_weight_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_attn_layers_1_conv_k_weight_to_fp16"), val = tensor<fp16, [96, 96, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1212416)))];
|
| 199 |
+
tensor<fp16, [96]> enc_p_encoder_attn_layers_1_conv_k_bias_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_attn_layers_1_conv_k_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1230912)))];
|
| 200 |
+
tensor<fp16, [1, 96, 512]> key_5_cast_fp16 = conv(bias = enc_p_encoder_attn_layers_1_conv_k_bias_to_fp16, dilations = key_5_dilations_0, groups = key_5_groups_0, pad = key_5_pad_0, pad_type = key_5_pad_type_0, strides = key_5_strides_0, weight = enc_p_encoder_attn_layers_1_conv_k_weight_to_fp16, x = input_31_cast_fp16)[name = tensor<string, []>("key_5_cast_fp16")];
|
| 201 |
+
tensor<string, []> value_5_pad_type_0 = const()[name = tensor<string, []>("value_5_pad_type_0"), val = tensor<string, []>("valid")];
|
| 202 |
+
tensor<int32, [1]> value_5_strides_0 = const()[name = tensor<string, []>("value_5_strides_0"), val = tensor<int32, [1]>([1])];
|
| 203 |
+
tensor<int32, [2]> value_5_pad_0 = const()[name = tensor<string, []>("value_5_pad_0"), val = tensor<int32, [2]>([0, 0])];
|
| 204 |
+
tensor<int32, [1]> value_5_dilations_0 = const()[name = tensor<string, []>("value_5_dilations_0"), val = tensor<int32, [1]>([1])];
|
| 205 |
+
tensor<int32, []> value_5_groups_0 = const()[name = tensor<string, []>("value_5_groups_0"), val = tensor<int32, []>(1)];
|
| 206 |
+
tensor<fp16, [96, 96, 1]> enc_p_encoder_attn_layers_1_conv_v_weight_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_attn_layers_1_conv_v_weight_to_fp16"), val = tensor<fp16, [96, 96, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1231168)))];
|
| 207 |
+
tensor<fp16, [96]> enc_p_encoder_attn_layers_1_conv_v_bias_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_attn_layers_1_conv_v_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1249664)))];
|
| 208 |
+
tensor<fp16, [1, 96, 512]> value_5_cast_fp16 = conv(bias = enc_p_encoder_attn_layers_1_conv_v_bias_to_fp16, dilations = value_5_dilations_0, groups = value_5_groups_0, pad = value_5_pad_0, pad_type = value_5_pad_type_0, strides = value_5_strides_0, weight = enc_p_encoder_attn_layers_1_conv_v_weight_to_fp16, x = input_31_cast_fp16)[name = tensor<string, []>("value_5_cast_fp16")];
|
| 209 |
+
tensor<int32, [4]> var_236 = const()[name = tensor<string, []>("op_236"), val = tensor<int32, [4]>([1, 2, 48, 512])];
|
| 210 |
+
tensor<fp16, [1, 2, 48, 512]> var_237_cast_fp16 = reshape(shape = var_236, x = query_5_cast_fp16)[name = tensor<string, []>("op_237_cast_fp16")];
|
| 211 |
+
tensor<int32, [4]> query_7_perm_0 = const()[name = tensor<string, []>("query_7_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
|
| 212 |
+
tensor<int32, [4]> var_239 = const()[name = tensor<string, []>("op_239"), val = tensor<int32, [4]>([1, 2, 48, 512])];
|
| 213 |
+
tensor<fp16, [1, 2, 48, 512]> var_240_cast_fp16 = reshape(shape = var_239, x = key_5_cast_fp16)[name = tensor<string, []>("op_240_cast_fp16")];
|
| 214 |
+
tensor<int32, [4]> var_242 = const()[name = tensor<string, []>("op_242"), val = tensor<int32, [4]>([1, 2, 48, 512])];
|
| 215 |
+
tensor<fp16, [1, 2, 48, 512]> var_243_cast_fp16 = reshape(shape = var_242, x = value_5_cast_fp16)[name = tensor<string, []>("op_243_cast_fp16")];
|
| 216 |
+
tensor<fp16, []> _inversed_246_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_246_y_0_to_fp16"), val = tensor<fp16, []>(0x1.278p-3)];
|
| 217 |
+
tensor<fp16, [1, 2, 512, 48]> query_7_cast_fp16 = transpose(perm = query_7_perm_0, x = var_237_cast_fp16)[name = tensor<string, []>("transpose_15")];
|
| 218 |
+
tensor<fp16, [1, 2, 512, 48]> _inversed_246_cast_fp16 = mul(x = query_7_cast_fp16, y = _inversed_246_y_0_to_fp16)[name = tensor<string, []>("_inversed_246_cast_fp16")];
|
| 219 |
+
tensor<bool, []> scores_7_transpose_x_0 = const()[name = tensor<string, []>("scores_7_transpose_x_0"), val = tensor<bool, []>(false)];
|
| 220 |
+
tensor<bool, []> scores_7_transpose_y_0 = const()[name = tensor<string, []>("scores_7_transpose_y_0"), val = tensor<bool, []>(false)];
|
| 221 |
+
tensor<fp16, [1, 2, 512, 512]> scores_7_cast_fp16 = matmul(transpose_x = scores_7_transpose_x_0, transpose_y = scores_7_transpose_y_0, x = _inversed_246_cast_fp16, y = var_240_cast_fp16)[name = tensor<string, []>("scores_7_cast_fp16")];
|
| 222 |
+
tensor<bool, []> x_35_transpose_x_0 = const()[name = tensor<string, []>("x_35_transpose_x_0"), val = tensor<bool, []>(false)];
|
| 223 |
+
tensor<bool, []> x_35_transpose_y_0 = const()[name = tensor<string, []>("x_35_transpose_y_0"), val = tensor<bool, []>(false)];
|
| 224 |
+
tensor<fp16, [1, 1, 48, 1023]> var_256_to_fp16 = const()[name = tensor<string, []>("op_256_to_fp16"), val = tensor<fp16, [1, 1, 48, 1023]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1249920)))];
|
| 225 |
+
tensor<fp16, [1, 2, 512, 1023]> x_35_cast_fp16 = matmul(transpose_x = x_35_transpose_x_0, transpose_y = x_35_transpose_y_0, x = _inversed_246_cast_fp16, y = var_256_to_fp16)[name = tensor<string, []>("x_35_cast_fp16")];
|
| 226 |
+
tensor<int32, [8]> x_37_pad_0 = const()[name = tensor<string, []>("x_37_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 0, 1])];
|
| 227 |
+
tensor<string, []> x_37_mode_0 = const()[name = tensor<string, []>("x_37_mode_0"), val = tensor<string, []>("constant")];
|
| 228 |
+
tensor<fp16, []> const_9_to_fp16 = const()[name = tensor<string, []>("const_9_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
|
| 229 |
+
tensor<fp16, [1, 2, 512, 1024]> x_37_cast_fp16 = pad(constant_val = const_9_to_fp16, mode = x_37_mode_0, pad = x_37_pad_0, x = x_35_cast_fp16)[name = tensor<string, []>("x_37_cast_fp16")];
|
| 230 |
+
tensor<int32, [3]> var_260 = const()[name = tensor<string, []>("op_260"), val = tensor<int32, [3]>([1, 2, 524288])];
|
| 231 |
+
tensor<fp16, [1, 2, 524288]> input_35_cast_fp16 = reshape(shape = var_260, x = x_37_cast_fp16)[name = tensor<string, []>("input_35_cast_fp16")];
|
| 232 |
+
tensor<int32, [6]> x_flat_5_pad_0 = const()[name = tensor<string, []>("x_flat_5_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 0, 511])];
|
| 233 |
+
tensor<string, []> x_flat_5_mode_0 = const()[name = tensor<string, []>("x_flat_5_mode_0"), val = tensor<string, []>("constant")];
|
| 234 |
+
tensor<fp16, []> const_10_to_fp16 = const()[name = tensor<string, []>("const_10_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
|
| 235 |
+
tensor<fp16, [1, 2, 524799]> x_flat_5_cast_fp16 = pad(constant_val = const_10_to_fp16, mode = x_flat_5_mode_0, pad = x_flat_5_pad_0, x = input_35_cast_fp16)[name = tensor<string, []>("x_flat_5_cast_fp16")];
|
| 236 |
+
tensor<int32, [4]> var_264 = const()[name = tensor<string, []>("op_264"), val = tensor<int32, [4]>([1, 2, 513, 1023])];
|
| 237 |
+
tensor<fp16, [1, 2, 513, 1023]> var_265_cast_fp16 = reshape(shape = var_264, x = x_flat_5_cast_fp16)[name = tensor<string, []>("op_265_cast_fp16")];
|
| 238 |
+
tensor<int32, [4]> var_268_begin_0 = const()[name = tensor<string, []>("op_268_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
|
| 239 |
+
tensor<int32, [4]> var_268_end_0 = const()[name = tensor<string, []>("op_268_end_0"), val = tensor<int32, [4]>([1, 2, 512, 1023])];
|
| 240 |
+
tensor<bool, [4]> var_268_end_mask_0 = const()[name = tensor<string, []>("op_268_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
|
| 241 |
+
tensor<fp16, [1, 2, 512, 1023]> var_268_cast_fp16 = slice_by_index(begin = var_268_begin_0, end = var_268_end_0, end_mask = var_268_end_mask_0, x = var_265_cast_fp16)[name = tensor<string, []>("op_268_cast_fp16")];
|
| 242 |
+
tensor<int32, [4]> var_269_begin_0 = const()[name = tensor<string, []>("op_269_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 511])];
|
| 243 |
+
tensor<int32, [4]> var_269_end_0 = const()[name = tensor<string, []>("op_269_end_0"), val = tensor<int32, [4]>([1, 2, 512, 1023])];
|
| 244 |
+
tensor<bool, [4]> var_269_end_mask_0 = const()[name = tensor<string, []>("op_269_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
|
| 245 |
+
tensor<fp16, [1, 2, 512, 512]> var_269_cast_fp16 = slice_by_index(begin = var_269_begin_0, end = var_269_end_0, end_mask = var_269_end_mask_0, x = var_268_cast_fp16)[name = tensor<string, []>("op_269_cast_fp16")];
|
| 246 |
+
tensor<fp16, [1, 2, 512, 512]> scores_9_cast_fp16 = add(x = scores_7_cast_fp16, y = var_269_cast_fp16)[name = tensor<string, []>("scores_9_cast_fp16")];
|
| 247 |
+
tensor<fp16, [1, 2, 512, 512]> scores_11_cast_fp16 = select(a = var_38_to_fp16, b = scores_9_cast_fp16, cond = var_134_cast_fp16)[name = tensor<string, []>("scores_11_cast_fp16")];
|
| 248 |
+
tensor<fp16, [1, 2, 512, 512]> p_attn_3_cast_fp16 = softmax(axis = var_42, x = scores_11_cast_fp16)[name = tensor<string, []>("p_attn_3_cast_fp16")];
|
| 249 |
+
tensor<bool, []> output_5_transpose_x_1 = const()[name = tensor<string, []>("output_5_transpose_x_1"), val = tensor<bool, []>(false)];
|
| 250 |
+
tensor<bool, []> output_5_transpose_y_1 = const()[name = tensor<string, []>("output_5_transpose_y_1"), val = tensor<bool, []>(true)];
|
| 251 |
+
tensor<fp16, [1, 2, 512, 48]> output_5_cast_fp16 = matmul(transpose_x = output_5_transpose_x_1, transpose_y = output_5_transpose_y_1, x = p_attn_3_cast_fp16, y = var_243_cast_fp16)[name = tensor<string, []>("output_5_cast_fp16")];
|
| 252 |
+
tensor<int32, [8]> x_39_pad_0 = const()[name = tensor<string, []>("x_39_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 0, 511])];
|
| 253 |
+
tensor<string, []> x_39_mode_0 = const()[name = tensor<string, []>("x_39_mode_0"), val = tensor<string, []>("constant")];
|
| 254 |
+
tensor<fp16, []> const_11_to_fp16 = const()[name = tensor<string, []>("const_11_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
|
| 255 |
+
tensor<fp16, [1, 2, 512, 1023]> x_39_cast_fp16 = pad(constant_val = const_11_to_fp16, mode = x_39_mode_0, pad = x_39_pad_0, x = p_attn_3_cast_fp16)[name = tensor<string, []>("x_39_cast_fp16")];
|
| 256 |
+
tensor<int32, [3]> var_277 = const()[name = tensor<string, []>("op_277"), val = tensor<int32, [3]>([1, 2, 523776])];
|
| 257 |
+
tensor<fp16, [1, 2, 523776]> input_37_cast_fp16 = reshape(shape = var_277, x = x_39_cast_fp16)[name = tensor<string, []>("input_37_cast_fp16")];
|
| 258 |
+
tensor<int32, [6]> x_flat_7_pad_0 = const()[name = tensor<string, []>("x_flat_7_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 512, 0])];
|
| 259 |
+
tensor<string, []> x_flat_7_mode_0 = const()[name = tensor<string, []>("x_flat_7_mode_0"), val = tensor<string, []>("constant")];
|
| 260 |
+
tensor<fp16, []> const_12_to_fp16 = const()[name = tensor<string, []>("const_12_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
|
| 261 |
+
tensor<fp16, [1, 2, 524288]> x_flat_7_cast_fp16 = pad(constant_val = const_12_to_fp16, mode = x_flat_7_mode_0, pad = x_flat_7_pad_0, x = input_37_cast_fp16)[name = tensor<string, []>("x_flat_7_cast_fp16")];
|
| 262 |
+
tensor<int32, [4]> var_281 = const()[name = tensor<string, []>("op_281"), val = tensor<int32, [4]>([1, 2, 512, 1024])];
|
| 263 |
+
tensor<fp16, [1, 2, 512, 1024]> var_282_cast_fp16 = reshape(shape = var_281, x = x_flat_7_cast_fp16)[name = tensor<string, []>("op_282_cast_fp16")];
|
| 264 |
+
tensor<int32, [4]> x_41_begin_0 = const()[name = tensor<string, []>("x_41_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 1])];
|
| 265 |
+
tensor<int32, [4]> x_41_end_0 = const()[name = tensor<string, []>("x_41_end_0"), val = tensor<int32, [4]>([1, 2, 512, 1024])];
|
| 266 |
+
tensor<bool, [4]> x_41_end_mask_0 = const()[name = tensor<string, []>("x_41_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
|
| 267 |
+
tensor<fp16, [1, 2, 512, 1023]> x_41_cast_fp16 = slice_by_index(begin = x_41_begin_0, end = x_41_end_0, end_mask = x_41_end_mask_0, x = var_282_cast_fp16)[name = tensor<string, []>("x_41_cast_fp16")];
|
| 268 |
+
tensor<bool, []> var_292_transpose_x_0 = const()[name = tensor<string, []>("op_292_transpose_x_0"), val = tensor<bool, []>(false)];
|
| 269 |
+
tensor<bool, []> var_292_transpose_y_0 = const()[name = tensor<string, []>("op_292_transpose_y_0"), val = tensor<bool, []>(false)];
|
| 270 |
+
tensor<fp16, [1, 1, 1023, 48]> var_291_to_fp16 = const()[name = tensor<string, []>("op_291_to_fp16"), val = tensor<fp16, [1, 1, 1023, 48]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1348224)))];
|
| 271 |
+
tensor<fp16, [1, 2, 512, 48]> var_292_cast_fp16 = matmul(transpose_x = var_292_transpose_x_0, transpose_y = var_292_transpose_y_0, x = x_41_cast_fp16, y = var_291_to_fp16)[name = tensor<string, []>("op_292_cast_fp16")];
|
| 272 |
+
tensor<fp16, [1, 2, 512, 48]> output_7_cast_fp16 = add(x = output_5_cast_fp16, y = var_292_cast_fp16)[name = tensor<string, []>("output_7_cast_fp16")];
|
| 273 |
+
tensor<int32, [4]> var_294_perm_0 = const()[name = tensor<string, []>("op_294_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
|
| 274 |
+
tensor<int32, [3]> var_296 = const()[name = tensor<string, []>("op_296"), val = tensor<int32, [3]>([1, 96, 512])];
|
| 275 |
+
tensor<fp16, [1, 2, 48, 512]> var_294_cast_fp16 = transpose(perm = var_294_perm_0, x = output_7_cast_fp16)[name = tensor<string, []>("transpose_14")];
|
| 276 |
+
tensor<fp16, [1, 96, 512]> input_41_cast_fp16 = reshape(shape = var_296, x = var_294_cast_fp16)[name = tensor<string, []>("input_41_cast_fp16")];
|
| 277 |
+
tensor<string, []> input_43_pad_type_0 = const()[name = tensor<string, []>("input_43_pad_type_0"), val = tensor<string, []>("valid")];
|
| 278 |
+
tensor<int32, [1]> input_43_strides_0 = const()[name = tensor<string, []>("input_43_strides_0"), val = tensor<int32, [1]>([1])];
|
| 279 |
+
tensor<int32, [2]> input_43_pad_0 = const()[name = tensor<string, []>("input_43_pad_0"), val = tensor<int32, [2]>([0, 0])];
|
| 280 |
+
tensor<int32, [1]> input_43_dilations_0 = const()[name = tensor<string, []>("input_43_dilations_0"), val = tensor<int32, [1]>([1])];
|
| 281 |
+
tensor<int32, []> input_43_groups_0 = const()[name = tensor<string, []>("input_43_groups_0"), val = tensor<int32, []>(1)];
|
| 282 |
+
tensor<fp16, [96, 96, 1]> enc_p_encoder_attn_layers_1_conv_o_weight_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_attn_layers_1_conv_o_weight_to_fp16"), val = tensor<fp16, [96, 96, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1446528)))];
|
| 283 |
+
tensor<fp16, [96]> enc_p_encoder_attn_layers_1_conv_o_bias_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_attn_layers_1_conv_o_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1465024)))];
|
| 284 |
+
tensor<fp16, [1, 96, 512]> input_43_cast_fp16 = conv(bias = enc_p_encoder_attn_layers_1_conv_o_bias_to_fp16, 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 = enc_p_encoder_attn_layers_1_conv_o_weight_to_fp16, x = input_41_cast_fp16)[name = tensor<string, []>("input_43_cast_fp16")];
|
| 285 |
+
tensor<fp16, [1, 96, 512]> x_43_cast_fp16 = add(x = input_31_cast_fp16, y = input_43_cast_fp16)[name = tensor<string, []>("x_43_cast_fp16")];
|
| 286 |
+
tensor<int32, [3]> input_45_perm_0 = const()[name = tensor<string, []>("input_45_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
|
| 287 |
+
tensor<int32, [1]> x_45_axes_0 = const()[name = tensor<string, []>("x_45_axes_0"), val = tensor<int32, [1]>([-1])];
|
| 288 |
+
tensor<fp16, [96]> enc_p_encoder_norm_layers_1_1_gamma_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_norm_layers_1_1_gamma_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1465280)))];
|
| 289 |
+
tensor<fp16, [96]> enc_p_encoder_norm_layers_1_1_beta_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_norm_layers_1_1_beta_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1465536)))];
|
| 290 |
+
tensor<fp16, [1, 512, 96]> input_45_cast_fp16 = transpose(perm = input_45_perm_0, x = x_43_cast_fp16)[name = tensor<string, []>("transpose_13")];
|
| 291 |
+
tensor<fp16, [1, 512, 96]> x_45_cast_fp16 = layer_norm(axes = x_45_axes_0, beta = enc_p_encoder_norm_layers_1_1_beta_to_fp16, epsilon = var_20_to_fp16, gamma = enc_p_encoder_norm_layers_1_1_gamma_to_fp16, x = input_45_cast_fp16)[name = tensor<string, []>("x_45_cast_fp16")];
|
| 292 |
+
tensor<int32, [3]> x_47_perm_0 = const()[name = tensor<string, []>("x_47_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
|
| 293 |
+
tensor<fp16, [1, 96, 512]> x_47_cast_fp16 = transpose(perm = x_47_perm_0, x = x_45_cast_fp16)[name = tensor<string, []>("transpose_12")];
|
| 294 |
+
tensor<fp16, [1, 96, 512]> input_47_cast_fp16 = mul(x = x_47_cast_fp16, y = x_mask_to_fp16)[name = tensor<string, []>("input_47_cast_fp16")];
|
| 295 |
+
tensor<int32, [6]> input_49_pad_0 = const()[name = tensor<string, []>("input_49_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 1, 1])];
|
| 296 |
+
tensor<string, []> input_49_mode_0 = const()[name = tensor<string, []>("input_49_mode_0"), val = tensor<string, []>("constant")];
|
| 297 |
+
tensor<fp16, []> const_14_to_fp16 = const()[name = tensor<string, []>("const_14_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
|
| 298 |
+
tensor<fp16, [1, 96, 514]> input_49_cast_fp16 = pad(constant_val = const_14_to_fp16, mode = input_49_mode_0, pad = input_49_pad_0, x = input_47_cast_fp16)[name = tensor<string, []>("input_49_cast_fp16")];
|
| 299 |
+
tensor<string, []> x_49_pad_type_0 = const()[name = tensor<string, []>("x_49_pad_type_0"), val = tensor<string, []>("valid")];
|
| 300 |
+
tensor<int32, [1]> x_49_strides_0 = const()[name = tensor<string, []>("x_49_strides_0"), val = tensor<int32, [1]>([1])];
|
| 301 |
+
tensor<int32, [2]> x_49_pad_0 = const()[name = tensor<string, []>("x_49_pad_0"), val = tensor<int32, [2]>([0, 0])];
|
| 302 |
+
tensor<int32, [1]> x_49_dilations_0 = const()[name = tensor<string, []>("x_49_dilations_0"), val = tensor<int32, [1]>([1])];
|
| 303 |
+
tensor<int32, []> x_49_groups_0 = const()[name = tensor<string, []>("x_49_groups_0"), val = tensor<int32, []>(1)];
|
| 304 |
+
tensor<fp16, [768, 96, 3]> enc_p_encoder_ffn_layers_1_conv_1_weight_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_ffn_layers_1_conv_1_weight_to_fp16"), val = tensor<fp16, [768, 96, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1465792)))];
|
| 305 |
+
tensor<fp16, [768]> enc_p_encoder_ffn_layers_1_conv_1_bias_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_ffn_layers_1_conv_1_bias_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1908224)))];
|
| 306 |
+
tensor<fp16, [1, 768, 512]> x_49_cast_fp16 = conv(bias = enc_p_encoder_ffn_layers_1_conv_1_bias_to_fp16, dilations = x_49_dilations_0, groups = x_49_groups_0, pad = x_49_pad_0, pad_type = x_49_pad_type_0, strides = x_49_strides_0, weight = enc_p_encoder_ffn_layers_1_conv_1_weight_to_fp16, x = input_49_cast_fp16)[name = tensor<string, []>("x_49_cast_fp16")];
|
| 307 |
+
tensor<fp16, [1, 768, 512]> input_51_cast_fp16 = relu(x = x_49_cast_fp16)[name = tensor<string, []>("input_51_cast_fp16")];
|
| 308 |
+
tensor<fp16, [1, 768, 512]> input_53_cast_fp16 = mul(x = input_51_cast_fp16, y = x_mask_to_fp16)[name = tensor<string, []>("input_53_cast_fp16")];
|
| 309 |
+
tensor<int32, [6]> input_55_pad_0 = const()[name = tensor<string, []>("input_55_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 1, 1])];
|
| 310 |
+
tensor<string, []> input_55_mode_0 = const()[name = tensor<string, []>("input_55_mode_0"), val = tensor<string, []>("constant")];
|
| 311 |
+
tensor<fp16, []> const_15_to_fp16 = const()[name = tensor<string, []>("const_15_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
|
| 312 |
+
tensor<fp16, [1, 768, 514]> input_55_cast_fp16 = pad(constant_val = const_15_to_fp16, mode = input_55_mode_0, pad = input_55_pad_0, x = input_53_cast_fp16)[name = tensor<string, []>("input_55_cast_fp16")];
|
| 313 |
+
tensor<string, []> x_53_pad_type_0 = const()[name = tensor<string, []>("x_53_pad_type_0"), val = tensor<string, []>("valid")];
|
| 314 |
+
tensor<int32, [1]> x_53_strides_0 = const()[name = tensor<string, []>("x_53_strides_0"), val = tensor<int32, [1]>([1])];
|
| 315 |
+
tensor<int32, [2]> x_53_pad_0 = const()[name = tensor<string, []>("x_53_pad_0"), val = tensor<int32, [2]>([0, 0])];
|
| 316 |
+
tensor<int32, [1]> x_53_dilations_0 = const()[name = tensor<string, []>("x_53_dilations_0"), val = tensor<int32, [1]>([1])];
|
| 317 |
+
tensor<int32, []> x_53_groups_0 = const()[name = tensor<string, []>("x_53_groups_0"), val = tensor<int32, []>(1)];
|
| 318 |
+
tensor<fp16, [96, 768, 3]> enc_p_encoder_ffn_layers_1_conv_2_weight_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_ffn_layers_1_conv_2_weight_to_fp16"), val = tensor<fp16, [96, 768, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1909824)))];
|
| 319 |
+
tensor<fp16, [96]> enc_p_encoder_ffn_layers_1_conv_2_bias_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_ffn_layers_1_conv_2_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2352256)))];
|
| 320 |
+
tensor<fp16, [1, 96, 512]> x_53_cast_fp16 = conv(bias = enc_p_encoder_ffn_layers_1_conv_2_bias_to_fp16, dilations = x_53_dilations_0, groups = x_53_groups_0, pad = x_53_pad_0, pad_type = x_53_pad_type_0, strides = x_53_strides_0, weight = enc_p_encoder_ffn_layers_1_conv_2_weight_to_fp16, x = input_55_cast_fp16)[name = tensor<string, []>("x_53_cast_fp16")];
|
| 321 |
+
tensor<fp16, [1, 96, 512]> input_57_cast_fp16 = mul(x = x_53_cast_fp16, y = x_mask_to_fp16)[name = tensor<string, []>("input_57_cast_fp16")];
|
| 322 |
+
tensor<fp16, [1, 96, 512]> x_55_cast_fp16 = add(x = x_47_cast_fp16, y = input_57_cast_fp16)[name = tensor<string, []>("x_55_cast_fp16")];
|
| 323 |
+
tensor<int32, [3]> input_59_perm_0 = const()[name = tensor<string, []>("input_59_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
|
| 324 |
+
tensor<int32, [1]> x_57_axes_0 = const()[name = tensor<string, []>("x_57_axes_0"), val = tensor<int32, [1]>([-1])];
|
| 325 |
+
tensor<fp16, [96]> enc_p_encoder_norm_layers_2_1_gamma_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_norm_layers_2_1_gamma_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2352512)))];
|
| 326 |
+
tensor<fp16, [96]> enc_p_encoder_norm_layers_2_1_beta_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_norm_layers_2_1_beta_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2352768)))];
|
| 327 |
+
tensor<fp16, [1, 512, 96]> input_59_cast_fp16 = transpose(perm = input_59_perm_0, x = x_55_cast_fp16)[name = tensor<string, []>("transpose_11")];
|
| 328 |
+
tensor<fp16, [1, 512, 96]> x_57_cast_fp16 = layer_norm(axes = x_57_axes_0, beta = enc_p_encoder_norm_layers_2_1_beta_to_fp16, epsilon = var_20_to_fp16, gamma = enc_p_encoder_norm_layers_2_1_gamma_to_fp16, x = input_59_cast_fp16)[name = tensor<string, []>("x_57_cast_fp16")];
|
| 329 |
+
tensor<int32, [3]> input_61_perm_0 = const()[name = tensor<string, []>("input_61_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
|
| 330 |
+
tensor<string, []> query_9_pad_type_0 = const()[name = tensor<string, []>("query_9_pad_type_0"), val = tensor<string, []>("valid")];
|
| 331 |
+
tensor<int32, [1]> query_9_strides_0 = const()[name = tensor<string, []>("query_9_strides_0"), val = tensor<int32, [1]>([1])];
|
| 332 |
+
tensor<int32, [2]> query_9_pad_0 = const()[name = tensor<string, []>("query_9_pad_0"), val = tensor<int32, [2]>([0, 0])];
|
| 333 |
+
tensor<int32, [1]> query_9_dilations_0 = const()[name = tensor<string, []>("query_9_dilations_0"), val = tensor<int32, [1]>([1])];
|
| 334 |
+
tensor<int32, []> query_9_groups_0 = const()[name = tensor<string, []>("query_9_groups_0"), val = tensor<int32, []>(1)];
|
| 335 |
+
tensor<fp16, [96, 96, 1]> enc_p_encoder_attn_layers_2_conv_q_weight_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_attn_layers_2_conv_q_weight_to_fp16"), val = tensor<fp16, [96, 96, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2353024)))];
|
| 336 |
+
tensor<fp16, [96]> enc_p_encoder_attn_layers_2_conv_q_bias_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_attn_layers_2_conv_q_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2371520)))];
|
| 337 |
+
tensor<fp16, [1, 96, 512]> input_61_cast_fp16 = transpose(perm = input_61_perm_0, x = x_57_cast_fp16)[name = tensor<string, []>("transpose_10")];
|
| 338 |
+
tensor<fp16, [1, 96, 512]> query_9_cast_fp16 = conv(bias = enc_p_encoder_attn_layers_2_conv_q_bias_to_fp16, dilations = query_9_dilations_0, groups = query_9_groups_0, pad = query_9_pad_0, pad_type = query_9_pad_type_0, strides = query_9_strides_0, weight = enc_p_encoder_attn_layers_2_conv_q_weight_to_fp16, x = input_61_cast_fp16)[name = tensor<string, []>("query_9_cast_fp16")];
|
| 339 |
+
tensor<string, []> key_9_pad_type_0 = const()[name = tensor<string, []>("key_9_pad_type_0"), val = tensor<string, []>("valid")];
|
| 340 |
+
tensor<int32, [1]> key_9_strides_0 = const()[name = tensor<string, []>("key_9_strides_0"), val = tensor<int32, [1]>([1])];
|
| 341 |
+
tensor<int32, [2]> key_9_pad_0 = const()[name = tensor<string, []>("key_9_pad_0"), val = tensor<int32, [2]>([0, 0])];
|
| 342 |
+
tensor<int32, [1]> key_9_dilations_0 = const()[name = tensor<string, []>("key_9_dilations_0"), val = tensor<int32, [1]>([1])];
|
| 343 |
+
tensor<int32, []> key_9_groups_0 = const()[name = tensor<string, []>("key_9_groups_0"), val = tensor<int32, []>(1)];
|
| 344 |
+
tensor<fp16, [96, 96, 1]> enc_p_encoder_attn_layers_2_conv_k_weight_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_attn_layers_2_conv_k_weight_to_fp16"), val = tensor<fp16, [96, 96, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2371776)))];
|
| 345 |
+
tensor<fp16, [96]> enc_p_encoder_attn_layers_2_conv_k_bias_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_attn_layers_2_conv_k_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2390272)))];
|
| 346 |
+
tensor<fp16, [1, 96, 512]> key_9_cast_fp16 = conv(bias = enc_p_encoder_attn_layers_2_conv_k_bias_to_fp16, dilations = key_9_dilations_0, groups = key_9_groups_0, pad = key_9_pad_0, pad_type = key_9_pad_type_0, strides = key_9_strides_0, weight = enc_p_encoder_attn_layers_2_conv_k_weight_to_fp16, x = input_61_cast_fp16)[name = tensor<string, []>("key_9_cast_fp16")];
|
| 347 |
+
tensor<string, []> value_9_pad_type_0 = const()[name = tensor<string, []>("value_9_pad_type_0"), val = tensor<string, []>("valid")];
|
| 348 |
+
tensor<int32, [1]> value_9_strides_0 = const()[name = tensor<string, []>("value_9_strides_0"), val = tensor<int32, [1]>([1])];
|
| 349 |
+
tensor<int32, [2]> value_9_pad_0 = const()[name = tensor<string, []>("value_9_pad_0"), val = tensor<int32, [2]>([0, 0])];
|
| 350 |
+
tensor<int32, [1]> value_9_dilations_0 = const()[name = tensor<string, []>("value_9_dilations_0"), val = tensor<int32, [1]>([1])];
|
| 351 |
+
tensor<int32, []> value_9_groups_0 = const()[name = tensor<string, []>("value_9_groups_0"), val = tensor<int32, []>(1)];
|
| 352 |
+
tensor<fp16, [96, 96, 1]> enc_p_encoder_attn_layers_2_conv_v_weight_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_attn_layers_2_conv_v_weight_to_fp16"), val = tensor<fp16, [96, 96, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2390528)))];
|
| 353 |
+
tensor<fp16, [96]> enc_p_encoder_attn_layers_2_conv_v_bias_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_attn_layers_2_conv_v_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2409024)))];
|
| 354 |
+
tensor<fp16, [1, 96, 512]> value_9_cast_fp16 = conv(bias = enc_p_encoder_attn_layers_2_conv_v_bias_to_fp16, dilations = value_9_dilations_0, groups = value_9_groups_0, pad = value_9_pad_0, pad_type = value_9_pad_type_0, strides = value_9_strides_0, weight = enc_p_encoder_attn_layers_2_conv_v_weight_to_fp16, x = input_61_cast_fp16)[name = tensor<string, []>("value_9_cast_fp16")];
|
| 355 |
+
tensor<int32, [4]> var_373 = const()[name = tensor<string, []>("op_373"), val = tensor<int32, [4]>([1, 2, 48, 512])];
|
| 356 |
+
tensor<fp16, [1, 2, 48, 512]> var_374_cast_fp16 = reshape(shape = var_373, x = query_9_cast_fp16)[name = tensor<string, []>("op_374_cast_fp16")];
|
| 357 |
+
tensor<int32, [4]> query_perm_0 = const()[name = tensor<string, []>("query_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
|
| 358 |
+
tensor<int32, [4]> var_376 = const()[name = tensor<string, []>("op_376"), val = tensor<int32, [4]>([1, 2, 48, 512])];
|
| 359 |
+
tensor<fp16, [1, 2, 48, 512]> var_377_cast_fp16 = reshape(shape = var_376, x = key_9_cast_fp16)[name = tensor<string, []>("op_377_cast_fp16")];
|
| 360 |
+
tensor<int32, [4]> var_379 = const()[name = tensor<string, []>("op_379"), val = tensor<int32, [4]>([1, 2, 48, 512])];
|
| 361 |
+
tensor<fp16, [1, 2, 48, 512]> var_380_cast_fp16 = reshape(shape = var_379, x = value_9_cast_fp16)[name = tensor<string, []>("op_380_cast_fp16")];
|
| 362 |
+
tensor<fp16, []> _inversed_383_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_383_y_0_to_fp16"), val = tensor<fp16, []>(0x1.278p-3)];
|
| 363 |
+
tensor<fp16, [1, 2, 512, 48]> query_cast_fp16 = transpose(perm = query_perm_0, x = var_374_cast_fp16)[name = tensor<string, []>("transpose_9")];
|
| 364 |
+
tensor<fp16, [1, 2, 512, 48]> _inversed_383_cast_fp16 = mul(x = query_cast_fp16, y = _inversed_383_y_0_to_fp16)[name = tensor<string, []>("_inversed_383_cast_fp16")];
|
| 365 |
+
tensor<bool, []> scores_13_transpose_x_0 = const()[name = tensor<string, []>("scores_13_transpose_x_0"), val = tensor<bool, []>(false)];
|
| 366 |
+
tensor<bool, []> scores_13_transpose_y_0 = const()[name = tensor<string, []>("scores_13_transpose_y_0"), val = tensor<bool, []>(false)];
|
| 367 |
+
tensor<fp16, [1, 2, 512, 512]> scores_13_cast_fp16 = matmul(transpose_x = scores_13_transpose_x_0, transpose_y = scores_13_transpose_y_0, x = _inversed_383_cast_fp16, y = var_377_cast_fp16)[name = tensor<string, []>("scores_13_cast_fp16")];
|
| 368 |
+
tensor<bool, []> x_61_transpose_x_0 = const()[name = tensor<string, []>("x_61_transpose_x_0"), val = tensor<bool, []>(false)];
|
| 369 |
+
tensor<bool, []> x_61_transpose_y_0 = const()[name = tensor<string, []>("x_61_transpose_y_0"), val = tensor<bool, []>(false)];
|
| 370 |
+
tensor<fp16, [1, 1, 48, 1023]> var_393_to_fp16 = const()[name = tensor<string, []>("op_393_to_fp16"), val = tensor<fp16, [1, 1, 48, 1023]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2409280)))];
|
| 371 |
+
tensor<fp16, [1, 2, 512, 1023]> x_61_cast_fp16 = matmul(transpose_x = x_61_transpose_x_0, transpose_y = x_61_transpose_y_0, x = _inversed_383_cast_fp16, y = var_393_to_fp16)[name = tensor<string, []>("x_61_cast_fp16")];
|
| 372 |
+
tensor<int32, [8]> x_63_pad_0 = const()[name = tensor<string, []>("x_63_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 0, 1])];
|
| 373 |
+
tensor<string, []> x_63_mode_0 = const()[name = tensor<string, []>("x_63_mode_0"), val = tensor<string, []>("constant")];
|
| 374 |
+
tensor<fp16, []> const_17_to_fp16 = const()[name = tensor<string, []>("const_17_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
|
| 375 |
+
tensor<fp16, [1, 2, 512, 1024]> x_63_cast_fp16 = pad(constant_val = const_17_to_fp16, mode = x_63_mode_0, pad = x_63_pad_0, x = x_61_cast_fp16)[name = tensor<string, []>("x_63_cast_fp16")];
|
| 376 |
+
tensor<int32, [3]> var_397 = const()[name = tensor<string, []>("op_397"), val = tensor<int32, [3]>([1, 2, 524288])];
|
| 377 |
+
tensor<fp16, [1, 2, 524288]> input_65_cast_fp16 = reshape(shape = var_397, x = x_63_cast_fp16)[name = tensor<string, []>("input_65_cast_fp16")];
|
| 378 |
+
tensor<int32, [6]> x_flat_9_pad_0 = const()[name = tensor<string, []>("x_flat_9_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 0, 511])];
|
| 379 |
+
tensor<string, []> x_flat_9_mode_0 = const()[name = tensor<string, []>("x_flat_9_mode_0"), val = tensor<string, []>("constant")];
|
| 380 |
+
tensor<fp16, []> const_18_to_fp16 = const()[name = tensor<string, []>("const_18_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
|
| 381 |
+
tensor<fp16, [1, 2, 524799]> x_flat_9_cast_fp16 = pad(constant_val = const_18_to_fp16, mode = x_flat_9_mode_0, pad = x_flat_9_pad_0, x = input_65_cast_fp16)[name = tensor<string, []>("x_flat_9_cast_fp16")];
|
| 382 |
+
tensor<int32, [4]> var_401 = const()[name = tensor<string, []>("op_401"), val = tensor<int32, [4]>([1, 2, 513, 1023])];
|
| 383 |
+
tensor<fp16, [1, 2, 513, 1023]> var_402_cast_fp16 = reshape(shape = var_401, x = x_flat_9_cast_fp16)[name = tensor<string, []>("op_402_cast_fp16")];
|
| 384 |
+
tensor<int32, [4]> var_405_begin_0 = const()[name = tensor<string, []>("op_405_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
|
| 385 |
+
tensor<int32, [4]> var_405_end_0 = const()[name = tensor<string, []>("op_405_end_0"), val = tensor<int32, [4]>([1, 2, 512, 1023])];
|
| 386 |
+
tensor<bool, [4]> var_405_end_mask_0 = const()[name = tensor<string, []>("op_405_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
|
| 387 |
+
tensor<fp16, [1, 2, 512, 1023]> var_405_cast_fp16 = slice_by_index(begin = var_405_begin_0, end = var_405_end_0, end_mask = var_405_end_mask_0, x = var_402_cast_fp16)[name = tensor<string, []>("op_405_cast_fp16")];
|
| 388 |
+
tensor<int32, [4]> var_406_begin_0 = const()[name = tensor<string, []>("op_406_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 511])];
|
| 389 |
+
tensor<int32, [4]> var_406_end_0 = const()[name = tensor<string, []>("op_406_end_0"), val = tensor<int32, [4]>([1, 2, 512, 1023])];
|
| 390 |
+
tensor<bool, [4]> var_406_end_mask_0 = const()[name = tensor<string, []>("op_406_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
|
| 391 |
+
tensor<fp16, [1, 2, 512, 512]> var_406_cast_fp16 = slice_by_index(begin = var_406_begin_0, end = var_406_end_0, end_mask = var_406_end_mask_0, x = var_405_cast_fp16)[name = tensor<string, []>("op_406_cast_fp16")];
|
| 392 |
+
tensor<fp16, [1, 2, 512, 512]> scores_15_cast_fp16 = add(x = scores_13_cast_fp16, y = var_406_cast_fp16)[name = tensor<string, []>("scores_15_cast_fp16")];
|
| 393 |
+
tensor<fp16, [1, 2, 512, 512]> scores_cast_fp16 = select(a = var_38_to_fp16, b = scores_15_cast_fp16, cond = var_134_cast_fp16)[name = tensor<string, []>("scores_cast_fp16")];
|
| 394 |
+
tensor<fp16, [1, 2, 512, 512]> p_attn_cast_fp16 = softmax(axis = var_42, x = scores_cast_fp16)[name = tensor<string, []>("p_attn_cast_fp16")];
|
| 395 |
+
tensor<bool, []> output_9_transpose_x_1 = const()[name = tensor<string, []>("output_9_transpose_x_1"), val = tensor<bool, []>(false)];
|
| 396 |
+
tensor<bool, []> output_9_transpose_y_1 = const()[name = tensor<string, []>("output_9_transpose_y_1"), val = tensor<bool, []>(true)];
|
| 397 |
+
tensor<fp16, [1, 2, 512, 48]> output_9_cast_fp16 = matmul(transpose_x = output_9_transpose_x_1, transpose_y = output_9_transpose_y_1, x = p_attn_cast_fp16, y = var_380_cast_fp16)[name = tensor<string, []>("output_9_cast_fp16")];
|
| 398 |
+
tensor<int32, [8]> x_65_pad_0 = const()[name = tensor<string, []>("x_65_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 0, 511])];
|
| 399 |
+
tensor<string, []> x_65_mode_0 = const()[name = tensor<string, []>("x_65_mode_0"), val = tensor<string, []>("constant")];
|
| 400 |
+
tensor<fp16, []> const_19_to_fp16 = const()[name = tensor<string, []>("const_19_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
|
| 401 |
+
tensor<fp16, [1, 2, 512, 1023]> x_65_cast_fp16 = pad(constant_val = const_19_to_fp16, mode = x_65_mode_0, pad = x_65_pad_0, x = p_attn_cast_fp16)[name = tensor<string, []>("x_65_cast_fp16")];
|
| 402 |
+
tensor<int32, [3]> var_414 = const()[name = tensor<string, []>("op_414"), val = tensor<int32, [3]>([1, 2, 523776])];
|
| 403 |
+
tensor<fp16, [1, 2, 523776]> input_67_cast_fp16 = reshape(shape = var_414, x = x_65_cast_fp16)[name = tensor<string, []>("input_67_cast_fp16")];
|
| 404 |
+
tensor<int32, [6]> x_flat_pad_0 = const()[name = tensor<string, []>("x_flat_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 512, 0])];
|
| 405 |
+
tensor<string, []> x_flat_mode_0 = const()[name = tensor<string, []>("x_flat_mode_0"), val = tensor<string, []>("constant")];
|
| 406 |
+
tensor<fp16, []> const_20_to_fp16 = const()[name = tensor<string, []>("const_20_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
|
| 407 |
+
tensor<fp16, [1, 2, 524288]> x_flat_cast_fp16 = pad(constant_val = const_20_to_fp16, mode = x_flat_mode_0, pad = x_flat_pad_0, x = input_67_cast_fp16)[name = tensor<string, []>("x_flat_cast_fp16")];
|
| 408 |
+
tensor<int32, [4]> var_418 = const()[name = tensor<string, []>("op_418"), val = tensor<int32, [4]>([1, 2, 512, 1024])];
|
| 409 |
+
tensor<fp16, [1, 2, 512, 1024]> var_419_cast_fp16 = reshape(shape = var_418, x = x_flat_cast_fp16)[name = tensor<string, []>("op_419_cast_fp16")];
|
| 410 |
+
tensor<int32, [4]> x_67_begin_0 = const()[name = tensor<string, []>("x_67_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 1])];
|
| 411 |
+
tensor<int32, [4]> x_67_end_0 = const()[name = tensor<string, []>("x_67_end_0"), val = tensor<int32, [4]>([1, 2, 512, 1024])];
|
| 412 |
+
tensor<bool, [4]> x_67_end_mask_0 = const()[name = tensor<string, []>("x_67_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
|
| 413 |
+
tensor<fp16, [1, 2, 512, 1023]> x_67_cast_fp16 = slice_by_index(begin = x_67_begin_0, end = x_67_end_0, end_mask = x_67_end_mask_0, x = var_419_cast_fp16)[name = tensor<string, []>("x_67_cast_fp16")];
|
| 414 |
+
tensor<bool, []> var_429_transpose_x_0 = const()[name = tensor<string, []>("op_429_transpose_x_0"), val = tensor<bool, []>(false)];
|
| 415 |
+
tensor<bool, []> var_429_transpose_y_0 = const()[name = tensor<string, []>("op_429_transpose_y_0"), val = tensor<bool, []>(false)];
|
| 416 |
+
tensor<fp16, [1, 1, 1023, 48]> var_428_to_fp16 = const()[name = tensor<string, []>("op_428_to_fp16"), val = tensor<fp16, [1, 1, 1023, 48]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2507584)))];
|
| 417 |
+
tensor<fp16, [1, 2, 512, 48]> var_429_cast_fp16 = matmul(transpose_x = var_429_transpose_x_0, transpose_y = var_429_transpose_y_0, x = x_67_cast_fp16, y = var_428_to_fp16)[name = tensor<string, []>("op_429_cast_fp16")];
|
| 418 |
+
tensor<fp16, [1, 2, 512, 48]> output_cast_fp16 = add(x = output_9_cast_fp16, y = var_429_cast_fp16)[name = tensor<string, []>("output_cast_fp16")];
|
| 419 |
+
tensor<int32, [4]> var_431_perm_0 = const()[name = tensor<string, []>("op_431_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
|
| 420 |
+
tensor<int32, [3]> var_433 = const()[name = tensor<string, []>("op_433"), val = tensor<int32, [3]>([1, 96, 512])];
|
| 421 |
+
tensor<fp16, [1, 2, 48, 512]> var_431_cast_fp16 = transpose(perm = var_431_perm_0, x = output_cast_fp16)[name = tensor<string, []>("transpose_8")];
|
| 422 |
+
tensor<fp16, [1, 96, 512]> input_71_cast_fp16 = reshape(shape = var_433, x = var_431_cast_fp16)[name = tensor<string, []>("input_71_cast_fp16")];
|
| 423 |
+
tensor<string, []> input_73_pad_type_0 = const()[name = tensor<string, []>("input_73_pad_type_0"), val = tensor<string, []>("valid")];
|
| 424 |
+
tensor<int32, [1]> input_73_strides_0 = const()[name = tensor<string, []>("input_73_strides_0"), val = tensor<int32, [1]>([1])];
|
| 425 |
+
tensor<int32, [2]> input_73_pad_0 = const()[name = tensor<string, []>("input_73_pad_0"), val = tensor<int32, [2]>([0, 0])];
|
| 426 |
+
tensor<int32, [1]> input_73_dilations_0 = const()[name = tensor<string, []>("input_73_dilations_0"), val = tensor<int32, [1]>([1])];
|
| 427 |
+
tensor<int32, []> input_73_groups_0 = const()[name = tensor<string, []>("input_73_groups_0"), val = tensor<int32, []>(1)];
|
| 428 |
+
tensor<fp16, [96, 96, 1]> enc_p_encoder_attn_layers_2_conv_o_weight_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_attn_layers_2_conv_o_weight_to_fp16"), val = tensor<fp16, [96, 96, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2605888)))];
|
| 429 |
+
tensor<fp16, [96]> enc_p_encoder_attn_layers_2_conv_o_bias_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_attn_layers_2_conv_o_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2624384)))];
|
| 430 |
+
tensor<fp16, [1, 96, 512]> input_73_cast_fp16 = conv(bias = enc_p_encoder_attn_layers_2_conv_o_bias_to_fp16, dilations = input_73_dilations_0, groups = input_73_groups_0, pad = input_73_pad_0, pad_type = input_73_pad_type_0, strides = input_73_strides_0, weight = enc_p_encoder_attn_layers_2_conv_o_weight_to_fp16, x = input_71_cast_fp16)[name = tensor<string, []>("input_73_cast_fp16")];
|
| 431 |
+
tensor<fp16, [1, 96, 512]> x_69_cast_fp16 = add(x = input_61_cast_fp16, y = input_73_cast_fp16)[name = tensor<string, []>("x_69_cast_fp16")];
|
| 432 |
+
tensor<int32, [3]> input_75_perm_0 = const()[name = tensor<string, []>("input_75_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
|
| 433 |
+
tensor<int32, [1]> x_71_axes_0 = const()[name = tensor<string, []>("x_71_axes_0"), val = tensor<int32, [1]>([-1])];
|
| 434 |
+
tensor<fp16, [96]> enc_p_encoder_norm_layers_1_2_gamma_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_norm_layers_1_2_gamma_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2624640)))];
|
| 435 |
+
tensor<fp16, [96]> enc_p_encoder_norm_layers_1_2_beta_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_norm_layers_1_2_beta_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2624896)))];
|
| 436 |
+
tensor<fp16, [1, 512, 96]> input_75_cast_fp16 = transpose(perm = input_75_perm_0, x = x_69_cast_fp16)[name = tensor<string, []>("transpose_7")];
|
| 437 |
+
tensor<fp16, [1, 512, 96]> x_71_cast_fp16 = layer_norm(axes = x_71_axes_0, beta = enc_p_encoder_norm_layers_1_2_beta_to_fp16, epsilon = var_20_to_fp16, gamma = enc_p_encoder_norm_layers_1_2_gamma_to_fp16, x = input_75_cast_fp16)[name = tensor<string, []>("x_71_cast_fp16")];
|
| 438 |
+
tensor<int32, [3]> x_73_perm_0 = const()[name = tensor<string, []>("x_73_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
|
| 439 |
+
tensor<fp16, [1, 96, 512]> x_73_cast_fp16 = transpose(perm = x_73_perm_0, x = x_71_cast_fp16)[name = tensor<string, []>("transpose_6")];
|
| 440 |
+
tensor<fp16, [1, 96, 512]> input_77_cast_fp16 = mul(x = x_73_cast_fp16, y = x_mask_to_fp16)[name = tensor<string, []>("input_77_cast_fp16")];
|
| 441 |
+
tensor<int32, [6]> input_79_pad_0 = const()[name = tensor<string, []>("input_79_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 1, 1])];
|
| 442 |
+
tensor<string, []> input_79_mode_0 = const()[name = tensor<string, []>("input_79_mode_0"), val = tensor<string, []>("constant")];
|
| 443 |
+
tensor<fp16, []> const_22_to_fp16 = const()[name = tensor<string, []>("const_22_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
|
| 444 |
+
tensor<fp16, [1, 96, 514]> input_79_cast_fp16 = pad(constant_val = const_22_to_fp16, mode = input_79_mode_0, pad = input_79_pad_0, x = input_77_cast_fp16)[name = tensor<string, []>("input_79_cast_fp16")];
|
| 445 |
+
tensor<string, []> x_75_pad_type_0 = const()[name = tensor<string, []>("x_75_pad_type_0"), val = tensor<string, []>("valid")];
|
| 446 |
+
tensor<int32, [1]> x_75_strides_0 = const()[name = tensor<string, []>("x_75_strides_0"), val = tensor<int32, [1]>([1])];
|
| 447 |
+
tensor<int32, [2]> x_75_pad_0 = const()[name = tensor<string, []>("x_75_pad_0"), val = tensor<int32, [2]>([0, 0])];
|
| 448 |
+
tensor<int32, [1]> x_75_dilations_0 = const()[name = tensor<string, []>("x_75_dilations_0"), val = tensor<int32, [1]>([1])];
|
| 449 |
+
tensor<int32, []> x_75_groups_0 = const()[name = tensor<string, []>("x_75_groups_0"), val = tensor<int32, []>(1)];
|
| 450 |
+
tensor<fp16, [768, 96, 3]> enc_p_encoder_ffn_layers_2_conv_1_weight_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_ffn_layers_2_conv_1_weight_to_fp16"), val = tensor<fp16, [768, 96, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2625152)))];
|
| 451 |
+
tensor<fp16, [768]> enc_p_encoder_ffn_layers_2_conv_1_bias_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_ffn_layers_2_conv_1_bias_to_fp16"), val = tensor<fp16, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3067584)))];
|
| 452 |
+
tensor<fp16, [1, 768, 512]> x_75_cast_fp16 = conv(bias = enc_p_encoder_ffn_layers_2_conv_1_bias_to_fp16, dilations = x_75_dilations_0, groups = x_75_groups_0, pad = x_75_pad_0, pad_type = x_75_pad_type_0, strides = x_75_strides_0, weight = enc_p_encoder_ffn_layers_2_conv_1_weight_to_fp16, x = input_79_cast_fp16)[name = tensor<string, []>("x_75_cast_fp16")];
|
| 453 |
+
tensor<fp16, [1, 768, 512]> input_81_cast_fp16 = relu(x = x_75_cast_fp16)[name = tensor<string, []>("input_81_cast_fp16")];
|
| 454 |
+
tensor<fp16, [1, 768, 512]> input_83_cast_fp16 = mul(x = input_81_cast_fp16, y = x_mask_to_fp16)[name = tensor<string, []>("input_83_cast_fp16")];
|
| 455 |
+
tensor<int32, [6]> input_85_pad_0 = const()[name = tensor<string, []>("input_85_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 1, 1])];
|
| 456 |
+
tensor<string, []> input_85_mode_0 = const()[name = tensor<string, []>("input_85_mode_0"), val = tensor<string, []>("constant")];
|
| 457 |
+
tensor<fp16, []> const_23_to_fp16 = const()[name = tensor<string, []>("const_23_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
|
| 458 |
+
tensor<fp16, [1, 768, 514]> input_85_cast_fp16 = pad(constant_val = const_23_to_fp16, mode = input_85_mode_0, pad = input_85_pad_0, x = input_83_cast_fp16)[name = tensor<string, []>("input_85_cast_fp16")];
|
| 459 |
+
tensor<string, []> x_79_pad_type_0 = const()[name = tensor<string, []>("x_79_pad_type_0"), val = tensor<string, []>("valid")];
|
| 460 |
+
tensor<int32, [1]> x_79_strides_0 = const()[name = tensor<string, []>("x_79_strides_0"), val = tensor<int32, [1]>([1])];
|
| 461 |
+
tensor<int32, [2]> x_79_pad_0 = const()[name = tensor<string, []>("x_79_pad_0"), val = tensor<int32, [2]>([0, 0])];
|
| 462 |
+
tensor<int32, [1]> x_79_dilations_0 = const()[name = tensor<string, []>("x_79_dilations_0"), val = tensor<int32, [1]>([1])];
|
| 463 |
+
tensor<int32, []> x_79_groups_0 = const()[name = tensor<string, []>("x_79_groups_0"), val = tensor<int32, []>(1)];
|
| 464 |
+
tensor<fp16, [96, 768, 3]> enc_p_encoder_ffn_layers_2_conv_2_weight_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_ffn_layers_2_conv_2_weight_to_fp16"), val = tensor<fp16, [96, 768, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3069184)))];
|
| 465 |
+
tensor<fp16, [96]> enc_p_encoder_ffn_layers_2_conv_2_bias_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_ffn_layers_2_conv_2_bias_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3511616)))];
|
| 466 |
+
tensor<fp16, [1, 96, 512]> x_79_cast_fp16 = conv(bias = enc_p_encoder_ffn_layers_2_conv_2_bias_to_fp16, dilations = x_79_dilations_0, groups = x_79_groups_0, pad = x_79_pad_0, pad_type = x_79_pad_type_0, strides = x_79_strides_0, weight = enc_p_encoder_ffn_layers_2_conv_2_weight_to_fp16, x = input_85_cast_fp16)[name = tensor<string, []>("x_79_cast_fp16")];
|
| 467 |
+
tensor<fp16, [1, 96, 512]> input_87_cast_fp16 = mul(x = x_79_cast_fp16, y = x_mask_to_fp16)[name = tensor<string, []>("input_87_cast_fp16")];
|
| 468 |
+
tensor<fp16, [1, 96, 512]> x_81_cast_fp16 = add(x = x_73_cast_fp16, y = input_87_cast_fp16)[name = tensor<string, []>("x_81_cast_fp16")];
|
| 469 |
+
tensor<int32, [3]> input_89_perm_0 = const()[name = tensor<string, []>("input_89_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
|
| 470 |
+
tensor<int32, [1]> x_83_axes_0 = const()[name = tensor<string, []>("x_83_axes_0"), val = tensor<int32, [1]>([-1])];
|
| 471 |
+
tensor<fp16, [96]> enc_p_encoder_norm_layers_2_2_gamma_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_norm_layers_2_2_gamma_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3511872)))];
|
| 472 |
+
tensor<fp16, [96]> enc_p_encoder_norm_layers_2_2_beta_to_fp16 = const()[name = tensor<string, []>("enc_p_encoder_norm_layers_2_2_beta_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3512128)))];
|
| 473 |
+
tensor<fp16, [1, 512, 96]> input_89_cast_fp16 = transpose(perm = input_89_perm_0, x = x_81_cast_fp16)[name = tensor<string, []>("transpose_5")];
|
| 474 |
+
tensor<fp16, [1, 512, 96]> x_83_cast_fp16 = layer_norm(axes = x_83_axes_0, beta = enc_p_encoder_norm_layers_2_2_beta_to_fp16, epsilon = var_20_to_fp16, gamma = enc_p_encoder_norm_layers_2_2_gamma_to_fp16, x = input_89_cast_fp16)[name = tensor<string, []>("x_83_cast_fp16")];
|
| 475 |
+
tensor<int32, [3]> x_85_perm_0 = const()[name = tensor<string, []>("x_85_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
|
| 476 |
+
tensor<fp16, [1, 96, 512]> x_85_cast_fp16 = transpose(perm = x_85_perm_0, x = x_83_cast_fp16)[name = tensor<string, []>("transpose_4")];
|
| 477 |
+
tensor<fp16, [1, 96, 512]> input_91_cast_fp16 = mul(x = x_85_cast_fp16, y = x_mask_to_fp16)[name = tensor<string, []>("input_91_cast_fp16")];
|
| 478 |
+
tensor<string, []> var_494_pad_type_0 = const()[name = tensor<string, []>("op_494_pad_type_0"), val = tensor<string, []>("valid")];
|
| 479 |
+
tensor<int32, [1]> var_494_strides_0 = const()[name = tensor<string, []>("op_494_strides_0"), val = tensor<int32, [1]>([1])];
|
| 480 |
+
tensor<int32, [2]> var_494_pad_0 = const()[name = tensor<string, []>("op_494_pad_0"), val = tensor<int32, [2]>([0, 0])];
|
| 481 |
+
tensor<int32, [1]> var_494_dilations_0 = const()[name = tensor<string, []>("op_494_dilations_0"), val = tensor<int32, [1]>([1])];
|
| 482 |
+
tensor<int32, []> var_494_groups_0 = const()[name = tensor<string, []>("op_494_groups_0"), val = tensor<int32, []>(1)];
|
| 483 |
+
tensor<fp16, [384, 96, 1]> enc_p_proj_weight_to_fp16 = const()[name = tensor<string, []>("enc_p_proj_weight_to_fp16"), val = tensor<fp16, [384, 96, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3512384)))];
|
| 484 |
+
tensor<fp16, [384]> enc_p_proj_bias_to_fp16 = const()[name = tensor<string, []>("enc_p_proj_bias_to_fp16"), val = tensor<fp16, [384]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3586176)))];
|
| 485 |
+
tensor<fp16, [1, 384, 512]> var_494_cast_fp16 = conv(bias = enc_p_proj_bias_to_fp16, dilations = var_494_dilations_0, groups = var_494_groups_0, pad = var_494_pad_0, pad_type = var_494_pad_type_0, strides = var_494_strides_0, weight = enc_p_proj_weight_to_fp16, x = input_91_cast_fp16)[name = tensor<string, []>("op_494_cast_fp16")];
|
| 486 |
+
tensor<fp16, [1, 384, 512]> var_495_cast_fp16 = mul(x = var_494_cast_fp16, y = x_mask_to_fp16)[name = tensor<string, []>("op_495_cast_fp16")];
|
| 487 |
+
tensor<int32, [2]> tile_0 = const()[name = tensor<string, []>("tile_0"), val = tensor<int32, [2]>([192, 192])];
|
| 488 |
+
tensor<int32, []> var_498_axis_0 = const()[name = tensor<string, []>("op_498_axis_0"), val = tensor<int32, []>(1)];
|
| 489 |
+
tensor<fp16, [1, 192, 512]> m_p, tensor<fp16, [1, 192, 512]> logs_p = split(axis = var_498_axis_0, split_sizes = tile_0, x = var_495_cast_fp16)[name = tensor<string, []>("op_498_cast_fp16")];
|
| 490 |
+
tensor<fp16, [1, 96, 512]> input_93_cast_fp16 = mul(x = input_91_cast_fp16, y = x_mask_to_fp16)[name = tensor<string, []>("input_93_cast_fp16")];
|
| 491 |
+
tensor<string, []> x_89_pad_type_0 = const()[name = tensor<string, []>("x_89_pad_type_0"), val = tensor<string, []>("custom")];
|
| 492 |
+
tensor<int32, [2]> x_89_pad_0 = const()[name = tensor<string, []>("x_89_pad_0"), val = tensor<int32, [2]>([1, 1])];
|
| 493 |
+
tensor<int32, [1]> x_89_strides_0 = const()[name = tensor<string, []>("x_89_strides_0"), val = tensor<int32, [1]>([1])];
|
| 494 |
+
tensor<int32, [1]> x_89_dilations_0 = const()[name = tensor<string, []>("x_89_dilations_0"), val = tensor<int32, [1]>([1])];
|
| 495 |
+
tensor<int32, []> x_89_groups_0 = const()[name = tensor<string, []>("x_89_groups_0"), val = tensor<int32, []>(1)];
|
| 496 |
+
tensor<fp16, [256, 96, 3]> dp_conv_1_weight_to_fp16 = const()[name = tensor<string, []>("dp_conv_1_weight_to_fp16"), val = tensor<fp16, [256, 96, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3587008)))];
|
| 497 |
+
tensor<fp16, [256]> dp_conv_1_bias_to_fp16 = const()[name = tensor<string, []>("dp_conv_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3734528)))];
|
| 498 |
+
tensor<fp16, [1, 256, 512]> x_89_cast_fp16 = conv(bias = dp_conv_1_bias_to_fp16, dilations = x_89_dilations_0, groups = x_89_groups_0, pad = x_89_pad_0, pad_type = x_89_pad_type_0, strides = x_89_strides_0, weight = dp_conv_1_weight_to_fp16, x = input_93_cast_fp16)[name = tensor<string, []>("x_89_cast_fp16")];
|
| 499 |
+
tensor<fp16, [1, 256, 512]> x_91_cast_fp16 = relu(x = x_89_cast_fp16)[name = tensor<string, []>("x_91_cast_fp16")];
|
| 500 |
+
tensor<int32, [3]> input_95_perm_0 = const()[name = tensor<string, []>("input_95_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
|
| 501 |
+
tensor<int32, [1]> x_93_axes_0 = const()[name = tensor<string, []>("x_93_axes_0"), val = tensor<int32, [1]>([-1])];
|
| 502 |
+
tensor<fp16, [256]> dp_norm_1_gamma_to_fp16 = const()[name = tensor<string, []>("dp_norm_1_gamma_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3735104)))];
|
| 503 |
+
tensor<fp16, [256]> dp_norm_1_beta_to_fp16 = const()[name = tensor<string, []>("dp_norm_1_beta_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3735680)))];
|
| 504 |
+
tensor<fp16, []> var_504_to_fp16 = const()[name = tensor<string, []>("op_504_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
|
| 505 |
+
tensor<fp16, [1, 512, 256]> input_95_cast_fp16 = transpose(perm = input_95_perm_0, x = x_91_cast_fp16)[name = tensor<string, []>("transpose_3")];
|
| 506 |
+
tensor<fp16, [1, 512, 256]> x_93_cast_fp16 = layer_norm(axes = x_93_axes_0, beta = dp_norm_1_beta_to_fp16, epsilon = var_504_to_fp16, gamma = dp_norm_1_gamma_to_fp16, x = input_95_cast_fp16)[name = tensor<string, []>("x_93_cast_fp16")];
|
| 507 |
+
tensor<int32, [3]> input_97_perm_0 = const()[name = tensor<string, []>("input_97_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
|
| 508 |
+
tensor<fp16, [1, 256, 512]> input_97_cast_fp16 = transpose(perm = input_97_perm_0, x = x_93_cast_fp16)[name = tensor<string, []>("transpose_2")];
|
| 509 |
+
tensor<fp16, [1, 256, 512]> input_99_cast_fp16 = mul(x = input_97_cast_fp16, y = x_mask_to_fp16)[name = tensor<string, []>("input_99_cast_fp16")];
|
| 510 |
+
tensor<string, []> x_97_pad_type_0 = const()[name = tensor<string, []>("x_97_pad_type_0"), val = tensor<string, []>("custom")];
|
| 511 |
+
tensor<int32, [2]> x_97_pad_0 = const()[name = tensor<string, []>("x_97_pad_0"), val = tensor<int32, [2]>([1, 1])];
|
| 512 |
+
tensor<int32, [1]> x_97_strides_0 = const()[name = tensor<string, []>("x_97_strides_0"), val = tensor<int32, [1]>([1])];
|
| 513 |
+
tensor<int32, [1]> x_97_dilations_0 = const()[name = tensor<string, []>("x_97_dilations_0"), val = tensor<int32, [1]>([1])];
|
| 514 |
+
tensor<int32, []> x_97_groups_0 = const()[name = tensor<string, []>("x_97_groups_0"), val = tensor<int32, []>(1)];
|
| 515 |
+
tensor<fp16, [256, 256, 3]> dp_conv_2_weight_to_fp16 = const()[name = tensor<string, []>("dp_conv_2_weight_to_fp16"), val = tensor<fp16, [256, 256, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3736256)))];
|
| 516 |
+
tensor<fp16, [256]> dp_conv_2_bias_to_fp16 = const()[name = tensor<string, []>("dp_conv_2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4129536)))];
|
| 517 |
+
tensor<fp16, [1, 256, 512]> x_97_cast_fp16 = conv(bias = dp_conv_2_bias_to_fp16, dilations = x_97_dilations_0, groups = x_97_groups_0, pad = x_97_pad_0, pad_type = x_97_pad_type_0, strides = x_97_strides_0, weight = dp_conv_2_weight_to_fp16, x = input_99_cast_fp16)[name = tensor<string, []>("x_97_cast_fp16")];
|
| 518 |
+
tensor<fp16, [1, 256, 512]> x_99_cast_fp16 = relu(x = x_97_cast_fp16)[name = tensor<string, []>("x_99_cast_fp16")];
|
| 519 |
+
tensor<int32, [3]> input_101_perm_0 = const()[name = tensor<string, []>("input_101_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
|
| 520 |
+
tensor<int32, [1]> x_101_axes_0 = const()[name = tensor<string, []>("x_101_axes_0"), val = tensor<int32, [1]>([-1])];
|
| 521 |
+
tensor<fp16, [256]> dp_norm_2_gamma_to_fp16 = const()[name = tensor<string, []>("dp_norm_2_gamma_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4130112)))];
|
| 522 |
+
tensor<fp16, [256]> dp_norm_2_beta_to_fp16 = const()[name = tensor<string, []>("dp_norm_2_beta_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4130688)))];
|
| 523 |
+
tensor<fp16, [1, 512, 256]> input_101_cast_fp16 = transpose(perm = input_101_perm_0, x = x_99_cast_fp16)[name = tensor<string, []>("transpose_1")];
|
| 524 |
+
tensor<fp16, [1, 512, 256]> x_101_cast_fp16 = layer_norm(axes = x_101_axes_0, beta = dp_norm_2_beta_to_fp16, epsilon = var_504_to_fp16, gamma = dp_norm_2_gamma_to_fp16, x = input_101_cast_fp16)[name = tensor<string, []>("x_101_cast_fp16")];
|
| 525 |
+
tensor<int32, [3]> input_103_perm_0 = const()[name = tensor<string, []>("input_103_perm_0"), val = tensor<int32, [3]>([0, -1, 1])];
|
| 526 |
+
tensor<fp16, [1, 256, 512]> input_103_cast_fp16 = transpose(perm = input_103_perm_0, x = x_101_cast_fp16)[name = tensor<string, []>("transpose_0")];
|
| 527 |
+
tensor<fp16, [1, 256, 512]> input_cast_fp16 = mul(x = input_103_cast_fp16, y = x_mask_to_fp16)[name = tensor<string, []>("input_cast_fp16")];
|
| 528 |
+
tensor<string, []> x_pad_type_0 = const()[name = tensor<string, []>("x_pad_type_0"), val = tensor<string, []>("valid")];
|
| 529 |
+
tensor<int32, [1]> x_strides_0 = const()[name = tensor<string, []>("x_strides_0"), val = tensor<int32, [1]>([1])];
|
| 530 |
+
tensor<int32, [2]> x_pad_0 = const()[name = tensor<string, []>("x_pad_0"), val = tensor<int32, [2]>([0, 0])];
|
| 531 |
+
tensor<int32, [1]> x_dilations_0 = const()[name = tensor<string, []>("x_dilations_0"), val = tensor<int32, [1]>([1])];
|
| 532 |
+
tensor<int32, []> x_groups_0 = const()[name = tensor<string, []>("x_groups_0"), val = tensor<int32, []>(1)];
|
| 533 |
+
tensor<fp16, [1, 256, 1]> dp_proj_weight_to_fp16 = const()[name = tensor<string, []>("dp_proj_weight_to_fp16"), val = tensor<fp16, [1, 256, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4131264)))];
|
| 534 |
+
tensor<fp16, [1]> dp_proj_bias_to_fp16 = const()[name = tensor<string, []>("dp_proj_bias_to_fp16"), val = tensor<fp16, [1]>([0x1.3e8p-1])];
|
| 535 |
+
tensor<fp16, [1, 1, 512]> x_cast_fp16 = conv(bias = dp_proj_bias_to_fp16, dilations = x_dilations_0, groups = x_groups_0, pad = x_pad_0, pad_type = x_pad_type_0, strides = x_strides_0, weight = dp_proj_weight_to_fp16, x = input_cast_fp16)[name = tensor<string, []>("x_cast_fp16")];
|
| 536 |
+
tensor<fp16, [1, 1, 512]> logw = mul(x = x_cast_fp16, y = x_mask_to_fp16)[name = tensor<string, []>("op_555_cast_fp16")];
|
| 537 |
+
} -> (m_p, logs_p, logw);
|
| 538 |
+
}
|
micro/encoder.mlmodelc/weights/weight.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:fb58e350102274ec50f4027e3a8488075a5af0bf14ce43b7f0af7f2cfeab2c19
|
| 3 |
+
size 4131840
|
micro/synthesizer_f1024.mlmodelc/analytics/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:2398cb0b3e176844c8252236a21d2c8bd4ab4e198772307482561875898025b6
|
| 3 |
+
size 243
|
micro/synthesizer_f1024.mlmodelc/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:4c5a3333442c6d3829eb5c6a9f325747e546a4125b17b52ab1d413c824e7fd4b
|
| 3 |
+
size 395
|
micro/synthesizer_f1024.mlmodelc/model.mil
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
micro/synthesizer_f1024.mlmodelc/weights/weight.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f7ef4ad0f9258746904f9e69e88fd2f1f13b32b81fe5db0dae9c4c1477a6d0c0
|
| 3 |
+
size 15163864
|
micro/synthesizer_f2048.mlmodelc/analytics/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:13ae54fbfb2cd6bc01c517c5314b8693c73c9f2bea7284be5d14fb19935cc2fd
|
| 3 |
+
size 243
|
micro/synthesizer_f2048.mlmodelc/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:eafe87cd9324f524f476ba6b812b5f607c1c86d99269d03fef9be567084967fa
|
| 3 |
+
size 395
|
micro/synthesizer_f2048.mlmodelc/model.mil
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
micro/synthesizer_f2048.mlmodelc/weights/weight.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f7ef4ad0f9258746904f9e69e88fd2f1f13b32b81fe5db0dae9c4c1477a6d0c0
|
| 3 |
+
size 15163864
|
micro/synthesizer_f256.mlmodelc/analytics/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:0f36dea7ffb50b4708463af039d3fce7083976950722fa8ba3eddf630382d72d
|
| 3 |
+
size 243
|
micro/synthesizer_f256.mlmodelc/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:5a2ff8a284923d269b31836a2f4f956daba11912919de8dcec3ce0c2cd757b2c
|
| 3 |
+
size 395
|
micro/synthesizer_f256.mlmodelc/model.mil
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
micro/synthesizer_f256.mlmodelc/weights/weight.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f7ef4ad0f9258746904f9e69e88fd2f1f13b32b81fe5db0dae9c4c1477a6d0c0
|
| 3 |
+
size 15163864
|
micro/synthesizer_f384.mlmodelc/analytics/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:2ee5401829791359a1ca258ba03fcc13a2a13f09da2442b0b25c0e949aeff7ae
|
| 3 |
+
size 243
|
micro/synthesizer_f384.mlmodelc/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:a1b5248bae13edaf5722bebc91a543c27afd7869cfc702e14b935aeccb66683c
|
| 3 |
+
size 395
|
micro/synthesizer_f384.mlmodelc/model.mil
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
micro/synthesizer_f384.mlmodelc/weights/weight.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f7ef4ad0f9258746904f9e69e88fd2f1f13b32b81fe5db0dae9c4c1477a6d0c0
|
| 3 |
+
size 15163864
|
micro/synthesizer_f512.mlmodelc/analytics/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:65dce49ac32cf8a603543b10a66b2a64a48918c668e08fe0ea47181426818283
|
| 3 |
+
size 243
|
micro/synthesizer_f512.mlmodelc/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:18dad0e2894bf159ac2ebd33b314da19697c8a61299f309d2d37621341247159
|
| 3 |
+
size 395
|
micro/synthesizer_f512.mlmodelc/model.mil
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
micro/synthesizer_f512.mlmodelc/weights/weight.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f7ef4ad0f9258746904f9e69e88fd2f1f13b32b81fe5db0dae9c4c1477a6d0c0
|
| 3 |
+
size 15163864
|
micro/synthesizer_f640.mlmodelc/analytics/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:2fefa6e0150a4ab71e88c896821b1fecd8bdbb1dd780af90eb84fb82763a09b6
|
| 3 |
+
size 243
|
micro/synthesizer_f640.mlmodelc/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:77033d1f8fde4047f2c3417bbee99238630d0c4708f2051ae5d95d3f93d0dd1d
|
| 3 |
+
size 395
|
micro/synthesizer_f640.mlmodelc/model.mil
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
micro/synthesizer_f640.mlmodelc/weights/weight.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f7ef4ad0f9258746904f9e69e88fd2f1f13b32b81fe5db0dae9c4c1477a6d0c0
|
| 3 |
+
size 15163864
|
micro/synthesizer_f768.mlmodelc/analytics/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f3be2ba4bb69d57065c1a0dec5e46889d86586d4ec1214476ecb8c98e135bc7a
|
| 3 |
+
size 243
|
micro/synthesizer_f768.mlmodelc/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:bbffaf432931b5bd47f1d0947455574b7770e4d9d44c84a1cd31d50dca6065df
|
| 3 |
+
size 395
|
micro/synthesizer_f768.mlmodelc/model.mil
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
micro/synthesizer_f768.mlmodelc/weights/weight.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f7ef4ad0f9258746904f9e69e88fd2f1f13b32b81fe5db0dae9c4c1477a6d0c0
|
| 3 |
+
size 15163864
|
micro/synthesizer_f896.mlmodelc/analytics/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:50a294626581a728be43d6f816d4356ded256ed8d89b06864a384b56051a000b
|
| 3 |
+
size 243
|
micro/synthesizer_f896.mlmodelc/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:06095f1eb658cc17e95a8d20b56808976a45555a5c917a7115831dec24261b05
|
| 3 |
+
size 395
|
micro/synthesizer_f896.mlmodelc/model.mil
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
micro/synthesizer_f896.mlmodelc/weights/weight.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f7ef4ad0f9258746904f9e69e88fd2f1f13b32b81fe5db0dae9c4c1477a6d0c0
|
| 3 |
+
size 15163864
|