[VITS] Fix init test (huggingface#25945)

* [VITS] Fix init test * add flaky decorator * style * max attempts Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com> * style --------- Co-authored-by: Yih-Dar <2521628+ydshieh@users.noreply.github.com>
blbadger · Nov 8, 2023 · 89a36a0 · 89a36a0
1 parent a49b103
commit 89a36a0
Showing 1 changed file with 34 additions and 18 deletions.
diff --git a/tests/models/vits/test_modeling_vits.py b/tests/models/vits/test_modeling_vits.py
@@ -24,6 +24,7 @@
 
 from transformers import PretrainedConfig, VitsConfig
 from transformers.testing_utils import (
+    is_flaky,
     is_torch_available,
     require_torch,
     slow,
@@ -80,6 +81,10 @@ def __init__(
         duration_predictor_filter_channels=16,
         prior_encoder_num_flows=2,
         upsample_initial_channel=16,
+        upsample_rates=[8, 2],
+        upsample_kernel_sizes=[16, 4],
+        resblock_kernel_sizes=[3, 7],
+        resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]],
     ):
         self.parent = parent
         self.batch_size = batch_size
@@ -96,6 +101,10 @@ def __init__(
         self.duration_predictor_filter_channels = duration_predictor_filter_channels
         self.prior_encoder_num_flows = prior_encoder_num_flows
         self.upsample_initial_channel = upsample_initial_channel
+        self.upsample_rates = upsample_rates
+        self.upsample_kernel_sizes = upsample_kernel_sizes
+        self.resblock_kernel_sizes = resblock_kernel_sizes
+        self.resblock_dilation_sizes = resblock_dilation_sizes
 
     def prepare_config_and_inputs(self):
         input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(2)
@@ -126,6 +135,10 @@ def get_config(self):
             duration_predictor_filter_channels=self.duration_predictor_filter_channels,
             posterior_encoder_num_wavenet_layers=self.num_hidden_layers,
             upsample_initial_channel=self.upsample_initial_channel,
+            upsample_rates=self.upsample_rates,
+            upsample_kernel_sizes=self.upsample_kernel_sizes,
+            resblock_kernel_sizes=self.resblock_kernel_sizes,
+            resblock_dilation_sizes=self.resblock_dilation_sizes,
         )
 
     def create_and_check_model_forward(self, config, inputs_dict):
@@ -135,7 +148,7 @@ def create_and_check_model_forward(self, config, inputs_dict):
         attention_mask = inputs_dict["attention_mask"]
 
         result = model(input_ids, attention_mask=attention_mask)
-        self.parent.assertEqual(result.waveform.shape, (self.batch_size, 11008))
+        self.parent.assertEqual((self.batch_size, 624), result.waveform.shape)
 
 
 @require_torch
@@ -168,30 +181,33 @@ def test_model_forward(self):
     def test_determinism(self):
         pass
 
-    # TODO: Fix me (ydshieh)
-    @unittest.skip("currently failing")
+    @is_flaky(
+        max_attempts=3,
+        description="Weight initialisation for the VITS conv layers sometimes exceeds the kaiming normal range",
+    )
     def test_initialization(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
 
+        uniform_init_parms = [
+            "emb_rel_k",
+            "emb_rel_v",
+            "conv_1",
+            "conv_2",
+            "conv_pre",
+            "conv_post",
+            "conv_proj",
+            "conv_dds",
+            "project",
+            "wavenet.in_layers",
+            "wavenet.res_skip_layers",
+            "upsampler",
+            "resblocks",
+        ]
+
         configs_no_init = _config_zero_init(config)
         for model_class in self.all_model_classes:
             model = model_class(config=configs_no_init)
             for name, param in model.named_parameters():
-                uniform_init_parms = [
-                    "emb_rel_k",
-                    "emb_rel_v",
-                    "conv_1",
-                    "conv_2",
-                    "conv_pre",
-                    "conv_post",
-                    "conv_proj",
-                    "conv_dds",
-                    "project",
-                    "wavenet.in_layers",
-                    "wavenet.res_skip_layers",
-                    "upsampler",
-                    "resblocks",
-                ]
                 if param.requires_grad:
                     if any(x in name for x in uniform_init_parms):
                         self.assertTrue(