[speech2text] fix init of sinusoidal embeddings (#37931)

* fix init (meta device -> bad numbers)

* fast test

* dont init sinusoidal twice

* make fixup

src/transformers/models/musicgen/modeling_musicgen.py

@@ -125,9 +125,7 @@ class MusicgenSinusoidalPositionalEmbedding(nn.Module):
             # in forward put the weights on the correct dtype and device of the param
             emb_weights = emb_weights.to(dtype=self.weights.dtype, device=self.weights.device)
 
-        self.weights = nn.Parameter(emb_weights)
+        self.register_buffer("weights", emb_weights, persistent=False)
-        self.weights.requires_grad = False
-        self.weights.detach_()
 
     @staticmethod
     def get_embedding(num_embeddings: int, embedding_dim: int):
@@ -718,11 +716,6 @@ class MusicgenPreTrainedModel(PreTrainedModel):
             module.weight.data.normal_(mean=0.0, std=std)
             if module.padding_idx is not None:
                 module.weight.data[module.padding_idx].zero_()
-        elif isinstance(module, MusicgenSinusoidalPositionalEmbedding):
-            weights = module.get_embedding(*module.weights.shape)
-            weights = nn.Parameter(weights, requires_grad=False)
-            weights.detach_()
-            module.weights = weights
 
 
 MUSICGEN_START_DOCSTRING = r"""

src/transformers/models/musicgen_melody/modeling_musicgen_melody.py

@@ -140,9 +140,7 @@ class MusicgenMelodySinusoidalPositionalEmbedding(nn.Module):
             # in forward put the weights on the correct dtype and device of the param
             emb_weights = emb_weights.to(dtype=self.weights.dtype, device=self.weights.device)
 
-        self.weights = nn.Parameter(emb_weights)
+        self.register_buffer("weights", emb_weights, persistent=False)
-        self.weights.requires_grad = False
-        self.weights.detach_()
 
     @staticmethod
     def get_embedding(num_embeddings: int, embedding_dim: int):
@@ -677,11 +675,6 @@ class MusicgenMelodyPreTrainedModel(PreTrainedModel):
             module.weight.data.normal_(mean=0.0, std=std)
             if module.padding_idx is not None:
                 module.weight.data[module.padding_idx].zero_()
-        elif isinstance(module, MusicgenMelodySinusoidalPositionalEmbedding):
-            weights = module.get_embedding(*module.weights.shape)
-            weights = nn.Parameter(weights, requires_grad=False)
-            weights.detach_()
-            module.weights = weights
 
 
 MUSICGEN_MELODY_START_DOCSTRING = r"""

src/transformers/models/speech_to_text/modeling_speech_to_text.py

@@ -113,9 +113,7 @@ class Speech2TextSinusoidalPositionalEmbedding(nn.Module):
             # in forward put the weights on the correct dtype and device of the param
             emb_weights = emb_weights.to(dtype=self.weights.dtype, device=self.weights.device)
 
-        self.weights = nn.Parameter(emb_weights)
+        self.register_buffer("weights", emb_weights, persistent=False)
-        self.weights.requires_grad = False
-        self.weights.detach_()
 
     @staticmethod
     def get_embedding(num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None):

src/transformers/models/speecht5/modeling_speecht5.py

@@ -299,9 +299,7 @@ class SpeechT5SinusoidalPositionalEmbedding(nn.Module):
             # in forward put the weights on the correct dtype and device of the param
             emb_weights = emb_weights.to(dtype=self.weights.dtype, device=self.weights.device)
 
-        self.weights = nn.Parameter(emb_weights)
+        self.register_buffer("weights", emb_weights, persistent=False)
-        self.weights.requires_grad = False
-        self.weights.detach_()
 
     @staticmethod
     def get_embedding(num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None):

tests/models/musicgen/test_modeling_musicgen.py

@@ -1556,7 +1556,7 @@ class MusicgenIntegrationTests(unittest.TestCase):
         self.assertTrue(
             output_values.shape == (2, 1, 36480)
         )  # input values take shape 32000 and we generate from there
-        torch.testing.assert_close(output_values[0, 0, -16:].cpu(), EXPECTED_VALUES, rtol=1e-4, atol=1e-4)
+        torch.testing.assert_close(output_values[0, 0, -16:].cpu(), EXPECTED_VALUES, rtol=2e-4, atol=2e-4)
 
 
 @require_torch
@@ -1631,5 +1631,5 @@ class MusicgenStereoIntegrationTests(unittest.TestCase):
         # (bsz, channels, seq_len)
         self.assertTrue(output_values.shape == (2, 2, 37760))
         # input values take shape 32000 and we generate from there - we check the last (generated) values
-        torch.testing.assert_close(output_values[0, 0, -16:].cpu(), EXPECTED_VALUES_LEFT, rtol=1e-4, atol=1e-4)
+        torch.testing.assert_close(output_values[0, 0, -16:].cpu(), EXPECTED_VALUES_LEFT, rtol=2e-4, atol=2e-4)
-        torch.testing.assert_close(output_values[0, 1, -16:].cpu(), EXPECTED_VALUES_RIGHT, rtol=1e-4, atol=1e-4)
+        torch.testing.assert_close(output_values[0, 1, -16:].cpu(), EXPECTED_VALUES_RIGHT, rtol=2e-4, atol=2e-4)

tests/models/speech_to_text/test_modeling_speech_to_text.py

@@ -19,6 +19,8 @@ import os
 import tempfile
 import unittest
 
+from datasets import load_dataset
+
 from transformers import Speech2TextConfig
 from transformers.testing_utils import (
     is_torch_available,
@@ -27,10 +29,8 @@ from transformers.testing_utils import (
     require_torch,
     require_torch_fp16,
     require_torchaudio,
-    slow,
     torch_device,
 )
-from transformers.utils import cached_property
 
 from ...generation.test_utils import GenerationTesterMixin
 from ...test_configuration_common import ConfigTester
@@ -701,32 +701,23 @@ class Speech2TextModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTest
 @require_torchaudio
 @require_sentencepiece
 @require_tokenizers
-@slow
 class Speech2TextModelIntegrationTests(unittest.TestCase):
-    @cached_property
+    @classmethod
-    def default_processor(self):
+    def setUpClass(cls):
-        return Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr")
+        model_name = "facebook/s2t-small-librispeech-asr"
-
+        cls.model = Speech2TextForConditionalGeneration.from_pretrained(model_name, device_map="auto")
-    def _load_datasamples(self, num_samples):
+        cls.processor = Speech2TextProcessor.from_pretrained(model_name)
-        from datasets import load_dataset
+        # loads 4 samples
-
         ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
-        # automatic decoding with librispeech
+        speech_samples = ds.sort("id").select(range(4))[:4]["audio"]
-        speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
+        cls.dataset = [x["array"] for x in speech_samples]
-
-        return [x["array"] for x in speech_samples]
 
     def test_generation_librispeech(self):
-        model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr")
+        input_speech = [self.dataset[0]]
-        model.to(torch_device)
+        input_features = self.processor(input_speech, return_tensors="pt").input_features.to(torch_device)
-        processor = self.default_processor
 
-        input_speech = self._load_datasamples(1)
+        generated_ids = self.model.generate(input_features)
-
+        generated_transcript = self.processor.batch_decode(generated_ids, skip_special_tokens=True)
-        input_features = processor(input_speech, return_tensors="pt").input_features.to(torch_device)
-
-        generated_ids = model.generate(input_features)
-        generated_transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)
 
         EXPECTED_TRANSCRIPTIONS = [
             "mister quilter is the apostle of the middle classes and we are glad to welcome his gospel"
@@ -734,19 +725,14 @@ class Speech2TextModelIntegrationTests(unittest.TestCase):
         self.assertListEqual(generated_transcript, EXPECTED_TRANSCRIPTIONS)
 
     def test_generation_librispeech_batched(self):
-        model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr")
+        input_speech = self.dataset
-        model.to(torch_device)
+        inputs = self.processor(input_speech, return_tensors="pt", padding=True)
-        processor = self.default_processor
-
-        input_speech = self._load_datasamples(4)
-
-        inputs = processor(input_speech, return_tensors="pt", padding=True)
 
         input_features = inputs.input_features.to(torch_device)
         attention_mask = inputs.attention_mask.to(torch_device)
 
-        generated_ids = model.generate(input_features, attention_mask=attention_mask)
+        generated_ids = self.model.generate(input_features, attention_mask=attention_mask)
-        generated_transcripts = processor.batch_decode(generated_ids, skip_special_tokens=True)
+        generated_transcripts = self.processor.batch_decode(generated_ids, skip_special_tokens=True)
 
         EXPECTED_TRANSCRIPTIONS = [
             "mister quilter is the apostle of the middle classes and we are glad to welcome his gospel",