[Wav2Vec2 Conformer] Fix inference float16 (#25985)

* [Wav2Vec2 Conformer] Fix inference float16

* fix test

* fix test more

* clean pipe test

src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py

@@ -406,13 +406,15 @@ class Wav2Vec2ConformerRotaryPositionalEmbedding(nn.Module):
             return self.cached_rotary_positional_embedding
 
         self.cached_sequence_length = sequence_length
+        # Embeddings are computed in the dtype of the inv_freq constant
         time_stamps = torch.arange(sequence_length).type_as(self.inv_freq)
         freqs = torch.einsum("i,j->ij", time_stamps, self.inv_freq)
         embeddings = torch.cat((freqs, freqs), dim=-1)
 
         cos_embeddings = embeddings.cos()[:, None, None, :]
         sin_embeddings = embeddings.sin()[:, None, None, :]
-        self.cached_rotary_positional_embedding = torch.stack([cos_embeddings, sin_embeddings])
+        # Computed embeddings are cast to the dtype of the hidden state inputs
+        self.cached_rotary_positional_embedding = torch.stack([cos_embeddings, sin_embeddings]).type_as(hidden_states)
         return self.cached_rotary_positional_embedding
 
 

tests/models/wav2vec2_conformer/test_modeling_wav2vec2_conformer.py

@@ -13,15 +13,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """ Testing suite for the PyTorch Wav2Vec2-Conformer model. """
-
 import math
+import tempfile
 import unittest
 
 import numpy as np
 from datasets import load_dataset
 
 from transformers import Wav2Vec2ConformerConfig, is_torch_available
-from transformers.testing_utils import is_pt_flax_cross_test, require_torch, slow, torch_device
+from transformers.testing_utils import is_pt_flax_cross_test, require_torch, require_torch_gpu, slow, torch_device
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import (
@@ -215,6 +215,23 @@ class Wav2Vec2ConformerModelTester:
             (self.batch_size, self.adapter_output_seq_length, config.output_hidden_size),
         )
 
+    def create_and_check_model_float16(self, config, input_values, attention_mask):
+        model = Wav2Vec2ConformerModel(config=config)
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            model.save_pretrained(tmpdirname)
+            model = Wav2Vec2ConformerModel.from_pretrained(tmpdirname, torch_dtype=torch.float16)
+
+        model.to(torch_device)
+        model.eval()
+
+        with torch.no_grad():
+            result = model(input_values.type(dtype=torch.float16), attention_mask=attention_mask)
+
+        self.parent.assertEqual(
+            result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size)
+        )
+
     def create_and_check_batch_inference(self, config, input_values, *args):
         # test does not pass for models making use of `group_norm`
         # check: https://github.com/pytorch/fairseq/issues/3227
@@ -451,6 +468,16 @@ class Wav2Vec2ConformerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model_with_adapter_proj_dim(*config_and_inputs)
 
+    @require_torch_gpu
+    def test_model_float16_with_relative(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="relative")
+        self.model_tester.create_and_check_model_float16(*config_and_inputs)
+
+    @require_torch_gpu
+    def test_model_float16_with_rotary(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="rotary")
+        self.model_tester.create_and_check_model_float16(*config_and_inputs)
+
     def test_ctc_loss_inference(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_ctc_loss(*config_and_inputs)

tests/pipelines/test_pipelines_automatic_speech_recognition.py

@@ -901,6 +901,26 @@ class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase):
         output = speech_recognizer(filename)
         self.assertEqual(output, {"text": "a man said to the universe sir i exist"})
 
+    @slow
+    @require_torch_gpu
+    def test_wav2vec2_conformer_float16(self):
+        speech_recognizer = pipeline(
+            task="automatic-speech-recognition",
+            model="facebook/wav2vec2-conformer-rope-large-960h-ft",
+            device="cuda:0",
+            torch_dtype=torch.float16,
+            framework="pt",
+        )
+
+        dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+        sample = dataset[0]["audio"]
+
+        output = speech_recognizer(sample)
+        self.assertEqual(
+            output,
+            {"text": "MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL"},
+        )
+
     @require_torch
     def test_chunking_fast(self):
         speech_recognizer = pipeline(