Fix FP16 inference in TextGenerationPipeline (#20913)

* add torch_dtype attribute to Pipeline

* Use torch_dtype to cast input tensor type in AutomaticSpeechRecognitionPipeline

* Fix code quality

* Add TextGenerationPipeline fp16 test

* Fix code quality

* Remove useless require in tests
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>

src/transformers/pipelines/automatic_speech_recognition.py

@@ -242,8 +242,6 @@ class AutomaticSpeechRecognitionPipeline(ChunkPipeline):
             preprocess_params["stride_length_s"] = kwargs["stride_length_s"]
         if "ignore_warning" in kwargs:
             preprocess_params["ignore_warning"] = kwargs["ignore_warning"]
-        if "torch_dtype" in kwargs:
-            preprocess_params["dtype"] = kwargs["torch_dtype"]
 
         postprocess_params = {}
         if "decoder_kwargs" in kwargs:
@@ -253,7 +251,7 @@ class AutomaticSpeechRecognitionPipeline(ChunkPipeline):
 
         return preprocess_params, {}, postprocess_params
 
-    def preprocess(self, inputs, chunk_length_s=0, stride_length_s=None, ignore_warning=False, dtype=None):
+    def preprocess(self, inputs, chunk_length_s=0, stride_length_s=None, ignore_warning=False):
         if isinstance(inputs, str):
             if inputs.startswith("http://") or inputs.startswith("https://"):
                 # We need to actually check for a real protocol, otherwise it's impossible to use a local file
@@ -336,14 +334,16 @@ class AutomaticSpeechRecognitionPipeline(ChunkPipeline):
                 raise ValueError("Chunk length must be superior to stride length")
 
             # make sure that
-            for item in chunk_iter(inputs, self.feature_extractor, chunk_len, stride_left, stride_right, dtype):
+            for item in chunk_iter(
+                inputs, self.feature_extractor, chunk_len, stride_left, stride_right, self.torch_dtype
+            ):
                 yield item
         else:
             processed = self.feature_extractor(
                 inputs, sampling_rate=self.feature_extractor.sampling_rate, return_tensors="pt"
             )
-            if dtype is not None:
-                processed = processed.to(dtype=dtype)
+            if self.torch_dtype is not None:
+                processed = processed.to(dtype=self.torch_dtype)
             if stride is not None:
                 if self.model.__class__ in MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING.values():
                     raise ValueError("Stride is only usable with CTC models, try removing it")

src/transformers/pipelines/base.py

@@ -748,6 +748,7 @@ class Pipeline(_ScikitCompat):
         task: str = "",
         args_parser: ArgumentHandler = None,
         device: Union[int, str, "torch.device"] = -1,
+        torch_dtype: Optional[Union[str, "torch.dtype"]] = None,
         binary_output: bool = False,
         **kwargs,
     ):
@@ -771,6 +772,7 @@ class Pipeline(_ScikitCompat):
                 self.device = torch.device(f"cuda:{device}")
         else:
             self.device = device
+        self.torch_dtype = torch_dtype
         self.binary_output = binary_output
 
         # Special handling

tests/pipelines/test_pipelines_text_generation.py

@@ -300,3 +300,11 @@ class TextGenerationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseM
                 }
             ],
         )
+
+    @require_torch
+    @require_torch_gpu
+    def test_small_model_fp16(self):
+        import torch
+
+        pipe = pipeline(model="hf-internal-testing/tiny-random-bloom", device=0, torch_dtype=torch.float16)
+        pipe("This is a test")