fix prompt strip to support tensors and np arrays (#27818)

* fix prompt strip to support tensors and np arrays

* framework agnostic

* change logic check before converting prompt into list

Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>

* adding _convert_to_list to tokenization_whisper_fast

* adding tests for prompt decoding

* adding comment

Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>

* adding comment

Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>

* revert minor

* make style formatting

* style formatting after update

* Update src/transformers/models/whisper/tokenization_whisper_fast.py

Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>

* fixing _strip_prompt to handle _decode_with_timestamps

* fix copies

---------

Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>

src/transformers/models/whisper/tokenization_whisper.py

@@ -851,9 +851,16 @@ class WhisperTokenizer(PreTrainedTokenizer):
         batch_encoding.convert_to_tensors(tensor_type=return_tensors)
         return batch_encoding["input_ids"]
 
-    @staticmethod
-    def _strip_prompt(token_ids: List[int], prompt_token_id: int, decoder_start_token_id: int):
-        has_prompt = isinstance(token_ids, list) and token_ids and token_ids[0] == prompt_token_id
+    def _strip_prompt(self, token_ids: List[int], prompt_token_id: int, decoder_start_token_id: int):
+        if not isinstance(token_ids, list):
+            token_ids = self._convert_to_list(token_ids)
+
+        # handle case of empty token_ids for decoding with timestamps.
+        # at this point token_ids is a list, so it is safe to use if not check.
+        if not token_ids:
+            return token_ids
+
+        has_prompt = token_ids[0] == prompt_token_id
         if has_prompt:
             if decoder_start_token_id in token_ids:
                 return token_ids[token_ids.index(decoder_start_token_id) :]
@@ -862,6 +869,16 @@ class WhisperTokenizer(PreTrainedTokenizer):
 
         return token_ids
 
+    @staticmethod
+    def _convert_to_list(token_ids):
+        # convert type to ndarray if necessary
+        if "torch" in str(type(token_ids)) or "tensorflow" in str(type(token_ids)) and hasattr(token_ids, "numpy"):
+            token_ids = token_ids.numpy()
+        # now the token ids are either a numpy array, or a list of lists
+        if isinstance(token_ids, np.ndarray):
+            token_ids = token_ids.tolist()
+        return token_ids
+
 
 def _decode_asr(tokenizer, model_outputs, *, return_timestamps, return_language, time_precision):
     """

src/transformers/models/whisper/tokenization_whisper_fast.py

@@ -582,10 +582,17 @@ class WhisperTokenizerFast(PreTrainedTokenizerFast):
         batch_encoding.convert_to_tensors(tensor_type=return_tensors)
         return batch_encoding["input_ids"]
 
-    @staticmethod
     # Copied from transformers.models.whisper.tokenization_whisper.WhisperTokenizer._strip_prompt
-    def _strip_prompt(token_ids: List[int], prompt_token_id: int, decoder_start_token_id: int):
-        has_prompt = isinstance(token_ids, list) and token_ids and token_ids[0] == prompt_token_id
+    def _strip_prompt(self, token_ids: List[int], prompt_token_id: int, decoder_start_token_id: int):
+        if not isinstance(token_ids, list):
+            token_ids = self._convert_to_list(token_ids)
+
+        # handle case of empty token_ids for decoding with timestamps.
+        # at this point token_ids is a list, so it is safe to use if not check.
+        if not token_ids:
+            return token_ids
+
+        has_prompt = token_ids[0] == prompt_token_id
         if has_prompt:
             if decoder_start_token_id in token_ids:
                 return token_ids[token_ids.index(decoder_start_token_id) :]
@@ -593,3 +600,14 @@ class WhisperTokenizerFast(PreTrainedTokenizerFast):
                 return []
 
         return token_ids
+
+    @staticmethod
+    # Copied from transformers.models.whisper.tokenization_whisper.WhisperTokenizer._convert_to_list
+    def _convert_to_list(token_ids):
+        # convert type to ndarray if necessary
+        if "torch" in str(type(token_ids)) or "tensorflow" in str(type(token_ids)) and hasattr(token_ids, "numpy"):
+            token_ids = token_ids.numpy()
+        # now the token ids are either a numpy array, or a list of lists
+        if isinstance(token_ids, np.ndarray):
+            token_ids = token_ids.tolist()
+        return token_ids

tests/models/whisper/test_tokenization_whisper.py

@@ -14,6 +14,8 @@
 
 import unittest
 
+import numpy as np
+
 from transformers.models.whisper import WhisperTokenizer, WhisperTokenizerFast
 from transformers.models.whisper.tokenization_whisper import _combine_tokens_into_words, _find_longest_common_sequence
 from transformers.testing_utils import slow
@@ -251,6 +253,39 @@ class WhisperTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
 
         self.assertListEqual(tokenizer_prompt_ids.tolist(), fast_tokenizer_prompt_ids.tolist())
 
+    def test_tokenizer_decode_prompt(self):
+        prompt_text = "What does the fox say?"
+        input_text = "Hatee hatee hatee ho"
+
+        tokenizer = self.get_tokenizer()
+        rust_tokenizer = self.get_rust_tokenizer()
+
+        # encode prompt and input text using tokenizer
+        prompt_ids = tokenizer.get_prompt_ids(prompt_text, return_tensors="np")
+        input_ids = tokenizer(input_text, return_tensors="np").input_ids[0]
+        input_ids = np.hstack([prompt_ids, input_ids])
+
+        # encode using fast tokenizer
+        rust_prompt_ids = rust_tokenizer.get_prompt_ids(prompt_text, return_tensors="np")
+        rust_input_ids = rust_tokenizer(input_text, return_tensors="np").input_ids[0]
+        rust_input_ids = np.hstack([rust_prompt_ids, rust_input_ids])
+
+        # check with prompt in output
+        pred_text = tokenizer.decode(input_ids, skip_special_tokens=False)
+        rust_pred_text = rust_tokenizer.decode(rust_input_ids, skip_special_tokens=False)
+
+        # check correctness for both tokenizers
+        expected_text = f"<|startofprev|> {prompt_text}<|startoftranscript|><|notimestamps|>{input_text}<|endoftext|>"
+        self.assertEqual(pred_text.strip(), expected_text)
+        self.assertEqual(rust_pred_text.strip(), expected_text)
+
+        # check stripping prompt from output
+        pred_text = tokenizer.decode(input_ids, skip_special_tokens=True)
+        rust_pred_text = tokenizer.decode(input_ids, skip_special_tokens=True)
+
+        self.assertEqual(pred_text.strip(), input_text)
+        self.assertEqual(rust_pred_text.strip(), input_text)
+
     def test_combine_tokens_into_words(self):
         tokenizer = self.get_tokenizer()
         rust_tokenizer = self.get_rust_tokenizer()