[TextGeneration] Fix llama tokenizer (#1635)

* add llama tokenizer fix

* fix generated string

* only run for streaming

* add TODO

---------

Co-authored-by: Dipika Sikka <dipikasikka1@gmail.coom>

src/deepsparse/transformers/pipelines/text_generation/prep_for_generation.py

@@ -101,6 +101,7 @@ def run(
             else [],
             "finished_reason": [],
             "token_generator": token_generator,
+            "past_tokens_queue": copy.copy(tokens),
         }
 
         if kv_cache is None:

src/deepsparse/transformers/pipelines/text_generation/process_outputs.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import datetime
-from typing import Optional
+from typing import List, Optional
 
 import numpy
 
@@ -54,6 +54,33 @@ def _create_generated_text_output(
             finished=False,
         )
 
+    def _generate_streamed_text_from_past_tokens(
+        self, generated_tokens: numpy.ndarray, past_tokens_queue: List[int]
+    ) -> str:
+        """
+        An auxiliary method that helps to properly generate the streamed text.
+        Some models like llama2 and mistral are using LlamaTokenizer which is
+        based on SentencePiece tokenizer. This specific tokenizer doesn't seem
+        to output appropriate prefix spaces when decoding token by token.
+        One can make it work if the previously generated tokens are included.
+        This allows the tokenizer to figure out that the appropriate spaces
+        from last n consecutive tokens.
+
+        :param generated_tokens: the generated tokens from the engine
+        :param past_tokens_queue: the queue of last n tokens (n is the
+            original prompt length in tokens)
+        :return: the generated string
+        """
+        string_from_n_tokens = self.tokenizer.decode(
+            past_tokens_queue, skip_special_tokens=True
+        )
+        past_tokens_queue.append(generated_tokens[0])
+        string_from_n_plus_1_tokens = self.tokenizer.decode(
+            past_tokens_queue, skip_special_tokens=True
+        )
+        past_tokens_queue.pop(0)
+        return [string_from_n_plus_1_tokens[len(string_from_n_tokens) :]]
+
     def run(
         self,
         generated_tokens: numpy.ndarray,
@@ -64,9 +91,24 @@ def run(
     ):
         generation_config = inference_state.current_state.get("generation_config")
         generated_logits = generated_logits if generation_config.output_scores else None
-        sequences = self.tokenizer.batch_decode(
-            generated_tokens, skip_special_tokens=True
-        )
+
+        import transformers
+
+        # Fix for LLAMA-specific models when running streaming
+        # TODO: make streaming a conditional input to this operator. using inference
+        # state is a quick fix.
+        if isinstance(
+            self.tokenizer,
+            (transformers.LlamaTokenizer, transformers.LlamaTokenizerFast),
+        ) and inference_state.current_state.get("streaming"):
+            past_tokens_queue = inference_state.current_state.get("past_tokens_queue")
+            sequences = self._generate_streamed_text_from_past_tokens(
+                generated_tokens, past_tokens_queue
+            )
+        else:
+            sequences = self.tokenizer.batch_decode(
+                generated_tokens, skip_special_tokens=True
+            )
 
         try:
             finished_reason = [f[-1] for f in finished_reason]