[Text Generation][Tests] Text Generation Pipeline

src/deepsparse/transformers/engines/nl_decoder_engine.py

@@ -16,16 +16,16 @@
 from typing import Any, Dict, List, Optional, Tuple
 
 import numpy
-import onnx
 from transformers import AutoTokenizer
 
 from deepsparse.engine import Context
 from deepsparse.pipeline import DEEPSPARSE_ENGINE, create_engine
 from deepsparse.transformers.utils.decoder_kv_cache import DecoderKVCache
-from deepsparse.transformers.utils.helpers import generate_session_id
+from deepsparse.transformers.utils.helpers import (
+    generate_session_id,
+    overwrite_onnx_model_inputs,
+)
 from deepsparse.utils.data import numpy_softmax
-from deepsparse.utils.onnx import translate_onnx_type_to_numpy
-from sparsezoo.utils.onnx import save_onnx
 
 
 _LOGGER = logging.getLogger(__name__)
@@ -71,7 +71,11 @@ def __init__(
         # flag to indicate if the model is quantized or not
         self.kv_cache_data_type = None
 
-        onnx_file_path, output_indices_to_be_cached = self.overwrite_onnx_model_inputs(
+        (
+            onnx_file_path,
+            output_indices_to_be_cached,
+            kv_cache_data_type,
+        ) = overwrite_onnx_model_inputs(
             onnx_file_path=onnx_file_path,
             batch_size=engine_args.get("batch_size", 1),
             sequence_length=sequence_length,
@@ -80,6 +84,7 @@ def __init__(
         kv_cache_enabled = False
         if sum(output_indices_to_be_cached):
             kv_cache_enabled = True
+            self.kv_cache_data_type = kv_cache_data_type
             if use_deepsparse_cache and engine_type == DEEPSPARSE_ENGINE:
                 # inform the engine, that are using the kv cache
                 engine_args["cache_output_bools"] = output_indices_to_be_cached
@@ -192,74 +197,6 @@ def transfer_cache_state(self, cache: DecoderKVCache):
         cache_to_copy.set_capacity(target_cache_capacity)
         self.kv_cache = cache_to_copy
 
-    def overwrite_onnx_model_inputs(
-        self,
-        onnx_file_path: str,
-        sequence_length: int,
-        input_ids_length: int,
-        batch_size: int = 1,
-    ) -> Tuple[str, List[int]]:
-        """
-        Enforces the appropriate input shapes for the onnx model, as well as
-        checks whether kv cache is enabled or not.
-
-        :param onnx_file_path: The path to the onnx model file that will be
-            overwritten with the new input shapes
-        :param batch_size: The batch size to use for the input
-        :param sequence_length: The sequence length to use for the input
-        :param input_ids_length: The length of input_ids
-        :return: The path to the onnx model file that has been overwritten
-            with the new input shapes, as well as the indices of the inputs
-            that should be cached
-        """
-        model = onnx.load(onnx_file_path, load_external_data=False)
-        initializer_input_names = set(node.name for node in model.graph.initializer)
-        external_inputs = [
-            inp for inp in model.graph.input if inp.name not in initializer_input_names
-        ]
-        for external_input in external_inputs:
-            # overwrite the batch size for all the inputs
-            external_input.type.tensor_type.shape.dim[0].dim_value = batch_size
-
-            if external_input.name in ["input_ids", "positions"]:
-                external_input.type.tensor_type.shape.dim[
-                    1
-                ].dim_value = input_ids_length
-            elif external_input.name == "attention_mask":
-                external_input.type.tensor_type.shape.dim[1].dim_value = sequence_length
-            elif external_input.name.startswith(_CACHE_INPUT_NAME):
-                external_input.type.tensor_type.shape.dim[2].dim_value = (
-                    sequence_length - input_ids_length
-                )
-            elif external_input.name.startswith("causal_mask"):
-                external_input.type.tensor_type.shape.dim[
-                    2
-                ].dim_value = input_ids_length
-                external_input.type.tensor_type.shape.dim[3].dim_value = sequence_length
-            else:
-                raise ValueError(
-                    f"Unexpected external input name: {external_input.name}"
-                )
-
-        _LOGGER.info(
-            "Overwriting in-place the input shapes "
-            f"of the transformer model at {onnx_file_path}"
-        )
-        save_onnx(model, onnx_file_path)
-
-        output_indices_to_be_cached = [
-            1 if inp.name.startswith("present") else 0 for inp in model.graph.output
-        ]
-        if any(output_indices_to_be_cached):
-            kv_cache_elem_type = next(
-                inp
-                for inp in model.graph.input
-                if inp.name.startswith(_CACHE_INPUT_NAME)
-            ).type.tensor_type.elem_type
-            self.kv_cache_data_type = translate_onnx_type_to_numpy(kv_cache_elem_type)
-
-        return onnx_file_path, output_indices_to_be_cached
-
     def generate_token(self, logits: numpy.ndarray) -> numpy.ndarray:
         """
         Samples a token from the logits using the sampling temperature.
@@ -283,7 +220,7 @@ def reset_kv_cache(self):
         kv_cache_state = self._initialize_kv_cache_state(
             self.sequence_length - self.input_ids_length
         )
-        self.kv_cache.setup_session(
+        self.kv_cache.setup(
             session_id=self._session_id,
             state=kv_cache_state,
             num_processed_tokens=0,
@@ -328,7 +265,7 @@ def update_kv_cache(
             name: array for name, array in zip(cache_onnx_names, kv_cache_state)
         }
 
-        self.kv_cache.update_session(
+        self.kv_cache.update(
             state=kv_cache_state,
             input_ids_len=input_ids_len,
         )
@@ -364,6 +301,6 @@ def _should_freeze_first_position(tokenizer) -> bool:
         # (True if tokenizer has a prefix for a BOS token)
         if tokenizer is None:
             return False
-        if hasattr(tokenizer, "bos_token"):
+        if hasattr(tokenizer, "add_bos_token"):
             return True
         return False

src/deepsparse/transformers/pipelines/text_generation.py

@@ -432,9 +432,8 @@ def prompt_inference(
             with self.timer_manager.current.time(
                 _TextGenerationTimings.PROMPT_PREFILL_SINGLE
             ):
-                new_token, new_logits = self.autoregressive_inference(
-                    run_tokens, shift_positions_by_one=not bool(num_tokens_processed)
-                )
+                new_token, new_logits = self.autoregressive_inference(run_tokens)
+
             prompt_logits.append(new_logits)
 
         tokens.append(new_token)
@@ -444,16 +443,12 @@ def prompt_inference(
     def autoregressive_inference(
         self,
         tokens: List[int],
-        shift_positions_by_one: bool = False,
     ) -> Tuple[int, numpy.ndarray]:
         """
         An inference run that processes the last token to generate
         a new token and new logits.
 
         :param tokens: The current context (prompt + generated tokens so far)
-        :param shift_positions_by_one: Whether to shift the positions
-            by one. Used if we are processing the prompt from the scratch
-            (i.e. not using the multitoken engine)
         :return: The new, generated token and the logits for the new token
             (with dimensions ['batch_size', 'num_tokens', 'vocab_size'])
         """
@@ -465,8 +460,7 @@ def autoregressive_inference(
         num_tokens_processed = min(len(tokens), self.sequence_length)  # cap by seq len
         attention_mask[:, -num_tokens_processed:] = 1
         positions = numpy.array([[len(tokens)]], dtype=numpy.int64)
-        if shift_positions_by_one:
-            positions -= 1
+        positions -= 1
         input_ids = numpy.array([[new_token]])
         causal_mask = create_causal_mask(input_ids, attention_mask)
 
@@ -523,28 +517,28 @@ def engine_inputs_for_prefill(
         num_batches = len(tokens) // self.prompt_processing_sequence_length
 
         token_batches = [
-            tokens[i : i + self.prompt_processing_sequence_length]
-            for i in range(num_batches)
+            tokens[
+                i
+                * self.prompt_processing_sequence_length : (i + 1)
+                * self.prompt_processing_sequence_length
+            ]
+            for i in range(0, num_batches)
         ]
 
         for idx, token_batch in enumerate(token_batches):
             engine_inputs = []
-
+            num_cached_entries = self.multitoken_engine.num_non_blank_cache_entries
             for name in self.multitoken_engine.onnx_input_names_no_cache:
                 if name == "input_ids":
                     engine_input = numpy.array([token_batch])
 
                 elif name == "attention_mask":
-                    num_cached_entries = (
-                        self.multitoken_engine.num_non_blank_cache_entries
-                    )
-
                     # create an empty attention mask
                     engine_input = numpy.zeros(
                         (1, self.sequence_length), dtype=numpy.int64
                     )
                     # fill it out with 1s (from the right), so that the number
-                    # of unmaksed entries is equal to the sum of:
+                    # of unmasked entries is equal to the sum of:
                     engine_input[
                         :,
                         -(
@@ -564,7 +558,11 @@ def engine_inputs_for_prefill(
                         engine_input = numpy.array([[idx]], dtype=numpy.int64)
                     else:
                         engine_input = (
-                            numpy.arange(self.prompt_processing_sequence_length)
+                            numpy.arange(
+                                num_cached_entries,
+                                num_cached_entries
+                                + self.prompt_processing_sequence_length,
+                            )
                             .reshape(1, -1)
                             .astype(numpy.int64)
                         )

src/deepsparse/transformers/utils/helpers.py

@@ -12,18 +12,93 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import logging
 import uuid
-from typing import List, Union
+from typing import List, Tuple, Union
 
 import numpy
+import onnx
+
+from deepsparse.utils.onnx import translate_onnx_type_to_numpy
+from sparsezoo.utils import save_onnx
 
 
 __all__ = [
     "generate_session_id",
     "pad_to_fixed_length",
     "create_causal_mask",
+    "overwrite_onnx_model_inputs",
 ]
 
+_LOGGER = logging.getLogger(__name__)
+
+
+def overwrite_onnx_model_inputs(
+    onnx_file_path: str,
+    sequence_length: int,
+    input_ids_length: int,
+    batch_size: int = 1,
+) -> Tuple[str, List[int]]:
+    """
+    Enforces the appropriate input shapes for the onnx model, as well as
+    checks whether kv cache is enabled or not.
+
+    :param onnx_file_path: The path to the onnx model file that will be
+        overwritten with the new input shapes
+    :param batch_size: The batch size to use for the input
+    :param sequence_length: The sequence length to use for the input
+    :param input_ids_length: The length of input_ids
+    :return: A tuple that contains:
+        -   the path to the onnx model file that has been overwritten
+            with the new input shapes
+        -   boolean list, where elements are set to True if the
+            corresponding model output should be cached or False
+            if not.
+        -   the data type of the kv cache. If the model does not
+            use kv cache, then the data type is None
+    """
+    model = onnx.load(onnx_file_path, load_external_data=False)
+    initializer_input_names = set(node.name for node in model.graph.initializer)
+    external_inputs = [
+        inp for inp in model.graph.input if inp.name not in initializer_input_names
+    ]
+    for external_input in external_inputs:
+        # overwrite the batch size for all the inputs
+        external_input.type.tensor_type.shape.dim[0].dim_value = batch_size
+
+        if external_input.name in ["input_ids", "positions"]:
+            external_input.type.tensor_type.shape.dim[1].dim_value = input_ids_length
+        elif external_input.name == "attention_mask":
+            external_input.type.tensor_type.shape.dim[1].dim_value = sequence_length
+        elif external_input.name.startswith("past_key_values"):
+            external_input.type.tensor_type.shape.dim[2].dim_value = (
+                sequence_length - input_ids_length
+            )
+        elif external_input.name.startswith("causal_mask"):
+            external_input.type.tensor_type.shape.dim[2].dim_value = input_ids_length
+            external_input.type.tensor_type.shape.dim[3].dim_value = sequence_length
+        else:
+            raise ValueError(f"Unexpected external input name: {external_input.name}")
+
+    _LOGGER.info(
+        "Overwriting in-place the input shapes "
+        f"of the transformer model at {onnx_file_path}"
+    )
+    save_onnx(model, onnx_file_path)
+
+    output_indices_to_be_cached = [
+        1 if inp.name.startswith("present") else 0 for inp in model.graph.output
+    ]
+
+    kv_cache_data_type = None
+    if any(output_indices_to_be_cached):
+        kv_cache_elem_type = next(
+            inp for inp in model.graph.input if inp.name.startswith("past_key_values")
+        ).type.tensor_type.elem_type
+        kv_cache_data_type = translate_onnx_type_to_numpy(kv_cache_elem_type)
+
+    return onnx_file_path, output_indices_to_be_cached, kv_cache_data_type
+
 
 def generate_session_id() -> str:
     """