initial implementation

src/deepsparse/transformers/pipelines/text_generation.py

@@ -13,12 +13,14 @@
 # limitations under the License.
 
 import logging
+import warnings
 from typing import List, Optional, Tuple, Type, Union
 
 import numpy
 from pydantic import BaseModel, Field
 
 from deepsparse import Pipeline
+from deepsparse.cpu import cpu_avx512_compatible
 from deepsparse.pipeline import DEEPSPARSE_ENGINE
 from deepsparse.transformers.engines import NLDecoderEngine
 from deepsparse.transformers.pipelines import TransformersPipeline
@@ -115,9 +117,19 @@ def __init__(
         # TODO: Set this to 64 once we modify the OPT injection logic
         prompt_processing_sequence_length: int = 128,
         force_max_tokens: bool = False,
-        use_deepsparse_cache: bool = False,
+        use_deepsparse_cache: bool = True,
         **kwargs,
     ):
+        print(cpu_avx512_compatible())
+        if not cpu_avx512_compatible() and kwargs["engine_type"] == DEEPSPARSE_ENGINE:
+            warnings.warn(
+                "Detected CPU is not AVX512 compatible. "
+                "The kv cache management will not be supported "
+                "by the optimized engine. The user may experience "
+                "non optimal performance."
+            )
+            use_deepsparse_cache = False
+
         if use_deepsparse_cache:
             if kwargs["engine_type"] != DEEPSPARSE_ENGINE:
                 raise ValueError(
@@ -126,10 +138,6 @@ def __init__(
                     f"is {kwargs['engine_type']}. "
                     f"Make sure to set `engine_type` to {DEEPSPARSE_ENGINE}"
                 )
-            raise NotImplementedError(
-                "The deepsparse kv cache is not yet "
-                "supported for text generation pipelines"
-            )
 
         super().__init__(
             **kwargs, _delay_engine_initialize=True, _delay_overwriting_inputs=True