[LLM] llm transformers format interface first part (#8276)

* llm-transformers-format

* update

* fix style

python/llm/src/bigdl/llm/ggml/model/generation/__init__.py

@@ -0,0 +1,22 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# This would makes sure Python is aware there is more than one sub-package within bigdl,
+# physically located elsewhere.
+# Otherwise there would be module not found error in non-pip's setting as Python would
+# only search the first bigdl package and end up finding only one sub-package.
+
+from .utils import GenerationMixin

python/llm/src/bigdl/llm/ggml/model/generation/utils.py

@@ -0,0 +1,86 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# This would makes sure Python is aware there is more than one sub-package within bigdl,
+# physically located elsewhere.
+# Otherwise there would be module not found error in non-pip's setting as Python would
+# only search the first bigdl package and end up finding only one sub-package.
+
+
+from typing import Optional, Union, Sequence, List
+from bigdl.llm.utils.common import invalidInputError
+from bigdl.llm.ggml.model.gptneox import gptneox_cpp
+
+
+class GenerationMixin:
+    """
+    A class containing all functions for auto-regressive text generation
+
+    Pass custom parameter values to 'generate' .
+    """
+    def generate(
+        self,
+        inputs: Union[Optional[Sequence[int]], Sequence[gptneox_cpp.gptneox_token]]=None,
+        max_new_tokens: int = 128,
+        top_k: int = 40,
+        top_p: float = 0.95,
+        temperature: float = 0.80,
+        repetition_penalty: float = 1.1,
+        reset: bool = True,
+        frequency_penalty: float = 0.0,
+        presence_penalty: float = 0.0,
+        tfs_z: float = 1.0,
+        mirostat_mode: int = 0,
+        mirostat_tau: float = 5.0,
+        mirostat_eta: float = 0.1,
+        stop: Optional[Union[str, List[str]]]=[],
+        **kwargs,
+    ) -> Union[Optional[Sequence[int]], Optional[Sequence[gptneox_cpp.gptneox_token]], None]:
+        # TODO: modify docs
+        """Create a generator of tokens from a prompt.
+
+        Examples:
+            >>> llama = Llama("models/ggml-7b.bin")
+            >>> tokens = llama.tokenize(b"Hello, world!")
+            >>> for token in llama.generate(tokens, top_k=40, top_p=0.95,
+            >>>                             temp=1.0, repeat_penalty=1.1):
+            ...     print(llama.detokenize([token]))
+
+        Args:
+            tokens: The prompt tokens.
+            top_k: The top-k sampling parameter.
+            top_p: The top-p sampling parameter.
+            temp: The temperature parameter.
+            repeat_penalty: The repeat penalty parameter.
+            reset: Whether to reset the model state.
+
+        Yields:
+            The generated tokens.
+        """
+        # TODO: stop & max_token
+        self._generate(tokens=inputs,
+                       top_k=top_k,
+                       top_p=top_p,
+                       temp=temperature,
+                       repeat_penalty=repetition_penalty,
+                       reset=reset,
+                       frequency_penalty=frequency_penalty,
+                       presence_penalty=presence_penalty,
+                       tfs_z=tfs_z,
+                       mirostat_mode=mirostat_mode,
+                       mirostat_tau=mirostat_tau,
+                       mirostat_eta=mirostat_eta,
+                       **kwargs)

python/llm/src/bigdl/llm/ggml/model/gptneox/gptneox.py

@@ -497,13 +497,13 @@ class Gptneox:
             mirostat_eta=gptneox_cpp.c_float(mirostat_eta),
         )
 
-    def generate(
+    def _generate(
         self,
         tokens: Sequence[gptneox_cpp.gptneox_token],
-        top_k: int,
-        top_p: float,
-        temp: float,
-        repeat_penalty: float,
+        top_k: int = 40,
+        top_p: float = 0.95,
+        temp: float = 0.80,
+        repeat_penalty: float = 1.1,
         reset: bool = True,
         frequency_penalty: float = 0.0,
         presence_penalty: float = 0.0,
@@ -700,7 +700,7 @@ class Gptneox:
 
         finish_reason = "length"
         multibyte_fix = 0
-        for token in self.generate(
+        for token in self._generate(
             prompt_tokens,
             top_k=top_k,
             top_p=top_p,

python/llm/src/bigdl/llm/ggml/model/llama/llama.py

@@ -55,6 +55,7 @@ import multiprocessing
 from typing import List, Optional, Union, Generator, Sequence, Iterator, Deque, Tuple
 from collections import deque, OrderedDict
 from bigdl.llm.utils.common import invalidInputError
+from bigdl.llm.ggml.model.generation import GenerationMixin
 from . import llama_cpp
 from .llama_types import *
 
@@ -119,7 +120,7 @@ class LlamaState:
         self.llama_state_size = llama_state_size
 
 
-class Llama:
+class Llama(GenerationMixin):
     """High-level Python wrapper for a llama.cpp model."""
 
     def __init__(
@@ -515,7 +516,7 @@ class Llama:
             penalize_nl=penalize_nl,
         )
 
-    def generate(
+    def _generate(
         self,
         tokens: Sequence[int],
         top_k: int = 40,
@@ -730,7 +731,7 @@ class Llama:
 
         finish_reason = "length"
         multibyte_fix = 0
-        for token in self.generate(
+        for token in self._generate(
             prompt_tokens,
             top_k=top_k,
             top_p=top_p,