LLM: optimize namespace and remove unused import logic (#8302)

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

@@ -19,6 +19,5 @@
 # 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 .quantize import quantize
+from bigdl.llm.utils.common import LazyImport
-from .convert import _convert_to_ggml
+convert_model = LazyImport('bigdl.llm.ggml.convert_model.convert_model')
-from .convert_model import convert_model

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

@@ -60,7 +60,7 @@ class Bloom:
                  n_ctx: int = 512,
                  seed: int = 1337,
                  logits_all: bool = False,
-                 n_threads: int = -1,
+                 n_threads: int = 2,
                  n_batch: int = 8,
                  last_n_tokens_size: int = 64,
                  verbose: bool = True,
@@ -72,8 +72,7 @@ class Bloom:
             n_ctx: Maximum context size.
             seed: Random seed. 0 for random.
             logits_all: Return logits for all tokens, not just the last token.
-            n_threads: Number of threads to use.
+            n_threads: Number of threads to use. Default to be 2.
-                       If None, the number of threads is automatically determined.
             n_batch: Maximum number of prompt tokens to batch together when calling llama_eval.
             last_n_tokens_size: Maximum number of tokens to keep in the last_n_tokens deque.
             verbose: Print verbose output to stderr.

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

@@ -139,7 +139,7 @@ class Gptneox(GenerationMixin):
         use_mmap: bool = True,
         use_mlock: bool = False,
         embedding: bool = False,
-        n_threads: Optional[int] = None,
+        n_threads: Optional[int] = 2,
         n_batch: int = 512,
         last_n_tokens_size: int = 64,
         lora_base: Optional[str] = None,
@@ -160,8 +160,7 @@ class Gptneox(GenerationMixin):
             use_mmap: Use mmap if possible.
             use_mlock: Force the system to keep the model in RAM.
             embedding: Embedding mode only.
-            n_threads: Number of threads to use. If None, the number of threads
+            n_threads: Number of threads to use. Default to be 2.
-            is automatically determined.
             n_batch: Maximum number of prompt tokens to batch together when calling gptneox_eval.
             last_n_tokens_size: Maximum number of tokens to keep in the last_n_tokens deque.
             lora_base: Optional path to base model, useful if using a quantized base model and
@@ -197,7 +196,7 @@ class Gptneox(GenerationMixin):
 
         self.cache: Optional[GptneoxCache] = None
 
-        self.n_threads = n_threads or max(multiprocessing.cpu_count() // 2, 1)
+        self.n_threads = n_threads
 
         self.lora_base = lora_base
         self.lora_path = lora_path

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

@@ -137,7 +137,7 @@ class Llama(GenerationMixin):
         use_mmap: bool = True,
         use_mlock: bool = False,
         embedding: bool = False,
-        n_threads: Optional[int] = None,
+        n_threads: Optional[int] = 2,
         n_batch: int = 512,
         last_n_tokens_size: int = 64,
         lora_base: Optional[str] = None,
@@ -158,8 +158,7 @@ class Llama(GenerationMixin):
             use_mmap: Use mmap if possible.
             use_mlock: Force the system to keep the model in RAM.
             embedding: Embedding mode only.
-            n_threads: Number of threads to use. If None, the number of threads is
+            n_threads: Number of threads to use. Default to be 2.
-            automatically determined.
             n_batch: Maximum number of prompt tokens to batch together when calling llama_eval.
             last_n_tokens_size: Maximum number of tokens to keep in the last_n_tokens deque.
             lora_base: Optional path to base model, useful if using a quantized base model and
@@ -194,7 +193,7 @@ class Llama(GenerationMixin):
 
         self.cache: Optional[LlamaCache] = None
 
-        self.n_threads = n_threads or max(multiprocessing.cpu_count() // 2, 1)
+        self.n_threads = n_threads
 
         self.lora_base = lora_base
         self.lora_path = lora_path

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

@@ -21,9 +21,7 @@
 
 import os
 import traceback
-from huggingface_hub import snapshot_download
 from bigdl.llm.utils.common import invalidInputError
-from bigdl.llm.ggml import convert_model
 
 
 class AutoModelForCausalLM:
@@ -71,6 +69,7 @@ class AutoModelForCausalLM:
         if not os.path.exists(pretrained_model_name_or_path):
             try:
                 # download from huggingface based on repo id
+                from huggingface_hub import snapshot_download
                 pretrained_model_name_or_path = snapshot_download(
                     repo_id=pretrained_model_name_or_path)
             except Exception as e:
@@ -90,6 +89,7 @@ class AutoModelForCausalLM:
         # points to a huggingface checkpoint
         if not os.path.isfile(pretrained_model_name_or_path):
             # huggingface checkpoint
+            from bigdl.llm.ggml import convert_model
             ggml_model_path = convert_model(input_path=pretrained_model_name_or_path,
                                             output_path=cache_dir,
                                             model_family=model_family,

python/llm/src/bigdl/llm/models.py

@@ -0,0 +1,24 @@
+#
+# 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 bigdl.llm.ggml.model.llama import Llama
+from bigdl.llm.ggml.model.gptneox import Gptneox
+from bigdl.llm.ggml.model.bloom import Bloom

python/llm/src/bigdl/llm/utils/common/__init__.py

@@ -20,3 +20,4 @@
 # only search the first bigdl package and end up finding only one sub-package.
 
 from .log4Error import invalidInputError, invalidOperationError
+from .lazyimport import LazyImport

python/llm/src/bigdl/llm/utils/common/lazyimport.py

@@ -0,0 +1,76 @@
+#
+# 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.
+#
+
+import importlib
+import sys
+
+
+# code adaptted from https://github.com/intel/neural-compressor/
+#                    blob/master/neural_compressor/utils/utility.py#L88
+class LazyImport:
+    """
+    Lazy import python module until use.
+
+    Example:
+        >>> from bigdl.llm.utils.common import LazyImport
+        >>> _convert_to_ggml = LazyImport('bigdl.llm.ggml.convert._convert_to_ggml')
+        >>> _convert_to_ggml(model_path, outfile_dir)
+    """
+    def __init__(self, module_name: str):
+        """
+        :param module_name: Import module name.
+        """
+        self.module_name = module_name
+
+    def __getattr__(self, name):
+        absolute_name = importlib.util.resolve_name(self.module_name)
+        # not reload modules
+        try:
+            return getattr(sys.modules[absolute_name], name)
+        except (KeyError, AttributeError):
+            pass
+
+        if "." in absolute_name:
+            # Split module name to prevent class name from being introduced as package
+            parent_name, _, child_name = absolute_name.rpartition('.')
+        else:
+            parent_name, child_name = absolute_name, None
+
+        try:
+            # For import parent module and get the submodule with getattr.
+            module = importlib.import_module(parent_name)
+            module = getattr(module, child_name) if child_name else module
+        except AttributeError:
+            # Triggered when the parent module cannot get the child module using getattr.
+            # More common when calling staticmethods or classmethods. e.g. from_tsdataset.
+            full_module_name = parent_name+'.'+child_name if child_name else parent_name
+            spec = importlib.util.find_spec(full_module_name)
+            module = importlib.util.module_from_spec(spec)
+            spec.loader.exec_module(module)
+
+        return getattr(module, name)
+
+    def __call__(self, *args, **kwargs):
+        function_name = self.module_name.rpartition('.')[-1]
+        module_name = self.module_name.rpartition(f'.{function_name}')[0]
+        try:
+            module = sys.modules[module_name]
+        except KeyError:
+            pass
+
+        module = importlib.import_module(module_name)
+        function = getattr(module, function_name)
+        return function(*args, **kwargs)