LLM: enhancements for convert_model (#8278)

* update convert

* change output name

* add discription for input_path, add check for input_values

* basic support for command line

* fix style

* update based on comment

* update based on comment

python/llm/setup.py

@@ -42,7 +42,7 @@ BIGDL_PYTHON_HOME = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 VERSION = open(os.path.join(BIGDL_PYTHON_HOME, 'version.txt'), 'r').read().strip()
 llm_home = os.path.join(os.path.dirname(os.path.abspath(__file__)), "src")
 libs_dir = os.path.join(llm_home, "bigdl", "llm", "libs")
-CONVERT_DEP = ['numpy', 'torch', 'transformers', 'sentencepiece']
+CONVERT_DEP = ['numpy', 'torch', 'transformers', 'sentencepiece', 'accelerate']
 
 
 def get_llm_packages():
@@ -151,6 +151,11 @@ def setup_package():
         package_dir={"": "src"},
         package_data={"bigdl.llm": package_data[platform_name]},
         include_package_data=True,
+        entry_points={
+            "console_scripts": [
+                'convert_model=bigdl.llm.ggml.convert_model:main'
+            ]
+        },
         extras_require={"all": all_requires},
         classifiers=[
             'License :: OSI Approved :: Apache Software License',

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

@@ -77,7 +77,11 @@ def _convert_to_ggml(model_path: str, outfile_dir: str,
     """
     Convert Hugging Face llama-like / gpt-neox-like / bloom-like model to ggml format.
 
-    :param model_path: str, path of model, for example `./llama-7b-hf`.
+    :param input_path: Path to a *directory*  for huggingface checkpoint that are directly
+            pulled from huggingface hub, for example `./llama-7b-hf`. This should be a dir
+            path that contains: weight bin, tokenizer config, tokenizer.model (required for
+            llama) and added_tokens.json (if applied).
+            For lora finetuned model, the path should be pointed to a merged weight.
     :param outfile_dir: str, the directory to save ggml compatible file, for example `./models`.
     :param model_family: Which model family your input model belongs to. Default to `llama`.
             Now only `llama`/`bloom`/`gptneox` are supported.

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

@@ -16,47 +16,99 @@
 import os
 import time
 from pathlib import Path
-
 from bigdl.llm.ggml.convert import _convert_to_ggml
 from bigdl.llm.ggml.quantize import quantize
+from bigdl.llm.utils.common import invalidInputError
+import argparse
+import tempfile
 
 
 def convert_model(input_path: str,
                   output_path: str,
                   model_family: str,
                   dtype: str = 'int4',
-                  tmp_path: str = '/tmp'):
+                  tmp_path: str = None):
     """
     Convert Hugging Face llama-like / gpt-neox-like / bloom-like model to lower precision
 
-    :param input_path: str, path of model, for example `./llama-7b-hf`.
+    :param input_path: Path to a *directory*  for huggingface checkpoint that are directly
-    :param output_path: Save path of output quantized model. Default to `None`.
+            pulled from huggingface hub, for example `./llama-7b-hf`. This should be a dir
-            If you don't specify this parameter, quantized model will be saved in
+            path that contains: weight bin, tokenizer config, tokenizer.model (required for
-            the same directory as the input and just replace precision with quantize_type
+            llama) and added_tokens.json (if applied).
-            like `./ggml-model-q4_0.bin`.
+            For lora finetuned model, the path should be pointed to a merged weight.
+    :param output_path: Save path of output quantized model. You must pass a *directory* to
+            save all related output.
     :param model_family: Which model family your input model belongs to.
             Now only `llama`/`bloom`/`gptneox` are supported.
     :param dtype: Which quantized precision will be converted.
-            Now only int4 supported.
+            Now only int4 is supported.
     :param tmp_path: Which path to store the intermediate model during the conversion process.
+            Default to `None` so that intermediate model will not be saved.
 
-    :return: the path str to the converted lower precision checkpoint
+    :return: the path string to the converted lower precision checkpoint.
     """
 
     dtype = dtype.lower()
+    # make sure directory exists
+    os.makedirs(output_path, exist_ok=True)
+    # check input value
+    invalidInputError(model_family in ['llama', 'bloom', 'gptneox'],
+                      "Now we only support quantization of model \
+                       family('llama', 'bloom', 'gptneox')",
+                      "{} is not in the list.".format(model_family))
+    invalidInputError(os.path.isdir(output_path),
+                      "The output_path {} was not a directory".format(output_path))
+    invalidInputError(dtype == 'int4',
+                      "Now only int4 is supported.")
+    # check for input_path
+    invalidInputError(os.path.exists(input_path),
+                      "The input path {} was not found".format(input_path))
+    invalidInputError(os.path.isdir(input_path),
+                      "The input path {} was not a directory".format(input_path))
+    # shall we support model_id or just model directory?
+
     if dtype == 'int4':
         dtype = 'q4_0'
 
+    if tmp_path is not None:
         model_name = Path(input_path).stem
         tmp_ggml_file_path = os.path.join(tmp_path, f'{model_name}_{int(time.time())}')
         _convert_to_ggml(model_path=input_path,
                          outfile_dir=tmp_ggml_file_path,
                          model_family=model_family,
                          outtype="fp16")
-
         tmp_ggml_file_path = next(Path(tmp_ggml_file_path).iterdir())
-
         return quantize(input_path=tmp_ggml_file_path,
                         output_path=output_path,
                         model_family=model_family,
                         dtype=dtype)
+    else:
+        with tempfile.TemporaryDirectory() as tmp_ggml_file_path:
+            _convert_to_ggml(model_path=input_path,
+                             outfile_dir=tmp_ggml_file_path,
+                             model_family=model_family,
+                             outtype="fp16")
+            tmp_ggml_file_path = next(Path(tmp_ggml_file_path).iterdir())
+            return quantize(input_path=tmp_ggml_file_path,
+                            output_path=output_path,
+                            model_family=model_family,
+                            dtype=dtype)
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Model Convert Parameters')
+    parser.add_argument('-i', '--input_path', type=str, required=True,
+                        help=("input_path, a path to a *directory* containing model weights"))
+    parser.add_argument('-o', '--output_path', type=str, required=True,
+                        help=("output_path,save path of output quantized model."))
+    parser.add_argument('-x', '--model_family', type=str, required=True,
+                        help=("model_family: Which model family your input model belongs to."
+                              "Now only `llama`/`bloom`/`gptneox` are supported."))
+    parser.add_argument('-t', '--dtype', type=str, default="int4",
+                        help="Which quantized precision will be converted.")
+    parser.add_argument('-p', '--tmp_path', type=str, default=None,
+                        help="Which path to store the intermediate model during the"
+                        "conversion process.")
+    args = parser.parse_args()
+    params = vars(args)
+    convert_model(**params)

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

@@ -42,24 +42,23 @@ _quantize_type = {"llama": _llama_quantize_type,
                   "gptneox": _gptneox_quantize_type}
 
 
-def quantize(input_path: str, output_path: str=None,
+def quantize(input_path: str, output_path: str,
-             model_family: str = 'llama', dtype: str='q4_0'):
+             model_family: str, dtype: str='q4_0'):
     """
     Quantize ggml file to lower precision.
 
     :param input_path: Path of input ggml file, for example `./ggml-model-f16.bin`.
-    :param output_path: Save path of output quantized model. Default to `None`.
+    :param output_path: Save path of output quantized model. You must pass a directory to
-            If you don't specify this parameter, quantized model will be saved in
+            save all related output. Filename of quantized model will be like
-            the same directory as the input and just replace precision with quantize_type
+            `bigdl_llm_llama_q4_0.bin`.
-            like `./ggml-model-q4_0.bin`.
+    :param model_family: Which model family your input model belongs to.
-    :param model_family: Which model family your input model belongs to. Default to `llama`.
             Now only `llama`/`bloom`/`gptneox` are supported.
     :param dtype: Quantization method which differs in the resulting model disk size and
-            inference speed. Defalut to `q4_0`. Difference model family may support different types,
+            inference speed. Defalut to `q4_0`. Difference model family may support
-            now the supported list is:
+            different types, now the supported list is:
             llama : "q4_0", "q4_1", "q4_2"
             bloom : "q4_0", "q4_1"
-            gptneox : "q4_0", "q4_1", "q4_2", "q5_0", "q5_1", "q8_0"
+            gptneox : "q4_0", "q4_1", "q5_0", "q5_1", "q8_0"
 
     :return: the path str to the converted ggml binary checkpoint
     """
@@ -69,12 +68,14 @@ def quantize(input_path: str, output_path: str=None,
                       "{} is not in the list.".format(model_family))
     invalidInputError(os.path.isfile(input_path),
                       "The file {} was not found".format(input_path))
-    # TODO : multi input model path
+    invalidInputError(os.path.isdir(output_path),
-    if output_path is None:
+                      "The output_path {} was not a directory".format(output_path))
-        output_path = Path(str(input_path).replace('f16', dtype))
     # convert quantize type str into corresponding int value
     quantize_type_map = _quantize_type[model_family]
-    invalidInputError(dtype in quantize_type_map, "{0} model just accept {1} now, \
+    output_filename = "bigdl_llm_{}_{}.bin".format(model_family,
+                                                   dtype.lower())
+    output_path = os.path.join(output_path, output_filename)
+    invalidInputError(dtype.lower() in quantize_type_map, "{0} model just accept {1} now, \
                       but you pass in {2}.".format(
                       model_family,
                       list(quantize_type_map.keys()),

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

@@ -42,7 +42,7 @@ class AutoModelForCausalLM:
         """
         :param pretrained_model_name_or_path: We support 3 kinds of pretrained model checkpoint
 
-               1. path for huggingface checkpoint that are directly pulled from hugginface hub.
+               1. path for huggingface checkpoint that are directly pulled from huggingface hub.
                   This should be a dir path that contains: weight bin, tokenizer config,
                   tokenizer.model (required for llama) and added_tokens.json (if applied).
                   For lora fine tuned model, the path should be pointed to a merged weight.