LLM: Align converting GPTQ model API with transformer style (#8365)

* LLM: Align GPTQ API with transformer style

python/llm/README.md

@@ -39,7 +39,7 @@ Here is an example to use `llm-convert` command line tool.
 # pth model
 llm-convert "/path/to/llama-7b-hf/" --model-format pth --outfile "/path/to/llama-7b-int4/" --model-family "llama"
 # gptq model
-llm-convert "/path/to/vicuna-13B-1.1-GPTQ-4bit-128g.pt" --model-format gptq --outfile "/path/to/out.bin" --tokenizer-path "/path/to/tokenizer.model" --model-family "llama"
+llm-convert "/path/to/vicuna-13B-1.1-GPTQ-4bit-128g/" --model-format gptq --outfile "/path/to/out.bin" --model-family "llama"
 ```
 
 Here is an example to use `llm_convert` python API.

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

@@ -24,9 +24,7 @@ import argparse
 def _special_kwarg_check(kwargs, check_args):
     _used_args = {}
     for arg in kwargs:
-        if arg not in check_args:
+        if arg in check_args:
-            return False, {arg: kwargs[arg]}
-        else:
             _used_args[arg] = kwargs[arg]
     return True, _used_args
 
@@ -38,9 +36,8 @@ def llm_convert(model,
                 model_format="pth",
                 **kwargs):
     if model_format == "pth":
-        check, _used_args = _special_kwarg_check(kwargs=kwargs,
+        _, _used_args = _special_kwarg_check(kwargs=kwargs,
                                              check_args=["tmp_path"])
-        invalidInputError(check, f"Invaid input kwargs found: {_used_args}")
         return ggml_convert_model(input_path=model,
                                   output_path=outfile,
                                   model_family=model_family,
@@ -48,21 +45,15 @@ def llm_convert(model,
                                   **_used_args,
                                   )
     elif model_format == "gptq":
-        invalidInputError(model.endswith(".pt"), "only support pytorch's .pt format now.")
         invalidInputError(model_family == "llama" and outtype == 'int4',
                           "Convert GPTQ models should always "
                           "specify `--model-family llama --dtype int4` in the command line.")
-        check, _used_args = _special_kwarg_check(kwargs=kwargs,
+        _, _used_args = _special_kwarg_check(kwargs=kwargs,
                                              check_args=["tokenizer_path"])
-        invalidInputError(check, f"Invaid input kwargs found: {_used_args}")
-        invalidInputError("tokenizer_path" in _used_args,
-                          "The GPT-Q model requires the `tokenizer_path` parameter to be provided."
-                          "Usage: convert-model --model-type gptq"
-                          "--model-family llama --input-path llamaXXb-4bit.pt"
-                          "--tokenizer-path tokenizer.model --output-path out.bin")
         convert_gptq2ggml(input_path=model,
+                          output_path=outfile,
                           tokenizer_path=_used_args["tokenizer_path"],
-                          output_path=outfile)
+                          )
         return outfile
     else:
         invalidInputError(False, f"Unsupported input model_type: {model_format}")

python/llm/src/bigdl/llm/gptq/convert/convert_gptq_to_ggml.py

@@ -30,6 +30,15 @@ from sentencepiece import SentencePieceProcessor
 from bigdl.llm.utils.common.log4Error import invalidInputError
 
 
+def find_pt_files(directory):
+    pt_files = []
+    for root, _, files in os.walk(directory):
+        for file in files:
+            if file.endswith(".pt"):
+                pt_files.append(os.path.join(root, file))
+    return pt_files
+
+
 def write_header(fout, shape, dst_name, ftype_cur):
     sname = dst_name.encode('utf-8')
     fout.write(struct.pack("iii", len(shape), len(sname), ftype_cur))
@@ -155,8 +164,13 @@ def convert_q4(src_name, dst_name, model, fout, n_head, permute=False):
     blob.tofile(fout)
 
 
-def convert_gptq2ggml(input_path, tokenizer_path, output_path):
+def convert_gptq2ggml(input_path, output_path, tokenizer_path=None):
-    model = torch.load(input_path, map_location="cpu")
+    input_models = find_pt_files(input_path)
+    invalidInputError(len(input_models) == 1,
+                      "Only support pytorch's .pt format now."
+                      f"There should be only one .pt under {input_path}, "
+                      f"but found {len(input_models)} file(s).")
+    model = torch.load(input_models[0], map_location="cpu")
 
     n_vocab, n_embd = model['model.embed_tokens.weight'].shape
     layer_re = r'model\.layers\.([0-9]+)'
@@ -167,6 +181,12 @@ def convert_gptq2ggml(input_path, tokenizer_path, output_path):
     n_mult = 256
     n_head = {32: 32, 40: 40, 60: 52, 80: 64}[n_layer]
 
+    if not tokenizer_path:
+        tokenizer_path = os.path.join(input_path, "tokenizer.model")
+        invalidInputError(os.path.isfile(tokenizer_path),
+                          f"tokenizer.model was not found under {tokenizer_path}."
+                          f"Please specify the tokenizer-path")
+
     tokenizer = SentencePieceProcessor(tokenizer_path)
 
     invalidInputError(tokenizer.vocab_size() == n_vocab, "vocab size not match.")
@@ -241,4 +261,4 @@ if __name__ == "__main__":
     fname_tokenizer = sys.argv[2]
     out_path = sys.argv[3]
     invalidInputError(fname_model.endswith(".pt"), "only support pytorch's .pt format now.")
-    convert_gptq2ggml(fname_model, fname_tokenizer, out_path)
+    convert_gptq2ggml(fname_model, out_path, tokenizer_path=fname_tokenizer)