fix export for phi1.5 (#400)

Author: eaidova

llm_bench/python/convert.py

@@ -1201,6 +1201,44 @@ def convert_falcon(args):
         unpatch_gptq(cuda, post_init)
 
 
+def convert_phi(args):
+    trust_remote_code = False
+    try:
+        config = AutoConfig.from_pretrained(args.model_id)
+    except Exception:
+        config = AutoConfig.from_pretrained(args.model_id, trust_remote_code=True)
+        trust_remote_code = True
+    cuda, post_init = patch_gptq(config)
+    model_kwargs = {}
+    if trust_remote_code:
+        model_kwargs["trust_remote_code"] = trust_remote_code
+    precision = args.precision
+    compression_only = (
+        args.compress_weights
+        and not args.force_convert
+        and not is_torch_compression(args)
+        and is_ov_model_provided(args.model_id, args.output_dir, args.precision)
+    )
+    if post_init is not None:
+        model_kwargs["torch_dtype"] = torch.float32
+    pt_model = None
+    gptq_applied = is_gptq(config)
+    precision = precision if not gptq_applied else GPTQ_DIR.format(precision=args.precision)
+    if not compression_only:
+        pt_model = AutoModelForCausalLM.from_pretrained(
+            args.model_id,
+            config=AutoConfig.from_pretrained(args.model_id),
+            **model_kwargs,
+        )
+        pt_model.config.use_cache = True
+        pt_model.eval()
+
+    convert_optimum_causallm_base(pt_model, args, config, compression_only)
+
+    if post_init is not None:
+        unpatch_gptq(cuda, post_init)
+
+
 def convert_baichaun(args):
     config = AutoConfig.from_pretrained(args.model_id, trust_remote_code=True)
     cuda, post_init = patch_gptq(config)
@@ -1304,6 +1342,7 @@ def convert_aquilachat(args):
     "lcm": convert_lcm,
     "ldm": convert_ldm_super_res,
     "mpt": convert_mpt,
+    "phi-": convert_phi,
     "replit": convert_mpt,
     "chatglm2": convert_causal_lm,
     "chatglm3": convert_causal_lm,

llm_bench/python/utils/ov_utils.py

@@ -141,40 +141,24 @@ def create_text_gen_model(model_path, device, **kwargs):
     if not model_path_existed:
         raise RuntimeError(f'==Failure ==: model path:{model_path} does not exist')
     else:
-        if model_type in ['replit', 'codegen2', 'chatglm']:
-            start = time.perf_counter()
-            ov_model = model_class.from_pretrained(
-                model_path,
-                device=device,
-                ov_config=ov_config,
-                config=AutoConfig.from_pretrained(model_path, trust_remote_code=True),
-                stateful=kwargs.get("stateful", None)
-            )
-            end = time.perf_counter()
-        elif model_type in ['falcon', "mpt"]:
-            start = time.perf_counter()
-            ov_model = model_class.from_pretrained(
-                model_path,
-                device=device,
-                ov_config=ov_config,
-                stateful=kwargs.get("stateful", None),
-                trust_remote_code=False
-            )
-            end = time.perf_counter()
-        else:
-            start = time.perf_counter()
-            config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
-            ov_model = model_class.from_pretrained(
-                model_path,
-                device=device,
-                ov_config=ov_config,
-                config=config,
-                compile=False,
-                stateful=kwargs.get("stateful", None)
-            )
-            if not isinstance(ov_model, OV_MODEL_CLASSES_MAPPING['t5']):
-                patch_inter_processing_and_compile(ov_model, **kwargs)
-            end = time.perf_counter()
+        remote_code = False
+        try:
+            model_config = AutoConfig.from_pretrained(model_path)
+        except Exception:
+            model_config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
+            remote_code = True
+        start = time.perf_counter()
+        ov_model = model_class.from_pretrained(
+            model_path,
+            device=device,
+            ov_config=ov_config,
+            config=model_config,
+            stateful=kwargs.get("stateful", None),
+            trust_remote_code=remote_code
+        )
+        if not isinstance(ov_model, OV_MODEL_CLASSES_MAPPING['t5']):
+            patch_inter_processing_and_compile(ov_model, **kwargs)
+        end = time.perf_counter()
     if kwargs['num_beams'] > 1:
         bench_hook = utils.hook_beam_search.BeamSearchHook()
     else: