removed NPU-related code to make HF happy

deps/intel_npu_acceleration_library-1.3.0-cp310-cp310-win_amd64.whl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:af65c7048377c8000a48dd00174cbe47f5c9264fca511dc0d1669148df22214d
-size 39727569

repository/intel_npu.py

@@ -1,58 +1,58 @@
-import json
-from pathlib import Path
-
-from intel_npu_acceleration_library import NPUModelForCausalLM, int4
-from intel_npu_acceleration_library.compiler import CompilerConfig
-from transformers import AutoTokenizer
-
-from repository.repository_abc import Repository, Model
-
-
-class IntelNpuRepository(Repository):
-    def __init__(self, model_info: Model, system_msg: str = None, log_to_file: Path = None):
-        self.model_info: Model = model_info
-        self.message_history: list[dict[str, str]] = []
-        self.set_message_for_role(self.model_info.roles.system_role, system_msg)
-        self.model = None
-        self.tokenizer = None
-        self.terminators = None
-        self.log_to_file = log_to_file
-
-    def get_model_info(self) -> Model:
-        return self.model_info
-
-    def get_message_history(self) -> list[dict[str, str]]:
-        return self.message_history
-
-    def init(self):
-        compiler_conf = CompilerConfig(dtype=int4)
-        self.model = NPUModelForCausalLM.from_pretrained(self.model_info.name, use_cache=True, config=compiler_conf,
-                                                          export=True, temperature=0).eval()
-        self.tokenizer = AutoTokenizer.from_pretrained(self.model_info.name)
-        self.terminators = [self.tokenizer.eos_token_id, self.tokenizer.convert_tokens_to_ids("<|eot_id|>")]
-
-    def send_prompt(self, prompt: str, add_to_history: bool = True) -> dict[str, str]:
-        pass
-        print("prompt to be sent: " + prompt)
-        user_prompt = {"role": self.model_info.roles.user_role, "content": prompt}
-        if self.log_to_file:
-            with open(self.log_to_file, "a+") as log_file:
-                log_file.write(json.dumps(user_prompt, indent=2))
-                log_file.write("\n")
-        self.get_message_history().append(user_prompt)
-        input_ids = (self.tokenizer.apply_chat_template(self.get_message_history(), add_generation_prompt=True,
-                                                        return_tensors="pt")
-                     .to(self.model.device))
-        outputs = self.model.generate(input_ids, eos_token_id=self.terminators, do_sample=True, max_new_tokens=2000, cache_position=None)
-        generated_token_array = outputs[0][len(input_ids[0]):]
-        generated_tokens = "".join(self.tokenizer.batch_decode(generated_token_array, skip_special_tokens=True))
-        answer = {"role": self.get_model_info().roles.ai_role, "content": generated_tokens}
-        if self.log_to_file:
-            with open(self.log_to_file, "a+") as log_file:
-                log_file.write(json.dumps(answer, indent=2))
-                log_file.write("\n")
-        if add_to_history:
-            self.message_history.append(answer)
-        else:
-            self.message_history.pop()
-        return answer
+# import json
+# from pathlib import Path
+#
+# from intel_npu_acceleration_library import NPUModelForCausalLM, int4
+# from intel_npu_acceleration_library.compiler import CompilerConfig
+# from transformers import AutoTokenizer
+#
+# from repository.repository_abc import Repository, Model
+#
+#
+# class IntelNpuRepository(Repository):
+#     def __init__(self, model_info: Model, system_msg: str = None, log_to_file: Path = None):
+#         self.model_info: Model = model_info
+#         self.message_history: list[dict[str, str]] = []
+#         self.set_message_for_role(self.model_info.roles.system_role, system_msg)
+#         self.model = None
+#         self.tokenizer = None
+#         self.terminators = None
+#         self.log_to_file = log_to_file
+#
+#     def get_model_info(self) -> Model:
+#         return self.model_info
+#
+#     def get_message_history(self) -> list[dict[str, str]]:
+#         return self.message_history
+#
+#     def init(self):
+#         compiler_conf = CompilerConfig(dtype=int4)
+#         self.model = NPUModelForCausalLM.from_pretrained(self.model_info.name, use_cache=True, config=compiler_conf,
+#                                                           export=True, temperature=0).eval()
+#         self.tokenizer = AutoTokenizer.from_pretrained(self.model_info.name)
+#         self.terminators = [self.tokenizer.eos_token_id, self.tokenizer.convert_tokens_to_ids("<|eot_id|>")]
+#
+#     def send_prompt(self, prompt: str, add_to_history: bool = True) -> dict[str, str]:
+#         pass
+#         print("prompt to be sent: " + prompt)
+#         user_prompt = {"role": self.model_info.roles.user_role, "content": prompt}
+#         if self.log_to_file:
+#             with open(self.log_to_file, "a+") as log_file:
+#                 log_file.write(json.dumps(user_prompt, indent=2))
+#                 log_file.write("\n")
+#         self.get_message_history().append(user_prompt)
+#         input_ids = (self.tokenizer.apply_chat_template(self.get_message_history(), add_generation_prompt=True,
+#                                                         return_tensors="pt")
+#                      .to(self.model.device))
+#         outputs = self.model.generate(input_ids, eos_token_id=self.terminators, do_sample=True, max_new_tokens=2000, cache_position=None)
+#         generated_token_array = outputs[0][len(input_ids[0]):]
+#         generated_tokens = "".join(self.tokenizer.batch_decode(generated_token_array, skip_special_tokens=True))
+#         answer = {"role": self.get_model_info().roles.ai_role, "content": generated_tokens}
+#         if self.log_to_file:
+#             with open(self.log_to_file, "a+") as log_file:
+#                 log_file.write(json.dumps(answer, indent=2))
+#                 log_file.write("\n")
+#         if add_to_history:
+#             self.message_history.append(answer)
+#         else:
+#             self.message_history.pop()
+#         return answer

repository/repository.py

@@ -1,6 +1,6 @@
 from pathlib import Path
 
-from repository.intel_npu import IntelNpuRepository
+# from repository.intel_npu import IntelNpuRepository
 from repository.ollama import OllamaRepository
 from repository.ondemand import OndemandRepository
 from repository.repository_abc import Model
@@ -13,8 +13,8 @@ def get_repository(implementation: str, model: Model, system_msg: str = None, lo
         raise ValueError(f"Unknown implementation {implementation}. Known implementations: {known_implementations}")
     if "ollama" == implementation:
         return OllamaRepository(model, system_msg)
-    if "intel_npu" == implementation:
-        return IntelNpuRepository(model, system_msg, log_to_file)
+    # if "intel_npu" == implementation:
+    #     return IntelNpuRepository(model, system_msg, log_to_file)
     if "ondemand" == implementation:
         return OndemandRepository(model, system_msg, log_to_file)
     if "testing" == implementation:

requirements.txt

@@ -2,4 +2,4 @@ PyPDFForm
 ollama
 transformers
 streamlit
-deps/intel_npu_acceleration_library-1.3.0-cp310-cp310-win_amd64.whl
+# deps/intel_npu_acceleration_library-1.3.0-cp310-cp310-win_amd64.whl