removed dependency from intel-npu library otherwise builds in huggingface will fail

repository/intel_npu.py

@@ -1,8 +1,8 @@
 import json
 from pathlib import Path
 
-from intel_npu_acceleration_library import NPUModelForCausalLM, int4
-from intel_npu_acceleration_library.compiler import CompilerConfig
+# 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
@@ -25,33 +25,35 @@ class IntelNpuRepository(Repository):
         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|>")]
+        pass
+        # 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]:
-        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
+        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

requirements.txt

@@ -1,4 +1,4 @@
 PyPDFForm
 ollama
 transformers
-intel-npu-acceleration-library
+# intel-npu-acceleration-library