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