Refresh request map for every proces (nutanix#38)

* Refresh request map for every proces

* fix lint

* Update transformer version

llm/cleanup.py

@@ -5,6 +5,7 @@
 Attributes:
     dirpath (str): Stores parent directory of module
 """
+
 import os
 from utils.shell_utils import rm_dir
 import utils.tsutils as ts

llm/generate.py

@@ -6,6 +6,7 @@
     during download and validation of model files.
     MAR_CONFIG_PATH (str): Path of model_config.json.
 """
+
 import os
 import argparse
 import json

llm/handler.py

@@ -3,6 +3,7 @@
 The handler provides functions to preprocess input data, make predictions using the model, 
 and post-process the output for a particular use case.
 """
+
 import logging
 import os
 from abc import ABC
@@ -70,16 +71,12 @@ class LLMHandler(BaseHandler, ABC):
     def __init__(self):
         super().__init__()
         self.initialized = False
-        self.request = {
-            "request_list": defaultdict(int),
-            "request_ids": defaultdict(int),
-            "request_type": defaultdict(int),
-        }
         self.tokenizer = None
         self.map_location = None
         self.device = None
         self.model = None
         self.device_map = None
+        self.request = None
 
     def initialize(self, context):
         """
@@ -147,6 +144,11 @@ def preprocess(self, data: str) -> torch.Tensor:
             Tensor: Tokenized input data
         """
         input_list = []
+        self.request = {
+            "request_list": defaultdict(int),
+            "request_ids": defaultdict(int),
+            "request_type": defaultdict(int),
+        }
 
         for idx, input_data in enumerate(data):
             # Pre-process for Kserve v2 format
@@ -175,7 +177,6 @@ def preprocess(self, data: str) -> torch.Tensor:
                 self.request["request_type"][idx] = "raw"
                 input_list.append(row_input)
 
-        logger.info("Received text: %s", ", ".join(map(str, input_list)))
         encoded_input = self.tokenizer(input_list, padding=True, return_tensors="pt")[
             "input_ids"
         ].to(self.device)
@@ -218,7 +219,6 @@ def inference(self, data: torch.Tensor, *args, **kwargs) -> List[str]:
 
         inference = []
         inference = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
-        logger.info("Generated text is: %s", ", ".join(map(str, inference)))
         return inference
 
     def postprocess(self, data: List[str]) -> List[str]:

llm/requirements.txt

@@ -4,7 +4,7 @@ torchtext==0.15.2
 fastai==2.7.12
 tokenizers==0.15.0
 torchdata==0.6.1
-transformers== 4.36.0
+transformers== 4.38.1
 accelerate==0.22.0
 nvgpu==0.10.0
 torchserve==0.8.2

llm/tests/test_generate.py

@@ -8,6 +8,7 @@
     MODEL_CONFIG_PATH: Path to model_config.json file.
     MODEL_TEMP_CONFIG_PATH: Path to backup model_config.json file.
 """
+
 import os
 import argparse
 import shutil

llm/tests/test_torchserve_run.py

@@ -4,6 +4,7 @@
 Attributes:
     INPUT_PATH: Path to input data folder.
 """
+
 import os
 import subprocess
 from typing import List

llm/torchserve_run.py

@@ -5,6 +5,7 @@
 Attributes:
     MODEL_CONFIG_PATH (str): Path to model_config.json file.
 """
+
 import os
 import argparse
 import json

llm/utils/inference_data_model.py

@@ -2,6 +2,7 @@
 This module stores the dataclasses InferenceDataModel, TorchserveStartData
 and function prepare_settings to set the InferenceDataModel's ts_data.
 """
+
 import argparse
 import os
 import dataclasses

llm/utils/inference_utils.py

@@ -1,6 +1,7 @@
 """
 This module contains utilities to start and manage Torchserve server.
 """
+
 import os
 import sys
 import time

llm/utils/marsgen.py

@@ -4,6 +4,7 @@
 Attributes:
     MAR_NAME_LEN (int): Number of characters to include from repo_version in MAR name
 """
+
 import os
 import sys
 import time

llm/utils/shell_utils.py

@@ -2,6 +2,7 @@
 This module contains utilities to run shell operations namely:
 remove files, remove folder, move file
 """
+
 import os
 import shutil
 import glob

llm/utils/system_utils.py

@@ -4,6 +4,7 @@
 Attributes:
     nvidia_smi_cmd (dict): Contains the nvidia-smi command in different operating systems.
 """
+
 import os
 import sys
 from typing import List

llm/utils/tsutils.py

@@ -7,6 +7,7 @@
     torch_model_archiver_command (dict): Contains the torch-model-archiver command in
                                          different operating systems.
 """
+
 import os
 import sys
 import platform