Initial commit

ktransformers/local_chat.py

@@ -0,0 +1,115 @@
+# Copyright 2024 Shaoyuan Chen
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import platform
+import sys
+project_dir = os.path.dirname(os.path.dirname(__file__))
+sys.path.insert(0, project_dir)
+import torch
+import logging
+from transformers import (
+    AutoTokenizer,
+    AutoConfig,
+    AutoModelForCausalLM,
+    GenerationConfig,
+    TextStreamer,
+)
+import json
+import fire
+from ktransformers.optimize.optimize import optimize_and_load_gguf
+from ktransformers.models.modeling_deepseek import DeepseekV2ForCausalLM
+from ktransformers.models.modeling_qwen2_moe import Qwen2MoeForCausalLM
+from ktransformers.util.utils import prefill_and_generate
+from ktransformers.server.config.config import Config
+
+custom_models = {
+    "DeepseekV2ForCausalLM": DeepseekV2ForCausalLM,
+    "Qwen2MoeForCausalLM": Qwen2MoeForCausalLM,
+}
+
+ktransformer_rules_dir = os.path.dirname(os.path.abspath(__file__)) + "/optimize/optimize_rules/"
+default_optimize_rules ={
+    "DeepseekV2ForCausalLM": ktransformer_rules_dir + "DeepSeek-V2-Chat.yaml",
+    "Qwen2MoeForCausalLM": ktransformer_rules_dir + "Qwen2-57B-A14B-Instruct.yaml",
+}
+
+def local_chat(
+    model_path: str,
+    optimize_rule_path: str = None,
+    gguf_path: str = None,
+    max_new_tokens: int = 1000,
+    cpu_infer: int = Config().cpu_infer
+):
+    torch.set_grad_enabled(False)
+    
+    Config().cpu_infer = cpu_infer
+    tokenizer = AutoTokenizer.from_pretrained(model_path)
+    config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
+    torch.set_default_dtype(config.torch_dtype)
+
+    with torch.device("meta"):
+        if config.architectures[0] in custom_models:
+            print("using custom modeling_xxx.py.")
+            if "Qwen2Moe" in config.architectures[0]: # Qwen2Moe must use flash_attention_2 to avoid overflow.
+                config._attn_implementation = "flash_attention_2"
+            model = custom_models[config.architectures[0]](config)
+        else:
+            model = AutoModelForCausalLM.from_config(
+                config, trust_remote_code=True, attn_implementation="flash_attention_2"
+            )
+
+    if optimize_rule_path is None:
+        if config.architectures[0] in default_optimize_rules:
+            print("using default_optimize_rule for", config.architectures[0])
+            optimize_rule_path = default_optimize_rules[config.architectures[0]]
+        else:
+            optimize_rule_path = input(
+                "please input the path of your rule file(yaml file containing optimize rules):"
+            )
+
+    if gguf_path is None:
+        gguf_path = input(
+            "please input the path of your gguf file(gguf file in the dir containing input gguf file must all belong to current model):"
+        )
+    optimize_and_load_gguf(model, optimize_rule_path, gguf_path, config)
+
+    model.generation_config = GenerationConfig.from_pretrained(model_path)
+    if model.generation_config.pad_token_id is None:
+        model.generation_config.pad_token_id = model.generation_config.eos_token_id
+    model.eval()
+
+    logging.basicConfig(level=logging.INFO)
+
+    system = platform.system()
+    if (system == u'Windows'):
+        os.system('cls')
+    else:
+        os.system('clear')
+
+    while True:
+        content = input("Chat: ")
+        # if content is num
+        if content == "":
+            content = "Please write a piece of quicksort code in C++." 
+
+        messages = [{"role": "user", "content": content}]
+        input_tensor = tokenizer.apply_chat_template(
+            messages, add_generation_prompt=True, return_tensors="pt"
+        )
+        torch.set_default_dtype(torch.bfloat16) # TODO: Remove this, replace dtype using config
+        generated = prefill_and_generate(model, tokenizer, input_tensor.cuda(), max_new_tokens)
+
+if __name__ == "__main__":
+    fire.Fire(local_chat)