[Feature] Initial support for multi-LoRA serving (#1307)

scripts/playground/lora/lora_hf_play.py

@@ -0,0 +1,62 @@
+import torch
+from peft import PeftModel
+from transformers import LlamaForCausalLM, LlamaTokenizer
+
+MODEL = "mistralai/Mistral-7B-Instruct-v0.3"
+# ADAPTER = "winddude/wizardLM-LlaMA-LoRA-7B"
+ADAPTER = "/home/ying/test_lora"
+HF_TOKEN = "..."
+
+
+prompt = """
+### Instruction:
+Write a poem about the transformers Python library. 
+Mention the word "large language models" in that poem.
+### Response:
+The Transformers are large language models,
+They're used to make predictions on text.
+"""
+
+
+tokenizer = LlamaTokenizer.from_pretrained(MODEL)
+
+base_model = LlamaForCausalLM.from_pretrained(
+    MODEL,
+    device_map="auto",
+    # load_in_8bit=True,
+    torch_dtype=torch.float16,
+    # use_auth_token=HF_TOKEN,
+).cuda()
+
+
+# base model generate
+with torch.no_grad():
+    output_tensors = base_model.generate(
+        input_ids=tokenizer(prompt, return_tensors="pt").input_ids.cuda(),
+        max_new_tokens=32,
+        do_sample=False,
+    )[0]
+
+output = tokenizer.decode(output_tensors, skip_special_tokens=True)
+print("======= base output ========")
+print(output)
+
+
+# peft model generate
+model = PeftModel.from_pretrained(
+    base_model,
+    ADAPTER,
+    torch_dtype=torch.float16,
+    is_trainable=False,
+)
+
+with torch.no_grad():
+    output_tensors = model.generate(
+        input_ids=tokenizer(prompt, return_tensors="pt").input_ids.cuda(),
+        max_new_tokens=32,
+        do_sample=False,
+    )[0]
+
+output = tokenizer.decode(output_tensors, skip_special_tokens=True)
+print("======= peft output ========")
+print(output)