[Doc] Move examples into categories (#11840)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
2025-01-08 13:09:53 +00:00
parent 2a0596bc48
commit aba8d6ee00
116 changed files with 153 additions and 124 deletions
--- a/examples/offline_inference/lora_with_quantization_inference.py
+++ b/examples/offline_inference/lora_with_quantization_inference.py
@@ -0,0 +1,134 @@
+"""
+This example shows how to use LoRA with different quantization techniques
+for offline inference.
+
+Requires HuggingFace credentials for access.
+"""
+
+import gc
+from typing import List, Optional, Tuple
+
+import torch
+from huggingface_hub import snapshot_download
+
+from vllm import EngineArgs, LLMEngine, RequestOutput, SamplingParams
+from vllm.lora.request import LoRARequest
+
+
+def create_test_prompts(
+        lora_path: str
+) -> List[Tuple[str, SamplingParams, Optional[LoRARequest]]]:
+    return [
+        # this is an example of using quantization without LoRA
+        ("My name is",
+         SamplingParams(temperature=0.0,
+                        logprobs=1,
+                        prompt_logprobs=1,
+                        max_tokens=128), None),
+        # the next three examples use quantization with LoRA
+        ("my name is",
+         SamplingParams(temperature=0.0,
+                        logprobs=1,
+                        prompt_logprobs=1,
+                        max_tokens=128),
+         LoRARequest("lora-test-1", 1, lora_path)),
+        ("The capital of USA is",
+         SamplingParams(temperature=0.0,
+                        logprobs=1,
+                        prompt_logprobs=1,
+                        max_tokens=128),
+         LoRARequest("lora-test-2", 1, lora_path)),
+        ("The capital of France is",
+         SamplingParams(temperature=0.0,
+                        logprobs=1,
+                        prompt_logprobs=1,
+                        max_tokens=128),
+         LoRARequest("lora-test-3", 1, lora_path)),
+    ]
+
+
+def process_requests(engine: LLMEngine,
+                     test_prompts: List[Tuple[str, SamplingParams,
+                                              Optional[LoRARequest]]]):
+    """Continuously process a list of prompts and handle the outputs."""
+    request_id = 0
+
+    while test_prompts or engine.has_unfinished_requests():
+        if test_prompts:
+            prompt, sampling_params, lora_request = test_prompts.pop(0)
+            engine.add_request(str(request_id),
+                               prompt,
+                               sampling_params,
+                               lora_request=lora_request)
+            request_id += 1
+
+        request_outputs: List[RequestOutput] = engine.step()
+        for request_output in request_outputs:
+            if request_output.finished:
+                print("----------------------------------------------------")
+                print(f"Prompt: {request_output.prompt}")
+                print(f"Output: {request_output.outputs[0].text}")
+
+
+def initialize_engine(model: str, quantization: str,
+                      lora_repo: Optional[str]) -> LLMEngine:
+    """Initialize the LLMEngine."""
+
+    if quantization == "bitsandbytes":
+        # QLoRA (https://arxiv.org/abs/2305.14314) is a quantization technique.
+        # It quantizes the model when loading, with some config info from the
+        # LoRA adapter repo. So need to set the parameter of load_format and
+        # qlora_adapter_name_or_path as below.
+        engine_args = EngineArgs(model=model,
+                                 quantization=quantization,
+                                 qlora_adapter_name_or_path=lora_repo,
+                                 load_format="bitsandbytes",
+                                 enable_lora=True,
+                                 max_lora_rank=64)
+    else:
+        engine_args = EngineArgs(model=model,
+                                 quantization=quantization,
+                                 enable_lora=True,
+                                 max_loras=4)
+    return LLMEngine.from_engine_args(engine_args)
+
+
+def main():
+    """Main function that sets up and runs the prompt processing."""
+
+    test_configs = [{
+        "name": "qlora_inference_example",
+        'model': "huggyllama/llama-7b",
+        'quantization': "bitsandbytes",
+        'lora_repo': 'timdettmers/qlora-flan-7b'
+    }, {
+        "name": "AWQ_inference_with_lora_example",
+        'model': 'TheBloke/TinyLlama-1.1B-Chat-v0.3-AWQ',
+        'quantization': "awq",
+        'lora_repo': 'jashing/tinyllama-colorist-lora'
+    }, {
+        "name": "GPTQ_inference_with_lora_example",
+        'model': 'TheBloke/TinyLlama-1.1B-Chat-v0.3-GPTQ',
+        'quantization': "gptq",
+        'lora_repo': 'jashing/tinyllama-colorist-lora'
+    }]
+
+    for test_config in test_configs:
+        print(
+            f"~~~~~~~~~~~~~~~~ Running: {test_config['name']} ~~~~~~~~~~~~~~~~"
+        )
+        engine = initialize_engine(test_config['model'],
+                                   test_config['quantization'],
+                                   test_config['lora_repo'])
+        lora_path = snapshot_download(repo_id=test_config['lora_repo'])
+        test_prompts = create_test_prompts(lora_path)
+        process_requests(engine, test_prompts)
+
+        # Clean up the GPU memory for the next test
+        del engine
+        gc.collect()
+        torch.cuda.empty_cache()
+
+
+if __name__ == '__main__':
+    main()