[example] Update Inference Example (#5725)

* [example] update inference example

Author: yuanheng-zhao

colossalai/inference/spec/README.md

@@ -0,0 +1,47 @@
+## Run Inference
+
+The provided example `llama_generation.py` is an example to configure, initialize the engine, and run inference on provided model. We've added `AutoModelForCausalLM` and `NoPaddingLlamaModelInferPolicy` as model class and policy class, and the script is good to run inference with Llama 3.
+
+For a basic setting, you could run the example by:
+```bash
+colossalai run --nproc_per_node 1 llama_generation.py -m PATH_MODEL --max_length 128
+```
+
+Run multi-GPU inference (Tensor Parallelism), as in the following example using 2 GPUs:
+```bash
+colossalai run --nproc_per_node 2 llama_generation.py -m PATH_MODEL --max_length 128 --tp_size 2
+```
+
+## Run Speculative Decoding
+
+Colossal-Inference supports speculative decoding using the inference engine, with optimized kernels and cache management for the main model.
+
+Both a drafter model (small model) and a main model (large model) will be used during speculative decoding process. The drafter model will generate a few tokens sequentially, and then the main model will validate those candidate tokens in parallel and accept validated ones. The decoding process will be speeded up, for the latency of speculating multiple tokens by the drafter model is lower than that by the main model.
+
+Moreover, Colossal-Inference also supports GLIDE, a modified draft model architecture that reuses key and value caches from the main model, which improves the acceptance rate and increment the speed-up ratio. Details can be found in research paper GLIDE with a CAPE - A Low-Hassle Method to Accelerate Speculative Decoding on [arXiv](https://arxiv.org/pdf/2402.02082.pdf).
+
+Right now, Colossal-Inference offers a GLIDE model compatible with vicuna7B (https://huggingface.co/lmsys/vicuna-7b-v1.5). You can find the fine-tuned GLIDE drafter model `cxdu/glide-vicuna7b` on the HuggingFace Hub: https://huggingface.co/cxdu/glide-vicuna7b.
+
+Benchmarking with gsm8k and MT-Bench dataset with batch size 1 on H800, the speed increase for using speculative decoding is around 1.28x, and the speed increase for using speculative decoding with Glide model (as drafter model) is around 1.5x.
+
+## Usage
+
+For main model, you might want to use model card  `lmsys/vicuna-7b-v1.5` at [HuggingFace Hub](https://huggingface.co/lmsys/vicuna-7b-v1.5).
+For regular drafter model, you might want to use model card `JackFram/llama-68m` at [HuggingFace Hub](https://huggingface.co/JackFram/llama-68m).
+For the GLIDE drafter model, you could use model card `cxdu/glide-vicuna7b` at [HuggingFace Hub](https://huggingface.co/cxdu/glide-vicuna7b).
+
+
+You could run speculative decoding by
+```bash
+colossalai run --nproc_per_node 1 llama_generation.py -m PATH_MODEL --drafter_model PATH_DRAFTER_MODEL --max_length 128
+```
+
+Run multi-GPU inference (Tensor Parallelism), as in the following example using 2 GPUs.
+```bash
+colossalai run --nproc_per_node 2 llama_generation.py -m PATH_MODEL --drafter_model PATH_DRAFTER_MODEL --max_length 128 --tp_size 2
+```
+
+If you want to try the GLIDE model (glide-vicuna7b) as the drafter model with vicuna-7B, you could provide the GLIDE model path or model card as drafter model and enable the feature by
+```python
+engine.enable_spec_dec(drafter_model, use_glide_drafter=True)
+```

examples/inference/llama/README.md

@@ -27,7 +27,7 @@ def infer(args):
     model = MODEL_CLS.from_pretrained(model_path_or_name)
     tokenizer = AutoTokenizer.from_pretrained(model_path_or_name)
     tokenizer.pad_token = tokenizer.eos_token
-    coordinator.print_on_master(f"Model Config:\n{model.config}")
+    # coordinator.print_on_master(f"Model Config:\n{model.config}")
 
     # ==============================
     # Initialize InferenceEngine
@@ -52,20 +52,39 @@ def infer(args):
         pad_token_id=tokenizer.eos_token_id,
         eos_token_id=tokenizer.eos_token_id,
         max_length=args.max_length,
-        do_sample=True,
+        do_sample=args.do_sample,
+        temperature=args.temperature,
+        top_k=args.top_k,
+        top_p=args.top_p,
     )
     coordinator.print_on_master(f"Generating...")
     out = engine.generate(prompts=[args.prompt], generation_config=generation_config)
-    coordinator.print_on_master(out[0])
+    coordinator.print_on_master(out)
+
+    # ==============================
+    # Optionally, load drafter model and proceed speculative decoding
+    # ==============================
+    drafter_model_path_or_name = args.drafter_model
+    if drafter_model_path_or_name is not None:
+        drafter_model = AutoModelForCausalLM.from_pretrained(drafter_model_path_or_name)
+        # turn on speculative decoding with the drafter model
+        engine.enable_spec_dec(drafter_model)
+        coordinator.print_on_master(f"Generating...")
+        out = engine.generate(prompts=[args.prompt], generation_config=generation_config)
+        coordinator.print_on_master(out)
+
+        engine.disable_spec_dec()
 
 
 # colossalai run --nproc_per_node 1 llama_generation.py -m MODEL_PATH
+# colossalai run --nproc_per_node 2 llama_generation.py -m MODEL_PATH --tp_size 2
 if __name__ == "__main__":
     # ==============================
     # Parse Arguments
     # ==============================
     parser = argparse.ArgumentParser()
     parser.add_argument("-m", "--model", type=str, help="Path to the model or model name")
+    parser.add_argument("--drafter_model", type=str, help="Path to the drafter model or model name")
     parser.add_argument(
         "-p", "--prompt", type=str, default="Introduce some landmarks in the United Kingdom, such as", help="Prompt"
     )
@@ -75,7 +94,12 @@ def infer(args):
     parser.add_argument("-t", "--tp_size", type=int, default=1, help="Tensor Parallelism size")
     parser.add_argument("-d", "--dtype", type=str, default="fp16", help="Data type", choices=["fp16", "fp32", "bf16"])
     parser.add_argument("--use_cuda_kernel", action="store_true", help="Use CUDA kernel, use Triton by default")
-    parser.add_argument("--max_length", type=int, default=32, help="Max length for generation")
+    # Generation configs
+    parser.add_argument("--max_length", type=int, default=64, help="Max length for generation")
+    parser.add_argument("--do_sample", action="store_true", help="Use sampling for generation")
+    parser.add_argument("--temperature", type=float, default=1.0, help="Temperature for generation")
+    parser.add_argument("--top_k", type=int, default=50, help="Top k for generation")
+    parser.add_argument("--top_p", type=float, default=1.0, help="Top p for generation")
     args = parser.parse_args()
 
     infer(args)