Support FP8-E5M2 KV Cache (#2279)

Co-authored-by: zhaoyang <zhao.yang16@zte.com.cn>
Co-authored-by: Zhuohan Li <zhuohan123@gmail.com>

docs/source/quantization/fp8_e5m2_kv_cache.rst

@@ -0,0 +1,32 @@
+.. _fp8_e5m2_kv_cache:
+
+FP8 E5M2 KV Cache
+==================
+
+The int8/int4 quantization scheme requires additional scale GPU memory storage, which reduces the expected GPU memory benefits.
+The FP8 data format retains 2~3 mantissa bits and can convert float/fp16/bflaot16 and fp8 to each other.
+
+Here is an example of how to enable this feature:
+
+.. code-block:: python
+    from vllm import LLM, SamplingParams
+    # Sample prompts.
+    prompts = [
+        "Hello, my name is",
+        "The president of the United States is",
+        "The capital of France is",
+        "The future of AI is",
+    ]
+    # Create a sampling params object.
+    sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+    # Create an LLM.
+    llm = LLM(model="facebook/opt-125m", kv_cache_dtype="fp8_e5m2")
+    # Generate texts from the prompts. The output is a list of RequestOutput objects
+    # that contain the prompt, generated text, and other information.
+    outputs = llm.generate(prompts, sampling_params)
+    # Print the outputs.
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+