init

docs/source/quantization/auto_awq.rst

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+.. _auto_awq:
+
+AutoAWQ
+==================
+
+.. warning::
+
+   Please note that AWQ support in vLLM is under-optimized at the moment. We would recommend using the unquantized version of the model for better
+   accuracy and higher throughput. Currently, you can use AWQ as a way to reduce memory footprint. As of now, it is more suitable for low latency
+   inference with small number of concurrent requests. vLLM's AWQ implementation have lower throughput than unquantized version.
+
+To create a new 4-bit quantized model, you can leverage `AutoAWQ <https://github.com/casper-hansen/AutoAWQ>`_. 
+Quantizing reduces the model's precision from FP16 to INT4 which effectively reduces the file size by ~70%.
+The main benefits are lower latency and memory usage.
+
+You can quantize your own models by installing AutoAWQ or picking one of the `400+ models on Huggingface <https://huggingface.co/models?sort=trending&search=awq>`_. 
+
+.. code-block:: console
+
+    $ pip install autoawq
+
+After installing AutoAWQ, you are ready to quantize a model. Here is an example of how to quantize Vicuna 7B v1.5:
+
+.. code-block:: python
+
+    from awq import AutoAWQForCausalLM
+    from transformers import AutoTokenizer
+
+    model_path = 'lmsys/vicuna-7b-v1.5'
+    quant_path = 'vicuna-7b-v1.5-awq'
+    quant_config = { "zero_point": True, "q_group_size": 128, "w_bit": 4, "version": "GEMM" }
+
+    # Load model
+    model = AutoAWQForCausalLM.from_pretrained(model_path, **{"low_cpu_mem_usage": True})
+    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+
+    # Quantize
+    model.quantize(tokenizer, quant_config=quant_config)
+
+    # Save quantized model
+    model.save_quantized(quant_path)
+    tokenizer.save_pretrained(quant_path)
+
+To run an AWQ model with vLLM, you can use `TheBloke/Llama-2-7b-Chat-AWQ <https://huggingface.co/TheBloke/Llama-2-7b-Chat-AWQ>`_ with the following command:
+
+.. code-block:: console
+
+    $ python examples/llm_engine_example.py --model TheBloke/Llama-2-7b-Chat-AWQ --quantization awq
+
+AWQ models are also supported directly through the LLM entrypoint:
+
+.. 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="TheBloke/Llama-2-7b-Chat-AWQ", quantization="AWQ")
+    # 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}")

docs/source/quantization/fp8_e4m3_kvcache.rst

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+.. _fp8_e4m3_kvcache:
+
+FP8 E4M3 KV Cache
+==================
+
+Quantizing the KV cache to FP8 reduces its memory footprint. This increases the number of tokens that can be stored in the cache, 
+improving throughput. OCP (Open Compute Project www.opencompute.org) specifies two common 8-bit floating point data formats: E5M2 
+(5 exponent bits and 2 mantissa bits) and E4M3FN (4 exponent bits and 3 mantissa bits), often shortened as E4M3. One benefit of 
+the E4M3 format over E5M2 is that floating point numbers are represented in higher precision. However, the small dynamic range of 
+FP8 E4M3 (±240.0 can be represented) typically necessitates the use of a higher-precision (typically FP32) scaling factor alongside 
+each quantized tensor. For now, only per-tensor (scalar) scaling factors are supported. Development is ongoing to support scaling 
+factors of a finer granularity (e.g. per-channel).
+
+These scaling factors can be specified by passing an optional quantization param JSON to the LLM engine at load time. If 
+this JSON is not specified, scaling factors default to 1.0. These scaling factors are typically obtained when running an 
+unquantized model through a quantizer tool (e.g. AMD quantizer or NVIDIA AMMO). 
+
+To install AMMO (AlgorithMic Model Optimization):
+
+.. code-block:: console
+
+        $ pip install --no-cache-dir --extra-index-url https://pypi.nvidia.com nvidia-ammo
+
+Studies have shown that FP8 E4M3 quantization typically only minimally degrades inference accuracy. The most recent silicon 
+offerings e.g. AMD MI300, NVIDIA Hopper or later support native hardware conversion to and from fp32, fp16, bf16, etc. 
+Thus, LLM inference is greatly accelerated with minimal accuracy loss.
+
+
+Here is an example of how to enable this feature:
+
+.. code-block:: python
+
+        # two float8_e4m3fn kv cache scaling factor files are provided under tests/fp8_kv, please refer to 
+        # https://github.com/vllm-project/vllm/blob/main/examples/fp8/README.md to generate kv_cache_scales.json of your own.
+
+        from vllm import LLM, SamplingParams
+        sampling_params = SamplingParams(temperature=1.3, top_p=0.8)
+        llm = LLM(model="meta-llama/Llama-2-7b-chat-hf",
+                  kv_cache_dtype="fp8",
+                  quantization_param_path="./tests/fp8_kv/llama2-7b-fp8-kv/kv_cache_scales.json")
+        prompt = "London is the capital of"
+        out = llm.generate(prompt, sampling_params)[0].outputs[0].text
+        print(out)
+
+        # output w/ scaling factors:  England, the United Kingdom, and one of the world's leading financial,
+        # output w/o scaling factors:  England, located in the southeastern part of the country. It is known 
+
+Note, current prefix caching doesn't work with FP8 KV cache enabled, forward_prefix kernel should handle different KV and cache type.
+

docs/source/quantization/fp8_e5m2_kvcache.rst

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+.. _fp8_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")
+    # 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}")
+
+
+Note, current prefix caching doesn't work with FP8 KV cache enabled, forward_prefix kernel should handle different KV and cache type.
+