Quantization docs update

docs/source/index.rst

@@ -60,6 +60,7 @@ For more information, browse the developer docs for your area of interest in the
    nlp/models
    nlp/machine_translation/machine_translation
    nlp/megatron_onnx_export
+   nlp/quantization
    nlp/api
 
 

docs/source/nlp/quantization.rst

@@ -3,35 +3,35 @@
 Quantization
 ==========================
 
-Post Training Quantization (PTQ)
+Post-Training Quantization (PTQ)
 --------------------------------
 
-PTQ enables deploying a model in a low-precision format -- FP8, INT4 or INT8 -- for efficient serving. Different quantization methods are available including FP8 quantization, INT8 SmoothQuant and INT4 AWQ.
+PTQ enables deploying a model in a low-precision format -- FP8, INT4, or INT8 -- for efficient serving. Different quantization methods are available including FP8 quantization, INT8 SmoothQuant, and INT4 AWQ.
 
 Model quantization has two primary benefits: reduced model memory requirements and increased inference throughput.
 
 In NeMo, quantization is enabled by the Nvidia AMMO library -- a unified algorithmic model optimization & deployment toolkit.
 
 The quantization process consists of the following steps:
 
-1. Loading a model checkpoint using appropriate parallelism strategy for evaluation
+1. Loading a model checkpoint using an appropriate parallelism strategy for evaluation
 2. Calibrating the model to obtain appropriate algorithm-specific scaling factors
-3. Producing output directory or .qnemo tarball with model config (json), quantized weights (safetensors) and tokenizer config (yaml).
+3. Producing an output directory or .qnemo tarball with model config (json), quantized weights (safetensors) and tokenizer config (yaml).
 
-Loading models requires using AMMO spec defined in `megatron.core.deploy.gpt.model_specs module <https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/deploy/gpt/model_specs.py>`_. Typically the calibration step is lightweight and uses a small dataset to obtain appropriate statistics for scaling tensors. The output directory produced (or a .qnemo tarball) is ready to be used to build a serving engine with the Nvidia TensorRT-LLM library. The engine build step is also soon to be the part of NeMo project and ``nemo.deploy`` and ``nemo.export`` modules, see https://github.com/NVIDIA/NeMo/pull/8690.
+Loading models requires using an AMMO spec defined in `megatron.core.inference.gpt.model_specs.py <https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/inference/gpt/model_specs.py>`_ module. Typically the calibration step is lightweight and uses a small dataset to obtain appropriate statistics for scaling tensors. The output directory produced (or a .qnemo tarball) is ready to be used to build a serving engine with the Nvidia TensorRT-LLM library. The engine build step is also soon to be the part of NeMo project and ``nemo.deploy`` and ``nemo.export`` modules, see https://github.com/NVIDIA/NeMo/pull/8743.
 
 Quantization algorithm can also be conveniently set to ``"null"`` to perform only the weights export step using default precision for TensorRT-LLM deployment. This is useful to obtain baseline performance and accuracy results for comparison.
 
 
 Example
 ^^^^^^^
-The example below shows how to quantize the Llama2 70b model into FP8 precision, using tensor parallelism of 8 on a single DGX H100 node. The quantized model is intended for serving using 2 GPUs specified with ``export.inference_tensor_parallel`` parameter.
+The example below shows how to quantize the Llama2 70b model into FP8 precision, using tensor parallelism of 8 on a single DGX H100 node. The quantized model is designed for serving using 2 GPUs specified with the ``export.inference_tensor_parallel`` parameter.
 
-The script should be launched correctly with the number of processes equal to tensor parallelism. This is achieved with the ``mpirun`` command below.
+The script must be launched correctly with the number of processes equal to tensor parallelism. This is achieved with the ``torchrun`` command below.
 
 .. code-block:: bash
 
-    mpirun -n 8 python examples/nlp/language_modeling/megatron_llama_quantization.py \
+    torchrun --nproc-per-node 8 examples/nlp/language_modeling/megatron_llama_quantization.py \
         model_file=llama2-70b-base-bf16.nemo \
         tensor_model_parallel_size=8 \
         pipeline_model_parallel_size=1 \
@@ -66,22 +66,24 @@ The TensorRT-LLM engine can be build with ``trtllm-build`` command, see `TensorR
         --output_dir engine_dir \
         --max_batch_size 8 \
         --max_input_len 2048 \
-        --max_output_len 512
+        --max_output_len 512 \
+        --strongly_typed
 
 
 
 Known issues
 ^^^^^^^^^^^^
-* Currently in NeMo quantizing and building TensorRT-LLM engines is limited to single-node use cases.
-* Supported and tested model family is Llama2. Quantizing other model types is experimental and may not be fully supported.
-* For INT8 SmoothQuant ``quantization.algorithm=int8_sq``, the TensorRT-LLM engine cannot be build with CLI ``trtllm-build`` command -- Python API and ``tensorrt_llm.builder`` should be used instead.
+* Currently in NeMo, quantizing and building TensorRT-LLM engines is limited to single-node use cases.
+* The supported and tested model family is Llama2. Quantizing other model types is experimental and may not be fully supported.
 
 
 Please refer to the following papers for more details on quantization techniques.
 
 References
 ----------
 
+`Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation, 2020 <https://arxiv.org/abs/2004.09602>`_
+
 `FP8 Formats for Deep Learning, 2022 <https://arxiv.org/abs/2209.05433>`_
 
 `SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models, 2022 <https://arxiv.org/abs/2211.10438>`_