[Kernel] Initial Activation Quantization Support #4506
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dsikka wants to merge 7 commits intovllm-project:mainfrom
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[Kernel] Initial Activation Quantization Support #4506dsikka wants to merge 7 commits intovllm-project:mainfrom
dsikka wants to merge 7 commits intovllm-project:mainfrom
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…for static W8A8 per tensor (#195) - Depending on how we end up parsing `ignore` and `targets` (layer_name vs layer_type) we may not need layer_name to be added to the linear_method. Will experiment using a compressed-tensors function in a follow-up PR - Initial implementation for Compressed Config support + Activation Quantization for static per tensor w8a8 - Includes fused kernels added by @varun-sundar-rabindranath ```python from vllm import LLM, SamplingParams import torch prompts = [ "Hello, my name is", "The capital of France is", "The US president is", "The future of AI is" ] sampling_params = SamplingParams(temperature=0.80, top_p=0.95) llm = LLM(model="nm-testing/tinyllama-one-shot-static-quant-test", enforce_eager=True, dtype=torch.float32, quantization="sparseml") outputs = llm.generate(prompts, sampling_params) for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") ``` - Verification of the different inputs expected for `targets` and `ignore` --> use functions to parse the layer names which can be shared by sparseml and vllm; would live in compressed tensors (https://github.com/neuralmagic/compressed-tensors/blob/67005d76107d4659787f1efd53fe7e6b1d192818/src/compressed_tensors/quantization/lifecycle/apply.py#L86) - Updates to further optimize fake qunat --------- Co-authored-by: Varun Sundar Rabindranath <varunsundar08@gmail.com> Co-authored-by: Varun Sundar Rabindranath <varun@neuralmagic.com>
dsikka
changed the title
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I'm very interested in this PR, curious if vllm can add nvidia-cutlass |
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cc @pcmoritz @robertgshaw2-neuralmagic @tlrmchlsmth |
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Note: this is from nm |
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@dsikka I accidentally closed this, can you reopen? |
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Summary
CompressedTensorsConfigandCompressedTensorsLinearMethodto support models quantized through sparseml and saved through compressed-tensorslayer_nameparameter tocreate_weights. Thelayer_namecan be used to match the appropriate quantization scheme from theCompressedTensorsConfigfor a given layerPR Checklist (Click to Expand)
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