[Misc] Update GPTQ to use vLLMParameters (#7976)
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@@ -4,6 +4,12 @@ gptq_marlin, TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ, main
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gptq_marlin, TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ, gptq-8bit--1g-actorder_True
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gptq_marlin, TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ, gptq-8bit-32g-actorder_True
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gptq_marlin, TechxGenus/gemma-1.1-2b-it-GPTQ, main
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gptq, robertgshaw2/zephyr-7b-beta-channelwise-gptq, main
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gptq, TheBloke/Llama-2-7B-GPTQ, main
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gptq, TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ, main
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gptq, TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ, gptq-8bit--1g-actorder_True
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gptq, TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ, gptq-8bit-32g-actorder_True
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gptq, TechxGenus/gemma-1.1-2b-it-GPTQ, main
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compressed-tensors, nm-testing/tinyllama-oneshot-w8w8-test-static-shape-change, main
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compressed-tensors, nm-testing/tinyllama-oneshot-w8-channel-a8-tensor, main
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compressed-tensors, nm-testing/tinyllama-oneshot-w8a8-dynamic-token-v2, main
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@@ -1,5 +1,7 @@
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import os
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import torch
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MAX_MODEL_LEN = 1024
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MODEL_NAME = os.environ.get("MODEL_NAME",
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"robertgshaw2/zephyr-7b-beta-channelwise-gptq")
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@@ -8,9 +10,12 @@ QUANTIZATION = os.environ.get("QUANTIZATION", "gptq_marlin")
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def test_weight_loading(vllm_runner):
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"""
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Test parameter weight loading with tp>1.
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"""
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with vllm_runner(model_name=MODEL_NAME,
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revision=REVISION,
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dtype="auto",
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dtype=torch.half if QUANTIZATION == "gptq" else "auto",
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quantization=QUANTIZATION,
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max_model_len=MAX_MODEL_LEN,
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tensor_parallel_size=2) as model:
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