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https://github.com/kvcache-ai/ktransformers.git
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40 lines
No EOL
1.2 KiB
Python
40 lines
No EOL
1.2 KiB
Python
import os
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os.environ["CUDA_VISIBLE_DEVICES"]="1"
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# add path
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import sys
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sys.path.append("../..")
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import pycuda.autoinit
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import pycuda.driver as cuda
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from pycuda.compiler import SourceModule
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import numpy as np
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from ktransformers.operators.linear import KTransformersLinear, KLinearMarlin
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from ktransformers.operators.experts import KTransformersExperts, KExpertsTorch
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from ktransformers.util.custom_loader import GGUFLoader, dequantize_q4_k_gpu, dequantize_q4_k
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import torch
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import KTransformersOps
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torch.set_default_dtype(torch.bfloat16)
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import time
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from transformers import (
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AutoConfig,
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)
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gguf_config = GGUFLoader("/data/Qwen2-57B-A14B-Instruct-GGUF/q4_k_m")
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model_name = "/data/Qwen2-57B-A14B-Instruct"
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key = "blk.0."
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target = "ffn_up_exps.weight"
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data = gguf_config.get_mmap_tensor(key + target)
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_, factors, offsets, qs1, qs2= dequantize_q4_k(data)
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factors_cpu = torch.from_numpy(factors)
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offsets_cpu = torch.from_numpy(offsets)
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qs1_cpu = torch.from_numpy(qs1)
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qs2_cpu = torch.from_numpy(qs2)
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_, factors, offsets, qs1, qs2 = dequantize_q4_k_gpu(data)
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print(torch.allclose(factors.cpu(), factors_cpu))
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print(torch.allclose(offsets.cpu(), offsets_cpu))
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print(torch.allclose(qs1.cpu(), qs1_cpu))
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print(torch.allclose(qs2.cpu(), qs2_cpu)) |