mirror of
https://github.com/kvcache-ai/ktransformers.git
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121 lines
4.1 KiB
Python
121 lines
4.1 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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'''
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Description :
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Author : chenht2022
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Date : 2024-07-25 10:31:59
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Version : 1.0.0
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LastEditors : chenht2022
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LastEditTime : 2024-08-06 10:35:35
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Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
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'''
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import os, sys
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import time
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sys.path.append(os.path.dirname(__file__) + '/../build')
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import cpuinfer_ext
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import torch
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input_size = 16384
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output_size = 5120
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stride = 16
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group_max_len = 1024
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layer_num = 10
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qlen = 1
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CPUInfer = cpuinfer_ext.CPUInfer(64)
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warm_up_iter = 1000
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test_iter = 10000
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def bench_linear(quant_mode: str):
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with torch.inference_mode(mode=True):
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hidden_type = 30 # ggml_type::GGML_TYPE_BF16
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if quant_mode == "fp32":
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proj_type = 0 # ggml_type::GGML_TYPE_F32
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bytes_per_elem = 4.000000
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elif quant_mode == "fp16":
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proj_type = 1 # ggml_type::GGML_TYPE_F16
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bytes_per_elem = 2.000000
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elif quant_mode == "bf16":
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proj_type = 30 # ggml_type::GGML_TYPE_BF16
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bytes_per_elem = 2.000000
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elif quant_mode == "q8_0":
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proj_type = 8 # ggml_type::GGML_TYPE_Q8_0
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bytes_per_elem = 1.062500
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elif quant_mode == "q6_k":
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proj_type = 14 # ggml_type::GGML_TYPE_Q6_K
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bytes_per_elem = 0.820312
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elif quant_mode == "q5_k_m":
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proj_type = 13 # ggml_type::GGML_TYPE_Q5_K
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bytes_per_elem = 0.687500
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elif quant_mode == "q4_k_m":
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proj_type = 12 # ggml_type::GGML_TYPE_Q4_K
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bytes_per_elem = 0.562500
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elif quant_mode == "q3_k_m":
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proj_type = 11 # ggml_type::GGML_TYPE_Q3_K
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bytes_per_elem = 0.429688
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elif quant_mode == "q2_k":
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proj_type = 10 # ggml_type::GGML_TYPE_Q2_K
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bytes_per_elem = 0.328125
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elif quant_mode == "iq3_xs":
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proj_type = 21 # ggml_type::GGML_TYPE_IQ3_S
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bytes_per_elem = 0.429688
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elif quant_mode == "iq2_xxs":
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proj_type = 16 # ggml_type::GGML_TYPE_IQ2_XXS
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bytes_per_elem = 0.257812
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else:
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assert(False)
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linears = []
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projs = []
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for _ in range(layer_num):
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proj = torch.randn((output_size, input_size), dtype=torch.float32, device = "cuda").to("cpu").contiguous()
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config = cpuinfer_ext.linear.LinearConfig(input_size, output_size, stride, group_max_len, proj.data_ptr(), proj_type, hidden_type)
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linear = cpuinfer_ext.linear.Linear(config)
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projs.append(proj)
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linears.append(linear)
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input = torch.randn((layer_num, qlen, input_size), dtype=torch.bfloat16, device = "cuda").to("cpu").contiguous()
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output = torch.empty((layer_num, qlen, output_size), dtype=torch.bfloat16, device = "cuda").to("cpu").contiguous()
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# warm up
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for i in range(warm_up_iter):
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CPUInfer.submit(
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linears[i % layer_num].forward(
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qlen,
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input[i % layer_num].data_ptr(),
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output[i % layer_num].data_ptr()
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)
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)
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CPUInfer.sync()
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# test
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start = time.perf_counter()
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for i in range(test_iter):
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CPUInfer.submit(
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linears[i % layer_num].forward(
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qlen,
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input[i % layer_num].data_ptr(),
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output[i % layer_num].data_ptr()
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)
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)
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CPUInfer.sync()
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end = time.perf_counter()
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total_time = end - start
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print('Quant mode: ', quant_mode)
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print('Time(s): ', total_time)
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print('Iteration: ', test_iter)
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print('Time(us) per iteration: ', total_time / test_iter * 1000000)
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print('Bandwidth: ', input_size * output_size * bytes_per_elem * test_iter / total_time / 1000 / 1000 / 1000, 'GB/s')
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print('')
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bench_linear("fp32")
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bench_linear("fp16")
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bench_linear("bf16")
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bench_linear("q8_0")
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bench_linear("q6_k")
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bench_linear("q5_k_m")
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bench_linear("q4_k_m")
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bench_linear("q3_k_m")
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bench_linear("q2_k")
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# Not supported on __x86_64__
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# bench_linear("iq3_xs")
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# bench_linear("iq2_xxs")
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