mirror of
https://github.com/kvcache-ai/ktransformers.git
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98 lines
No EOL
3.7 KiB
Python
98 lines
No EOL
3.7 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 10:32:05
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Version : 1.0.0
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LastEditors : chenht2022
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LastEditTime : 2024-07-25 10:34:03
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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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with torch.inference_mode(mode=True):
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hidden_size = 5120
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intermediate_size = 3072
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stride = 32
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gate_type = 1 # ggml_type::GGML_TYPE_F16
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up_type = 1 # ggml_type::GGML_TYPE_F16
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down_type = 1 # ggml_type::GGML_TYPE_F16
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hidden_type = 1 # ggml_type::GGML_TYPE_F16
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layer_num = 10
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CPUInfer = cpuinfer_ext.CPUInfer(48)
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validation_iter = 100
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warm_up_iter = 1000
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test_iter = 10000
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mlps = []
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gate_projs = []
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up_projs = []
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down_projs = []
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for _ in range(layer_num):
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gate_proj = torch.randn((intermediate_size, hidden_size), dtype=torch.float16, device = "cuda").to("cpu").contiguous()
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up_proj = torch.randn((intermediate_size, hidden_size), dtype=torch.float16, device = "cuda").to("cpu").contiguous()
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down_proj = torch.randn((hidden_size, intermediate_size), dtype=torch.float16, device = "cuda").to("cpu").contiguous()
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config = cpuinfer_ext.mlp.MLPConfig(hidden_size, intermediate_size, stride, gate_proj.data_ptr(), up_proj.data_ptr(), down_proj.data_ptr(), gate_type, up_type, down_type, hidden_type)
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mlp = cpuinfer_ext.mlp.MLP(config)
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gate_projs.append(gate_proj)
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up_projs.append(up_proj)
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down_projs.append(down_proj)
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mlps.append(mlp)
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# validation
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for i in range(validation_iter):
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mlp = mlps[i % layer_num]
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input = torch.randn((1, hidden_size), dtype=torch.float16).contiguous()
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output = torch.empty((1, hidden_size), dtype=torch.float16).contiguous()
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input = input / 100
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CPUInfer.submit(mlp.forward, input.data_ptr(), output.data_ptr())
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CPUInfer.sync()
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# print('cpuinfer output', output)
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def act_fn(x):
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return x / (1.0 + torch.exp(-x))
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gate_proj = gate_projs[i%layer_num]
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up_proj = up_projs[i%layer_num]
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down_proj = down_projs[i%layer_num]
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gate_buf = torch.mm(input, gate_proj.t())
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up_buf = torch.mm(input, up_proj.t())
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intermediate = act_fn(gate_buf) * up_buf
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t_output = torch.mm(intermediate, down_proj.t())
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# print('torch output', t_output)
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diff = torch.mean(torch.abs(output - t_output)) / torch.mean(torch.abs(t_output))
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print('diff = ', diff)
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assert(diff < 0.001)
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# warm up
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for i in range(warm_up_iter):
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mlp = mlps[i % layer_num]
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input = torch.randn((1, hidden_size), dtype=torch.float16).contiguous()
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output = torch.empty((1, hidden_size), dtype=torch.float16).contiguous()
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input = input / 100
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CPUInfer.submit(mlp.forward, input.data_ptr(), output.data_ptr())
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CPUInfer.sync()
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# test
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total_time = 0
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for i in range(test_iter):
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mlp = mlps[i % layer_num]
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input = torch.randn((1, hidden_size), dtype=torch.float16).contiguous()
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output = torch.empty((1, hidden_size), dtype=torch.float16).contiguous()
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input = input / 100
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start = time.time()
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CPUInfer.submit(mlp.forward, input.data_ptr(), output.data_ptr())
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CPUInfer.sync()
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end = time.time()
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total_time += end - start
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print('Time: ', total_time)
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print('Iteration: ', test_iter)
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print('Time per iteration: ', total_time / test_iter)
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print('Bandwidth: ', hidden_size * intermediate_size * 3 * 2 * test_iter / total_time / 1024 / 1024 / 1024, 'GB/s')
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print("All tasks completed.") |