Initial commit

ktransformers/ktransformers_ext/examples/test_linear.py

@@ -0,0 +1,83 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Description  :  
+Author       : chenht2022
+Date         : 2024-07-25 10:32:05
+Version      : 1.0.0
+LastEditors  : chenht2022 
+LastEditTime : 2024-07-25 10:34:00
+Copyright (c) 2024 by KVCache.AI, All Rights Reserved. 
+'''
+import os, sys
+import time
+sys.path.append(os.path.dirname(__file__) + '/../build')
+import cpuinfer_ext
+import torch
+
+with torch.inference_mode(mode=True):
+    input_size = 16384
+    output_size = 5120
+    stride = 32
+    proj_type = 1 # ggml_type::GGML_TYPE_F16
+    hidden_type = 1 # ggml_type::GGML_TYPE_F16
+    layer_num = 10
+    CPUInfer = cpuinfer_ext.CPUInfer(48)
+    validation_iter = 100
+    warm_up_iter = 1000
+    test_iter = 10000
+
+    linears = []
+    projs = []
+    for _ in range(layer_num):
+        proj = torch.randn((output_size, input_size), dtype=torch.float16, device = "cuda").to("cpu").contiguous()
+        config = cpuinfer_ext.linear.LinearConfig(input_size, output_size, stride, proj.data_ptr(), proj_type, hidden_type)
+        linear = cpuinfer_ext.linear.Linear(config)
+        projs.append(proj)
+        linears.append(linear)
+
+    # validation
+    for i in range(validation_iter):
+        linear = linears[i % layer_num]
+        input = torch.randn((1, input_size), dtype=torch.float16).contiguous()
+        output = torch.empty((1, output_size), dtype=torch.float16).contiguous()
+        input = input / 100
+
+        CPUInfer.submit(linear.forward, input.data_ptr(), output.data_ptr())
+        CPUInfer.sync()
+        # print('cpuinfer output', output)
+
+        proj = projs[i%layer_num]
+        t_output = torch.mm(input, proj.t())
+        # print('torch output', t_output)
+
+        diff = torch.mean(torch.abs(output - t_output)) / torch.mean(torch.abs(t_output))
+        print('diff = ', diff)
+        assert(diff < 0.001)
+
+    # warm up
+    for i in range(warm_up_iter):
+        linear = linears[i % layer_num]
+        input = torch.randn((1, input_size), dtype=torch.float16).contiguous()
+        output = torch.empty((1, output_size), dtype=torch.float16).contiguous()
+        input = input / 100
+        CPUInfer.submit(linear.forward, input.data_ptr(), output.data_ptr())
+        CPUInfer.sync()
+
+    # test
+    total_time = 0
+    for i in range(test_iter):
+        linear = linears[i % layer_num]
+        input = torch.randn((1, input_size), dtype=torch.float16).contiguous()
+        output = torch.empty((1, output_size), dtype=torch.float16).contiguous()
+        input = input / 100
+        start = time.perf_counter()
+        CPUInfer.submit(linear.forward, input.data_ptr(), output.data_ptr())
+        CPUInfer.sync()
+        end = time.perf_counter()
+        total_time += end - start
+    print('Time: ', total_time)
+    print('Iteration: ', test_iter) 
+    print('Time per iteration: ', total_time / test_iter)
+    print('Bandwidth: ', input_size * output_size * 2 * test_iter / total_time / 1000 / 1000 / 1000, 'GB/s')
+    print("All tasks completed.")