chore: import upstream snapshot with attribution

kt-kernel/examples/test_linear.py

@@ -0,0 +1,59 @@
+#!/usr/bin/env python
+# coding=utf-8
+"""
+Description  :
+Author       : chenht2022
+Date         : 2024-07-25 10:32:05
+Version      : 1.0.0
+LastEditors  : chenht2022
+LastEditTime : 2024-08-06 10:36:59
+Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
+"""
+import os, sys
+import time
+
+sys.path.append(os.path.dirname(__file__) + "/../build")
+from kt_kernel import kt_kernel_ext
+import torch
+
+input_size = 16384
+output_size = 5120
+stride = 32
+group_max_len = 1024
+proj_type = 1  # ggml_type::GGML_TYPE_F16
+hidden_type = 1  # ggml_type::GGML_TYPE_F16
+qlen = 30
+layer_num = 10
+CPUInfer = kt_kernel_ext.CPUInfer(48)
+validation_iter = 100
+
+with torch.inference_mode(mode=True):
+    linears = []
+    projs = []
+    for _ in range(layer_num):
+        proj = torch.randn((output_size, input_size), dtype=torch.float16, device="cuda").to("cpu").contiguous()
+        config = kt_kernel_ext.linear.LinearConfig(
+            input_size, output_size, stride, group_max_len, proj.data_ptr(), proj_type, hidden_type
+        )
+        linear = kt_kernel_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((qlen, input_size), dtype=torch.float16).contiguous()
+        output = torch.empty((qlen, output_size), dtype=torch.float16).contiguous()
+        input = input / 100
+
+        CPUInfer.submit(linear.forward(qlen, 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