refactor folders

ktransformers/ktransformers_ext/examples/test_mlp.py

@@ -1,82 +0,0 @@
-#!/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 10:37:28
-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
-
-hidden_size = 5120
-intermediate_size = 3072
-stride = 32
-group_max_len = 1024
-gate_type = 1 # ggml_type::GGML_TYPE_F16
-up_type = 1 # ggml_type::GGML_TYPE_F16
-down_type = 1 # ggml_type::GGML_TYPE_F16
-hidden_type = 1 # ggml_type::GGML_TYPE_F16
-qlen = 30
-layer_num = 10
-CPUInfer = cpuinfer_ext.CPUInfer(48)
-validation_iter = 100
-
-def act_fn(x):
-    return x / (1.0 + torch.exp(-x))
-
-def mlp_torch(input, gate_proj, up_proj, down_proj):
-    gate_buf = torch.mm(input, gate_proj.t())
-    up_buf = torch.mm(input, up_proj.t())
-    intermediate = act_fn(gate_buf) * up_buf
-    ret = torch.mm(intermediate, down_proj.t())
-    return ret
-
-with torch.inference_mode(mode=True):
-    mlps = []
-    gate_projs = []
-    up_projs = []
-    down_projs = []
-    for _ in range(layer_num):
-        gate_proj = torch.randn((intermediate_size, hidden_size), dtype=torch.float16, device = "cuda").to("cpu").contiguous()
-        up_proj = torch.randn((intermediate_size, hidden_size), dtype=torch.float16, device = "cuda").to("cpu").contiguous()
-        down_proj = torch.randn((hidden_size, intermediate_size), dtype=torch.float16, device = "cuda").to("cpu").contiguous()
-        config = cpuinfer_ext.mlp.MLPConfig(hidden_size, intermediate_size, stride, group_max_len, gate_proj.data_ptr(), up_proj.data_ptr(), down_proj.data_ptr(), gate_type, up_type, down_type, hidden_type)
-        mlp = cpuinfer_ext.mlp.MLP(config)
-        gate_projs.append(gate_proj)
-        up_projs.append(up_proj)
-        down_projs.append(down_proj)
-        mlps.append(mlp)
-
-    # validation
-    for i in range(validation_iter):
-        mlp = mlps[i % layer_num]
-        input = torch.randn((qlen, hidden_size), dtype=torch.float16).contiguous()
-        output = torch.empty((qlen, hidden_size), dtype=torch.float16).contiguous()
-        input = input / 100
-
-        CPUInfer.submit(
-            mlp.forward(
-                qlen,
-                input.data_ptr(), 
-                output.data_ptr()
-            )
-        )
-        CPUInfer.sync()
-        # print('cpuinfer output', output)
-
-        gate_proj = gate_projs[i%layer_num]
-        up_proj = up_projs[i%layer_num]
-        down_proj = down_projs[i%layer_num]
-        t_output = mlp_torch(input, gate_proj, up_proj, down_proj)
-        # 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)