kvcache-ai-ktransformers/archive/csrc/ktransformers_ext/examples/test_moe.py

#!/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 10:38:05
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

expert_num = 160
hidden_size = 5120
intermediate_size = 1536
stride = 32
group_min_len = 10
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
n_routed_experts = 6
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

def moe_torch(input, expert_ids, weights, gate_proj, up_proj, down_proj):
    cnts = expert_ids.new_zeros((expert_ids.shape[0], expert_num))
    cnts.scatter_(1, expert_ids, 1)
    tokens_per_expert = cnts.sum(dim=0)
    idxs = expert_ids.view(-1).argsort()
    sorted_tokens = input[idxs // expert_ids.shape[1]]

    outputs = []
    start_idx = 0
    for i, num_tokens in enumerate(tokens_per_expert):
        end_idx = start_idx + num_tokens
        if num_tokens == 0:
            continue
        tokens_for_this_expert = sorted_tokens[start_idx:end_idx]
        expert_out = mlp_torch(tokens_for_this_expert, gate_proj[i], up_proj[i], down_proj[i])
        outputs.append(expert_out)
        start_idx = end_idx

    outs = torch.cat(outputs, dim=0) if len(outputs) else sorted_tokens.new_empty(0)

    new_x = torch.empty_like(outs)
    new_x[idxs] = outs
    t_output = (
        new_x.view(*expert_ids.shape, -1)
        .type(weights.dtype)
        .mul_(weights.unsqueeze(dim=-1))
        .sum(dim=1)
        .type(new_x.dtype)
    )
    return t_output

with torch.inference_mode(mode=True):
    moes = []
    gate_projs = []
    up_projs = []
    down_projs = []
    for _ in range(layer_num):
        gate_proj = torch.randn((expert_num, intermediate_size, hidden_size), dtype=torch.float16, device = "cuda").to("cpu").contiguous()
        up_proj = torch.randn((expert_num, intermediate_size, hidden_size), dtype=torch.float16, device = "cuda").to("cpu").contiguous()
        down_proj = torch.randn((expert_num, hidden_size, intermediate_size), dtype=torch.float16, device = "cuda").to("cpu").contiguous()
        config = cpuinfer_ext.moe.MOEConfig(expert_num, n_routed_experts, hidden_size, intermediate_size, stride, group_min_len, group_max_len, gate_proj.data_ptr(), up_proj.data_ptr(), down_proj.data_ptr(), gate_type, up_type, down_type, hidden_type)
        moe = cpuinfer_ext.moe.MOE(config)
        gate_projs.append(gate_proj)
        up_projs.append(up_proj)
        down_projs.append(down_proj)
        moes.append(moe)

    # validation
    for i in range(validation_iter):
        expert_ids = torch.stack([torch.randperm(expert_num)[:n_routed_experts] for _ in range(qlen)]).contiguous()
        weights = torch.rand((qlen, n_routed_experts), dtype=torch.float32).contiguous()
        input = torch.randn((qlen, hidden_size), dtype=torch.float16).contiguous()
        output = torch.empty((qlen, hidden_size), dtype=torch.float16).contiguous()
        input = input / 100
        
        moe = moes[i % layer_num]
        CPUInfer.submit(
            moe.forward( 
                qlen,
                n_routed_experts, 
                expert_ids.data_ptr(), 
                weights.data_ptr(), 
                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 = moe_torch(input, expert_ids, weights, 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)