Witllm/qwen/research_attention.py

import torch
import sys
import math
from modelscope import snapshot_download
from transformers import AutoTokenizer
from transformers import AutoConfig

from modeling_qwen import QWenLMHeadModel
from modeling_qwen import QwenRunner

import torch.nn.functional as F

sys.path.append("..")
from tools import show

seed = 4321
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)

model_dir = snapshot_download("qwen/Qwen-1_8B-Chat")
# model_dir = "/home/colin/.cache/modelscope/hub/qwen/Qwen-1_8B-Chat"

config, kwargs = AutoConfig.from_pretrained(
    "./",
    return_unused_kwargs=True,
    trust_remote_code=True,
    code_revision=None,
    _commit_hash=None,
)
model = QWenLMHeadModel(config)

print(model)

tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
model = model.from_pretrained(model_dir).cuda()
model = model.eval()


class ResearchRunner(QwenRunner):
    def attention(self, attention, query, key, value, causal_mask):
        query = query.permute(0, 2, 1, 3)
        key = key.permute(0, 2, 1, 3)
        value = value.permute(0, 2, 1, 3)

        scale_factor = 1 / math.sqrt(query.size(-1))
        attn_weight = query @ key.transpose(-2, -1) * scale_factor
        attn_weight = torch.softmax(attn_weight, dim=-1)
        size = query.shape[2]
        attn_mask = torch.ones(causal_mask.shape, dtype=query.dtype, device=query.device)
        attn_mask.masked_fill_(causal_mask.logical_not(), float(0))
        qk = attn_weight * attn_mask
        qk = qk[0]
        prePath = "./temp/"
        show.DumpTensorToImage(qk, prePath + "q@k_seq_" + str(size) + "_layer_" + str(attention.index) + ".png")

        attn_output = F.scaled_dot_product_attention(query, key, value, attn_mask=causal_mask).transpose(1, 2)
        context_layer = attention._merge_heads(attn_output, attention.num_heads, attention.head_dim)
        attn_output = attention.c_proj(context_layer)
        return attn_output


runner = ResearchRunner(model)

# 第一轮对话
response, history, decode_tokens = runner.Chat(tokenizer, "东南亚国家日本的首都是什么市", "日本的首都是")
print(decode_tokens)
# <|im_start|>system
# You are a helpful assistant.<|im_end|>
# <|im_start|>user
# 东南亚国家日本的首都是什么市<|im_end|>
# <|im_start|>assistant
# 日本的首都东京。<|im_end|>
# <|endoftext|>
                                    #    日本的首都是东京。<|im_end|>
if decode_tokens.split("\n")[-2] != """日本的首都东京。<|im_end|>""":
    raise ()