77 lines
2.5 KiB
Python
77 lines
2.5 KiB
Python
import torch
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import sys
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import math
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from modelscope import snapshot_download
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from transformers import AutoTokenizer
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from transformers import AutoConfig
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from modeling_qwen import QWenLMHeadModel
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from modeling_qwen import QwenRunner
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import torch.nn.functional as F
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sys.path.append("..")
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from tools import show
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seed = 4321
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torch.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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model_dir = snapshot_download("qwen/Qwen-1_8B-Chat")
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# model_dir = "/home/colin/.cache/modelscope/hub/qwen/Qwen-1_8B-Chat"
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config, kwargs = AutoConfig.from_pretrained(
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"./",
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return_unused_kwargs=True,
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trust_remote_code=True,
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code_revision=None,
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_commit_hash=None,
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)
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model = QWenLMHeadModel(config)
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print(model)
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tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
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model = model.from_pretrained(model_dir).cuda()
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model = model.eval()
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class ResearchRunner(QwenRunner):
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def attention(self, attention, query, key, value, causal_mask):
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query = query.permute(0, 2, 1, 3)
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key = key.permute(0, 2, 1, 3)
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value = value.permute(0, 2, 1, 3)
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scale_factor = 1 / math.sqrt(query.size(-1))
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attn_weight = query @ key.transpose(-2, -1) * scale_factor
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attn_weight = torch.softmax(attn_weight, dim=-1)
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size = query.shape[2]
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attn_mask = torch.ones(causal_mask.shape, dtype=query.dtype, device=query.device)
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attn_mask.masked_fill_(causal_mask.logical_not(), float(0))
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qk = attn_weight * attn_mask
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qk = qk[0]
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prePath = "./temp/"
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show.DumpTensorToImage(qk, prePath + "q@k_seq_" + str(size) + "_layer_" + str(attention.index) + ".png")
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attn_output = F.scaled_dot_product_attention(query, key, value, attn_mask=causal_mask).transpose(1, 2)
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context_layer = attention._merge_heads(attn_output, attention.num_heads, attention.head_dim)
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attn_output = attention.c_proj(context_layer)
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return attn_output
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runner = ResearchRunner(model)
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# 第一轮对话
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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")
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print(decode_tokens)
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# <|im_start|>system
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# You are a helpful assistant.<|im_end|>
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# <|im_start|>user
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# 东南亚国家日本的首都是什么市<|im_end|>
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# <|im_start|>assistant
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# 日本的首都东京。<|im_end|>
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# <|endoftext|>
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# 日本的首都是东京。<|im_end|>
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if decode_tokens.split("\n")[-2] != """日本的首都东京。<|im_end|>""":
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raise ()
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