Witllm/qwen/research_token.py

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import torch
import sys
# from modelscope import snapshot_download
from transformers import AutoTokenizer
from transformers import AutoConfig
from modeling_qwen import QWenLMHeadModel
from modeling_qwen import QwenRunner
from qwen_generation_utils import (
make_context,
decode_tokens,
)
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)
if torch.cuda.device_count() > 0:
model = model.cuda()
model = model.eval()
class ResearchRunner(QwenRunner):
def __init__(self, model):
super().__init__(model)
def forwardQWen(
self,
input_ids=None,
labels=None,
):
transfm = self.qwen.transformer
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
hidden_states = transfm.wte(input_ids)
kv_seq_len = hidden_states.size()[1]
transfm.update_rotary_pos_emb_cache(kv_seq_len, ntk_alpha=1.0)
cos, sin = transfm._rotary_pos_emb_cache
rotary_pos_emb_list = [[cos[:, :kv_seq_len], sin[:, :kv_seq_len]]]
hidden_states = transfm.drop(hidden_states)
output_shape = input_shape + (hidden_states.size(-1),)
for block in transfm.h:
self.forwardQWenBlock(block, hidden_states, rotary_pos_emb_list=rotary_pos_emb_list)
break
def forwardQWenBlock(
self,
block,
hidden_states,
rotary_pos_emb_list=None,
):
layernorm_output = block.ln_1(hidden_states)
self.forwardAttention(block.attn, layernorm_output, rotary_pos_emb_list)
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)
global q
global k
query = query[:, head_group_index, :, :]
key = key[:, head_group_index, :, :]
q = torch.cat([q, query], 1)
k = torch.cat([k, key], 1)
head_group_index = 0
total_token = 151851
topk = 10
tokens_str = []
for token in range(total_token):
decoded, response, end_reason = decode_tokens(
[token],
tokenizer,
raw_text_len=0,
context_length=0,
errors="replace",
)
tokens_str.append(repr(decoded))
patch_end = list(range(0, total_token, 1000))
patch_end = patch_end[1:] + [total_token]
patch_start = 0
q = torch.zeros((1, 0, 128), dtype=float).to(next(model.parameters()).device)
k = torch.zeros((1, 0, 128), dtype=float).to(next(model.parameters()).device)
for end in patch_end:
tokens = list(range(patch_start, end))
patch_start = end
input_ids = torch.tensor([tokens]).to(next(model.parameters()).device)
runner = ResearchRunner(model)
runner.forwardQWen(input_ids)
q = q[0, :, :]
k = k[0, :, :].permute(1, 0)
token_topk = []
for i in range(total_token):
subq = q[i, :]
qk = subq @ k
values, indices = torch.topk(qk, topk)
item = str(i).zfill(7) + " " + tokens_str[i] + " : "
for index in indices:
item += tokens_str[index] + " "
token_topk.append(item)
show.DumpListToFile(token_topk, "./temp/qwen_token_qk_topk_head_group_" + str(head_group_index) + ".txt")
print("decoded")