401 lines
16 KiB
Python
401 lines
16 KiB
Python
import copy
|
|
import math
|
|
import os
|
|
import sys
|
|
import gc
|
|
from tqdm import auto as tqdm_lib
|
|
import json
|
|
from typing import Optional, Tuple, Union, Callable, List, Any, Generator
|
|
from einops import rearrange
|
|
|
|
import torch
|
|
import torch.nn.functional as F
|
|
import torch.utils.checkpoint
|
|
from torch.nn import CrossEntropyLoss
|
|
from torch import nn
|
|
from safetensors.torch import load_file as safe_load_file
|
|
from safetensors.torch import save_file as safe_save_file
|
|
|
|
from qwen_generation_utils import (
|
|
make_context,
|
|
decode_tokens,
|
|
)
|
|
|
|
sys.path.append("..")
|
|
from tools import show
|
|
from tools import mem_tracker
|
|
|
|
# tracker = mem_tracker.MemTracker()
|
|
# tracker.track()
|
|
|
|
|
|
class RMSNorm(torch.nn.Module):
|
|
def __init__(self, dim: int, eps: float = 1e-6):
|
|
super().__init__()
|
|
self.eps = eps
|
|
self.weight = nn.Parameter(torch.ones(dim))
|
|
|
|
def _norm(self, x):
|
|
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
|
|
|
|
def forward(self, x):
|
|
return self._norm(x.float()).type_as(x) * self.weight
|
|
|
|
|
|
class QWenAttention(nn.Module):
|
|
def __init__(self, config, index):
|
|
super().__init__()
|
|
self.hidden_size = config.hidden_size
|
|
self.split_size = config.hidden_size
|
|
self.num_heads = config.num_attention_heads
|
|
self.head_dim = self.hidden_size // self.num_heads
|
|
self.c_attn = nn.Linear(config.hidden_size, 3 * self.hidden_size)
|
|
self.c_proj = nn.Linear(config.hidden_size, self.hidden_size, bias=not config.no_bias)
|
|
self.attn_dropout = nn.Dropout(config.attn_dropout_prob)
|
|
self.index = index
|
|
|
|
def _split_heads(self, tensor, num_heads, attn_head_size):
|
|
new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)
|
|
tensor = tensor.view(new_shape)
|
|
return tensor
|
|
|
|
def _merge_heads(self, tensor, num_heads, attn_head_size):
|
|
tensor = tensor.contiguous()
|
|
new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,)
|
|
return tensor.view(new_shape)
|
|
|
|
|
|
class QWenMLP(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
ff_dim_in = config.intermediate_size // 2
|
|
self.w1 = nn.Linear(config.hidden_size, ff_dim_in, bias=not config.no_bias)
|
|
self.w2 = nn.Linear(config.hidden_size, ff_dim_in, bias=not config.no_bias)
|
|
self.c_proj = nn.Linear(ff_dim_in, config.hidden_size, bias=not config.no_bias)
|
|
|
|
|
|
class QWenBlock(nn.Module):
|
|
def __init__(self, config, index):
|
|
super().__init__()
|
|
self.ln_1 = RMSNorm(
|
|
config.hidden_size,
|
|
eps=config.layer_norm_epsilon,
|
|
)
|
|
self.attn = QWenAttention(config, index)
|
|
self.ln_2 = RMSNorm(
|
|
config.hidden_size,
|
|
eps=config.layer_norm_epsilon,
|
|
)
|
|
self.mlp = QWenMLP(config)
|
|
self.index = index
|
|
|
|
|
|
class QWenModel(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.wte = nn.Embedding(config.vocab_size, config.hidden_size)
|
|
self.drop = nn.Dropout(config.emb_dropout_prob)
|
|
self.dim = config.hidden_size // config.num_attention_heads
|
|
|
|
self.h = nn.ModuleList([QWenBlock(config, i) for i in range(config.num_hidden_layers)])
|
|
self.ln_f = RMSNorm(
|
|
config.hidden_size,
|
|
eps=config.layer_norm_epsilon,
|
|
)
|
|
self.base = config.rotary_emb_base
|
|
inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float() / self.dim))
|
|
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
|
self._rotary_pos_emb_cache = None
|
|
self._seq_len_cached = 0
|
|
self._ntk_alpha_cached = 1.0
|
|
|
|
def update_rotary_pos_emb_cache(self, seqlen, ntk_alpha=1.0):
|
|
if seqlen > self._seq_len_cached or ntk_alpha != self._ntk_alpha_cached:
|
|
base = self.base * ntk_alpha ** (self.dim / (self.dim - 2))
|
|
self.inv_freq = 1.0 / (
|
|
base ** (torch.arange(0, self.dim, 2, device=self.inv_freq.device).float() / self.dim)
|
|
)
|
|
self._seq_len_cached = max(2 * seqlen, 16)
|
|
self._ntk_alpha_cached = ntk_alpha
|
|
seq = torch.arange(self._seq_len_cached, device=self.inv_freq.device)
|
|
freqs = torch.outer(seq.type_as(self.inv_freq), self.inv_freq)
|
|
|
|
emb = torch.cat((freqs, freqs), dim=-1)
|
|
emb = rearrange(emb, "n d -> 1 n 1 d")
|
|
|
|
cos, sin = emb.cos(), emb.sin()
|
|
self._rotary_pos_emb_cache = [cos, sin]
|
|
|
|
|
|
class QWenLMHeadModel(nn.Module):
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
self.transformer = QWenModel(config)
|
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
token_type_ids: Optional[torch.LongTensor] = None,
|
|
**kwargs,
|
|
):
|
|
runner = QwenRunner(self)
|
|
return runner.forwardQWen(input_ids, labels)
|
|
|
|
def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]]):
|
|
pretrained_model_name_or_path = str(pretrained_model_name_or_path)
|
|
resolved_archive_file = os.path.join(pretrained_model_name_or_path, "model.safetensors.index.json")
|
|
print(f"loading weights file {resolved_archive_file}")
|
|
with open(resolved_archive_file, "r") as f:
|
|
index = json.loads(f.read())
|
|
shard_filenames = sorted(set(index["weight_map"].values()))
|
|
resolved_archive_file = [os.path.join(pretrained_model_name_or_path, f) for f in shard_filenames]
|
|
model = cls._load_pretrained_model(resolved_archive_file)
|
|
return model
|
|
|
|
def _load_state_dict_into_model(self, model_to_load, state_dict, start_prefix):
|
|
metadata = getattr(state_dict, "_metadata", None)
|
|
state_dict = state_dict.copy()
|
|
if metadata is not None:
|
|
state_dict._metadata = metadata
|
|
error_msgs = []
|
|
|
|
def load(module: nn.Module, state_dict, prefix=""):
|
|
local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
|
|
args = (state_dict, prefix, local_metadata, True, [], [], error_msgs)
|
|
if len([key for key in state_dict if key.startswith(prefix)]) > 0:
|
|
module._load_from_state_dict(*args)
|
|
|
|
for name, child in module._modules.items():
|
|
if child is not None:
|
|
load(child, state_dict, prefix + name + ".")
|
|
|
|
load(model_to_load, state_dict, prefix=start_prefix)
|
|
del state_dict
|
|
return error_msgs
|
|
|
|
def _load_pretrained_model(cls, resolved_archive_file):
|
|
start_prefix = ""
|
|
model_to_load = cls
|
|
if len(resolved_archive_file) > 1:
|
|
resolved_archive_file = tqdm_lib.tqdm(resolved_archive_file, desc="Loading checkpoint shards")
|
|
for shard_file in resolved_archive_file:
|
|
state_dict = safe_load_file(shard_file)
|
|
cls._load_state_dict_into_model(model_to_load, state_dict, start_prefix)
|
|
del state_dict # force memory release
|
|
gc.collect()
|
|
print(f"All model checkpoint weights were used when initializing {cls.__class__.__name__}.\n")
|
|
return cls
|
|
|
|
|
|
class QwenRunner:
|
|
def __init__(self, qwen):
|
|
self.qwen = qwen
|
|
|
|
@torch.no_grad()
|
|
def Chat(
|
|
self,
|
|
tokenizer,
|
|
query: str,
|
|
query_assistant: str,
|
|
gen_length=0,
|
|
system: str = "You are a helpful assistant.",
|
|
history=[],
|
|
):
|
|
qwen = self.qwen
|
|
history = copy.deepcopy(history)
|
|
self.qwen.config.pad_token_id = tokenizer.eod_id
|
|
self.qwen.config.eos_token_id = tokenizer.eod_id
|
|
raw_text, context_tokens = self.prepareInput(tokenizer, query, query_assistant, history, system)
|
|
input_ids = torch.tensor([context_tokens]).to(next(qwen.parameters()).device)
|
|
self.unfinished_sequences = torch.ones(input_ids.shape[0], dtype=torch.long, device=input_ids.device)
|
|
input_length = input_ids.shape[1]
|
|
while True:
|
|
outputs = self.forwardQWen(input_ids)
|
|
next_token_scores = outputs[:, -1, :]
|
|
|
|
next_token_scores = self.repetition_penalty(input_ids, next_token_scores)
|
|
next_token_scores = self.top_p(next_token_scores)
|
|
next_tokens = self.sample(next_token_scores)
|
|
finish, next_tokens = self.isFinish(next_tokens)
|
|
if finish:
|
|
break
|
|
input_ids = torch.cat([input_ids, next_tokens], dim=-1)
|
|
if gen_length != 0 and (input_length + gen_length) < input_ids.shape[1]:
|
|
break
|
|
|
|
decoded, response, end_reason = decode_tokens(
|
|
input_ids[0],
|
|
tokenizer,
|
|
raw_text_len=len(raw_text),
|
|
context_length=len(context_tokens),
|
|
errors="replace",
|
|
)
|
|
history.append((query, response))
|
|
return input_ids[0].cpu().tolist(), history, decoded
|
|
|
|
def _rotate_half(self, x):
|
|
x = rearrange(x, "... (j d) -> ... j d", j=2)
|
|
x1, x2 = x.unbind(dim=-2)
|
|
return torch.cat((-x2, x1), dim=-1)
|
|
|
|
def apply_rotary_pos_emb(self, t, freqs):
|
|
rot_dim = freqs[0].shape[-1]
|
|
cos, sin = freqs
|
|
t_float = t.float()
|
|
t_rot = t_float[..., :rot_dim]
|
|
t_pass = t_float[..., rot_dim:]
|
|
t_rot = (t_rot * cos) + (self._rotate_half(t_rot) * sin)
|
|
return torch.cat((t_rot, t_pass), dim=-1).type_as(t)
|
|
|
|
def split_heads(
|
|
self,
|
|
attention,
|
|
hidden_states: Optional[Tuple[torch.FloatTensor]],
|
|
):
|
|
atten = attention
|
|
mixed_x_layer = atten.c_attn(hidden_states)
|
|
query, key, value = mixed_x_layer.split(atten.split_size, dim=2)
|
|
query = atten._split_heads(query, atten.num_heads, atten.head_dim)
|
|
key = atten._split_heads(key, atten.num_heads, atten.head_dim)
|
|
value = atten._split_heads(value, atten.num_heads, atten.head_dim)
|
|
return query, key, value
|
|
|
|
def pos_emb(self, query, key, rotary_pos_emb_list):
|
|
rotary_pos_emb = rotary_pos_emb_list[0]
|
|
rotary_pos_emb = [i[:, -query.shape[1] :, :, :] for i in rotary_pos_emb]
|
|
rotary_pos_emb = (rotary_pos_emb,) * 2
|
|
query = self.apply_rotary_pos_emb(query, rotary_pos_emb[0])
|
|
key = self.apply_rotary_pos_emb(key, rotary_pos_emb[1])
|
|
return query, key
|
|
|
|
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)
|
|
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
|
|
|
|
def build_mask(self, query):
|
|
size = query.size(1)
|
|
causal_mask = torch.tril(torch.ones((size, size), dtype=torch.bool, device=query.device)).view(1, 1, size, size)
|
|
return causal_mask
|
|
|
|
def forwardAttention(
|
|
self,
|
|
attention,
|
|
hidden_states: Optional[Tuple[torch.FloatTensor]],
|
|
rotary_pos_emb_list: Optional[List[List[torch.Tensor]]] = None,
|
|
):
|
|
query, key, value = self.split_heads(attention, hidden_states)
|
|
query, key = self.pos_emb(query, key, rotary_pos_emb_list)
|
|
causal_mask = self.build_mask(query)
|
|
return self.attention(attention, query, key, value, causal_mask)
|
|
|
|
def forwardQWenBlock(
|
|
self,
|
|
block,
|
|
hidden_states: Optional[Tuple[torch.FloatTensor]],
|
|
rotary_pos_emb_list: Optional[List[List[torch.Tensor]]] = None,
|
|
):
|
|
layernorm_output = block.ln_1(hidden_states)
|
|
|
|
attn_outputs = self.forwardAttention(block.attn, layernorm_output, rotary_pos_emb_list)
|
|
attn_output = attn_outputs[0]
|
|
layernorm_input = attn_output + hidden_states
|
|
|
|
layernorm_output = block.ln_2(layernorm_input)
|
|
a1 = block.mlp.w1(layernorm_output)
|
|
a2 = block.mlp.w2(layernorm_output)
|
|
intermediate_parallel = a1 * F.silu(a2)
|
|
mlp_output = block.mlp.c_proj(intermediate_parallel)
|
|
|
|
hidden_states = layernorm_input + mlp_output
|
|
return hidden_states
|
|
|
|
def forwardQWen(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
labels: Optional[torch.LongTensor] = 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:
|
|
hidden_states = self.forwardQWenBlock(block, hidden_states, rotary_pos_emb_list=rotary_pos_emb_list)
|
|
|
|
hidden_states = transfm.ln_f(hidden_states)
|
|
hidden_states = hidden_states.view(output_shape)
|
|
|
|
lm_logits = self.qwen.lm_head(hidden_states)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
labels = labels.to(lm_logits.device)
|
|
shift_labels = labels[..., 1:].contiguous().view(-1)
|
|
shift_logits = lm_logits[..., :-1, :].contiguous()
|
|
shift_logits = shift_logits.view(-1, shift_logits.size(-1))
|
|
mask = shift_labels < self.qwen.config.vocab_size
|
|
shift_labels = shift_labels[mask]
|
|
shift_logits = shift_logits[mask]
|
|
# m = torch.max(shift_logits, 1).indices.cpu().numpy()
|
|
# ll = shift_labels.cpu().numpy()
|
|
loss = CrossEntropyLoss()(shift_logits, shift_labels)
|
|
|
|
return lm_logits, loss
|
|
|
|
def prepareInput(self, tokenizer, query, query_assistant, history, system):
|
|
return make_context(tokenizer, query, query_assistant, history=history, system=system)
|
|
|
|
def repetition_penalty(self, input_ids, next_token_scores):
|
|
penalty = self.qwen.config.repetition_penalty
|
|
score = torch.gather(next_token_scores, 1, input_ids)
|
|
# if score < 0 then repetition penalty has to be multiplied to reduce the token probabilities
|
|
score = torch.where(score < 0, score * penalty, score / penalty)
|
|
next_token_scores = next_token_scores.scatter_(1, input_ids, score)
|
|
return next_token_scores
|
|
|
|
def top_p(self, next_token_scores):
|
|
top_p = self.qwen.config.top_p
|
|
filter_value = -float("Inf")
|
|
min_tokens_to_keep = 1
|
|
sorted_logits, sorted_indices = torch.sort(next_token_scores, descending=False)
|
|
cumulative_probs = sorted_logits.softmax(dim=-1).cumsum(dim=-1)
|
|
# Remove tokens with cumulative top_p above the threshold (token with 0 are kept)
|
|
sorted_indices_to_remove = cumulative_probs <= (1 - top_p)
|
|
# Keep at least min_tokens_to_keep
|
|
sorted_indices_to_remove[..., -min_tokens_to_keep:] = 0
|
|
# scatter sorted tensors to original indexing
|
|
indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
|
|
next_token_scores = next_token_scores.masked_fill(indices_to_remove, filter_value)
|
|
return next_token_scores
|
|
|
|
def sample(self, next_token_scores):
|
|
probs = nn.functional.softmax(next_token_scores, dim=-1)
|
|
next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
|
|
return next_tokens
|
|
|
|
def isFinish(self, next_tokens):
|
|
pad_token_id = self.qwen.config.pad_token_id
|
|
eos_token_id_tensor = torch.tensor([self.qwen.config.eos_token_id]).to(next_tokens.device)
|
|
|
|
next_tokens = next_tokens * self.unfinished_sequences + pad_token_id * (1 - self.unfinished_sequences)
|
|
self.unfinished_sequences = self.unfinished_sequences.mul(
|
|
next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0)
|
|
)
|
|
return self.unfinished_sequences.max() == 0, next_tokens[:, None]
|