390 lines
16 KiB
Python
390 lines
16 KiB
Python
import copy
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import math
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import os
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import sys
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import gc
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from tqdm import auto as tqdm_lib
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import json
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from typing import Optional, Tuple, Union, Callable, List, Any, Generator
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from einops import rearrange
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import torch
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import torch.nn.functional as F
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import torch.utils.checkpoint
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from torch.nn import CrossEntropyLoss
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from torch import nn
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from safetensors.torch import load_file as safe_load_file
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from safetensors.torch import save_file as safe_save_file
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from qwen_generation_utils import (
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make_context,
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decode_tokens,
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)
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sys.path.append("..")
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from tools import show
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from tools import mem_tracker
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# tracker = mem_tracker.MemTracker()
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# tracker.track()
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class RMSNorm(torch.nn.Module):
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def __init__(self, dim: int, eps: float = 1e-6):
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super().__init__()
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self.eps = eps
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self.weight = nn.Parameter(torch.ones(dim))
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def _norm(self, x):
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return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
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def forward(self, x):
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return self._norm(x.float()).type_as(x) * self.weight
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class QWenAttention(nn.Module):
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def __init__(self, config, index):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.split_size = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.head_dim = self.hidden_size // self.num_heads
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self.projection_size = config.kv_channels * config.num_attention_heads
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self.c_attn = nn.Linear(config.hidden_size, 3 * self.projection_size)
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self.c_proj = nn.Linear(config.hidden_size, self.projection_size, bias=not config.no_bias)
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self.attn_dropout = nn.Dropout(config.attn_dropout_prob)
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self.index = index
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def _split_heads(self, tensor, num_heads, attn_head_size):
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new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)
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tensor = tensor.view(new_shape)
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return tensor
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def _merge_heads(self, tensor, num_heads, attn_head_size):
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tensor = tensor.contiguous()
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new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,)
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return tensor.view(new_shape)
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class QWenMLP(nn.Module):
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def __init__(self, config):
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super().__init__()
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ff_dim_in = config.intermediate_size // 2
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self.w1 = nn.Linear(config.hidden_size, ff_dim_in, bias=not config.no_bias)
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self.w2 = nn.Linear(config.hidden_size, ff_dim_in, bias=not config.no_bias)
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self.c_proj = nn.Linear(ff_dim_in, config.hidden_size, bias=not config.no_bias)
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class QWenBlock(nn.Module):
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def __init__(self, config, index):
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super().__init__()
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self.ln_1 = RMSNorm(
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config.hidden_size,
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eps=config.layer_norm_epsilon,
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)
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self.attn = QWenAttention(config, index)
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self.ln_2 = RMSNorm(
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config.hidden_size,
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eps=config.layer_norm_epsilon,
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)
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self.mlp = QWenMLP(config)
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self.index = index
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class QWenModel(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.wte = nn.Embedding(config.vocab_size, config.hidden_size)
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self.drop = nn.Dropout(config.emb_dropout_prob)
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dim = config.kv_channels
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self.h = nn.ModuleList([QWenBlock(config, i) for i in range(config.num_hidden_layers)])
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self.ln_f = RMSNorm(
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config.hidden_size,
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eps=config.layer_norm_epsilon,
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)
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self.dim = dim
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self.base = config.rotary_emb_base
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inv_freq = 1.0 / (self.base ** (torch.arange(0, dim, 2).float() / dim))
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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self._rotary_pos_emb_cache = None
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self._seq_len_cached = 0
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self._ntk_alpha_cached = 1.0
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def update_rotary_pos_emb_cache(self, seqlen, ntk_alpha=1.0):
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if seqlen > self._seq_len_cached or ntk_alpha != self._ntk_alpha_cached:
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base = self.base * ntk_alpha ** (self.dim / (self.dim - 2))
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self.inv_freq = 1.0 / (
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base ** (torch.arange(0, self.dim, 2, device=self.inv_freq.device).float() / self.dim)
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)
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self._seq_len_cached = max(2 * seqlen, 16)
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self._ntk_alpha_cached = ntk_alpha
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seq = torch.arange(self._seq_len_cached, device=self.inv_freq.device)
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freqs = torch.outer(seq.type_as(self.inv_freq), self.inv_freq)
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emb = torch.cat((freqs, freqs), dim=-1)
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emb = rearrange(emb, "n d -> 1 n 1 d")
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cos, sin = emb.cos(), emb.sin()
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self._rotary_pos_emb_cache = [cos, sin]
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class QWenLMHeadModel(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.transformer = QWenModel(config)
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self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
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def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]]):
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pretrained_model_name_or_path = str(pretrained_model_name_or_path)
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resolved_archive_file = os.path.join(pretrained_model_name_or_path, "model.safetensors.index.json")
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print(f"loading weights file {resolved_archive_file}")
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with open(resolved_archive_file, "r") as f:
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index = json.loads(f.read())
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shard_filenames = sorted(set(index["weight_map"].values()))
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resolved_archive_file = [os.path.join(pretrained_model_name_or_path, f) for f in shard_filenames]
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model = cls._load_pretrained_model(resolved_archive_file)
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return model
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def _load_state_dict_into_model(self, model_to_load, state_dict, start_prefix):
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metadata = getattr(state_dict, "_metadata", None)
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state_dict = state_dict.copy()
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if metadata is not None:
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state_dict._metadata = metadata
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error_msgs = []
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def load(module: nn.Module, state_dict, prefix=""):
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local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
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args = (state_dict, prefix, local_metadata, True, [], [], error_msgs)
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if len([key for key in state_dict if key.startswith(prefix)]) > 0:
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module._load_from_state_dict(*args)
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for name, child in module._modules.items():
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if child is not None:
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load(child, state_dict, prefix + name + ".")
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load(model_to_load, state_dict, prefix=start_prefix)
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del state_dict
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return error_msgs
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def _load_pretrained_model(cls, resolved_archive_file):
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start_prefix = ""
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model_to_load = cls
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if len(resolved_archive_file) > 1:
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resolved_archive_file = tqdm_lib.tqdm(resolved_archive_file, desc="Loading checkpoint shards")
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for shard_file in resolved_archive_file:
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state_dict = safe_load_file(shard_file)
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cls._load_state_dict_into_model(model_to_load, state_dict, start_prefix)
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del state_dict # force memory release
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gc.collect()
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print(f"All model checkpoint weights were used when initializing {cls.__class__.__name__}.\n")
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return cls
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class QwenRunner:
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def __init__(self, qwen):
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self.qwen = qwen
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@torch.no_grad()
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def Chat(
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self,
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tokenizer,
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query: str,
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query_assistant: str,
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system: str = "You are a helpful assistant.",
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history=[],
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):
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qwen = self.qwen
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history = copy.deepcopy(history)
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raw_text, context_tokens = self.prepareInput(tokenizer, query, query_assistant, history, system)
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input_ids = torch.tensor([context_tokens]).to(next(qwen.parameters()).device)
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self.unfinished_sequences = torch.ones(input_ids.shape[0], dtype=torch.long, device=input_ids.device)
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while True:
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outputs = self.forwardQWen(input_ids)
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next_token_scores = outputs[:, -1, :]
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next_token_scores = self.repetition_penalty(input_ids, next_token_scores)
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next_token_scores = self.top_p(next_token_scores)
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next_tokens = self.sample(next_token_scores)
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finish, next_tokens = self.isFinish(next_tokens)
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if finish:
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break
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input_ids = torch.cat([input_ids, next_tokens], dim=-1)
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decoded, response, end_reason = decode_tokens(
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input_ids[0],
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tokenizer,
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raw_text_len=len(raw_text),
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context_length=len(context_tokens),
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errors="replace",
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)
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history.append((query, response))
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return input_ids[0].cpu().tolist(), history, decoded
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def _rotate_half(self, x):
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x = rearrange(x, "... (j d) -> ... j d", j=2)
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x1, x2 = x.unbind(dim=-2)
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return torch.cat((-x2, x1), dim=-1)
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def apply_rotary_pos_emb(self, t, freqs):
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rot_dim = freqs[0].shape[-1]
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cos, sin = freqs
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t_float = t.float()
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t_rot, t_pass = t_float[..., :rot_dim], t_float[..., rot_dim:]
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t_rot = (t_rot * cos) + (self._rotate_half(t_rot) * sin)
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return torch.cat((t_rot, t_pass), dim=-1).type_as(t)
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def split_heads(
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self,
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attention,
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hidden_states: Optional[Tuple[torch.FloatTensor]],
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):
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atten = attention
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mixed_x_layer = atten.c_attn(hidden_states)
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query, key, value = mixed_x_layer.split(atten.split_size, dim=2)
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query = atten._split_heads(query, atten.num_heads, atten.head_dim)
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key = atten._split_heads(key, atten.num_heads, atten.head_dim)
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value = atten._split_heads(value, atten.num_heads, atten.head_dim)
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return query, key, value
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def pos_emb(self, query, key, rotary_pos_emb_list):
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rotary_pos_emb = rotary_pos_emb_list[0]
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rotary_pos_emb = [i[:, -query.shape[1] :, :, :] for i in rotary_pos_emb]
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rotary_pos_emb = (rotary_pos_emb,) * 2
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query = self.apply_rotary_pos_emb(query, rotary_pos_emb[0])
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key = self.apply_rotary_pos_emb(key, rotary_pos_emb[1])
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return query, key
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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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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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def build_mask(self, query):
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size = query.size(1)
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causal_mask = torch.tril(torch.ones((size, size), dtype=torch.bool, device=query.device)).view(1, 1, size, size)
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return causal_mask
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def forwardAttention(
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self,
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attention,
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hidden_states: Optional[Tuple[torch.FloatTensor]],
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rotary_pos_emb_list: Optional[List[List[torch.Tensor]]] = None,
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):
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query, key, value = self.split_heads(attention, hidden_states)
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query, key = self.pos_emb(query, key, rotary_pos_emb_list)
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causal_mask = self.build_mask(query)
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return self.attention(attention, query, key, value, causal_mask)
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def forwardQWenBlock(
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self,
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block,
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hidden_states: Optional[Tuple[torch.FloatTensor]],
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rotary_pos_emb_list: Optional[List[List[torch.Tensor]]] = None,
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):
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layernorm_output = block.ln_1(hidden_states)
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attn_outputs = self.forwardAttention(block.attn, layernorm_output, rotary_pos_emb_list)
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attn_output = attn_outputs[0]
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layernorm_input = attn_output + hidden_states
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layernorm_output = block.ln_2(layernorm_input)
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a1 = block.mlp.w1(layernorm_output)
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a2 = block.mlp.w2(layernorm_output)
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intermediate_parallel = a1 * F.silu(a2)
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mlp_output = block.mlp.c_proj(intermediate_parallel)
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hidden_states = layernorm_input + mlp_output
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return hidden_states
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def forwardQWen(
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self,
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input_ids: Optional[torch.LongTensor] = None,
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labels: Optional[torch.LongTensor] = None,
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):
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transfm = self.qwen.transformer
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input_shape = input_ids.size()
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input_ids = input_ids.view(-1, input_shape[-1])
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hidden_states = transfm.wte(input_ids)
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kv_seq_len = hidden_states.size()[1]
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transfm.update_rotary_pos_emb_cache(kv_seq_len, ntk_alpha=1.0)
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cos, sin = transfm._rotary_pos_emb_cache
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rotary_pos_emb_list = [[cos[:, :kv_seq_len], sin[:, :kv_seq_len]]]
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hidden_states = transfm.drop(hidden_states)
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output_shape = input_shape + (hidden_states.size(-1),)
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for block in transfm.h:
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hidden_states = self.forwardQWenBlock(block, hidden_states, rotary_pos_emb_list=rotary_pos_emb_list)
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hidden_states = transfm.ln_f(hidden_states)
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hidden_states = hidden_states.view(output_shape)
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lm_logits = self.qwen.lm_head(hidden_states)
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loss = None
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if labels is not None:
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labels = labels.to(lm_logits.device)
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shift_logits = lm_logits[..., :-1, :].contiguous()
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shift_labels = labels[..., 1:].contiguous()
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loss_fct = CrossEntropyLoss()
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loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
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# shift_labels = torch.ones([1,19]).to(lm_logits.device).to(torch.int64)
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# shift_logits = lm_logits[..., :-1, :].contiguous()
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# loss_fct = CrossEntropyLoss()
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# loss = loss_fct(
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# shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)
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# )
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# loss.backward()
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return lm_logits
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def prepareInput(self, tokenizer, query, query_assistant, history, system):
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return make_context(tokenizer, query, query_assistant, history=history, system=system)
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def repetition_penalty(self, input_ids, next_token_scores):
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penalty = self.qwen.config.repetition_penalty
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score = torch.gather(next_token_scores, 1, input_ids)
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# if score < 0 then repetition penalty has to be multiplied to reduce the token probabilities
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score = torch.where(score < 0, score * penalty, score / penalty)
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next_token_scores = next_token_scores.scatter_(1, input_ids, score)
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return next_token_scores
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def top_p(self, next_token_scores):
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top_p = self.qwen.config.top_p
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filter_value = -float("Inf")
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min_tokens_to_keep = 1
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sorted_logits, sorted_indices = torch.sort(next_token_scores, descending=False)
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cumulative_probs = sorted_logits.softmax(dim=-1).cumsum(dim=-1)
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# Remove tokens with cumulative top_p above the threshold (token with 0 are kept)
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sorted_indices_to_remove = cumulative_probs <= (1 - top_p)
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# Keep at least min_tokens_to_keep
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sorted_indices_to_remove[..., -min_tokens_to_keep:] = 0
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# scatter sorted tensors to original indexing
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indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
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next_token_scores = next_token_scores.masked_fill(indices_to_remove, filter_value)
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return next_token_scores
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def sample(self, next_token_scores):
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probs = nn.functional.softmax(next_token_scores, dim=-1)
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next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
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return next_tokens
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def isFinish(self, next_tokens):
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pad_token_id = self.qwen.config.pad_token_id
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eos_token_id_tensor = torch.tensor([self.qwen.config.eos_token_id]).to(next_tokens.device)
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next_tokens = next_tokens * self.unfinished_sequences + pad_token_id * (1 - self.unfinished_sequences)
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self.unfinished_sequences = self.unfinished_sequences.mul(
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next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0)
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)
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return self.unfinished_sequences.max() == 0, next_tokens[:, None]
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