dataset/MNBVC.py

@@ -1,5 +0,0 @@
-from datasets import load_dataset
-
-dataset = load_dataset("liwu/MNBVC", "wikipedia", split="train", streaming=True)
-
-print(next(iter(dataset)))  # get the first line

qwen/research_silu.py

@@ -1,123 +0,0 @@
-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 numpy as np
-
-import torch.nn.functional as F
-
-from qwen_generation_utils import (
-    make_context,
-    decode_tokens,
-)
-
-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()
-
-index = 0
-
-
-class ResearchRunner(QwenRunner):
-    def __init__(self, model):
-        super().__init__(model)
-
-    def prepareInput(self, tokenizer, query, query_assistant, history, system):
-        start_to = [151644]
-        n_to = [198]
-        end_to = [151645]
-        system_str = "system\nYou are a helpful assistant."
-        user_str = "user\n" + query
-        aassistant_str = "assistant\n" + query_assistant
-
-        system_token = start_to + tokenizer.encode(system_str, allowed_special=set()) + end_to + n_to
-        user_token = start_to + tokenizer.encode(user_str, allowed_special=set()) + end_to + n_to
-        aassistant_token = start_to + tokenizer.encode(aassistant_str, allowed_special=set())
-
-        tokens = system_token + user_token + aassistant_token
-        tokens = user_token + aassistant_token
-        tokens = start_to + tokenizer.encode("user\nHi你好\nassistant\n我是", allowed_special=set())
-
-        return "", tokens
-
-    def forwardQWenBlock(
-        self,
-        block,
-        hidden_states,
-        rotary_pos_emb_list=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)
-
-        activation = (F.relu(a2) > 0).to(float)
-        act_mean = torch.mean(activation, 2)
-        print("Layer:" + str(block.index))
-        print(act_mean.cpu())
-        global index
-        if index == 0:
-            activation = activation.reshape(activation.shape[1], 64, -1)
-            show.DumpTensorToImage(activation, "./temp/activation_layer_" + str(block.index) + ".png")
-
-        intermediate_parallel = a1 * F.silu(a2)
-        mlp_output = block.mlp.c_proj(intermediate_parallel)
-
-        hidden_states = layernorm_input + mlp_output
-        return hidden_states
-
-    def isFinish(self, next_tokens):
-        global index
-        index = index + 1
-        finish, next = super().isFinish(next_tokens)
-        return finish, next
-
-
-para = list(model.parameters())
-runner = ResearchRunner(model)
-
-output_ids, history, decoded = runner.Chat(tokenizer, "你好!!", "")
-print(decoded)
-
-
-tokens = []
-for i, token in enumerate(output_ids):
-    de = tokenizer.decode([token])
-    de = str(i + 1).zfill(3) + " : " + repr(de)
-    tokens.append(de)
-
-show.DumpListToFile(tokens, "./temp/token_decode_list.txt")

wit/__init__.py

@@ -1,2 +0,0 @@
-from qwen.modeling_qwen import QWenLMHeadModel
-from qwen.configuration_qwen import QWenConfig

wit/configuration_qwen.py

@@ -1,45 +0,0 @@
-# Copyright (c) Alibaba Cloud.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-
-class QWenConfig:
-    def __init__(self):
-        self.vocab_size = 151936
-        self.hidden_size = 2048
-        self.num_hidden_layers = 24
-        self.num_attention_heads = 16
-        self.emb_dropout_prob = 0.0
-        self.attn_dropout_prob = 0.0
-        self.layer_norm_epsilon = 1e-6
-        self.initializer_range = 0.02
-        self.max_position_embeddings = 8192
-        self.scale_attn_weights = True
-        self.use_cache = True
-        self.bf16 = False
-        self.fp16 = False
-        self.fp32 = False
-        self.kv_channels = 128
-        self.rotary_pct = 1.0
-        self.rotary_emb_base = 10000
-        self.use_dynamic_ntk = True
-        self.use_logn_attn = True
-        self.use_flash_attn = "auto"
-        self.intermediate_size = 11008
-        self.no_bias = True
-        self.tie_word_embeddings = False
-        self.use_cache_quantization = False
-        self.use_cache_kernel = False
-        self.softmax_in_fp32 = False
-
-        self.chat_format = "chatml"
-        self.eos_token_id = 151643
-        self.pad_token_id = 151643
-        self.max_window_size = 6144
-        self.max_new_tokens = 512
-        self.do_sample = True
-        self.top_k = 0
-        self.top_p = 0.8
-        self.repetition_penalty = 1.1
-        self.model_max_length = 8192

wit/demo.py

@@ -1,31 +0,0 @@
-import torch
-from modelscope import snapshot_download
-
-from modeling_wit import QWenLMHeadModel
-from modeling_wit import QwenRunner
-from configuration_qwen import QWenConfig
-from tokenization_qwen import QWenTokenizer
-
-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 = QWenConfig()
-model = QWenLMHeadModel(config)
-
-print(model)
-
-tokenizer = QWenTokenizer("./qwen.tiktoken")
-model = model.from_pretrained(model_dir).cuda()
-
-model = model.eval()
-# model = model.train()  # control by @torch.no_grad()
-
-
-runner = QwenRunner(model)
-
-response, history, decode_tokens = runner.Chat(tokenizer, "东南亚国家日本的首都是什么市", "")
-print(decode_tokens)

wit/model.safetensors.index.json

@@ -1,202 +0,0 @@
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-    "transformer.h.0.mlp.w1.weight": "model-00001-of-00002.safetensors",
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-    "transformer.h.5.ln_1.weight": "model-00001-of-00002.safetensors",
-    "transformer.h.5.ln_2.weight": "model-00001-of-00002.safetensors",
-    "transformer.h.5.mlp.c_proj.weight": "model-00001-of-00002.safetensors",
-    "transformer.h.5.mlp.w1.weight": "model-00001-of-00002.safetensors",
-    "transformer.h.5.mlp.w2.weight": "model-00001-of-00002.safetensors",
-    "transformer.h.6.attn.c_attn.bias": "model-00001-of-00002.safetensors",
-    "transformer.h.6.attn.c_attn.weight": "model-00001-of-00002.safetensors",
-    "transformer.h.6.attn.c_proj.weight": "model-00001-of-00002.safetensors",
-    "transformer.h.6.ln_1.weight": "model-00001-of-00002.safetensors",
-    "transformer.h.6.ln_2.weight": "model-00001-of-00002.safetensors",
-    "transformer.h.6.mlp.c_proj.weight": "model-00001-of-00002.safetensors",
-    "transformer.h.6.mlp.w1.weight": "model-00001-of-00002.safetensors",
-    "transformer.h.6.mlp.w2.weight": "model-00001-of-00002.safetensors",
-    "transformer.h.7.attn.c_attn.bias": "model-00001-of-00002.safetensors",
-    "transformer.h.7.attn.c_attn.weight": "model-00001-of-00002.safetensors",
-    "transformer.h.7.attn.c_proj.weight": "model-00001-of-00002.safetensors",
-    "transformer.h.7.ln_1.weight": "model-00001-of-00002.safetensors",
-    "transformer.h.7.ln_2.weight": "model-00001-of-00002.safetensors",
-    "transformer.h.7.mlp.c_proj.weight": "model-00001-of-00002.safetensors",
-    "transformer.h.7.mlp.w1.weight": "model-00001-of-00002.safetensors",
-    "transformer.h.7.mlp.w2.weight": "model-00001-of-00002.safetensors",
-    "transformer.h.8.attn.c_attn.bias": "model-00001-of-00002.safetensors",
-    "transformer.h.8.attn.c_attn.weight": "model-00001-of-00002.safetensors",
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-    "transformer.h.8.ln_1.weight": "model-00001-of-00002.safetensors",
-    "transformer.h.8.ln_2.weight": "model-00001-of-00002.safetensors",
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-    "transformer.h.8.mlp.w1.weight": "model-00001-of-00002.safetensors",
-    "transformer.h.8.mlp.w2.weight": "model-00001-of-00002.safetensors",
-    "transformer.h.9.attn.c_attn.bias": "model-00001-of-00002.safetensors",
-    "transformer.h.9.attn.c_attn.weight": "model-00001-of-00002.safetensors",
-    "transformer.h.9.attn.c_proj.weight": "model-00001-of-00002.safetensors",
-    "transformer.h.9.ln_1.weight": "model-00001-of-00002.safetensors",
-    "transformer.h.9.ln_2.weight": "model-00001-of-00002.safetensors",
-    "transformer.h.9.mlp.c_proj.weight": "model-00001-of-00002.safetensors",
-    "transformer.h.9.mlp.w1.weight": "model-00001-of-00002.safetensors",
-    "transformer.h.9.mlp.w2.weight": "model-00001-of-00002.safetensors",
-    "transformer.ln_f.weight": "model-00002-of-00002.safetensors",
-    "transformer.wte.weight": "model-00001-of-00002.safetensors"
-  }
-}

wit/modeling_wit.py

@@ -1,389 +0,0 @@
-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.projection_size = config.kv_channels * config.num_attention_heads
-        self.c_attn = nn.Linear(config.hidden_size, 3 * self.projection_size)
-        self.c_proj = nn.Linear(config.hidden_size, self.projection_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)
-        dim = config.kv_channels
-
-        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.dim = dim
-        self.base = config.rotary_emb_base
-        inv_freq = 1.0 / (self.base ** (torch.arange(0, dim, 2).float() / 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 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,
-        system: str = "You are a helpful assistant.",
-        history=[],
-    ):
-        qwen = self.qwen
-        history = copy.deepcopy(history)
-        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)
-        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)
-
-        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_pass = t_float[..., :rot_dim], 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_logits = lm_logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            loss_fct = CrossEntropyLoss()
-            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
-
-        # shift_labels = torch.ones([1,19]).to(lm_logits.device).to(torch.int64)
-        # shift_logits = lm_logits[..., :-1, :].contiguous()
-        # loss_fct = CrossEntropyLoss()
-        # loss = loss_fct(
-        #     shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)
-        # )
-        # loss.backward()
-
-        return lm_logits
-
-    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]

wit/qwen_generation_utils.py

@@ -1,109 +0,0 @@
-# Copyright (c) Alibaba Cloud.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""Generation support."""
-
-from typing import Tuple, List, Union, Iterable
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from transformers import PreTrainedTokenizer
-from transformers import logging
-from transformers.generation import LogitsProcessor
-
-logger = logging.get_logger(__name__)
-
-# Types.
-HistoryType = List[Tuple[str, str]]
-TokensType = List[int]
-BatchTokensType = List[List[int]]
-
-
-def make_context(
-    tokenizer: PreTrainedTokenizer,
-    query: str,
-    query_assistant: str = "",
-    history: List[Tuple[str, str]] = None,
-    system: str = "",
-    max_window_size: int = 6144,
-):
-    if history is None:
-        history = []
-
-    im_start, im_end = "<|im_start|>", "<|im_end|>"
-    im_start_tokens = [tokenizer.im_start_id]
-    im_end_tokens = [tokenizer.im_end_id]
-    nl_tokens = tokenizer.encode("\n")
-
-    def _tokenize_str(role, content):
-        return f"{role}\n{content}", tokenizer.encode(role, allowed_special=set()) + nl_tokens + tokenizer.encode(
-            content, allowed_special=set()
-        )
-
-    system_text, system_tokens_part = _tokenize_str("system", system)
-    system_tokens = im_start_tokens + system_tokens_part + im_end_tokens
-    assistant_tokens = tokenizer.encode(query_assistant, allowed_special=set())
-    raw_text = ""
-    context_tokens = []
-
-    for turn_query, turn_response in reversed(history):
-        query_text, query_tokens_part = _tokenize_str("user", turn_query)
-        query_tokens = im_start_tokens + query_tokens_part + im_end_tokens
-        response_text, response_tokens_part = _tokenize_str("assistant", turn_response)
-        response_tokens = im_start_tokens + response_tokens_part + im_end_tokens
-
-        next_context_tokens = nl_tokens + query_tokens + nl_tokens + response_tokens
-        prev_chat = f"\n{im_start}{query_text}{im_end}\n{im_start}{response_text}{im_end}"
-
-        current_context_size = len(system_tokens) + len(next_context_tokens) + len(context_tokens)
-        if current_context_size < max_window_size:
-            context_tokens = next_context_tokens + context_tokens
-            raw_text = prev_chat + raw_text
-        else:
-            break
-
-    context_tokens = system_tokens + context_tokens
-    raw_text = f"{im_start}{system_text}{im_end}" + raw_text
-    context_tokens += (
-        nl_tokens
-        + im_start_tokens
-        + _tokenize_str("user", query)[1]
-        + im_end_tokens
-        + nl_tokens
-        + im_start_tokens
-        + tokenizer.encode("assistant")
-        + nl_tokens
-        + assistant_tokens
-    )
-    raw_text += f"\n{im_start}user\n{query}{im_end}\n{im_start}assistant\n{query_assistant}"
-
-    return raw_text, context_tokens
-
-
-def decode_tokens(
-    tokens: Union[torch.LongTensor, TokensType],
-    tokenizer: PreTrainedTokenizer,
-    raw_text_len: int = 0,
-    context_length: int = 0,
-    errors: str = "replace",
-) -> str:
-    if torch.is_tensor(tokens):
-        tokens = tokens.cpu().numpy().tolist()
-
-    end_reason = f"Gen length {len(tokens)}"
-    eod_token_idx = context_length
-    for eod_token_idx in range(context_length, len(tokens)):
-        if tokens[eod_token_idx] in [tokenizer.im_start_id, tokenizer.im_end_id]:
-            end_reason = f"Gen {tokenizer.decode([tokens[eod_token_idx]])!r}"
-            break
-
-    decoded = tokenizer.decode(tokens, errors=errors)
-
-    decode_tokens = tokenizer.decode(tokens[:eod_token_idx], errors=errors)
-    trim_decode_tokens = decode_tokens[raw_text_len:]
-    trim_decode_tokens = trim_decode_tokens.strip()
-
-    return decoded, trim_decode_tokens, end_reason

wit/tokenization_qwen.py

@@ -1,266 +0,0 @@
-# Copyright (c) Alibaba Cloud.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""Tokenization classes for QWen."""
-
-import base64
-import logging
-import os
-import unicodedata
-from typing import Collection, Dict, List, Set, Tuple, Union
-
-import tiktoken
-from transformers import PreTrainedTokenizer, AddedToken
-
-logger = logging.getLogger(__name__)
-
-
-VOCAB_FILES_NAMES = {"vocab_file": "qwen.tiktoken"}
-
-PAT_STR = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
-ENDOFTEXT = "<|endoftext|>"
-IMSTART = "<|im_start|>"
-IMEND = "<|im_end|>"
-# as the default behavior is changed to allow special tokens in
-# regular texts, the surface forms of special tokens need to be
-# as different as possible to minimize the impact
-EXTRAS = tuple((f"<|extra_{i}|>" for i in range(205)))
-# changed to use actual index to avoid misconfiguration with vocabulary expansion
-SPECIAL_START_ID = 151643
-SPECIAL_TOKENS = tuple(
-    enumerate(
-        (
-            (
-                ENDOFTEXT,
-                IMSTART,
-                IMEND,
-            )
-            + EXTRAS
-        ),
-        start=SPECIAL_START_ID,
-    )
-)
-SPECIAL_TOKENS_SET = set(t for i, t in SPECIAL_TOKENS)
-
-
-def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
-    with open(tiktoken_bpe_file, "rb") as f:
-        contents = f.read()
-    return {
-        base64.b64decode(token): int(rank) for token, rank in (line.split() for line in contents.splitlines() if line)
-    }
-
-
-class QWenTokenizer(PreTrainedTokenizer):
-    """QWen tokenizer."""
-
-    vocab_files_names = VOCAB_FILES_NAMES
-
-    def __init__(
-        self,
-        vocab_file,
-        errors="replace",
-        extra_vocab_file=None,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-
-        # how to handle errors in decoding UTF-8 byte sequences
-        # use ignore if you are in streaming inference
-        self.errors = errors
-
-        self.mergeable_ranks = _load_tiktoken_bpe(vocab_file)  # type: Dict[bytes, int]
-        self.special_tokens = {token: index for index, token in SPECIAL_TOKENS}
-
-        # try load extra vocab from file
-        if extra_vocab_file is not None:
-            used_ids = set(self.mergeable_ranks.values()) | set(self.special_tokens.values())
-            extra_mergeable_ranks = _load_tiktoken_bpe(extra_vocab_file)
-            for token, index in extra_mergeable_ranks.items():
-                if token in self.mergeable_ranks:
-                    logger.info(f"extra token {token} exists, skipping")
-                    continue
-                if index in used_ids:
-                    logger.info(f"the index {index} for extra token {token} exists, skipping")
-                    continue
-                self.mergeable_ranks[token] = index
-            # the index may be sparse after this, but don't worry tiktoken.Encoding will handle this
-
-        enc = tiktoken.Encoding(
-            "Qwen",
-            pat_str=PAT_STR,
-            mergeable_ranks=self.mergeable_ranks,
-            special_tokens=self.special_tokens,
-        )
-        assert (
-            len(self.mergeable_ranks) + len(self.special_tokens) == enc.n_vocab
-        ), f"{len(self.mergeable_ranks) + len(self.special_tokens)} != {enc.n_vocab} in encoding"
-
-        self.decoder = {v: k for k, v in self.mergeable_ranks.items()}  # type: dict[int, bytes|str]
-        self.decoder.update({v: k for k, v in self.special_tokens.items()})
-
-        self.tokenizer = enc  # type: tiktoken.Encoding
-
-        self.eod_id = self.tokenizer.eot_token
-        self.im_start_id = self.special_tokens[IMSTART]
-        self.im_end_id = self.special_tokens[IMEND]
-
-    def __getstate__(self):
-        # for pickle lovers
-        state = self.__dict__.copy()
-        del state["tokenizer"]
-        return state
-
-    def __setstate__(self, state):
-        # tokenizer is not python native; don't pass it; rebuild it
-        self.__dict__.update(state)
-        enc = tiktoken.Encoding(
-            "Qwen",
-            pat_str=PAT_STR,
-            mergeable_ranks=self.mergeable_ranks,
-            special_tokens=self.special_tokens,
-        )
-        self.tokenizer = enc
-
-    def __len__(self) -> int:
-        return self.tokenizer.n_vocab
-
-    def get_vocab(self) -> Dict[bytes, int]:
-        return self.mergeable_ranks
-
-    def convert_tokens_to_ids(self, tokens: Union[bytes, str, List[Union[bytes, str]]]) -> List[int]:
-        ids = []
-        if isinstance(tokens, (str, bytes)):
-            if tokens in self.special_tokens:
-                return self.special_tokens[tokens]
-            else:
-                return self.mergeable_ranks.get(tokens)
-        for token in tokens:
-            if token in self.special_tokens:
-                ids.append(self.special_tokens[token])
-            else:
-                ids.append(self.mergeable_ranks.get(token))
-        return ids
-
-    def _add_tokens(
-        self,
-        new_tokens: Union[List[str], List[AddedToken]],
-        special_tokens: bool = False,
-    ) -> int:
-        if not special_tokens and new_tokens:
-            raise ValueError("Adding regular tokens is not supported")
-        for token in new_tokens:
-            surface_form = token.content if isinstance(token, AddedToken) else token
-            if surface_form not in SPECIAL_TOKENS_SET:
-                raise ValueError("Adding unknown special tokens is not supported")
-        return 0
-
-    def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]:
-        """
-        Save only the vocabulary of the tokenizer (vocabulary).
-
-        Returns:
-            `Tuple(str)`: Paths to the files saved.
-        """
-        file_path = os.path.join(save_directory, "qwen.tiktoken")
-        with open(file_path, "w", encoding="utf8") as w:
-            for k, v in self.mergeable_ranks.items():
-                line = base64.b64encode(k).decode("utf8") + " " + str(v) + "\n"
-                w.write(line)
-        return (file_path,)
-
-    def tokenize(
-        self,
-        text: str,
-        allowed_special: Union[Set, str] = "all",
-        disallowed_special: Union[Collection, str] = (),
-        **kwargs,
-    ) -> List[Union[bytes, str]]:
-        """
-        Converts a string in a sequence of tokens.
-
-        Args:
-            text (`str`):
-                The sequence to be encoded.
-            allowed_special (`Literal["all"]` or `set`):
-                The surface forms of the tokens to be encoded as special tokens in regular texts.
-                Default to "all".
-            disallowed_special (`Literal["all"]` or `Collection`):
-                The surface forms of the tokens that should not be in regular texts and trigger errors.
-                Default to an empty tuple.
-
-            kwargs (additional keyword arguments, *optional*):
-                Will be passed to the underlying model specific encode method.
-
-        Returns:
-            `List[bytes|str]`: The list of tokens.
-        """
-        tokens = []
-        text = unicodedata.normalize("NFC", text)
-
-        # this implementation takes a detour: text -> token id -> token surface forms
-        for t in self.tokenizer.encode(text, allowed_special=allowed_special, disallowed_special=disallowed_special):
-            tokens.append(self.decoder[t])
-        return tokens
-
-    def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
-        """
-        Converts a sequence of tokens in a single string.
-        """
-        text = ""
-        temp = b""
-        for t in tokens:
-            if isinstance(t, str):
-                if temp:
-                    text += temp.decode("utf-8", errors=self.errors)
-                    temp = b""
-                text += t
-            elif isinstance(t, bytes):
-                temp += t
-            else:
-                raise TypeError("token should only be of type types or str")
-        if temp:
-            text += temp.decode("utf-8", errors=self.errors)
-        return text
-
-    @property
-    def vocab_size(self):
-        return self.tokenizer.n_vocab
-
-    def _convert_id_to_token(self, index: int) -> Union[bytes, str]:
-        """Converts an id to a token, special tokens included"""
-        if index in self.decoder:
-            return self.decoder[index]
-        raise ValueError("unknown ids")
-
-    def _convert_token_to_id(self, token: Union[bytes, str]) -> int:
-        """Converts a token to an id using the vocab, special tokens included"""
-        if token in self.special_tokens:
-            return self.special_tokens[token]
-        if token in self.mergeable_ranks:
-            return self.mergeable_ranks[token]
-        raise ValueError("unknown token")
-
-    def _tokenize(self, text: str, **kwargs):
-        """
-        Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based
-        vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).
-
-        Do NOT take care of added tokens.
-        """
-        raise NotImplementedError
-
-    def _decode(
-        self,
-        token_ids: Union[int, List[int]],
-        skip_special_tokens: bool = False,
-        errors: str = None,
-        **kwargs,
-    ) -> str:
-        if isinstance(token_ids, int):
-            token_ids = [token_ids]
-        if skip_special_tokens:
-            token_ids = [i for i in token_ids if i < self.eod_id]
-        return self.tokenizer.decode(token_ids, errors=errors or self.errors)

wit/tokenizer_config.json

@@ -1,10 +0,0 @@
-{
-  "model_max_length": 8192,
-  "tokenizer_class": "QWenTokenizer",
-  "auto_map": {
-    "AutoTokenizer": [
-      "tokenization_qwen.QWenTokenizer",
-      null
-      ]
-  }
-}