Refine model of qwen.

qwen/config.json

@@ -33,5 +33,14 @@
   "use_dynamic_ntk": true,
   "use_flash_attn": "auto",
   "use_logn_attn": true,
-  "vocab_size": 151936
+  "vocab_size": 151936,
+  "chat_format": "chatml",
+  "eos_token_id": 151643,
+  "pad_token_id": 151643,
+  "max_window_size": 6144,
+  "max_new_tokens": 512,
+  "do_sample": true,
+  "top_k": 0,
+  "top_p": 0.8,
+  "repetition_penalty": 1.1
 }

qwen/demo.py

@@ -52,10 +52,10 @@ print(model)
 
 
 tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
-model = model.from_pretrained(model_dir, config=config, device_map="auto", trust_remote_code=True)
+model = model.from_pretrained(model_dir).cuda()
 
 # model = model.eval()
-model = model.train() # control by @torch.no_grad()
+model = model.train()  # control by @torch.no_grad()
 
 # 可指定不同的生成长度、top_p等相关超参
 # model.generation_config = GenerationConfig.from_pretrained(
@@ -80,7 +80,7 @@ print(decode_tokens)
 
 # 第二轮对话
 response, history, decode_tokens = model.chat(tokenizer, "给我讲一个年轻人奋斗创业最终取得成功的故事。", "", history=None)
-print(response)
+print(decode_tokens)
 
 
 # <|im_start|>system

qwen/generation_config.json

@@ -1,12 +0,0 @@
-{
-  "chat_format": "chatml",
-  "eos_token_id": 151643,
-  "pad_token_id": 151643,
-  "max_window_size": 6144,
-  "max_new_tokens": 512,
-  "do_sample": true,
-  "top_k": 0,
-  "top_p": 0.8,
-  "repetition_penalty": 1.1,
-  "transformers_version": "4.31.0"
-}

qwen/modeling_qwen.py

@@ -1,11 +1,10 @@
-# 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.
-
 import copy
 import math
 import inspect
+import os
+import gc
+from tqdm import auto as tqdm_lib
+import json
 from typing import TYPE_CHECKING, Optional, Tuple, Union, Callable, List, Any, Generator
 
 import torch
@@ -37,6 +36,11 @@ from qwen_generation_utils import (
     StopWordsLogitsProcessor,
 )
 
+from safetensors import safe_open
+from safetensors.torch import load_file as safe_load_file
+from safetensors.torch import save_file as safe_save_file
+
+
 import sys
 
 sys.path.append("..")
@@ -96,7 +100,6 @@ class QWenAttention(nn.Module):
         self,
         hidden_states: Optional[Tuple[torch.FloatTensor]],
         rotary_pos_emb_list: Optional[List[List[torch.Tensor]]] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
     ):
         mixed_x_layer = self.c_attn(hidden_states)
         query, key, value = mixed_x_layer.split(self.split_size, dim=2)
@@ -120,32 +123,21 @@ class QWenAttention(nn.Module):
             query = query * logn_tensor.expand_as(query)
 
         key_size = key.size(1)
-        if query.size(1) == key_size:
+        causal_mask = torch.tril(torch.ones((key_size, key_size), dtype=torch.bool, device=query.device)).view(
-            causal_mask = torch.tril(torch.ones((key_size, key_size), dtype=torch.bool, device=query.device)).view(
+            1, 1, key_size, key_size
-                1, 1, key_size, key_size
+        )
-            )
-        else:
-            causal_mask = None
         query = query.permute(0, 2, 1, 3)
         key = key.permute(0, 2, 1, 3)
         value = value.permute(0, 2, 1, 3)
 
-        if attention_mask is not None:
-            attention_mask = attention_mask.expand(-1, -1, causal_mask.size(2), -1)
-            if causal_mask is not None:
-                attention_mask = attention_mask.masked_fill(~causal_mask, torch.finfo(query.dtype).min)
-        else:
-            attention_mask = causal_mask
-
         # qk = query @ key.transpose(-2, -1)
         # qk = qk[0]
-        # show.DumpTensorToImage(qk,"q_matmul_k_layer_"+str(self.index)+".png")
+        # prePath = "../generated/query_matmul_key/img/"
-        # print("layer:" + str(self.index) + "  query.shape:"+ str(query.shape))
+        # show.DumpTensorToImage(
-        # print("layer:" + str(self.index) + "  key.shape:"+ str(key.shape))
+        #     qk, prePath + "q_matmul_k_sequence_" + str(key_size) + "_layer_" + str(self.index) + ".png"
-        # print("layer:" + str(self.index) + "  value.shape:"+ str(value.shape))
+        # )
-        # print("\n")
 
-        attn_output = F.scaled_dot_product_attention(query, key, value, attn_mask=attention_mask).transpose(1, 2)
+        attn_output = F.scaled_dot_product_attention(query, key, value, attn_mask=causal_mask).transpose(1, 2)
         context_layer = self._merge_heads(attn_output, self.num_heads, self.head_dim)
         attn_output = self.c_proj(context_layer)
 
@@ -189,15 +181,10 @@ class QWenBlock(nn.Module):
         self,
         hidden_states: Optional[Tuple[torch.FloatTensor]],
         rotary_pos_emb_list: Optional[List[List[torch.Tensor]]] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
     ):
         layernorm_output = self.ln_1(hidden_states)
 
-        attn_outputs = self.attn(
+        attn_outputs = self.attn(layernorm_output, rotary_pos_emb_list)
-            layernorm_output,
-            rotary_pos_emb_list,
-            attention_mask=attention_mask,
-        )
         attn_output = attn_outputs[0]
         residual = hidden_states
         layernorm_input = attn_output + residual
@@ -209,7 +196,7 @@ class QWenBlock(nn.Module):
         return hidden_states
 
 
-class QWenPreTrainedModel(PreTrainedModel):
+class QWenPreTrainedModel(nn.Module):
     config_class = QWenConfig
     base_model_prefix = "transformer"
     is_parallelizable = False
@@ -217,7 +204,7 @@ class QWenPreTrainedModel(PreTrainedModel):
     _no_split_modules = ["QWenBlock"]
 
     def __init__(self, *inputs, **kwargs):
-        super().__init__(*inputs, **kwargs)
+        super().__init__()
 
 
 class QWenModel(QWenPreTrainedModel):
@@ -248,8 +235,6 @@ class QWenModel(QWenPreTrainedModel):
             eps=config.layer_norm_epsilon,
         )
 
-        self.post_init()
-
     def get_ntk_alpha(self, true_seq_len):
         context_value = math.log(true_seq_len / self.seq_length, 2) + 1
         ntk_alpha = 2 ** math.ceil(context_value) - 1
@@ -259,8 +244,6 @@ class QWenModel(QWenPreTrainedModel):
     def forward(
         self,
         input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
         inputs_embeds: Optional[torch.FloatTensor] = None,
     ):
         if input_ids is not None and inputs_embeds is not None:
@@ -275,14 +258,6 @@ class QWenModel(QWenPreTrainedModel):
         else:
             raise ValueError("You have to specify either input_ids or inputs_embeds")
 
-        if attention_mask is not None:
-            attention_mask = attention_mask.view(batch_size, -1)
-            attention_mask = attention_mask[:, None, None, :]
-            attention_mask = attention_mask.to(dtype=self.dtype)
-            attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
-
-        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
-
         if inputs_embeds is None:
             inputs_embeds = self.wte(input_ids)
         hidden_states = inputs_embeds
@@ -295,15 +270,8 @@ class QWenModel(QWenPreTrainedModel):
             ntk_alpha_list = self.rotary_emb._ntk_alpha_cached_list
         else:
             ntk_alpha_list = []
-            if attention_mask is not None and kv_seq_len > self.seq_length:
+            ntk_alpha = self.get_ntk_alpha(kv_seq_len)
-                true_seq_lens = attention_mask.squeeze(1).squeeze(1).eq(0).sum(dim=-1, dtype=torch.int32)
+            ntk_alpha_list.append(ntk_alpha)
-                for i in range(hidden_states.size()[0]):
-                    true_seq_len = true_seq_lens[i].item()
-                    ntk_alpha = self.get_ntk_alpha(true_seq_len)
-                    ntk_alpha_list.append(ntk_alpha)
-            else:
-                ntk_alpha = self.get_ntk_alpha(kv_seq_len)
-                ntk_alpha_list.append(ntk_alpha)
         self.rotary_emb._ntk_alpha_cached_list = ntk_alpha_list
         rotary_pos_emb_list = [self.rotary_emb(kv_seq_len, ntk_alpha=ntk_alpha) for ntk_alpha in ntk_alpha_list]
 
@@ -312,52 +280,34 @@ class QWenModel(QWenPreTrainedModel):
 
         all_hidden_states = None
         for block in self.h:
-            hidden_states = block(
+            hidden_states = block(hidden_states, rotary_pos_emb_list=rotary_pos_emb_list)
-                hidden_states,
-                rotary_pos_emb_list=rotary_pos_emb_list,
-                attention_mask=attention_mask,
-            )
 
         hidden_states = self.ln_f(hidden_states)
         hidden_states = hidden_states.view(output_shape)
         return BaseModelOutputWithPast(last_hidden_state=hidden_states, hidden_states=all_hidden_states)
 
 
-class QWenLMHeadModel(QWenPreTrainedModel):
+class QWenLMHeadModel(nn.Module):
     def __init__(self, config):
-        super().__init__(config)
+        super().__init__()
+        self.config = config
 
         self.transformer = QWenModel(config)
         self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-        self.post_init()
+        self.generation_config = GenerationConfig.from_model_config(config)
 
     def prepare_inputs_for_generation(self, input_ids, inputs_embeds=None, **kwargs):
-        if input_ids.size(0) == 1:
-            attention_mask = None
-        else:
-            attention_mask = kwargs.get("attention_mask", None)
-
         model_inputs = {"input_ids": input_ids}
-
-        model_inputs.update(
-            {
-                "attention_mask": attention_mask,
-            }
-        )
         return model_inputs
 
     def forward(
         self,
         input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
         inputs_embeds: Optional[torch.FloatTensor] = None,
         labels: Optional[torch.LongTensor] = None,
     ) -> Union[Tuple, CausalLMOutputWithPast]:
         transformer_outputs = self.transformer(
             input_ids,
-            attention_mask=attention_mask,
-            head_mask=head_mask,
             inputs_embeds=inputs_embeds,
         )
         hidden_states = transformer_outputs[0]
@@ -387,6 +337,59 @@ class QWenLMHeadModel(QWenPreTrainedModel):
             attentions=transformer_outputs.attentions,
         )
 
+    def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]]):
+        load_in_8bit = False
+        load_in_4bit = False
+
+        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)
+        model.is_loaded_in_4bit = load_in_4bit
+        model.is_loaded_in_8bit = load_in_8bit
+        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
+        error_msgs = []
+        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)
+
+            error_msgs += 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
+
     @torch.no_grad()
     def chat(
         self,
@@ -406,18 +409,14 @@ class QWenLMHeadModel(QWenPreTrainedModel):
 
         stop_words_ids = []
 
-        max_window_size = kwargs.get("max_window_size", None)
+        raw_text, context_tokens = make_context(tokenizer, query, query_assistant, history=history, system=system)
-        if max_window_size is None:
-            max_window_size = generation_config.max_window_size
-        raw_text, context_tokens = make_context(
-            tokenizer, query, query_assistant, history=history, system=system, max_window_size=max_window_size
-        )
 
         stop_words_ids.extend([[tokenizer.im_end_id], [tokenizer.im_start_id]])
-        input_ids = torch.tensor([context_tokens]).to(self.device)
+        input_ids = torch.tensor([context_tokens]).to(next(self.parameters()).device)
         outputs = self.generate(
             input_ids,
             stop_words_ids=stop_words_ids,
+            tokenizer=tokenizer,
             **kwargs,
         )
         decoded, response, end_reason = decode_tokens(
@@ -432,110 +431,53 @@ class QWenLMHeadModel(QWenPreTrainedModel):
 
     def generate(
         self,
-        inputs: Optional[torch.Tensor] = None,
+        input_ids: Optional[torch.Tensor] = None,
         stop_words_ids=[],
+        tokenizer=None,
         prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
         **kwargs,
     ) -> Union[GenerateOutput, torch.LongTensor]:
         generation_config = self.generation_config
-
-        # 1. Handle `generation_config` and kwargs that might update it, and validate the `.generate()` call
-        self._validate_model_class()
-
         generation_config = copy.deepcopy(generation_config)
         model_kwargs = generation_config.update(**kwargs)  # All unused kwargs must be model kwargs
         generation_config.validate()
-        self._validate_model_kwargs(model_kwargs.copy())
-
-        # 2. Set generation parameters if not already defined
-
-        if generation_config.pad_token_id is None and generation_config.eos_token_id is not None:
-            eos_token_id = generation_config.eos_token_id
-            if isinstance(eos_token_id, list):
-                eos_token_id = eos_token_id[0]
-            generation_config.pad_token_id = eos_token_id
-
-        # 3. Define model inputs
-        inputs_tensor, model_input_name, model_kwargs = self._prepare_model_inputs(
-            inputs, generation_config.bos_token_id, model_kwargs
-        )
-        # 4. Define other model kwargs
-
-        accepts_attention_mask = "attention_mask" in set(inspect.signature(self.forward).parameters.keys())
-        requires_attention_mask = "encoder_outputs" not in model_kwargs
-
-        if model_kwargs.get("attention_mask", None) is None and requires_attention_mask and accepts_attention_mask:
-            model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
-                inputs_tensor,
-                generation_config.pad_token_id,
-                generation_config.eos_token_id,
-            )
-
-        # 5. Prepare `input_ids` which will be used for auto-regressive generation
-        input_ids = inputs_tensor if model_input_name == "input_ids" else model_kwargs.pop("input_ids")
-
-        # 6. Prepare `max_length` depending on other stopping criteria.
-        input_ids_length = input_ids.shape[-1]
-        has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
-        generation_config.max_length = generation_config.max_new_tokens + input_ids_length
-        self._validate_generated_length(generation_config, input_ids_length, has_default_max_length)
-
-        stop_words_logits_processor = StopWordsLogitsProcessor(
-            stop_words_ids=stop_words_ids,
-            eos_token_id=generation_config.eos_token_id,
-        )
-        logits_processor = LogitsProcessorList([stop_words_logits_processor])
-        logits_processor = self._get_logits_processor(
-            generation_config=generation_config,
-            input_ids_seq_length=input_ids_length,
-            encoder_input_ids=inputs_tensor,
-            prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
-            logits_processor=logits_processor,
-            model_kwargs=model_kwargs,
-            negative_prompt_ids=None,
-            negative_prompt_attention_mask=None,
-        )
-
-        # 12. expand input_ids with `num_return_sequences` additional sequences per batch
-        input_ids, model_kwargs = self._expand_inputs_for_generation(
-            input_ids=input_ids,
-            expand_size=generation_config.num_return_sequences,
-            is_encoder_decoder=False,
-            **model_kwargs,
-        )
-
-        # 13. run sample
 
         pad_token_id = generation_config.pad_token_id
         eos_token_id_tensor = torch.tensor([generation_config.eos_token_id]).to(input_ids.device)
 
-        # init values
-        stopping_criteria = self._get_stopping_criteria(
-            generation_config=generation_config, stopping_criteria=StoppingCriteriaList()
-        )
-
-        logits_warper = self._get_logits_warper(generation_config)
-
-        # init attention / hidden states / scores tuples
         scores = None
-
         # keep track of which sequences are already finished
         unfinished_sequences = torch.ones(input_ids.shape[0], dtype=torch.long, device=input_ids.device)
 
         this_peer_finished = False
         # auto-regressive generation
         while True:
-            # prepare model inputs
             model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
 
             # forward pass to get next token
             outputs = self(**model_inputs)
-
             next_token_scores = outputs.logits[:, -1, :]
 
-            # pre-process distribution
+            # repetition_penalty
-            next_token_scores = logits_processor(input_ids, next_token_scores)
+            penalty = self.config.repetition_penalty
-            next_token_scores = logits_warper(input_ids, next_token_scores)
+            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)
+
+            # top_p
+            top_p = self.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)
 
             # sample
             probs = nn.functional.softmax(next_token_scores, dim=-1)
@@ -545,20 +487,24 @@ class QWenLMHeadModel(QWenPreTrainedModel):
 
             # update generated ids, model inputs, and length for next step
             input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
-            model_kwargs = self._update_model_kwargs_for_generation(outputs, model_kwargs, is_encoder_decoder=False)
 
             unfinished_sequences = unfinished_sequences.mul(
                 next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0)
             )
 
+            # decoded, response, end_reason = decode_tokens(
+            #     next_tokens,
+            #     tokenizer,
+            #     raw_text_len=0,
+            #     context_length=0,
+            #     errors="replace",
+            # )
+            # print(decoded)
+
             # stop when each sentence is finished
             if unfinished_sequences.max() == 0:
                 this_peer_finished = True
 
-            # stop if we exceed the maximum length
-            if stopping_criteria(input_ids, scores):
-                this_peer_finished = True
-
             if this_peer_finished:
                 break
         return input_ids