# 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 importlib import math from typing import TYPE_CHECKING, Optional, Tuple, Union, Callable, List, Any, Generator import torch import torch.nn.functional as F import torch.utils.checkpoint from torch.cuda.amp import autocast from torch.nn import CrossEntropyLoss from transformers import PreTrainedTokenizer, GenerationConfig, StoppingCriteriaList from transformers.generation.logits_process import LogitsProcessorList if TYPE_CHECKING: from transformers.generation.streamers import BaseStreamer from transformers.generation.utils import GenerateOutput from transformers.modeling_outputs import ( BaseModelOutputWithPast, CausalLMOutputWithPast, ) from transformers.modeling_utils import PreTrainedModel from transformers.utils import logging try: from einops import rearrange except ImportError: rearrange = None from torch import nn SUPPORT_CUDA = torch.cuda.is_available() SUPPORT_BF16 = SUPPORT_CUDA and torch.cuda.is_bf16_supported() SUPPORT_FP16 = SUPPORT_CUDA and torch.cuda.get_device_capability(0)[0] >= 7 from .configuration_qwen import QWenConfig from .qwen_generation_utils import ( HistoryType, make_context, decode_tokens, get_stop_words_ids, StopWordsLogitsProcessor, ) from .visual import VisionTransformer logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "qwen" _CONFIG_FOR_DOC = "QWenConfig" QWen_PRETRAINED_MODEL_ARCHIVE_LIST = ["qwen-7b"] _ERROR_BAD_CHAT_FORMAT = """\ We detect you are probably using the pretrained model (rather than chat model) for chatting, since the chat_format in generation_config is not "chatml". If you are directly using the model downloaded from Huggingface, please make sure you are using our "Qwen/Qwen-7B-Chat" Huggingface model (rather than "Qwen/Qwen-7B") when you call model.chat(). 我们检测到您可能在使用预训练模型（而非chat模型）进行多轮chat，因为您当前在generation_config指定的chat_format，并未设置为我们在对话中所支持的"chatml"格式。 如果您在直接使用我们从Huggingface提供的模型，请确保您在调用model.chat()时，使用的是"Qwen/Qwen-7B-Chat"模型（而非"Qwen/Qwen-7B"预训练模型）。 """ _SENTINEL = object() _ERROR_STREAM_IN_CHAT = """\ Pass argument `stream` to model.chat() is buggy, deprecated, and marked for removal. Please use model.chat_stream(...) instead of model.chat(..., stream=True). 向model.chat()传入参数stream的用法可能存在Bug，该用法已被废弃，将在未来被移除。请使用model.chat_stream(...)代替model.chat(..., stream=True)。 """ apply_rotary_emb_func = None rms_norm = None # Copied from transformers.models.bart.modeling_bart._make_causal_mask def _make_causal_mask( input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0 ): """ Make causal mask used for bi-directional self-attention. """ bsz, tgt_len = input_ids_shape mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) mask_cond = torch.arange(mask.size(-1), device=device) mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) mask = mask.to(dtype) if past_key_values_length > 0: mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1) return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) # Copied from transformers.models.bart.modeling_bart._expand_mask def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): """ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. """ bsz, src_len = mask.size() tgt_len = tgt_len if tgt_len is not None else src_len expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) inverted_mask = 1.0 - expanded_mask return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) class QWenAttention(nn.Module): def __init__(self, config): super().__init__() self.register_buffer("masked_bias", torch.tensor(-1e4), persistent=False) self.seq_length = config.seq_length 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.scale_attn_weights = True self.projection_size = config.kv_channels * config.num_attention_heads assert self.projection_size % config.num_attention_heads == 0 self.hidden_size_per_attention_head = ( self.projection_size // 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.is_fp32 = not (config.bf16 or config.fp16) self.bf16 = config.bf16 self.use_dynamic_ntk = config.use_dynamic_ntk self.use_logn_attn = config.use_logn_attn logn_list = [ math.log(i, self.seq_length) if i > self.seq_length else 1 for i in range(1, 32768) ] self.logn_tensor = torch.tensor(logn_list)[None, :, None, None] self.attn_dropout = nn.Dropout(config.attn_dropout_prob) def _attn(self, query, key, value, registered_causal_mask, attention_mask=None, head_mask=None): attn_weights = torch.matmul(query, key.transpose(-1, -2)) if self.scale_attn_weights: attn_weights = attn_weights / torch.full( [], value.size(-1) ** 0.5, dtype=attn_weights.dtype, device=attn_weights.device, ) query_length, key_length = query.size(-2), key.size(-2) # causal_mask = self.bias[ # :, :, key_length - query_length : key_length, :key_length # ] # mask_value = torch.finfo(attn_weights.dtype).min # mask_value = torch.full([], mask_value, dtype=attn_weights.dtype).to( # attn_weights.device # ) # attn_weights = torch.where( # causal_mask, attn_weights.to(attn_weights.dtype), mask_value # ) attn_weights = attn_weights + attention_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1) attn_weights = attn_weights.type(value.dtype) attn_weights = self.attn_dropout(attn_weights) if head_mask is not None: attn_weights = attn_weights * head_mask attn_output = torch.matmul(attn_weights, value) attn_output = attn_output.transpose(1, 2) return attn_output, attn_weights def _upcast_and_reordered_attn( self, query, key, value, registered_causal_mask, attention_mask=None, head_mask=None ): bsz, num_heads, q_seq_len, dk = query.size() _, _, k_seq_len, _ = key.size() attn_weights = torch.empty( bsz * num_heads, q_seq_len, k_seq_len, dtype=torch.float32, device=query.device, ) scale_factor = 1.0 if self.scale_attn_weights: scale_factor /= float(value.size(-1)) ** 0.5 with autocast(enabled=False): q, k = query.reshape(-1, q_seq_len, dk), key.transpose(-1, -2).reshape( -1, dk, k_seq_len ) attn_weights = torch.baddbmm( attn_weights, q.float(), k.float(), beta=0, alpha=scale_factor ) attn_weights = attn_weights.reshape(bsz, num_heads, q_seq_len, k_seq_len) query_length, key_length = query.size(-2), key.size(-2) causal_mask = registered_causal_mask[ :, :, key_length - query_length : key_length, :key_length ] mask_value = torch.finfo(attn_weights.dtype).min mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to( attn_weights.device ) attn_weights = torch.where(causal_mask, attn_weights, mask_value) if attention_mask is not None: attn_weights = attn_weights + attention_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1) if attn_weights.dtype != torch.float32: raise RuntimeError( "Error with upcasting, attn_weights does not have dtype torch.float32" ) attn_weights = attn_weights.type(value.dtype) attn_weights = self.attn_dropout(attn_weights) if head_mask is not None: attn_weights = attn_weights * head_mask attn_output = torch.matmul(attn_weights, value) return attn_output, attn_weights 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) def forward( self, hidden_states: Optional[Tuple[torch.FloatTensor]], rotary_pos_emb: Optional[List[torch.Tensor]] = None, registered_causal_mask: Optional[torch.Tensor] = None, layer_past: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = False, ): mixed_x_layer = self.c_attn(hidden_states) query, key, value = mixed_x_layer.split(self.split_size, dim=2) query = self._split_heads(query, self.num_heads, self.head_dim) key = self._split_heads(key, self.num_heads, self.head_dim) value = self._split_heads(value, self.num_heads, self.head_dim) if rotary_pos_emb is not None: cur_len = query.shape[1] rotary_pos_emb = [i[:, -cur_len:, :, :] for i in rotary_pos_emb] rotary_pos_emb = (rotary_pos_emb,) * 2 q_pos_emb, k_pos_emb = rotary_pos_emb # Slice the pos emb for current inference query = apply_rotary_pos_emb(query, q_pos_emb) key = apply_rotary_pos_emb(key, k_pos_emb) if layer_past is not None: past_key, past_value = layer_past[0], layer_past[1] key = torch.cat((past_key, key), dim=1) value = torch.cat((past_value, value), dim=1) if use_cache: present = (key, value) else: present = None if self.use_logn_attn and not self.training: if self.logn_tensor.device != query.device or self.logn_tensor.dtype != query.dtype: self.logn_tensor = self.logn_tensor.to(query.device).type_as(query) seq_start = key.size(1) - query.size(1) seq_end = key.size(1) logn_tensor = self.logn_tensor[:, seq_start:seq_end, :, :] query = query * logn_tensor.expand_as(query) query = query.permute(0, 2, 1, 3) key = key.permute(0, 2, 1, 3) value = value.permute(0, 2, 1, 3) attn_output, attn_weight = self._attn( query, key, value, registered_causal_mask, attention_mask, head_mask ) context_layer = self._merge_heads( attn_output, self.num_heads, self.head_dim ) attn_output = self.c_proj(context_layer) outputs = (attn_output, present) if output_attentions: outputs += (attn_weight,) return outputs class QWenMLP(nn.Module): def __init__(self, config): super().__init__() self.w1 = nn.Linear( config.hidden_size, config.intermediate_size // 2, bias=not config.no_bias ) self.w2 = nn.Linear( config.hidden_size, config.intermediate_size // 2, bias=not config.no_bias ) ff_dim_in = config.intermediate_size // 2 self.c_proj = nn.Linear(ff_dim_in, config.hidden_size, bias=not config.no_bias) def forward(self, hidden_states): a1 = self.w1(hidden_states) a2 = self.w2(hidden_states) intermediate_parallel = a1 * F.silu(a2) output = self.c_proj(intermediate_parallel) return output class QWenBlock(nn.Module): def __init__(self, config): super().__init__() hidden_size = config.hidden_size self.bf16 = config.bf16 self.ln_1 = RMSNorm( hidden_size, eps=config.layer_norm_epsilon, ) self.attn = QWenAttention(config) self.ln_2 = RMSNorm( hidden_size, eps=config.layer_norm_epsilon, ) self.mlp = QWenMLP(config) def forward( self, hidden_states: Optional[Tuple[torch.FloatTensor]], rotary_pos_emb: Optional[List[torch.Tensor]] = None, registered_causal_mask: Optional[torch.Tensor] = None, layer_past: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False, ): layernorm_output = self.ln_1(hidden_states) attn_outputs = self.attn( layernorm_output, rotary_pos_emb, registered_causal_mask=registered_causal_mask, layer_past=layer_past, attention_mask=attention_mask, head_mask=head_mask, use_cache=use_cache, output_attentions=output_attentions, ) attn_output = attn_outputs[0] outputs = attn_outputs[1:] residual = hidden_states layernorm_input = attn_output + residual layernorm_output = self.ln_2(layernorm_input) residual = layernorm_input mlp_output = self.mlp(layernorm_output) hidden_states = residual + mlp_output if use_cache: outputs = (hidden_states,) + outputs else: outputs = (hidden_states,) + outputs[1:] return outputs class QWenPreTrainedModel(PreTrainedModel): config_class = QWenConfig base_model_prefix = "transformer" is_parallelizable = False supports_gradient_checkpointing = True _no_split_modules = ["QWenBlock"] def __init__(self, *inputs, **kwargs): super().__init__(*inputs, **kwargs) def _init_weights(self, module): """Initialize the weights.""" if isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, RMSNorm): module.weight.data.fill_(1.0) for name, p in module.named_parameters(): if name == "c_proj.weight": p.data.normal_( mean=0.0, std=( self.config.initializer_range / math.sqrt(2 * self.config.num_hidden_layers) ), ) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, QWenModel): module.gradient_checkpointing = value class QWenModel(QWenPreTrainedModel): _keys_to_ignore_on_load_missing = ["attn.masked_bias"] def __init__(self, config): super().__init__(config) self.vocab_size = config.vocab_size self.num_hidden_layers = config.num_hidden_layers self.embed_dim = config.hidden_size self.gradient_checkpointing = False self.use_dynamic_ntk = config.use_dynamic_ntk self.seq_length = config.seq_length self.wte = nn.Embedding(self.vocab_size, self.embed_dim) self.drop = nn.Dropout(config.emb_dropout_prob) if config.rotary_pct == 1.0: self.rotary_ndims = None else: assert config.rotary_pct < 1 self.rotary_ndims = int( config.kv_channels * config.rotary_pct ) dim = ( self.rotary_ndims if self.rotary_ndims is not None else config.kv_channels ) self.rotary_emb = RotaryEmbedding(dim, base=config.rotary_emb_base) self.use_flash_attn = config.use_flash_attn self.is_fp32 = not (config.bf16 or config.fp16) self.registered_causal_mask = None # if ( # self.use_flash_attn # and flash_attn_unpadded_func is not None # and not self.is_fp32 # ): # self.registered_causal_mask = None # else: # max_positions = config.max_position_embeddings # self.register_buffer( # "registered_causal_mask", # torch.tril( # torch.ones((max_positions, max_positions), dtype=torch.bool) # ).view(1, 1, max_positions, max_positions), # persistent=False, # ) self.h = nn.ModuleList( [ QWenBlock( config ) for i in range(config.num_hidden_layers) ] ) self.ln_f = RMSNorm( self.embed_dim, eps=config.layer_norm_epsilon, ) self.visual = VisionTransformer(**config.visual) self.post_init() def get_input_embeddings(self): return self.wte def set_input_embeddings(self, new_embeddings): self.wte = new_embeddings # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length): # create causal mask # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] combined_attention_mask = None if input_shape[-1] > 1: combined_attention_mask = _make_causal_mask( input_shape, inputs_embeds.dtype, device=inputs_embeds.device, past_key_values_length=past_key_values_length, ) if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to( inputs_embeds.device ) combined_attention_mask = ( expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask ) return combined_attention_mask def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): if past_key_values is None and torch.any(input_ids == self.config.visual['image_start_id']): bos_pos = torch.where(input_ids == self.config.visual['image_start_id']) eos_pos = torch.where(input_ids == self.config.visual['image_start_id'] + 1) assert (bos_pos[0] == eos_pos[0]).all() img_pos = torch.stack((bos_pos[0], bos_pos[1], eos_pos[1]), dim=1) images = [] for i, a, b in img_pos: image = input_ids[i][a + 1 : b - 1].tolist() image = image[ : image.index(self.config.visual['image_start_id'] + 2)] images.append(bytes(image).decode('utf-8')) images = self.visual.encode(images) assert images.shape[0] == len(images) fake_images = None elif self.training: fake_images=torch.zeros(1,3,224,224).to( dtype=self.visual.conv1.weight.dtype, device=self.visual.conv1.weight.device) images = self.visual(fake_images) else: fake_images = None images = None output_attentions = ( output_attentions if output_attentions is not None else self.config.output_attentions ) output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = ( return_dict if return_dict is not None else self.config.use_return_dict ) if input_ids is not None and inputs_embeds is not None: raise ValueError( "You cannot specify both input_ids and inputs_embeds at the same time" ) elif input_ids is not None: input_shape = input_ids.size() input_ids = input_ids.view(-1, input_shape[-1]) batch_size = input_ids.shape[0] elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] batch_size = inputs_embeds.shape[0] else: raise ValueError("You have to specify either input_ids or inputs_embeds") device = input_ids.device if input_ids is not None else inputs_embeds.device if token_type_ids is not None: token_type_ids = token_type_ids.view(-1, input_shape[-1]) if position_ids is not None: position_ids = position_ids.view(-1, input_shape[-1]) if past_key_values is None: past_length = 0 past_key_values = tuple([None] * len(self.h)) else: past_length = past_key_values[0][0].size(-2) if position_ids is None: position_ids = torch.arange( past_length, input_shape[-1] + past_length, dtype=torch.long, device=device, ) position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1]) encoder_attention_mask = None head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) if inputs_embeds is None: inputs_embeds = self.wte(input_ids) if batch_size <= 0: raise ValueError("batch_size has to be defined and > 0") attention_mask = self._prepare_decoder_attention_mask( attention_mask, input_shape, inputs_embeds, past_length ) hidden_states = inputs_embeds kv_seq_len = hidden_states.size()[1] if past_key_values[0] is not None: # past key values[0][0] shape: bs * seq_len * head_num * dim kv_seq_len += past_key_values[0][0].shape[1] if ( self.use_dynamic_ntk and kv_seq_len == hidden_states.size()[1] and not self.training ): context_value = math.log(kv_seq_len / self.seq_length, 2) + 1 ntk_alpha = 2 ** math.ceil(context_value) - 1 ntk_alpha = max(ntk_alpha, 1) else: ntk_alpha = self.rotary_emb._ntk_alpha_cached rotary_pos_emb = self.rotary_emb(kv_seq_len, ntk_alpha=ntk_alpha) for idx in range(len(rotary_pos_emb)): rotary_pos_emb[idx] = rotary_pos_emb[idx].to(hidden_states.device) hidden_states = self.drop(hidden_states).clone() if fake_images is not None: hidden_states = hidden_states + images.mean()*0 elif images is not None: for idx, (i, a, b) in enumerate(img_pos): hidden_states[i][a + 1 : b] = images[idx] output_shape = input_shape + (hidden_states.size(-1),) if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False presents = () if use_cache else None all_self_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): # None for past_key_value return module(*inputs, use_cache, output_attentions) return custom_forward outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(block), hidden_states, rotary_pos_emb, self.registered_causal_mask, None, attention_mask, head_mask[i], encoder_hidden_states, encoder_attention_mask, ) else: outputs = block( hidden_states, layer_past=layer_past, rotary_pos_emb=rotary_pos_emb, registered_causal_mask=self.registered_causal_mask, attention_mask=attention_mask, head_mask=head_mask[i], encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=use_cache, output_attentions=output_attentions, ) hidden_states = outputs[0] if use_cache is True: presents = presents + (outputs[1],) if output_attentions: all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) hidden_states = self.ln_f(hidden_states) hidden_states = hidden_states.view(output_shape) # Add last hidden state if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [hidden_states, presents, all_hidden_states] if v is not None ) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_self_attentions, ) class QWenLMHeadModel(QWenPreTrainedModel): _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.rotary_emb\.inv_freq"] _keys_to_ignore_on_load_unexpected = [r"h\.\d+\.attn\.masked_bias"] def __init__(self, config): super().__init__(config) assert ( config.bf16 + config.fp16 + config.fp32 <= 1 ), "Only one of \"bf16\", \"fp16\", \"fp32\" can be true" autoset_precision = config.bf16 + config.fp16 + config.fp32 == 0 if autoset_precision: if SUPPORT_BF16: logger.warn( "The model is automatically converting to bf16 for faster inference. " "If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"." ) config.bf16 = True elif SUPPORT_FP16: logger.warn( "The model is automatically converting to fp16 for faster inference. " "If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"." ) config.fp16 = True else: config.fp32 = True if config.bf16 and SUPPORT_CUDA and not SUPPORT_BF16: logger.warn("Your device does NOT seem to support bf16, you can switch to fp16 or fp32 by by passing fp16/fp32=True in \"AutoModelForCausalLM.from_pretrained\".") if config.fp16 and SUPPORT_CUDA and not SUPPORT_FP16: logger.warn("Your device does NOT support faster inference with fp16, please switch to fp32 which is likely to be faster") if config.fp32: if SUPPORT_BF16: logger.warn("Your device support faster inference by passing bf16=True in \"AutoModelForCausalLM.from_pretrained\".") elif SUPPORT_FP16: logger.warn("Your device support faster inference by passing fp16=True in \"AutoModelForCausalLM.from_pretrained\".") self.transformer = QWenModel(config) self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) if config.bf16: self.transformer.bfloat16() self.lm_head.bfloat16() if config.fp16: self.transformer.half() self.lm_head.half() self.post_init() def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def prepare_inputs_for_generation( self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs ): token_type_ids = kwargs.get("token_type_ids", None) if past_key_values: input_ids = input_ids[:, -1].unsqueeze(-1) if token_type_ids is not None: token_type_ids = token_type_ids[:, -1].unsqueeze(-1) attention_mask = kwargs.get("attention_mask", None) position_ids = kwargs.get("position_ids", None) if attention_mask is not None and position_ids is None: position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) if past_key_values: position_ids = position_ids[:, -1].unsqueeze(-1) else: position_ids = None if inputs_embeds is not None and past_key_values is None: model_inputs = {"inputs_embeds": inputs_embeds} else: model_inputs = {"input_ids": input_ids} model_inputs.update( { "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), "position_ids": position_ids, "attention_mask": attention_mask, "token_type_ids": token_type_ids, } ) return model_inputs def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CausalLMOutputWithPast]: return_dict = ( return_dict if return_dict is not None else self.config.use_return_dict ) transformer_outputs = self.transformer( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = transformer_outputs[0] lm_logits = self.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) ) if not return_dict: output = (lm_logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return CausalLMOutputWithPast( loss=loss, logits=lm_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) @staticmethod def _reorder_cache( past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor ) -> Tuple[Tuple[torch.Tensor]]: return tuple( tuple( past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past ) for layer_past in past_key_values ) def chat( self, tokenizer: PreTrainedTokenizer, query: str, history: Optional[HistoryType], system: str = "You are a helpful assistant.", append_history: bool = True, stream: Optional[bool] = _SENTINEL, stop_words_ids: Optional[List[List[int]]] = None, generation_config: Optional[GenerationConfig] = None, **kwargs, ) -> Tuple[str, HistoryType]: generation_config = generation_config if generation_config is not None else self.generation_config assert stream is _SENTINEL, _ERROR_STREAM_IN_CHAT assert generation_config.chat_format == 'chatml', _ERROR_BAD_CHAT_FORMAT if history is None: history = [] if stop_words_ids is None: stop_words_ids = [] max_window_size = kwargs.get('max_window_size', None) if max_window_size is None: max_window_size = generation_config.max_window_size raw_text, context_tokens = make_context( tokenizer, query, history=history, system=system, max_window_size=max_window_size, chat_format=generation_config.chat_format, ) stop_words_ids.extend(get_stop_words_ids( generation_config.chat_format, tokenizer )) input_ids = torch.tensor([context_tokens]).to(self.device) outputs = self.generate( input_ids, stop_words_ids=stop_words_ids, return_dict_in_generate=False, generation_config=generation_config, **kwargs, ) response = decode_tokens( outputs[0], tokenizer, raw_text_len=len(raw_text), context_length=len(context_tokens), chat_format=generation_config.chat_format, verbose=False, errors='replace' ) if append_history: history.append((query, response)) return response, history def chat_stream( self, tokenizer: PreTrainedTokenizer, query: str, history: Optional[HistoryType], system: str = "You are a helpful assistant.", stop_words_ids: Optional[List[List[int]]] = None, logits_processor: Optional[LogitsProcessorList] = None, generation_config: Optional[GenerationConfig] = None, **kwargs, ) -> Generator[str, Any, None]: generation_config = generation_config if generation_config is not None else self.generation_config assert generation_config.chat_format == 'chatml', _ERROR_BAD_CHAT_FORMAT if history is None: history = [] if stop_words_ids is None: stop_words_ids = [] max_window_size = kwargs.get('max_window_size', None) if max_window_size is None: max_window_size = generation_config.max_window_size raw_text, context_tokens = make_context( tokenizer, query, history=history, system=system, max_window_size=max_window_size, chat_format=generation_config.chat_format, ) stop_words_ids.extend(get_stop_words_ids( generation_config.chat_format, tokenizer )) if stop_words_ids is not None: stop_words_logits_processor = StopWordsLogitsProcessor( stop_words_ids=stop_words_ids, eos_token_id=generation_config.eos_token_id, ) if logits_processor is None: logits_processor = LogitsProcessorList([stop_words_logits_processor]) else: logits_processor.append(stop_words_logits_processor) input_ids = torch.tensor([context_tokens]).to(self.device) from transformers_stream_generator.main import NewGenerationMixin, StreamGenerationConfig self.__class__.generate_stream = NewGenerationMixin.generate self.__class__.sample_stream = NewGenerationMixin.sample_stream stream_config = StreamGenerationConfig(**generation_config.to_dict(), do_stream=True) def stream_generator(): outputs = [] for token in self.generate_stream( input_ids, return_dict_in_generate=False, generation_config=stream_config, logits_processor=logits_processor, seed=-1, **kwargs): outputs.append(token.item()) yield tokenizer.decode(outputs, skip_special_tokens=True, errors='ignore', keep_image_special=True) return stream_generator() def generate( self, inputs: Optional[torch.Tensor] = None, generation_config: Optional[GenerationConfig] = None, logits_processor: Optional[LogitsProcessorList] = None, stopping_criteria: Optional[StoppingCriteriaList] = None, prefix_allowed_tokens_fn: Optional[ Callable[[int, torch.Tensor], List[int]] ] = None, synced_gpus: Optional[bool] = None, assistant_model: Optional["PreTrainedModel"] = None, streamer: Optional["BaseStreamer"] = None, **kwargs, ) -> Union[GenerateOutput, torch.LongTensor]: generation_config = generation_config if generation_config is not None else self.generation_config # Process stop_words_ids. stop_words_ids = kwargs.pop("stop_words_ids", None) if stop_words_ids is None and generation_config is not None: stop_words_ids = getattr(generation_config, "stop_words_ids", None) if stop_words_ids is None: stop_words_ids = getattr(generation_config, "stop_words_ids", None) if stop_words_ids is not None: stop_words_logits_processor = StopWordsLogitsProcessor( stop_words_ids=stop_words_ids, eos_token_id=generation_config.eos_token_id, ) if logits_processor is None: logits_processor = LogitsProcessorList([stop_words_logits_processor]) else: logits_processor.append(stop_words_logits_processor) return super().generate( inputs, generation_config=generation_config, logits_processor=logits_processor, stopping_criteria=stopping_criteria, prefix_allowed_tokens_fn=prefix_allowed_tokens_fn, synced_gpus=synced_gpus, assistant_model=assistant_model, streamer=streamer, **kwargs, ) class RotaryEmbedding(torch.nn.Module): def __init__(self, dim, base=10000): super().__init__() self.dim = dim self.base = base self.inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim)) if importlib.util.find_spec("einops") is None: raise RuntimeError("einops is required for Rotary Embedding") self._rotary_pos_emb_cache = None self._seq_len_cached = 0 self._ntk_alpha_cached = 1.0 def update_rotary_pos_emb_cache(self, max_seq_len, offset=0, ntk_alpha=1.0): seqlen = max_seq_len + offset 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) from einops import rearrange emb = rearrange(emb, "n d -> 1 n 1 d") cos, sin = emb.cos(), emb.sin() self._rotary_pos_emb_cache = [cos, sin] def forward(self, max_seq_len, offset=0, ntk_alpha=1.0): self.update_rotary_pos_emb_cache(max_seq_len, offset, ntk_alpha) cos, sin = self._rotary_pos_emb_cache return [cos[:, offset : offset + max_seq_len], sin[:, offset : offset + max_seq_len]] def _rotate_half(x): from einops import rearrange 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(t, freqs): cos, sin = freqs if apply_rotary_emb_func is not None and t.is_cuda: t_ = t.float() cos = cos.squeeze(0).squeeze(1)[:, : cos.shape[-1] // 2] sin = sin.squeeze(0).squeeze(1)[:, : sin.shape[-1] // 2] output = apply_rotary_emb_func(t_, cos, sin).type_as(t) return output else: rot_dim = freqs[0].shape[-1] cos, sin = freqs t_, t_pass_ = t[..., :rot_dim], t[..., rot_dim:] t_ = t_.float() t_pass_ = t_pass_.float() t_ = (t_ * cos) + (_rotate_half(t_) * sin) return torch.cat((t_, t_pass_), dim=-1).type_as(t) 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): if rms_norm is not None and x.is_cuda: return rms_norm(x, self.weight, self.eps) else: output = self._norm(x.float()).type_as(x) return output * self.weight