# 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 importlib import math import pathlib from typing import TYPE_CHECKING, Optional, Tuple, Union, Callable, List, Any, Generator import torch import torch.nn.functional as F import torch.utils.checkpoint import warnings 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 SUPPORT_TORCH2 = hasattr(torch, '__version__') and int(torch.__version__.split(".")[0]) >= 2 from .configuration_qwen import QWenConfig from .qwen_generation_utils import ( HistoryType, make_context, decode_tokens, get_stop_words_ids, StopWordsLogitsProcessor, ) 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)。 """ _ERROR_INPUT_CPU_QUERY_WITH_FLASH_ATTN_ACTIVATED = """\ We detect you have activated flash attention support, but running model computation on CPU. Please make sure that your input data has been placed on GPU. If you actually want to run CPU computation, please following the readme and set device_map="cpu" to disable flash attention when loading the model (calling AutoModelForCausalLM.from_pretrained). 检测到您的模型已激活了flash attention支持，但正在执行CPU运算任务。如使用flash attention，请您确认模型输入已经传到GPU上。如果您确认要执行CPU运算，请您在载入模型（调用AutoModelForCausalLM.from_pretrained）时，按照readme说法，指定device_map="cpu"以禁用flash attention。 """ apply_rotary_emb_func = None rms_norm = None flash_attn_unpadded_func = None flash_attn_func = None def _import_flash_attn(): global apply_rotary_emb_func, rms_norm, flash_attn_unpadded_func, flash_attn_func try: from flash_attn.layers.rotary import apply_rotary_emb_func as __apply_rotary_emb_func apply_rotary_emb_func = __apply_rotary_emb_func except ImportError: logger.warn( "Warning: import flash_attn rotary fail, please install FlashAttention rotary to get higher efficiency " "https://github.com/Dao-AILab/flash-attention/tree/main/csrc/rotary" ) try: from flash_attn.ops.rms_norm import rms_norm as __rms_norm rms_norm = __rms_norm except ImportError: logger.warn( "Warning: import flash_attn rms_norm fail, please install FlashAttention layer_norm to get higher efficiency " "https://github.com/Dao-AILab/flash-attention/tree/main/csrc/layer_norm" ) try: import flash_attn _flash_attn_func = None if not hasattr(flash_attn, '__version__'): from flash_attn.flash_attn_interface import flash_attn_unpadded_func as __flash_attn_unpadded_func else: if int(flash_attn.__version__.split(".")[0]) >= 2: if int(flash_attn.__version__.split(".")[1]) >= 1: from flash_attn.flash_attn_interface import flash_attn_func as _flash_attn_func from flash_attn.flash_attn_interface import flash_attn_varlen_func as __flash_attn_unpadded_func else: from flash_attn.flash_attn_interface import flash_attn_unpadded_func as __flash_attn_unpadded_func flash_attn_unpadded_func = __flash_attn_unpadded_func flash_attn_func = _flash_attn_func except ImportError: logger.warn( "Warning: import flash_attn fail, please install FlashAttention to get higher efficiency " "https://github.com/Dao-AILab/flash-attention" ) def quantize_cache_v(fdata, bits, qmax, qmin): # b, s, head, h-dim->b, head, s, h-dim qtype = torch.uint8 device = fdata.device shape = fdata.shape fdata_cal = torch.flatten(fdata, 2) fmax = torch.amax(fdata_cal, dim=-1, keepdim=True) fmin = torch.amin(fdata_cal, dim=-1, keepdim=True) # Compute params if qmax.device != fmax.device: qmax = qmax.to(device) qmin = qmin.to(device) scale = (fmax - fmin) / (qmax - qmin) zero = qmin - fmin / scale scale = scale.unsqueeze(-1).repeat(1,1,shape[2],1).contiguous() zero = zero.unsqueeze(-1).repeat(1,1,shape[2],1).contiguous() # Quantize res_data = fdata / scale + zero qdata = torch.clamp(res_data, qmin, qmax).to(qtype) return qdata.contiguous(), scale, zero def dequantize_cache_torch(qdata, scale, zero): data = scale * (qdata - zero) return data class FlashSelfAttention(torch.nn.Module): def __init__( self, causal=False, softmax_scale=None, attention_dropout=0.0, ): super().__init__() assert flash_attn_unpadded_func is not None, ( "Please install FlashAttention first, " "e.g., with pip install flash-attn" ) assert ( rearrange is not None ), "Please install einops first, e.g., with pip install einops" self.causal = causal self.softmax_scale = softmax_scale self.dropout_p = attention_dropout def unpad_input(self, hidden_states, attention_mask): valid_mask = attention_mask.squeeze(1).squeeze(1).eq(0) seqlens_in_batch = valid_mask.sum(dim=-1, dtype=torch.int32) indices = torch.nonzero(valid_mask.flatten(), as_tuple=False).flatten() max_seqlen_in_batch = seqlens_in_batch.max().item() cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)) hidden_states = hidden_states[indices] return hidden_states, indices, cu_seqlens, max_seqlen_in_batch def pad_input(self, hidden_states, indices, batch, seqlen): output = torch.zeros(batch * seqlen, *hidden_states.shape[1:], device=hidden_states.device, dtype=hidden_states.dtype) output[indices] = hidden_states return rearrange(output, '(b s) ... -> b s ...', b=batch) def forward(self, q, k, v, attention_mask=None): assert all((i.dtype in [torch.float16, torch.bfloat16] for i in (q, k, v))) assert all((i.is_cuda for i in (q, k, v))) batch_size, seqlen_q = q.shape[0], q.shape[1] seqlen_k = k.shape[1] seqlen_out = seqlen_q if flash_attn_func is not None and batch_size == 1: dropout_p = self.dropout_p if self.training else 0 output = flash_attn_func(q, k, v, dropout_p, softmax_scale=self.softmax_scale, causal=self.causal) return output q, k, v = [rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v]] cu_seqlens_q = torch.arange( 0, (batch_size + 1) * seqlen_q, step=seqlen_q, dtype=torch.int32, device=q.device, ) if batch_size > 1 and attention_mask is not None: k, indices_k, cu_seqlens_k, seqlen_k = self.unpad_input(k, attention_mask) if q.size(0) == v.size(0): q = q[indices_k] cu_seqlens_q = cu_seqlens_k seqlen_q = seqlen_k v = v[indices_k] else: cu_seqlens_k = torch.arange( 0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int32, device=q.device, ) if self.training: assert seqlen_k == seqlen_q is_causal = self.causal dropout_p = self.dropout_p else: is_causal = seqlen_q == seqlen_k dropout_p = 0 output = flash_attn_unpadded_func( q, k, v, cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k, dropout_p, softmax_scale=self.softmax_scale, causal=is_causal, ) if batch_size > 1 and attention_mask is not None and seqlen_q == seqlen_k: output = self.pad_input(output, indices_k, batch_size, seqlen_out) else: new_shape = (batch_size, output.shape[0] // batch_size) + output.shape[1:] output = output.view(new_shape) return output 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.use_flash_attn = config.use_flash_attn 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) if ( self.use_flash_attn and flash_attn_unpadded_func is not None and not self.is_fp32 ): self.core_attention_flash = FlashSelfAttention( causal=True, attention_dropout=config.attn_dropout_prob ) 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) ] logn_tensor = torch.tensor(logn_list)[None, :, None, None] self.register_buffer("logn_tensor", logn_tensor, persistent=False) self.attn_dropout = nn.Dropout(config.attn_dropout_prob) self.softmax_in_fp32 = config.softmax_in_fp32 if hasattr(config, 'softmax_in_fp32') else False self.use_cache_quantization = config.use_cache_quantization if hasattr(config, 'use_cache_quantization') else False self.use_cache_kernel = config.use_cache_kernel if hasattr(config,'use_cache_kernel') else False cache_dtype = torch.float if self.bf16: cache_dtype=torch.bfloat16 elif config.fp16: cache_dtype = torch.float16 self.cache_qmax = torch.tensor(torch.iinfo(torch.uint8).max, dtype=cache_dtype) self.cache_qmin = torch.tensor(torch.iinfo(torch.uint8).min, dtype=cache_dtype) if config.use_cache_quantization and config.use_cache_kernel: # pre check if the support files existing module_root = pathlib.Path(__file__).parent src_files = ("cache_autogptq_cuda_256.cpp", "cache_autogptq_cuda_kernel_256.cu") if any(not (module_root/src).is_file() for src in src_files): warnings.warn("KV cache kernel source files (.cpp and .cu) not found.") self.cache_kernels = None else: try: from .cpp_kernels import cache_autogptq_cuda_256 self.cache_kernels = cache_autogptq_cuda_256 except ImportError: warnings.warn("Failed to import KV cache kernels.") self.cache_kernels = None def _attn(self, query, key, value, causal_mask=None, attention_mask=None, head_mask=None): device = query.device if self.use_cache_quantization: qk, qk_scale, qk_zero = key if self.use_cache_kernel and self.cache_kernels is not None: shape = query.shape[:-1] + (qk.shape[-2],) attn_weights = torch.zeros(shape, dtype=torch.float16, device=device) self.cache_kernels.vecquant8matmul_batched_faster_old( query.contiguous() if query.dtype == torch.float16 else query.to(torch.float16).contiguous(), qk.transpose(-1, -2).contiguous(), attn_weights, qk_scale.contiguous() if qk_scale.dtype == torch.float16 else qk_scale.to(torch.float16).contiguous(), qk_zero.contiguous()if qk_zero.dtype == torch.float16 else qk_zero.to(torch.float16).contiguous()) # attn_weights = attn_weights.to(query.dtype).contiguous() else: key = dequantize_cache_torch(qk, qk_scale, qk_zero) attn_weights = torch.matmul(query, key.transpose(-1, -2)) else: attn_weights = torch.matmul(query, key.transpose(-1, -2)) if self.scale_attn_weights: if self.use_cache_quantization: size_temp = value[0].size(-1) else: size_temp = value.size(-1) attn_weights = attn_weights / (size_temp ** 0.5) mask_value = torch.finfo(attn_weights.dtype).min if causal_mask is not None: attn_weights = torch.where( causal_mask, attn_weights.to(attn_weights.dtype), mask_value ) if attention_mask is not None: attn_weights = attn_weights + attention_mask if self.softmax_in_fp32: attn_weights = nn.functional.softmax(attn_weights.float(), dim=-1) else: attn_weights = nn.functional.softmax(attn_weights, dim=-1) attn_weights = attn_weights.type(query.dtype) attn_weights = self.attn_dropout(attn_weights) if head_mask is not None: attn_weights = attn_weights * head_mask if self.use_cache_quantization: qv, qv_scale, qv_zero = value if self.use_cache_kernel and self.cache_kernels is not None: shape = attn_weights.shape[:-1] + (query.shape[-1],) attn_output = torch.zeros(shape, dtype=torch.float16, device=device) self.cache_kernels.vecquant8matmul_batched_column_compression_faster_old( attn_weights.contiguous() if attn_weights.dtype == torch.float16 else attn_weights.to(torch.float16).contiguous(), qv.contiguous(), # dtype: int32 attn_output, qv_scale.contiguous() if qv_scale.dtype == torch.float16 else qv_scale.to(torch.float16).contiguous(), qv_zero.contiguous() if qv_zero.dtype == torch.float16 else qv_zero.to(torch.float16).contiguous()) if attn_output.dtype != query.dtype: attn_output = attn_output.to(query.dtype) attn_weights = attn_weights.to(query.dtype) else: value = dequantize_cache_torch(qv, qv_scale, qv_zero) attn_output = torch.matmul(attn_weights, value) else: attn_output = torch.matmul(attn_weights, value) attn_output = attn_output.transpose(1, 2) 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_list: Optional[List[List[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_list is not None: cur_len = query.shape[1] if len(rotary_pos_emb_list) == 1: rotary_pos_emb = rotary_pos_emb_list[0] 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) else: query_list = [] key_list = [] for i, rotary_pos_emb in enumerate(rotary_pos_emb_list): 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_list += [apply_rotary_pos_emb(query[i:i+1, :, :], q_pos_emb)] key_list += [apply_rotary_pos_emb(key[i:i+1, :, :], k_pos_emb)] query = torch.cat(query_list, dim=0) key = torch.cat(key_list, dim=0) if self.use_cache_quantization: key = quantize_cache_v(key.permute(0, 2, 1, 3), bits=8, qmin=self.cache_qmin, qmax=self.cache_qmax) value = quantize_cache_v(value.permute(0, 2, 1, 3), bits=8, qmin=self.cache_qmin, qmax=self.cache_qmax) if layer_past is not None: past_key, past_value = layer_past[0], layer_past[1] if self.use_cache_quantization: # use_cache_quantization: # present=((q_key,key_scale,key_zero_point), # (q_value,value_scale,value_zero_point)) key = (torch.cat((past_key[0], key[0]), dim=2), torch.cat((past_key[1], key[1]), dim=2), torch.cat((past_key[2], key[2]), dim=2)) value = (torch.cat((past_value[0], value[0]), dim=2), torch.cat((past_value[1], value[1]), dim=2), torch.cat((past_value[2], value[2]), dim=2)) else: # not use_cache_quantization: # present=(key,value) 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 key_size = key[0].size(2) if self.use_cache_quantization else key.size(1) if key_size > self.seq_length and self.use_logn_attn and not self.training: if self.use_cache_quantization: seq_start = key[0].size(2) - query.size(1) seq_end = key[0].size(2) else: seq_start = key.size(1) - query.size(1) seq_end = key.size(1) logn_tensor = self.logn_tensor[:, seq_start:seq_end, :, :].type_as(query) query = query * logn_tensor.expand_as(query) if ( self.use_flash_attn and flash_attn_unpadded_func is not None and not self.is_fp32 and query.is_cuda ): q, k, v = query, key, value attn_output = self.core_attention_flash(q, k, v, attention_mask=attention_mask) else: key_size = key[0].size(2) if self.use_cache_quantization else 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(1, 1, key_size, key_size) else: causal_mask = None query = query.permute(0, 2, 1, 3) if not self.use_cache_quantization: key = key.permute(0, 2, 1, 3) value = value.permute(0, 2, 1, 3) if ( causal_mask is None and self.use_flash_attn and flash_attn_unpadded_func is not None and not self.is_fp32 and not query.is_cuda ): raise Exception(_ERROR_INPUT_CPU_QUERY_WITH_FLASH_ATTN_ACTIVATED) if not self.use_cache_quantization and SUPPORT_TORCH2: if attention_mask is not None: attention_mask = attention_mask.expand(-1, -1, query.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 attn_output = F.scaled_dot_product_attention( query, key, value, attn_mask=attention_mask ).transpose(1, 2) attn_weight = None else: attn_output, attn_weight = self._attn( query, key, value, 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: if ( self.use_flash_attn and flash_attn_unpadded_func is not None and not self.is_fp32 ): raise ValueError("Cannot output attentions while using flash-attn") elif not self.use_cache_quantization and SUPPORT_TORCH2: raise ValueError("Cannot output attentions while using scaled_dot_product_attention") else: 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_list: Optional[List[List[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_list, 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"] _skip_keys_device_placement = "past_key_values" 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.use_cache_quantization = self.config.use_cache_quantization if hasattr(self.config, 'use_cache_quantization') else False 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.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.post_init() def get_input_embeddings(self): return self.wte def set_input_embeddings(self, new_embeddings): self.wte = new_embeddings 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 ntk_alpha = max(ntk_alpha, 1) return ntk_alpha 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, ): 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: if self.use_cache_quantization: past_length = past_key_values[0][0][0].size(2) 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]) if attention_mask is not None: if batch_size <= 0: raise ValueError("batch_size has to be defined and > 0") 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 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) 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 if self.use_cache_quantization: kv_seq_len += past_key_values[0][0][0].shape[2] else: kv_seq_len += past_key_values[0][0].shape[1] if self.training or not self.use_dynamic_ntk: ntk_alpha_list = [1.0] elif kv_seq_len != hidden_states.size()[1]: 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: true_seq_lens = attention_mask.squeeze(1).squeeze(1).eq(0).sum(dim=-1, dtype=torch.int32) 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 ] hidden_states = self.drop(hidden_states) 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_list, None, attention_mask, head_mask[i], encoder_hidden_states, encoder_attention_mask, ) else: outputs = block( hidden_states, layer_past=layer_past, rotary_pos_emb_list=rotary_pos_emb_list, 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\".") if config.use_flash_attn == "auto": if config.bf16 or config.fp16: logger.warn("Try importing flash-attention for faster inference...") config.use_flash_attn = True else: config.use_flash_attn = False if config.use_flash_attn and config.fp32: logger.warn("Flash attention will be disabled because it does NOT support fp32.") if config.use_flash_attn: _import_flash_attn() 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 ): if past_key_values: input_ids = input_ids[:, -1].unsqueeze(-1) if input_ids.size(0) == 1: attention_mask = None else: attention_mask = kwargs.get("attention_mask", 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"), "attention_mask": attention_mask, } ) 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.", 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 = [] else: # make a copy of the user's input such that is is left untouched history = copy.deepcopy(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' ) # as history is a copy of the user inputs, # we can always return the new turn to the user. # separating input history and output history also enables the user # to implement more complex history management 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') 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 inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) 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 self._ntk_alpha_cached_list = [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) 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, ntk_alpha=1.0): self.update_rotary_pos_emb_cache(max_seq_len, ntk_alpha) cos, sin = self._rotary_pos_emb_cache return [cos[:, :max_seq_len], sin[:, :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): """ Apply rotary embedding to the first rotary_dim of the iput Arguments: t (tensor(batch_size, seq_len, n_head, head_dim)): the input embedding/hidden states freqs (list[tensor(1, seq_len, 1, rotary_dim), tensor(1, seq_len, 1, rotary_dim)]): the cached cos/sin position embeddings """ rot_dim = freqs[0].shape[-1] cos, sin = freqs t_float = t.float() if apply_rotary_emb_func is not None and t.is_cuda: # apply_rotary_emb in flash_attn requires cos/sin to be of # shape (seqlen, rotary_dim / 2) and apply rotary embedding # to the first rotary_dim of the input cos = cos.squeeze(0).squeeze(1)[:, : rot_dim // 2] sin = sin.squeeze(0).squeeze(1)[:, : rot_dim // 2] return apply_rotary_emb_func(t_float, cos, sin).type_as(t) else: t_rot, t_pass = t_float[..., :rot_dim], t_float[..., rot_dim:] t_rot = (t_rot * cos) + (_rotate_half(t_rot) * sin) return torch.cat((t_rot, 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