diff --git a/demo.py b/chatglm/demo.py similarity index 87% rename from demo.py rename to chatglm/demo.py index 2efbe61..4392766 100644 --- a/demo.py +++ b/chatglm/demo.py @@ -1,19 +1,23 @@ +import sys + +sys.path.append("..") + import json import torch -from chatglm import ChatGLMForConditionalGeneration -from chatglm import ChatGLMTokenizer +from modeling_chatglm import ChatGLMForConditionalGeneration +from tokenization_chatglm import ChatGLMTokenizer +from modelscope import snapshot_download +from transformers import AutoConfig from tools import show -from transformers import AutoConfig - seed = 4321 torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) +pretrained_model_name_or_path = snapshot_download("ZhipuAI/chatglm3-6b") -pretrained_model_name_or_path = "../ZhipuAI/chatglm3-6b" config, kwargs = AutoConfig.from_pretrained( pretrained_model_name_or_path, return_unused_kwargs=True, @@ -24,7 +28,7 @@ config, kwargs = AutoConfig.from_pretrained( glm = ChatGLMForConditionalGeneration(config) -tokenizer_config_file = "./chatglm/tokenizer_config.json" +tokenizer_config_file = "./tokenizer_config.json" if tokenizer_config_file is not None: with open(tokenizer_config_file, encoding="utf-8") as tokenizer_config_handle: init_kwargs = json.load(tokenizer_config_handle) @@ -32,7 +36,7 @@ if tokenizer_config_file is not None: init_kwargs.pop("tokenizer_file", None) saved_init_inputs = init_kwargs.pop("init_inputs", ()) init_inputs = saved_init_inputs -init_kwargs["vocab_file"] = "./chatglm/tokenizer.model" +init_kwargs["vocab_file"] = "./tokenizer.model" init_kwargs["added_tokens_file"] = None init_kwargs["special_tokens_map_file"] = None init_kwargs["tokenizer_file"] = None diff --git a/graph.md b/chatglm/graph.md similarity index 100% rename from graph.md rename to chatglm/graph.md diff --git a/chatglm/modeling_chatglm.py b/chatglm/modeling_chatglm.py index d4abdff..74cb5db 100644 --- a/chatglm/modeling_chatglm.py +++ b/chatglm/modeling_chatglm.py @@ -19,14 +19,16 @@ from safetensors.torch import storage_ptr, storage_size from transformers.configuration_utils import PretrainedConfig from transformers.generation import GenerationConfig -from chatglm import ChatGLMConfig +from configuration_chatglm import ChatGLMConfig from tools import show class RotaryEmbedding(nn.Module): def __init__(self, dim: int, original_impl=False, device=None, dtype=None): super().__init__() - inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, device=device).to(dtype=dtype) / dim)) + inv_freq = 1.0 / ( + 10000 ** (torch.arange(0, dim, 2, device=device).to(dtype=dtype) / dim) + ) self.register_buffer("inv_freq", inv_freq) self.dim = dim self.original_impl = original_impl @@ -35,7 +37,13 @@ class RotaryEmbedding(nn.Module): dtype = self.inv_freq.dtype device = self.inv_freq.device # $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$ - theta = 1.0 / (base ** (torch.arange(0, self.dim, 2, dtype=torch.float, device=device) / self.dim)) + theta = 1.0 / ( + base + ** ( + torch.arange(0, self.dim, 2, dtype=torch.float, device=device) + / self.dim + ) + ) # Create position indexes `[0, 1, ..., max_seq_len - 1]` seq_idx = torch.arange(max_seq_len, dtype=torch.float, device=device) # Calculate the product of position index and $\theta_i$ @@ -50,7 +58,9 @@ class RotaryEmbedding(nn.Module): class RMSNorm(torch.nn.Module): def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None, **kwargs): super().__init__() - self.weight = torch.nn.Parameter(torch.empty(normalized_shape, device=device, dtype=dtype)) + self.weight = torch.nn.Parameter( + torch.empty(normalized_shape, device=device, dtype=dtype) + ) self.eps = eps def forward(self, hidden_states: torch.Tensor): @@ -70,7 +80,9 @@ class CoreAttention(torch.nn.Module): projection_size = config.kv_channels * config.num_attention_heads # Per attention head and per partition values. self.hidden_size_per_partition = projection_size - self.hidden_size_per_attention_head = projection_size // config.num_attention_heads + self.hidden_size_per_attention_head = ( + projection_size // config.num_attention_heads + ) self.num_attention_heads_per_partition = config.num_attention_heads coeff = None @@ -82,13 +94,17 @@ class CoreAttention(torch.nn.Module): self.attention_dropout = torch.nn.Dropout(config.attention_dropout) def forward(self, query_layer, key_layer, value_layer): - query_layer, key_layer, value_layer = [k.permute(1, 2, 0, 3) for k in [query_layer, key_layer, value_layer]] + query_layer, key_layer, value_layer = [ + k.permute(1, 2, 0, 3) for k in [query_layer, key_layer, value_layer] + ] if query_layer.shape[2] == key_layer.shape[2]: context_layer = torch.nn.functional.scaled_dot_product_attention( query_layer, key_layer, value_layer, is_causal=True ) context_layer = context_layer.permute(2, 0, 1, 3) - new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) + new_context_layer_shape = context_layer.size()[:-2] + ( + self.hidden_size_per_partition, + ) context_layer = context_layer.reshape(*new_context_layer_shape) return context_layer @@ -98,13 +114,16 @@ class SelfAttention(torch.nn.Module): super(SelfAttention, self).__init__() self.layer_number = max(1, layer_number) self.projection_size = config.kv_channels * config.num_attention_heads - self.hidden_size_per_attention_head = self.projection_size // config.num_attention_heads + self.hidden_size_per_attention_head = ( + self.projection_size // config.num_attention_heads + ) self.num_attention_heads_per_partition = config.num_attention_heads self.multi_query_attention = config.multi_query_attention self.qkv_hidden_size = 3 * self.projection_size self.num_multi_query_groups_per_partition = config.multi_query_group_num self.qkv_hidden_size = ( - self.projection_size + 2 * self.hidden_size_per_attention_head * config.multi_query_group_num + self.projection_size + + 2 * self.hidden_size_per_attention_head * config.multi_query_group_num ) self.query_key_value = nn.Linear( config.hidden_size, @@ -144,9 +163,12 @@ class SelfAttention(torch.nn.Module): (query_layer, key_layer, value_layer) = mixed_x_layer.split( [ - self.num_attention_heads_per_partition * self.hidden_size_per_attention_head, - self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, - self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head, + self.num_attention_heads_per_partition + * self.hidden_size_per_attention_head, + self.num_multi_query_groups_per_partition + * self.hidden_size_per_attention_head, + self.num_multi_query_groups_per_partition + * self.hidden_size_per_attention_head, ], dim=-1, ) @@ -182,7 +204,8 @@ class SelfAttention(torch.nn.Module): -1, -1, -1, - self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, + self.num_attention_heads_per_partition + // self.num_multi_query_groups_per_partition, -1, ) key_layer = key_layer.contiguous().view( @@ -197,7 +220,8 @@ class SelfAttention(torch.nn.Module): -1, -1, -1, - self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, + self.num_attention_heads_per_partition + // self.num_multi_query_groups_per_partition, -1, ) value_layer = value_layer.contiguous().view( @@ -224,9 +248,11 @@ class MLP(torch.nn.Module): device=device, dtype=config.torch_dtype, ) + def swiglu(x): x = torch.chunk(x, 2, dim=-1) return F.silu(x[0]) * x[1] + self.activation_func = swiglu self.dense_4h_to_h = nn.Linear( config.ffn_hidden_size, @@ -254,7 +280,9 @@ class GLMBlock(torch.nn.Module): super(GLMBlock, self).__init__() self.layer_number = layer_number - self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm + self.apply_residual_connection_post_layernorm = ( + config.apply_residual_connection_post_layernorm + ) self.fp32_residual_connection = config.fp32_residual_connection @@ -286,7 +314,9 @@ class GLMBlock(torch.nn.Module): attention_output = self.self_attention(layernorm_output, rotary_pos_emb) residual = hidden_states - layernorm_input = torch.nn.functional.dropout(attention_output, p=self.hidden_dropout, training=self.training) + layernorm_input = torch.nn.functional.dropout( + attention_output, p=self.hidden_dropout, training=self.training + ) layernorm_input = residual + layernorm_input # Layer norm post the self attention. @@ -297,7 +327,9 @@ class GLMBlock(torch.nn.Module): residual = layernorm_input - output = torch.nn.functional.dropout(mlp_output, p=self.hidden_dropout, training=self.training) + output = torch.nn.functional.dropout( + mlp_output, p=self.hidden_dropout, training=self.training + ) output = residual + output return output @@ -365,7 +397,9 @@ class ChatGLMModel(nn.Module): # Rotary positional embeddings self.seq_length = config.seq_length rotary_dim = ( - config.hidden_size // config.num_attention_heads if config.kv_channels is None else config.kv_channels + config.hidden_size // config.num_attention_heads + if config.kv_channels is None + else config.kv_channels ) self.rotary_pos_emb = RotaryEmbedding( @@ -392,7 +426,9 @@ class ChatGLMModel(nn.Module): tokenizer=None, ): output_hidden_states = ( - output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states ) inputs_embeds = self.embedding(input_ids) @@ -410,7 +446,7 @@ class ChatGLMModel(nn.Module): probs = nn.functional.softmax(next_token_logits, dim=-1) next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1) - return next_tokens + return probs, next_tokens class ChatGLMForConditionalGeneration(nn.Module): @@ -427,21 +463,26 @@ class ChatGLMForConditionalGeneration(nn.Module): self.warnings_issued = {} self.generation_config = GenerationConfig.from_model_config(config) - def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]]): + 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, "pytorch_model.bin.index.json") + resolved_archive_file = os.path.join( + pretrained_model_name_or_path, "pytorch_model.bin.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] + 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 - model.eval() # Set model in evaluation mode to deactivate DropOut modules by default return model def _load_state_dict_into_model(self, model_to_load, state_dict, start_prefix): @@ -470,15 +511,21 @@ class ChatGLMForConditionalGeneration(nn.Module): 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") + resolved_archive_file = tqdm_lib.tqdm( + resolved_archive_file, desc="Loading checkpoint shards" + ) for shard_file in resolved_archive_file: state_dict = torch.load(shard_file, map_location="cpu") - error_msgs += cls._load_state_dict_into_model(model_to_load, state_dict, start_prefix) + 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") + print( + f"All model checkpoint weights were used when initializing {cls.__class__.__name__}.\n" + ) return cls @torch.inference_mode() @@ -496,7 +543,9 @@ class ChatGLMForConditionalGeneration(nn.Module): generation_config = copy.deepcopy(self.generation_config) inputs_tensor = inputs["input_ids"] - input_ids = inputs_tensor.repeat_interleave(generation_config.num_return_sequences, dim=0) + input_ids = inputs_tensor.repeat_interleave( + generation_config.num_return_sequences, dim=0 + ) outputs = self.sample( input_ids, @@ -523,17 +572,21 @@ class ChatGLMForConditionalGeneration(nn.Module): eos_token_id = [eos_token_id] eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device) - isFinished = torch.zeros(input_ids.shape[0], dtype=torch.long, device=input_ids.device) + isFinished = torch.zeros( + input_ids.shape[0], dtype=torch.long, device=input_ids.device + ) # token_count = 0 while True: input_ids_in = input_ids batch_size, seq_length = input_ids_in.shape position_ids_in = ( - torch.arange(seq_length, dtype=torch.long, device=input_ids.device).unsqueeze(0).repeat(batch_size, 1) + torch.arange(seq_length, dtype=torch.long, device=input_ids.device) + .unsqueeze(0) + .repeat(batch_size, 1) ) model_inputs = {"input_ids": input_ids_in, "position_ids": position_ids_in} - next_tokens = self.transformer( + probs, next_tokens = self.transformer( **model_inputs, output_hidden_states=output_hidden_states, tokenizer=tokenizer, @@ -549,3 +602,41 @@ class ChatGLMForConditionalGeneration(nn.Module): input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1) return input_ids + + def backward( + self, + tokenizer, + query: str, + ): + inputs = tokenizer.build_chat_input(query, history=[], role="user") + inputs = inputs.to(next(self.parameters()).device) + + generation_config = copy.deepcopy(self.generation_config) + inputs_tensor = inputs["input_ids"] + input_ids = inputs_tensor.repeat_interleave( + generation_config.num_return_sequences, dim=0 + ) + + input_ids_in = input_ids + batch_size, seq_length = input_ids_in.shape + position_ids_in = ( + torch.arange(seq_length, dtype=torch.long, device=input_ids.device) + .unsqueeze(0) + .repeat(batch_size, 1) + ) + model_inputs = {"input_ids": input_ids_in, "position_ids": position_ids_in} + + probs, next_tokens = self.transformer( + **model_inputs, + output_hidden_states=None, + tokenizer=tokenizer, + ) + + next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1) + # probs_target = probs + # probs_target[0, next_tokens] = probs_target[0, next_tokens] * 1.1 + + loss = probs[0, next_tokens] + loss.backward() + + return loss diff --git a/test_tokenizer.py b/chatglm/test_tokenizer.py similarity index 83% rename from test_tokenizer.py rename to chatglm/test_tokenizer.py index a88c168..4242e53 100644 --- a/test_tokenizer.py +++ b/chatglm/test_tokenizer.py @@ -1,13 +1,18 @@ +import sys + +sys.path.append("..") + import json import torch from tools import show + from chatglm import ChatGLMTokenizer +from modelscope import snapshot_download -pretrained_model_name_or_path = "../ZhipuAI/chatglm3-6b" +pretrained_model_name_or_path = snapshot_download("ZhipuAI/chatglm3-6b") - -tokenizer_config_file = "./chatglm/tokenizer_config.json" +tokenizer_config_file = "./tokenizer_config.json" if tokenizer_config_file is not None: with open(tokenizer_config_file, encoding="utf-8") as tokenizer_config_handle: init_kwargs = json.load(tokenizer_config_handle) @@ -15,7 +20,7 @@ if tokenizer_config_file is not None: init_kwargs.pop("tokenizer_file", None) saved_init_inputs = init_kwargs.pop("init_inputs", ()) init_inputs = saved_init_inputs -init_kwargs["vocab_file"] = "./chatglm/tokenizer.model" +init_kwargs["vocab_file"] = "./tokenizer.model" init_kwargs["added_tokens_file"] = None init_kwargs["special_tokens_map_file"] = None init_kwargs["tokenizer_file"] = None @@ -30,7 +35,7 @@ b = tokenizer.decode([236, 173, 140]) token = [] for i in range(64798): token.append(str(i) + " : " + tokenizer.decode(i)) -show.DumpListToFile(token, "generated/token.log") +show.DumpListToFile(token, "../generated/token.log") # print("=======================") # for i in range(hidden_states_en.shape[0]): diff --git a/qwen/demo.py b/qwen/demo.py new file mode 100644 index 0000000..05d58d6 --- /dev/null +++ b/qwen/demo.py @@ -0,0 +1,25 @@ +from modelscope import snapshot_download +from transformers import AutoModelForCausalLM, AutoTokenizer +from transformers.generation import GenerationConfig + +model_dir = snapshot_download("qwen/Qwen-1_8B-Chat") + +tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True) +model = AutoModelForCausalLM.from_pretrained( + model_dir, device_map="auto", trust_remote_code=True +).eval() + + +# 可指定不同的生成长度、top_p等相关超参 +model.generation_config = GenerationConfig.from_pretrained( + model_dir, trust_remote_code=True +) + +# 第一轮对话 +response, history = model.chat(tokenizer, "你好", history=None) +print(response) +# 你好！很高兴为你提供帮助。 + +# 第二轮对话 +response, history = 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=history) +print(response) diff --git a/qwen/modeling_qwen.py b/qwen/modeling_qwen.py new file mode 100644 index 0000000..6500378 --- /dev/null +++ b/qwen/modeling_qwen.py @@ -0,0 +1,1363 @@ +# 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 \ No newline at end of file diff --git a/embedding.py b/test/embedding.py similarity index 100% rename from embedding.py rename to test/embedding.py diff --git a/tensor.py b/test/tensor.py similarity index 100% rename from tensor.py rename to test/tensor.py diff --git a/train.py b/train.py new file mode 100644 index 0000000..e543bce --- /dev/null +++ b/train.py @@ -0,0 +1,45 @@ +import json +import torch + +from chatglm import ChatGLMForConditionalGeneration +from chatglm import ChatGLMTokenizer + +from tools import show + +from transformers import AutoConfig + +seed = 4321 +torch.manual_seed(seed) +torch.cuda.manual_seed_all(seed) + + +pretrained_model_name_or_path = "../ZhipuAI/chatglm3-6b" +config, kwargs = AutoConfig.from_pretrained( + pretrained_model_name_or_path, + return_unused_kwargs=True, + trust_remote_code=True, + code_revision=None, + _commit_hash=None, +) +glm = ChatGLMForConditionalGeneration(config) + + +tokenizer_config_file = "./chatglm/tokenizer_config.json" +if tokenizer_config_file is not None: + with open(tokenizer_config_file, encoding="utf-8") as tokenizer_config_handle: + init_kwargs = json.load(tokenizer_config_handle) + init_kwargs.pop("tokenizer_class", None) + init_kwargs.pop("tokenizer_file", None) + saved_init_inputs = init_kwargs.pop("init_inputs", ()) + init_inputs = saved_init_inputs +init_kwargs["vocab_file"] = "./chatglm/tokenizer.model" +init_kwargs["added_tokens_file"] = None +init_kwargs["special_tokens_map_file"] = None +init_kwargs["tokenizer_file"] = None +init_kwargs["name_or_path"] = pretrained_model_name_or_path +tokenizer = ChatGLMTokenizer(*init_inputs, **init_kwargs) + + +glm = glm.from_pretrained(pretrained_model_name_or_path).half().cuda() +query = "你好" +response = glm.backward(tokenizer, query)