""" PyTorch ChatGLM model. """ import math import copy import warnings import re import sys import torch import torch.utils.checkpoint import torch.nn.functional as F from torch import nn from torch.nn import CrossEntropyLoss, LayerNorm from torch.nn import CrossEntropyLoss, LayerNorm, MSELoss, BCEWithLogitsLoss from torch.nn.utils import skip_init from typing import Optional, Tuple, Union, List, Callable, Dict, Any from transformers.modeling_outputs import ( BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast, ) from transformers.modeling_utils import PreTrainedModel from transformers.utils import logging from transformers.generation.logits_process import LogitsProcessor from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig, ModelOutput from .configuration_chatglm import ChatGLMConfig # flags required to enable jit fusion kernels if sys.platform != 'darwin': torch._C._jit_set_profiling_mode(False) torch._C._jit_set_profiling_executor(False) torch._C._jit_override_can_fuse_on_cpu(True) torch._C._jit_override_can_fuse_on_gpu(True) logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "THUDM/ChatGLM2-6B" _CONFIG_FOR_DOC = "ChatGLM6BConfig" CHATGLM_6B_PRETRAINED_MODEL_ARCHIVE_LIST = [ "THUDM/chatglm2-6b", # See all ChatGLM models at https://huggingface.co/models?filter=chatglm ] def default_init(cls, *args, **kwargs): return cls(*args, **kwargs) class InvalidScoreLogitsProcessor(LogitsProcessor): def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor: if torch.isnan(scores).any() or torch.isinf(scores).any(): scores.zero_() scores[..., 5] = 5e4 return scores class PrefixEncoder(torch.nn.Module): """ The torch.nn model to encode the prefix Input shape: (batch-size, prefix-length) Output shape: (batch-size, prefix-length, 2*layers*hidden) """ def __init__(self, config: ChatGLMConfig): super().__init__() self.prefix_projection = config.prefix_projection if self.prefix_projection: # Use a two-layer MLP to encode the prefix kv_size = config.num_layers * config.kv_channels * config.multi_query_group_num * 2 self.embedding = torch.nn.Embedding(config.pre_seq_len, kv_size) self.trans = torch.nn.Sequential( torch.nn.Linear(kv_size, config.hidden_size), torch.nn.Tanh(), torch.nn.Linear(config.hidden_size, kv_size) ) else: self.embedding = torch.nn.Embedding(config.pre_seq_len, config.num_layers * config.kv_channels * config.multi_query_group_num * 2) def forward(self, prefix: torch.Tensor): if self.prefix_projection: prefix_tokens = self.embedding(prefix) past_key_values = self.trans(prefix_tokens) else: past_key_values = self.embedding(prefix) return past_key_values def split_tensor_along_last_dim( tensor: torch.Tensor, num_partitions: int, contiguous_split_chunks: bool = False, ) -> List[torch.Tensor]: """Split a tensor along its last dimension. Arguments: tensor: input tensor. num_partitions: number of partitions to split the tensor contiguous_split_chunks: If True, make each chunk contiguous in memory. Returns: A list of Tensors """ # Get the size and dimension. last_dim = tensor.dim() - 1 last_dim_size = tensor.size()[last_dim] // num_partitions # Split. tensor_list = torch.split(tensor, last_dim_size, dim=last_dim) # Note: torch.split does not create contiguous tensors by default. if contiguous_split_chunks: return tuple(chunk.contiguous() for chunk in tensor_list) return tensor_list class RotaryEmbedding(nn.Module): def __init__(self, dim, 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)) self.register_buffer("inv_freq", inv_freq) self.dim = dim self.original_impl = original_impl def forward_impl( self, seq_len: int, n_elem: int, dtype: torch.dtype, device: torch.device, base: int = 10000 ): """Enhanced Transformer with Rotary Position Embedding. Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/ transformers/rope/__init__.py. MIT License: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license. """ # $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$ theta = 1.0 / (base ** (torch.arange(0, n_elem, 2, dtype=dtype, device=device) / n_elem)) # Create position indexes `[0, 1, ..., seq_len - 1]` seq_idx = torch.arange(seq_len, dtype=dtype, device=device) # Calculate the product of position index and $\theta_i$ idx_theta = torch.outer(seq_idx, theta).float() cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1) # this is to mimic the behaviour of complex32, else we will get different results if dtype in (torch.float16, torch.bfloat16, torch.int8): cache = cache.bfloat16() if dtype == torch.bfloat16 else cache.half() return cache def forward(self, max_seq_len, offset=0): return self.forward_impl( max_seq_len, self.dim, dtype=self.inv_freq.dtype, device=self.inv_freq.device ) @torch.jit.script def apply_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor: # x: [sq, b, np, hn] sq, b, np, hn = x.size(0), x.size(1), x.size(2), x.size(3) rot_dim = rope_cache.shape[-2] * 2 x, x_pass = x[..., :rot_dim], x[..., rot_dim:] # truncate to support variable sizes rope_cache = rope_cache[:sq] xshaped = x.reshape(sq, -1, np, rot_dim // 2, 2) rope_cache = rope_cache.view(sq, -1, 1, xshaped.size(3), 2) x_out2 = torch.stack( [ xshaped[..., 0] * rope_cache[..., 0] - xshaped[..., 1] * rope_cache[..., 1], xshaped[..., 1] * rope_cache[..., 0] + xshaped[..., 0] * rope_cache[..., 1], ], -1, ) x_out2 = x_out2.flatten(3) return torch.cat((x_out2, x_pass), dim=-1) 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.eps = eps def forward(self, hidden_states: torch.Tensor): input_dtype = hidden_states.dtype variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.eps) return (self.weight * hidden_states).to(input_dtype) class CoreAttention(torch.nn.Module): def __init__(self, config: ChatGLMConfig, layer_number): super(CoreAttention, self).__init__() self.apply_query_key_layer_scaling = config.apply_query_key_layer_scaling self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32 if self.apply_query_key_layer_scaling: self.attention_softmax_in_fp32 = True self.layer_number = max(1, layer_number) 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.num_attention_heads_per_partition = config.num_attention_heads coeff = None self.norm_factor = math.sqrt(self.hidden_size_per_attention_head) if self.apply_query_key_layer_scaling: coeff = self.layer_number self.norm_factor *= coeff self.coeff = coeff self.attention_dropout = torch.nn.Dropout(config.attention_dropout) def forward(self, query_layer, key_layer, value_layer, attention_mask): pytorch_major_version = int(torch.__version__.split('.')[0]) if pytorch_major_version >= 2: query_layer, key_layer, value_layer = [k.permute(1, 2, 0, 3) for k in [query_layer, key_layer, value_layer]] if attention_mask is None and 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) else: if attention_mask is not None: attention_mask = ~attention_mask context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer, attention_mask) context_layer = context_layer.permute(2, 0, 1, 3) new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) context_layer = context_layer.reshape(*new_context_layer_shape) else: # Raw attention scores # [b, np, sq, sk] output_size = (query_layer.size(1), query_layer.size(2), query_layer.size(0), key_layer.size(0)) # [sq, b, np, hn] -> [sq, b * np, hn] query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1) # [sk, b, np, hn] -> [sk, b * np, hn] key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1) # preallocting input tensor: [b * np, sq, sk] matmul_input_buffer = torch.empty( output_size[0] * output_size[1], output_size[2], output_size[3], dtype=query_layer.dtype, device=query_layer.device ) # Raw attention scores. [b * np, sq, sk] matmul_result = torch.baddbmm( matmul_input_buffer, query_layer.transpose(0, 1), # [b * np, sq, hn] key_layer.transpose(0, 1).transpose(1, 2), # [b * np, hn, sk] beta=0.0, alpha=(1.0 / self.norm_factor), ) # change view to [b, np, sq, sk] attention_scores = matmul_result.view(*output_size) # =========================== # Attention probs and dropout # =========================== # attention scores and attention mask [b, np, sq, sk] if self.attention_softmax_in_fp32: attention_scores = attention_scores.float() if self.coeff is not None: attention_scores = attention_scores * self.coeff if attention_mask is None and attention_scores.shape[2] == attention_scores.shape[3]: attention_mask = torch.ones(output_size[0], 1, output_size[2], output_size[3], device=attention_scores.device, dtype=torch.bool) attention_mask.tril_() attention_mask = ~attention_mask if attention_mask is not None: attention_scores = attention_scores.masked_fill(attention_mask, float("-inf")) attention_probs = F.softmax(attention_scores, dim=-1) attention_probs = attention_probs.type_as(value_layer) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.attention_dropout(attention_probs) # ========================= # Context layer. [sq, b, hp] # ========================= # value_layer -> context layer. # [sk, b, np, hn] --> [b, np, sq, hn] # context layer shape: [b, np, sq, hn] output_size = (value_layer.size(1), value_layer.size(2), query_layer.size(0), value_layer.size(3)) # change view [sk, b * np, hn] value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -1) # change view [b * np, sq, sk] attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1) # matmul: [b * np, sq, hn] context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1)) # change view [b, np, sq, hn] context_layer = context_layer.view(*output_size) # [b, np, sq, hn] --> [sq, b, np, hn] context_layer = context_layer.permute(2, 0, 1, 3).contiguous() # [sq, b, np, hn] --> [sq, b, hp] new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,) context_layer = context_layer.view(*new_context_layer_shape) return context_layer class SelfAttention(torch.nn.Module): """Parallel self-attention layer abstract class. Self-attention layer takes input with size [s, b, h] and returns output of the same size. """ def __init__(self, config: ChatGLMConfig, layer_number, device=None): super(SelfAttention, self).__init__() self.layer_number = max(1, layer_number) self.projection_size = config.kv_channels * config.num_attention_heads # Per attention head and per partition values. 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 if self.multi_query_attention: 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.query_key_value = nn.Linear(config.hidden_size, self.qkv_hidden_size, bias=config.add_bias_linear or config.add_qkv_bias, device=device, **_config_to_kwargs(config) ) self.core_attention = CoreAttention(config, self.layer_number) # Output. self.dense = nn.Linear(self.projection_size, config.hidden_size, bias=config.add_bias_linear, device=device, **_config_to_kwargs(config) ) def _allocate_memory(self, inference_max_sequence_len, batch_size, device=None, dtype=None): if self.multi_query_attention: num_attention_heads = self.num_multi_query_groups_per_partition else: num_attention_heads = self.num_attention_heads_per_partition return torch.empty( inference_max_sequence_len, batch_size, num_attention_heads, self.hidden_size_per_attention_head, dtype=dtype, device=device, ) def forward( self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True ): # hidden_states: [sq, b, h] # ================================================= # Pre-allocate memory for key-values for inference. # ================================================= # ===================== # Query, Key, and Value # ===================== # Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)] mixed_x_layer = self.query_key_value(hidden_states) if self.multi_query_attention: (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, ], dim=-1, ) query_layer = query_layer.view( query_layer.size()[:-1] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) ) key_layer = key_layer.view( key_layer.size()[:-1] + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head) ) value_layer = value_layer.view( value_layer.size()[:-1] + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head) ) else: new_tensor_shape = mixed_x_layer.size()[:-1] + \ (self.num_attention_heads_per_partition, 3 * self.hidden_size_per_attention_head) mixed_x_layer = mixed_x_layer.view(*new_tensor_shape) # [sq, b, np, 3 * hn] --> 3 [sq, b, np, hn] (query_layer, key_layer, value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3) # apply relative positional encoding (rotary embedding) if rotary_pos_emb is not None: query_layer = apply_rotary_pos_emb(query_layer, rotary_pos_emb) key_layer = apply_rotary_pos_emb(key_layer, rotary_pos_emb) # adjust key and value for inference if kv_cache is not None: cache_k, cache_v = kv_cache key_layer = torch.cat((cache_k, key_layer), dim=0) value_layer = torch.cat((cache_v, value_layer), dim=0) if use_cache: kv_cache = (key_layer, value_layer) else: kv_cache = None if self.multi_query_attention: key_layer = key_layer.unsqueeze(-2) key_layer = key_layer.expand( -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1 ) key_layer = key_layer.contiguous().view( key_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) ) value_layer = value_layer.unsqueeze(-2) value_layer = value_layer.expand( -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1 ) value_layer = value_layer.contiguous().view( value_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) ) # ================================== # core attention computation # ================================== context_layer = self.core_attention(query_layer, key_layer, value_layer, attention_mask) # ================= # Output. [sq, b, h] # ================= output = self.dense(context_layer) return output, kv_cache def _config_to_kwargs(args): common_kwargs = { "dtype": args.torch_dtype, } return common_kwargs class MLP(torch.nn.Module): """MLP. MLP will take the input with h hidden state, project it to 4*h hidden dimension, perform nonlinear transformation, and project the state back into h hidden dimension. """ def __init__(self, config: ChatGLMConfig, device=None): super(MLP, self).__init__() self.add_bias = config.add_bias_linear # Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf self.dense_h_to_4h = nn.Linear( config.hidden_size, config.ffn_hidden_size * 2, bias=self.add_bias, device=device, **_config_to_kwargs(config) ) def swiglu(x): x = torch.chunk(x, 2, dim=-1) return F.silu(x[0]) * x[1] self.activation_func = swiglu # Project back to h. self.dense_4h_to_h = nn.Linear( config.ffn_hidden_size, config.hidden_size, bias=self.add_bias, device=device, **_config_to_kwargs(config) ) def forward(self, hidden_states): # [s, b, 4hp] intermediate_parallel = self.dense_h_to_4h(hidden_states) intermediate_parallel = self.activation_func(intermediate_parallel) # [s, b, h] output = self.dense_4h_to_h(intermediate_parallel) return output class GLMBlock(torch.nn.Module): """A single transformer layer. Transformer layer takes input with size [s, b, h] and returns an output of the same size. """ def __init__(self, config: ChatGLMConfig, layer_number, device=None): super(GLMBlock, self).__init__() self.layer_number = layer_number self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm self.fp32_residual_connection = config.fp32_residual_connection LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm # Layernorm on the input data. self.input_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, dtype=config.torch_dtype) # Self attention. self.self_attention = SelfAttention(config, layer_number, device=device) self.hidden_dropout = config.hidden_dropout # Layernorm on the attention output self.post_attention_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, dtype=config.torch_dtype) # MLP self.mlp = MLP(config, device=device) def forward( self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True, ): # hidden_states: [s, b, h] # Layer norm at the beginning of the transformer layer. layernorm_output = self.input_layernorm(hidden_states) # Self attention. attention_output, kv_cache = self.self_attention( layernorm_output, attention_mask, rotary_pos_emb, kv_cache=kv_cache, use_cache=use_cache ) # Residual connection. if self.apply_residual_connection_post_layernorm: residual = layernorm_output else: residual = hidden_states 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. layernorm_output = self.post_attention_layernorm(layernorm_input) # MLP. mlp_output = self.mlp(layernorm_output) # Second residual connection. if self.apply_residual_connection_post_layernorm: residual = layernorm_output else: residual = layernorm_input output = torch.nn.functional.dropout(mlp_output, p=self.hidden_dropout, training=self.training) output = residual + output return output, kv_cache class GLMTransformer(torch.nn.Module): """Transformer class.""" def __init__(self, config: ChatGLMConfig, device=None): super(GLMTransformer, self).__init__() self.fp32_residual_connection = config.fp32_residual_connection self.post_layer_norm = config.post_layer_norm # Number of layers. self.num_layers = config.num_layers # Transformer layers. def build_layer(layer_number): return GLMBlock(config, layer_number, device=device) self.layers = torch.nn.ModuleList([build_layer(i + 1) for i in range(self.num_layers)]) if self.post_layer_norm: LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm # Final layer norm before output. self.final_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device, dtype=config.torch_dtype) self.gradient_checkpointing = False def _get_layer(self, layer_number): return self.layers[layer_number] def forward( self, hidden_states, attention_mask, rotary_pos_emb, kv_caches=None, use_cache: Optional[bool] = True, output_hidden_states: Optional[bool] = False, ): if not kv_caches: kv_caches = [None for _ in range(self.num_layers)] presents = () if use_cache else None 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 all_self_attentions = None all_hidden_states = () if output_hidden_states else None for index in range(self.num_layers): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer = self._get_layer(index) if self.gradient_checkpointing and self.training: layer_ret = torch.utils.checkpoint.checkpoint( layer, hidden_states, attention_mask, rotary_pos_emb, kv_caches[index], use_cache ) else: layer_ret = layer( hidden_states, attention_mask, rotary_pos_emb, kv_cache=kv_caches[index], use_cache=use_cache ) hidden_states, kv_cache = layer_ret if use_cache: presents = presents + (kv_cache,) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) # Final layer norm. if self.post_layer_norm: hidden_states = self.final_layernorm(hidden_states) return hidden_states, presents, all_hidden_states, all_self_attentions class ChatGLMPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ is_parallelizable = False supports_gradient_checkpointing = True config_class = ChatGLMConfig base_model_prefix = "transformer" _no_split_modules = ["GLMBlock"] def _init_weights(self, module: nn.Module): """Initialize the weights.""" return def get_masks(self, input_ids, past_key_values, padding_mask=None): batch_size, seq_length = input_ids.shape full_attention_mask = torch.ones(batch_size, seq_length, seq_length, device=input_ids.device) full_attention_mask.tril_() past_length = 0 if past_key_values: past_length = past_key_values[0][0].shape[0] if past_length: full_attention_mask = torch.cat((torch.ones(batch_size, seq_length, past_length, device=input_ids.device), full_attention_mask), dim=-1) if padding_mask is not None: full_attention_mask = full_attention_mask * padding_mask.unsqueeze(1) if not past_length and padding_mask is not None: full_attention_mask -= padding_mask.unsqueeze(-1) - 1 full_attention_mask = (full_attention_mask < 0.5).bool() full_attention_mask.unsqueeze_(1) return full_attention_mask def get_position_ids(self, input_ids, device): batch_size, seq_length = input_ids.shape position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1) return position_ids def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, GLMTransformer): module.gradient_checkpointing = value class Embedding(torch.nn.Module): """Language model embeddings.""" def __init__(self, config: ChatGLMConfig, device=None): super(Embedding, self).__init__() self.hidden_size = config.hidden_size # Word embeddings (parallel). self.word_embeddings = nn.Embedding( config.padded_vocab_size, self.hidden_size, dtype=config.torch_dtype, device=device ) self.fp32_residual_connection = config.fp32_residual_connection def forward(self, input_ids): # Embeddings. words_embeddings = self.word_embeddings(input_ids) embeddings = words_embeddings # Data format change to avoid explicit tranposes : [b s h] --> [s b h]. embeddings = embeddings.transpose(0, 1).contiguous() # If the input flag for fp32 residual connection is set, convert for float. if self.fp32_residual_connection: embeddings = embeddings.float() return embeddings class ChatGLMModel(ChatGLMPreTrainedModel): def __init__(self, config: ChatGLMConfig, device=None, empty_init=True): super().__init__(config) if empty_init: init_method = skip_init else: init_method = default_init init_kwargs = {} if device is not None: init_kwargs["device"] = device self.embedding = init_method(Embedding, config, **init_kwargs) self.num_layers = config.num_layers self.multi_query_group_num = config.multi_query_group_num self.kv_channels = config.kv_channels # 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 ) self.rotary_pos_emb = RotaryEmbedding(rotary_dim // 2, original_impl=config.original_rope, device=device, dtype=config.torch_dtype) self.encoder = init_method(GLMTransformer, config, **init_kwargs) self.output_layer = init_method(nn.Linear, config.hidden_size, config.padded_vocab_size, bias=False, dtype=config.torch_dtype, **init_kwargs) self.pre_seq_len = config.pre_seq_len self.prefix_projection = config.prefix_projection if self.pre_seq_len is not None: for param in self.parameters(): param.requires_grad = False self.prefix_tokens = torch.arange(self.pre_seq_len).long() self.prefix_encoder = PrefixEncoder(config) self.dropout = torch.nn.Dropout(0.1) def get_input_embeddings(self): return self.embedding.word_embeddings def get_prompt(self, batch_size, device, dtype=torch.half): prefix_tokens = self.prefix_tokens.unsqueeze(0).expand(batch_size, -1).to(device) past_key_values = self.prefix_encoder(prefix_tokens).type(dtype) past_key_values = past_key_values.view( batch_size, self.pre_seq_len, self.num_layers * 2, self.multi_query_group_num, self.kv_channels ) # seq_len, b, nh, hidden_size past_key_values = self.dropout(past_key_values) past_key_values = past_key_values.permute([2, 1, 0, 3, 4]).split(2) return past_key_values def forward( self, input_ids, position_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.BoolTensor] = None, full_attention_mask: Optional[torch.BoolTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, inputs_embeds: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): 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 batch_size, seq_length = input_ids.shape if inputs_embeds is None: inputs_embeds = self.embedding(input_ids) if self.pre_seq_len is not None: if past_key_values is None: past_key_values = self.get_prompt(batch_size=batch_size, device=input_ids.device, dtype=inputs_embeds.dtype) if attention_mask is not None: attention_mask = torch.cat([attention_mask.new_ones((batch_size, self.pre_seq_len)), attention_mask], dim=-1) if full_attention_mask is None: if (attention_mask is not None and not attention_mask.all()) or (past_key_values and seq_length != 1): full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask) # Rotary positional embeddings rotary_pos_emb = self.rotary_pos_emb(self.seq_length) if position_ids is not None: rotary_pos_emb = rotary_pos_emb[position_ids] else: rotary_pos_emb = rotary_pos_emb[None, :seq_length] rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous() # Run encoder. hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder( inputs_embeds, full_attention_mask, rotary_pos_emb=rotary_pos_emb, kv_caches=past_key_values, use_cache=use_cache, output_hidden_states=output_hidden_states ) if not return_dict: return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] 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, ) def quantize(self, weight_bit_width: int): from .quantization import quantize quantize(self.encoder, weight_bit_width) return self class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel): def __init__(self, config: ChatGLMConfig, empty_init=True, device=None): super().__init__(config) self.max_sequence_length = config.max_length self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device) self.config = config self.quantized = False if self.config.quantization_bit: self.quantize(self.config.quantization_bit, empty_init=True) def _update_model_kwargs_for_generation( self, outputs: ModelOutput, model_kwargs: Dict[str, Any], is_encoder_decoder: bool = False, standardize_cache_format: bool = False, ) -> Dict[str, Any]: # update past_key_values model_kwargs["past_key_values"] = self._extract_past_from_model_output( outputs, standardize_cache_format=standardize_cache_format ) # update attention mask if "attention_mask" in model_kwargs: attention_mask = model_kwargs["attention_mask"] model_kwargs["attention_mask"] = torch.cat( [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1 ) # update position ids if "position_ids" in model_kwargs: position_ids = model_kwargs["position_ids"] new_position_id = position_ids[..., -1:].clone() new_position_id += 1 model_kwargs["position_ids"] = torch.cat( [position_ids, new_position_id], dim=-1 ) model_kwargs["is_first_forward"] = False return model_kwargs def prepare_inputs_for_generation( self, input_ids: torch.LongTensor, past_key_values: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None, is_first_forward: bool = True, **kwargs ) -> dict: # only last token for input_ids if past is not None if position_ids is None: position_ids = self.get_position_ids(input_ids, device=input_ids.device) if not is_first_forward: if past_key_values is not None: position_ids = position_ids[..., -1:] input_ids = input_ids[:, -1:] return { "input_ids": input_ids, "past_key_values": past_key_values, "position_ids": position_ids, "attention_mask": attention_mask, "return_last_logit": True, "use_cache": use_cache } def forward( self, input_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[Tuple[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, return_last_logit: Optional[bool] = False, ): 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 transformer_outputs = self.transformer( input_ids=input_ids, position_ids=position_ids, attention_mask=attention_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = transformer_outputs[0] if return_last_logit: hidden_states = hidden_states[-1:] lm_logits = self.transformer.output_layer(hidden_states) lm_logits = lm_logits.transpose(0, 1).contiguous() loss = None if labels is not None: lm_logits = lm_logits.to(torch.float32) # Shift so that tokens < n predict n shift_logits = lm_logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens loss_fct = CrossEntropyLoss(ignore_index=-100) loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) lm_logits = lm_logits.to(hidden_states.dtype) loss = loss.to(hidden_states.dtype) 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: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]: """ This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct beam_idx at every generation step. Output shares the same memory storage as `past`. """ return tuple( ( layer_past[0].index_select(1, beam_idx.to(layer_past[0].device)), layer_past[1].index_select(1, beam_idx.to(layer_past[1].device)), ) for layer_past in past ) def process_response(self, response): response = response.strip() response = response.replace("[[训练时间]]", "2023年") return response def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = None): prompt = tokenizer.build_prompt(query, history=history) inputs = tokenizer([prompt], return_tensors="pt") inputs = inputs.to(self.device) return inputs def build_stream_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = None): if history: prompt = "\n\n[Round {}]\n\n问：{}\n\n答：".format(len(history) + 1, query) input_ids = tokenizer.encode(prompt, add_special_tokens=False) input_ids = input_ids[1:] inputs = tokenizer.batch_encode_plus([(input_ids, None)], return_tensors="pt", add_special_tokens=False) else: prompt = "[Round {}]\n\n问：{}\n\n答：".format(len(history) + 1, query) inputs = tokenizer([prompt], return_tensors="pt") inputs = inputs.to(self.device) return inputs @torch.inference_mode() def chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, max_length: int = 8192, num_beams=1, do_sample=True, top_p=0.8, temperature=0.8, logits_processor=None, **kwargs): if history is None: history = [] if logits_processor is None: logits_processor = LogitsProcessorList() logits_processor.append(InvalidScoreLogitsProcessor()) gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p, "temperature": temperature, "logits_processor": logits_processor, **kwargs} inputs = self.build_inputs(tokenizer, query, history=history) outputs = self.generate(**inputs, **gen_kwargs) outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):] response = tokenizer.decode(outputs) response = self.process_response(response) history = history + [(query, response)] return response, history @torch.inference_mode() def stream_chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, past_key_values=None, max_length: int = 8192, do_sample=True, top_p=0.8, temperature=0.8, logits_processor=None, return_past_key_values=False, **kwargs): if history is None: history = [] if logits_processor is None: logits_processor = LogitsProcessorList() logits_processor.append(InvalidScoreLogitsProcessor()) gen_kwargs = {"max_length": max_length, "do_sample": do_sample, "top_p": top_p, "temperature": temperature, "logits_processor": logits_processor, **kwargs} if past_key_values is None and not return_past_key_values: inputs = self.build_inputs(tokenizer, query, history=history) else: inputs = self.build_stream_inputs(tokenizer, query, history=history) if past_key_values is not None: past_length = past_key_values[0][0].shape[0] if self.transformer.pre_seq_len is not None: past_length -= self.transformer.pre_seq_len inputs.position_ids += past_length attention_mask = inputs.attention_mask attention_mask = torch.cat((attention_mask.new_ones(1, past_length), attention_mask), dim=1) inputs['attention_mask'] = attention_mask for outputs in self.stream_generate(**inputs, past_key_values=past_key_values, return_past_key_values=return_past_key_values, **gen_kwargs): if return_past_key_values: outputs, past_key_values = outputs outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):] response = tokenizer.decode(outputs) if response and response[-1] != "�": response = self.process_response(response) new_history = history + [(query, response)] if return_past_key_values: yield response, new_history, past_key_values else: yield response, new_history @torch.inference_mode() def stream_generate( self, input_ids, 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, return_past_key_values=False, **kwargs, ): batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1] if generation_config is None: generation_config = self.generation_config generation_config = copy.deepcopy(generation_config) model_kwargs = generation_config.update(**kwargs) model_kwargs["use_cache"] = generation_config.use_cache bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id if isinstance(eos_token_id, int): eos_token_id = [eos_token_id] has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None if has_default_max_length and generation_config.max_new_tokens is None: warnings.warn( f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. " "This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we" " recommend using `max_new_tokens` to control the maximum length of the generation.", UserWarning, ) elif generation_config.max_new_tokens is not None: generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length if not has_default_max_length and generation_config.max_length is not None: logger.warn( f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(=" f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. " "Please refer to the documentation for more information. " "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)", UserWarning, ) if input_ids_seq_length >= generation_config.max_length: input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids" logger.warning( f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to" f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider" " increasing `max_new_tokens`." ) # 2. Set generation parameters if not already defined logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList() stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList() logits_processor = self._get_logits_processor( generation_config=generation_config, input_ids_seq_length=input_ids_seq_length, encoder_input_ids=input_ids, prefix_allowed_tokens_fn=prefix_allowed_tokens_fn, logits_processor=logits_processor, ) stopping_criteria = self._get_stopping_criteria( generation_config=generation_config, stopping_criteria=stopping_criteria ) logits_warper = self._get_logits_warper(generation_config) unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1) scores = None while True: model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs) # forward pass to get next token outputs = self( **model_inputs, return_dict=True, output_attentions=False, output_hidden_states=False, ) next_token_logits = outputs.logits[:, -1, :] # pre-process distribution next_token_scores = logits_processor(input_ids, next_token_logits) next_token_scores = logits_warper(input_ids, next_token_scores) # sample probs = nn.functional.softmax(next_token_scores, dim=-1) if generation_config.do_sample: next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1) else: next_tokens = torch.argmax(probs, dim=-1) # update generated ids, model inputs, and length for next step input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1) model_kwargs = self._update_model_kwargs_for_generation( outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder ) unfinished_sequences = unfinished_sequences.mul((sum(next_tokens != i for i in eos_token_id)).long()) if return_past_key_values: yield input_ids, outputs.past_key_values else: yield input_ids # stop when each sentence is finished, or if we exceed the maximum length if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores): break def quantize(self, bits: int, empty_init=False, device=None, **kwargs): if bits == 0: return from .quantization import quantize if self.quantized: logger.info("Already quantized.") return self self.quantized = True self.config.quantization_bit = bits self.transformer.encoder = quantize(self.transformer.encoder, bits, empty_init=empty_init, device=device, **kwargs) return self class ChatGLMForSequenceClassification(ChatGLMPreTrainedModel): def __init__(self, config: ChatGLMConfig, empty_init=True, device=None): super().__init__(config) self.num_labels = config.num_labels self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device) self.classifier_head = nn.Linear(config.hidden_size, config.num_labels, bias=True, dtype=torch.half) if config.classifier_dropout is not None: self.dropout = nn.Dropout(config.classifier_dropout) else: self.dropout = None self.config = config if self.config.quantization_bit: self.quantize(self.config.quantization_bit, empty_init=True) def forward( self, input_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, full_attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, inputs_embeds: Optional[torch.LongTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor, ...], SequenceClassifierOutputWithPast]: return_dict = return_dict if return_dict is not None else self.config.use_return_dict transformer_outputs = self.transformer( input_ids=input_ids, position_ids=position_ids, attention_mask=attention_mask, full_attention_mask=full_attention_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = transformer_outputs[0] pooled_hidden_states = hidden_states[-1] if self.dropout is not None: pooled_hidden_states = self.dropout(pooled_hidden_states) logits = self.classifier_head(pooled_hidden_states) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze().float(), labels.squeeze()) else: loss = loss_fct(logits.float(), labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels).float(), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits.float(), labels.view(-1, self.num_labels)) if not return_dict: output = (logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutputWithPast( loss=loss, logits=logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, )