""" The ChatGLM2-6B License 1. Definitions “Licensor” means the ChatGLM2-6B Model Team that distributes its Software. “Software” means the ChatGLM2-6B model parameters made available under this license. 2. License Grant Subject to the terms and conditions of this License, the Licensor hereby grants to you a non-exclusive, worldwide, non-transferable, non-sublicensable, revocable, royalty-free copyright license to use the Software solely for your non-commercial research purposes. The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. 3. Restriction You will not use, copy, modify, merge, publish, distribute, reproduce, or create derivative works of the Software, in whole or in part, for any commercial, military, or illegal purposes. You will not use the Software for any act that may undermine China's national security and national unity, harm the public interest of society, or infringe upon the rights and interests of human beings. 4. Disclaimer THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 5. Limitation of Liability EXCEPT TO THE EXTENT PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL THEORY, WHETHER BASED IN TORT, NEGLIGENCE, CONTRACT, LIABILITY, OR OTHERWISE WILL ANY LICENSOR BE LIABLE TO YOU FOR ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES, OR ANY OTHER COMMERCIAL LOSSES, EVEN IF THE LICENSOR HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. 6. Dispute Resolution This license shall be governed and construed in accordance with the laws of People’s Republic of China. Any dispute arising from or in connection with this License shall be submitted to Haidian District People's Court in Beijing. Note that the license is subject to update to a more comprehensive version. For any questions related to the license and copyright, please contact us at glm-130b@googlegroups.com. """ """ PyTorch ChatGLM model. """ import copy import math import sys import warnings from typing import Any, Callable, Dict, List, Optional, Tuple import torch import torch.nn.functional as F import torch.utils.checkpoint from torch import nn from torch.nn import CrossEntropyLoss, LayerNorm from torch.nn.utils import skip_init from transformers.generation.logits_process import LogitsProcessor from transformers.generation.utils import GenerationConfig, LogitsProcessorList, ModelOutput, StoppingCriteriaList from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast from transformers.modeling_utils import PreTrainedModel from transformers.utils import logging 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 behavior 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.elementwise_affine = True self.normalized_shape = normalized_shape self.weight = torch.nn.Parameter(torch.ones(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) # preallocating 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, # config has no attribute 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, 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: 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, } 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.no_grad() 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.no_grad() 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.no_grad() 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) 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 behavior 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: 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