""" PyTorch ChatGLM model. """ import math import copy import os 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.utils import skip_init from typing import Optional, Tuple, Union, List, Callable, Dict, Any from transformers.utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, ) from transformers.modeling_outputs import ( BaseModelOutputWithPast, CausalLMOutputWithPast, BaseModelOutputWithPastAndCrossAttentions, ) 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/ChatGLM-6B" _CONFIG_FOR_DOC = "ChatGLM6BConfig" CHATGLM_6B_PRETRAINED_MODEL_ARCHIVE_LIST = [ "THUDM/chatglm-6b", # See all ChatGLM-6B models at https://huggingface.co/models?filter=chatglm ] 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[..., 20005] = 5e4 return scores def load_tf_weights_in_chatglm_6b(model, config, tf_checkpoint_path): """Load tf checkpoints in a pytorch model.""" try: import re import numpy as np import tensorflow as tf except ImportError: logger.error( "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see " "https://www.tensorflow.org/install/ for installation instructions." ) raise tf_path = os.path.abspath(tf_checkpoint_path) logger.info(f"Converting TensorFlow checkpoint from {tf_path}") # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] arrays = [] for name, shape in init_vars: logger.info(f"Loading TF weight {name} with shape {shape}") array = tf.train.load_variable(tf_path, name) names.append(name) arrays.append(array) for name, array in zip(names, arrays): name = name.split("/") # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v # which are not required for using pretrained model if any( n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"] for n in name ): logger.info(f"Skipping {'/'.join(name)}") continue pointer = model for m_name in name: if re.fullmatch(r"[A-Za-z]+_\d+", m_name): scope_names = re.split(r"_(\d+)", m_name) else: scope_names = [m_name] if scope_names[0] == "kernel" or scope_names[0] == "gamma": pointer = getattr(pointer, "weight") elif scope_names[0] == "output_bias" or scope_names[0] == "beta": pointer = getattr(pointer, "bias") elif scope_names[0] == "output_weights": pointer = getattr(pointer, "weight") elif scope_names[0] == "squad": pointer = getattr(pointer, "classifier") else: try: pointer = getattr(pointer, scope_names[0]) except AttributeError: logger.info(f"Skipping {'/'.join(name)}") continue if len(scope_names) >= 2: num = int(scope_names[1]) pointer = pointer[num] if m_name[-11:] == "_embeddings": pointer = getattr(pointer, "weight") elif m_name == "kernel": array = np.transpose(array) try: assert ( pointer.shape == array.shape ), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched" except AssertionError as e: e.args += (pointer.shape, array.shape) raise logger.info(f"Initialize PyTorch weight {name}") pointer.data = torch.from_numpy(array) return model 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): super().__init__() self.prefix_projection = config.prefix_projection if self.prefix_projection: # Use a two-layer MLP to encode the prefix self.embedding = torch.nn.Embedding(config.pre_seq_len, config.hidden_size) self.trans = torch.nn.Sequential( torch.nn.Linear(config.hidden_size, config.hidden_size), torch.nn.Tanh(), torch.nn.Linear(config.hidden_size, config.num_layers * config.hidden_size * 2) ) else: self.embedding = torch.nn.Embedding(config.pre_seq_len, config.num_layers * config.hidden_size * 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 @torch.jit.script def gelu_impl(x): """OpenAI's gelu implementation.""" return 0.5 * x * (1.0 + torch.tanh(0.7978845608028654 * x * (1.0 + 0.044715 * x * x))) def gelu(x): return gelu_impl(x) class RotaryEmbedding(torch.nn.Module): def __init__(self, dim, base=10000, precision=torch.half, learnable=False): super().__init__() inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim)) inv_freq = inv_freq.half() self.learnable = learnable if learnable: self.inv_freq = torch.nn.Parameter(inv_freq) self.max_seq_len_cached = None else: self.register_buffer('inv_freq', inv_freq) self.max_seq_len_cached = None self.cos_cached = None self.sin_cached = None self.precision = precision def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs): pass def forward(self, x, seq_dim=1, seq_len=None): if seq_len is None: seq_len = x.shape[seq_dim] if self.max_seq_len_cached is None or (seq_len > self.max_seq_len_cached): self.max_seq_len_cached = None if self.learnable else seq_len t = torch.arange(seq_len, device=x.device, dtype=self.inv_freq.dtype) freqs = torch.einsum('i,j->ij', t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1).to(x.device) if self.precision == torch.bfloat16: emb = emb.float() # [sx, 1 (b * np), hn] cos_cached = emb.cos()[:, None, :] sin_cached = emb.sin()[:, None, :] if self.precision == torch.bfloat16: cos_cached = cos_cached.bfloat16() sin_cached = sin_cached.bfloat16() if self.learnable: return cos_cached, sin_cached self.cos_cached, self.sin_cached = cos_cached, sin_cached return self.cos_cached[:seq_len, ...], self.sin_cached[:seq_len, ...] def _apply(self, fn): if self.cos_cached is not None: self.cos_cached = fn(self.cos_cached) if self.sin_cached is not None: self.sin_cached = fn(self.sin_cached) return super()._apply(fn) def rotate_half(x): x1, x2 = x[..., :x.shape[-1] // 2], x[..., x.shape[-1] // 2:] return torch.cat((-x2, x1), dim=x1.ndim - 1) # dim=-1 triggers a bug in earlier torch versions @torch.jit.script def apply_rotary_pos_emb_index(q, k, cos, sin, position_id): # position_id: [sq, b], q, k: [sq, b, np, hn], cos: [sq, 1, hn] -> [sq, b, 1, hn] cos, sin = F.embedding(position_id, cos.squeeze(1)).unsqueeze(2), \ F.embedding(position_id, sin.squeeze(1)).unsqueeze(2) q, k = (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin) return q, k def attention_fn( self, query_layer, key_layer, value_layer, attention_mask, hidden_size_per_partition, layer_id, layer_past=None, scaling_attention_score=True, use_cache=False, ): if layer_past is not None: past_key, past_value = layer_past[0], layer_past[1] key_layer = torch.cat((past_key, key_layer), dim=0) value_layer = torch.cat((past_value, value_layer), dim=0) # seqlen, batch, num_attention_heads, hidden_size_per_attention_head seq_len, b, nh, hidden_size = key_layer.shape if use_cache: present = (key_layer, value_layer) else: present = None query_key_layer_scaling_coeff = float(layer_id + 1) if scaling_attention_score: query_layer = query_layer / (math.sqrt(hidden_size) * query_key_layer_scaling_coeff) # =================================== # Raw attention scores. [b, np, s, s] # =================================== # [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) matmul_result = torch.empty( output_size[0] * output_size[1], output_size[2], output_size[3], dtype=query_layer.dtype, device=query_layer.device, ) matmul_result = torch.baddbmm( matmul_result, 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, ) # change view to [b, np, sq, sk] attention_scores = matmul_result.view(*output_size) if self.scale_mask_softmax: self.scale_mask_softmax.scale = query_key_layer_scaling_coeff attention_probs = self.scale_mask_softmax(attention_scores, attention_mask.contiguous()) else: if not (attention_mask == 0).all(): # if auto-regressive, skip attention_scores.masked_fill_(attention_mask, -10000.0) dtype = attention_scores.dtype attention_scores = attention_scores.float() attention_scores = attention_scores * query_key_layer_scaling_coeff attention_probs = F.softmax(attention_scores, dim=-1) attention_probs = attention_probs.type(dtype) # ========================= # 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] + (hidden_size_per_partition,) context_layer = context_layer.view(*new_context_layer_shape) outputs = (context_layer, present, attention_probs) return outputs class SelfAttention(torch.nn.Module): def __init__(self, hidden_size, num_attention_heads, layer_id, hidden_size_per_attention_head=None, bias=True, params_dtype=torch.float, position_encoding_2d=True): super(SelfAttention, self).__init__() self.layer_id = layer_id self.hidden_size = hidden_size self.hidden_size_per_partition = hidden_size self.num_attention_heads = num_attention_heads self.num_attention_heads_per_partition = num_attention_heads self.position_encoding_2d = position_encoding_2d self.rotary_emb = RotaryEmbedding( self.hidden_size // (self.num_attention_heads * 2) if position_encoding_2d else self.hidden_size // self.num_attention_heads, base=10000, precision=torch.half, learnable=False, ) self.scale_mask_softmax = None if hidden_size_per_attention_head is None: self.hidden_size_per_attention_head = hidden_size // num_attention_heads else: self.hidden_size_per_attention_head = hidden_size_per_attention_head self.inner_hidden_size = num_attention_heads * self.hidden_size_per_attention_head # Strided linear layer. self.query_key_value = skip_init( torch.nn.Linear, hidden_size, 3 * self.inner_hidden_size, bias=bias, dtype=params_dtype, ) self.dense = skip_init( torch.nn.Linear, self.inner_hidden_size, hidden_size, bias=bias, dtype=params_dtype, ) @staticmethod def attention_mask_func(attention_scores, attention_mask): attention_scores.masked_fill_(attention_mask, -10000.0) return attention_scores def split_tensor_along_last_dim(self, tensor, num_partitions, contiguous_split_chunks=False): """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. """ # 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 def forward( self, hidden_states: torch.Tensor, position_ids, attention_mask: torch.Tensor, layer_id, layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, use_cache: bool = False, output_attentions: bool = False, ): """ hidden_states: [seq_len, batch, hidden_size] attention_mask: [(1, 1), seq_len, seq_len] """ # [seq_len, batch, 3 * hidden_size] mixed_raw_layer = self.query_key_value(hidden_states) # [seq_len, batch, 3 * hidden_size] --> [seq_len, batch, num_attention_heads, 3 * hidden_size_per_attention_head] new_tensor_shape = mixed_raw_layer.size()[:-1] + ( self.num_attention_heads_per_partition, 3 * self.hidden_size_per_attention_head, ) mixed_raw_layer = mixed_raw_layer.view(*new_tensor_shape) # [seq_len, batch, num_attention_heads, hidden_size_per_attention_head] (query_layer, key_layer, value_layer) = self.split_tensor_along_last_dim(mixed_raw_layer, 3) if self.position_encoding_2d: q1, q2 = query_layer.chunk(2, dim=(query_layer.ndim - 1)) k1, k2 = key_layer.chunk(2, dim=(key_layer.ndim - 1)) cos, sin = self.rotary_emb(q1, seq_len=position_ids.max() + 1) position_ids, block_position_ids = position_ids[:, 0, :].transpose(0, 1).contiguous(), \ position_ids[:, 1, :].transpose(0, 1).contiguous() q1, k1 = apply_rotary_pos_emb_index(q1, k1, cos, sin, position_ids) q2, k2 = apply_rotary_pos_emb_index(q2, k2, cos, sin, block_position_ids) query_layer = torch.concat([q1, q2], dim=(q1.ndim - 1)) key_layer = torch.concat([k1, k2], dim=(k1.ndim - 1)) else: position_ids = position_ids.transpose(0, 1) cos, sin = self.rotary_emb(value_layer, seq_len=position_ids.max() + 1) # [seq_len, batch, num_attention_heads, hidden_size_per_attention_head] query_layer, key_layer = apply_rotary_pos_emb_index(query_layer, key_layer, cos, sin, position_ids) # [seq_len, batch, hidden_size] context_layer, present, attention_probs = attention_fn( self=self, query_layer=query_layer, key_layer=key_layer, value_layer=value_layer, attention_mask=attention_mask, hidden_size_per_partition=self.hidden_size_per_partition, layer_id=layer_id, layer_past=layer_past, use_cache=use_cache ) output = self.dense(context_layer) outputs = (output, present) if output_attentions: outputs += (attention_probs,) return outputs # output, present, attention_probs class GEGLU(torch.nn.Module): def __init__(self): super().__init__() self.activation_fn = F.gelu def forward(self, x): # dim=-1 breaks in jit for pt<1.10 x1, x2 = x.chunk(2, dim=(x.ndim - 1)) return x1 * self.activation_fn(x2) class GLU(torch.nn.Module): def __init__(self, hidden_size, inner_hidden_size=None, layer_id=None, bias=True, activation_func=gelu, params_dtype=torch.float): super(GLU, self).__init__() self.layer_id = layer_id self.activation_func = activation_func # Project to 4h. self.hidden_size = hidden_size if inner_hidden_size is None: inner_hidden_size = 4 * hidden_size self.inner_hidden_size = inner_hidden_size self.dense_h_to_4h = skip_init( torch.nn.Linear, self.hidden_size, self.inner_hidden_size, bias=bias, dtype=params_dtype, ) # Project back to h. self.dense_4h_to_h = skip_init( torch.nn.Linear, self.inner_hidden_size, self.hidden_size, bias=bias, dtype=params_dtype, ) def forward(self, hidden_states): """ hidden_states: [seq_len, batch, hidden_size] """ # [seq_len, batch, inner_hidden_size] intermediate_parallel = self.dense_h_to_4h(hidden_states) intermediate_parallel = self.activation_func(intermediate_parallel) output = self.dense_4h_to_h(intermediate_parallel) return output class GLMBlock(torch.nn.Module): def __init__( self, hidden_size, num_attention_heads, layernorm_epsilon, layer_id, inner_hidden_size=None, hidden_size_per_attention_head=None, layernorm=LayerNorm, use_bias=True, params_dtype=torch.float, num_layers=28, position_encoding_2d=True ): super(GLMBlock, self).__init__() # Set output layer initialization if not provided. self.layer_id = layer_id # Layernorm on the input data. self.input_layernorm = layernorm(hidden_size, eps=layernorm_epsilon) self.position_encoding_2d = position_encoding_2d # Self attention. self.attention = SelfAttention( hidden_size, num_attention_heads, layer_id, hidden_size_per_attention_head=hidden_size_per_attention_head, bias=use_bias, params_dtype=params_dtype, position_encoding_2d=self.position_encoding_2d ) # Layernorm on the input data. self.post_attention_layernorm = layernorm(hidden_size, eps=layernorm_epsilon) self.num_layers = num_layers # GLU self.mlp = GLU( hidden_size, inner_hidden_size=inner_hidden_size, bias=use_bias, layer_id=layer_id, params_dtype=params_dtype, ) def forward( self, hidden_states: torch.Tensor, position_ids, attention_mask: torch.Tensor, layer_id, layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, use_cache: bool = False, output_attentions: bool = False, ): """ hidden_states: [seq_len, batch, hidden_size] attention_mask: [(1, 1), seq_len, seq_len] """ # Layer norm at the begining of the transformer layer. # [seq_len, batch, hidden_size] attention_input = self.input_layernorm(hidden_states) # Self attention. attention_outputs = self.attention( attention_input, position_ids, attention_mask=attention_mask, layer_id=layer_id, layer_past=layer_past, use_cache=use_cache, output_attentions=output_attentions ) attention_output = attention_outputs[0] outputs = attention_outputs[1:] # Residual connection. alpha = (2 * self.num_layers) ** 0.5 hidden_states = attention_input * alpha + attention_output mlp_input = self.post_attention_layernorm(hidden_states) # MLP. mlp_output = self.mlp(mlp_input) # Second residual connection. output = mlp_input * alpha + mlp_output if use_cache: outputs = (output,) + outputs else: outputs = (output,) + outputs[1:] return outputs # hidden_states, present, 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__(self, *inputs, **kwargs): super().__init__(*inputs, **kwargs) def _init_weights(self, module: nn.Module): """Initialize the weights.""" return def get_masks(self, input_ids, device): batch_size, seq_length = input_ids.shape context_lengths = [seq.tolist().index(self.config.bos_token_id) for seq in input_ids] attention_mask = torch.ones((batch_size, seq_length, seq_length), device=device) attention_mask.tril_() for i, context_length in enumerate(context_lengths): attention_mask[i, :, :context_length] = 1 attention_mask.unsqueeze_(1) attention_mask = (attention_mask < 0.5).bool() return attention_mask def get_position_ids(self, input_ids, mask_positions, device, gmask=False): batch_size, seq_length = input_ids.shape context_lengths = [seq.tolist().index(self.config.bos_token_id) for seq in input_ids] if self.position_encoding_2d: position_ids = torch.arange(seq_length, dtype=torch.long, device=device).expand(batch_size, seq_length) for i, context_length in enumerate(context_lengths): position_ids[i, context_length:] = mask_positions[i] block_position_ids = [torch.cat(( torch.zeros(context_length, dtype=torch.long, device=device), torch.arange(seq_length - context_length, dtype=torch.long, device=device) + 1 )) for context_length in context_lengths] block_position_ids = torch.stack(block_position_ids, dim=0) position_ids = torch.stack((position_ids, block_position_ids), dim=1) else: position_ids = torch.arange(seq_length, dtype=torch.long, device=device).expand(batch_size, seq_length) if not gmask: for i, context_length in enumerate(context_lengths): position_ids[context_length:] = mask_positions[i] return position_ids def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, ChatGLMModel): module.gradient_checkpointing = value CHATGLM_6B_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`~ChatGLM6BConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ CHATGLM_6B_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`ChatGLM6BTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert *input_ids* indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare ChatGLM-6B Model transformer outputting raw hidden-states without any specific head on top.", CHATGLM_6B_START_DOCSTRING, ) class ChatGLMModel(ChatGLMPreTrainedModel): """ The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in [Attention is all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. """ def __init__(self, config: ChatGLMConfig): super().__init__(config) # recording parameters self.max_sequence_length = config.max_sequence_length self.hidden_size = config.hidden_size self.params_dtype = torch.half self.num_attention_heads = config.num_attention_heads self.vocab_size = config.vocab_size self.num_layers = config.num_layers self.layernorm_epsilon = config.layernorm_epsilon self.inner_hidden_size = config.inner_hidden_size self.hidden_size_per_attention_head = self.hidden_size // self.num_attention_heads self.position_encoding_2d = config.position_encoding_2d self.pre_seq_len = config.pre_seq_len self.prefix_projection = config.prefix_projection self.word_embeddings = skip_init( torch.nn.Embedding, num_embeddings=self.vocab_size, embedding_dim=self.hidden_size, dtype=self.params_dtype ) self.gradient_checkpointing = False def get_layer(layer_id): return GLMBlock( self.hidden_size, self.num_attention_heads, self.layernorm_epsilon, layer_id, inner_hidden_size=self.inner_hidden_size, hidden_size_per_attention_head=self.hidden_size_per_attention_head, layernorm=LayerNorm, use_bias=True, params_dtype=self.params_dtype, position_encoding_2d=self.position_encoding_2d, ) self.layers = torch.nn.ModuleList( [get_layer(layer_id) for layer_id in range(self.num_layers)] ) # Final layer norm before output. self.final_layernorm = LayerNorm(self.hidden_size, eps=self.layernorm_epsilon) 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) # total_params = sum(p.numel() for p in self.parameters()) # trainable_params = sum(p.numel() for p in self.parameters() if p.requires_grad) # print("Using p-tuning v2: # trainable_params = {} / {}".format(trainable_params, total_params)) def get_input_embeddings(self): return self.word_embeddings def set_input_embeddings(self, new_embeddings: torch.Tensor): self.word_embeddings = new_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.num_attention_heads, self.hidden_size // self.num_attention_heads ) # 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) # past_key_values = [(v[0], v[1]) for v in past_key_values] return past_key_values @add_start_docstrings_to_model_forward(CHATGLM_6B_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPastAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, inputs_embeds: 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[torch.Tensor, ...], BaseModelOutputWithPast]: 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 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 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: batch_size, seq_length = input_ids.shape[:2] elif inputs_embeds is not None: batch_size, seq_length, _ = inputs_embeds.shape[:2] else: raise ValueError("You have to specify either input_ids or inputs_embeds") if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) if past_key_values is None: if self.pre_seq_len is not None: past_key_values = self.get_prompt(batch_size=input_ids.shape[0], device=input_ids.device, dtype=inputs_embeds.dtype) else: past_key_values = tuple([None] * len(self.layers)) if attention_mask is None: attention_mask = self.get_masks( input_ids, device=input_ids.device ) if position_ids is None: MASK, gMASK = 150000, 150001 mask_token = MASK if MASK in input_ids else gMASK use_gmask = False if MASK in input_ids else gMASK mask_positions = [seq.tolist().index(mask_token) for seq in input_ids] position_ids = self.get_position_ids( input_ids, mask_positions=mask_positions, device=input_ids.device, gmask=use_gmask ) if self.pre_seq_len is not None and attention_mask is not None: prefix_attention_mask = torch.ones(batch_size, 1, input_ids.size(-1), self.pre_seq_len).to( attention_mask.device) prefix_attention_mask = (prefix_attention_mask < 0.5).bool() attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=3) # [seq_len, batch, hidden_size] hidden_states = inputs_embeds.transpose(0, 1) presents = () if use_cache else None all_self_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None if attention_mask is None: attention_mask = torch.zeros(1, 1, device=input_ids.device).bool() else: attention_mask = attention_mask.to(input_ids.device) for i, layer in enumerate(self.layers): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_past = past_key_values[i] if self.gradient_checkpointing and self.training: layer_ret = torch.utils.checkpoint.checkpoint( layer, hidden_states, position_ids, attention_mask, torch.tensor(i), layer_past, use_cache, output_attentions ) else: layer_ret = layer( hidden_states, position_ids=position_ids, attention_mask=attention_mask, layer_id=torch.tensor(i), layer_past=layer_past, use_cache=use_cache, output_attentions=output_attentions ) hidden_states = layer_ret[0] if use_cache: presents = presents + (layer_ret[1],) if output_attentions: all_self_attentions = all_self_attentions + (layer_ret[2 if use_cache else 1],) # Final layer norm. hidden_states = self.final_layernorm(hidden_states) 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, 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, ) class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel): def __init__(self, config: ChatGLMConfig): super().__init__(config) # self.hidden_size = config.hidden_size # self.params_dtype = torch.half # self.vocab_size = config.vocab_size self.max_sequence_length = config.max_sequence_length self.position_encoding_2d = config.position_encoding_2d self.transformer = ChatGLMModel(config) self.lm_head = skip_init( nn.Linear, config.hidden_size, config.vocab_size, bias=False, dtype=torch.half ) self.config = config self.quantized = False if self.config.quantization_bit: self.quantize(self.config.quantization_bit, empty_init=True) def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings 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"] if attention_mask is not None and attention_mask.dtype == torch.bool: attention_mask = torch.cat( [attention_mask, attention_mask.new_ones((*attention_mask.shape[:3], 1))], dim=3) new_attention_mask = attention_mask[:, :, -1:].clone() new_attention_mask[..., -1] = False model_kwargs["attention_mask"] = torch.cat( [attention_mask, new_attention_mask], dim=2 ) # 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, :] += 1 model_kwargs["position_ids"] = torch.cat( [position_ids, new_position_id], dim=-1 ) return model_kwargs def prepare_inputs_for_generation( self, input_ids: torch.LongTensor, past: Optional[torch.Tensor] = None, past_key_values: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, **kwargs ) -> dict: batch_size, seq_length = input_ids.shape MASK, gMASK = 150000, 150001 mask_token = MASK if MASK in input_ids else gMASK use_gmask = False if MASK in input_ids else gMASK seqs = input_ids.tolist() mask_positions = [seq.index(mask_token) for seq in seqs] # only last token for input_ids if past is not None if past is not None or past_key_values is not None: last_token = input_ids[:, -1].unsqueeze(-1) if attention_mask is not None and attention_mask.dtype == torch.bool: attention_mask = attention_mask[:, :, -1:] else: attention_mask = None if position_ids is not None: position_ids = position_ids[..., -1:] else: context_lengths = [seq.index(self.config.bos_token_id) for seq in seqs] if self.position_encoding_2d: position_ids = torch.tensor( [[mask_position, seq_length - context_length] for mask_position, context_length in zip(mask_positions, context_lengths)], dtype=torch.long, device=input_ids.device).unsqueeze(-1) else: position_ids = torch.tensor([mask_position for mask_position in mask_positions], dtype=torch.long, device=input_ids.device).unsqueeze(-1) if past is None: past = past_key_values return { "input_ids": last_token, "past_key_values": past, "position_ids": position_ids, "attention_mask": attention_mask } else: if attention_mask is not None and attention_mask.dtype != torch.bool: logger.warning_once(f"The dtype of attention mask ({attention_mask.dtype}) is not bool") attention_mask = None if attention_mask is None: attention_mask = self.get_masks( input_ids, device=input_ids.device ) if position_ids is None: position_ids = self.get_position_ids( input_ids, device=input_ids.device, mask_positions=mask_positions, gmask=use_gmask ) return { "input_ids": input_ids, "past_key_values": past, "position_ids": position_ids, "attention_mask": attention_mask } 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, ): 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_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).permute(1, 0, 2).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") punkts = [ [",", "，"], ["!", "！"], [":", "："], [";", "；"], ["\?", "？"], ] for item in punkts: response = re.sub(r"([\u4e00-\u9fff])%s" % item[0], r"\1%s" % item[1], response) response = re.sub(r"%s([\u4e00-\u9fff])" % item[0], r"%s\1" % item[1], response) return response @torch.no_grad() def chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, max_length: int = 2048, num_beams=1, do_sample=True, top_p=0.7, temperature=0.95, 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} if not history: prompt = query else: prompt = "" for i, (old_query, response) in enumerate(history): prompt += "[Round {}]\n问：{}\n答：{}\n".format(i, old_query, response) prompt += "[Round {}]\n问：{}\n答：".format(len(history), query) inputs = tokenizer([prompt], return_tensors="pt") inputs = inputs.to(self.device) 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, max_length: int = 2048, do_sample=True, top_p=0.7, temperature=0.95, 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, "do_sample": do_sample, "top_p": top_p, "temperature": temperature, "logits_processor": logits_processor, **kwargs} if not history: prompt = query else: prompt = "" for i, (old_query, response) in enumerate(history): prompt += "[Round {}]\n问：{}\n答：{}\n".format(i, old_query, response) prompt += "[Round {}]\n问：{}\n答：".format(len(history), query) inputs = tokenizer([prompt], return_tensors="pt") inputs = inputs.to(self.device) for outputs in self.stream_generate(**inputs, **gen_kwargs): outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):] response = tokenizer.decode(outputs) response = self.process_response(response) new_history = history + [(query, response)] 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, **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 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: 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()) # 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 yield input_ids def quantize(self, bits: int, empty_init=False, **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 = quantize(self.transformer, bits, empty_init=empty_init, **kwargs) return self