support transformers 4.44

README.md

@@ -17,6 +17,8 @@ pipeline_tag: text-generation
 
 Read this in [English](README_en.md)
 
+**2024/08/12, 本仓库代码已更新并使用 `transforemrs>=4.44.0`, 请及时更新依赖。**
+
 GLM-4-9B 是智谱 AI 推出的最新一代预训练模型 GLM-4 系列中的开源版本。 在语义、数学、推理、代码和知识等多方面的数据集测评中，
 **GLM-4-9B** 及其人类偏好对齐的版本 **GLM-4-9B-Chat** 均表现出超越 Llama-3-8B 的卓越性能。除了能进行多轮对话，GLM-4-9B-Chat
 还具备网页浏览、代码执行、自定义工具调用（Function Call）和长文本推理（支持最大 128K 上下文）等高级功能。本代模型增加了多语言支持，支持包括日语，韩语，德语在内的

README_en.md

@@ -1,5 +1,7 @@
 # GLM-4-9B
 
+**2024/08/12, The repository code has been updated and now requires `transformers>=4.44.0`. Please update your dependencies accordingly.**
+
 ## Model Introduction
 
 GLM-4-9B is the open-source version of the latest generation of pre-trained models in the GLM-4 series launched by Zhipu

config.json

@@ -36,7 +36,7 @@
  "seq_length": 8192,
  "use_cache": true,
  "torch_dtype": "bfloat16",
- "transformers_version": "4.42.4",
+ "transformers_version": "4.44.0",
  "tie_word_embeddings": false,
  "eos_token_id": [151329, 151336, 151338],
  "pad_token_id": 151329

generation_config.json

@@ -9,5 +9,5 @@
  "temperature": 0.8,
  "max_length": 8192,
  "top_p": 0.8,
- "transformers_version": "4.42.4"
+ "transformers_version": "4.44.0"
 }

modeling_chatglm.py

@@ -1,1144 +0,0 @@
-""" PyTorch ChatGLM model. """
-import json
-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, MSELoss, BCEWithLogitsLoss
-from torch.nn.utils import skip_init
-from typing import Optional, Tuple, Union, List, Callable, Dict, Any
-from copy import deepcopy
-
-from transformers.modeling_outputs import (
- BaseModelOutputWithPast,
- CausalLMOutputWithPast,
- SequenceClassifierOutputWithPast,
-)
-from transformers.modeling_utils import PreTrainedModel
-from transformers.utils import logging, is_torch_npu_available
-from transformers.generation.logits_process import LogitsProcessor
-from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig, ModelOutput
-
-from .configuration_chatglm import ChatGLMConfig
-
-try:
- from transformers.utils import is_flash_attn_greater_or_equal_2_10, is_flash_attn_2_available
- if is_flash_attn_2_available():
- from flash_attn import flash_attn_func, flash_attn_varlen_func
- from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
-except:
- pass
-
-
-# flags required to enable jit fusion kernels
-
-if sys.platform != 'darwin' and not is_torch_npu_available():
- 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"
-_CONFIG_FOR_DOC = "ChatGLMConfig"
-
-
-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[..., 198] = 5e4
- return scores
-
-
-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, rope_ratio=1, 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
- self.rope_ratio = rope_ratio
-
- 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.
- """
- # $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$
- base = base * self.rope_ratio
- theta = 1.0 / (base ** (torch.arange(0, n_elem, 2, dtype=torch.float, device=device) / n_elem))
-
- # Create position indexes `[0, 1, ..., seq_len - 1]`
- seq_idx = torch.arange(seq_len, dtype=torch.float, 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: [b, np, sq, hn]
- b, np, sq, 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(b, np, sq, rot_dim // 2, 2)
- rope_cache = rope_cache.view(-1, 1, sq, 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.config = config
- 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)
- self.is_causal = True
-
- 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):
- # [b, np, sq, sk]
- output_size = (query_layer.size(0), query_layer.size(1), query_layer.size(2), key_layer.size(2))
-
- # [b, np, sq, hn] -> [b * np, sq, hn]
- query_layer = query_layer.view(output_size[0] * output_size[1], output_size[2], -1)
- # [b, np, sk, hn] -> [b * np, sk, hn]
- key_layer = key_layer.view(output_size[0] * output_size[1], output_size[3], -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, # [b * np, sq, hn]
- key_layer.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)
-
- # query layer shape: [b * np, sq, hn]
- # value layer shape: [b, np, sk, hn]
- # attention shape: [b, np, sq, sk]
- # context layer shape: [b, np, sq, hn]
- output_size = (value_layer.size(0), value_layer.size(1), query_layer.size(1), value_layer.size(3))
- # change view [b * np, sk, hn]
- value_layer = value_layer.view(output_size[0] * output_size[1], value_layer.size(2), -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)
- # change view [b, np, sq, hn]
- context_layer = context_layer.view(*output_size)
- # [b, np, sq, hn] --> [b, sq, np, hn]
- context_layer = context_layer.transpose(1, 2).contiguous()
- # [b, sq, np, hn] --> [b, sq, hp]
- new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,)
- context_layer = context_layer.reshape(*new_context_layer_shape)
-
- return context_layer
-
-
-class SdpaAttention(CoreAttention):
- def forward(self, query_layer, key_layer, value_layer, attention_mask):
- 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,
- dropout_p=self.config.attention_dropout if self.training else 0.0)
- 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,
- dropout_p=self.config.attention_dropout if self.training else 0.0)
- context_layer = context_layer.transpose(1, 2).contiguous()
- new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,)
- context_layer = context_layer.reshape(*new_context_layer_shape)
- return context_layer
-
-
-def _get_unpad_data(attention_mask):
- seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
- indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
- max_seqlen_in_batch = seqlens_in_batch.max().item()
- cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
- return (
- indices,
- cu_seqlens,
- max_seqlen_in_batch,
- )
-
-
-# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2
-class FlashAttention2(CoreAttention):
- def __init__(self, *args, **kwargs):
- super().__init__(*args, **kwargs)
- self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
-
- def forward(self, query_states, key_states, value_states, attention_mask):
- query_states = query_states.transpose(1, 2)
- key_states = key_states.transpose(1, 2)
- value_states = value_states.transpose(1, 2)
- batch_size, query_length = query_states.shape[:2]
- if not self._flash_attn_uses_top_left_mask:
- causal = self.is_causal
- else:
- # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
- causal = self.is_causal and query_length != 1
- dropout = self.config.attention_dropout if self.training else 0.0
- # Contains at least one padding token in the sequence
- if attention_mask is not None:
- query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
- query_states, key_states, value_states, attention_mask, query_length
- )
-
- cu_seqlens_q, cu_seqlens_k = cu_seq_lens
- max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
-
- attn_output_unpad = flash_attn_varlen_func(
- query_states,
- key_states,
- value_states,
- cu_seqlens_q=cu_seqlens_q,
- cu_seqlens_k=cu_seqlens_k,
- max_seqlen_q=max_seqlen_in_batch_q,
- max_seqlen_k=max_seqlen_in_batch_k,
- dropout_p=dropout,
- softmax_scale=None,
- causal=causal,
- )
-
- attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
- else:
- attn_output = flash_attn_func(
- query_states, key_states, value_states, dropout, softmax_scale=None, causal=causal
- )
- attn_output = attn_output.reshape(batch_size, query_length, self.hidden_size_per_partition).contiguous()
- return attn_output
-
- def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
- indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
- batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
-
- key_layer = index_first_axis(
- key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
- )
- value_layer = index_first_axis(
- value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
- )
- if query_length == kv_seq_len:
- query_layer = index_first_axis(
- query_layer.reshape(batch_size * kv_seq_len, self.num_attention_heads_per_partition, head_dim), indices_k
- )
- cu_seqlens_q = cu_seqlens_k
- max_seqlen_in_batch_q = max_seqlen_in_batch_k
- indices_q = indices_k
- elif query_length == 1:
- max_seqlen_in_batch_q = 1
- cu_seqlens_q = torch.arange(
- batch_size + 1, dtype=torch.int32, device=query_layer.device
- ) # There is a memcpy here, that is very bad.
- indices_q = cu_seqlens_q[:-1]
- query_layer = query_layer.squeeze(1)
- else:
- # The -q_len: slice assumes left padding.
- attention_mask = attention_mask[:, -query_length:]
- query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
-
- return (
- query_layer,
- key_layer,
- value_layer,
- indices_q,
- (cu_seqlens_q, cu_seqlens_k),
- (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
- )
-
-
-CORE_ATTENTION_CLASSES = {
- "eager": CoreAttention,
- "sdpa": SdpaAttention,
- "flash_attention_2": FlashAttention2
-}
-
-
-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 = CORE_ATTENTION_CLASSES[config._attn_implementation](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: [b, sq, h]
-
- # =================================================
- # Pre-allocate memory for key-values for inference.
- # =================================================
- # =====================
- # Query, Key, and Value
- # =====================
-
- # Attention heads [b, sq, h] --> [b, sq, (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)
-
- # [b, sq, np, 3 * hn] --> 3 [b, sq, np, hn]
- (query_layer, key_layer, value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3)
-
- # [b, sq, np, hn] -> [b, np, sq, hn]
- query_layer, key_layer, value_layer = [k.transpose(1, 2) for k in [query_layer, key_layer, value_layer]]
-
- # 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=2)
- value_layer = torch.cat((cache_v, value_layer), dim=2)
- if use_cache:
- if kv_cache is None:
- kv_cache = torch.cat((key_layer.unsqueeze(0).unsqueeze(0), value_layer.unsqueeze(0).unsqueeze(0)),
- dim=1)
- else:
- 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, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1, -1
- )
- key_layer = key_layer.contiguous().view(
- key_layer.size()[:1] + (self.num_attention_heads_per_partition,) + key_layer.size()[3:]
- )
- value_layer = value_layer.unsqueeze(2)
- value_layer = value_layer.expand(
- -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1, -1
- )
- value_layer = value_layer.contiguous().view(
- value_layer.size()[:1] + (self.num_attention_heads_per_partition,) + value_layer.size()[3:]
- )
-
- # ==================================
- # 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,
- use_reentrant=False
- )
- 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:
- # token by token decoding, use tuple format
- if kv_caches[0] is not None:
- presents = presents + (kv_cache,)
- # prefilling in decoding, use tensor format to save cuda memory
- else:
- if len(presents) == 0:
- presents = kv_cache
- else:
- presents = torch.cat((presents, kv_cache.to(presents.device)), dim=0)
-
- 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"]
- _supports_flash_attn_2 = True
- _supports_sdpa = True
-
- def _init_weights(self, module: nn.Module):
- """Initialize the weights."""
- return
-
- def get_masks(self, input_ids, past_key_values, padding_mask=None):
- if self.config._attn_implementation == "flash_attention_2":
- if padding_mask is not None and not padding_mask.all():
- return padding_mask
- return 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[2]
- 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
-
-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
- # 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, rope_ratio=config.rope_ratio,
- 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)
-
- def get_input_embeddings(self):
- return self.embedding.word_embeddings
-
- def set_input_embeddings(self, value):
- self.embedding.word_embeddings = value
-
- 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_attentions: 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 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]
-
- # 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 presents is not None and type(presents) is torch.Tensor:
- presents = presents.split(1, dim=0)
- presents = list(presents)
- presents = [list(x.squeeze(0).split(1, dim=0)) for x in presents]
- presents = [tuple([x.squeeze(0) for x in y]) for y in presents]
- presents = tuple(presents)
-
- 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, 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
-
- 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
- cache_name, cache = self._extract_past_from_model_output(
- outputs, standardize_cache_format=standardize_cache_format
- )
- model_kwargs[cache_name] = cache
-
- # 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)
-
- 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(0, beam_idx.to(layer_past[0].device)),
- layer_past[1].index_select(0, beam_idx.to(layer_past[1].device)),
- )
- for layer_past in past
- )
-
-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=config.torch_dtype)
- if config.classifier_dropout is not None:
- self.dropout = nn.Dropout(config.classifier_dropout)
- else:
- self.dropout = None
- self.config = config
-
- 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_attentions: 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_attentions=output_attentions,
- 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,
- )